Functional brain reconfiguration during sustained pain

  1. Jae-Joong Lee
  2. Sungwoo Lee
  3. Dong Hee Lee
  4. Choong-Wan Woo  Is a corresponding author
  1. Center for Neuroscience Imaging Research, Institute for Basic Science, Republic of Korea
  2. Department of Biomedical Engineering, Sungkyunkwan University, Republic of Korea
  3. Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Republic of Korea

Abstract

Pain is constructed through complex interactions among multiple brain systems, but it remains unclear how functional brain networks are reconfigured over time while experiencing pain. Here, we investigated the time-varying changes in the functional brain networks during 20 min capsaicin-induced sustained orofacial pain. In the early stage, the orofacial areas of the primary somatomotor cortex were separated from other areas of the somatosensory cortex and integrated with subcortical and frontoparietal regions, constituting an extended brain network of sustained pain. As pain decreased over time, the subcortical and frontoparietal regions were separated from this brain network and connected to multiple cerebellar regions. Machine-learning models based on these network features showed significant predictions of changes in pain experience across two independent datasets (n = 48 and 74). This study provides new insights into how multiple brain systems dynamically interact to construct and modulate pain experience, advancing our mechanistic understanding of sustained pain.

Editor's evaluation

This article will be of great interest to researchers interested in the brain mechanisms of pain. It shows how the connectivity of brain networks associated with sustained pain changes over time. These findings are supported by compelling fMRI analyses of a tonic pain paradigm in two cohorts of healthy human participants. These important insights advance the understanding of the brain mechanisms of sustained pain, which is the hallmark of chronic pain as a major healthcare problem.

https://doi.org/10.7554/eLife.74463.sa0

Introduction

The experience of pain unfolds over time and dynamically changes (Kucyi and Davis, 2015), and the sustained and spontaneously fluctuating nature is a key characteristic of clinical chronic pain (Apkarian et al., 2001). Pain is known to consist of multiple component processes ranging from sensory and affective to cognitive and motivational processes (Melzack and Casey, 1968), and thus the construction and modulation of pain are subserved by distributed brain systems (Coghill, 2020; Mano and Seymour, 2015). Importantly, the degree to which each component process contributes to pain experience changes over time (Hashmi et al., 2013). For example, fear-avoidance (Vlaeyen and Linton, 2012), maladaptive coping (Jensen et al., 1991), and learning and memory (Apkarian et al., 2009; Phelps et al., 2021) become more important in explaining pain experience as the pain becomes chronic. Therefore, identifying the whole-brain network features that can explain and predict the natural fluctuations and changes of sustained pain (Farmer et al., 2012; Kucyi and Davis, 2015) is crucial to understanding why pain naturally decreases in some cases and individuals but not in others (Ploner et al., 2011). In this study, we examined the reconfiguration of whole-brain functional networks underlying the natural fluctuation in sustained pain to provide a mechanistic understanding of the brain responses to sustained pain. To this end, our key research questions include (Figure 1A): How does the brain community structure change (i) for the sustained pain condition compared to the resting condition and (ii) over the course of sustained pain from its initiation to remission? (iii) Can we develop predictive models of sustained pain based on the patterns of brain network changes?

Study overview and behavioral results.

(A) Three main research questions of the current study. We aimed to answer the research questions by examining the time-varying patterns of functional brain network reconfiguration during 20 min of tonic orofacial pain experience and comparing them to the pain-free resting state. (B) Behavioral results. We asked participants to continuously report their pain in the scanner using either a pain avoidance rating scale (‘how much do you want to avoid this experience in the future?’) for Study 1 (‘discovery dataset,’ n = 48) or a pain intensity rating scale (‘how intense is your pain?’) for Study 2 (‘independent test dataset,’ n = 74). The anchors of the scale (the horizontal dashed lines) were based on a modified version of the general labeled magnitude scale (gLMS). The vertical dashed lines show how we define the early, middle, and late periods of sustained pain. The solid lines represent group mean ratings (red for capsaicin, and blue for control), and the shading represents standard errors of the mean (SEM). (C) The overview of main analyses. Voxel-level functional connectivity was estimated in the native space using 2 min moving windows and thresholded at 0.05 network density (see ‘Materials and methods’ for details). We identified the time-evolving network community structures from these suprathreshold connectivity matrices using the multilayer community detection method (Mucha et al., 2010), and calculated the module allegiance (Bassett et al., 2011). Using the module allegiance values as input features, we either identified group-level consensus community structures or conducted predictive modeling and tested the models on Study 2 independent test dataset (n = 74). Colored circles in the multilayer community detection and module allegiance analysis represent different community labels. Black- and white-colored boxes in the module allegiance matrices indicate whether or not the two different network nodes have the same community affiliation, respectively.

To answer these questions, we conducted two functional magnetic resonance imaging (fMRI) experiments with 48 and 74 healthy participants while they were experiencing 20 and 10min of capsaicin-induced tonic orofacial pain, respectively. Tonic pain has long been used as an experimental model of clinical pain (Dubuisson and Dennis, 1977), and our previous study demonstrated that capsaicin-induced tonic orofacial pain shows network-level brain representations similar to clinical pain, suggesting its clinical relevance (Lee et al., 2021). The application of a capsaicin-rich hot sauce on a participant’s tongue can effectively elicit pain sensation for approximately 10 minutes, followed by a slow remission of pain toward the end of the scan. This experimental paradigm is particularly suitable for identifying reliable features of the brain network for different periods of pain (i.e., initiation to remission), which has been challenging in clinical fMRI studies because of the small variance and heterogeneity of clinical pain trajectories within a few sessions of fMRI scans.

We first identified the time-evolving functional brain network structures using a community detection method for multi-layer networks (Mucha et al., 2010) using data from Study 1 (‘discovery dataset,’ n = 48). We conducted this network analysis at the voxel level on an individual’s native brain space to fully utilize personalized and fine-grained pattern information. We then compared the group-level representative brain community structures between the sustained pain condition versus the no-pain resting condition (the first research question) and between different phases of pain, including its early, middle, and late periods (the second research question). Finally, we developed machine-learning models based on the brain community patterns using the data from Study 1, either to classify pain versus no-pain conditions or to predict the dynamic changes in pain ratings, and tested the performances of these models on an unseen dataset (Study 2, ‘independent test dataset,’ n = 74) (the third research question).

The results showed that the orofacial areas (i.e., ventral part) of the primary somatomotor regions were separated from the other (i.e., dorsal) primary somatomotor regions and instead integrated with subcortical (e.g., thalamus, basal ganglia) and frontoparietal regions (e.g., dorsolateral prefrontal cortex) during sustained pain. Interestingly, this pain-induced somatomotor-dominant brain community structure changed over time. The subcortical and frontoparietal regions affiliated with the somatomotor-dominant network in the early period of pain were gradually separated from the somatomotor network and strongly connected to multiple cerebellar regions as pain decreased. Machine-learning models based on these brain network organization patterns could discriminate the sustained pain from the pain-free control condition and predict dynamic changes in pain experience including pain avoidance and pain intensity over time. These models were further generalized to the independent tonic pain dataset (Study 2).

Overall, this study contributes to the understanding of dynamic interactions among multiple functional brain networks in response to sustained pain, offering new insights into the mechanistic understanding of chronic pain.

Results

Experimental design and behavioral results

In Study 1 (‘discovery dataset’), we scanned 48 participants while we delivered the capsaicin-rich hot sauce onto the participants’ tongues (‘capsaicin’ condition) or had the participants rest without stimuli (‘control’ condition). The fMRI scan duration was 20 min for both conditions, which was sufficient to cover the entire period of sustained pain from its initiation to the complete remission. During the scan, we asked participants to report the continuous changes in pain avoidance by asking the following question, “How much do you want to avoid this experience in the future?” (For details of the rating procedure, please see Appendix 1—figure 1.) With this question, we aimed to measure the continuous changes in the avoidance motivation induced by sustained pain, which is known as a core component of clinical chronic pain (Vlaeyen and Linton, 2012). We employed a modified version of the general labeled magnitude scale (gLMS) to better represent pain experience at the super-high pain range (Bartoshuk et al., 2004). The order of the experimental conditions was counterbalanced across participants.

The pain avoidance ratings for the capsaicin condition were higher than those for the control condition throughout the 20 min of the experiment (Figure 1B), β^ = 0.11 ± 0.01 (mean ± standard error of the mean [SEM]), z = 4.25, p=2.10 × 10–5 (multilevel general linear model with bootstrap tests, 10,000 iterations, two-tailed, gender and the order of experimental conditions were modeled as covariates), indicating that the capsaicin stimulation effectively induced sustained pain experience and avoidance motivation. The pain avoidance ratings during the capsaicin condition exhibit an evident rise and fall. The differences in the pain avoidance between the capsaicin versus control conditions became nonsignificant toward the end of the scan (from 17.3 min to the end, two-tailed ps>0.05, paired t-test, BF01 = 1.01–4.71), suggesting that pain subsided. A similar rise-and-fall pattern of pain ratings was observed in Study 2 with a pain intensity question, “How intense is your pain?”, except the short duration of pain because of the smaller amount of pain stimulus (i.e., hot sauce) compared to Study 1. Note that the overall trend of pain ratings over time was similar across participants because of the characteristics of our experimental design, which has also been observed in the previous studies that used oral capsaicin (Berry and Simons, 2020; Lu et al., 2013; Ngom et al., 2001). However, also note that each individual’s time course of pain ratings was not entirely the same (Appendix 1—figures 2 and 3).

Community detection of time-evolving brain networks

To examine the reconfiguration of the whole-brain functional networks over time, we used a multilayer community detection approach (Mucha et al., 2010) based on the Louvain community detection algorithm (Blondel et al., 2008). This approach is designed to find community structures of time-evolving networks by connecting the same nodes across different time points (Figure 1C) and has been successfully used in previous studies on brain network reconfigurations for learning (Bassett et al., 2011; Bassett et al., 2015), working memory (Braun et al., 2015; Finc et al., 2020), planning and reasoning (Pedersen et al., 2018), emotion (Betzel et al., 2017), and pharmacological intervention (Braun et al., 2016). In this study, we used this approach to examine the temporal changes of brain network structures during sustained pain, which cannot be done with conventional functional connectivity-based analyses (Lee et al., 2021).

We first divided the fMRI data into 2 min nonoverlapping time windows (i.e., 10 time-bins) and estimated the functional connectivity for each time window using Pearson’s correlation for each participant and each condition. Although the long duration of the time window without overlaps may obscure the fine-grained temporal dynamics in functional connectivity patterns, we chose to use this long time window to obtain more reliable estimates of network structures and their transitions as previous literature (Bassett et al., 2011; Robinson et al., 2015). Importantly, we computed the functional connectivity at the voxel level on each participant’s native brain space to avoid an arbitrary choice of brain parcellation schemes and minimize the potential loss of information due to anatomical normalization. Also, since many of the graph analytics were developed for sparse networks (Newman, 2010) and voxel-level connectivity data were likely to contain many spurious correlations, we applied proportional thresholding to functional connectivity matrices. Because an arbitrary choice of the threshold can have a substantial impact on the results (van den Heuvel et al., 2017), we selected the optimal threshold level (0.05; top 5% of connections) that maximized the differences in commonly used network attributes between the capsaicin versus control conditions (Appendix 1—figure 4; for details of how we determined the optimal threshold level, see ‘Materials and methods’). We used this threshold level for the remaining analyses.

Differences in consensus community structure between sustained pain versus no pain (Q1)

To address the first research question, “How does the brain community structure change for the sustained pain condition compared to the no-pain resting condition?”, we compared the consensus brain community structures for the capsaicin versus control conditions. Here, the consensus community means the group-level representative structures of the distinct community partitions of individuals. To determine the consensus community across different individuals and times, we first obtained the module allegiance (Bassett et al., 2011) from the community assignment of each individual. Module allegiance assesses how much a pair of nodes is likely to be affiliated with the same community label and is defined as a matrix T whose element Tij is 1 when nodes i and j are assigned to the same community and 0 when assigned to different communities. This conversion of the categorical community assignments to the continuous module allegiance values allows group-level summarization of different community structures of individuals. We projected the module allegiance values onto the common MNI space and then averaged them across participants and time, which yielded a group-level module allegiance matrix that ranges from 0 to 1 (Figure 2A). Finally, we obtained the consensus community by applying a community detection algorithm to the group-level module allegiance matrix (Bassett et al., 2013). For more details on the consensus community detection, see ‘Materials and methods’.

Reconfiguration of community structure for the sustained pain versus control conditions.

(A) Analysis overview. Individual module allegiance values computed in the native spaces were projected onto MNI space and averaged across all participants and all time-bins (i.e., ten bins) to yield a group-level module allegiance matrix. Then, we identified the group-level consensus community structures by decomposing the group-level module allegiance matrix into distinct modules using the Louvain community detection algorithm (Blondel et al., 2008). (B) Consensus community structure for the capsaicin versus control conditions. Community colors were determined based on the canonical network membership of the largest proportion of voxels across the two conditions. V-shaped arrows indicate the regions that showed marked changes in communities. (C) Voxel-wise changes in community assignments between the capsaicin and control conditions. (D) Proportions of 10 canonical brain networks—7 resting-state large-scale networks (Yeo et al., 2011), subcortical, cerebellum, and brainstem regions—for different communities. The large square on the left shows the network composition of the voxels common across the capsaicin and control conditions, and the squares on the upper and lower right represent the voxels uniquely assigned to the community for the capsaicin or control conditions, respectively. The sizes of the squares are proportional to the number of voxels.

The consensus community structures of capsaicin and control conditions are illustrated in Figure 2B. We identified four main brain communities across both conditions and found the most prominent differences between the capsaicin versus control conditions in Community 2, of which the size became larger in the capsaicin condition than in the control condition (Figure 2C). Figure 2D shows the network decomposition of the communities using Yeo’s large-scale network scheme (Yeo et al., 2011)—the major player for each community was the visual network for Community 1, the somatomotor network for Community 2, the default mode network for Community 3, and the cerebellum for Community 4. The expansion of Community 2 during the capsaicin condition was mainly driven by the frontoparietal network regions from Community 3 (including the dorsolateral prefrontal and inferior parietal regions) and subcortical regions from Community 4 (including the thalamus and basal ganglia regions; Figure 2D and Appendix 1—figure 5). Permutation tests confirmed that the community assignment in the frontoparietal and subcortical regions showed significant changes between the capsaicin versus control conditions (Appendix 1—figure 6A). These results suggested that sustained pain induced a global functional reconfiguration of multiple brain networks that included segregation from the default mode network and cerebellar regions and integration with the somatomotor network to form an extended brain system for pain, which is a replication of the previous study that termed this enlarged somatomotor-dominant network as a ‘pain supersystem’ (Zheng et al., 2019).

Changes in consensus community structure over the course of sustained pain (Q2)

Next, to address the second research question, “How does the brain community structure change throughout sustained pain from its initiation to the remission?”, we first divided the capsaicin run into three time periods, ‘early’ (1–3 bins, 0–6 min), ‘middle’ (4–7 bins, 6–14 min), and ‘late’ (8–10 bins, 14–20 min). These three time periods corresponded to (i) initiation and maintenance of pain, (ii) gradual pain decrease, and (iii) full remission of pain, respectively (Figure 1B). Similar to the analysis shown in Figure 2A, we then averaged module allegiance matrices across participants for each period and calculated the consensus community structures from the averaged allegiance matrices.

As shown in Figure 3, the algorithm detected six consensus communities across time periods. The first four communities (communities 1–4) were consistent with the communities identified in the previous analysis based on the averaged data for the capsaicin and control conditions (Figure 2). Patterns of changes in communities 1–4 were similar to those from previous results. For example, Community 2 (somatomotor-dominant) was larger in the early period than in the middle and late periods (i.e., the emergence of the extended somatomotor-dominant community during capsaicin condition), and the frontoparietal network and subcortical regions were the main drivers of this change (Figure 3A and C and Appendix 1—figure 7A).

Reconfiguration of community structure over the time course of sustained pain experience.

(A) Consensus community structures of the early (0–6 min), middle (6–14 min), and late (14–20 min) periods of sustained pain. Colors indicate distinct community assignments and were determined based on the canonical network membership with the largest proportion of voxels across the periods. (B) Voxel-wise changes in community assignments from the early to late periods of pain. (C) Proportions of 10 canonical brain networks for different communities. The large square on the upper left shows the network composition of the voxels common across all periods, and the squares on the upper right, lower right, lower left represent the voxels of the early, middle, and late periods of pain after removing the common voxels, respectively. The sizes of the squares are proportional to the number of voxels.

Community 6, which was one of the newly detected communities, displayed an interesting pattern of changes over the course of sustained pain. The community mainly consisted of the frontoparietal and dorsal attention network regions and gradually increased in size over time (Figure 3A and B). The increase in community size in the later periods was mainly driven by the frontoparietal network regions that were among Community 2 (somatomotor-dominant) during the early period. In the later periods, these frontoparietal regions, including the dorsolateral prefrontal and inferior parietal regions, changed their affiliation to Community 6, forming a separate frontoparietal-dominant community (Appendix 1—figure 7C).

Community 5 also showed an interesting pattern. Community 5 mainly consisted of the limbic cortices and subcortical and brainstem regions during the early and middle periods. However, the subcortical and brainstem regions changed their affiliation to Community 4 (cerebellum-dominant community), leaving only a small number of voxels within the limbic regions in Community 5 in the late period (Figure 3 and Appendix 1—figure 8A). The composition of Community 5 during the early period suggested that the brainstem regions were closely linked with the limbic brain regions, including the medial temporal lobe structures (e.g., hippocampus, amygdala, and parahippocampal gyrus) and the temporal pole, consistent with recent findings that showed the involvement of the spino-parabrachio-amygdaloid circuit in sustained pain (Chiang et al., 2019; Huang et al., 2019; Rodriguez et al., 2017). In addition, the reconfiguration of Community 4 showed that the cerebellum-dominant community extended its connections to the brainstem and thalamus during the late period of sustained pain (Figure 3C and Appendix 1—figure 8B), suggesting an important, though less investigated, pain-modulatory role of the cerebellum in pain (Claassen et al., 2020; Moulton et al., 2010).

These results suggested that decrease in sustained pain induced the dissociation of frontoparietal-somatomotor and limbic-subcortical-brainstem coupling, and the emergence of a separate frontoparietal-dominant community and an enlarged cerebellum-dominant community. Permutation tests further confirmed that the community assignment in the frontoparietal, subcortical, brainstem, and cerebellar regions showed significant changes between the early versus late period of pain (Appendix 1—figure 6B).

Module allegiance-based classifier for sustained pain versus no pain (Q3-1)

To further characterize the functional network changes induced by sustained pain, we conducted predictive modeling using the module allegiance patterns (third research question, “Can we develop predictive models of sustained pain based on the patterns of brain network changes?”). To obtain module allegiance matrices on a common feature space and also to reduce the computational burden for model fitting, we projected a whole-brain parcellation comprising 263 regions defined on the MNI space (Schaefer atlas [Schaefer et al., 2018] with the additional brainstem, cerebellum, and subcortical regions; see ‘Materials and methods’ for details) onto an individual’s native space (Figure 4A). Then, we averaged module allegiance for each region, resulting in a 263 × 263 module allegiance matrix for each participant and for each time window. Here, high module allegiance indicates the voxels of two regions are likely to be in the same community affiliation, and vice versa. With the module allegiance matrices, we conducted two different types of predictive modeling: classification (developing a support vector machine [SVM] classifier to discriminate the capsaicin condition from the control condition) and regression (developing a principal component regression [PCR] model to predict the fluctuations in sustained pain ratings). We chose to use the SVM and PCR because they are representative linear algorithms for finding the low-dimensional latent components of highly correlated data such as brain networks.

Module allegiance-based classifier for sustained pain versus resting.

(A) The analysis overview of the module allegiance-based predictive modeling. We first projected the whole-brain atlas onto an individual’s native brain space. We then averaged the voxel-level module allegiance values for each region to create region-level module allegiance matrices. These region-level module allegiance values were used to predict whether an individual was in pain or not. (B) Group-level module allegiance matrix. We averaged the region-level module allegiance matrices across all participants and all time-bins, and sorted the brain regions according to their canonical functional network membership. Lower and upper triangles represent the allegiance matrices of the capsaicin and control conditions, respectively. (C) Raw predictive weights of the support vector machine (SVM) classifier. (D) To obtain an unbiased estimate of the classifier’s classification performance, we conducted the forced-choice test with leave-one-participant-out cross-validation. Left: receiver-operating characteristics (ROC) curve. Right: cross-validated model responses for different conditions. Each line connecting dots represents an individual participant’s paired data (red: correct classification, blue: incorrect classification). p-Value was based on a binomial test, two-tailed. (E) Thresholded weights based on bootstrap tests with 10,000 iterations. Left: predictive weights thresholded at false discovery rate (FDR)-corrected q < 0.05 (which corresponds to uncorrected p<0.003), two-tailed. We indicated the brain region with the largest weighted degree centrality for positive and negative weights based on the thresholded model with the black arrow on the plot. Right: top 50 stable predictive weights (FDR q < 1.32 × 10–4, uncorrected p<1.91 × 10–7, two-tailed). (F) Seed-based allegiance map for the hub region that we identified in (E), left primary somatosensory and motor cortex (tongue region). The bottommost row shows the contrast map for the capsaicin versus control conditions, thresholded at t47 = 2.82, FDR q < 0.05 (uncorrected p<0.007), two-tailed, paired t-test. Put, putamen; dlPFC, dorsolateral prefrontal cortex; Thal, thalamus; SMC, somatomotor cortex; VN, visual network; SMN, somatomotor network; DAN, dorsal attention network; VAN, ventral attention network; LN, limbic network; FPN, frontoparietal network; DMN, default mode network; SCTX, subcortical regions; CB, cerebellum; BS, brainstem.

To develop a classifier for the capsaicin versus control conditions, we averaged the module allegiance data across time points, creating one allegiance matrix per person and experimental condition. Figure 4B displays the group averages of the module allegiance matrices for the capsaicin condition (lower triangle) and the control condition (upper triangle). As shown in Figure 4C and D, the module allegiance-based classifier showed a high classification accuracy for the capsaicin versus control conditions in a forced-choice test (accuracy = 98%, p=3.48 × 10–13, binomial test, two-tailed; Figure 4D), suggesting that the individuals’ brain community structures had enough information to detect the existence of sustained pain. For the classification accuracy across all the participants instead of the forced-choice test, please see Appendix 1. When we thresholded the classifier weights to examine the important features of the model based on bootstrap tests (false discovery rate [FDR]-corrected q < 0.05, which corresponds to uncorrected p<0.003, two-tailed; the left panel of Figure 4E), the results suggested that the important network features of sustained pain included (i) segregation within the somatomotor network (negative weights among the brain regions within the somatomotor network in Figure 4E) and (ii) integration between the subcortical regions and the somatomotor network regions (positive weights between the somatomotor network regions and the subcortical regions in Figure 4E). We could not observe the segregation within the somatomotor network in the group-level analysis presented in the previous section (Figure 2), suggesting that the individual-level analysis based on module allegiance provides additional insights into the brain network changes during sustained pain.

To further examine the important connections for the dynamic features, we visualized the top 50 stable connections (FDR q < 1.32 × 10–4, uncorrected p<1.91 × 10–7, two-tailed) using a glass brain (the right panel of Figure 4E and Appendix 1—table 1). We also displayed these important connections focusing on the somatosensory and insular cortical regions. We then placed them on the sensory homunculus (Appendix 1—figure 9) to highlight that the negative weight connections were between the ventral (tongue area) and dorsal parts (other body areas) of the primary somatomotor cortex, and the positive weight connections between the tongue primary somatomotor cortex and some subcortical regions including the thalamus and basal ganglia.

We selected hub regions with the largest weighted degree centrality to provide a more detailed picture of the network reconfiguration, separately for the positive and negative weights, based on the thresholded model at FDR q < 0.05. The left ventral primary somatomotor region (tongue area) was selected as the sole hub region for both positive and negative weights. We then obtained a seed-based module allegiance map using the hub region as the seed. As shown in Figure 4F, the results reconfirmed that the hub region showed substantially decreased allegiance with the dorsal primary somatomotor regions and increased allegiance with the basal ganglia and thalamus in the capsaicin condition (thresholded at t47 = 2.82, FDR q < 0.05, uncorrected p<0.007, paired t-test between capsaicin and control conditions, two-tailed). Moreover, the hub region showed increased allegiance with dorsolateral prefrontal cortex regions, consistent with previous findings that sustained pain induced integration between the frontoparietal and somatomotor networks (Figure 2).

Module allegiance-based prediction model of pain ratings (Q3-2)

Next, we developed a PCR model to predict pain ratings. We used region-level module allegiance matrices across 10 time-bins of all participants as features. Because the number of features (263C2 = 34,453) was higher than the number of observations (10 ratings × 48 participants = 480), we first reduced the dimensionality of the features using principal component analysis (PCA). We then regressed the pain ratings on the principal components (PCs) of module allegiance. We selected the number of PCs that yielded the best predictive performance in leave-one-participant-out cross-validation (Appendix 1—figure 10). The newly developed PCR model (Figure 5A) showed significantly high prediction performance (mean prediction–outcome correlation r = 0.29, p=7.27 × 10–6, bootstrap test, two-tailed; mean squared error = 0.043 ± 0.006 [mean ± SEM]; Figure 5B). For the between-individual prediction–outcome correlation of mean pain ratings, please see Appendix 1. Note that this model performance is biased because we conducted hyperparameter tuning (i.e., the number of PCs) using the same dataset. To obtain a less biased estimate of performance in the training data, we used nested leave-one-participant-out cross-validation that separates the hyperparameter tuning and testing (see ‘Materials and methods’ for details). The results showed that prediction performance was significant (mean prediction–outcome correlation r = 0.28, p=1.00 × 10–5, bootstrap test, two-tailed, mean squared error = 0.044 ± 0.006 [mean ± SEM], number of PCs = 13.94 ± 0.14 [mean ± SEM]), suggesting that the individuals’ brain community structures are predictive of the temporal change of sustained pain.

Module allegiance-based prediction model of pain rating.

(A) The raw predictive weights of the principal component regression (PCR) model based on region-level time-bin module allegiance matrices. (B) Actual versus predicted ratings. Each colored line (and symbol) represents individual participant’s ratings (10 time-bin average ratings per participant) during the capsaicin run (red: higher r; yellow: lower r; blue: r < 0). p-Value is based on bootstrap tests, two-tailed. (C) Thresholded weights based on bootstrap tests with 10,000 iterations. Left: top 50 stable predictive weights (false discovery rate [FDR]-corrected q < 0.043, which corresponds to uncorrected p<6.09 × 10–5, two-tailed). Right: FDR q < 0.05 (which corresponds to uncorrected p<9.24 × 10–5, vermillion and blue colors) or uncorrected p<0.05 (reddish purple and sky blue), two-tailed. The brain region with the largest weighted degree centrality separately for positive and negative weights is indicated with the black arrows. (D, E) Seed-based allegiance maps for the hub regions across different periods of sustained pain. The right posterior cingulate cortex (PCC) and left cerebellum lobule VIIb were identified as hub regions for the positive and negative weights, respectively. The bottommost row shows the contrast map for the late versus early periods, thresholded at t47 = 3.13 (D) and 3.22 (E), FDR q < 0.05 (which corresponds to uncorrected p<0.003 [D] and 0.002 [E]), two-tailed, paired t-test. ACC, anterior cingulate cortex; Cb, cerebellum; dlPFC, dorsolateral prefrontal cortex; Thal, thalamus; SMC, somatomotor cortex; pre-SMA, pre-supplementary motor area; Put, putamen; Ins, insula; VN, visual network; SMN, somatomotor network; DAN, dorsal attention network; VAN, ventral attention network; LN, limbic network; FPN, frontoparietal network; DMN, default mode network; SCTX, subcortical regions; CB, cerebellum; BS, brainstem.

When we thresholded the predictive model based on bootstrap tests (Figure 5C, FDR-corrected q < 0.05, uncorrected p<9.24 × 10–5, two-tailed), the results showed a dissociation between the cerebellum and the subcortical and frontoparietal regions for the high levels of sustained pain (negative weights in the circos plot in Figure 5C). Because few connections with positive weights were survived at the FDR correction, we examined the weight patterns at a more liberal threshold (uncorrected p<0.05, two-tailed; the sky blue and reddish purple connections in Figure 5D). We observed many positive connections between the somatomotor and frontoparietal network regions, suggesting integration between the somatomotor and frontoparietal networks at high levels of sustained pain. The top 50 stable features (FDR q < 0.043, uncorrected p<6.09 × 10–5, two-tailed; the left panel of Figure 5C and Appendix 1—table 2) were mostly negative weight connections that were connected to multiple cerebellar regions.

When we selected the hub regions with the largest weighted degree centrality based on the thresholded model at uncorrected p<0.05, the right posterior cingulate cortex (PCC) within the frontoparietal network (MNI center: 6, –26, 30) and the lobule VIIb of the left cerebellum (MNI center: −26, –66, –50) were selected for the positive and negative weights, respectively. Figure 5D and E show seed-based module allegiance maps. The right PCC region showed decreased connections with the somatomotor regions and increased connections with the dorsolateral prefrontal cortex, anterior cingulate cortex, thalamus, and cerebellar regions during the late period of pain compared to the early period (thresholded at t47 = 3.13, FDR q < 0.05, uncorrected p<0.003, paired t-test, two-tailed). The left cerebellar lobule VIIb showed increased connections with the dorsolateral prefrontal cortex, anterior cingulate cortex and pre-supplementary motor area, insula, thalamus, and basal ganglia during the late period of pain compared to the early period (thresholded at t47 = 3.22, FDR q < 0.05, uncorrected p<0.002, two-tailed). This implicates that both the right PCC and the left cerebellum may play essential roles in integrating frontoparietal and subcortical regions to construct functional communities separate from the somatomotor regions as pain decreased.

These results suggested that the frontoparietal network interacted with the somatomotor network during the early period of sustained pain. However, these connections were weakened, and the frontoparietal and subcortical regions were connected to the cerebellar regions as pain decreased.

Specificity of the module allegiance-based predictive models

To examine whether the predictive models were specific to pain and the prediction performances were not influenced by confounding variables such as head motion and physiological changes, we conducted additional analyses as shown in Appendix 1—figures 1113. The SVM and PCR models showed significant prediction performances even after controlling for head motion (i.e., framewise displacement) and physiological responses (i.e., heart rate and respiratory rate) (SVM: accuracy = 89%, p=1.41 × 10–7, binomial test, two-tailed; PCR: r = 0.20, p=0.003, bootstrap test, two-tailed, mean squared error = 0.159 ± 0.022; Appendix 1—figures 11 and 12) and did not respond to the nonpainful but aversive conditions including the bitter taste (SVM: accuracy = 79%, p=6.17 × 10–5, binomial test, two-tailed; PCR: r = 0.05, p=0.41, bootstrap test, two-tailed, mean squared error = 0.036 ± 0.006) and aversive odor conditions (SVM: accuracy = 83%, p=3.31 × 10–6, binomial test, two-tailed; PCR: r = 0.12, p=0.06, bootstrap test, two-tailed, mean squared error = 0.044 ± 0.004; Appendix 1—figure 13), supporting the specificity of our predictive models to pain. For details, please see Appendix 1.

Testing the module allegiance-based predictive models on an independent dataset

Although our module allegiance-based predictive models demonstrated significant cross-validated prediction performances in our discovery dataset (n = 48), these results could be biased toward the training data. Thus, to provide unbiased estimates of model performance, we tested our models on an independent test dataset (Study 2, n = 74). Study 2 dataset had the same experimental design, but with a shorter scan duration—the ‘capsaicin’ run was 10 min, and the ‘control’ run was 6 min. Also, the pain rating was collected using pain intensity scale, not the pain avoidance scale, to ensure generalizability of the allegiance-based PCR model. The test results showed significant classification and prediction performances. The accuracy of the SVM model in classifying the capsaicin versus control conditions was 81%, p=6.22 × 10–8, binomial test, two-tailed (Figure 6A), and the average prediction–outcome correlation of the PCR model was r = 0.32, p=1.20 × 10–7, bootstrap test, two-tailed, mean squared error = 0.041 ± 0.004 (Figure 6B).

Model performance on an independent test dataset.

To provide unbiased estimates of prediction performance and test the generalizability of the module allegiance-based predictive models, we tested the support vector machine (SVM) and principal component regression (PCR) models on an independent test dataset (Study 2, n = 74). (A) We conducted a forced-choice test to compare the model responses for the capsaicin versus control conditions. Left: receiver-operating characteristics (ROC) curve. Right: model responses for different conditions. Each line connecting dots represents an individual participant’s paired data (red: correct classification; blue: incorrect classification). p-Value was based on a binomial test, two-tailed. (B) Actual versus predicted ratings. Each colored line (and symbol) represents individual participant’s ratings during the capsaicin run (red: higher r; yellow: lower r; blue: r < 0). p-Value was based on bootstrap tests, two-tailed.

Discussion

In this study, we investigated the reconfiguration of functional brain networks during sustained pain. The main findings of this study are summarized in Figure 7. We compared the brain network structures for the capsaicin versus control conditions (Figure 7A) and found that (i) the somatomotor dominant community was enlarged during sustained pain by incorporating multiple subcortical and frontoparietal regions, resulting in the emergence of the ‘pain supersystem’ (Zheng et al., 2019). (ii) The ventral primary somatomotor region (tongue area) was segregated from the dorsal primary somatomotor regions and (iii) integrated with the subcortical and frontoparietal regions. When we examined the brain network changes over time within the capsaicin condition (Figure 7B), we observed that (iv) the brainstem regions were connected to the limbic brain regions (e.g., hippocampus, amygdala, parahippocampal gyrus, and temporal pole) during the early period of pain, but soon these connections were lost as pain decreased. (v) During the late period, the frontoparietal regions were dissociated from the somatomotor-dominant community and formed their own community. Lastly, (vi) a module allegiance-based predictive model of pain ratings showed that cerebellar connections with the frontoparietal and subcortical regions were important for pain decrease.

Graphical summary of the main findings.

We summarized the main findings to determine the main conclusion of this study. (A) Three main findings from the comparisons between the functional brain architectures of sustained pain (i.e., capsaicin) versus pain-free resting state (i.e., control) conditions (related to Q1 and Q3 in Figure 1A). During sustained pain, (i) an extended somatomotor-dominant community emerged. More specifically, the ventral part of somatomotor cortex regions (i.e., tongue area) was (ii) segregated from the original somatomotor-dominant community and (iii) integrated with the subcortical (e.g., basal ganglia, thalamus) and frontoparietal (e.g., dorsolateral prefrontal cortex) regions during sustained pain. (B) Three main findings from the comparisons between the early versus late periods of sustained pain (related to Q2 and Q3 in Figure 1A). (iv) In the early period, the brainstem was connected to the limbic brain regions including hippocampus, amygdala, parahippocampal gyrus, and temporal pole (c.f., the spino-brachio-amygdaloid circuit). (v) In the late period, the frontoparietal regions were dissociated from the extended somatomotor-dominant community and formed their own community. (vi) The cerebellar connections to the subcortical and frontoparietal regions were predictive of pain decrease.

Although researchers have highlighted the importance of understanding the ‘dynamic pain connectome’ (Kucyi and Davis, 2015), previous studies were limited mainly to examining summary metrics of dynamic connectivity, such as mean and standard deviation (Bosma et al., 2018; Cheng et al., 2018; Cottam et al., 2018). Therefore, a detailed characterization of how multiple brain networks dynamically interact throughout pain has yet to be conducted. A few studies examined multiple brain states over a period of pain (Cheng et al., 2022; Lee et al., 2019b; Robinson et al., 2015), but their experimental designs and analyses were not suitable to associate the changes in brain states with different phases of pain, that is, from initiation, peak, and to remission. To better characterize the dynamic changes in functional brain community structures for different phases of pain experience, we used oral delivery of capsaicin to induce sustained and gradually decreasing pain for approximately 20 min. Although future studies need to examine whether our findings can be generalized to other stimulus modalities of sustained pain, this study provides a reliable experimental paradigm to study time-varying network changes during sustained pain and spontaneous pain decrease.

One of the most critical observations in this study is the emergence of an extended somatomotor-dominant brain community in response to sustained pain. This finding is consistent with a previous study in which subcortical and frontoparietal regions were integrated with the somatomotor network to constitute the ‘pain supersystem’ for a brief phasic heat pain stimulus (Zheng et al., 2019). The integration of the somatomotor and frontoparietal regions also correspond to the global workspace theory (Baars, 2002), in which the experience of pain requires frontoparietal involvement to enable recurrent processing of nociceptive information across multiple brain areas (Bastuji et al., 2016). Also, considering that subcortical structures, such as the thalamus, are important for the early processing of nociceptive signals from the peripheral nervous system, our finding suggests that the recruitment of both bottom-up (subcortical) and top-down (frontoparietal) systems and their integration are crucial to the experience of sustained pain. Previous studies that reported modular reorganization of the somatomotor network (Mano et al., 2018) and frontoparietal network (Barroso et al., 2021; Mano et al., 2018) in chronic pain are also in line with our results. Importantly, this systems-level integration occurred in a somatotopy-specific way—the SVM classifier (Figure 4) showed that the ventral primary somatomotor area, which subserves tongue sensation, was segregated from the other somatomotor regions and integrated with the subcortical regions during the early period of pain. These results are consistent with previous studies of sustained pain, which showed that the functional connectivity of the somatotopic primary somatomotor area was decreased for the other somatomotor areas, while being increased for other networks, such as the salience network (Cheng et al., 2022; Kim et al., 2015; Kim et al., 2013; Kim et al., 2019; Lee et al., 2019a).

In addition, we characterized how the functional brain networks were dynamically reconfigured over the period of sustained pain from the initiation to its resolution and found that the cerebellar regions played an important role in pain decrease. During the early period of pain, the subcortical and frontoparietal regions were integrated with the somatomotor network. However, as pain is resolved, these regions shifted their connections to multiple cerebellar regions. Although pain neuroimaging studies have consistently reported cerebellar activations (Moulton et al., 2010), its functional role in pain remains unclear. Interestingly, the cerebellum not only projects efferent nociceptive neurons to the thalamus (Liu et al., 1993), but it also has afferent and efferent interconnections via the thalamus to the dorsolateral prefrontal (Middleton and Strick, 2001) and inferior parietal (Clower et al., 2001) cortices, which are the main components of the frontoparietal network (Allen et al., 2005; Dosenbach et al., 2008). These neuronal connections implicate the cerebellum’s functional roles in top-down pain regulation. Furthermore, recent studies suggest that cerebellum plays an important role in pain-related prediction and its error (Ernst et al., 2019), which are keys to the fear-avoidance (Vlaeyen and Linton, 2012) and motivation-decision (Fields, 2018) model of sustained pain. Therefore, our findings provide additional evidence that the cerebellar regions may be important drivers of endogenous modulation of pain avoidance and spontaneous coping responses to sustained pain. A previous lesion study in which patients with cerebellar damage exhibited increased pain sensitivity and decreased placebo analgesia (Ruscheweyh et al., 2014) supports our interpretation.

The predictive modeling based on individual-level module allegiance values (Figures 4 and 5) provided novel findings complementary to the group-level consensus community analysis (Figures 2 and 3). For example, the dissociation between the ventral versus dorsal primary somatomotor regions during sustained pain was not evident in the group-level analysis of the consensus community structure. This finding suggests that group-level averaging could obscure fine-grained information of functional anatomy (Gordon et al., 2017) with distinct regions merging together as the same network community (Braga and Buckner, 2017). In this regard, our multivariate pattern-based predictive modeling approach can provide an alternative way to study dynamic changes in functional network structures while preserving fine-grained information about individuals (Finn et al., 2015; Rosenberg et al., 2017). Moreover, developing models to directly predict the pain ratings is helpful to complement the group-level analysis because the changes in consensus community structure over the early, middle, and late periods only indirectly reflect the different levels of pain. Lastly, the predictive modeling approach can provide information about the robustness and usefulness of the multilayer community detection method by allowing us to test the prediction performance for the discovery dataset and generalizability for the independent test dataset.

An interesting future direction would be to examine whether the current results can be generalized to clinical pain. Experimental tonic pain has been known to share similar characteristics with clinical pain (Rainville et al., 1992; Stohler and Kowalski, 1999). In addition, in a recent study, we showed that an fMRI connectivity-based signature for capsaicin-induced orofacial tonic pain can be generalized to chronic back pain (Lee et al., 2021). Therefore, a detailed characterization of the brain responses to tonic pain has the potential to provide useful information about clinical pain. However, there are also differences between the characteristics of capsaicin-induced tonic pain versus clinical pain. For example, clinical pain continuously fluctuates over time in an idiosyncratic pattern (Apkarian et al., 2001), whereas capsaicin-induced tonic pain showed a similar time-course pattern across the participants—that is, increasing rapidly and then decreasing gradually (Figure 1B). This typical time course of pain ratings has been reported in previous studies that used oral capsaicin (Berry and Simons, 2020; Lu et al., 2013; Ngom et al., 2001). Although we would expect our results to reflect the general pattern of brain network changes during the rise and fall of sustained pain, it remains an empirical question how much they will be generalizable across different clinical pain conditions. Interestingly, a recent fMRI study on the temporal summation of pain in fibromyalgia patients reported results similar to ours (Cheng et al., 2022), including the intra-network dissociation within the somatomotor network and the inter-network integration between the somatomotor and other networks during pain. Although we cannot directly examine whether the temporal summation of pain gave rise to these network-level changes due to the limitation of our experimental paradigm, these consistent findings between the two studies may suggest that our findings could be generalized to clinical conditions and that delineating the role of temporal summation of pain would be a promising future direction.

Note that Study 1 used a pain avoidance measure that is not yet fully validated in healthy participants. However, we chose to use the pain avoidance measure, which can provide integrative information on the multidimensional aspects of pain (Melzack, 1999; Waddell et al., 1993). It also has a clinical implication considering that the maladaptive associations of pain avoidance to innocuous environments have been suggested as a putative mechanism of transition to chronic pain (Vlaeyen and Linton, 2012). Lastly, the avoidance measure can provide a common scale across different modalities of aversive experience, allowing us to compare their distinct brain representations (Ceko et al., 2022) or test the specificity of their predictive models (Lee et al., 2021; Appendix 1—figure 13). Although the psychometric properties of the pain avoidance measure should be a topic of future investigation, we expect that the pain avoidance measure would have a high level of convergent validity with pain intensity given the observed similarity between pain avoidance (Study 1) and pain intensity (Study 2) in their temporal profiles. The generalizability of our PCR model across studies 1 and 2 also supports this speculation. However, there would also be situations in which pain avoidance is dissociated from pain intensity. For example, capsaicin can be experienced to be intense but less aversive or even appetitive in some contexts, such as cravings for spicy food (Stevenson and Yeomans, 1993). In addition, the gradual rise in avoidance ratings during the late period of the control condition in Study 1 would not be observed if the intensity measure was used. Future studies need to examine the relationship between pain avoidance and the other pain assessments and the advantage of using the pain avoidance measure.

This study also had some limitations. First, with the current experimental paradigm, it is difficult to dissociate the pain duration from the level of pain because the delivery of oral capsaicin commonly induces initial bursting and then a gradual decrease of pain over time. Though we aimed to model the effects of pain duration and pain avoidance ratings with our two primary analyses, that is, consensus community detection and predictive modeling, we cannot fully dissociate the impact of time duration versus pain ratings. Second, although the prediction performances of our predictive models were significant, their effect sizes were moderate (SVM model: 81% accuracy; PCR model: mean r = 0.32). These moderate levels of model performance may be due to information loss during a few of the multilayer community detection analysis steps, for example, thresholding and binarization of the connectivity matrices. For example, a previous predictive model of sustained pain based on unthresholded functional connectivity values showed better performances (Lee et al., 2021). However, the previous model provides a limited level of mechanistic understanding because of the high dimensionality of the model and its features. In addition, functional connectivity itself provides only limited insight into how functional brain networks are structured and reconfigured over time. Considering that the mechanistic understanding of brain responses to sustained pain is the main focus of this study, our moderate levels of model performance may be acceptable, but future studies can still investigate how to improve the predictive performance of the predictive models based on time-varying network features. Third, the choice of two parameters for the multilayer community detection algorithm (i.e., intra- and inter-layer coupling parameters, γ and ω) can affect the overall results (Mucha et al., 2010). Here, we followed the conventional choice (γ = 1 and ω = 1) from a previous study (Bassett et al., 2015), but the effects of these parameters on our results remain unclear. We used the predictive modeling approach and tested the model generalizability on an independent dataset to avoid excessive reverse-inference from these parameter-sensitive results alone. However, future studies will have to assess the effects of parameter choices on the overall results in a systematic way. Lastly, the optimization of the network density threshold to maximize the differences in a priori network attributes between the capsaicin versus control conditions may influence the results comparing the community structures between the two conditions. However, our main results were based on temporal dynamics across all time-bins, whereas the global-level network attributes that we used to determine the level of threshold were based on the averaged data across all time-bins. In addition, our SVM and PCR models were generalized to the independent test dataset, which was not used to determine the threshold level. Therefore, the confounding effect of thresholding on our main results should be minimal.

Overall, this study contributes to a deeper understanding of how multiple brain systems dynamically interact in response to pain, paving the way for developing novel brain-based interventions for sustained pain. Although further studies are needed to show that our findings can be generalized to clinically relevant sustained pain conditions, we believe that this study provides new insights into the reconfiguration of functional brain networks for pain and a novel framework to investigate the neural mechanisms of sustained pain from a dynamic network perspective.

Materials and methods

Participants

Study 1 (discovery) dataset included 48 healthy, right-handed participants (age = 22.8 ± 2.4 [mean ± SD], 21 females) after we excluded 4 participants who provided avoidance rating scores higher in the control run than the capsaicin run and 1 participant whose brain coverage of MRI was insufficient. This dataset was included in a previous publication as an independent test dataset (as Study 3 dataset) (Lee et al., 2021). Study 2 (independent test) dataset included 74 healthy, right-handed participants (age = 22.1 ± 2.4 [mean ± SD], 34 females). All participants were recruited from the Suwon area in South Korea. The institutional review board of Sungkyunkwan University approved the study (IRB 2017-05-001). All participants provided written informed consent. The preliminary eligibility of the participants was determined through an online questionnaire. Participants with psychiatric, neurological, or systemic disorders and MRI contraindications were excluded.

Capsaicin stimulation

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In Study 1, we applied capsaicin-containing hot sauce (a food ingredient, Capsaicin Hot Sauce from Jinmifood, Inc) to the participants’ tongues to induce tonic pain with minimal risk. We did not include pre-calibration to match the subjective level of pain. To deliver the hot sauce inside the scanner, we dropped a small amount of hot sauce (0.1 ml) onto filter paper (2 cm × 6.5 cm). We spread the hot sauce in a circular format (diameter = 1 cm) on the upper 1/3 of the filter paper. While the participants were lying in the scanner, we handed the filter paper to the participants. The participants carefully placed the capsaicin side of the paper on their tongue (the participants had an opportunity to practice this procedure with a paper without capsaicin inside of the scanner). We then asked them to close their mouths. After 30 s, we asked them to open their mouths and put the paper on the towel on their chest. We then asked participants to keep opening their mouths and breathing only through the mouth for 1 min to prevent the capsaicin liquid from flowing into the throat. In this way, the liquid dried up, and the capsaicin settled down on a specific area of the tongue. After 1 min, we asked the participants to close their mouths (and keep closing their mouths) while starting the fMRI scan. The participants provided their ratings using an MR-compatible trackball device when a rating scale appeared on the screen. We used this particular procedure for the following reasons: (i) it reduces the risk of coughing in the scanner, (ii) can keep the pain within a tolerable range while maximizing the pain intensity, and (iii) simplifies the delivery method without additional equipment.

In Study 2, we used an almost similar capsaicin delivery procedure, but there were also some differences. First, we used a smaller amount of hot sauce (0.05 ml) because the scan duration of the independent test dataset was shorter (approximately half) than the duration of the discovery dataset. Second, capsaicin was delivered in the middle of the scan. More specifically, participants held the filter paper with their hands for the first 43 s (13 s for additional removal of volumes for image stabilization) since the start of the scan, and then placed the filter paper on their tongue and closed their mouths for 20 s. The participants then removed the filter paper, opened their mouths for 20 s, and then closed their mouths. The total duration of sustained pain was approximately 10 min and 20 s.

Aversive tastant stimulation

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To induce aversive, but not painful, oral sensation for the specificity testing, we used quinine that has a bitter taste in Study 1. A small amount of quinine sulfate (50 mg) was dissolved in distilled water (0.1 ml), which was sufficient to induce aversive and bitter taste. The quinine solution was subsequently dropped onto a filter paper, and the rest of this procedure was same as the ‘Capsaicin stimulation’ above.

Aversive odor stimulation

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We used fermented skate as an additional aversive stimulus for specificity testing in Study 1. The fermented skate (Hongeo) is a food in South Korea famous for its bad smell. We chose to use the fermented skate among many other options (including multiple strong-smell cheese, Maroilles Fauquet, Bons Mayennais Lingot, Gorgonzolla Piccante) based on aversiveness ratings in a pilot study (n = 15). We attached a slice of fermented skate, covered with a filter paper, to the interior nasal part of a mask. The mask was designed to cover the nose and the mouth. For the delivery, we first moved the bed out of the scanner and unlocked and lifted the head coil. While the participants were breathing through their mouth, we placed the mask to cover participants’ nose and mouth. After the head coil was installed again, the participants were re-entered into the scanner. We asked the participants to start breathing through the nose after we started the scanning. We instructed the participants to breathe only through the nose until the end of the scan.

Experimental design

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In Study 1, there were four experimental conditions: (i) capsaicin, (ii) bitter taste, (iii) aversive odor, and (iv) control. After a structural scan, we administered the four condition runs, and the order of the conditions was counterbalanced across participants. Each run lasted for 20 min, and participants provided the avoidance ratings continuously throughout the run. We designed the experiment to have long scans to capture the full rise and fall of each sensation. For the rating scale, we used a modified version of the gLMS (Bartoshuk et al., 2004): the anchors began with ‘Not at all’ [0] to the far left of the scale and continued to the right in a graded fashion with anchors of ‘A little bit’ [0.061], ‘Moderately’ [0.172], ‘Strongly’ [0.354], and ‘Very strongly’ [0.533], until ‘Most (I never want to experience this again in my life)’ [1] on the far right. To prevent participants from falling asleep and to help maintain a certain level of alertness during the scan, we used an intermittent simple response task, in which the color of the rating bar on the screen was changed from orange to red for 1 s every minute with a jitter, and the participants had to respond to the color change by clicking the button on the trackball device. During the preprocessing of the data, we included additional regressors of the color changes and button clicks to remove confounding effects related to the task. After the scan, we asked the participants multiple post-scan questions regarding their thought contents during the scan, which were not included in this study.

Study 2 had three experimental conditions: (i) capsaicin, (ii) control, and (iii) phasic heat stimulation. We used only the capsaicin and control runs. The control and capsaicin runs were conducted at the start and the end of the session, respectively. The control run lasted for 6 min and 13 s, and the capsaicin run lasted for 11 min and 43 s. After the first 13 s (13 s data were discarded before further analyses), participants provided intensity ratings continuously throughout the run using a trackball. After the scan, we asked the participants multiple post-scan questions regarding their thought contents during the scan.

fMRI data acquisition

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Whole-brain fMRI data were acquired using a 3T Siemens Prisma scanner at Sungkyunkwan University. High-resolution T1-weighted structural images were also acquired. Functional EPI images were acquired with TR = 460 ms, TE = 29.0 ms, multiband acceleration factor = 8, field of view = 248 mm, 82 × 82 matrix, 3 × 3 × 3 mm3 voxels, 56 interleaved slices. Stimulus presentation and behavioral data acquisition were controlled using MATLAB (MathWorks) and Psychtoolbox (http://psychtoolbox.org/).

fMRI data preprocessing

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Structural and functional MRI data were preprocessed using our in-house preprocessing pipeline (https://github.com/cocoanlab/surface_preprocessing, Lee and Woo, 2020) based on AFNI, FSL, and Freesurfer. This is similar to the Human Connectome Project (HCP) preprocessing pipeline (Glasser et al., 2013). For structural T1-weighted images, magnetic field bias was corrected, and non-brain tissues were removed using Freesurfer. Nonlinear transformation parameters projecting native T1 space to MNI 2 × 2 × 2 mm3 template space were also calculated using FSL. For functional EPI images, the initial volumes (22 images [10 s] for Study 1, 29 images [13 s] for Study 2) of fMRI images were removed to allow for image intensity stabilization. Then, the images were motion-corrected using AFNI and distortion-corrected using FSL. These EPI images were co-registered to T1-weighted images using the boundary-based registration (BBR) technique (Greve and Fischl, 2009) that used FSL for the first registration and Freesurfer for refinement, similar to the HCP pipeline. We then removed motion-related signals from co-registered EPI images using ICA-AROMA (Pruim et al., 2015). Additional preprocessing modules, including (i) removal of nuisance covariates, (ii) linear de-trending, and (iii) low-pass filtering at 0.1 Hz, were combined and conducted in one step using the 3dTproject function in AFNI to avoid introducing unwanted artifacts (Lindquist et al., 2019). We included mean BOLD signals from white matter (WM) and cerebrospinal fluid (CSF) (Pruim et al., 2015), and timepoints where intermittent arousal maintenance tasks appeared (total of 20 times) as nuisance covariates. For computational efficiency in further analyses, including community detection, these de-noised EPI images were resampled to 4 × 4 × 4 mm3 spatial resolution and masked with an individually defined gray matter boundary image. This gray matter mask obtained using Freesurfer was dilated and eroded five times to create smooth edges, and then resampled to 4 × 4 × 4 mm3 spatial resolution to match the spatial dimension of EPI images.

Functional connectivity calculation and proportional thresholding

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Whole-brain voxel-wise functional connectivity was computed using Pearson’s correlation. To determine the optimal threshold level, we tested multiple network density thresholding options (0.01, 0.05, 0.10, 0.20, 0.30, and 0.40) and compared the global-level network characteristics between the capsaicin versus control conditions. Five network attributes, including assortativity, transitivity, characteristic path, global efficiency, and modularity, were used for comparison. We calculated the network attributes using the Brain Connectivity Toolbox (https://sites.google.com/site/bctnet/; Rubinov and Sporns, 2010).

A brief description of these measures is as follows: (i) Assortativity: This attribute is related to how often each node (here, a voxel) is connected to the other nodes that have a similar number of links (here, functional connectivity) (Newman, 2002). It can be measured using Pearson’s correlation between the degrees of every pair of connected nodes. High assortativity means that there are mutual connections between high-degree hub nodes, reflecting the overall resilience of a network. The ‘assortativity_bin’ function of the toolbox was used. (ii) Transitivity: This attribute is related to how often the two nodes that are connected to the same node are also connected to each other (Newman, 2003) and measured as the ratio of the number of interconnected triplets of nodes to the number of all triplets of nodes. High transitivity indicates that nodes of a network are more likely to be clustered together. The ‘transitivity_bu’ function is used. (iii) Characteristic path: This attribute is the average of the shortest path between all pairs of connected nodes (estimated using ‘distance_bin’ function), reflecting the functional dissociation of a network. A high characteristic path length suggests that the network is in a disintegrated state. (iv) Global efficiency: This attribute is the average of the inverse of the shortest path between all pairs of connected nodes (estimated from ‘distance_bin’ function), reflecting the functional integration of a network. A high global efficiency suggests that a network is in an integrated state. (v) Modularity: This attribute is related to how much a network can be clearly divided into a set of modular structures. A network with fewer between-module connections and more within-module connections has a higher level of modularity. The Louvain community detection algorithm (Blondel et al., 2008) was used to determine the inherent community structure of a network (‘community_louvain’ function).

Using these global-level network attributes, we calculated the group-level z-statistics of the differences between conditions for different network density levels. Then, we selected the density level that maximized the sum of absolute z-scores as the optimal threshold level. As shown in Appendix 1—figure 4, we selected a network density of 0.05 (i.e., only the top 5% connections were survived) as the optimal threshold level.

Dynamic functional connectivity (moving time window)

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Whole-brain voxel-wise connectivity was computed for every 2 min nonoverlapping time window within individuals using Pearson’s correlation. Proportional thresholding at a predetermined network density of 0.05 was applied to each of these dynamic connectivity matrices (Study 1 dataset: 10 matrices per run and participant; Study 2 dataset: five matrices per participant for the capsaicin run and three matrices per participant for the control run), without binarization of connectivity values.

Multilayer community detection

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To identify the time-evolving network community structure, we used the multilayer community detection approach (Mucha et al., 2010), which is a generalized version of the Louvain algorithm (Blondel et al., 2008). This method finds the optimal community structure that maximizes the ‘multilayer modularity’ function Q, which is defined as

Q=12μijlrAijl- γlPijlδlr+δijωjlrδgil,gjr ,

where Aijl is the adjacency matrix of layer l (i.e., dynamic connectivity matrices), Pijl is the optimization null model of layer l (Newman–Girvan null model Pij=kikj2m , where ki=jAij and m=12ijAij), γl is the intra-layer resolution parameter of layer l, ωjlr is the inter-layer coupling parameter between node j in layer l and node j in layer r, µ is the total sum of edge weights μ=12jrkjr+lωjlr , the Kronecker delta δgil,gjr is equal to 1 when the community assignment of node i in layer l (gil) is the same as the community assignment of node j in layer r (gjr), and equal to 0 otherwise. Here, we set all the γl and ωjlr parameters to 1 as in previous studies (Bassett et al., 2015).

This modularity-maximizing community detection method is inherently nondeterministic (NP-hard problem). Therefore, we iterated this procedure 100 times to determine the consensus community structure, as described in previous studies (Bassett et al., 2013; Bassett et al., 2015). Details of the within-individual consensus community detection procedure are as follows:

  1. Module allegiance, which is defined as a matrix T whose element Tij is equal to 1 if nodes i and node j are assigned to the same community and 0 otherwise, was calculated for each iteration of multilayer community detection.

  2. The module allegiance matrices from (i) were averaged across all iterations.

  3. Null models of module allegiance matrices were obtained by repeating (i) and (ii) with a random permutation of the original community assignments. Permutation was performed once per each of the original community assignments.

  4. The averaged allegiance matrix from (ii) was thresholded at the maximum value of the null-model allegiance matrix from (iii).

  5. The Louvain community detection algorithm was applied to the thresholded allegiance matrix with 100 iterations. If all the 100 community assignment vectors were identical, the community assignment was determined to be the consensus community. Otherwise, steps (i)–(v) were repeated, using the 100 community assignment vectors as the input of (i).

Using this within-individual consensus community detection procedure, we obtained a consensus community for each layer (i.e., 2 min time window), run, and participant.

Projecting individuals’ community assignments into MNI space

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For analyses requiring group-level inferences, we computed one-to-one mapping between the voxels in an individual’s native space and the voxels in the MNI space as follows: First, every voxel within the resampled gray matter mask (4 × 4 × 4 mm3 native space; obtained from the preprocessing steps) was labeled with unique indices. Next, the labeled gray matter mask was resampled to 2 × 2 × 2 mm3 native space, projected to the MNI space using the pre-computed parameters for nonlinear transformation, and then resampled to 4 × 4 × 4 mm3 space, with the nearest neighbor interpolation method. The output contains voxel labels indicating the original location in the native space, which then can be used as a mapping rule for projecting community assignments defined in the native space onto the MNI space.

Group-level consensus community detection (Figures 2 and 3)

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To examine the group-level consensus community structure, we performed the following consensus community detection procedure:

  1. The individual-level consensus community assignments in the native space were projected onto the MNI space using the pre-computed one-to-one mapping rule.

  2. The projected module community assignments were converted into module allegiance matrices.

  3. The module allegiance matrices were averaged across all individuals and across time depending on the type of consensus community. For the analyses on the capsaicin versus control conditions (as in Figure 2), allegiance values were averaged across all time-bins for each run. For the analyses on the early/middle/late periods (as in Figure 3), the allegiance values of the capsaicin run were averaged within three separate time-bins; that is, averaging 1–3, 4–7, and 8–10 layers into early, middle, and late-period module allegiance matrices, respectively.

  4. Null models of module allegiance matrices were obtained by repeating (i)–(iii) with a random permutation of the original community assignments. Permutation was performed once per each of the original community assignments.

  5. The averaged allegiance matrices from (iii) were thresholded at the maximum value of the null-model allegiance matrix from (iv).

  6. The Louvain community detection algorithm was applied to the thresholded allegiance matrix with 100 iterations. If all the 100 community assignment vectors were identical, the community assignment was determined to be the consensus community. Otherwise, steps (i)–(vi) were repeated using the 100 community assignment vectors as the input of (i).

We excluded voxels that were disconnected after the iterative consensus community detection procedure or that were assigned to a community with a small number of voxels, that is, <20 voxels.

Permutation tests for regional differences in community structures

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To test the statistical significance of the voxel-level difference of consensus community structures (Figures 2 and 3), we performed the following Phi-test (Alexander-Bloch et al., 2012; Lerman-Sinkoff and Barch, 2016). First, for each given voxel, we compared the community label of the voxel to the community label of all the voxels, generating a list of voxel-seed module allegiance values that allow quantitative comparison of voxel-level community profile (e.g., [1, 0, 1, 1, 0, 0,…], whose element is equal to 1 if the seed and target voxels were assigned to the same community and 0 otherwise). Next, a correlation coefficient was calculated between the module allegiance values of the two different brain community structures (i.e., capsaicin versus control, and early versus late). This correlation coefficient is an estimate of the regional similarity of community profiles (here, the correlation coefficient is Phi coefficient because module allegiance is a binary variable). To estimate the statistical significance of the Phi coefficient, we performed permutation tests, in which we randomly shuffled the labels and then obtained the group-level consensus community structures from the shuffled data. Then, the Phi coefficient between the module allegiance values of the two shuffled consensus community structures was calculated. We repeated this procedure 1000 times to generate the null distribution of the Phi coefficient for each voxel. Lastly, we examined the probability to observe a smaller Phi coefficient (i.e., a more dissimilar community profile) than the one from the non-shuffled original data, which corresponds to the p-value of the permutation test. All the p-values were one-tailed as the hypothesis of this permutation test is unidirectional.

Module allegiance-based predictive modeling (Figures 4 and 5)

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To quantify the relative contribution of network communities to sustained pain experience, we conducted predictive modeling using module allegiance values as input features. We first projected the whole-brain atlas onto each individual’s native brain space. The whole-brain atlas originally consisted of 265 regions, but we had to exclude two cerebellar regions (vermis VIIb and X) because these regions had a small number of voxels (23 and 42 in 2 × 2 × 2 mm3 space, respectively) and thus were not successfully transformed to native space in a few participants, resulting in a total of 263 regions. This atlas included 200 cortical regions from the Schaefer atlas (Schaefer et al., 2018), 61 subcortical and cerebellar regions from the Brainnetome atlas (Fan et al., 2016), and the periaqueductal gray and brainstem regions used in previous studies (Beissner et al., 2014; Roy et al., 2014). Then, voxel-wise module allegiance matrices were obtained from the individual’s consensus community structure and grouped and averaged into 263 × 263 region-wise allegiance matrices.

For the classification problem, as shown in Figure 4, we averaged 10 time-varying region-wise module allegiance matrices for each run (capsaicin and control runs). Then we trained an SVM classifier to determine whether a participant was in pain or not (i.e., capsaicin versus control conditions) using region-level allegiance matrices across participants (i.e., 34,453 allegiances × 2 runs × 48 participants). A regularization hyperparameter C was set to 1, which is a conventional choice for SVM. To obtain unbiased estimates of classification performance, we used leave-one-participant-out cross-validation on the training dataset (Study 1) and tested the model on an independent test dataset (Study 2). To quantify the model performance, we conducted a forced two-choice classification test, which directly compared the predicted values (here, distances from the hyperplane) of two conditions for each individual. This test did not require the assumption that all participants’ brain responses to stimuli are on the same scale (Wager et al., 2013).

For regression-based modeling, as shown in Figure 5, we trained a PCR model (Hastie et al., 2009) to predict pain avoidance ratings (10 time-bins × 48 participants) based on concatenated module allegiance matrices of capsaicin run across 10 time-bins and participants (i.e., 34,453 allegiances × 10 time-bins × 48 participants). We selected 14 PCs for the regression modeling because the PC number yielded the best leave-one-participant-out cross-validated predictive performance on the training dataset (Study 1) (Appendix 1—figure 10). To overcome the potential bias in the performance estimation due to the optimal selection of PC number, we additionally conducted nested cross-validation that has double loops of leave-one-participant-out cross-validation; the inner loop where the hyperparameter (i.e., the PC number) was selected, and the outer loop where the actual prediction was done using the hyperparameters chosen from the inner loop. Since the hyperparameter tuning and testing were separated into the inner and outer loops, this procedure provides a less biased estimate of prediction performance even in the training dataset (Study 1). We further tested the prediction model on an independent test dataset (Study 2).

To identify important features for the classification and prediction models, we used bootstrap tests. We randomly sampled 48 participants 10,000 times with replacement and conducted model training with the resampled data. We calculated the statistical significance of predictive weights using z-statistics based on 10,000 samples of 34,453 predictive weights.

Seed-based allegiance analysis (Figures 4F and 5D and E)

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Using the voxel-wise module allegiance in the native space, we calculated the individual’s seed-based module allegiance map, which consisted of averaged module allegiance values between voxels within a seed region and the rest of the brain. This seed-based allegiance map was then transformed into MNI space using the pre-computed one-to-one mapping between the native and MNI spaces and averaged across participants. We then conducted a paired t-test between the capsaicin versus control conditions for the classification analysis (Figure 4F) and between the late versus early period of pain for the regression analysis (Figure 5D and E).

Model response calculation (Figure 6)

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To test the allegiance-based classification and prediction models, we calculated a model response score (the intensity of pattern expression) using a dot product of vectorized functional connectivity with model weights:

Modelresponse=wx=i=1nwixi

where n is the number of connections within the connectivity-based predictive models, w is the connection-level predictive weights, and x is the test data.

Statistical analysis

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In Figures 4D and 6A, we used binomial tests for significance testing of whether forced two-choice classification accuracies were significantly higher than the probability of chance (here, 50%). The sample sizes for Figures 4D and 6A were n = 48 and 74, respectively. In Figures 5B and 6B, we conducted bootstrap tests with 10,000 iterations to test whether the sampling distribution of the within-individual prediction–outcome correlations was significantly higher than zero. Note that we performed the r-to-z transformation before the bootstrap tests. The sample sizes for Figures 5B and 6B were n = 48 and 74, respectively. In Figures 4E and 5C, we used bootstrap tests (with 10,000 iterations) to threshold each model’s predictive weights. In Figures 4F and 5D and E, we used paired t-tests either between the seed-based allegiance maps of the capsaicin versus control conditions (Figure 4F) or between the seed-based allegiance maps of the late versus the early period of pain (Figure 5D and E). Further details of the statistical analyses are provided in each relevant description in the article.

Data availability

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All the data that were used to generate the main figures are available at https://github.com/cocoanlab/brain_reconfig_pain, (copy archived at swh:1:rev:077a65b3d3905182a207349919697e550226fbe5, Lee, 2022b). The data that were not used in the main figures will be shared upon request.

Code availability

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The code for generating the main figures is available at https://github.com/cocoanlab/brain_reconfig_pain. In-house MATLAB codes for fMRI data analyses are available at https://github.com/canlab/CanlabCore, (copy archived at swh:1:rev:8d22b1b51ce3696ecd81c3f614e972791ea23df5, Sun, 2022) and https://github.com/cocoanlab/cocoanCORE, (copy archived at swh:1:rev:cdcb8a60a65e6dfb8edbd536862d760b54dd39a4, Lee, 2022a).

Appendix 1

Results

Specificity analysis (Appendix 1—figures 1113)

To examine whether the predictive models (i.e., SVM and PCR models) were specific to pain and not influenced by confounding noises, we conducted additional specificity analysis assessing the independence of the models from head movement and physiology variables and specificity of our models to pain versus nonpainful aversive conditions (i.e., bitter taste and aversive odor) in Study 1.

First, we examined the overall changes of framewise displacement (FD) (Power et al., 2012), heart rate (HR), and respiratory rate (RR) in sustained pain (Appendix 1—figure 11). For the univariate comparison between capsaicin versus control conditions (Appendix 1—figure 11A), the results showed that, as expected, capsaicin condition caused significant changes in motion and autonomic responses. The mean FD and HR were significantly higher, and the RR was lower in the capsaicin condition compared to the control condition (FD: t47 = 5.30, p=2.98 × 10–6; HR: t43 = 4.98, p=1.10 × 10–5; RR: t43 = –1.91, p=0.063, paired t-test). For the temporal changes of movement and physiology variables (Appendix 1—figure 11B), the results showed that the increased motion and autonomic responses are more prominent in the early period of pain. The 10-binned (2 min per time-chunk) FD and HR showed decreasing trend while the RR showed increasing trend over time in capsaicin condition. Additional univariate comparisons between early (1–3 bins, 0–6 min) versus late (8–10 bins, 14–20 min) period of capsaicin condition showed that differences were significant for FD and HR (FD: t47 = 6.45, p=8.12 × 10–8; HR: t43 = 6.52, p=6.41 × 10–8; RR: t43 = –1.61, p=0.11, paired t-test). This suggests that while participants were experiencing tonic pain, particularly in the early period, motion and heart rate was increased but breathing was slowed. Note that we needed to exclude four participants’ data due to technical issues with physiological data acquisition.

Next, we examined whether the head movement and physiological responses are the main driver of our predictive models (Appendix 1—figure 12). For all the original model responses from SVM model (2 conditions × 44 participants = 88), we regressed out the mean FD, HR, and RR (concatenated across conditions and participants as the SVM model was trained) and calculated the classification accuracy (Appendix 1—figure 12A). Although the model responses were controlled for movement and physiology variables, the SVM model still showed a high classification accuracy for the capsaicin versus control conditions in a forced-choice test (n = 44, accuracy = 89%, p=1.41 × 10–7, binomial test, two-tailed). Similarly, for all the original model responses from PCR model (10 time-bins × 44 participants = 440), we regressed out the 10-binned FD, HR, and RR (concatenated across time-bins and participants as the PCR model was trained) and calculated the within-individual prediction–outcome correlation (Appendix 1—figure 12B). Again, the PCR model showed a significantly high predictive performance (n = 44, mean prediction–outcome correlation r = 0.20, p=0.003, bootstrap test, two-tailed, mean squared error = 0.159 ± 0.022 [mean ± SEM]) while controlling for movement and physiology variables. These results suggest that our SVM and PCR models capture unique variance in tonic pain above and beyond the head movement and physiological changes.

Lastly, we examined the specificity of our predictive models to pain by testing the models onto the nonpainful but tonic aversive conditions including bitter taste (induced by quinine) and aversive odor (induced by fermented skate) (Appendix 1—figure 13). All the model responses were obtained using leave-one-participant-out cross-validation. The results showed that the overall model responses of SVM model for bitter taste and aversive odor conditions were higher than those for control conditions, but lower than capsaicin condition (Appendix 1—figure 13A). Classification accuracy between capsaicin versus bitter taste and versus aversive odor was all significantly high (capsaicin versus bitter taste: accuracy = 79%, p=6.17 × 10–5, binomial test, two-tailed, Appendix 1—figure 13C; capsaicin versus aversive odor: accuracy = 83%, p=3.31 × 10–6, binomial test, two-tailed, Appendix 1—figure 13E), suggesting the specificity of SVM model to pain. Similarly, the temporal trajectories of the model responses of PCR model for bitter taste and aversive odor conditions were not overlapping with that of the capsaicin condition (Appendix 1—figure 13B). Furthermore, the model responses of bitter taste and aversive odor conditions do not have significant relationship with the actual avoidance ratings (bitter taste: mean prediction–outcome correlation r = 0.05, p=0.41, bootstrap test, two-tailed, mean squared error = 0.036 ± 0.006 [mean ± SEM], Appendix 1—figure 13D; aversive odor: mean prediction–outcome correlation r = 0.12, p=0.06, bootstrap test, two-tailed, mean squared error = 0.044 ± 0.004 [mean ± SEM], Appendix 1—figure 13F), suggesting the specificity of PCR model to pain.

Overall, we have provided evidence that the module allegiance-based models can predict pain ratings above and beyond the movement and physiological changes, and are more responsive to pain compared to nonpainful aversive conditions, which suggest the specificity of our results to pain.

Between-individual predictive performances

For SVM model, we evaluated the classification accuracy for the capsaicin versus control conditions across all the participants, instead of the forced-choice test that compared the two conditions within individuals. The results for Study 1 were as follows: accuracy with an optimal threshold = 88% (p=1.82 × 10–14, binomial test, two-tailed), 85% sensitivity, 90% specificity, area under the curve (AUC) = 0.94. The results for Study 2 were as follows: accuracy with an optimal threshold = 76% (p=2.84 × 10–10, binomial test, two-tailed), 72% sensitivity, 80% specificity, AUC = 0.80.

For PCR model, we calculated the correlation between mean pain ratings and mean model responses (i.e., between-individual prediction–outcome correlation) for capsaicin condition. The results for Study 1 were as follows: r = 0.41, p=0.004, one-sample t-test, two-tailed, mean squared error = 0.024. The results for Study 2 were as follows: r = 0.27, p=0.018, one-sample t-test, two-tailed, mean squared error = 0.022.

Appendix 1—figure 1
Avoidance rating in Study 1.

(A) Participants reported their subjective ratings of avoidance continuously throughout the scan by moving the yellow-colored rating bar on the rating scale (gray triangle). The rating question was “how much do you want to avoid this experience in the future?” During the scan, we showed only the two extreme descriptors (i.e., ‘Not at all’ and ‘Most’) on the screen. (B) To enhance the reliability, we provided a detailed explanation of the locations and meaning of the anchors before the scan. The anchors included ‘Not at all,’ ‘A little bit,’ ‘Moderately,’ ‘Strongly,’ ‘Very strongly,’ and ‘Most.’ These anchors were not displayed during the scan.

Appendix 1—figure 2
Pain avoidance ratings of individual participants.

These plots show the pain avoidance ratings of 48 participants from Study 1. The horizontal dashed lines indicate the anchors of the general labeled magnitude scale (gLMS). The vertical dashed lines show the time boundaries for the early, middle, and late periods of sustained pain. The red and blue solid lines show the pain avoidance ratings (red for the capsaicin run and blue for the control run).

Appendix 1—figure 3
Distribution of avoidance ratings in Study 1.

We used the violin and box plots to show the distributions of the avoidance ratings, for the timepoints of 0 min, 6 min, 14 min, and 20 min. For each timepoint, 10 s time window was applied for averaging the ratings. The box was bounded by the first and third quartiles, and the whiskers stretched to the greatest and lowest values within median ± 1.5 interquartile range. The red dots outside of the whiskers were marked as outliers. Each gray line between gray dots represents each individual participant’s paired data.

Appendix 1—figure 4
Global-level network attributes.

We compared the five global-level network attributes (i.e., assortativity, transitivity, characteristics path, global efficiency, and modularity) of the capsaicin and control conditions (z-values from paired z-test) across different levels of network density. Note that the overall differences of network attributes between the capsaicin versus control conditions were maximal at the network density of 0.05. All network attributes were measured from the binarized static connectivity matrices (for details, see ‘Materials and methods’).

Appendix 1—figure 5
Reconfiguration of the consensus Community 2.

(A) We compared the spatial distributions of the consensus Community 2 (i.e., the somatomotor dominant community) of the capsaicin and control conditions. The purple regions show the overlap between the two conditions, and the red and blue regions show the unique regions for the capsaicin and control conditions, respectively. Blue region was comparatively small, indicating that the somatomotor community mainly expanded primarily during the capsaicin condition compared to the control condition. (B) The spatial distribution of the 10 canonical brain networks within the consensus Community 2. The expansion of Community 2 during the capsaicin condition (red regions in [A]) was mainly driven by the brain voxels within the frontoparietal network (e.g., dorsolateral prefrontal cortex and inferior parietal cortex) and the subcortical regions (e.g., thalamus and basal ganglia).

Appendix 1—figure 6
Permutation tests for the community structure changes.

We examined the regional differences in community structures between (A) the capsaicin versus control or (B) the early versus late period of pain. To quantitatively compare the group-level consensus community structures between the capsaicin versus control conditions and the early versus late periods, we first obtained a seed-based module allegiance map for each voxel (i.e., using each voxel as a seed). Then, we calculated a correlation coefficient of the module allegiance values between two different conditions for each voxel. This correlation coefficient can serve as an estimate of the voxel-level similarity of the consensus community profile. Because module allegiance is a binary variable, these correlation values are Phi coefficients (φ). To calculate the statistical significance of the Phi coefficient, we conducted permutation tests, in which we randomly shuffled the condition labels in each participant and obtained the group-level consensus community structure for each shuffled condition. Then, we calculated the voxel-level correlations of the module allegiance values between the two shuffled conditions. We repeated this procedure 1000 times to generate the null distribution of the Phi coefficients, and calculated the proportion of null samples that have a smaller Phi coefficient (i.e., a more dissimilar regional community structure) than the nonshuffled original data, which is the p-value, one-tailed. First row: -log10p values from the permutation tests. Second row: thresholded maps based on the p-values from the permutation tests. Colored as purple for false discovery rate (FDR)-corrected q < 0.05, pink for uncorrected p<0.05, and pale pink for nonsignificance. Third and last row: Group-level consensus community structures from Figures 2 and 3.

Appendix 1—figure 7
Reconfiguration of the consensus communities 2, 3, and 6.

The reconfiguration pattern of the community assignments of the brain voxels that were assigned to (A) the consensus Community 2 (somatomotor network dominant community) in the early period of sustained pain, (B) the consensus Community 3 (default-mode network dominant community) in the early period of sustained pain, and (C) the consensus Community 6 (frontoparietal network dominant community) in the late period of sustained pain. E, early; L, late.

Appendix 1—figure 8
Reconfiguration of the consensus communities 4 and 5.

The reconfiguration pattern of the community assignments of the brain voxels that were assigned to (A) the consensus Community 5 (limbic network dominant community) in the early period of sustained pain, and (B) the consensus Community 4 (cerebellum-dominant community) in the late period of sustained pain. E, early; L, late.

Appendix 1—figure 9
Classifier weights of the somatosensory and insular cortical regions.

(A) Thresholded connections (false discovery rate [FDR] q < 0.05, which corresponds to uncorrected p<0.003, two-tailed, bootstrap test with 10,000 iterations) showing the predictive weights between the somatosensory and insular cortical regions and the other remaining whole brain regions. Line thickness and transparency indicate the absolute magnitude of predictive weights. There were strong negative weights within the somatosensory cortical regions and strong positive weights between the tongue primary somatosensory regions and subcortical regions (e.g., basal ganglia). (B) Thresholded connections (FDR q < 0.05, which corresponds to uncorrected p<0.003, two-tailed, bootstrap test with 10,000 iterations) showing the predictive weights between the primary and secondary somatosensory and insular cortical regions. Line thickness indicates the absolute magnitude of the predictive weights. Note that the location of the nodes on the brain map may not reflect the exact center coordinates of the regions-of-interests, though we marked them on the closest locations on the map. The somatosensory homunculus (modified from Fig 17 in Penfield and Rasmussen, 1950, p.44) represents the overall somatotopic gradients.

Appendix 1—figure 10
Predictive performances across different numbers of principal components (PCs).

We tested principal component regression (PCR) with a different number of PCs to find the best model to predict the within-individual variation of sustained pain ratings and calculated the mean correlation between actual and predicted pain ratings (10 ratings). Predictive performances were based on leave-one-subject-out cross-validation. The best model used 14 PCs for prediction.

Appendix 1—figure 11
Head movement and physiology variables in Study 1.

(A) Comparisons of head motion (framewise displacement) and physiological measures (heart and respiratory rate) between the capsaicin versus control conditions. For significance testing, we conducted paired t-tests. (B) Temporal changes of head motion and physiology variables. Data were divided into 10 time-bins (2 min per bin). The vertical dashed lines show how we define the early, middle, and late periods of sustained pain. The solid lines represent group mean ratings (red for capsaicin, and blue for control), and the shading represents standard errors of the mean (SEM). Note that we needed to exclude four participants’ data due to technical issues with the physiological data acquisition.

Appendix 1—figure 12
Prediction performance after controlling for head motion and physiological variables.

To examine whether head motion and physiology responses influenced the predictive model performance, we regressed out the framewise displacement (FD), heart rate (HR), and respiratory rate (RR) from the cross-validated model predictions in Study 1 (n = 44). (A) A forced-choice test to compare the model responses of the support vector machine (SVM) model for the capsaicin versus control conditions after regressing out the mean FD, HR, and RR. For regression, all the conditions and participants were concatenated as we trained the original SVM model. Left: receiver-operating characteristics (ROC) curve. Right: model responses for different conditions. Each line connecting dots represents an individual participant’s paired data (red: correct classification; blue: incorrect classification). p-Value was based on a binomial test, two-tailed. (B) Actual versus predicted values of the principal component regression (PCR) model after regressing out the 10-binned (2 min per bin) FD, HR, and RR. For regression, all the time-bins and participants were concatenated as we trained the original PCR model. Each colored line (and symbol) represents individual participant’s ratings during the capsaicin run (red: higher r; yellow: lower r; blue: r < 0). p-Value was based on bootstrap tests, two-tailed.

Appendix 1—figure 13
Specificity test results.

We tested the module allegiance-based support vector machine (SVM) and principal component regression (PCR) models of pain on the bitter taste (quinine) or aversive odor (fermented skate) conditions in Study 1 (n = 48) to examine the specificity of the models, using leave-one-participant-out cross-validation. (A, B) Model responses of the SVM model (A) and the PCR model (B) for capsaicin, bitter taste, aversive odor, and control conditions. The solid lines and shading in (B) represent the group mean ratings and standard errors of the mean (SEM), respectively. (C, E) Forced-choice test results of comparing the model responses for the capsaicin versus bitter taste (C) or aversive odor (E) conditions. Left: receiver-operating characteristics (ROC) curve. Right: model responses for different conditions. Each line connecting dots represents an individual participant’s paired data (red: correct classification; blue: incorrect classification). p-Values were based on a binomial test, two-tailed. (D, F) Actual versus predicted ratings. Each colored line (and symbol) represents individual participant’s ratings during the bitter taste run (D) or aversive odor run (F) (red: higher r; yellow: lower r; blue: r < 0). p-Value was based on bootstrap tests, two-tailed.

Appendix 1—table 1
Top 50 stable connections of the classification model.
RankWeightsROI namesMNI coordinates
Positive connections
#10.0222LH_SomMot_6 - BG_L_6_6 (dlPu)(−56,–8,30) - (−28,–6,2)
#20.0200LH_SomMot_6 - BG_R_6_6 (dlPu)(−56,–8,30) - (30,−4,2)
#30.0200RH_SomMot_7 - BG_L_6_6 (dlPu)(58,−4,30) - (−28,–6,2)
#40.0181LH_SomMot_6 - BG_L_6_2 (GP)(−56,–8,30) - (−22,–2,4)
#50.0170RH_SomMot_7 - BG_R_6_6 (dlPu)(58,−4,30) - (30,−4,2)
#60.0162LH_SomMot_7 - BG_R_6_6 (dlPu)(−48,–8,46) - (30,−4,2)
#70.0161RH_SomMot_7 - BG_L_6_2 (GP)(58,−4,30) - (−22,–2,4)
#80.0160LH_SomMot_7 - BG_L_6_6 (dlPu)(−48,–8,46) - (−28,–6,2)
#90.0156RH_DorsAttn_Post_9 - RH_SalVentAttn_TempOccPar_2(8,-56,62) - (60,−38,16)
#100.0154LH_SomMot_6 - BG_R_6_2 (GP)(−56,–8,30) - (22,−2,4)
#110.0154RH_SalVentAttn_PrC_1 - RH_SalVentAttn_FrOperIns_3(50,4,40) - (36,24,4)
#120.0154LH_Cont_Par_3 - RH_SalVentAttn_Med_3(−46,–42,46) - (8,4,66)
#130.0153LH_SomMot_2 - BG_R_6_6 (dlPu)(−52,–24,10) - (30,−4,2)
#140.0161LH_SomMot_6 - Cb_Right_VIIb(−56,–8,30) - (−24,–58,–52)
#150.0160RH_SomMot_1 - BG_L_6_6 (dlPu)(52,−14,6) - (−28,–6,2)
#160.0156LH_DorsAttn_FEF_1 - RH_Cont_PFCl_5(−32,–4,54) - (30,48,28)
#170.0154LH_SomMot_6 - Tha_L_8_2 (mPMtha)(−56,–8,30) - (−18,–14,4)
#180.0154LH_SomMot_6 - Cb_Right_VI(−56,–8,30) - (24,−58,−26)
#190.0153RH_SomMot_1 - BG_R_6_6 (dlPu)(52,−14,6) - (30,−4,2)
#200.0139RH_SomMot_2 - BG_R_6_6 (dlPu)(64,−24,8) - (30,−4,2)
#210.0133LH_SomMot_2 - BG_R_6_2 (GP)(−52,–24,10) - (22,−2,4)
#220.0131RH_SomMot_2 - BG_R_6_2 (GP)(64,−24,8) - (22,−2,4)
#230.0130RH_SomMot_7 - BG_R_6_2 (GP)(58,−4,30) - (22,−2,4)
#240.0127LH_SomMot_7 - BG_L_6_2 (GP)(−48,–8,46) - (−22,–2,4)
#250.0127LH_SomMot_6 - Cb_Left_VI(−56,–8,30) - (−22,–58,–24)
#260.0125RH_SomMot_10 - BG_R_6_6 (dlPu)(46,−12,48) - (30,−4,2)
#270.0118LH_SomMot_6 - Cb_Left_VIIb(−56,–8,30) - (−26,–66,–50)
#280.0113LH_SomMot_6 - Tha_R_8_8 (lPFtha)(−56,–8,30) - (12,−16,6)
#290.0112LH_SomMot_7 - BG_R_6_2 (GP)(−48,–8,46) - (22,−2,4)
#300.0111LH_Vis_8 - Tha_L_8_2 (mPMtha)(−48,–70,10) - (−18,–14,4)
#310.0104LH_SomMot_13 - LH_Default_PFC_7(−26,–38,68) - (–8,58,20)
#320.0104LH_Vis_8 - LH_Cont_Cing_1(−48,–70,10) - (−4,–28,26)
#330.0103RH_SomMot_2 - Tha_L_8_2 (mPMtha)(64,−24,8) - (−18,–14,4)
#340.0095LH_SomMot_13 - LH_Default_PFC_2(−26,–38,68) - (−6,36,–10)
#350.0092LH_SomMot_7 - Cb_Right_VI(−48,–8,46) - (24,−58,−26)
#360.0092LH_SomMot_13 - RH_Default_PFCdPFCm_1(−26,–38,68) - (4,36,−14)
#370.0077LH_Cont_Cing_1 - RH_Vis_5(−4,–28,26) - (48,−72,−6)
#380.0067LH_SomMot_13 - LH_Limbic_OFC_2(−26,–38,68) - (−10,36,–20)
Negative connections
#1–0.0235RH_SomMot_7 - RH_SomMot_12(58,−4,30) - (40,−24,58)
#2–0.0231LH_SomMot_6 - RH_SomMot_12(−56,–8,30) - (40,−24,58)
#3–0.0160LH_SomMot_4 - RH_SomMot_12(−54,–4,10) - (40,−24,58)
#4–0.0145RH_SomMot_6 - RH_SomMot_14(56,−12,14) - (32,−22,64)
#5–0.0143LH_SomMot_6 - LH_Default_Temp_3(−56,–8,30) - (−56,–6,–12)
#6–0.0141LH_SomMot_6 - LH_Default_Temp_4(−56,–8,30) - (−58,–30,–4)
#7–0.0135LH_SomMot_6 - LH_Default_pCunPCC_1(−56,–8,30) - (−12,–56,14)
#8–0.0124LH_SomMot_4 - LH_SomMot_13(−54,–4,10) - (−26,–38,68)
#9–0.0115LH_SomMot_4 - RH_SomMot_14(−54,–4,10) - (32,−22,64)
#10–0.0092RH_SalVentAttn_Med_1 - Cb_Left_VIIIb(8,8,42) - (0,–64,–42)
#11–0.0084LH_SalVentAttn_FrOperIns_4 - Cb_Left_VIIIb(–52,8,10) - (0,–64,–42)
#12–0.0083LH_SalVentAttn_Med_1 - Cb_Left_VIIIb(–6,10,42) - (0,–64,–42)
  1. Top 50 stable connections based on bootstrap tests with 10,000 iterations (edge-level p<1.9 × 10–7, FDR q < 1.4 × 10–4).

  2. FDR, false discovery rate; ROI, region of interest.

Appendix 1—table 2
Top 50 stable connections of the regression model.
RankWeightsROI namesMNI coordinates
Positive connections
#10.000496LH_SomMot_12 - RH_Default_pCunPCC_3(−32,–22,64) - (6,−58,44)
#20.000460LH_SomMot_10 - RH_Default_pCunPCC_3(−40,–26,58) - (6,−58,44)
Negative connections
#1–0.000768RH_Cont_Cing_1 - Cb_Left_Crus_II(6,–26,30) - (−26,–74,–42)
#2–0.000756RH_Cont_Cing_1 - Cb_Left_Crus_I(6,–26,30) - (−36,–68,–32)
#3–0.000743Tha_R_8_1 (mPFtha) - Cb_Vermis_VI(8,–10,6) - (0,–70,–22)
#4–0.000711Tha_R_8_1 (mPFtha) - Cb_Vermis_VIIIa(8,–10,6) - (26,–58,–54)
#5–0.000697RH_Cont_Cing_1 - Cb_Right_Crus_I(6,–26,30) - (38,–68,–32)
#6–0.000695RH_Cont_Cing_1 - Cb_Vermis_IX(6,–26,30) - (6,–54,–48)
#7–0.000694Tha_R_8_1 (mPFtha) - Cb_Left_VIIb(8,–10,6) - (−26,–66,–50)
#8–0.000673RH_Cont_Cing_1 - Cb_Right_Crus_II(6,–26,30) - (26,–76,–42)
#9–0.000662Tha_R_8_1 (mPFtha) - Cb_Left_Crus_I(8,–10,6) - (−36,–68,–32)
#10–0.000655Tha_R_8_1 (mPFtha) - Cb_Vermis_IX(8,–10,6) - (6,–54,–48)
#11–0.000655LH_Cont_Cing_1 - Cb_Left_Crus_I(−4,–28,26) - (−36,–68,–32)
#12–0.000654RH_Cont_PFCmp_1 - Cb_Left_Crus_I(8,30,28) - (−36,–68,–32)
#13–0.000652RH_Cont_PFCmp_1 - Cb_Left_Crus_II(8,30,28) - (−26,–74,–42)
#14–0.000650Tha_R_8_1 (mPFtha) - Cb_Left_Crus_II(8,-10,6) - (−26,–74,–42)
#15–0.000648LH_Cont_Cing_1 - Cb_Left_Crus_II(−4,–28,26) - (−26,–74,–42)
#16–0.000647Tha_L_8_1 (mPFtha) - Cb_Vermis_VI(−6,–12,6) - (0,–70,–22)
#17–0.000643LH_Cont_Cing_1 - Cb_Right_Crus_I(−4,–28,26) - (38,–68,–32)
#18–0.000642Tha_R_8_1 (mPFtha) - Cb_Right_VIIb(8,–10,6) - (−24,–58,–52)
#19–0.000626BG_R_6_1 (vCa) - Cb_Vermis_VI(14,14,–2) - (0,–70,–22)
#20–0.000624LH_Cont_Cing_1 - Cb_Right_Crus_II(−4,–28,26) - (26,–76,–42)
#21–0.000598Tha_R_8_1 (mPFtha) - Cb_Right_VI(8,–10,6) - (24,–58,–26)
#22–0.000592Tha_R_8_1 (mPFtha) - Cb_Right_Crus_I(8,–10,6) - (38,–68,–32)
#23–0.000578RH_SalVentAttn_FrOperIns_3 - Cb_Left_VIIb(36,24,4) - (−26,–66,–50)
#24–0.000565LH_SalVentAttn_Med_1 - Cb_Left_VIIb(–6,10,42) - (−26,–66,–50)
#25–0.000549Tha_L_8_7 (cTtha) - Cb_Left_Crus_I(−10,–22,14) - (−36,–68,–32)
#26–0.000543Tha_L_8_7 (cTtha) - Cb_Left_VIIb(−10,–22,14) - (−26,–66,–50)
#27–0.000538BG_L_6_5 (dCa) - Cb_Vermis_VIIIa(–14,2,16) - (26,–58,-54)
#28–0.000532LH_SalVentAttn_Med_1 - Cb_Right_VIIb(–6,10,42) - (−24,–58,–52)
#29–0.000530BG_L_6_5 (dCa) - Cb_Vermis_VI(–14,2,16) - (0,–70,–22)
#30–0.000530Tha_L_8_7 (cTtha) - Cb_Left_Crus_II(−10,–22,14) - (−26,–74,–42)
#31–0.000523BG_R_6_5 (dCa) - Cb_Left_Crus_I(14,6,14) - (−36,–68,–32)
#32–0.000520BG_R_6_5 (dCa) - Cb_Left_VIIb(14,6,14) - (−26,–66,–50)
#33–0.000519Tha_L_8_1 (mPFtha) - Cb_Left_Crus_I(−6,–12,6) - (−36,–68,–32)
#34–0.000515BG_R_6_5 (dCa) - Cb_Left_Crus_II(14,6,14) - (−26,–74,–42)
#35–0.000494BG_L_6_5 (dCa) - Cb_Left_VIIb(–14,2,16) - (−26,–66,–50)
#36–0.000481BG_L_6_4 (vmPu) - Cb_Right_VI(−22,6,–4) - (24,–58,-26)
#37–0.000477Tha_R_8_4 (rTtha) - Cb_Left_Crus_II(2,–12,6) - (−26,–74,–42)
#38–0.000473Tha_R_8_4 (rTtha) - Cb_Left_Crus_I(2,–12,6) - (−36,–68,–32)
#39–0.000470BG_L_6_5 (dCa) - Cb_Left_Crus_I(–14,2,16) - (−36,–68,–32)
#40–0.000469BG_R_6_5 (dCa) - Cb_Right_Crus_I(14,6,14) - (38,–68,–32)
#41–0.000454BG_R_6_5 (dCa) - Cb_Right_VIIb(14,6,14) - (−24,–58,–52)
#42–0.000448BG_L_6_5 (dCa) - Cb_Left_Crus_II(–14,2,16) - (−26,–74,–42)
#43–0.000444RH_Cont_PFCv_1 - Cb_Left_Crus_II(34,22,–8) - (−26,–74,–42)
#44–0.000443BG_L_6_5 (dCa) - Cb_Right_VIIb(–14,2,16) - (−24,–58,–52)
#45–0.000434BG_R_6_5 (dCa) - Cb_Right_VI(14,6,14) - (24,–58,–26)
#46–0.000421BG_L_6_5 (dCa) - Cb_Right_Crus_I(–14,2,16) - (38,–68,–32)
#47–0.000405BG_L_6_5 (dCa) - Cb_Right_VI(–14,2,16) - (24,–58,–26)
#48–0.000393BG_L_6_5 (dCa) - Cb_Left_VI(–14,2,16) - (−22,–58,–24)
  1. Top 50 stable connections based on bootstrap tests with 10,000 iterations (edge-level p<6.1 × 10–5, FDR q < 0.043).

  2. FDR, false discovery rate; ROI, region of interest.

Data availability

All the data that were used to generate the main figures are available at https://github.com/cocoanlab/brain_reconfig_pain (copy archived at swh:1:rev:077a65b3d3905182a207349919697e550226fbe5).

References

    1. Clower DM
    2. West RA
    3. Lynch JC
    4. Strick PL
    (2001)
    The inferior parietal lobule is the target of output from the superior colliculus, hippocampus, and cerebellum
    The Journal of Neuroscience 21:6283–6291.
    1. Melzack R
    2. Casey KL
    (1968)
    Sensory, motivational, and central control determinants of pain: a new conceptual model
    The Skin Senses 1:423–443.
    1. Middleton FA
    2. Strick PL
    (2001)
    Cerebellar projections to the prefrontal cortex of the primate
    The Journal of Neuroscience 21:700–712.
  1. Book
    1. Penfield W
    2. Rasmussen T
    (1950)
    The Cerebral Cortex of Man; a Clinical Study of Localization of Function
    Macmillan.

Decision letter

  1. Markus Ploner
    Reviewing Editor; Technische Universität München, Germany
  2. Timothy E Behrens
    Senior Editor; University of Oxford, United Kingdom
  3. Markus Ploner
    Reviewer; Technische Universität München, Germany
  4. Tamas Spisak
    Reviewer; Essen University Hospital, Germany

Our editorial process produces two outputs: i) public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Dynamic Functional Brain Reconfiguration During Sustained Pain" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, including Markus Ploner as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by a Reviewing Editor and Timothy Behrens as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Tamas Spisak (Reviewer #3).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1) It remains unclear whether the changes of brain networks over time simply reflect the duration of sustained pain or whether they essentially reflect different levels of pain intensity/avoidance. Therefore, analyzing and/or discussing whether brain network changes reflect pain duration or pain intensity would be crucial for the interpretation of the findings.

2) Although the manuscript is very well-written it might benefit from an even clearer and simpler explanation of what the consensus community structure and the underlying module allegiance measure assesses.

3) The added value of the assessment of the dynamics of brain networks remains unclear. Specifically, it is unclear whether the current analysis of brain networks dynamics allows for a clearer distinction between and prediction of pain and no-pain states than other measures of static or dynamic brain activity or static measures of brain connectivity. Therefore, clarifying the added value of the community structure analysis as compared to other more common analyses of brain activity and brain connectivity would significantly strengthen the case.

4) The Authors do not touch upon the concept of temporal summation of pain, historically associated with tonic pain. Please comment on the relationship of the present study to temporal summation particularly since chronic pain patients often exhibit increased temporal summation of pain.

5) Please consider a recent related paper by Cheng et al., Arthritis Rheumatol, 2021 that shares most of the methodological pipeline to highlight similarities and novelties and deepen the comparison with the associated literature.

6) The data analysis is entirely conducted on young healthy subjects. This is not a limitation per se, but the conclusion about offering new insights into understanding mechanisms at the basis of chronic pain is too far from the results. A similar pipeline has been actually applied to chronic pain patients (Cheng et al., Arthritis Rheumatol, 2021, Lee et al., Nat Med. 2021). Discussing the results of the present paper in relationship to those, could offer a more robust way to connect the Authors' results to networks behavior in pathological brains.

7) The behavioral measure used to assess evoked pain perception (avoidance ratings), has been developed for chronic pain patients and never validated on healthy controls. It might not be an appropriate measure considering the total absence of pain variability in the reported responses over forty-eight subjects. Please discuss this important point. Moreover, please address the following questions:

• How does the rating scale look like? Is the meaning of the anchors and of the thresholds for weak, moderate, strong, and very strong displayed on a screed during the scan? What are the instructions given to the participants before the start of the experiment? Do they induce any kind of pain expectancy (e.g., "After 6 minutes the pain should start decreasing" or analogous expressions)? A combination of those elements could explain the lack of pain variability.

• The behavioral outcome is called "pain avoidance" and the Authors hypothesize that it is proportional to the perceived pain. Is there any evidence proving this correlation? The collected ratings are the answer to the question "how much do you want to avoid this experience in the future?". Is it possible that this question is too generic to be called pain avoidance? Did the Authors quantify in any way the effect of laying down in a scanner for long time? It might play a role in the avoidance index.

8) The dynamic measure employed by the Authors is better described from the term "windowed functional connectivity". It is often considered a measure of dynamic functional connectivity and it gives information about fluctuations of the connectivity patterns over time. Nevertheless, the entire focus of the paper, including the title, is on dynamic networks, which inaccurately leads one to think of time-varying measures with higher temporal resolution. This allows one to follow network reorganization over time without averaging 2-min intervals in which several different brain mechanisms might play an important role. In summary, the assumption of constant response throughout 2-min periods of tonic pain and the use of Pearson correlations do not mirror the idea of dynamic analysis expressed by the Authors in title and introduction. Please consider removing "dynamic" from the title, reduce the emphasis on this concept, address possible confounds introduced by the choice of long windows and rephrase the aim of the study in terms of brain network reconfiguration over the main phases of tonic pain experience.

9) Procedure chosen for evoking sustained pain. The measures in figure 1B suggest that the intensity of the painful stimulation is not constant as expected for sustained pain (probably the effect washes out with the saliva). In this case, the first six-minute interval requires particular attention because it encapsulates the real tonic pain phase, and the following ones require more appropriate labels. Ideally the authors should cite previous studies showing that tongue evoked pain elicits a very specific behavioral response (summation, habituation/decrease of pain, absence of pain perception). Moreover, please address the following points:

• Does the procedure include a calibration phase? If yes, please add description in the Methods section. If not, how do the Authors explain the relatively small standard error of the mean reported in Figure 1?

• If possible, add citation proving that there is a very consistent behavioral (pain related) response to the capsaicin: no pain variability against what most of the evoked pain experiments showed.

• Please report in the supplementary material the dots distribution (box plot with visible dots) of the ratings at minute 0, 6, 14, 20.

10) Community detection analysis. Please clarify the following issues:

• The thresholding of the connectivity matrices for the binarization of the networks is certainly a weakness of the study and, more in general, of most of the connectivity analysis (only few estimators have known null distributions). Here, the chosen optimal threshold is the one that maximize the difference between conditions (capsaicin vs controls) in terms of global graph measures, including modularity. I suggest adding a comment on the effect of maximizing a measure depending on the modularity of the networks, on the subsequent community detection algorithm, also based on maximizing modularity (within the network this time). What is the Authors' opinion on this possible confound? Also, what is the rationale/hypothesis behind this procedure for obtaining sparse matrices?

• Did the Authors consider running the analysis with other resolution parameters (γ and omega)?

Group-level consensus community detection: I found this section difficult to follow, especially in terms of reasoning for specific choices and steps of the analysis.

• Step III: the standard definition of allegiance is a binary matrix whose elements are equal to one only when the two correspondent nodes belong to the same community (as the Authors described). After step III, a new index is computed as the mean of allegiance matrices over time and across subjects. Its value indicates the proportion (percentage) of subjects showing the two nodes in the same community at specific time points. Or how many times the two nodes belong to the same community during the early/middle/late stage of the experiment. Using the name allegiance (a binary measure) to indicate those percentages, might be misleading. I suggest using more appropriate names (measures like "agreement", "dwell time" and similar might be useful) and providing more explanatory examples on how to read the value of the computed measures.

• Step IV: please specify number of permutations.

• Step VI: in the same spirit as the two previous comments, I suggest either reconsidering the necessity of this computation, or explaining the reasons for applying a community detection algorithm twice. I believe that additional layers of complexity always require a clear question that they can answer.

11) It remains unclear, how specific the results are to pain. Differences between the control resting state and the capsaicin trials might be – at least partially – driven by other factors, like motion artifacts, saliency, attention, axiety, etc. Differences between stages over the time-course might, additionally, be driven by scanner drifts (to which the applied approach might be less sensitive, but the possibility is still there ) or other gradual processes, e.g. shifts in arousal, attention shifts, alertness, etc. All the above factors might emerge as confounding bias in both of the predictive models. This problem should be thoroughly discussed, and at least the following extra analyses are recommended, in order to attenuate concerns related to the overall specificity and neurobiological validity of the results:

• Reporting of, and testing for motion estimates (mean, max, median framewise displacement or anything similar).

• Examining whether these factors might, at least partially, drive the predictive models.

• e.g. Applying the PCR model on the resting state data and verifying of the predicted timecourse is flat (no inverse U-shape, that is characteristic to all capsaicin trials).

12) Statistical inference. An important issue is the (apparent) lack of statistical inference when analyzing the differences in the group-level consensus community structures (both when comparing capsaicin to control and when analysing changes over the time-course of the capsaicin-challenge). Although the observed changes seem biologically plausible and fit very well to previous results, without proper statistical inference we can't determine, how likely such differences are to emerge just by chance. This makes all results on Figures 2 and 3, and points 1, 4 and 5 in the discussion partially or fully speculative or weakly underpinned, comprising a large proportion of the current version of the manuscript. There are two main ways of handling this issue:

• Enhancing (or clarifying potential misunderstandings regarding) the methodology (see my concrete, and hopefully feasible, suggestions in the "private part" of the review). There are likely many ways to test the significance of these differences. Two permutation testing-based ideas are (i) permuting the labels ctr-capsaicin, or early-mid-late, repeating the analysis, constructing the proper null distribution of e.g. the community size changes and obtain the p-values and (ii) "trace back" communities to the individual level and do (nonparametric) statistical inference there.

• De-weighting the presentation and the discussion of the related results.

Reviewer #1 (Recommendations for the authors):

• The authors emphasize the term "pain supersystem". This term is not very well-introduced yet and the necessity for such a term is unclear. I recommend that the authors rely less on this term and omit it at least from the abstract.

• The statement in the abstract "In the early stage, the orofacial areas of the primary somatomotor cortex were separated from the other primary somatomotor cortices and integrated with…" is a bit ambiguous. It might better read "In the early stage, the orofacial areas of the primary somatomotor cortex were separated from other areas of the primary somatomotor cortex and integrated with…"

Reviewer #2 (Recommendations for the authors):

• I suggest reducing the amount of text in the figures. All the information needed to understand the illustrations should be included in the captions. Figure 1 and Figure 7 are the ones that require the most attention in this respect.

• It might be a good idea to specify when any previous evidence used to justify the current analysis or to make inferences on the obtained results actually come from the Authors' previous publications. Especially if they are extracted from the same dataset, this information is relevant.

• In their previous paper, the Authors had access to a dataset including the experimental conditions: tonic capsaicin pain, tonic aversive taste, and tonic aversive odor. Did the Authors analyze the communities structure during those controls conditions? Did they consider testing their classifier on them? In my opinion, it would add a lot of robustness to the study findings, and it would make the obtained results reliable and unquestionably pain related (thinking of the more general avoidance ratings).

• In terms of data availability, the Authors declared that data and codes will be shared upon publication. I would appreciate their availability if there will be a second loop of revisions before potential publication.

Reviewer #3 (Recommendations for the authors):

– As the authors mention the cross-validated evaluation of the PCR model is biased due to hyperparameter optimization. While the independent evaluation resolves any related concerns, the authors might consider applying a nested cross-validation framework, to have unbiased estimates for the discovery dataset, as well.

– Optimizing the network density threshold in the same dataset, especially on one of the conditions-of-interest (Q1: capsaicin vs. controls) may be circular (as the optimized global network metrics may well be associated to the community structure). On the other hand, this potential circularity does not affect all he results (e.g definitely not the results based on the independent test dataset) and in general, I don't think this would significantly affect the results. Nevertheless, performing (or reproducing) the optimization on independent data would be reassuring. Alternatively, this issue must be discussed as a potential limitation/bias.

– While this is not explicitly stated, prediction performance is evaluated only on the within subject-level. For better comparability to other methods, please report and discuss the "between-subject" estimates, too (i.e. how well can we classify/predict from a single session/window of a single subject).

– Introduction: discussing the possible relation of the present work to chronic pain or other clinical pain conditions is not sufficient.

– More information is needed about the individual variability of the pain-related behavioral time-courses (maybe in the supplementary info). Was remission complete in all participants?

– Some participants might be more tolerant for capsaicin than others, due to eating habits. Please discuss whether this could potentially affect the results.

– At many points, e.g. in paragraph 25 on page x or 5 on page 25, it is mentioned that the models generalized across two datasets. While the terminology is currently heterogenous, I kindly suggest to use the term "generalization" only to the independent test dataset (here the models really had to generalize to scanning parameters, paradigm differences, etc.)

– it's a bit unclear why the pain avoidance ratings fall. One would, somewhat naively, hypothesize that if the participant once though she would never repeat this experiment again, why would she change her mind a couple of minutes later, when the memories of pain are still vivid. Please comment on this.

– Please add a short discussion of the differences of the behavioral ratings and how they might affect the findings. (this might be positive thing: a sign of generalization across behavioral assessment protocols).

– Please clarify why *pain* avoidance (slightly) increased in the control resting state scan.

– Please provide more rationale for the choice of ML algorithms.

– How were the hyperparameters set for the SVM? Why were those not optimized, too?

– Why was only one hub selected for the seed-based analysis in the case of the classifier?

– While the prediction performances are obviously significant, testing for this with bootstrapping may be suboptimal, as bootstrap samples may inherit non-normality from the parent dataset. Permutation test would be more "elegant" in my opinion.

– Discussion: relation to consciousness might be somewhat speculative, should be hedged.

– Will the raw data also be shared?

https://doi.org/10.7554/eLife.74463.sa1

Author response

Essential revisions:

1) It remains unclear whether the changes of brain networks over time simply reflect the duration of sustained pain or whether they essentially reflect different levels of pain intensity/avoidance. Therefore, analyzing and/or discussing whether brain network changes reflect pain duration or pain intensity would be crucial for the interpretation of the findings.

We appreciate the editor and reviewer’s comment on this issue. With the current experimental paradigm, it is difficult to dissociate the pain duration from the level of pain because the delivery of oral capsaicin commonly induces initial bursting and then a gradual decrease of pain over time. That is, the pain duration is correlated with the pain intensity in our task.

However, when we examined the time-course of the ratings at each individual level (as shown in Appendix 1—figure 2 ), the time duration explained 53.7% of the rating variance, R2 = 0.537 ± 0.315 (mean ± standard deviation). In addition, if we constrain the β coefficient of the time duration to be negative (i.e., ratings should decrease over time), the explained variance decreases to 48.2%, R2 = 0.482 ± 0.457, leaving us enough variance (i.e., greater than 50%) for examining the distinct effects of time duration and ratings on the patterns of functional brain reorganization.

Indeed, the two main analyses included in the manuscript—consensus community detection and predictive modeling—were designed to examine those two aspects of the task, i.e., time duration and pain avoidance ratings, respectively. First, through the consensus community detection analysis, we examined the community structure that changes over time, i.e., across the early, middle, and late periods (as shown in Figure 3). We then developed predictive models of pain avoidance ratings in the second main analysis (as shown in Figure 5).

Though it is still a caveat that we cannot fully dissociate the effects of time duration versus pain ratings, we could interpret the first set of results to be more about time duration, while the second set of results is more about pain ratings.

We now added a description of the implication of predictive modeling for isolating the effects of pain ratings. In addition, a discussion on the caveat of the current experimental design and relevant future direction.

Revisions to the main manuscript:

p. 25:

“Moreover, developing models to directly predict the pain ratings is helpful to complement the group-level analysis, because the changes in consensus community structure over the early, middle, and late periods only indirectly reflect the different levels of pain.”

p. 27:

“This study also had some limitations. First, with the current experimental paradigm, it is difficult to dissociate the pain duration from the level of pain because the delivery of oral capsaicin commonly induces initial bursting and then a gradual decrease of pain over time. Though we aimed to model the effects of pain duration and pain avoidance ratings with our two primary analyses, i.e., consensus community detection and predictive modeling, we cannot fully dissociate the impact of time duration versus pain ratings.”

2) Although the manuscript is very well-written it might benefit from an even clearer and simpler explanation of what the consensus community structure and the underlying module allegiance measure assesses.

We thank you for the suggestion. Now we added additional (but simple) descriptions of module allegiance and consensus community detection methods.

Revisions to the main manuscript:

pp. 8-9:

“Here, the consensus community means the group-level representative structures of the distinct community partitions of individuals. To determine the consensus community across different individuals and times, we first obtained the module allegiance (Bassett et al., 2011) from the community assignment of each individual. Module allegiance assesses how much a pair of nodes is likely to be affiliated with the same community label, and is defined as a matrix T whose element Tij is 1 when nodes i and j are assigned to the same community and 0 when assigned to different communities. This conversion of the categorical community assignments to the continuous module allegiance values allows group-level summarization of different community structures of individuals.”

p. 14:

“Here, high module allegiance indicates the voxels of two regions are likely to be in the same community affiliation, and vice versa.”

3) The added value of the assessment of the dynamics of brain networks remains unclear. Specifically, it is unclear whether the current analysis of brain networks dynamics allows for a clearer distinction between and prediction of pain and no-pain states than other measures of static or dynamic brain activity or static measures of brain connectivity. Therefore, clarifying the added value of the community structure analysis as compared to other more common analyses of brain activity and brain connectivity would significantly strengthen the case.

The main goal (and thus, the added value) of the current study was to provide a “mechanistic” understanding of the brain processes of sustained pain, rather than the “prediction.” Even though we included the results from the predictive modeling, as in Figures 4-6, our focus was more on the interpretation of the model to quantitatively examine the functional changes in the brain, not on the maximization of the prediction performance.

Indeed, maximizing the prediction performance was the main goal of our previous study (Lee et al., 2021), in which we developed a predictive model of sustained pain based on the patterns of dynamic functional connectivity. The model showed better prediction performances compared to the current study, but it was challenging to interpret the model because of the high dimensionality of the model and its features. In addition, functional connectivity itself provides only limited insight into how functional brain networks are structured and reconfigured over time.

In this sense, the multi-layer community detection method has several advantages to achieving our goal. First, the community detection analysis allows us to summarize the complex, high-dimensional whole-brain connectivity patterns into neurobiologically interpretable subsystems. Second, the multi-layer community detection method allows us to study the temporal changes in community structure by connecting the same nodes across different time points.

Now we added a description of the rationale behind the choice of the multi-layer community detection analysis over the conventional functional connectivity methods, and the added value of our study.

Revisions to the main manuscript:

p. 3:

“In this study, we examined the reconfiguration of whole-brain functional networks underlying the natural fluctuation in sustained pain to provide a mechanistic understanding of the brain responses to sustained pain.”

p. 7:

“In this study, we used this approach to examine the temporal changes of brain network structures during sustained pain, which cannot be done with conventional functional connectivity-based analyses (Lee et al., 2021).”

p. 27:

“However, the previous model provides a limited level of mechanistic understanding because of the high dimensionality of the model and its features. In addition, functional connectivity itself provides only limited insight into how functional brain networks are structured and reconfigured over time.”

4) The Authors do not touch upon the concept of temporal summation of pain, historically associated with tonic pain. Please comment on the relationship of the present study to temporal summation particularly since chronic pain patients often exhibit increased temporal summation of pain.

We thank the reviewer and editor for the comment on this important topic. Temporal summation of pain indicates progressively increased sensation of pain during prolonged noxious stimulation (Price, Hu, Dubner, and Gracely, 1977), and has been suggested as a hallmark of chronic pain disorders including fibromyalgia (Cheng et al., 2022; Price et al., 2002). In a recent study by Cheng et al. (2022), the authors induced tonic pain using constantly high cuff pressure and examined whether the participants experienced increased pain in the late period compared to the early period of pain. On the contrary, in our experimental paradigm, the capsaicin liquid initially delivered into the oral cavity is being cleaned out by saliva, and thus overall pain intensity was decreasing over time, not increasing (Figure 1B). Therefore, the temporal summation of pain may occur in a limited period (e.g., the early period of the run), but it is difficult to examine its effect systematically in our study.

However, it is notable that Cheng et al.’s results overlap with our findings. For example, Cheng et al. reported the intra-network segregation within the somatomotor network and the inter-network integration between the somatomotor and other networks during the temporal summation of pressure pain in patients with fibromyalgia, which were similar to the findings we reported in Appendix 1—figure 9 and Figure 4. Although it is unclear whether these results reflect the temporal summation of pain, these network-level features shared across the two studies are likely to be an essential component of the sustained pain processes in the brain.

Now we added a comment on the temporal summation of pain in the main manuscript.

Revisions to the main manuscript (p. 26):

“Interestingly, a recent fMRI study on the temporal summation of pain in fibromyalgia patients reported results similar to ours (Cheng et al., 2022), including the intra-network dissociation within the somatomotor network and the inter-network integration between the somatomotor and other networks during pain. Although we cannot directly examine whether the temporal summation of pain gave rise to these network-level changes due to the limitation of our experimental paradigm, these consistent findings between the two studies may suggest that our findings could be generalized to clinical conditions.”

5) Please consider a recent related paper by Cheng et al., Arthritis Rheumatol, 2021 that shares most of the methodological pipeline to highlight similarities and novelties and deepen the comparison with the associated literature.

We thank the reviewer and editor for the information about this recent publication. Cheng et al. (2022) was not published at the time we wrote the manuscript, and we were surprised that Cheng et al. shares many aspects with our study, e.g., both used multilayer community detection and also reported similar findings, as described above.

However, there were some differences between the two studies as well.

First, the focus of our study was on the brain dynamics during the natural time-course of sustained pain from its initiation to remission in healthy participants, whereas the focus of Cheng et al. was on the temporal summation phenomenon of pain (TSP) and the enhanced TSP in patients with fibromyalgia patients. Because of this difference in the research focuses, our study and Cheng et al. are providing many nonoverlapping results and insights. For example, our study paid particular attention to the coping mechanisms of the brain (e.g., the network-level changes in the subcortical and frontoparietal network regions) and the brain systems that are correlated with the natural decrease of pain (e.g., the cerebellum in Figure 5). In contrast, Cheng et al. (2022) identified the brain connectivity and network features important for the increased TSP in fibromyalgia patients.

Second, our great interest was in identifying and visualizing the fine-grained spatiotemporal patterns of functional brain network changes over the period of sustained pain. To utilize fine-grained brain activity information, we conducted our main analyses at a voxel-level resolution and on the native brain space, such as in Figures 2-3 and Appendix 1—figures 5, 7, and 8. With this fine-grained spatiotemporal mapping, we were able to identify small, but important voxel-level dynamics.

We now cited Cheng et al. (2022) in multiple places and revised the manuscript accordingly.

Revisions to the main manuscript (p. 26):

“Interestingly, a recent fMRI study on the temporal summation of pain in fibromyalgia patients reported results similar to ours (Cheng et al., 2022), including the intra-network dissociation within the somatomotor network and the inter-network integration between the somatomotor and other networks during pain. Although we cannot directly examine whether the temporal summation of pain gave rise to these network-level changes due to the limitation of our experimental paradigm, these consistent findings between the two studies may suggest that our findings could be generalized to clinical conditions.”

6) The data analysis is entirely conducted on young healthy subjects. This is not a limitation per se, but the conclusion about offering new insights into understanding mechanisms at the basis of chronic pain is too far from the results. A similar pipeline has been actually applied to chronic pain patients (Cheng et al., Arthritis Rheumatol, 2021, Lee et al., Nat Med. 2021). Discussing the results of the present paper in relationship to those, could offer a more robust way to connect the Authors' results to networks behavior in pathological brains.

We are grateful for the opportunity to discuss the clinical implication of our study. First of all, we agree with the reviewer and editor that we cannot make a definitive claim about chronic pain with the current study, and thus, we revised the last sentence of the abstract to tone down our claim.

Revisions to the main manuscript (p. 2, in the abstract):

“This study provides new insights into how multiple brain systems dynamically interact to construct and modulate pain experience, advancing our mechanistic understanding of sustained pain.”

However, as we noted above in Essential revisions 4, some of our findings were consistent with the findings from a previous clinical study (Cheng et al., 2022), suggesting the potential to generalize our study to clinical pain conditions. In addition, we previously reported that a predictive model of sustained pain derived from healthy participants performed better at predicting the pain severity of chronic pain patients than the model derived directly from chronic pain patients (Lee et al., 2021), highlighting the advantage of the “component process approach.”

The component process approach aims to develop brain-based biomarkers for basic component processes first, which can then serve as intermediate features for the modeling of multiple clinical conditions (Woo, Chang, Lindquist, and Wager, 2017). This has been one of the core ideas of the Research Domain Criteria (RDoC) (Insel et al., 2010) and the Hierarchical Taxonomy of Psychopathology (HiTOP) (Kotov et al., 2017). If the clinical pain of a patient group is modeled as a whole, it becomes unclear what is being modeled because of the multidimensional and heterogeneous nature of clinical pain (Melzack, 1999) as well as other co-occurring health conditions (e.g., mental health issues, medication use, etc.). The component process approach, in contrast, can specify which components are being modeled and are relatively free from heterogeneity and comorbidity issues by experimentally manipulating the specific component of interest in healthy participants.

The current study was conducted on healthy young adults based on the component process approach. We used oral capsaicin to experimentally induce sustained pain, which unfolds over protracted time periods and has been suggested to reflect some of the essential features of clinical pain (Rainville, Feine, Bushnell, and Duncan, 1992; Stohler and Kowalski, 1999). Therefore, the detailed characterization of the brain processes of sustained pain will be able to serve as an intermediate feature of multiple clinical conditions in future studies.

Now we added the discussion on the clinical generalizability issue in the Discussion section.

Revisions to the main manuscript:

p. 26:

“An interesting future direction would be to examine whether the current results can be generalized to clinical pain. Experimental tonic pain has been known to share similar characteristics with clinical pain (Rainville et al., 1992; Stohler and Kowalski, 1999). In addition, in a recent study, we showed that an fMRI connectivity-based signature for capsaicin-induced orofacial tonic pain can be generalized to chronic back pain (Lee et al., 2021). Therefore, a detailed characterization of the brain responses to sustained pain has the potential to provide useful information about clinical pain.”

p. 26:

“Interestingly, a recent fMRI study on the temporal summation of pain in fibromyalgia patients reported results similar to ours (Cheng et al., 2022), including the intra-network dissociation within the somatomotor network and the inter-network integration between the somatomotor and other networks during pain. Although we cannot directly examine whether the temporal summation of pain gave rise to these network-level changes due to the limitation of our experimental paradigm, these consistent findings between the two studies may suggest that our findings could be generalized to clinical conditions.”

7) The behavioral measure used to assess evoked pain perception (avoidance ratings), has been developed for chronic pain patients and never validated on healthy controls. It might not be an appropriate measure considering the total absence of pain variability in the reported responses over forty-eight subjects. Please discuss this important point.

We acknowledge that pain avoidance measures are not fully validated in the healthy population. Nevertheless, we used this measure in this study for the following two main reasons that outweigh the limitations.

First, a pain avoidance rating provides an integrative measure that can reflect the multi-dimensional aspects of sustained pain. One of the essential functions of pain is to avoid harmful situations and promote survival, and the avoidance motivation induced by pain is composed of not only sensory-discriminative, but also cognitive components including learning, valuation, and contexts (Melzack, 1999). According to the fear-avoidance model (Vlaeyen and Linton, 2012), if the pain-induced avoidance motivation is not resolved for a long time and is maladaptively associated with innocuous environments, chronic pain is likely to develop, suggesting the importance and clinical relevance of pain avoidance measures. In addition, our experimental design is particularly suitable for the use of avoidance rating because the oral capsaicin stimulation is accompanied by the urge to avoid the painful sensation, but it cannot immediately be resolved similar to chronic pain. Moreover, capsaicin is sometimes experienced as intense but less aversive (or even appetitive) in some cases, e.g., spicy food craver (Stevenson and Yeomans, 1993). In this case, avoidance ratings can provide a more reasonable measure of pain compared to the intensity rating.

Second, the avoidance measure provides a common scale on which we can compare different types of aversive experiences, allowing us to conduct specificity tests for a predictive model of pain. For example, a recent study successfully compared the brain representations of two types of pain and two types of aversive, but non-painful experiences (e.g., aversive auditory and visual experiences) using the same avoidance measure (Ceko, Kragel, Woo, Lopez-Sola, and Wager, 2022). These comparisons were possible because the avoidance measure provided one common scale for all the aversive experiences regardless of their types of stimuli.

To provide a better justification for the use of the avoidance measure, we now included the specificity test results of our pain predictive models. More specifically, we tested our module allegiance-based SVM and PCR models of pain on the aversive taste and aversive odor conditions (Appendix 1—figure 13 ).

Despite these advantages, the use of avoidance rating without thorough validation is a limitation of the current study, and thus future studies need to examine the psychometric properties of the avoidance rating, e.g., examining the relationship among pain intensity, unpleasantness, and avoidance measures. However, the current study showed that the predictive models derived with pain avoidance rating (Study 1) could be used to predict the pain intensity rating (Study 2). In addition, the overall time-course of pain avoidance ratings in Study 1 was similar to the time-course of pain intensity ratings in Study 2, providing some supporting evidence for the convergent validity of the pain avoidance measure.

As to the following comment, “It might not be an appropriate measure considering the total absence of pain variability in the reported responses over forty-eight subjects,” there are pieces of evidence supporting that the low between-individual variability of ratings is due to the characteristics of our experimental design, not to the fact that we used the avoidance measure. As we discussed in more detail in our response to Essential revisions 1, our experimental procedure based on capsaicin liquid commonly induces the initial burst of painful sensation and the subsequent gradual relief for most of the participants (Figure 1B, left). A similar time-course pattern of ratings was observed in Study 2 (Figure 1B, right), which used the pain “intensity” rating, not the pain avoidance rating. In addition, previous studies with a similar experimental design (i.e., intra-oral capsaicin application) (Berry and Simons, 2020; Lu, Baad-Hansen, List, Zhang, and Svensson, 2013; Ngom, Dubray, Woda, and Dallel, 2001) also showed a similar time-course of pain ratings with low between-individual variability regardless of the rating types (e.g., VAS or irritation intensity), confirming that this observation is not unique to the pain avoidance rating.

Now we added descriptions on the small between-individual variability of pain ratings and the use of avoidance ratings.

Revisions to the main manuscript:

pp. 5-7:

“Note that the overall trend of pain ratings over time was similar across participants because of the characteristics of our experimental design, which has also been observed in the previous studies that used oral capsaicin (Berry and Simons, 2020; Lu et al., 2013; Ngom et al., 2001). However, also note that each individual’s time-course of pain ratings were not entirely the same (Appendix 1—figures 2 and 3).”

p. 26:

“However, there are also differences between the characteristics of capsaicin-induced tonic pain versus clinical pain. For example, clinical pain continuously fluctuates over time in an idiosyncratic pattern (Apkarian, Krauss, Fredrickson, and Szeverenyi, 2001), whereas capsaicin-induced tonic pain showed a similar time-course pattern across the participants—i.e., increasing rapidly and then decreasing gradually (Figure 1B). This typical time-course of pain ratings has been reported in previous studies that used oral capsaicin (Berry and Simons, 2020; Lu et al., 2013; Ngom et al., 2001).”

pp. 26-27:

“Note that Study 1 used a pain avoidance measure that is not yet fully validated in healthy participants. However, we chose to use the pain avoidance measure, which can provide integrative information on the multi-dimensional aspects of pain (Melzack, 1999; Waddell, Newton, Henderson, Somerville, and Main, 1993). It also has a clinical implication considering that the maladaptive associations of pain avoidance to innocuous environments have been suggested as a putative mechanism of transition to chronic pain (Vlaeyen and Linton, 2012). Lastly, the avoidance measure can provide a common scale across different modalities of aversive experience, allowing us to compare their distinct brain representations (Ceko et al., 2022) or test the specificity of their predictive models (Lee et al., 2021) (Appendix 1—figure 13). Although the psychometric properties of the pain avoidance measure should be a topic of future investigation, we expect that the pain avoidance measure would have a high level of convergent validity with pain intensity given the observed similarity between pain avoidance (Study 1) and pain intensity (Study 2) in their temporal profiles. The generalizability of our PCR model across Studies 1 and 2 also supports this speculation. However, there would also be situations in which pain avoidance is dissociated from pain intensity. For example, capsaicin can be experienced to be intense but less aversive or even appetitive in some contexts, such as cravings for spicy food (Stevenson and Yeomans, 1993). In addition, the gradual rise of avoidance ratings during the late period of the control condition in Study 1 would not be observed if the intensity measure was used. Future studies need to examine the relationship between pain avoidance and the other pain assessments and the advantage of using the pain avoidance measure.”

8) Moreover, please address the following questions:

• How does the rating scale look like? Is the meaning of the anchors and of the thresholds for weak, moderate, strong, and very strong displayed on a screed during the scan? What are the instructions given to the participants before the start of the experiment? Do they induce any kind of pain expectancy (e.g., "After 6 minutes the pain should start decreasing" or analogous expressions)? A combination of those elements could explain the lack of pain variability.

We appreciate this question. First, we now added the scale and rating instruction that we used in Study 1 to present the experimental procedure more clearly (Appendix 1—figure 1).

Next, below are the scripts for explaining the instruction of pain avoidance rating translated into English.

“We will explain the rating scale that you will use with a trackball mouse during the fMRI scan. You will be asked to report ratings during the experiment, so please carefully listen to the following instructions.

Throughout all sessions except for the structural scan, you will see the following scale on the screen. Use the trackball to move the yellow bar like this to report your ratings.

You will see the question on the screen, “how much do you want to avoid this experience in the future?”. You will report your ratings using the following anchors, ranging from “Not at all” to “Most.” These anchors will not be shown during the scan, so please remember the location of each indicator. To help understand the rating scale, you can think about “how much money would I be paid to do this experience again?”. “Not at all” is an experience you can have again without any monetary reward, whereas “Most” is an experience you do not want to do again no matter how much money you will be paid. “Moderately” is an experience you want to avoid moderately, and “Very strong” is an experience that you would want to avoid immediately, like grabbing and immediately dropping a cup of coffee that you just bought.

Please explain how you understood the scale back to me. (Participants explain) In case you intermittently experience very severe pain, you can rate your pain over “Very strongly.” It is totally okay to rate your pain higher than “Very strongly,” but if your ratings are continuously higher than “Very strongly,” we will consider it too much pain for you and immediately stop the experiment.”

As described above, we did not show anchors and descriptors during scans, and there was no instruction that may induce any kind of pain expectancy. Therefore, these factors are not likely to cause the low variability of pain ratings.

We also put substantial effort into the design and instruction of the rating scale to reduce between-individual variance potentially coming from the different understanding and different uses across participants. For example, we used the general labeled magnitude scale (gLMS) (Bartoshuk et al., 2004), which features intermediate anchors with quasi-logarithmic spacing to permit valid comparisons of sensory experiences between individuals. We explained the meaning of these anchors to participants in great detail and thoroughly checked whether they understood them correctly before the scan. In addition, we let the participants practice the use of the rating scale in the scanner before we started the experiment. We believe that these efforts helped reduce the unwanted between-subject variance in ratings.

9) The behavioral outcome is called "pain avoidance" and the Authors hypothesize that it is proportional to the perceived pain. Is there any evidence proving this correlation? The collected ratings are the answer to the question "how much do you want to avoid this experience in the future?". Is it possible that this question is too generic to be called pain avoidance? Did the Authors quantify in any way the effect of laying down in a scanner for long time? It might play a role in the avoidance index.

As discussed in Essential revisions 7 above, the pain avoidance measure is not fully validated in a healthy population, which is a limitation of our study. There is currently no previous literature that proved the correlation between continuous ratings of pain avoidance and those of pain intensity. However, we think that the pain avoidance and intensity are correlated at least within our experimental paradigm, considering the similar patterns over time between pain avoidance ratings in Study 1 and pain intensity ratings in Study 2 (Figure 1B). The two studies used the same experimental procedure for capsaicin delivery, except that Study 2 used half amount of capsaicin compared to Study 1 because of the shorter scan time.

However, pain avoidance and pain intensity are not identical of course, and there can be some conditions in which the two measures are dissociated (Rainville, Duncan, Price, Carrier, and Bushnell, 1997). Our research group will examine the relationship between pain intensity and avoidance in future studies.

The instruction for the avoidance rating was “Please continuously report how much you want to avoid this experience in the future” (Appendix 1-figure 1). Indeed, we did not specify “pain” in the instruction and intentionally made it generic to use it across different experimental conditions including the bitter taste and aversive odor conditions. The main reason for this was to compare these multiple conditions on the same scale (Appendix 1-figure 13). For this reason, we also observed a slow increase in the avoidance rating score during the control run (Figure 1B), and this is understandable because lying down in a scanner for a long time could become aversive.

Although we understand the concern that our rating was too generic to be called ‘pain avoidance,’ we called it ‘pain avoidance’ because the effect of pain on the changes in avoidance ratings was evident in our analyses (e.g., capsaicin vs. control, early vs. middle vs. late, etc.), and thus the use of ‘pain avoidance’ is not a misnomer or overstatement. However, we will be happy to reconsider it and change the term into an alternative name, such as the ‘avoidance rating,’ if the reviewer thinks it is better.

Now we added a detailed discussion on the use of pain avoidance ratings.

Revisions to the main manuscript (pp. 26-27):

“Note that Study 1 used a pain avoidance measure that is not yet fully validated in healthy participants. However, we chose to use the pain avoidance measure, which can provide integrative information on the multi-dimensional aspects of pain (Melzack, 1999; Waddell et al., 1993). It also has a clinical implication considering that the maladaptive associations of pain avoidance to innocuous environments have been suggested as a putative mechanism of transition to chronic pain (Vlaeyen and Linton, 2012). Lastly, the avoidance measure can provide a common scale across different modalities of aversive experience, allowing us to compare their distinct brain representations (Ceko et al., 2022) or test the specificity of their predictive models (Lee et al., 2021) (Appendix 1—figure 13). Although the psychometric properties of the pain avoidance measure should be a topic of future investigation, we expect that the pain avoidance measure would have a high level of convergent validity with pain intensity given the observed similarity between pain avoidance (Study 1) and pain intensity (Study 2) in their temporal profiles. The generalizability of our PCR model across Studies 1 and 2 also supports this speculation. However, there would also be situations in which pain avoidance is dissociated from pain intensity. For example, capsaicin can be experienced to be intense but less aversive or even appetitive in some contexts, such as cravings for spicy food (Stevenson and Yeomans, 1993). In addition, the gradual rise of avoidance ratings during the late period of the control condition in Study 1 would not be observed if the intensity measure was used. Future studies need to examine the relationship between pain avoidance and the other pain assessments and the advantage of using the pain avoidance measure.”

10) The dynamic measure employed by the Authors is better described from the term "windowed functional connectivity". It is often considered a measure of dynamic functional connectivity and it gives information about fluctuations of the connectivity patterns over time. Nevertheless, the entire focus of the paper, including the title, is on dynamic networks, which inaccurately leads one to think of time-varying measures with higher temporal resolution. This allows one to follow network reorganization over time without averaging 2-min intervals in which several different brain mechanisms might play an important role. In summary, the assumption of constant response throughout 2-min periods of tonic pain and the use of Pearson correlations do not mirror the idea of dynamic analysis expressed by the Authors in title and introduction. Please consider removing "dynamic" from the title, reduce the emphasis on this concept, address possible confounds introduced by the choice of long windows and rephrase the aim of the study in terms of brain network reconfiguration over the main phases of tonic pain experience.

Now we removed the word ‘dynamic’ from many places in the manuscript, including the title. In addition, we added a brief discussion on the reason we chose to use the long and non-overlapping windows for connectivity calculation.

Revisions to the main manuscript (p. 8):

“Although the long duration of the time window without overlaps may obscure the fine-grained temporal dynamics in functional connectivity patterns, we chose to use this long time window based on previous literature (Bassett et al., 2011; Robinson, Atlas, and Wager, 2015), which also used long time windows to obtain more reliable estimates of network structures and their transitions.”

11) Procedure chosen for evoking sustained pain. The measures in figure 1B suggest that the intensity of the painful stimulation is not constant as expected for sustained pain (probably the effect washes out with the saliva). In this case, the first six-minute interval requires particular attention because it encapsulates the real tonic pain phase, and the following ones require more appropriate labels. Ideally the authors should cite previous studies showing that tongue evoked pain elicits a very specific behavioral response (summation, habituation/decrease of pain, absence of pain perception).

We thank the reviewer for the important comment and suggestions. We indeed conducted an extensive search to find appropriate labels for the three phases (i.e., early, middle, and late) for orofacial capsaicin pain, but we could not find one that fits our experimental design well. In addition, we have been hesitant about using the suggested terms, such as habituation or summation. For example, Segerdahl et al. (2015) used the term “habituation” for the late period of the tonic leg pain induced by topical capsaicin cream. This term seems reasonable for the study in which there was a decrease in pain without the removal of topical capsaicin cream. However, in our case, the decrease in pain can be driven by 1) the gradual removal of capsaicin by saliva over time and 2) active top-down pain regulation. These cannot be described with the term, habituation. Segerdahl et al. (2015) also used “relief” to describe the remission of pain after the analgesic procedure (e.g., cooling), but our experiment included no analgesic procedure and thus labeling the late period of pain in our study as “relief” may mislead the readers.

We also had a detailed discussion about the relationship between our study and “temporal summation” in Essential revisions 4. Briefly here, the temporal summation of pain may occur in our experiment, but only in a limited period of time (e.g., the very early period of the run). In addition, it is difficult to systematically investigate the temporal summation effect in our study due to our experimental design. Therefore, it is not clear to us whether the term “summation” can convey the precise meaning of the early period of pain in our study.

According to our literature search, most of the previous studies on oral capsaicin used simple descriptive terms such as increasing or decreasing, which are similar to our description of the three phases of pain—(1) the initiation and maintenance of pain, (2) gradual decrease of pain, and (3) full remission of pain. Some of the previous studies labeled the late period of capsaicin-induced pain as the “waning” period (Chang, Arendt-Nielsen, Graven-Nielsen, Svensson, and Chen, 2001a, 2001b; Coghill, Sang, Berman, Bennett, and Iadarola, 1998; Iadarola et al., 1998). Although we could not come up with better labels than our current labels at this point, we are open to suggestions and will be happy to change the terms in the next round of revision if possible.

12) Moreover, please address the following points:

• Does the procedure include a calibration phase? If yes, please add description in the Methods section. If not, how do the Authors explain the relatively small standard error of the mean reported in Figure 1?

Now we added a description that there was no calibration phase before the capsaicin stimulation in the Methods section.

Revisions to the main manuscript (p. 29):

“We did not include pre-calibration to match the subjective level of pain.”

We think the small standard error of the mean in Study 1 is presumably attributed to the characteristics of our experimental design. For a more detailed explanation, please see Essential revisions 1, Essential revisions 7, and Essential revisions 8 above.

13) If possible, add citation proving that there is a very consistent behavioral (pain related) response to the capsaicin: no pain variability against what most of the evoked pain experiments showed.

We now added the citations of the studies showing the small between-individual variability of pain ratings with orofacial capsaicin stimulation.

Revisions to the main manuscript (pp. 5-7):

“Note that the overall trend of pain ratings over time was similar across participants because of the characteristics of our experimental design, which has also been observed in the previous studies that used oral capsaicin (Berry and Simons, 2020; Lu et al., 2013; Ngom et al., 2001). However, also note that each individual’s time-course of pain ratings were not entirely the same (Appendix 1—figures 2 and 3).”

14) Please report in the supplementary material the dots distribution (box plot with visible dots) of the ratings at minute 0, 6, 14, 20.

We now provided dots distribution of the ratings at 0, 6, 14, and 20 min.

15) Community detection analysis. Please clarify the following issues:

• The thresholding of the connectivity matrices for the binarization of the networks is certainly a weakness of the study and, more in general, of most of the connectivity analysis (only few estimators have known null distributions). Here, the chosen optimal threshold is the one that maximize the difference between conditions (capsaicin vs controls) in terms of global graph measures, including modularity. I suggest adding a comment on the effect of maximizing a measure depending on the modularity of the networks, on the subsequent community detection algorithm, also based on maximizing modularity (within the network this time). What is the Authors' opinion on this possible confound? Also, what is the rationale/hypothesis behind this procedure for obtaining sparse matrices?

We appreciate the comment and understand the concerns regarding the choice of thresholding parameters. In the following paragraphs, we tried our best to explain the rationale behind our connectivity thresholding and how we mitigated the possible confound from this procedure.

It is often considered a prerequisite to apply thresholding to functional connectivity matrices for brain network analyses (van den Heuvel et al., 2017) because many of the graph analytics were developed for sparse networks (Newman, 2010) and functional connectivity data were likely to contain many spurious correlations. What remains controversial is how to determine the level of thresholding. In an ideal condition, results for the multiple levels of thresholding can be separately reported and discussed. However, we could not repeat all the main analyses multiple times to test different threshold levels (e.g., 0.01, 0.05, 0.1, 0.2, 0.3, 0.4 as Appendix 1—figure 4), each of which takes approximately a few months because the current study used high-dimensional voxel-level connectome data and also the multiple iterations of community detection processes to obtain robust estimates of community structures. Because of the huge computational burden, we could not try all different options and had to determine the one threshold level among the possible candidate values, which should be based on a priori criteria to avoid an arbitrary decision like picking up a specific level of network density (e.g., 0.05 or 0.1). In the current study, we chose the threshold level that maximizes the contrast between the global-level network measures of the capsaicin and control conditions. The chosen threshold level was 0.05, which is a common value for the thresholding of functional connectivity in network neuroscience studies.

In addition, there could be a concern that we examined the network-level contrast between the capsaicin vs. control conditions (e.g., Figure 2) using the threshold that maximized the difference between the conditions. However, its confounding effect should be minimal for the following reasons. First, our main analysis (i.e., multi-layer community detection) was based on weighted networks from all ten time-bins, whereas the global-level network attributes that we used to determine the threshold level were obtained by averaging all time-bin network data and binarizing them to make it unweighted networks. Thus, two analyses were based on different data and the confounding effect should be minimal. Second, most of the global-level network measures used for determining the threshold level were not directly relevant to our community detection analyses except for the modularity measure. Even without the modularity measure, the selection of the threshold level is unchanged. Third, the time-varying changes in brain networks within the capsaicin condition (e.g., Figures 3 and 5) are not relevant to the choice of thresholding, which was based on the contrast between the capsaicin vs. control conditions. Lastly, our SVM (Figure 4) and PCR models (Figure 5) were generalized to the independent test dataset (Study 2, n = 74) that was not used to determine the threshold level.

Now we added a discussion on the choice of network density threshold.

Revisions to the main manuscript (p. 28):

“Lastly, the optimization of the network density threshold to maximize the differences in a priori network attributes between the capsaicin versus control conditions may influence the results comparing the community structures between the two conditions. However, our main results were based on temporal dynamics across all time bins, whereas the global-level network attributes that we used to determine the level of threshold were based on the averaged data across all time bins. In addition, our SVM and PCR models were generalized to the independent test dataset, which was not used to determine the threshold level. Therefore, the confounding effect of thresholding on our main results should be minimal.”

16) Did the Authors consider running the analysis with other resolution parameters (γ and omega)?

We thank the reviewer for the important suggestion. However, it is currently not feasible to try different options of parameter sets because of the extremely heavy computational load as described in Essential revisions 15. We followed the conventional choice (γ = 1 and ω = 1) (Bassett, Yang, Wymbs, and Grafton, 2015) similar to many previous studies (Bassett et al., 2011; Bassett, Wymbs, et al., 2013; Bassett et al., 2015; Betzel, Satterthwaite, Gold, and Bassett, 2017; Braun et al., 2016; Braun et al., 2015; Cocuzza, Ito, Schultz, Bassett, and Cole, 2020; Finc et al., 2020; Gifford et al., 2020; Han et al., 2020; Khambhati, Mattar, Wymbs, Grafton, and Bassett, 2018; Lydon-Staley, Ciric, Satterthwaite, and Bassett, 2019; Lydon-Staley, Kuehner, et al., 2019; Pedersen, Zalesky, Omidvarnia, and Jackson, 2018; Shine, Koyejo, and Poldrack, 2016; Telesford et al., 2016). However, we agree that it would be important to test multiple sets of resolution parameters (Betzel and Bassett, 2017) to examine the effects of the parameters. This is a part of the reason that we conducted the predictive modeling, which allows us to avoid excessive reverse-inference from the parameter-sensitive results alone (as described in the Discussion, pp. 27-28).

17) Group-level consensus community detection: I found this section difficult to follow, especially in terms of reasoning for specific choices and steps of the analysis.

• Step III: the standard definition of allegiance is a binary matrix whose elements are equal to one only when the two correspondent nodes belong to the same community (as the Authors described). After step III, a new index is computed as the mean of allegiance matrices over time and across subjects. Its value indicates the proportion (percentage) of subjects showing the two nodes in the same community at specific time points. Or how many times the two nodes belong to the same community during the early/middle/late stage of the experiment. Using the name allegiance (a binary measure) to indicate those percentages, might be misleading. I suggest using more appropriate names (measures like "agreement", "dwell time" and similar might be useful) and providing more explanatory examples on how to read the value of the computed measures.

To our best knowledge, the term “allegiance,” “mean allegiance,” or “averaged allegiance” have been used to indicate the time-averaged, subject-averaged, or both time-and subject-averaged module allegiance matrix in previous studies (Bassett et al., 2015; Farahani et al., 2022; Finc et al., 2020; Gifford et al., 2020). Therefore, the use of “allegiance” in our study does not seem to be problematic. However, we also know the term “agreement” has often been used as an alternative to the “allegiance” (Barroso et al., 2021; Baum et al., 2017; Cheng et al., 2022; Mano et al., 2018). Thus, we are willing to revise the term accordingly if the reviewer thinks we should change the term.

18) Step IV: please specify number of permutations.

Permutation was performed once per each of the original community assignments as in the previous study by Bassett (Bassett et al., 2015). We now specified the number of permutations.

Revisions to the main manuscript (p. 34):

“Permutation was performed once per each of the original community assignments.”

19) Step VI: in the same spirit as the two previous comments, I suggest either reconsidering the necessity of this computation, or explaining the reasons for applying a community detection algorithm twice. I believe that additional layers of complexity always require a clear question that they can answer.

Since the multilayer community detection algorithm is non-deterministic and has near-degeneracies, it has been recommended to repeat the community detection multiple times and obtain the consensus partition among the iterations (Bassett, Porter, et al., 2013). In the current study, we followed this recommendation as in many previous studies (Bassett, Porter, et al., 2013; Baum et al., 2017; Braun et al., 2015; Marquez-Legorreta et al., 2022; Mattar, Thompson-Schill, and Bassett, 2018; Petrican and Levine, 2018). Although we understand the considerate comment regarding the complexity of the consensus clustering method, we believe this method provides a reliable and representative community partition that can be replicated over repeated iterations, which is particularly beneficial for the comparison of group-level community structures between different conditions (Figure 2) or time (Figure 3).

20) It remains unclear, how specific the results are to pain. Differences between the control resting state and the capsaicin trials might be – at least partially – driven by other factors, like motion artifacts, saliency, attention, anxiety, etc. Differences between stages over the time-course might, additionally, be driven by scanner drifts (to which the applied approach might be less sensitive, but the possibility is still there) or other gradual processes, e.g. shifts in arousal, attention shifts, alertness, etc. All the above factors might emerge as confounding bias in both of the predictive models. This problem should be thoroughly discussed, and at least the following extra analyses are recommended, in order to attenuate concerns related to the overall specificity and neurobiological validity of the results:

• Reporting of, and testing for motion estimates (mean, max, median framewise displacement or anything similar).

• Examining whether these factors might, at least partially, drive the predictive models.

• e.g. Applying the PCR model on the resting state data and verifying of the predicted timecourse is flat (no inverse U-shape, that is characteristic to all capsaicin trials).

We thank the reviewer for this comment on the important issue regarding the specificity of our results and the potential influences of noise. The effects of head motion and physiological confounds are particularly relevant to pain studies because pain involves substantial physiological changes and often causes head motion. To address the related concerns of specificity, we conducted additional analyses assessing the independence of our predictive models (i.e., SVM and PCR models) from head movement and physiology variables and the specificity of our models to pain versus non-painful aversive conditions (i.e., bitter taste and aversive odor) in Study 1.

First, we examined the overall changes of framewise displacement (FD) (Power, Barnes, Snyder, Schlaggar, and Petersen, 2012), heart rate (HR), and respiratory rate (RR) in the capsaicin condition (Appendix 1—figure 11 ). For the univariate comparison between the capsaicin vs. control conditions (Appendix 1—figure 11A), the results showed that, as expected, the capsaicin condition caused significant changes in head motion and autonomic responses. The mean FD and HR were significantly higher, and the RR was lower in the capsaicin condition compared to the control condition (FD: t47 = 5.30, P = 2.98 × 10-6; HR: t43 = 4.98, P = 1.10 × 10-5; RR: t43 = -1.91, P = 0.063, paired t-test). In addition, the increased motion and autonomic responses were more prominent in the early period of pain (Appendix 1—figure 11B). The 10-binned (2 mins per time-bin) FD and HR showed a decreasing trend while the RR showed an increasing trend over time in the capsaicin condition. The comparisons between the early (1-3 bins, 0-6 min) vs. late (8-10 bins, 14-20 min) periods of the capsaicin condition showed significant differences both for FD and HR (FD: t47 = 6.45, P = 8.12 × 10-8; HR: t43 = 6.52, P = 6.41 × 10-8; RR: t43 = -1.61, P = 0.11, paired t-test). These results suggest that while participants were experiencing capsaicin tonic pain, particularly during the early period, head motion and heart rate were increased, while breathing was slowed down. Note that we needed to exclude 4 participants’ data in this analysis due to technical issues with the physiological data acquisition.

Next, we examined whether the changes in head motion and physiological responses influenced our predictive model performance (Appendix 1—figure 12). We first regressed out the mean FD, HR, and RR (concatenated across conditions and participants as we trained the SVM model) from the predicted values of the SVM model with leave-one-subject-out cross-validation (2 conditions × 44 participants = 88) and then calculated the classification accuracy again (Appendix 1—figure 12A). The results showed that the SVM model showed a reduced, but still significant classification accuracy for the capsaicin versus control conditions in a forced-choice test (n = 44, accuracy = 89%, P = 1.41 × 10-7, binomial test, two-tailed). We also did the same analysis for the PCR model (10 time-bins × 44 participants = 440) and the PCR model also showed a significant prediction performance (n = 44, mean prediction-outcome correlation r = 0.20, P = 0.003, bootstrap test, two-tailed, mean squared error = 0.159 ± 0.022 [mean ± s.e.m.]) (Appendix 1—figure 12B). These results suggest that our SVM and PCR models capture unique variance in tonic pain above and beyond the head movement and physiological changes.

Lastly, we examined the specificity of our predictive models to pain, by testing the models on the non-painful but aversive conditions including the bitter taste (induced by quinine) and aversive odor (induced by fermented skate) conditions (Appendix 1-figure 13, please see Essential revisions 7). All the model responses were obtained using leave-one-participant-out cross-validation. The results showed that the overall model responses of the SVM model for the bitter taste and aversive odor conditions were higher than those for the control condition but lower than the capsaicin condition (Appendix 1-figure 13A). Classification accuracies for comparing capsaicin vs. bitter taste and capsaicin vs. aversive odor were all significant (for capsaicin vs. bitter taste, accuracy = 79%, P = 6.17 × 10-5, binomial test, two-tailed, Appendix 1-figure 13C; for capsaicin vs. aversive odor, accuracy = 83%, P = 3.31 × 10-6, binomial test, two-tailed, Appendix 1-figure 13E), supporting the specificity of our SVM model of pain. Similarly, the model responses of the PCR model for the bitter taste and aversive odor conditions were lower than the capsaicin condition, and their temporal trajectories were less steep and fluctuating compared to the capsaicin condition (Appendix 1-figure 13B). The time-course of the model responses for the control condition was flatter than all other conditions and did not show the inverted U-shape. Furthermore, the model responses of the bitter taste and aversive odor conditions did not show the significant correlations with the actual avoidance ratings (bitter taste: mean prediction-outcome correlation r = 0.05, P = 0.41, bootstrap test, two-tailed, mean squared error = 0.036 ± 0.006 [mean ± s.e.m.], Appendix 1-figure 13D; aversive odor: mean prediction-outcome correlation r = 0.12, P = 0.06, bootstrap test, two-tailed, mean squared error = 0.044 ± 0.004 [mean ± s.e.m.], Appendix 1-figure 13F), suggesting the specificity of PCR model to pain.

Overall, we have provided evidence that our models can predict pain ratings above and beyond the head motion and physiological changes and that the models are more responsive to pain compared to non-painful aversive conditions.

Now we added descriptions on the specificity tests to the main manuscript and also to the Appendix 1.

Revisions to the main manuscript (p. 20):

“Specificity of the module allegiance-based predictive models

To examine whether the predictive models were specific to pain and the prediction performances were not influenced by confounding variables such as head motion and physiological changes, we conducted additional analyses as shown in Appendix 1—figures 11-13. The SVM and PCR models showed significant prediction performances even after controlling for head motion (i.e., framewise displacement) and physiological responses (i.e., heart rate and respiratory rate) (Appendix 1—figures 11 and 12) and did not respond to the non-painful but aversive conditions including the bitter taste and aversive odor conditions (Appendix 1—figure 13), supporting the specificity of our predictive to pain. For details, please see Appendix 1.”

Revisions to the Appendix 1 (pp. 2-4):

“Specificity analysis (Appendix 1—figures 11-13)

To examine whether the predictive models (i.e., SVM and PCR models) were specific to pain and not influenced by confounding noises, we conducted additional specificity analysis assessing the independence of the models from head movement and physiology variables and specificity of our models to pain versus non-painful aversive conditions (i.e., bitter taste and aversive odor) in Study 1.

[…]

Overall, we have provided evidence that the module allegiance-based models can predict pain ratings above and beyond the movement and physiological changes, and are more responsive to pain compared to non-painful aversive conditions, which suggest the specificity of our results to pain.”

21) Statistical inference. An important issue is the (apparent) lack of statistical inference when analyzing the differences in the group-level consensus community structures (both when comparing capsaicin to control and when analysing changes over the time-course of the capsaicin-challenge). Although the observed changes seem biologically plausible and fit very well to previous results, without proper statistical inference we can't determine, how likely such differences are to emerge just by chance. This makes all results on Figures 2 and 3, and points 1, 4 and 5 in the discussion partially or fully speculative or weakly underpinned, comprising a large proportion of the current version of the manuscript. There are two main ways of handling this issue:

• Enhancing (or clarifying potential misunderstandings regarding) the methodology (see my concrete, and hopefully feasible, suggestions in the "private part" of the review). There are likely many ways to test the significance of these differences. Two permutation testing-based ideas are (i) permuting the labels ctr-capsaicin, or early-mid-late, repeating the analysis, constructing the proper null distribution of e.g. the community size changes and obtain the p-values and (ii) "trace back" communities to the individual level and do (nonparametric) statistical inference there.

• De-weighting the presentation and the discussion of the related results.

We appreciate this important comment. We did not conduct statistical inference when comparing the group-level consensus community affiliations of the different conditions (Figure 2) or different phases (Figure 3) because of the difficulty in matching the community affiliation values of the networks to be compared.

For example, let us assume that the 800 out of 1,000 voxels of community #1 and 1,000 out of 4,000 voxels of community #2 in the control condition are commonly affiliated with the same community #3 in the capsaicin condition. To compare the community affiliation between two conditions, we should first match the community label of the capsaicin condition (i.e., #3) to that of the control condition (i.e., #1 or #2), and here a dilemma occurs; if we prioritize the proportion of the overlapping voxels for the matching, the common community should be labeled as #1, whereas if we prioritize the number of the overlapping voxels for the matching, the label of the common community should be #2. Although both choices look reasonable, none of them can be a perfect solution.

As the example above, it is impossible to exactly match the community affiliation of the different networks. We must choose an imperfect criterion for the matching procedure, which essentially affects the comparison of network structure. This was the main reason that we limited our results of Figures 2-3 to a qualitative description based on visual inspection. Moreover, the group-level consensus community structures in Figures 2-3 are not a simple group statistic like sample mean; they were obtained from multiple steps of analyses including permutation-based thresholding and unsupervised clustering, which could further complicate the interpretation of statistical tests.

Alternatively, there is a slightly different but more rigorous approach to the comparisons of the community structures, which is the Phi-test (Alexander-Bloch et al., 2012; Lerman-Sinkoff and Barch, 2016). Instead of direct use of the community labels, this method converts the community label of each voxel into a list of module allegiance values between the seed voxel and all the voxels of the brain (i.e., 1 if the seed and target voxels have the same community label and 0 otherwise). This allows quantitative comparisons of voxel-level community profiles between different conditions without an arbitrarily matching of the community labels. We adopted this Phi-test for our analyses to examine whether the regional community affiliation pattern is significantly different between (i) the capsaicin vs. control conditions and (ii) the early vs. late periods of pain (Appendix 1-figure 6), which correspond to the main findings of the Figures 2 and 3 in our manuscript, respectively.

More specifically, to compare the group-level consensus community structures between the capsaicin vs. control conditions and the early vs. late periods, we first obtained a seed-based module allegiance map for each voxel (i.e., using each voxel as a seed). Then, we calculated a correlation coefficient of the module allegiance values between two different conditions for each voxel. This correlation coefficient can serve as an estimate of the voxel-level similarity of the consensus community profile.

Because module allegiance is a binary variable, these correlation values are Phi coefficients. A small Phi coefficient means that the spatial pattern of brain regions that have the same community affiliation with the given voxel are different between the two conditions. For example, if a voxel is connected to the somatomotor-dominant community during the capsaicin condition and the default-mode-dominant community during the control condition, the brain regions that have the same community label with the voxel will be very different, and thus the Phi coefficient will become small. Moreover, the Phi coefficient can be small even if a voxel is affiliated as the same (matched) community label for both conditions, when the spatial patterns of the same community is different between conditions.

To calculate the statistical significance of the Phi coefficient, we conducted permutation tests, in which we randomly shuffled the condition labels in each participant and obtained the group-level consensus community structure for each shuffled condition. Then, we calculated the voxel-level correlations of the module allegiance values between the two shuffled conditions. We repeated this procedure 1,000 times to generate the null distribution of the Phi coefficients, and calculated the proportion of null samples that have a smaller Phi coefficient (i.e., a more dis-similar regional community structure) than the non-shuffled original data.

Results showed that there are multiple voxels with statistical significance (permutation tests with 1,000 iterations, one-tailed) in the area where the community affiliations of the two contrasting conditions were different (Appendix 1—figure 6). For example, the frontoparietal and subcortical regions for the capsaicin vs. control (c.f., Figure 2), and the frontoparietal, subcortical, brainstem, and cerebellar regions for the early vs. late period of pain (c.f., Figure 3) contain voxels that survived after thresholding with FDR-corrected q < 0.05, suggesting the robustness of our main results.

Particularly, the somatomotor and insular cortices showed statistical significance in the permutation test, and this may reflect the large changes in other areas that are connecting to the somatomotor and insular cortices across different conditions. The statistical significance was also observed in the visual cortex, which was unexpected. We interpret that the spatial distribution of the visual network community is too stable across conditions, and thus the null distribution from permutation formed a very narrow distribution of Phi coefficients. Therefore, a small change in the community structure could achieve statistical significance.

Now we added descriptions on the permutation tests.

Revisions to the main manuscript:

p. 9:

“Permutation tests confirmed that the community assignment in the frontoparietal and subcortical regions showed significant changes between the capsaicin versus control conditions (Appendix 1—figure 6A).”

pp. 13-14:

“Permutation tests further confirmed that the community assignment in the frontoparietal, subcortical, brainstem, and cerebellar regions showed significant changes between the early versus late period of pain (Appendix 1—figure 6B).”

pp. 36-37:

“Permutation tests for regional differences in community structures. To test the statistical significance of the voxel-level difference of consensus community structures (Figures 2 and 3), we performed the following Phi-test (Alexander-Bloch et al., 2012; Lerman-Sinkoff and Barch, 2016). First, for each given voxel, we compared the community label of the voxel to the community label of all the voxels, generating a list of voxel-seed module allegiance values that allow quantitative comparison of voxel-level community profile (e.g., [1, 0, 1, 1, 0, 0, …], whose element is equal to 1 if the seed and target voxels were assigned to the same community and 0 otherwise). Next, a correlation coefficient was calculated between the module allegiance values of the two different brain community structures (i.e., capsaicin versus control, and early versus late). This correlation coefficient is an estimate of the regional similarity of community profiles (here, the correlation coefficient is Phi coefficient because module allegiance is a binary variable). To estimate the statistical significance of the Phi coefficient, we performed permutation tests, in which we randomly shuffled the labels and then obtained the group-level consensus community structures from the shuffled data. Then, the Phi coefficient between the module allegiance values of the two shuffled consensus community structures was calculated. We repeated this procedure 1,000 times to generate the null distribution of the Phi coefficient for each voxel. Lastly, we examined the probability to observe a smaller Phi coefficient (i.e., a more dissimilar community profile) than the one from the non-shuffled original data, which corresponds to the P-value of the permutation test. All the P-values were one-tailed as the hypothesis of this permutation test is unidirectional.”

Reviewer #1 (Recommendations for the authors):

• The authors emphasize the term "pain supersystem". This term is not very well-introduced yet and the necessity for such a term is unclear. I recommend that the authors rely less on this term and omit it at least from the abstract.

We agree with the reviewer’s suggestion. The term “pain supersystem” is now mostly replaced with a more descriptive one (i.e., an extended somatomotor-dominant community), including the abstract.

• The statement in the abstract "In the early stage, the orofacial areas of the primary somatomotor cortex were separated from the other primary somatomotor cortices and integrated with…" is a bit ambiguous. It might better read "In the early stage, the orofacial areas of the primary somatomotor cortex were separated from other areas of the primary somatomotor cortex and integrated with…"

We thank the reviewer for this helpful comment, and now revised the statement in the abstract as suggested.

Reviewer #2 (Recommendations for the authors):

• I suggest reducing the amount of text in the figures. All the information needed to understand the illustrations should be included in the captions. Figure 1 and Figure 7 are the ones that require the most attention in this respect.

We agree with this suggestion, and now substantially reduced the amount of text in Figures 1 and 7.

• It might be a good idea to specify when any previous evidence used to justify the current analysis or to make inferences on the obtained results actually come from the Authors' previous publications. Especially if they are extracted from the same dataset, this information is relevant.

We now specified when we used our previous publication to interpret or justify our current findings.

Revisions to the main manuscript:

p. 3:

“Tonic pain has long been used as an experimental model of clinical pain (Dubuisson and Dennis, 1977), and our previous study demonstrated that capsaicin-induced tonic orofacial pain shows a network-level brain representations similar to clinical pain, suggesting its clinical relevance (Lee et al., 2021).”

p. 7:

“In this study, we used this approach to examine the temporal changes of brain network structures during sustained pain, which cannot be done with conventional functional connectivity-based analyses (Lee et al., 2021).”

p. 26:

“In addition, in a recent study, we showed that an fMRI connectivity-based signature for capsaicin-induced orofacial tonic pain can be generalized to chronic back pain (Lee et al., 2021).”

• In their previous paper, the Authors had access to a dataset including the experimental conditions: tonic capsaicin pain, tonic aversive taste, and tonic aversive odor. Did the Authors analyze the communities structure during those controls conditions? Did they consider testing their classifier on them? In my opinion, it would add a lot of robustness to the study findings, and it would make the obtained results reliable and unquestionably pain related (thinking of the more general avoidance ratings).

We thank the reviewer for this helpful comment. We conducted additional analyses to demonstrate the specificity of our predictive models to pain by testing the models on non-painful but aversive conditions including the bitter taste and aversive odor conditions in Essential revisions 20 (Appendix 1-figure 13). The results showed that the model responses of the SVM model were significantly higher in capsaicin condition than the bitter taste and aversive odor conditions, and that the PCR model was not predictive of the avoidance ratings of the bitter taste and aversive odor conditions, supporting the specificity of the predictive models.

• In terms of data availability, the Authors declared that data and codes will be shared upon publication. I would appreciate their availability if there will be a second loop of revisions before potential publication.

We are now ready to share the codes and processed data (e.g., brain community assignment) for generating the main figures (https://github.com/cocoanlab/brain_reconfig_pain). The raw data of Study 1 will be shared upon request, and the data of Study 2 will be shared later as a part of the large-scale dataset (including heat and capsaicin pain) that is still ongoing and will be publicly open.

Reviewer #3 (Recommendations for the authors):

– As the authors mention the cross-validated evaluation of the PCR model is biased due to hyperparameter optimization. While the independent evaluation resolves any related concerns, the authors might consider applying a nested cross-validation framework, to have unbiased estimates for the discovery dataset, as well.

We agree that the nested cross-validation could provide less biased estimates of prediction performance in the discovery dataset. Following the suggestion, we conducted a nested leave-one-participant-out cross-validation of the PCR model. The results also showed a significant prediction performance (mean prediction-outcome correlation r = 0.28, P = 1.00 × 10-5, bootstrap test, two-tailed, mean squared error = 0.044 ± 0.006 [mean ± s.e.m.], number of components = 13.94 ± 0.14 [mean ± s.e.m.]), which was comparable to the previous results (r = 0.29).

We now added the following description to the manuscript.

Revisions to the main manuscript:

p. 19:

“To obtain a less biased estimate of performance in the training data, we used nested leave-one-participant-out cross-validation that separates the hyper-parameter tuning and testing (see Materials and methods for details). The results showed that prediction performance was significant (mean prediction-outcome correlation r = 0.28, P = 1.00 × 10-5, bootstrap test, two-tailed, mean squared error = 0.044 ± 0.006 [mean ± s.e.m.], number of principal components = 13.94 ± 0.14 [mean ± s.e.m.]), suggesting that the individuals’ brain community structures are predictive of the temporal change of sustained pain.”

p. 38:

“To overcome the potential bias in the performance estimation due to the optimal selection of PC number, we additionally conducted nested cross-validation which has double loops of leave-one-participant-out cross-validation; the inner loop where the hyper-parameter (i.e., the PC number) was selected, and the outer loop where the actual prediction was done using the hyperparameters chosen from the inner loop. Since the hyper-parameter tuning and testing were separated into the inner and outer loops, this procedure provides a less biased estimate of prediction performance even in the training dataset (Study 1).”

– Optimizing the network density threshold in the same dataset, especially on one of the conditions-of-interest (Q1: capsaicin vs. controls) may be circular (as the optimized global network metrics may well be associated to the community structure). On the other hand, this potential circularity does not affect all he results (e.g definitely not the results based on the independent test dataset) and in general, I don't think this would significantly affect the results. Nevertheless, performing (or reproducing) the optimization on independent data would be reassuring. Alternatively, this issue must be discussed as a potential limitation/bias.

We addressed this comment in Essential revisions 15.

– While this is not explicitly stated, prediction performance is evaluated only on the within subject-level. For better comparability to other methods, please report and discuss the "between-subject" estimates, too (i.e. how well can we classify/predict from a single session/window of a single subject).

The reason we mainly focused on the within-individual prediction was that the main purpose of our study was to examine the brain network changes during sustained pain within individuals, not to examine the between-individual differences. Nonetheless, we agree that providing the between-individual prediction performance could be helpful to compare our models with other existing models. We now provide additional results of between-individual prediction.

For the SVM model, we evaluated the classification accuracy for the capsaicin versus control conditions across all the participants, instead of the forced-choice test that compared the two conditions within individuals. The results for Study 1 were as follows: accuracy with an optimal threshold = 88% (P = 1.82 × 10-14, binomial test, two-tailed), 85% sensitivity, 90% specificity, area under the curve (AUC) = 0.94. The results for Study 2 were as follows: accuracy with an optimal threshold = 76% (P = 2.84 × 10-10, binomial test, two-tailed), 72% sensitivity, 80% specificity, AUC = 0.80.

For the PCR model, we calculated the correlation between mean pain ratings and mean model responses (i.e., between-individual prediction-outcome correlation) for capsaicin condition. The results for Study 1 were as follows: r = 0.41, P = 0.004, one-sample t-test, two-tailed, mean squared error = 0.024. The results for Study 2 were as follows: r = 0.27, P = 0.018, one-sample t-test, two-tailed, mean squared error = 0.022.

Now we added descriptions on the between-individual prediction to the main manuscripts and also to the Appendix 1.

Revisions to the main manuscript:

p. 14:

“For the classification accuracy across all the participants instead of the forced-choice test, please see Appendix 1.”

p. 19:

“For the between-individual prediction-outcome correlation of mean pain ratings, please see Appendix 1.”

Revisions to the Appendix 1 (p. 4):

“Between-individual predictive performances

For SVM model, we evaluated the classification accuracy for the capsaicin versus control conditions across all the participants, instead of the forced-choice test that compared the two conditions within individuals. The results for Study 1 were as follows: accuracy with an optimal threshold = 88% (P = 1.82 × 10-14, binomial test, two-tailed), 85% sensitivity, 90% specificity, area under the curve (AUC) = 0.94. The results for Study 2 were as follows: accuracy with an optimal threshold = 76% (P = 2.84 × 10-10, binomial test, two-tailed), 72% sensitivity, 80% specificity, AUC = 0.80.

For PCR model, we calculated the correlation between mean pain ratings and mean signature responses (i.e., between-individual prediction-outcome correlation) for capsaicin condition. The results for Study 1 were as follows: r = 0.41, P = 0.004, one-sample t-test, two-tailed, mean squared error = 0.024. The results for Study 2 were as follows: r = 0.27, P = 0.018, one-sample t-test, two-tailed, mean squared error = 0.022.”

– Introduction: discussing the possible relation of the present work to chronic pain or other clinical pain conditions is not sufficient.

It seems that this comment is similar to the Essential revisions 6; we now added the discussion on the relationship with clinical pain conditions in the main manuscript. We are also happy to revise the current manuscript if more discussion is needed in the other sections including the Introduction.

Revisions to the main manuscript (p. 26):

“An interesting future direction would be to examine whether the current results can be generalized to clinical pain. Experimental tonic pain has been known to share similar characteristics with clinical pain (Rainville et al., 1992; Stohler and Kowalski, 1999). In addition, in a recent study, we showed that an fMRI connectivity-based signature for capsaicin-induced orofacial tonic pain can be generalized to chronic back pain (Lee et al., 2021). Therefore, a detailed characterization of the brain responses to sustained pain has the potential to provide useful information about clinical pain. However, there are also differences between the characteristics of capsaicin-induced tonic pain versus clinical pain. For example, clinical pain continuously fluctuates over time in an idiosyncratic pattern (Apkarian et al., 2001), whereas capsaicin-induced tonic pain showed a similar time-course pattern across the participants—i.e., increasing rapidly and then decreasing gradually (Figure 1B). This typical time-course of pain ratings has been reported in previous studies that used oral capsaicin (Berry and Simons, 2020; Lu et al., 2013; Ngom et al., 2001). Although we would expect our results reflect the general pattern of brain network changes during the rise and fall of sustained pain, it remains an empirical question how much they will be generalizable across different clinical pain conditions. Interestingly, a recent fMRI study on the temporal summation of pain in fibromyalgia patients reported results similar to ours (Cheng et al., 2022), including the intra-network dissociation within the somatomotor network and the inter-network integration between the somatomotor and other networks during pain. Although we cannot directly examine whether the temporal summation of pain gave rise to these network-level changes due to the limitation of our experimental paradigm, these consistent findings between the two studies may suggest that our findings could be generalized to clinical conditions.”

– More information is needed about the individual variability of the pain-related behavioral time-courses (maybe in the supplementary info). Was remission complete in all participants?

We think this comment is partially addressed in Essential revisions 1, Essential revisions 7 and Essential revisions 8. Although between-individual variability of avoidance ratings was small because of the characteristics of our experimental design that commonly induces initial burst of painful sensation and the subsequent gradual relief, each individual’s timecourse of pain ratings actually shows distinct patterns and there were some participants who report severe pain in the late period (Appendix 1-figure 2, please see Essential revisions 1). The term “remission” was used because the difference between avoidance ratings of capsaicin vs. control conditions became insignificant in the late period of pain (from 17.3 min to the end, two-tailed Ps > 0.05, paired t-test, BF01 = 1.01-4.71), which does not mean that all participants experienced complete remission of pain.

We also added descriptions on the between-individual variability of pain ratings.

Revisions to the main manuscript:

pp. 5-7:

“Note that the overall trend of pain ratings over time was similar across participants because of the characteristics of our experimental design, which has also been observed in the previous studies that used oral capsaicin (Berry and Simons, 2020; Lu et al., 2013; Ngom et al., 2001). However, also note that each individual’s time-course of pain ratings were not entirely the same (Appendix 1—figures 2 and 3).”

p. 26:

“However, there are also differences between the characteristics of capsaicin-induced tonic pain versus clinical pain. For example, clinical pain continuously fluctuates over time in an idiosyncratic pattern (Apkarian et al., 2001), whereas capsaicin-induced tonic pain showed a similar time-course pattern across the participants—i.e., increasing rapidly and then decreasing gradually (Figure 1B). This typical time-course of pain ratings has been reported in previous studies that used oral capsaicin (Berry and Simons, 2020; Lu et al., 2013; Ngom et al., 2001).”

– Some participants might be more tolerant for capsaicin than others, due to eating habits. Please discuss whether this could potentially affect the results.

Since we measured the pain avoidance that reflects multidimensional aspects of pain, multiple factors such as cravings for spicy food (Stevenson and Yeomans, 1993) can affect their subjective reports. However, we did not systematically screen out participants based on those factors. This is because we wanted to capture as much variance as possible, which potentially helps to get more generalizable results for different settings. Although the group-level consensus community may obscure the between-individual differences, predictive models can efficiently reflect those differences and provide novel findings compared to the group-level consensus community analysis.

We now added brief discussion on the individual differences in pain sensitivity.

Revisions to the main manuscript (p. 27):

“However, there would also be situations in which pain avoidance is dissociated from pain intensity. For example, capsaicin can be experienced to be intense but less aversive or even appetitive in some contexts, such as cravings for spicy food (Stevenson and Yeomans, 1993).”

– At many points, e.g. in paragraph 25 on page x or 5 on page 25, it is mentioned that the models generalized across two datasets. While the terminology is currently heterogenous, I kindly suggest to use the term "generalization" only to the independent test dataset (here the models really had to generalize to scanning parameters, paradigm differences, etc.)

We agree with the Reviewer’s opinion, and now revised the paragraph accordingly.

Revisions to the main manuscript:

p. 4:

“These models were further generalized to the independent tonic pain dataset (Study 2).”

p. 25:

“Lastly, the predictive modeling approach can provide information about the robustness and usefulness of the multi-layer community detection method by allowing us to test the prediction performance for the discovery dataset and generalizability for the independent test dataset.”

– it's a bit unclear why the pain avoidance ratings fall. One would, somewhat naively, hypothesize that if the participant once though she would never repeat this experiment again, why would she change her mind a couple of minutes later, when the memories of pain are still vivid. Please comment on this.

The gradual decrease of pain avoidance ratings was because we asked participants to continuously report the avoidance of the experience at the moment. For your information, the instruction was “How much do you want to avoid this experience in the future?”. If we asked this question only once per condition, the term ‘this experience’ would be understood as the experience of the whole experiment as the Reviewer’s comment. However, we gave the introduction that they should continuously answer the question during the scans. We provided detailed descriptions of the rating scale and introduction procedure in Essential revisions 8.

– Please add a short discussion of the differences of the behavioral ratings and how they might affect the findings. (this might be positive thing: a sign of generalization across behavioral assessment protocols).

We think this comment is partially addressed in Essential revisions 7 and Essential revisions 9. We think that the pain avoidance measure would have a high level of convergent validity with pain intensity, considering that the overall time-course of pain avoidance ratings in Study 1 was similar to the time-course of the pain intensity ratings in Study 2, and the predictive models derived with pain avoidance rating (Study 1) could be used to predict the pain intensity rating (Study 2). However, pain avoidance and pain intensity are not identical of course, and there can be some conditions in which the two measures are dissociated. Our research group will examine the relationship between pain intensity and avoidance in future studies.

We now added descriptions on the difference between pain avoidance and intensity ratings.

Revisions to the main manuscript (p. 27):

“Although the psychometric properties of the pain avoidance measure should be a topic of future investigation, we expect that the pain avoidance measure would have a high level of convergent validity with pain intensity given the observed similarity between pain avoidance (Study 1) and pain intensity (Study 2) in their temporal profiles. The generalizability of our PCR model across Studies 1 and 2 also supports this speculation. However, there would also be situations in which pain avoidance is dissociated from pain intensity. For example, capsaicin can be experienced to be intense but less aversive or even appetitive in some contexts, such as cravings for spicy food (Stevenson and Yeomans, 1993). In addition, the gradual rise of avoidance ratings during the late period of the control condition in Study 1 would not be observed if the intensity measure was used. Future studies need to examine the relationship between pain avoidance and the other pain assessments and the advantage of using the pain avoidance measure.”

– Please clarify why *pain* avoidance (slightly) increased in the control resting state scan.

We think this comment is partially addressed in Essential revisions 9. We could measure the avoidance rating in the control condition because the question does not contain the term “pain” (please see Essential revisions 8 for details of our rationale behind the rating scale) and thus can provide a generalizable rating scale shared across different conditions. The increase of avoidance rating in the control condition is expected because lying down in a scanner for a long time can cause participants to feel bored and tired, or even induce painful sensations (e.g., back pain). However, we think this effect did not significantly affect the current results including the predictive models, considering that the model responses of the PCR model for the control condition (Appendix 1-figure 13, please see Essential revisions 7) did not show a similar time-course as the avoidance rating of the control condition. Although we understand the concern that our rating was too generic to be called ‘pain avoidance,’ we called it ‘pain avoidance’ because the effect of pain on the changes in avoidance ratings was evident in our analyses (e.g., capsaicin vs. control, early vs. middle vs. late, etc.), and thus the use of ‘pain avoidance’ is not a misnomer or overstatement. However, we will be happy to reconsider it and change the term into an alternative name, such as the ‘avoidance rating,’ if the reviewer thinks it is better.

– Please provide more rationale for the choice of ML algorithms.

The SVM and PCR are widely accepted algorithms for finding the low-dimensional latent components of highly correlated data such as brain networks. Although the other types of algorithms are available for the current study, we think it may hamper the simplicity and interpretability of the predictive modeling if we try many different options of algorithms. Thus we chose the most representative algorithms for classification (SVM) and regression (PCR) of brain data, respectively.

We now added the descriptions for the choice of machine learning algorithms.

Revisions to the main manuscript (p. 14):

“We chose to use the SVM and PCR because they are representative linear algorithms for finding the low-dimensional latent components of highly correlated data such as brain networks.”

– How were the hyperparameters set for the SVM? Why were those not optimized, too?

We did not conduct hyperparameter tuning for the SVM model and used the conventional choice of hyperparameter C=1, which is a widely used, default value for SVM modeling. However, to our knowledge, there is no conventional choice of hyperparameter for the PCR algorithm. Sometimes the principal components that explained 90%, 95%, or 99% of the variance were used, but the level of threshold also varies by researchers. Therefore, we had no choice but to conduct an exhaustive search for the number of principal components despite the possibility of overfitting and then tested the chosen PCR model onto the independent dataset to prove its generalizability.

We now added the descriptions for the choice of hyperparameter of the SVM model.

Revisions to the main manuscript (p. 37):

“A regularization hyperparameter C was set to 1, which is a conventional choice for SVM.”

– Why was only one hub selected for the seed-based analysis in the case of the classifier?

It was coincidental that the left ventral primary somatomotor region (tongue area) was selected as the common hub region for both positive and negative weights. We speculate that this result shows the high importance of the hub region.

– While the prediction performances are obviously significant, testing for this with bootstrapping may be suboptimal, as bootstrap samples may inherit non-normality from the parent dataset. Permutation test would be more "elegant" in my opinion.

We thank the Reviewer’s suggestion and agree that bootstrapping does not always generate a normal distribution depending on the characteristics of the original data. Therefore, we examine the normality of the 10,000 bootstrapped mean prediction-outcome correlation coefficients of the PCR model for Study 1 (Author response image 1). The results from Lilliefors test did not reject the alternative hypothesis (P = 0.27, two-tailed), suggesting that we cannot assume the bootstrapped distribution as a non-normal one.

Author response image 1
The distribution of the bootstrapped prediction-outcome correlations.

We conducted bootstrap tests to examine whether the distribution of within-individual prediction-outcome correlation coefficients of the PCR model were significantly different from zero for Study 1. The distribution of correlation coefficients met normality assumption, P = 0.27, Lilliefors test, two-tailed.

For clarification, we additionally tried the permutation test to examine whether the mean of within-individual prediction-outcome correlation coefficients is different from zero. To generate the null distribution, we randomly flip the sign of each correlation value, e.g., 0.24 to -0.24, -0.51 to 0.51, etc., and then calculate the mean of those randomly sign-flipped correlation values. This procedure was repeated with 10,000 iterations to obtain the null distribution of the mean correlation. The two-tailed P-value from this permutation test was 0.0002 for Study 1 and 0.0002 for Study 2, all of which were far below 0.05.

– Discussion: relation to consciousness might be somewhat speculative, should be hedged.

We now removed the word ‘conscious’ or ‘consciousness’ from the whole manuscript.

– Will the raw data also be shared?

The raw data of Study 1 will be shared upon request. For the Study 2 data, we are planning to collect data from more than 100 participants with the goal of publicly sharing them as a part of a large-scale pain dataset (including heat and capsaicin pain). Thus, we will eventually publicly share the Study 2 data. Of course, the raw data of Study 2 can be shared upon request before we fully open the whole dataset.

References

Alexander-Bloch, A., Lambiotte, R., Roberts, B., Giedd, J., Gogtay, N., and Bullmore, E. (2012). The discovery of population differences in network community structure: new methods and applications to brain functional networks in schizophrenia. Neuroimage, 59(4), 3889-3900. doi:10.1016/j.neuroimage.2011.11.035

Apkarian, A. V., Krauss, B. R., Fredrickson, B. E., and Szeverenyi, N. M. (2001). Imaging the pain of low back pain: functional magnetic resonance imaging in combination with monitoring subjective pain perception allows the study of clinical pain states. Neurosci Lett, 299(1-2), 57-60. doi:10.1016/s0304-3940(01)01504-x

Barroso, J., Wakaizumi, K., Reis, A. M., Baliki, M., Schnitzer, T. J., Galhardo, V., and Apkarian, A. V. (2021). Reorganization of functional brain network architecture in chronic osteoarthritis pain. Hum Brain Mapp, 42(4), 1206-1222. doi:10.1002/hbm.25287

Bartoshuk, L. M., Duffy, V. B., Green, B. G., Hoffman, H. J., Ko, C. W., Lucchina, L. A.,... Weiffenbach, J. M. (2004). Valid across-group comparisons with labeled scales: the gLMS versus magnitude matching. Physiol Behav, 82(1), 109-114. doi:10.1016/j.physbeh.2004.02.033

Bassett, D. S., Porter, M. A., Wymbs, N. F., Grafton, S. T., Carlson, J. M., and Mucha, P. J. (2013). Robust detection of dynamic community structure in networks. Chaos, 23(1), 013142. doi:10.1063/1.4790830

Bassett, D. S., Wymbs, N. F., Porter, M. A., Mucha, P. J., Carlson, J. M., and Grafton, S. T. (2011). Dynamic reconfiguration of human brain networks during learning. Proc Natl Acad Sci U S A, 108(18), 7641-7646. doi:10.1073/pnas.1018985108

Bassett, D. S., Wymbs, N. F., Rombach, M. P., Porter, M. A., Mucha, P. J., and Grafton, S. T. (2013). Task-based core-periphery organization of human brain dynamics. PLoS Comput Biol, 9(9), e1003171. doi:10.1371/journal.pcbi.1003171

Bassett, D. S., Yang, M., Wymbs, N. F., and Grafton, S. T. (2015). Learning-induced autonomy of sensorimotor systems. Nat Neurosci, 18(5), 744-751. doi:10.1038/nn.3993

Baum, G. L., Ciric, R., Roalf, D. R., Betzel, R. F., Moore, T. M., Shinohara, R. T.,... Satterthwaite, T. D. (2017). Modular Segregation of Structural Brain Networks Supports the Development of Executive Function in Youth. Curr Biol, 27(11), 1561-1572 e1568. doi:10.1016/j.cub.2017.04.051

Berry, D. N., and Simons, C. T. (2020). Assessing regional sensitivity and desensitization to capsaicin among oral cavity mucosae. Chem Senses. doi:10.1093/chemse/bjaa033

Betzel, R. F., and Bassett, D. S. (2017). Multi-scale brain networks. Neuroimage, 160, 73-83. doi:10.1016/j.neuroimage.2016.11.006

Betzel, R. F., Satterthwaite, T. D., Gold, J. I., and Bassett, D. S. (2017). Positive affect, surprise, and fatigue are correlates of network flexibility. Sci Rep, 7(1), 520. doi:10.1038/s41598-017-00425-z

Boudreau, S. A., Wang, K., Svensson, P., Sessle, B. J., and Arendt-Nielsen, L. (2009). Vascular and psychophysical effects of topical capsaicin application to orofacial tissues. J Orofac Pain, 23(3), 253-264. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/19639105

Braun, U., Schafer, A., Bassett, D. S., Rausch, F., Schweiger, J. I., Bilek, E.,... Tost, H. (2016). Dynamic brain network reconfiguration as a potential schizophrenia genetic risk mechanism modulated by NMDA receptor function. Proc Natl Acad Sci U S A, 113(44), 12568-12573. doi:10.1073/pnas.1608819113

Braun, U., Schafer, A., Walter, H., Erk, S., Romanczuk-Seiferth, N., Haddad, L.,... Bassett, D. S. (2015). Dynamic reconfiguration of frontal brain networks during executive cognition in humans. Proc Natl Acad Sci U S A, 112(37), 11678-11683. doi:10.1073/pnas.1422487112

Ceko, M., Kragel, P. A., Woo, C. W., Lopez-Sola, M., and Wager, T. D. (2022). Common and stimulus-type-specific brain representations of negative affect. Nat Neurosci, 25(6), 760-770. doi:10.1038/s41593-022-01082-w

Chang, P. F., Arendt-Nielsen, L., Graven-Nielsen, T., Svensson, P., and Chen, A. C. (2001a). Different EEG topographic effects of painful and non-painful intramuscular stimulation in man. Exp Brain Res, 141(2), 195-203. doi:10.1007/s002210100864

Chang, P. F., Arendt-Nielsen, L., Graven-Nielsen, T., Svensson, P., and Chen, A. C. (2001b). Topographic effects of tonic cutaneous nociceptive stimulation on human electroencephalograph. Neurosci Lett, 305(1), 49-52. doi:10.1016/s0304-3940(01)01802-x

Cheng, J. C., Anzolin, A., Berry, M., Honari, H., Paschali, M., Lazaridou, A.,... Napadow, V. (2022). Dynamic Functional Brain Connectivity Underlying Temporal Summation of Pain in Fibromyalgia. Arthritis Rheumatol, 74(4), 700-710. doi:10.1002/art.42013

Cocuzza, C. V., Ito, T., Schultz, D., Bassett, D. S., and Cole, M. W. (2020). Flexible Coordinator and Switcher Hubs for Adaptive Task Control. J Neurosci, 40(36), 6949-6968. doi:10.1523/JNEUROSCI.2559-19.2020

Coghill, R. C., Sang, C. N., Berman, K. F., Bennett, G. J., and Iadarola, M. J. (1998). Global cerebral blood flow decreases during pain. J Cereb Blood Flow Metab, 18(2), 141-147. doi:10.1097/00004647-199802000-00003

Dubuisson, D., and Dennis, S. G. (1977). The formalin test: a quantitative study of the analgesic effects of morphine, meperidine, and brain stem stimulation in rats and cats. Pain, 4(2), 161-174. doi:10.1016/0304-3959(77)90130-0

Farahani, F. V., Karwowski, W., D'Esposito, M., Betzel, R. F., Douglas, P. K., Sobczak, A. M.,... Fafrowicz, M. (2022). Diurnal variations of resting-state fMRI data: A graph-based analysis. Neuroimage, 256, 119246. doi:10.1016/j.neuroimage.2022.119246

Finc, K., Bonna, K., He, X., Lydon-Staley, D. M., Kuhn, S., Duch, W., and Bassett, D. S. (2020). Dynamic reconfiguration of functional brain networks during working memory training. Nat Commun, 11(1), 2435. doi:10.1038/s41467-020-15631-z

Gifford, G., Crossley, N., Kempton, M. J., Morgan, S., Dazzan, P., Young, J., and McGuire, P. (2020). Resting state fMRI based multilayer network configuration in patients with schizophrenia. Neuroimage Clin, 25, 102169. doi:10.1016/j.nicl.2020.102169

Green, B. G. (1991). Temporal characteristics of capsaicin sensitization and desensitization on the tongue. Physiol Behav, 49(3), 501-505. doi:10.1016/0031-9384(91)90271-o

Han, S., Cui, Q., Wang, X., Li, L., Li, D., He, Z.,... Chen, H. (2020). Resting state functional network switching rate is differently altered in bipolar disorder and major depressive disorder. Hum Brain Mapp, 41(12), 3295-3304. doi:10.1002/hbm.25017

Iadarola, M. J., Berman, K. F., Zeffiro, T. A., Byas-Smith, M. G., Gracely, R. H., Max, M. B., and Bennett, G. J. (1998). Neural activation during acute capsaicin-evoked pain and allodynia assessed with PET. Brain, 121 ( Pt 5), 931-947. doi:10.1093/brain/121.5.931

Insel, T., Cuthbert, B., Garvey, M., Heinssen, R., Pine, D. S., Quinn, K.,... Wang, P. (2010). Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am J Psychiatry, 167(7), 748-751. doi:10.1176/appi.ajp.2010.09091379

Khambhati, A. N., Mattar, M. G., Wymbs, N. F., Grafton, S. T., and Bassett, D. S. (2018). Beyond modularity: Fine-scale mechanisms and rules for brain network reconfiguration. Neuroimage, 166, 385-399. doi:10.1016/j.neuroimage.2017.11.015

Kotov, R., Krueger, R. F., Watson, D., Achenbach, T. M., Althoff, R. R., Bagby, R. M.,... Zimmerman, M. (2017). The Hierarchical Taxonomy of Psychopathology (HiTOP): A dimensional alternative to traditional nosologies. J Abnorm Psychol, 126(4), 454-477. doi:10.1037/abn0000258

Lee, J.-J., Kim, H. J., Čeko, M., Park, B.-y., Lee, S. A., Park, H.,... Woo, C.-W. (2021). A neuroimaging biomarker for sustained experimental and clinical pain. Nature Medicine, 27(1), 174-182. doi:10.1038/s41591-020-1142-7

Lerman-Sinkoff, D. B., and Barch, D. M. (2016). Network community structure alterations in adult schizophrenia: identification and localization of alterations. Neuroimage Clin, 10, 96-106. doi:10.1016/j.nicl.2015.11.011

Lu, S., Baad-Hansen, L., List, T., Zhang, Z., and Svensson, P. (2013). Somatosensory profiling of intra-oral capsaicin and menthol in healthy subjects. Eur J Oral Sci, 121(1), 29-35. doi:10.1111/eos.12014

Lydon-Staley, D. M., Ciric, R., Satterthwaite, T. D., and Bassett, D. S. (2019). Evaluation of confound regression strategies for the mitigation of micromovement artifact in studies of dynamic resting-state functional connectivity and multilayer network modularity. Netw Neurosci, 3(2), 427-454. doi:10.1162/netn_a_00071

Lydon-Staley, D. M., Kuehner, C., Zamoscik, V., Huffziger, S., Kirsch, P., and Bassett, D. S. (2019). Repetitive negative thinking in daily life and functional connectivity among default mode, fronto-parietal, and salience networks. Transl Psychiatry, 9(1), 234. doi:10.1038/s41398-019-0560-0

Mano, H., Kotecha, G., Leibnitz, K., Matsubara, T., Nakae, A., Shenker, N.,... Seymour, B. (2018). Classification and characterisation of brain network changes in chronic back pain: A multicenter study [version 1; referees: 3 approved]. Wellcome Open Research, 3(19). doi:10.12688/wellcomeopenres.14069.1

Marquez-Legorreta, E., Constantin, L., Piber, M., Favre-Bulle, I. A., Taylor, M. A., Blevins, A. S.,... Scott, E. K. (2022). Brain-wide visual habituation networks in wild type and fmr1 zebrafish. Nat Commun, 13(1), 895. doi:10.1038/s41467-022-28299-4

Mattar, M. G., Thompson-Schill, S. L., and Bassett, D. S. (2018). The network architecture of value learning. Netw Neurosci, 2(2), 128-149. doi:10.1162/netn_a_00021

Melzack, R. (1999). From the gate to the neuromatrix. Pain, Suppl 6, S121-S126. doi:10.1016/S0304-3959(99)00145-1

Newman, M. E. J. (2010). Networks : an introduction. Oxford ; New York: Oxford University Press.

Ngom, P. I., Dubray, C., Woda, A., and Dallel, R. (2001). A human oral capsaicin pain model to assess topical anesthetic-analgesic drugs. Neurosci Lett, 316(3), 149-152. doi:10.1016/s0304-3940(01)02401-6

Pedersen, M., Zalesky, A., Omidvarnia, A., and Jackson, G. D. (2018). Multilayer network switching rate predicts brain performance. Proc Natl Acad Sci U S A, 115(52), 13376-13381. doi:10.1073/pnas.1814785115

Petrican, R., and Levine, B. T. (2018). Similarity in functional brain architecture between rest and specific task modes: A model of genetic and environmental contributions to episodic memory. Neuroimage, 179, 489-504. doi:10.1016/j.neuroimage.2018.06.057

Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., and Petersen, S. E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage, 59(3), 2142-2154. doi:10.1016/j.neuroimage.2011.10.018

Price, D. D., Hu, J. W., Dubner, R., and Gracely, R. H. (1977). Peripheral suppression of first pain and central summation of second pain evoked by noxious heat pulses. Pain, 3(1), 57-68. doi:10.1016/0304-3959(77)90035-5

Price, D. D., Staud, R., Robinson, M. E., Mauderli, A. P., Cannon, R., and Vierck, C. J. (2002). Enhanced temporal summation of second pain and its central modulation in fibromyalgia patients. Pain, 99(1-2), 49-59. doi:10.1016/s0304-3959(02)00053-2

Rainville, P., Duncan, G. H., Price, D. D., Carrier, B., and Bushnell, M. C. (1997). Pain affect encoded in human anterior cingulate but not somatosensory cortex. Science, 277(5328), 968-971. doi:10.1126/science.277.5328.968

Rainville, P., Feine, J. S., Bushnell, M. C., and Duncan, G. H. (1992). A psychophysical comparison of sensory and affective responses to four modalities of experimental pain. Somatosens Mot Res, 9(4), 265-277. doi:10.3109/08990229209144776

Robinson, L. F., Atlas, L. Y., and Wager, T. D. (2015). Dynamic functional connectivity using state-based dynamic community structure: method and application to opioid analgesia. Neuroimage, 108, 274-291. doi:10.1016/j.neuroimage.2014.12.034

Segerdahl, A. R., Mezue, M., Okell, T. W., Farrar, J. T., and Tracey, I. (2015). The dorsal posterior insula subserves a fundamental role in human pain. Nat Neurosci, 18(4), 499-500. doi:10.1038/nn.3969

Shine, J. M., Koyejo, O., and Poldrack, R. A. (2016). Temporal metastates are associated with differential patterns of time-resolved connectivity, network topology, and attention. Proc Natl Acad Sci U S A, 113(35), 9888-9891. doi:10.1073/pnas.1604898113

Stevenson, R. J., and Yeomans, M. R. (1993). Differences in ratings of intensity and pleasantness for the capsaicin burn between chili likers and non-likers; implications for liking development. Chemical Senses, 18(5), 471-482. doi:10.1093/chemse/18.5.471

Stohler, C. S., and Kowalski, C. J. (1999). Spatial and temporal summation of sensory and affective dimensions of deep somatic pain. Pain, 79(2-3), 165-173. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/10068162

Telesford, Q. K., Lynall, M. E., Vettel, J., Miller, M. B., Grafton, S. T., and Bassett, D. S. (2016). Detection of functional brain network reconfiguration during task-driven cognitive states. Neuroimage, 142, 198-210. doi:10.1016/j.neuroimage.2016.05.078

van den Heuvel, M. P., de Lange, S. C., Zalesky, A., Seguin, C., Yeo, B. T. T., and Schmidt, R. (2017). Proportional thresholding in resting-state fMRI functional connectivity networks and consequences for patient-control connectome studies: Issues and recommendations. Neuroimage, 152, 437-449. doi:10.1016/j.neuroimage.2017.02.005

Vlaeyen, J. W. S., and Linton, S. J. (2012). Fear-avoidance model of chronic musculoskeletal pain: 12 years on. Pain, 153(6), 1144-1147. doi:10.1016/j.pain.2011.12.009

Waddell, G., Newton, M., Henderson, I., Somerville, D., and Main, C. J. (1993). A Fear-Avoidance Beliefs Questionnaire (FABQ) and the role of fear-avoidance beliefs in chronic low back pain and disability. Pain, 52(2), 157-168. doi:10.1016/0304-3959(93)90127-B

Woo, C. W., Chang, L. J., Lindquist, M. A., and Wager, T. D. (2017). Building better biomarkers: brain models in translational neuroimaging. Nat Neurosci, 20(3), 365-377. doi:10.1038/nn.4478

https://doi.org/10.7554/eLife.74463.sa2

Article and author information

Author details

  1. Jae-Joong Lee

    1. Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
    2. Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
    Contribution
    Conceptualization, Resources, Data curation, Software, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7353-8683
  2. Sungwoo Lee

    1. Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
    2. Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
    3. Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
    Contribution
    Resources, Data curation, Writing - review and editing
    Competing interests
    No competing interests declared
  3. Dong Hee Lee

    1. Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
    2. Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
    3. Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
    Contribution
    Resources, Data curation, Writing - review and editing
    Competing interests
    No competing interests declared
  4. Choong-Wan Woo

    1. Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
    2. Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
    3. Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
    Contribution
    Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Project administration, Writing - review and editing
    For correspondence
    waniwoo@g.skku.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7423-5422

Funding

Institute for Basic Science (IBS-R015-D1)

  • Choong-Wan Woo

National Research Foundation of Korea (2019R1C1C1004512)

  • Choong-Wan Woo

National Research Foundation of Korea (2021M3E5D2A01022515)

  • Choong-Wan Woo

National Research Foundation of Korea (2021M3A9E4080780)

  • Choong-Wan Woo

Korea Institute of Science and Technology (2E31511-22-090)

  • Choong-Wan Woo

National Research Foundation of Korea (2018H1A2A1059844)

  • Jae-Joong Lee

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Hongji Kim and Soo Ahn Lee for their help with conducting experiments. This work was supported by IBS-R015-D1 (Institute for Basic Science; to C-WW), 2019R1C1C1004512, 2021M3E5D2A01022515, and 2021M3A9E4080780 (National Research Foundation of Korea; to C-WW), 2E31511-22-090 (KIST Institutional Program; to C-WW), and by 2018H1A2A1059844 (National Research Foundation of Korea; to J-JL).

Ethics

All participants were recruited from the Suwon area in South Korea. The institutional review board of Sungkyunkwan University approved the study (IRB 2017-05-001). All participants provided written informed consent.

Senior Editor

  1. Timothy E Behrens, University of Oxford, United Kingdom

Reviewing Editor

  1. Markus Ploner, Technische Universität München, Germany

Reviewers

  1. Markus Ploner, Technische Universität München, Germany
  2. Tamas Spisak, Essen University Hospital, Germany

Publication history

  1. Received: October 5, 2021
  2. Preprint posted: October 16, 2021 (view preprint)
  3. Accepted: September 9, 2022
  4. Version of Record published: September 29, 2022 (version 1)

Copyright

© 2022, Lee et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Jae-Joong Lee
  2. Sungwoo Lee
  3. Dong Hee Lee
  4. Choong-Wan Woo
(2022)
Functional brain reconfiguration during sustained pain
eLife 11:e74463.
https://doi.org/10.7554/eLife.74463

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