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Empathic pain evoked by sensory and emotional-communicative cues share common and process-specific neural representations

  1. Feng Zhou  Is a corresponding author
  2. Jialin Li
  3. Weihua Zhao
  4. Lei Xu
  5. Xiaoxiao Zheng
  6. Meina Fu
  7. Shuxia Yao
  8. Keith M Kendrick
  9. Tor D Wager
  10. Benjamin Becker  Is a corresponding author
  1. Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, China
  2. Department of Psychological and Brain Sciences, Dartmouth College, United States
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Cite this article as: eLife 2020;9:e56929 doi: 10.7554/eLife.56929

Abstract

Pain empathy can be evoked by multiple cues, particularly observation of acute pain inflictions or facial expressions of pain. Previous studies suggest that these cues commonly activate the insula and anterior cingulate, yet vicarious pain encompasses pain-specific responses as well as unspecific processes (e.g. arousal) and overlapping activations are not sufficient to determine process-specific shared neural representations. We employed multivariate pattern analyses to fMRI data acquired during observation of noxious stimulation of body limbs (NS) and painful facial expressions (FE) and found spatially and functionally similar cross-modality (NS versus FE) whole-brain vicarious pain-predictive patterns. Further analyses consistently identified shared neural representations in the bilateral mid-insula. The vicarious pain patterns were not sensitive to respond to non-painful high-arousal negative stimuli but predicted self-experienced thermal pain. Finally, a domain-general vicarious pain pattern predictive of self-experienced pain but not arousal was developed. Our findings demonstrate shared pain-associated neural representations of vicarious pain.

Introduction

Pain empathy, the capacity to resonate with, relate to, and share others’ pain, is an essential part of human experience. Among other functions, it motivates helping and cooperative behaviors and aids in learning to avoid harmful situations. Vicarious pain can be triggered by observing or imagining another individual’s painful state and can be elicited by multiple types of social cues, particularly the observation of an inflicted physical injury or a facial expression of pain (Decety and Ickes, 2009; Jauniaux et al., 2019; Vachon-Presseau et al., 2012; Yesudas and Lee, 2015). While stimuli depicting the noxious stimulation of body limbs [i.e. observation of noxious stimulation (NS) induced vicarious pain (NS vicarious pain)] provide objective cues about the sensory component of the observed pain, the observation of facial expressions of pain [i.e. facial expressions induced vicarious pain (FE vicarious pain)] is considered more subjective and indirect as the pain experience of the expresser needs to be interpreted by the observer (Hadjistavropoulos et al., 2011; Vachon-Presseau et al., 2012). Functional magnetic resonance imaging (fMRI) studies employing corresponding pictorial stimuli have identified distinct and common neural substrates of pain empathy across vicarious pain induction procedures (Jauniaux et al., 2019). For example, Vachon-Presseau et al., 2012 demonstrated that NS vicarious pain increased activity in core regions of the mirror neuron system, specifically inferior frontal and posterior regions engaged in coding sensory-somatic information (Rizzolatti and Craighero, 2004) while the presentation of a facial expression of pain led to stronger increases in the medial prefrontal cortex and precuneus which have been associated with social cognitive processes such as mentalizing and theory of mind (Amft et al., 2015; Gallo et al., 2018; Mitchell, 2009; Northoff et al., 2006). Despite the different psychological domains engaged in the pain empathic responses induced by NS and FE both elicit vicarious pain experience (Timmers et al., 2018), encompassing pain-specific processes such as recognizing and understanding the painful state of the other person and affective sharing of pain but also non-specific processes that are shared between pain and other non-painful experiences such as arousal and negative affect (Zaki et al., 2016). In line with the shared underlying mental processes previous neuroimaging meta-analyses revealed that the observation of acute pain infliction and painful facial expressions commonly activate core empathy and nociceptive pain regions specifically the insular and cingulate cortices (Jauniaux et al., 2019; Lamm et al., 2011; Timmers et al., 2018). The overlapping activations have been suggested to reflect shared neural representations of vicarious pain (Jauniaux et al., 2019; Lamm et al., 2011; Timmers et al., 2018).

However, overlapping functional activations within these regions do not necessarily reflect shared underlying neural representations of a specific mental process (Zaki et al., 2016), given that (1) due to local spatial dependencies the main focus of traditional mass-univariate fMRI analytic approaches (i.e. conducting massive number of tests on brain voxels one at a time) is not on single-voxel activity, but on smoothed, regional differences in brain activity across multiple tasks or stimuli (Haynes, 2015) and (2) brain regions may contain multiple, distinct populations of neurons and averaging across those neuron populations yields nonspecific signals (Haxby et al., 2014; Zaki et al., 2016). For instance, electrophysiological and optogenetic studies have identified distinct neuronal populations in the anterior cingulate and insular cortices that activate during several functional domains, including pain- and empathy-related processes as well as attention, salience, social observation learning and reward expectancy (Allman et al., 2011; Chen, 2018; Kvitsiani et al., 2013; Sakaguchi et al., 2018; Shidara and Richmond, 2002; Shura et al., 2014; Sikes and Vogt, 1992). Studies employing mass-univariate fMRI analyses suggest that both regions are engaged by various experimental paradigms including not only experienced and observed pain, but also reward, arousal, salience and attention (Cauda et al., 2012; Shackman et al., 2011; Uddin, 2015; Wager et al., 2016; Yarkoni et al., 2011). Despite the overlapping fMRI activation in response to different experimental manipulations the underlying brain representations may be separable (Corradi-Dell'Acqua et al., 2016; Krishnan et al., 2016; Woo et al., 2014), emphasizing that more fine-grained analyses are required to determine process-specific shared or distinct neural representations (Zaki et al., 2016).

In an effort to overcome these limitations, recent studies have proposed several strategies to investigate the ‘shared representation’ question, including pharmacological (see e.g. Rütgen et al., 2015) and multivariate pattern analysis (MVPA) approaches. Compared to conventional analytic approaches, MVPA can be effective in extracting information at much finer spatial scales (e.g. below the intrinsic resolution determined by the voxel size by pooling together weak feature-selective signals in each voxel; Kamitani and Tong, 2005; Woo et al., 2017) and represents a more suitable approach to support or reject claims about neural mechanisms that are shared between mental processes (Chikazoe et al., 2014; Peelen and Downing, 2007; Zaki et al., 2016). In support of this view, using MVPA approaches researchers have demonstrated shared neural representations across mental processes (including self-experienced and observed pain) in both humans and animals (Carrillo et al., 2019; Corradi-Dell'Acqua et al., 2011; Corradi-Dell'Acqua et al., 2016). Moreover, a growing number of recent studies have demonstrated functional independence of overlapping univariate activation in these brain regions using MVPA (Krishnan et al., 2016; Peelen et al., 2006; Woo et al., 2014), including separable neural representations of physical and social rejection pain within the dorsal anterior cingulate cortex (Woo et al., 2014) and of modality-specific aversive experience in the anterior insular cortex (Krishnan et al., 2016).

Nevertheless, shared multivariate patterns do not necessarily imply process-specific common neural representations per se given that the shared neural representations could simply reflect common demands on basal processing domains such as attention or arousal (Corradi-Dell'Acqua et al., 2011; Corradi-Dell'Acqua et al., 2016; Krishnan et al., 2016). For instance, Corradi-Dell'Acqua et al., 2016 found shared neural patterns between vicarious and self-experienced pain in the left anterior insula and further demonstrated that the common local patterns were not specific to pain-related processing, but also represented disgust and unfairness suggesting modality-unspecific processing of aversive and arousing experiences.

This leads to the questions of (1) whether or not NS and FE-induced vicarious pain share pain-associated common neural representations, and further (2) whether a general (i.e. across NS and FE vicarious pain modalities) neural signature of vicarious pain, which is specific to the pain empathic response rather than capturing unspecific processes such as negative emotional experience or arousal, can be determined. More specifically, we examined the following three questions in this study: (i) whether NS and FE-induced vicarious pain-predictive signatures share spatially (correlation and distribution) and functionally (predictions of cross-modality vicarious pain versus corresponding non-painful control stimuli) similar neural representations, (ii) whether a general and specific vicarious pain-predictive neural signature, which should (1) generalize across different vicarious pain stimuli and (2) not be sensitive to predict unspecific negative affect or arousal induced by non-painful negative stimuli, and (3) be ‘activated’ by the direct experience of somatic pain as reflected by an accurate prediction of self-experienced somatic pain, can be determined.

To this end, we employed MVPA to fMRI data from an experiment during which participants were presented with stimuli depicting the infliction of noxious stimulation of body limbs (NS vicarious pain) or painful facial expressions (FE vicarious pain) as well as corresponding non-painful control stimuli (Figure 1A). Given that relative to their control stimuli both sets of painful stimuli were perceived as more painful in terms of recognized and shared pain as well as more arousing and negative (details see Results and Figure 1B), we additionally asked participants to undergo an emotion processing paradigm with non-painful high-arousal negative stimuli and low-arousal neutral stimuli from the International Affective Picture System (IAPS) (details see Materials and methods) to further test the specificity of the shared neural representations with respect to the vicarious experience of pain rather than emotional arousal or negative affect. To determine the association of the vicarious pain signature with direct pain experience, we included an independent fMRI dataset that collected ratings of self-experienced pain during thermal pain induction (details are provided in Materials and methods and in Wager et al., 2013; Woo et al., 2015).

Examples and behavioral ratings of the experimental stimuli.

(A) Examples of stimuli for NS and FE vicarious pain as well as corresponding non-painful control stimuli. Of note, examples of the facial expressions (FE) were not included in the original stimulus set and written consent was obtained from the two volunteers. (B) Behavioral ratings of the stimuli from an independent sample (n = 38). Error bars represent standard errors of the mean. ‘Other’s pain’ indicates ‘how much pain do you think the person in the photo is feeling’, ‘self-pain’ indicates ‘how much pain do you experience when watching the picture’. All ratings were assessed by nine-point Likert scales ranging from ‘1 = not painful at all or very negative or very low arousing’ to ‘9 = extremely painful or very positive or very high arousing’. NS vicarious pain, observation of noxious stimulation of body limbs induced vicarious pain; FE vicarious pain, observation of facial expressions of pain induced vicarious pain; NS control stimuli depict body limbs in similar but innocuous situations, FE control stimuli show neutral facial expressions.

Given that small sample sizes may lead to a large cross-validation error which is the discrepancy between the prediction accuracy measured by cross-validation and the expected accuracy on new data (Varoquaux, 2018) and fMRI-based inferences on regions that are most predictive substantially benefit from larger samples (Chang et al., 2015) we included a comparably large sample of n = 238 individuals (details see Materials and methods and Li et al., 2018; Xu et al., 2020a).

Results

Evaluation of the stimuli

To match the instructions between the vicarious pain and negative emotion fMRI paradigms an implicit instruction was provided (attentively view the pictorial stimuli) (details see Materials and methods). Affective ratings of the stimuli in an independent sample confirmed that both sets of painful stimuli were rated as considerably more painful compared to their respective control stimuli, both in terms of recognizing and sharing pain, and additionally were rated as more arousing and negative. Specifically, both categories of painful stimuli elicited a substantial level of pain intensity perceived for the person displayed as well as in the observer. The NS vicarious pain stimuli were rated considerably higher on both dimensions (mean ± standard error (SE) pain intensity displayed = 6.73 ± 0.27; mean ± SE pain intensity self-experienced = 6.14 ± 0.36) as compared to the corresponding NS control stimuli (mean ± SE pain intensity displayed = 1.37 ± 0.11; mean ± SE pain intensity self-experienced = 1.54 ± 0.14; t37 = 18.11, p<0.001; t37 = 12.71, p<0.001, respectively). Similarly, the FE vicarious pain stimuli were also rated substantially higher on both pain-related dimensions (mean ± SE pain intensity displayed = 6.20 ± 0.25; mean ± SE pain intensity self-experienced = 5.05 ± 0.31) as compared to the corresponding FE control stimuli (mean ± SE pain intensity displayed = 1.78 ± 0.16; mean ± SE pain intensity self-experienced = 2.20 ± 0.18; t37 = 14.58, p<0.001; t37 = 9.00, p<0.001, respectively) (Figure 1B). Moreover, both categories of painful stimuli were rated as considerably more negative and induced stronger arousal in the participants as compared to their respective control stimuli (NS vicarious pain stimuli: mean ± SE valence=3.14 ± 0.22; mean ± SE arousal=5.81 ± 0.32; NS control stimuli: mean ± SE valence=5.12 ± 0.15; mean ± SE valence=2.68 ± 0.25; t37 = −7.99, p<0.001; t37 = 9.02, p<0.001, respectively; FE vicarious pain stimuli: mean ± SE valence=3.57 ± 0.21; mean ± SE arousal=5.03 ± 0.29; FE control stimuli: mean ± SE valence=4.83 ± 0.12; mean ± SE valence=3.34 ± 0.24; t37 = −5.24, p<0.001; t37 = 6.50, p<0.001, respectively) (Figure 1B).

Likewise, the non-painful negative IAPS pictures were rated as considerably more arousing and negative as compared to the corresponding neutral stimuli. Specifically, negative stimuli elicited substantial negative affect and arousal on numerical rating scales (mean ± SE valence=2.41 ± 0.16; mean ± SE arousal=6.34 ± 0.22) compared with neutral stimuli (mean ± SE valence=5.35 ± 0.08; mean ± SE arousal=3.22 ± 0.25; t36 = −16.09, p<0.001; t36 = 12.65, p<0.001, respectively).

Post-fmri ratings further confirmed that vicarious pain stimuli elicited higher recognizing pain and arousal as compared to the control stimuli (Supplementary file 1). Of note, although we found that the two vicarious pain-evoking stimulus sets were not fully matched in terms of vicarious pain intensity, arousal and valence, the differences between the stimulus sets may only have a small effect on our findings given that (1) this study focused on common rather than different empathic pain responses elicited by the two stimulus sets and (2) both categories of vicarious pain stimuli elicited substantial levels of pain empathy.

Univariate approach - shared activations for NS and FE vicarious pain

To test whether NS and FE vicarious pain share similar activation patterns as determined by traditional mass-univariate analyses, we performed a permutation-based correlation analysis to compare the spatial similarity between the unthresholded group-level NS vicarious pain activation (NS vicarious pain >NS control) and the FE vicarious pain activation (FE vicarious pain >FE control). We found that activation in response to NS vicarious pain was spatially correlated with that to FE vicarious pain (r = 0.171, p<0.1 based on permutation tests) (Figure 2A). Moreover, after multiple comparisons correction (FDR corrected, q < 0.05, two-tailed) (Figure 2B and C), distributed regions of overlapping activation were identified, including a network exhibiting increased activation during both modalities encompassing the bilateral anterior and mid-insula, dorsomedial prefrontal cortex, inferior parietal lobule, middle frontal gyrus and middle temporal gyrus, as well as a network of decreased activation, including the rostral and ventral anterior cingulate cortices, ventromedial and orbitofrontal cortices, and lingual and parahippocampal gyri (Figure 2D).

Results from the conventional univariate analyses.

(A) The NS vicarious pain activation pattern was spatially correlated with the FE vicarious pain pattern. (B) Results from the conventional univariate analysis comparing NS vicarious pain with the NS control stimuli thresholded at FDR q < 0.05 (two-tailed). (C) Results from the univariate analysis comparing FE vicarious pain with the FE control stimuli thresholded at FDR q < 0.05 (two-tailed). (D) Overlapping activation between NE and FE vicarious pain as determined by the conventional univariate approach. p<0.1. NS vicarious pain, observation of noxious stimulation of body limbs induced vicarious pain; FE vicarious pain, observation of facial expressions of pain induced pain; NS control stimuli depict limbs in similar but innocuous situations, FE control stimuli show neutral facial expressions.

Multivariate approach – modality general vicarious pain-predictive patterns

Previous studies have suggested that pain and negative emotional processes are distributed across brain regions (Chang et al., 2015; Krishnan et al., 2016; Wager et al., 2013) and that compared to whole-brain predictive models local regions explain considerably less variance in predicting these processes (Kragel et al., 2018; Woo et al., 2017). In an initial step, we therefore developed novel whole-brain patterns to decode NS and FE vicarious pain separately. The NS vicarious pain-predictive pattern yielded an average classification accuracy of 88 ± 1.5% SE, p<0.001, d = 2.13; d indicates effect size in terms of Cohen’s d (accuracy = 96 ± 1.2% SE, p<0.001, d = 2.17 based on a two-alternative forced-choice test) and the FE vicarious pain-predictive pattern discriminated FE vicarious pain versus FE control with 80 ± 1.8% SE accuracy, p<0.001, d = 1.64 (accuracy = 88 ± 2.1% SE, p<0.001, d = 1.57 based on a two-alternative forced-choice test) with a 10-fold cross-validation procedure which was repeated 10 times, yielding 10 random partitions of the original sample.

Next, permutation-based correlation analysis was employed to determine the similarity between the whole-brain patterns of NS and FE vicarious pain which confirmed that the modality-specific patterns were spatially correlated (r = 0.170, p<0.001 based on permutation tests) (Figure 3A). To further qualitatively determine shared but also distinct vicarious pain signatures we analyzed the spatial covariation between the unthresholded weight maps for NS and FE vicarious pain. To this end, we plotted the joint distribution of normalized (z-transformed) voxel weights of the FE vicarious pain-predictive pattern on the x-axis and the NS vicarious pain-predictive patterns on the y-axis in Figure 3B (for similar approach see Koban et al., 2019; Yu et al., 2019). Briefly, pattern weights in any given voxel are expressed as positive, negative or near-zero values for each of the vicarious pain-predictive patterns, which allows to divide voxels into eight equally sized Octants depending on the relative weights in each pattern. For visual presentation, the Octants were color-coded with different colors indicating either voxels of shared positive or shared negative weight (Octants 2 and 6, respectively), selectively positive weights for NS (Octant 1) and FE (Octant 3) vicarious pain-predictive patterns, selectively negative weights for either NS (Octant 5) or FE (Octant 7) vicarious pain-predictive patterns, or opposite weights in the two decoders such that voxels in Octants 4 and 8 express positive and negative weights for the FE vicarious pain-predictive pattern but negative and positive weights for NS vicarious pain-predictive pattern, respectively. Furthermore, to provide an overall measure for voxels in each Octant, we computed the sum of squared distances (SSD) from the origin, which accounts for both, absolute numbers of voxels in each Octant and their (squared) distance from the origin. This analysis of the spatial coactivation of NS and FE vicarious pain-predictive patterns revealed peak SSDs in Octants 2 and 6 as compared to other Octants, suggesting that a considerable number of voxels express positive or negative weights for both vicarious pain-predictive patterns. Overall, this analysis provides further supports largely shared, but also non-shared, neural representations for NS and FE vicarious pain. In support of this, between-modality classifications showed that the NS vicarious pain-predictive pattern could reliably discriminate FE vicarious pain versus FE control with 69% accuracy (±3.0% SE, p<0.001, d = 0.65) and that the FE vicarious pain-predictive pattern could discriminate NS vicarious pain versus NS control with 78% accuracy (±2.7% SE, p<0.001, d = 1.00) based on two-alternative forced-choice tests with a repeated 10-fold cross-validation procedure (Figure 3C). Taken together, our results confirmed shared neural representations between the different vicarious pain modalities at the whole-brain level, yet the reduced between-modality prediction effect sizes as compared to within-modality prediction effect sizes (<50%) additionally suggest distinguishable neural representations. Results remained significant after correcting for multiple comparisons using Bonferroni correction.

Results from the whole-brain multivariate pattern analyses.

(A) The NS vicarious pain-predictive pattern was spatially correlated with the FE vicarious pain-predictive pattern. (B) Scatter plot displaying normalized voxel weights for NS (y-axis) and FE (x-axis) vicarious pain-predictive patterns. Bars on the right represent the sum of squared distances from the origin (0,0) for each Octant. Different colors are assigned to the eight Octants that reflect voxels of shared positive or shared negative weights (Octants 2 and 6, respectively), selectively positive weights for the NS (Octant 1) or for FE (Octant 3) vicarious pain patterns, selectively negative weights for the NS (Octant 5) or FE (Octant 7) vicarious pain patterns, and voxels with opposite weights for the two neural signatures (Octants 4 and 8). The numbers on the top of each bar indicate the voxel counts for each Octant. (C) Cross-validation accuracy as determined by two-alternative forced-choice classification tests based on the whole-brain patterns. The results demonstrated significant within- and between- modality classifications for both NS and FE vicarious pain-predictive patterns. The dashed line indicates the chance level (50%), and the error bars represent the standard error of the mean across subjects. ***p < 0.001. SSD, sum of squared distances. Error bar indicates standard error. NS vicarious pain, observation of noxious stimulation of body limbs induced vicarious pain; FE vicarious pain, observation of facial expressions of pain induced pain.

Shared local representations for NS and FE vicarious pain

To identify brain regions which made reliable contributions to both whole-brain NS and FE vicarious pain-predictive patterns, we thresholded the corresponding neural patterns at FDR q < 0.05 (two-tailed, bootstrap tests with 10,000 iterations) separately and found overlapping regions in the bilateral mid-insula, left putamen and left inferior parietal lobule (Figure 4A, B and C), emphasizing the importance of these regions for encoding both NS and FE vicarious pain. Moreover, we employed a searchlight-based approach to locate regions which could predict both within-modality vicarious pain (e.g. NS vicarious pain-predictive patterns to predict NS vicarious pain versus NS control) as well as between-modality vicarious pain (e.g. NS vicarious pain-predictive patterns to predict FE vicarious pain versus FE control) using a cross-validation procedure. We found that a bilateral network encompassing the insula, striatum as well as the ventromedial prefrontal cortex (see Figure 4D, q < 0.05, FDR corrected, two-tailed) demonstrated significant within-modality cross-validation and between-modality cross-prediction accuracies between NS and FE vicarious pain, implying shared representation at the local pattern level. We additionally re-ran searchlight analyses with two different searchlight sizes (4-mm- and 10-mm-radius spheres) and found that the overlapping vicarious pain networks remained robust across different searchlight sizes (details see Figure 4—figure supplement 1).

Figure 4 with 1 supplement see all
Brain regions that made reliable contributions to decoding vicarious pain.

NS (A) and FE (B) vicarious pain-predictive patterns and (C) overlapping reliable predictive voxels (bootstrap thresholded at FDR q < 0.05, two-tailed). (D) Brain regions exhibiting significant within-modality cross-validation and between-modality cross-prediction accuracies between NS and FE vicarious pain (thresholded at FDR q < 0.05, two-tailed). NS vicarious pain, observation of noxious stimulation of body limbs induced vicarious pain; FE vicarious pain, observation of facial expressions of pain induced pain.

Shared representations in the mid-insula

Across the analyses, we observed overlapping activation and shared representations in the mid-insula (see also Figure 4—figure supplement 1 for convergent findings across searchlight sizes). Accumulating evidence suggest a critical role of the mid-insula in pain-related processes, including self-experienced as well as vicarious pain. In line with functional anatomical studies suggesting that the mid-insula receives nociceptive information from thalamic nuclei (Craig et al., 1994; Craig et al., 2000) intracerebral electrical stimulation of the mid-insula evokes pain sensations (Afif et al., 2010) and previous MVPA studies demonstrated distinct neural representations between pain and non-pain negative stimuli in the (right) mid-insula yet shared representations across self-experienced and vicarious pain (Corradi-Dell'Acqua et al., 2011), while a recent meta-analysis of conventional fMRI empathy studies reported that vicarious pain uniquely activates the bilateral mid-insula and MCC as compared to empathy for non-pain negative affective states (Timmers et al., 2018). Based on the specific role of the mid-insula in pain-related processes, we further explored whether the mid-insula shared neural representations of NS and FE could be sufficient to predict vicarious pain. The mid-insula was defined based on the Human Connectome Project (HCP) multi-modal parcellation atlas (Glasser et al., 2016) (encompassing PoI2, FOP2, FOP3 and MI and available from the Cognitive and Affective Neuroscience Laboratory Github repository at https://github.com/canlab/Neuroimaging_Pattern_Masks; Figure 5—figure supplement 1 displays the mid-insula mask). We found that NS vicarious pain activation in the insula was strongly positively correlated with FE vicarious pain activation (r = 0.737, p=0.006 based on permutation tests) and consistent with this, that the NS vicarious pain-predictive and FE vicarious pain-predictive pattern weights within the mid-insula were also strongly positively correlated (r = 0.538, p<0.001 based on permutation tests) (Figure 5A and B). Moreover, plotting the amount of shared positive, shared negative, and unique positive and negative voxel weights (z-scored) within the mid-insula for NS and FE vicarious pain-predictive patterns indicated that most voxels in the mid-insula exhibited shared positive weights (Octant 2) or negative weights (Octant 6), whereas only few voxels exhibited opposite weights directions (Octants 4 and 8) (Figure 5C). Consistent with the voxel-wise weight distribution two-alternative forced-choice tests revealed that the mid-insula partial NS vicarious pain-predictive pattern classified above chance for both, NS vicarious pain versus NS control (71 ± 2.9% SE, p<0.001, d = 0.72; within-modality) and FE vicarious pain versus FE control (61 ± 3.2% SE, p<0.001, d = 0.36; between-modality prediction) in out-of-sample participants through a repeated 10-fold cross-validation procedure. In line with this, the mid-insula partial FE vicarious pain-predictive pattern discriminated NS vicarious pain versus NS control with 65% accuracy (±3.1% SE, p<0.001, d = 0.58; between-modality) and FE vicarious pain versus FE control with 60% accuracy (±3.2% SE, p=0.004, d = 0.27; within-modality) (Figure 5D). Together, these findings converge on common representations of vicarious pain in the mid-insula across univariate and multivariate patterns for NS and FE vicarious pain. However, although statistically significant, thus reflecting that the mid-insula plays important roles in encoding NS and FE vicarious pain and that the neural representations of NS and FE vicarious pain in this region are similar, the much lower effect sizes (as compared with the whole-brain predictions) indicate that the mid-insula is not sufficient to capture vicarious pain processing alone. Results remained significant after correcting for multiple comparisons using Bonferroni correction.

Figure 5 with 1 supplement see all
Results of the mid-insula focused analyses.

(A) Mid-insula activation to NS vicarious pain was highly similar to activation to FE vicarious pain. (B) NS vicarious pain-predictive pattern in the mid-insula was spatially similar to the FE vicarious pain-predictive pattern. (C) Examining voxel-level similarity in bilateral mid-insula revealed that that the majority of mid-insula voxels exhibited shared positive or negative weights (Octants 2 and 6, respectively). Selective weights are depicted as: selective positive weights for NS (Octant 1) and for FE (Octant 3) vicarious pain patterns, selective negative weights for NS (Octant 5) and for FE (Octant 7) vicarious pain patterns. Voxels with opposite weights for the two signatures are depicted in Octants 4 and 8. (D) Cross-validation accuracy from the two-choice classification tests with mid-insula partial patterns. The results demonstrated significant within- and between-modality classifications for both NS and FE vicarious pain-predictive patterns. The dashed line indicates the chance level (50%), and error bars represent standard error of the mean across subjects. **p < 0.01; ***p < 0.001. SSD, sum of squared distances. Error bar indicates standard error.

Shared vicarious pain representations are not sensitive to arousal or negative affect

One key question is whether the developed vicarious pain-predictive patterns are specific to the vicarious sharing of pain or are rather generally sensitive to emotional arousal or negative affect. To test the functional specificity, whole-brain patterns were separately employed to discriminate processing of high-arousal non-painful negative from low-arousal neutral stimuli from the IAPS database with two-alternative forced-choice tests through a repeated 10-fold cross-validation procedure. This approach revealed statistically significant yet comparably low accuracies and small effect sizes (NS vicarious pain-predictive pattern: 58 ± 3.2%, p=0.024, d = 0.34; FE vicarious pain-predictive pattern: 61 ± 3.2% SE, p=0.001, d = 0.42). In contrast, testing whether shared representations in the mid-insula could discriminate negative versus neutral stimuli revealed that neither of the insula partial patterns could classify negative stimuli above chance level (NS: 56 ± 3.2% SE, p=0.079, d = 0.11; FE: 56 ± 3.2% SE, p=0.111, d = 0.09), suggesting a pain-specific representation in this region.

In addition, using the emotional processing data we developed a negative emotion-predictive pattern which could accurately classify non-painful negative vs. neutral stimuli (accuracy = 86 ± 1.6% SE, p<0.001, d = 2.07 using a repeated 10-fold cross-validation procedure). The negative emotion-predictive pattern could significant discriminate NS vicarious pain versus its control (cross-validated accuracy = 70 ± 3.0% SE, p<0.001, d = 0.88) and FE vicarious pain versus its control (cross-validated accuracy = 61 ± 3.2% SE, p<0.001, d = 0.28). However, accuracy and effect size are lower as compared to FE vicarious pain pattern’s prediction of NS vicarious pain (cross-validated accuracy = 78 ± 2.7% SE, p<0.001, d = 1.00) and vice versa (cross-validated accuracy = 69 ± 3.0% SE, p<0.001, d = 0.65) and the mid-insula negative-predictive pattern did not predict vicarious pain (accuracies = 40 ± 3.2% SE, 48 ± 3.2% SE for NS and FE vicarious pain, respectively). Moreover, in contrast to the pain-predictive patterns (see below for details) neither the whole-brain nor the mid-insula negative-predictive pattern could predict thermal pain intensity (whole-brain, r196 = 0.101, p=0.157; mid-insula, r196 = −0.319), which additionally emphasizes the functional specificity of the pain-predictive pattern in the domain of pain-related processing. Together these findings suggest that negative emotional processing might share some neural representations with vicarious pain, but that the whole-brain and mid-insula vicarious pain representations are more specific to the pain-related information. Results remained stable after correcting for multiple comparisons using Bonferroni correction.

A vicarious pain-predictive pattern that predicts both NS and FE vicarious pain

Given that the NS and FE vicarious pain-predictive patterns shared similar whole-brain as well as local neural representations, we developed a general vicarious pain pattern which yielded a classification accuracy of 82 ± 1.2% SE, p<0.001, d = 1.77 (accuracy = 91 ± 1.3% SE, p<0.001, d = 1.74 based on a two-alternative forced-choice test) in discriminating vicarious pain versus non-painful control. More specifically, the pattern could accurately predict both NS vicarious pain from the NS control (95 ± 1.4% accuracy, p<0.001, d = 2.10) and FE vicarious pain from the FE control (87 ± 2.1% accuracy, p<0.001, d = 1.45), but performed considerably worse classifying non-painful negative versus neutral stimuli (59 ± 2.1% accuracy, p=0.01, d = 0.30), in forced-choice classifications. In line with the spatially overlapping modality-specific vicarious pain patterns the general vicarious pain pattern was highly similar with both, the NS vicarious pain pattern (r = 0.587, permutated p<0.001 based on permutation tests) and FE vicarious pain pattern (r = 0.702, p<0.001 based on permutation tests). To functionally characterize the general vicarious pain-predictive pattern the Neurosynth decoder function was used to assess its similarity to the reverse inference meta-analysis maps generated for the entire set of terms included in the Neurosynth dataset. The most relevant features were ‘painful’ and ‘pain’ for the top 50 terms (excluding anatomical terms) ranked by the correlation strengths between the vicarious pain pattern and the meta-analytic maps (see word cloud, size of the font scaled by correlation strength, Figure 6A). After thresholding and correction for multiple comparisons (bootstrapping 10,000 samples, FDR q < 0.05, two-tailed), the general vicarious pain-predictive pattern revealed a distributed network engaged in vicarious pain processing encompassing the bilateral mid-insula, inferior parietal lobule and ventromedial prefrontal cortex (Figure 6B), further emphasizing the importance of these regions for encoding vicarious pain. All conclusions remained stable after controlling for multiple comparisons using Bonferroni correction.

A general vicarious pain-predictive pattern which predicts both observation of noxious stimulation of body limbs and facial expressions of pain induced vicarious pain.

(A) Word cloud showing the top 50 relevant terms (excluding anatomical terms) for the meta-analytic decoding of the general vicarious pain-predictive pattern. The size of the font was scaled by correlation strength. (B) When thresholded at FDR q < 0.05, two-tailed (bootstrapped 10,000 samples) the general vicarious pain-predictive pattern revealed a distributed network of vicarious pain empathy representation including bilateral mid-insula and ventromedial prefrontal cortex.

Association of the vicarious pain-predictive pattern with self-experienced somatic pain

To test the associations between the vicarious pain representation with directly experienced pain, we applied the whole-brain general vicarious pain-predictive pattern to self-experienced thermal pain data using dot-product of vectorized activation maps with the pattern classifier weights. We found that the general vicarious pain-predictive pattern expressions were highly correlated with both overall objective temperature levels (r196 = 0.538, p<0.001) and subjective pain ratings (r196 = 0.507, p<0.001). Moreover, the general pain-predictive pattern discriminated high thermal pain versus low thermal pain with a 94% accuracy (±4.2% SE, p<0.001, d = 2.00), high thermal pain versus medium thermal pain with a 91% accuracy (±5.0% SE, p<0.001, d = 1.56) and medium thermal pain versus low thermal pain with an 82% accuracy (±6.7% SE, p=0.001, d = 1.20) using two-alternative forced-choice tests (Figure 7A).

Figure 7 with 2 supplements see all
Generalizability of the general (across NS and FE) vicarious pain-predictive pattern.

Both whole-brain (A) and mid-insula (B) vicarious pain-predictive patterns could accurately predict the severity and classify the levels of self-experienced pain in an independent dataset. ***p < 0.001. Error bar indicates standard error.

When prediction focused on the mid-insula the general vicarious pain-predictive local pattern could discriminate high thermal pain versus low thermal pain (accuracy = 88 ± 5.7% SE, p<0.001, d = 1.56), high thermal pain versus medium thermal pain (accuracy = 88 ± 5.7% SE, p<0.001, d = 1.23) and medium thermal pain versus low thermal pain (accuracy = 82 ± 6.7% SE, p<0.001, d = 1.49) above chance levels (Figure 7B). In addition, the mid-insula partial pattern expressions (i.e. focusing on the mid-insula pattern) were highly correlated with temperature levels (r196 = 0.454, p<0.001) as well as individual pain ratings (r196 = 0.440, p<0.001). Together with the predictions using NS and FE vicarious pain-predictive patterns separately (Figure 7—figure supplement 1), our results demonstrate that the vicarious pain patterns respond to self-experienced somatic pain, confirming that the vicarious pain patterns reflect pain-associated information. All findings remained significant after correcting multiple comparisons via Bonferroni correction.

Discussion

Several studies have explored the neural underpinnings of vicarious pain in humans and suggested overlapping univariate fMRI activations in the anterior cingulate and insular cortices across different vicarious pain induction procedures (for meta-analyses see e.g. Jauniaux et al., 2019; Timmers et al., 2018). However, the conventional univariate approach lacks anatomical and functional specificity to test the question of whether vicarious pain across different modalities share common and process-specific neural representations (Iannetti et al., 2013; Krishnan et al., 2016; Woo et al., 2014; Zaki et al., 2016). Here, we employed a fine-grained MVPA approach which is sensitive and specific to particular types of mental processes including pain (Kragel et al., 2018; Peelen et al., 2006; Wager et al., 2013; Woo et al., 2017) to explore (1) whether shared neural representations of vicarious pain can be determined across different induction procedures (FE, NS) and (2) whether the shared neural representation is sensitive to pain-unspecific components of the vicarious pain response (arousal, negative affect) and related to the experience of somatic pain. We demonstrated that shared multivariate patterns encoding NS and FE vicarious pain can be determined at the whole-brain level and that across different analytic approaches the mid-insular cortex was consistently engaged across induction procedures. Furthermore, we demonstrated that these patterns were not sensitive to respond to the processing of non-painful high-arousal negative stimuli in the same sample, together with the findings showing that NS vicarious pain predicted FE vicarious pain (and vice versa) more accurately as compared with the predictions using a negative emotion decoder, suggesting that the common vicarious pain representations do not simply reflect shared unspecific processes of negative affect or arousal. Moreover, the shared vicarious pain representations predicted self-experienced thermal pain in an independent sample, suggesting an association between the neural expression and processes directly related to the experience of pain. Together these results provide evidence for a generalized neural representation of vicarious pain, particularly in the mid-insula, and demonstrated that the shared neural signature may specifically capture pain-associated aspects of the vicarious pain response rather unspecific processes such as aversive experience or arousal.

The idea that vicarious pain across different induction procedures share common neural representations has been supported by meta-analyses covering previous fMRI pain empathy studies that demonstrated overlapping activations in the insular and cingulate cortices (Jauniaux et al., 2019; Timmers et al., 2018). In line with these meta-analytic findings we found that these regions were consistently engaged during both NS and FE vicarious pain. However, the insular and anterior cingulate cortices are involved in a wide range of mental processes including representation of interoceptive and affective states as well as salience detection (Craig, 2009; Critchley et al., 2004; Timmers et al., 2018; Uddin, 2015), suggesting that the overlapping activity might be due to common underlying mental processes such as detecting and orienting attention toward salient stimuli or unspecific emotional arousal (Corradi-Dell'Acqua et al., 2011; Corradi-Dell'Acqua et al., 2016; Valentini and Koch, 2012).

To systematically test whether vicarious sharing of pain elicited by different social cues shares common neural representations, we developed and compared multivariate patterns that predicted NS and FE vicarious pain evoking stimuli respectively. While mass-univariate analysis results reflect the presence of intermingled neuronal populations related to stimulus-specific representations, MVPA investigates whether idiosyncratic spatial variations in the fMRI signal are shared or dissociated across different conditions and thus might be more suitable to determine process-specific representations in meso-scale neural circuits (Kamitani and Tong, 2005; Kriegeskorte et al., 2006; Peelen and Downing, 2007). Moreover, previous studies have suggested that whole-brain predictive models could better capture emotional processes compared to regional approaches, such as decoding of a single brain region or searchlight-based methods (Kragel et al., 2018; Woo et al., 2017). To this end, we first identified whole-brain fMRI patterns that accurately predicted NS and FE vicarious pain, respectively. We found that the NS and FE vicarious pain-predictive patterns were spatially correlated and both could classify within- and between-modality painful versus non-painful stimuli at the whole-brain level, suggesting that NS and FE vicarious pain share distributed processing across multiple systems and component processes. In line with previous studies demonstrating that while NS vicarious pain provides objective cues about the sensory component of the observed pain the FE vicarious lacks such information and is more subjective and indirect as the pain experience of the expresser need to be interpreted by the observer (Hadjistavropoulos et al., 2011; Vachon-Presseau et al., 2012), the decreased accuracies and effect sizes in the cross-modality predictions additionally suggest partly distinguishable neural representations of NS and FE vicarious pain possibly reflecting the engagement of different component processes.

In the context of previous studies suggesting that pain empathy deficits are mediated by regional-specific brain lesions and functional dysregulations (Leigh et al., 2013; Shamay-Tsoory et al., 2009; Xu et al., 2020b) the question for the contribution of specific brain regions arises. Thresholding the vicarious pain patterns (at FDR q < 0.05, two-tailed) allowed us to identify voxels that reliably contributed to the respective decoders and revealed that specifically the bilateral mid-insula provided important features to predict both NS and FE vicarious pain. Moreover, the mid-insula partial vicarious pain patterns were highly spatially correlated and both could significantly predict within- and between-modality vicarious pain-related experience. Consistent with this, searchlight-based classification analyses also demonstrated that mid-insula local patterns produced significant within- and between-modality predictions of vicarious pain. Our results are in line with a previous meta-analysis showing that the mid-insula responds specifically to empathy for pain across different task paradigms compared to empathy for non-pain negative affective states (Timmers et al., 2018), which together with the present findings suggests that the mid-insula represents a core neural substrate for vicarious pain.

Although multivariate predictive models can capture information at much finer spatial scales and consequently anatomical specificity (Kamitani and Tong, 2005; Woo et al., 2017), the question of the specific mental processes captured by our vicarious pain-predictive patterns remains unclear. Pain empathy is a multi-component process that includes the vicarious sharing of pain but may also evoke emotional arousal and negative affect, and these unspecific processes can be captured by the decoders. To determine the functional specificity of the neural representations, we applied the vicarious pain-predictive patterns to data from an emotion processing paradigm acquired in the same sample as well as to data from a thermal pain induction experiment in an independent sample and found that (1) the vicarious pain patterns performed only modest for discriminating high-arousal (non-painful) negative stimuli from low-arousal neutral stimuli and (2) the whole-brain and mid-insula patterns predicted levels of self-experienced thermal pain with high accuracies. Finally, we developed a general vicarious pain-predictive pattern across NS and FE vicarious pain induction procedures and demonstrated that it accurately predicted both NS and FE vicarious pain (accuracies > 87%) as well as thermal pain intensities (accuracies > 82%), yet classified non-painful negative versus neutral stimuli with comparably low accuracy (59%). In line with the prediction results, meta-analytic decoding analysis revealed that this general vicarious pain pattern was highly correlated with the domains of ‘painful’ and ‘pain’, but not with ‘arousal’, ‘valence’ or ‘negative’ (not shown in the top 100 relevant terms). Together these findings suggest a shared neural representation of vicarious pain and a high-specificity of the whole-brain and specifically the mid-insula patterns for the vicarious experience of pain. A previous study developed a vicarious pain signature (VPS) that was sensitive and specific to NS vicarious pain, but not sensitive to the intensity of self-experienced somatic pain (Krishnan et al., 2016). Examining similarities with our general vicarious pain-predictive pattern revealed only modest spatial correlations between the two patterns (r = 0.04). The different instructions employed in the experiments might have contributed to the low overlapping spatial distributions such that participants in the previous study were required to explicitly rate their emotional response to the stimuli, whereas we decided for an implicit processing (passive viewing) paradigm to match instructions across the vicarious pain and negative emotional processing paradigm and to additionally control for cognitive processes which can modulate empathic reactivity and painful experience as well as the specific neural networks engaged (Jauniaux et al., 2019; Urien and Wang, 2019). Moreover, we found that the present pattern could successfully predict pain experience during thermal heat stimulation while the VPS was not sensitive to self-experienced pain. The observed differences might be explained in terms of (1) the considerably larger sample size included in the present study and prediction accuracy (as reflected by prediction-outcome correlation) of self-experienced pain experience increased as a function of sample size used to develop the NS vicarious pain decoder (see additional analysis presented in Figure 7—figure supplement 2) and (2) differences between paradigms and instructions such that, for example, a recent meta-analysis of pain empathy studies showed that the mid-cingulate gyrus was more activated by explicit cognitive/evaluative paradigms while the right inferior frontal gyrus and anterior insula were more activated by implicit perceptual/affective paradigms (Timmers et al., 2018).

Our results highlighted the mid-insula as a key region sharing similar neural representations across NS and FE vicarious pain suggesting that it may contribute to the core vicarious pain experience that characterizes pain empathy. Consistent with the whole-brain results, the shared information in the mid-insula was specific to vicarious pain rather than negative affect or arousal. Previous non-human primate and human studies indicate that the posterior and mid-insula receive nociceptive information from thalamic nuclei (Craig et al., 1994; Craig et al., 2000) which are in turn conveyed to the anterior insula for progressive integration with higher level affective and interoceptive experience (Corradi-Dell'Acqua et al., 2011; Corradi-Dell'Acqua et al., 2016; Singer et al., 2009). Although overarching models of the neural basis of pain empathy and neuroimaging meta-analyses (Jauniaux et al., 2019; Timmers et al., 2018) emphasize the role of the anterior insula in pain empathy processing, accumulating evidence from studies examining shared and process-specific representations of vicarious pain suggest a specific role of the mid-insula in vicarious pain (Corradi-Dell'Acqua et al., 2011; Krishnan et al., 2016), whereas the (left) anterior insula also responded to negative stimuli in general (Corradi-Dell'Acqua et al., 2011) and across modalities (Corradi-Dell'Acqua et al., 2016). Importantly, the peak anterior insula coordinates identified in these previous studies did not overlap with our mid-insula mask or the mid-insula region that exhibited reliable predictive features in both NS and FE vicarious pain whole-brain patterns determined in the present study, suggesting a more specific role of the mid-insula in pain-related components of the vicarious pain response (see also recent meta-analysis by Timmers et al., 2018 demonstrating a specific role of the mid-insula in pain empthy). In support of our findings a previous study employed a similar whole-brain MVPA approach to predict NS vicarious pain induced by an evaluative paradigm also identified the bilateral mid-insula as reliable (q < 0.05, FDR corrected) predictive regions (Krishnan et al., 2016), further conforming the reliable contribution of this region in encoding vicarious pain. Studies examining the functional and anatomical organization of the insular cortex with intracerebral electrical stimulation have demonstrated that painful sensations can be elicited by stimulation of the middle but not the anterior insula (Afif et al., 2010). Together with the functional relevance of the mid-insula to predict objective and subjective pain experience in an independent sample and the contribution of this region to nociception as well as vicarious pain (Botvinick et al., 2005; Krishnan et al., 2016; Lamm et al., 2011; Timmers et al., 2018; Wager et al., 2013), our findings suggest that the shared representations in the mid-insula across vicarious pain induction procedures may specifically code the automatic pain sharing which resonates with embodies conceptualizations of vicarious pain (see e.g. Corradi-Dell'Acqua et al., 2011 for a convergent interpretation). However, consistent with previous evidence that (NS) vicarious pain representation is distributed across brain regions and single local regions exhibit considerably lower effect sizes compared to whole-brain predictive models (Krishnan et al., 2016), we found that the prediction effect sizes for the mid-insula were smaller than those observed in our whole brain analyses. These findings suggest that despite the key role of the mid-insula in vicarious pain experience this region is not sufficient to fully capture this process.

Consistent with previous studies suggesting that the anterior cingulate cortex represents a core brain region for emotional empathy in general and pain empathy in particular (Fan et al., 2011; Jauniaux et al., 2019; Timmers et al., 2018), we found overlapping deactivations in the rostral and ventral anterior cingulate cortex in mass-univariate analyses and shared patterns in the dorsal, rostral and ventral anterior cingulate cortex in searchlight-based prediction analyses between NS and FE vicarious pain. However, no overlapping reliable predictive voxels for whole-brain NS and FE pain-predictive patterns were found in cingulate regions suggesting a differential involvement of this region during FE and NS vicarious pain induction procedures. From a methodological perspective, these results may reflect that the whole-brain predictive model could provide a more specific neural description of a behavior or mental process (Kragel et al., 2018; Woo et al., 2017). In line with our findings, previous studies also showed that significant activation and searchlight-based prediction in local regions do not necessarily imply reliable predictive features in whole-brain predictive models (Krishnan et al., 2016; Woo et al., 2014). From a brain systems perspective, these findings may indicate that the anterior cingulate cortex is not specifically involved in vicarious pain elicited across induction procedures. Although the anterior cingulate cortex has been reliably identified in meta-analytic studies covering brain activation patterns during (pain) empathy induction procedures (see e.g. Jauniaux et al., 2019; Timmers et al., 2018), the anterior cingulate has also been associated with a number of basal processes, including arousal and salience, and activation in this region may reflect rather unspecific neural responses.

The present study has limitations that should be addressed in future studies. Compared to the homogeneous stimuli within the conditions of the vicarious pain and the self-experienced pain paradigm the stimuli displaying emotional evocative scenes from the IAPS database may have led to a higher inter-trial variance in the negative processing experiment. Although the inter-stimulus variance should not systematically differ between the experimental conditions employed to develop the corresponding decoder, we cannot fully exclude that this may have partly contributed to the low accuracies of the emotional processing decoder with respect to predicting self-experienced pain ratings. Moreover, the current study employed a passive observation paradigm and a recent meta-analysis revealed that vicarious pain induced by cognitive/evaluative and affective/perceptual paradigms elicited activations in overlapping yet also different brain regions (Timmers et al., 2018). Whether the present conclusions could generalize to more ‘active’ engagements in empathy (e.g. explicitly asking subjects to imagine that the injury occurring in the picture displayed was happening to them) remains to be determined.

In conclusion, by applying a novel whole-brain as well as local-region-based MVPA approaches in a large sample of healthy adults, our results provide the first neuroimaging evidence that NS and FE vicarious pain share common neural representations, especially in the mid-insula which may specifically encode the vicarious sharing of pain that specifically characterizes pain empathy. Moreover, we also provide a general vicarious pain-predictive pattern (across NS and FE vicarious pain stimuli), which may be employed in future studies to facilitate inferences about pain empathy across modalities as well as self-experienced pain. Our study offers a new approach to better understand pain empathy by exploring common neural representations and linking these shared representations to felt pain.

Materials and methods

Key resources table
Reagent type
(species) or resource
DesignationSource or referenceIdentifiersAdditional
information
Software, algorithmMatlab R2015bMathWorksRRID:SCR 001622
Software, algorithmSPM12Wellcome Trust Centre for NeuroimagingRRID:SCR_007037
Software, algorithmCANLab Core ToolsCANlabhttps://github.com/canlab
OtherThermal pain dataWager et al., 2013https://ndownloader.figshare.com/files/12708989
OtherVicarious pain signaturesThis paperhttps://neurovault.org/collections/6332/Deposited multivariate patterns
OtherData and codesThis paperhttps://figshare.com/articles/Vicarious_pain_dataset/11994498Deposited fMRI data and scripts for figures

Participants

N = 252 healthy young participants were enrolled in the current study and underwent a previously validated NS and FE vicarious pain empathy fMRI paradigm. The fMRI data on the basic group activation maps for NS and FE vicarious pain contrasts were previously published in a study examining dimensional associations with trait autism and alexithymia (Li et al., 2018) and a study investigating network-level communication during pain empathic processing using an exploratory inter-subject phase synchronization approach (Xu et al., 2020a). Of note, the aim, methodological approach and hypotheses of the current study were independent from these previous publications; here, we focus on identifying an fMRI multivariate pattern for NS and FE vicarious pain separately and assessing their relationship. To further examine the specificity of the determined pain patterns from general negative emotion processing the data from an emotion processing paradigm from the same subjects was additionally used. Due to technical issues during data acquisition (incomplete data, n = 6), left-handedness (n = 4) or excessive head motion (>3 mm translation or 3° rotation; n = 4) data from 14 participants were excluded leading to a sample of n = 238 participants (118 females; mean ± SD age=21.58 ± 2.32 years) for the pain empathy analyses; data from 15 participants (incomplete data n = 8; left-handedness, n = 4; excessive head motion, n = 3) was excluded from the emotion processing paradigm analyses (n = 237; 120 females; mean ± SD age=21.55 ± 2.30 years). Participants provided written consent, the study was approved by the ethics committee at the University of Electronic Science and Technology and was in accordance the Declaration of Helsinki. Consent authorization for publication has been obtained from individuals in Figure 1.

Experimental stimuli

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The main aim of the present study was to determine (1) shared neural representations of pain empathy and (2) to further differentiate the specificity of the neural representation of shared vicarious pain from unspecific arousal and negative processing. For aim (1) we employed two different sets of validated pain empathy experimental stimuli displaying noxious stimulation of body limbs (NS vicarious pain) and facial expressions of pain (FE vicarious pain) as well as respective non-painful control stimuli (see Figure 1A for examples). The NS vicarious stimuli displayed a person’s hand or foot in painful or non-painful everyday situations from the first-person perspective (e.g. the painful stimulus displays cutting a hand with a knife whereas the matched non-painful control stimulus shows cutting vegetables with a knife; for an evaluation of the stimuli see also Meng et al., 2012). The FE vicarious stimuli incorporated painful and neutral facial expressions from 16 Chinese actors (eight males; for an evaluation of the stimuli see also Sheng and Han, 2012). To further validate the stimulus properties, we recruited an independent sample of 40 subjects (two of them were excluded due to incomplete data; 17 females; Mean ± SD age=20.45 ± 1.43 years) to rate the intensity of pain the depicted person is experiencing, the intensity of (vicarious) pain they experience while seeing the picture, valence and arousal for each stimulus on nine-point Likert scales (1 = ‘not painful at all’, ‘very negative’ or ‘lowly arousing’, 9 = ‘extremely painful’, ‘very positive’ or ‘highly arousing’). In line with previous studies employing these stimulus sets (Meng et al., 2012; Sheng and Han, 2012) ratings in the present sample confirmed that both sets of painful stimuli were rated as considerably more painful in terms of the perceived level of pain the person in the picture is experiencing as well as level of vicarious pain experience in the observer (all Ps <0.001). As expected, both sets of painful stimuli were also rated as more negative and stronger arousing than the control stimuli (all Ps <0.001) (details see Figure 1B and Results). To determine whether the shared higher arousal and negative affect of both painful stimuli relative to their control stimuli may have contributed to the identified shared neural representation (aim 2) we additionally employed a stimulus set with non-painful high-arousal negative pictures and low-arousal neutral control stimuli. All stimuli were from the International Affective Picture System (IAPS) database. We recruited another independent sample of 37 subjects (16 females; Mean ± SD age=23.60 ± 2.86 years) to rate the valence and arousal for each stimulus with nine-point Likert scales (1 = ‘very negative’ or ‘lowly arousing’, 9 = ‘very positive’ or ‘highly arousing’). Given that the IAPS stimuli we selected were non-painful we did not ask subject to rate pain intensity. Negative stimuli elicited substantial negative affect and arousal on numerical rating scales as compared with neutral stimuli (details see Results).

Presentation of the stimuli

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The pain empathy paradigm employed a blocked design incorporating condition-specific blocks presenting the validated visual stimuli displaying painful everyday scenes (NS vicarious pain) and painful facial expressions (FE vicarious pain) as well as modality-specific control stimuli displaying non-painful scenes (NS control) or neutral facial expressions (FE control). A total of 16 blocks (four blocks per condition) were presented in a pseudo-randomized order and interspersed by a jittered red fixation cross (8, 10, or 12 s). Each block (16 s) incorporated four condition-specific stimuli (each presented for 3 s) separated by a white fixation cross (1 s). An implicit processing paradigm (passive viewing) was employed. To this end, participants were instructed to attentively watch the presented stimuli.

In line with the pain empathy paradigm, the emotion processing paradigm employed a block design incorporating three experimental conditions (positive, negative and neutral pictures). A total of 19 blocks (neutral, seven blocks; negative, six blocks; positive, six blocks) were presented in a pseudo-randomized order and interspersed by a jittered red fixation cross (8, 10, or 12 s). In each block (16 s), four condition-specific stimuli (3 s) were presented and separated by a white fixation cross (1 s). An implicit processing (passive viewing) paradigm was employed and participants were asked to attentively watch the stimuli. To ensure attentive processing, participants were required to press a button when a stimulus with a white frame (one in each block) was presented.

Thermal pain paradigm

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Thirty-three healthy (22 females; mean ± SD age=27.9 ± 9.0 years), right-handed subjects participated in the thermal pain study (details see Wager et al., 2013; Woo et al., 2015). Six levels of temperature (ranging from 44.3°C to 49.3°C in increments of 1°C) were delivered to the volar surface of the left inner forearm using a TSA-II Neurosensory Analyzer (Medoc Ltd.) with a 16 mm Peltier thermode end-plate during fMRI acquisition. The fMRI task included seven passive experience runs and two regulation runs where subjects were asked to cognitively 'increase' (regulate-up) or 'decrease' (regulate-down) pain intensity with each run encompassing 11 trials. Each trial consisted of a 12.5 s stimulus (3 s ramp-up and 2 s ramp-down periods and 7.5 s at the target temperature), a jittered 4.5–8.5 s delay, a 4 s painful/non-painful decision period, a 7 s continuous warmth or pain rating period (on a visual analogue scale) and 23–27 s rest. For the current study, we incorporated the data from the passive experience runs.

MRI data acquisition and preprocessing

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MRI data were collected on a 3.0 T GE Discovery MR750 system (General Electric Medical System, Milwaukee, WI). Functional MRI data was acquired using a T2*-weighted echo-planar imaging (EPI) pulse sequence (repetition time = 2 s, echo time = 30 ms, 39 slices, slice thickness = 3.4 mm, gap = 0.6 mm, field of view = 240 × 240 mm, resolution = 64 × 64, flip angle = 90°, voxel size = 3.75 × 3.75 × 4 mm). To improve spatial normalization and exclude participants with apparent brain pathologies a high-resolution, T1-weighted image was acquired using a 3D spoiled gradient recalled (SPGR) sequence (repetition time = 6 ms, echo time = minimum, 156 slices, slice thickness = 1 mm, no gap, field of view = 256 × 256 mm, acquisition matrix = 256 × 256, flip angle = 9°, voxel size = 1 × 1×1 mm). OptoActive MRI headphones (http://www.optoacoustics.com/) were used to reduce acoustic noise exposure for the participants during MRI data acquisition.

Functional MRI data was preprocessed using Statistical Parametric Mapping (SPM12; RRID:SCR_007037; https://www.fil.ion.ucl.ac.uk/spm/software/spm12/). The first 10 volumes of each run were discarded to allow MRI T1 equilibration and active noise cancelling by the headphones. The remaining volumes were spatially realigned to the first volume and unwarped to correct for nonlinear distortions related to head motion or magnetic field inhomogeneity. The anatomical image was segmented into grey matter, white matter, cerebrospinal fluid, bone, fat and air by registering tissue types to tissue probability maps. Next, the skull-stripped and bias corrected structural image was generated and the functional images were co-registered to this image. The functional images were subsequently normalized the Montreal Neurological Institute (MNI) space (interpolated to 2 × 2 × 2 mm voxel size) by applying the forward deformation parameters that were obtained from the segmentation procedure, and spatially smoothed using an 8 mm full-width at half maximum (FWHM) Gaussian kernel.

Pain empathy - univariate general linear model (GLM) analyses

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A two-level random effects GLM analysis was conducted on the fMRI signal to determine shared modality-specific activation patterns using a mass-univariate GLM approach. The first-level model included four condition-specific (NS vicarious pain, NS control, FE vicarious pain, and FE control) box-car regressors logged to the first stimulus presentation per block that were convolved with SPM12’s canonical hemodynamic response function (HRF). The fixation cross epoch during the inter-block interval served as implicit baseline, and a high-pass filter of 128 s was applied to remove low-frequency drifts. Regressors of non-interest (nuisance variables) included (1) six head movement parameters and their squares, their derivatives and squared derivatives (leading to 24 motion-related nuisance regressors in total) and (2) motion and signal-intensity outliers (based on Nipype’s rapidart function). Single-subject voxel-wise statistical parametric maps for the empathy modality-specific contrasts (NS vicarious pain >NS control and FE vicarious pain >FE control) were obtained and subjected to group-level one-sample t-tests. The corresponding analyses were thresholded and corrected for multiple comparisons within a grey matter mask based on false discovery rate (FDR q < 0.05, two-tailed) with a minimum extent of 100 mm3. The resulting thresholded activation maps were next used to identify common regions of activation across the modalities (NS and FE vicarious pain; i.e. masking the overlapping significant voxels).

To determine the activation similarity of NS and FE vicarious pain, a permutation-based correlation analysis was employed (Hong et al., 2019). Specifically, we (1) calculated Pearson’s correlation (r) between the modality-specific unthresholded statistical maps (NS vicarious pain >NS control versus FE vicarious pain >FE control), (2) shuffled the condition labels for the NS stimuli, obtained a new group-level statistical map for ‘NS vicarious pain >NS control’ and calculated the activation similarity of FE and the ‘modelled’ NS vicarious pain, (3) repeated step (2) 10,000 times, (4) repeated steps (2-3) with shuffled labels for FE instead of NS stimuli, and finally (5) calculate the probability of observing the activation similarity between the true NS and FE pain given the null distribution of permuted activation similarity. A p value < 0.05 was being considered statistically significant and between 0.05 and 0.1 was being considered as marginal significant.

Pain empathy - multivariate pattern analyses

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For the multivariate pattern analyses, nuisance regression (24 head motion parameters, motion and signal-intensity outliers, and linear trend) and high-pass filtering (cut off at 128 s) were initially simultaneous conducted on the preprocessed fMRI data. Next, the fMRI signal was averaged within the four condition-specific blocks (shifted by 3 TRs to account for the delay of the HRF). In line with previous studies (e.g. Krishnan et al., 2016; Wager et al., 2013; Woo et al., 2014), we used normalized and smoothed (8 mm FWHM Gaussian kernel) data to develop the population-level vicarious pain-predictive patterns as previous studies suggested that this smoothing level could improve inter-subject functional alignment while retaining sensitivity to mesoscopic activity patterns that are consistent across subjects (Op de Beeck, 2010; Shmuel et al., 2010). Linear support vector machines (SVMs, C = 1) were then employed to the whole-brain maps (restrict to a grey matter mask) to train multivariate pattern classifiers on the cleaned averaged fMRI signal to discriminate NS vicarious pain versus NS control and FE vicarious pain versus FE control separately. The classification performance was evaluated by a 10-fold cross-validation procedure during which all participants were randomly assigned to 10 subsamples of 23 or 24 participants using MATLAB's cvpartition function. The optimal hyperplane was computed based on the multivariate pattern of 214 or 215 participants (training set) and evaluated by the excluded 24 or 23 participants (test set). The training set was linearly scaled to [−1, 1], and the test set was next scaled using the same scaling parameters before applying SVM (Hsu et al., 2003). This procedure was repeated 10 times with each subsample being the testing set once. To avoid a potential bias of training-test splits, the cross-validation procedures throughout the study were repeated 10 times by producing different splits in each repetition and the resultant accuracy and p values were averaged to produce a convergent estimation (Zhou et al., 2018). In line with the mass-univariate analyses and to identify which brain regions made reliable contributions to the decoders (Wager et al., 2013; Zhou et al., 2019), the pattern maps were thresholded at FDR q < 0.05 (two-tailed) with a minimum extent of 100 mm3 using bootstrap procedures with 10,000 samples. Next the thresholded maps were subjected to a conjunction analysis to identify regions that robustly contributed to both NS and FE vicarious pain classifiers by masking overlapping significant voxels. Statistical maps were visualized using the Connectome Workbench provided by the Human Connectome Project (https://www.humanconnectome.org/software/connectome-workbench).

Similarity patterns between the modality-specific neural patterns were determined employing (1) Pearson’s correlation between the whole-brain unthresholded classifier weights using a permutation test (similar to the activation similarity analysis) and (2) ‘between - modality classification’ tests encompassing the following two steps: (a) pattern classifiers were trained separately for NS vicarious pain versus NS control and FE vicarious pain versus FE control with a 10-fold cross-validation procedure (repeated 10 times), and next (b) applying the identified patterns of NS and FE vicarious pain to out-of-sample participants for the FE vicarious pain versus FE control and NS vicarious pain versus NS control respectively using a two-alternative forced choice test, where pattern expression values were compared for two conditions with the image exhibiting the higher expression being determined as pain.

Pain empathy – within- and between- modality classification analyses employing local classifiers

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To further identify regions with shared neural expressions across NS and FE vicarious pain, a local pattern-based classification approach with three-voxel radius spherical searchlights around center voxels was employed (Corradi-Dell'Acqua et al., 2011; Kriegeskorte et al., 2006; Woo et al., 2014). Specifically, (1) multivariate pattern classifiers using a defined local region were trained to discriminate vicarious pain versus control within each modality (i.e. NS and FE stimuli) separately and (2) the patterns obtained were next applied to out-of-sample participants for within-modality cross-validation and between-modality cross-prediction. Steps (1) and (2) were repeated for each local region across the whole-brain. It was hypothesized that shared neural representations for NS and FE pain within a local region would be reflected by significant cross-validation and cross-prediction accuracies for each classifier. Given that the specific results of searchlight-based approaches strongly depend on the searchlight size if information is not present and detected equally at all spatial frequencies (Etzel et al., 2013), we repeated our analyses with two additional searchlight sizes (4-mm- and 10-mm-radius spheres).

Specificity of the NS and FE vicarious pain-predictive patterns

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To test whether the observed NS and FE vicarious pain-predictive patterns were specific to pain processing or rather reflect general aspects of negative emotional processing, the two pain-predictive patterns were applied to the data from the emotional task paradigm. The first-level model for the emotion processing data included the four experimental conditions (positive, negative, neutral and white framed stimuli) and high-pass filter and nuisance regressors were identical to the pain empathy GLM analysis. The two pain-predictive patterns were next applied to negative and neutral contrasts (via dot-products) using a repeated 10-fold cross-validation procedure separately, and subsequently two-alternative forced choice tests were employed to discriminate negative versus neutral stimuli.

Generalized vicarious pain-predictive pattern

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Given that we found shared neural representations between NS and FE vicarious pain (see Results for details), a general vicarious pain pattern was developed by classifying vicarious pain (NS and FE) versus control stimuli and further evaluated by predicting NS vicarious pain versus NS control and FE vicarious pain versus FE control separately through 10-fold cross validation procedures. We next constructed 10,000 bootstrap sample sets to visualize the voxels that made the most reliable contribution to the classification and to decode the cognitive relevance of the classifier with the resultant Z map using the Neurosynth (Yarkoni et al., 2011). Moreover, to compare the general vicarious pain pattern with the NS and FE vicarious patterns, we examined the similarities between this general vicarious pain pattern and the NS and FE vicarious pain patterns, respectively.

Generalizability of the vicarious pain pattern

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To test the functional relevance and generalizability of the empathic-induced neural pain pattern, the unthresholded whole-brain pattern of the general across NS and FE vicarious pain was applied to determine the behavioral and neural responses during actual pain induction. To this end, data from a previous study employing different levels of thermal pain induction during fMRI scanning (MRI data acquisition and preprocessing details see Wager et al., 2013; Woo et al., 2015). First-level GLM analysis included regressors for stimulation periods for each of the six levels and the 11 s rating periods as well as nuisance regressors including intercept for each run, linear drift across time within each run, indicator vectors for outliers and head movement. The general vicarious pain pattern from the current study was used to estimate the pattern expressions of each participant in each condition (stimulation period) and next the neural pattern expressions of the six pain levels were (1) correlated with the temperature levels (1-6) as well as the subjective pain ratings separately and (2) employed to discriminate high thermal pain stimulation (average of 48.3°C and 49.3°C) versus low stimulation (average of 44.3°C and 45.3°C), high stimulation versus medium stimulation (average of 46.3°C and 47.3°C), as well as medium stimulation versus low stimulation. Moreover, we conducted the same analyses with NS and FE vicarious pain patterns to determine the robustness of the prediction.

Data availability

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Statistical and pattern weight images are available on Neurovault (https://neurovault.org/collections/6332/). Vicarious pain dataset as well as numerical data and Matlab scripts that were used to generate the figures are available on figshare (https://figshare.com/articles/Vicarious_pain_dataset/11994498). Other data can be obtained from the corresponding authors upon reasonable request.

Code availability

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Code is available at https://github.com/canlab (Canlab, 2020) and from the corresponding authors upon reasonable request.

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Decision letter

  1. Alexander Shackman
    Reviewing Editor; University of Maryland, United States
  2. Christian Büchel
    Senior Editor; University Medical Center Hamburg-Eppendorf, Germany
  3. Mitul Mehta
    Reviewer
  4. Deniz Vatansever
    Reviewer; University of York, United Kingdom

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Thank you for submitting your article "Emotional contagion of pain across different social cues shares common and process-specific neural representations" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by Alex Shackman (Reviewing Editor) and Christian Büchel (Senior Editor). The following reviewers have agreed to reveal their identity: Mitul Mehta (Reviewer #2); Deniz Vatansever (Reviewer #3).

Dr Shackman has drafted this decision to help you prepare a revised submission:

Summary

In this study, Zhou and colleagues have sought to determine shared neural representations in the processing of physical and affective vicarious pain in a large-scale fMRI study. In addition to the conventional univariate analysis, the authors employed extensive multivariate pattern analyses which highlighted common representations centred largely on the bilateral mid-insula. In a further validation step, the authors have shown that the identified patterns did not predict arousal in a separate emotional fMRI task within the same cohort, but predicted self-experienced pain in a thermal stimulation experiment within an independent sample. Utilising these results, the authors have also developed a domain-general pattern that could be employed in future studies.

The reviewers were enthusiastic about your report:

- Overall, this is an excellent study with a clear design and objective, accompanied by a well-written manuscript. I have no doubt that the results will be of interest to a wide audience in both the scientific and the general public.

- Multiple methods across different data sets, and also look at validation against task parameters and subjective responses.

- There are many strengths and positive things in this paper, above all the very large sample size, the use of complimentary analysis methods, and the attempt to make inferences stronger by bringing in data from several tasks/paradigms.

- The overall approach of univariate and multivariate methods with within and between modality classification is thoughtful and the authors show awareness of the potential broad role of key areas in the network and attempt to support specificity through the comparative analysis with the negative stimuli and a Neurosynth summary of the region.

- Strengths include the number of participants and the use of different analysis approaches and separate data sets and validation against task parameters.

- Overall, this is an excellent study with a clear design and objective, accompanied by a well-written manuscript. I have no doubt that the results will be of interest to a wide audience in both the scientific and the general public.

- Analyses were conducted and reported with the utmost scientific rigour.

Nevertheless, the 3 reviewers did have some suggestions for strengthening your report, as detailed below.

I would like to thank them for their constructive suggestions and their investment in the paper and the process.

Major/Moderate Suggestions

1) Need for Clear and Precise Nomenclature; Jingle-Jangle Issues.

a) A reviewer noted, "Throughout the manuscript, the authors use "emotional contagion" to refer to vicarious pain, which is slightly confusing given that the results indicate how pain representations are separate from emotional arousal. Hence, empathic contagion or some other term which does not refer to "emotion" might be more suitable.

b) Another reviewer wrote that, "We do not have enough clarity about what conceptual difference or connection exists between the two paradigms, and thus how we should interpret the neural differences; someone who focuses on facial emotion perception might thus rather conclude that emotion expressions are preferentially processed in the mid insular cortex "

c) S/he went on to note that, "I do not follow why the authors speak of a physical and an affective vicarious pain, and that the latter should measure/tap into emotion contagion; what I see are two paradigms showing (possibly) the same type of event happening to a target person (pain), one being triggered by a (mild) physical injury, the other by expressing pain on the face, likely expressed in response to a similar event, or not (this doesn't become clear, but neither it was probably to participants, I am assuming that because the paradigms were labeled as passive observation); in order to label one as "emotion contagion" and the other as something else (it remains unclear what that would be), one would need to clearly define emotion contagion first, and then have some independent measures of that it occurs; e.g. increase in heart rate, matching facial expressions, etc. moreover, if the task was presented in a passive-viewing fashion without any specific instruction (see 2), subjects may have associated the different kinds of stimuli with each other in such an experimental context: Intense somatic pain will always (in healthy individuals) elicit a strong affective reaction, and on the other hand, intense facial expressions of pain are most likely associated with physical pain. To play devil's advocate, one could also label the two paradigms as "passive pain perception via facial expression" vs. "passive pain perception via somatosensory events"

2) Insufficient motivation for focusing on mid-insula

a) A reviewer notes that "The authors base their mid-insula-restricted analysis ("Shared representations in the mid-insula") of shared representations on a) their own findings within the same manuscript and b) one paper reporting a specific role of the middle portion of the insula. Judging from the presented maps in Figure 3D, activity in the insula stretches from anterior to posterior portions. Also, e.g. Corradi-dell'Acqua et al., 2016 report a specific role of the anterior insula in vicarious pain. Authors need to justify this approach more clearly (or expand their ROI), especially because it could clearly affect the outcome of other analyses, such as the mid-insula classification approach for the IAPS paradigm."

3) Additional Analyses? One of the reviewers wondered whether the authors have measured affect in the pain fMRI task besides emotional arousal, and whether the identified pattern would relate to how positive/negative the participants found the given pain stimuli. This might provide evidence that the results could be separated from general emotional arousal.

4) Additional Analyses? The authors show that the patterns in pain task could not predict ratings in the emotional task. Is it possible to test whether that the reverse pattern is evident? (i.e. the pattern in the emotional task does not predict responses in the pain task.)

5) Framing/Discussion of Differences

a) Authors need to crisply articulate how the significance of observed differences in light of the many perceptual and psychological differences across tasks.

b) For example, a reviewer notes that, "The matching between the two vicarious conditions, but also to the control or comparison conditions, is far from optimal, so that many differences as well as a lack of "sharedness"/common variance between paradigms could be simply explained by that. In one case, the "neutral" condition is not fully neutral, as the picture shows a potentially noxious object that could also be seen as "just having missed to inflict pain/injury"; in the other, a painful expression is compared to a fully neutral one, without a noxious object; thus (visual and affective) salience between conditions starkly differs, which makes it hard to compare them and to draw meaningful conclusions when doing so;"

c) S/he notes that, "things get even more problematic when comparing data from the IAPS and the thermal pain conditions, which differ in many more aspects. In the IAPS, pictures of various emotionally evocative scenes are shown, thus some of the differences may be explained by higher intertrial variance and thus also lower "SNR" in the IAPS runs; moreover, salience might have differed as well – so any difference between conditions might be related to that, and the conclusion "it is not just emotional arousal that triggers the vicarious pain responses" is not conclusive (although I would be very much in favor of it); what could have been done to better match conditions would be e.g. to compare different facial emotion expressions matched for emotional arousal, so the "modality is not changed";

d) And, "the same limitation (about limited matching between conditions) applies to the comparison to the thermal pain condition, which is a very different condition in terms of trial structure, "immediateness" of experience for the participants, and visual stimulation (apart from the sample size)

6) Need for a sober and more complete discussion of implications, and perhaps an additional analysis

a) A reviewer notes that "the vicarious pain pattern successfully distinguishes between different thermal pain levels, speaking for shared neural patterns between self-experienced pain and vicarious pain, which appears to be the most interesting (yet deemphasized) finding of the study. Yet, after repeatedly emphasizing the advantages of MVPA over mass-univariate techniques, the authors argue that "although the vicarious pain patterns could accurately predict the intensity of self-experienced pain this result does not imply that vicarious and somatic pain share common neural representations", without further elaboration. Dismissing this finding in such a way leaves the manuscript with the sole main finding that physical and affective vicarious pain show overlap also on a multivariate pattern level; thus what seems crucial is to carve out much better the discrepancy between the current results and the previous ones by Krishnan in eLife as well; as a reader, I am left wondering what is correct now – that there are, or there aren't "shared representations"? the interpretation "Krishnan et al. used different instructions" is of course one possible explanation, but what about sample size and power? Maybe Krishnan et al. was underpowered, and the shared patterns would have been found there as well? But then why should a passive observation paradigm be "better" in identifying shared patterns, if anything I would have expected the opposite as my own experience as well as prior work e.g. by Feng et al. (in NeuroImage) tell us that reduced attention if anything leads to less activation in insula, A/MCC, and the likes? (apart from the fact that a between sample prediction should also have less sensitivity). My proposal here would be to go much deeper into the discrepancy between the current and Krishnan's findings – that alone could be a very interesting paper, given the current sample size; one way to do that could be e.g. to "simulate" (e.g. by random drawing and permutation testing with N as in Krishnan) the lower sample size of Krishnan and to test whether or not the differences between studies remain."

b) S/he notes that, "the discrepancies between the uni and multivariate analyses are another "gold mine" of this paper, but they are hardly addressed or explained. For example, the effect size (r) of the univariare analyses in Figure 1 is very low (0.171 -> ~4% common variance), whereas the MVPA analyses suggest much stronger, up to ~50%, correspondence. The authors need to seriously grapple with this in the Discussion.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Empathic pain evoked by sensory and emotional-communicative cues share common and process-specific neural representation" for further consideration by eLife. Your revised article has been evaluated by Drs. Büchel (Senior Editor) and Shackman (Reviewing Editor). Dr. Shackman writes…

I am very pleased to accept your paper for publication pending receipt of a revision that addresses the few remaining suggestions. I will not send it out for another round of external review.

The reviewers and I were enthusiastic about the revision:

– The authors have taken on board all of my comments and presented detailed responses and further analyses of the data. All of these provide assurances that the results are robust. The new figures and supplementary figures are great and illustrate the data and the robustness of the findings well.

– The authors have diligently responded to all my prior concerns and comments, and carried out the additional analyses I have suggested. I think the current version of this study/manuscript presents major advancement in the field both from theoretical and technical perspectives. I have no further comments.

– The authors have been very responsive to the reviewer comments, and I would like to thank them for that. The paper has much improved as a consequence… Again, congratulations on a fine and huge piece of work, that certainly will advance the field.

Nevertheless, they did have some suggestions for further enhancing the report.

"It would benefit the paper to a. delineate more clearly/accessibly, what the mid-insula entails (there is a reference to the GitHub and paper, which is already great, but not all readers will want to check), and how it "differs"/can be disambiguated from "anterior" and possibly also "posterior" IC (anterior has been often reported in empathy but also in salience research)"

I leave it to the authors to determine whether they wish to tackle the following additional analysis…

"It might be useful to also test whether these other divisions (AI, PI) show similar or distinct effects as the mid-insula."

Or, whether they would prefer to simply amend the manuscript to address this constructive suggestion:

"Basically, it would be good to know whether the mid-insula cluster includes previously reported anterior insula clusters (at least partially), and whether what we should take away really is a stronger focus on mid rather than anterior insula in future empathy/vicarious pain studies and models."

Again, I am comfortable with the authors eschewing additional analyses if they wish, but they need to address the substance of the reviewer's very reasonable suggestion prior to publication.

https://doi.org/10.7554/eLife.56929.sa1

Author response

Major/Moderate Suggestions

1) Need for Clear and Precise Nomenclature; Jingle-Jangle Issues.

a) A reviewer noted, "Throughout the manuscript, the authors use "emotional contagion" to refer to vicarious pain, which is slightly confusing given that the results indicate how pain representations are separate from emotional arousal. Hence, empathic contagion or some other term which does not refer to "emotion" might be more suitable.

We fully agree with the reviewer that using the terms “emotional contagion” and “vicarious pain” interchangeably, and using them together with “emotional arousal” in this context, might not be specific enough to differentiate between the mental processes referred to. Moreover, recent overarching conceptualizations of empathy clearly separate contagion from empathic responses, at least in the respect that contagion lacks a clear self-other distinction (e.g., Bird and Viding, 2014; De Vignemont and Singer, 2006; Lamm et al., 2019). We thank the reviewer for raising this important issue and included a clearer definition of the shared mental process in the Introduction as follows and changed the corresponding wording throughout the revised version accordingly to “vicarious pain”:

“…Vicarious pain can be triggered by observing or imagining another individual’s painful state and can be elicited by multiple types of social cues, particularly the observation of an inflicted physical injury or a facial expression of pain (Decety and Ickes, 2009; Jauniaux et al., 2019; Vachon-Presseau et al., 2012; Yesudas and Lee, 2015). While stimulus depicting the noxious stimulation of body limbs [i.e., observation of noxious stimulation induced vicarious pain (NS vicarious pain)] provides objective cues about the sensory component of the observed pain, the observation of facial expressions of pain [i.e., facial expressions induced vicarious pain (FE vicarious pain)] is considered more subjective and indirect as the pain experience of the expresser need to be interpreted by the observer (Hadjistavropoulos et al., 2011; Vachon-Presseau et al., 2012)… Despite the different psychological domains engaged in the pain empathic response induced by NS and FE both elicit vicarious pain experience (Timmers et al., 2018), encompassing pain-specific processes such as recognizing and understanding the painful state of the other person and affective sharing of pain but also non-specific processes that are shared between pain and other non-painful experiences such as arousal and negative affect (Zaki et al., 2016).”

b) Another reviewer wrote that, "We do not have enough clarity about what conceptual difference or connection exists between the two paradigms, and thus how we should interpret the neural differences; someone who focuses on facial emotion perception might thus rather conclude that emotion expressions are preferentially processed in the mid insular cortex "

We thank the reviewer for raising this important point and agree that the conceptual background and rationale to include the different paradigms into the study needs to be clarified. For the difference and connection between the two sets of vicarious pain stimuli please see also our reply to comment #1a. The major aims of the present study were to determine (1) shared – rather than different – and specific neural representations of vicarious sharing of pain across two different sets of pain empathy evoking stimuli, (2) test the specificity of the shared neural representations (relative to general negative affect / arousal), and (3) determine associations with experienced somatic pain. The corresponding analytic approach thus tested (1) whether the vicarious pain signatures that were developed by two different sets of stimuli separately exhibit shared empathic and pain-specific neural representations (which might provide an initial evidence that vicarious pain across different pain empathy evoking stimuli share common representations or not), (2) whether a general vicarious pain signature which could generalize across different types of stimuli that elicit vicarious pain can be determined and (3) whether the direct experience of somatic pain could “activate” this general vicarious pain signature. To this end we trained and tested vicarious pain predictive patterns across (a) images of noxious stimulation of body limbs [observation of noxious stimulation induced vicarious pain (NS vicarious pain)], and (b) facial expressions of pain [observation of facial expressions of pain induced vicarious pain (FE vicarious pain)]. However, both sets of stimuli could also induce unspecific processes such as arousal and negative affect and the corresponding cross-modality representation could simply reflect these more general mental processes (Corradi-Dell’Acqua et al., 2016; Zaki et al., 2016). One approach to test the specificity of the neural representation is to elicit arousal and negative affect by other (non-painful) stimuli and determine whether the neural representation is sensitive to non-pain related induction of these mental processes (please see also our response to comment #5C).

Based on the comment from the reviewer we included a clearer description of the aims of the present study and the corresponding operationalization and analytic strategy in the Introduction. Moreover, to further determine the properties of the experimental stimuli we included new data from an independent sample who explicitly rated the pain empathic stimuli. Briefly, subjects were asked to rate (1) pain intensity experienced by the subjects displayed in the picture (“how much pain do you think the person in the photo is feeling”) assessed by a nine-point Likert scale ranging from “1 = not painful at all” to “9 = extremely painful”, (2) pain intensity experienced by the participant in response to the stimulus (“how much pain do you experience when watching the picture”) assessed using the same Likert scale ranging from “1 = not painful at all” to “9 = extremely painful”, (3) valence of the stimuli by means of a nine-point Likert scale ranging from “very negative” to “very positive”, and arousal of the stimuli by means of a nine-point Likert scale ranging from “very low arousing” to “very high arousing”. These ratings confirmed that relative to the respective control stimuli both sets of painful stimuli were perceived as more painful in terms of recognized and shared pain as well as more arousing and negative (details see Supplementary File 1 and Figure 1B). To control for the shared higher arousal and negative affect induced by both sets of pain-stimuli we thus incorporated a set of non-painful yet high-arousal negative stimuli from the IAPS database as well as corresponding low-arousal neutral pictures to induce arousal and negative affect independent of pain (empathy) aspects.

c) S/he went on to note that, "I do not follow why the authors speak of a physical and an affective vicarious pain, and that the latter should measure/tap into emotion contagion; what I see are two paradigms showing (possibly) the same type of event happening to a target person (pain), one being triggered by a (mild) physical injury, the other by expressing pain on the face, likely expressed in response to a similar event, or not (this doesn't become clear, but neither it was probably to participants, I am assuming that because the paradigms were labeled as passive observation); in order to label one as "emotion contagion" and the other as something else (it remains unclear what that would be), one would need to clearly define emotion contagion first, and then have some independent measures of that it occurs; e.g. increase in heart rate, matching facial expressions, etc. moreover, if the task was presented in a passive-viewing fashion without any specific instruction (see 2), subjects may have associated the different kinds of stimuli with each other in such an experimental context: Intense somatic pain will always (in healthy individuals) elicit a strong affective reaction, and on the other hand, intense facial expressions of pain are most likely associated with physical pain. To play devil's advocate, one could also label the two paradigms as "passive pain perception via facial expression" vs. "passive pain perception via somatosensory events"

We agree with the reviewer that it is necessary to conceptualize the mental process shared by the two stimuli sets in the context of the rationale of the present study. Based on the comments from the reviewers we included a more specific description of the shared mental process in the Introduction and in line with current conceptualizations of empathy which differentiate between contagion and empathy changed the wording to “vicarious sharing of pain”. We apologize for the unclear conceptual presentation in the initial version of the manuscript and included a clearer definition of the mental process in the Introduction. Moreover, we agree that referring to the stimuli sets as “physical” and “affective” pain is misleading. Based on the reviewers comment we changed the description of the two stimuli sets and included rationale for selecting these stimuli sets as follows: “Vicarious pain can be triggered by observing or imagining another individual’s painful state and can be elicited by multiple types of social cues, particularly the observation of an inflicted physical injury or a facial expression of pain (Decety and Ickes, 2009; Jauniaux et al., 2019; Vachon-Presseau et al., 2012; Yesudas and Lee, 2015). While stimulus depicting the noxious stimulation of body limbs [i.e., observation of noxious stimulation induced vicarious pain (NS vicarious pain)] provides objective cues about the sensory component of the observed pain, the observation of facial expressions of pain [i.e., facial expressions induced vicarious pain (FE vicarious pain)] is considered more subjective and indirect as the pain experience of the expresser need to be interpreted by the observer (Hadjistavropoulos et al., 2011; Vachon-Presseau et al., 2012)… Despite the different psychological domains engaged in the pain empathic response induced by NS and FE both elicit vicarious pain experience (Timmers et al., 2018), encompassing pain-specific processes such as recognizing and understanding the painful state of the other person and affective sharing of pain but also non-specific processes that are shared between pain and other non-painful experiences such as arousal and negative affect (Zaki et al., 2016)…”.

Finally, the reviewer mentions the “passive presentation” of the stimuli. More specifically the participants were asked to attentively watch the stimuli (we now also clarify this in the revised version). This approach is often referred to as “implicit” processing in contrast to “explicit” processing during which subjects are instructed to explicitly evaluate the painfulness of the presented stimuli. We decided to employ an implicit processing instruction because: (1) this allowed us to use similar instructions for the vicarious pain and negative emotional expression paradigm, and (2) our main focus was on pain empathic reactivity rather than more cognitive aspects of empathy. Previous studies using implicit / passive viewing paradigms including instructions to attentively or to passively view the stimuli reliably elicited pain empathic response [for acute pain infliction stimuli see e.g., (Meng et al., 2012; Yao et al., 2016); for painful faces see e.g. (Sheng and Han, 2012)] and a recent meta-analysis examined the effects of explicit instructions (e.g., to emphasize with the person or to explicitly indicate the level of experienced vicarious pain) versus implicit instructions on the neural correlates of pain empathy and, in line with the original studies, reported that both instructions increase neural activity in the core pain empathy networks (Timmers et al., 2018).

2) Insufficient motivation for focusing on mid-insula

a) A reviewer notes that "The authors base their mid-insula-restricted analysis ("Shared representations in the mid-insula") of shared representations on a) their own findings within the same manuscript and b) one paper reporting a specific role of the middle portion of the insula. Judging from the presented maps in Figure 3D, activity in the insula stretches from anterior to posterior portions. Also, e.g. Corradi-dell'Acqua et al. 2016 report a specific role of the anterior insula in vicarious pain. Authors need to justify this approach more clearly (or expand their ROI), especially because it could clearly affect the outcome of other analyses, such as the mid-insula classification approach for the IAPS paradigm."

We agree with the reviewer that the rationale for the specific focus on the mid-insula needs to be justified in detail. The main aim of the mid-insula focused analysis was to explore whether the shared neural representations in this region would be sufficient to predict vicarious pain. As pointed out by the reviewer the focus on the mid-insula was based on (1) our results suggesting convergent engagement of the bilateral mid-insula in vicarious pain processing and (2) previous literature suggesting a critical role of the mid-insula in pain-related processes including self-experienced as well as vicarious pain. Based on the comment from the reviewer we reformulated the specific aim of the mid-insula focused analyses clearer and included additional supporting information on the role of the mid-insula in pain and pain empathic processing as follows:

“…Across the analyses we observed overlapping activation and shared representations in the mid-insula (see also Figure 4—figure supplement 1 for convergent findings across searchlight sizes). Accumulating evidence suggest a critical role of the mid-insula in pain-related processes, including self-experienced as well as vicarious pain. In line with functional anatomical studies suggesting that the mid-insula receives nociceptive information from thalamic nuclei (Craig et al., 1994; Craig et al., 2000) intracerebral electrical stimulation of the mid-insula evokes pain sensations (Afif et al., 2010) and previous MVPA studies demonstrated distinct neural representations between pain and non-pain negative stimuli in the (right) mid-insula yet shared representations across self-experienced and vicarious pain (Corradi-Dell'Acqua et al., 2011), while a recent meta-analysis of conventional fMRI empathy studies reported that vicarious pain uniquely activates the bilateral mid-insula and MCC as compared to empathy for non-pain negative affective states (Timmers et al., 2018). Based on the specific role of the mid-insula in pain-related processes we further explored whether the mid-insula shared neural representations of NS and FE could be sufficient to predict vicarious pain”

The Corradi-Dell’Acqua et al., 2016, study reported a lateralized effect of AI, i.e., the right AI encoded vicarious pain and negative affect (disgust and unfair) in distinct patterns while the left AI exhibited shared representations for vicarious pain, self-pain and negative affect. Moreover, in another study (Corradi-Dell'Acqua et al., 2011) the authors showed shared neural representations between felt and seen pain as well as non-pain negative emotion with the bilateral AI ROI. Thus, we cannot make the conclusion that AI has a specific role in vicarious pain. Moreover, using whole-brain decoding approaches, which explains considerably more variance in predicting these processes as compared to local regions (which were used in Corradi-dell'Acqua et al. studies) (Kragel et al., 2018; Woo et al., 2017), we found that bilateral mid-insula, rather than AI, were reliable predictors to infer both NS and SE vicarious pain.

3) Additional Analyses? One of the reviewers wondered whether the authors have measured affect in the pain fMRI task besides emotional arousal, and whether the identified pattern would relate to how positive/negative the participants found the given pain stimuli. This might provide evidence that the results could be separated from general emotional arousal.

We thank the reviewer for this interesting idea which would allow us to better integrate the two paradigms and this could further strengthen our conclusion that the identified vicarious pain-predictive patterns are independent of general negative emotional arousal. However, the examination of associations between behavioral indices and neural decoders within (or across) the vicarious pain and negative emotional processing paradigm is limited due to (1) the use of a blocked design presentation and corresponding fMRI models on the individual level, and, (2) the use of an implicit processing paradigm during fMRI acquisition (subjects were instructed to attentively watch the stimuli, explicit ratings were acquired after the fMRI and rather served to additionally validate the properties of the stimuli). Given that multivariate patterns can be generalized to different paradigms and stimuli, for example, the NPS (Neurologic Pain Signature; Wager et al., 2013) responds to diverse types of evoked pain (heat, electrical, laser, mechanical and visceral) in N > 600 subjects across diverse population with an average effect size of d = 2.18 (Zunhammer et al., 2018), we employed a different strategy to determine the specificity of the vicarious pain predictive pattern. Briefly, we propose that if the vicarious pain decoders were actually tracking unspecific arousal experience the decoders should accurately predict negative vs. neutral stimuli in the emotional processing task although the paradigms were slightly different for a similar strategy of testing the specificity of a neural signature (see e.g., Chang et al., 2015). However, we found that vicarious pain decoders barely discriminated negative from neutral stimuli (although statistically significant the accuracies were less than 61%), suggesting that the identified patterns are not likely to relate to how negative/arousal the participants found the given pain stimuli. Moreover, we also developed a general negative experience decoder and found that it predicted vicarious pain less accurately than the cross-modality predictions (see below for details), further suggesting that the identified vicarious pain patterns capture information more than general negative experience.

4) Additional Analyses? The authors show that the patterns in pain task could not predict ratings in the emotional task. Is it possible to test whether that the reverse pattern is evident? (i.e. the pattern in the emotional task does not predict responses in the pain task.)

We thank the reviewer for this advice and agree that this analysis could reveal important additional information of the specificity of the neural representations and the association between negative processing and pain empathy. To address this question, we developed a negative emotion-predictive pattern based on the data from the IAPS paradigm. The pattern could accurately classify non-painful negative vs. neutral stimuli (accuracy = 86 ± 1.6% SE, P < 0.001, d = 2.07 with repeated 10-fold cross-validation procedures). We next applied the negative emotion-predictive pattern to the vicarious pain data and observed that this decoder could significantly predict NS vicarious pain vs. its control (cross-validated accuracy = 70 ± 3.0% SE, P < 0.001, d = 0.88) and FE vicarious pain vs. its control (cross-validated accuracy = 61 ± 3.2% SE, P < 0.001, d = 0.28). Given that vicarious pain stimuli were general more negative as compared to the control stimuli (and NS vicarious pain was more negative as compared with FE vicarious pain) these findings were expected.

Of note the accuracy and effect sizes were lower as compared to the between modality vicarious pain prediction for both, the prediction of FE vicarious pain signature on NS vicarious pain vs. its control (cross-validated accuracy = 78 ± 2.7% SE, P < 0.001, d = 1.00) and vice versa (cross-validated accuracy = 69 ± 3.0% SE, P < 0.001, d = 0.65), demonstrating that the vicarious pain decoders performed better for predicting cross-modality vicarious pain signatures as compared to the non-painful negative emotion decoder. Moreover, the mid-insula neural representation for the negative-predictive pattern could not significantly predict vicarious pain (accuracies = 40 ± 3.2% SE, 48 ± 3.2% SE for NS and FE vicarious pain respectively), suggesting that despite shared neural signatures between negative affective processing and vicarious pain processing in distributed neural networks the mid-insula plays a specific role in vicarious pain across modalities. Moreover, in contrast to the vicarious pain-predictive patterns neither the whole-brain or the mid-insula negative-predictive patterns could predict thermal pain intensity (whole-brain, r196 = 0.101, P = 0.157; mid-insula, r196 = -0.319), which additionally emphasizes the functional specificity of the pain-predictive pattern in the domain of pain-related processing. We included the new results in the revised version of the manuscript and suggest that these findings indicate that although non-painful negative emotional expressions share some neural representations with the representation of vicarious pain, the latter cannot be simply explained by the former and has a higher sensitivity and specificity to vicarious pain, in particular pain-associated processes.

“…using the emotional processing data, we developed a negative emotion-predictive pattern which could accurately classify non-painful negative vs. neutral stimuli (accuracy = 86 ± 1.6% SE, p < 0.001, d = 2.07 using repeated 10-fold cross-validation procedures). The negative emotion-predictive pattern could significant discriminate NS vicarious pain versus its control (cross-validated accuracy = 70 ± 3.0% SE, P < 0.001, d = 0.88) and FE vicarious pain versus its control (cross-validated accuracy = 61 ± 3.2% SE, P < 0.001, d = 0.28). Of note, accuracy and effect size are lower as compared to FE vicarious pain pattern’s prediction of NS vicarious pain (cross-validated accuracy = 78 ± 2.7% SE, P < 0.001, d = 1.00) and vice versa (cross-validated accuracy = 69 ± 3.0% SE, P < 0.001, d = 0.65) and the mid-insula negative-predictive pattern did not predict vicarious pain (accuracies = 40 ± 3.2% SE, 48 ± 3.2% SE for NS and FE vicarious pain, respectively). Moreover, in contrast to the pain-predictive patterns (see below for details) neither the whole-brain or the mid-insula negative-predictive pattern could predict thermal pain intensity (whole-brain, r196 = 0.101, P = 0.157; mid-insula, r196 = -0.319), which additionally emphasizes the functional specificity of the pain-predictive pattern in the domain of pain-related processing. Together these findings suggest that negative emotional processing might share some neural representations with vicarious pain, but that the whole-brain and mid-insula vicarious pain representations are more specific to the pain-related information.”

5) Framing/Discussion of Differences

a) Authors need to crisply articulate how the significance of observed differences in light of the many perceptual and psychological differences across tasks.

We agree with this comment and included a clearer description of the findings in the context of the task differences in Discussion.

“…In line with previous studies demonstrating that while NS vicarious pain provides objective cues about the sensory component of the observed pain the FE vicarious lacks such information and is more subjective and indirect as the pain experience of the expresser need to be interpreted by the observer (Hadjistavropoulos et al., 2011; Vachon-Presseau et al., 2012), the decreased accuracies and effect sizes in the cross-modality predictions additionally suggest partly distinguishable neural representations of NS and FE vicarious pain possibly reflecting the engagement of different component processes.”

b) For example, a reviewer notes that, "The matching between the two vicarious conditions, but also to the control or comparison conditions, is far from optimal, so that many differences as well as a lack of "sharedness"/common variance between paradigms could be simply explained by that. In one case, the "neutral" condition is not fully neutral, as the picture shows a potentially noxious object that could also be seen as "just having missed to inflict pain/injury"; in the other, a painful expression is compared to a fully neutral one, without a noxious object; thus (visual and affective) salience between conditions starkly differs, which makes it hard to compare them and to draw meaningful conclusions when doing so;"

We agree with the reviewer that the stimuli sets differ in several perceptual aspects, however, the main aim of the study was to determine shared representations between the stimuli sets rather than differences. Moreover, the cross-modality predictions employed two-alternative forced-choice tests, that is, we applied a signature (e.g., the NS vicarious pain pattern) to the cross-modality (e.g., FE stimuli) vicarious pain and control β maps using dot products (which generated one pattern expression per β map) and predicted the map with the higher pattern expression as vicarious pain condition for each subject separately. This procedure statistically equals applying the signature to the vicarious pain > control contrast (e.g., in this example, FE vicarious pain > FE control) for each subject and comparing the pattern expressions with 0 (higher than 0 means correct prediction). Given that within the same stimulus set the vicarious pain and control stimuli were well matched our procedure, to some extent, controlled for perceptual specific characteristics (e.g., general face processing).

Moreover, we agree with the reviewer that the painful conditions may differ with respect to additional characteristics such that the painful stimuli were additionally rated as more arousing and negative as compared to the control stimuli. However, the ratings clearly indicate that both sets of painful stimuli were rated as considerably more painful than the control stimuli which were generally rated as low painful (please see our response to 1b for details) and the lack of sensitivity of the vicarious pain patterns to differentiate high-arousal negative stimuli from neutral stimuli in the IAPS paradigm argues against the notion that unspecific differences may have contributed to the predictive models, specifically with respect to mid-insula expressions.

c) S/he notes that, "things get even more problematic when comparing data from the IAPS and the thermal pain conditions, which differ in many more aspects. In the IAPS, pictures of various emotionally evocative scenes are shown, thus some of the differences may be explained by higher intertrial variance and thus also lower "SNR" in the IAPS runs; moreover, salience might have differed as well – so any difference between conditions might be related to that, and the conclusion "it is not just emotional arousal that triggers the vicarious pain responses" is not conclusive (although I would be very much in favor of it); what could have been done to better match conditions would be e.g. to compare different facial emotion expressions matched for emotional arousal, so the "modality is not changed";

We agree with the reviewer that these two paradigms might differ in many aspects. However, multivariate patterns could be generalized to different paradigms and stimuli with common mental processes, for example, the NPS (Neurologic Pain Signature; Wager et al., 2013) responds to diverse types of evoked pain (heat, electrical, laser, mechanical and visceral) in N > 600 subjects across diverse population with an average effect size of d = 2.18 (Zunhammer et al., 2018). In support of this view, we used the IAPS data to develop a negative-predictive signature (please see response to comment #4 for more details) and applied this signature to negative emotional data from a previous study n = 182; (Chang et al., 2015), which employed a quite different paradigm (e.g., event design and trial by trail rating using a five-point Likert scale). We found that despite of the un-matched stimuli and paradigm our signature could predict high (average of rating 4 and 5) versus low (average of rating 1 and 2) negative stimuli with an accuracy of 92 ± 2% using a forced-choice test.

The logic of our approach is that a neural representation of vicarious pain should not simply reflect more general processes like negative affect or arousal (Zaki et al., 2016) and one way of testing this is by eliciting arousal and negative affect in other ways (e.g., non-painful high-arousal negative pictures) and testing the specificity of the vicarious pain signature. If our vicarious pain decoders were predicting arousal or negative affect they should also accurately predict non-painful negative stimuli from the IAPS data. Our results showed that the vicarious pain decoders could barely generalize to non-painful high-arousal negative stimuli, suggesting that the vicarious pain decoders are not sensitive to general processes like arousal and negative affect. Interesting, Chang et al., 2015, study included a few “painful” negative stimuli (e.g., heavily wounded people), and our general vicarious pain decoder predicted high versus low negative stimuli in their data with an accuracy of 67 ± 3.6%, which was higher than the accuracy (59 ± 2.1%) of predicting non-painful negative versus neutral stimuli used in the IAPS paradigm although the IAPS paradigm in our study was more similar to the vicarious pain paradigm (e.g., same sample and both employed block designs). Similar results were found with NS and FE vicarious pain decoders separately. These findings, to some extent, suggest that our vicarious pain decoders are more likely to predict “pain” specific information rather than general process like arousal or negative affect. However, we agree with the reviewer that between paradigm differences such as response amplitude and variance can influence decoding accuracies (e.g., Smith et al., 2011) and thus we cannot fully exclude that – in addition to the separable neural processes engaged in pain and emotional processing (Gilam et al., 2020) – may have contributed to the lack of a prediction of the negative emotion data with the vicarious pain signatures. Based on the suggestion from the reviewer we acknowledge this as limitations in the revised version as follows:

“Compared to the homogeneous stimuli within the conditions of the vicarious pain and the self-experienced pain paradigm the stimuli displaying emotional evocative scenes from the IAPS database may have led to a higher inter-trial variance in the negative processing experiment. Although the inter-stimulus variance should not systematically differ between the experimental conditions employed to develop the corresponding decoder we cannot fully exclude that this may have partly contributed to the low accuracies of the emotional processing decoder with respect to predicting self-experienced pain ratings.”

We fully agree with the reviewer that the conclusion "it is not just emotional arousal that triggers the vicarious pain responses" generally reaches far beyond the present findings. The inclusion of the IAPS data served to test whether the neural representation of vicarious pain – which due to the arousal differences between the vicarious pain and non-painful control stimuli may capture both mental processes – represents separable neural representations from general negative arousal. Together with the comparably low accuracies of the vicarious pain decoders to predict (non-painful) negative from neutral stimuli in the IAPS task the higher accuracies for NS vicarious pain to predict FE vicarious pain (and vice versa) suggest that the vicarious pain decoders share some overlapping neural representations with negative arousal yet that these are not able to fully capture the empathic pain responses. Based on the comment from the reviewer we emphasized this point in the revised Discussion as follows:

“…we demonstrated that these patterns were not sensitive to respond to the processing of non-painful high-arousal negative stimuli in the same sample, together with the findings showing that NS vicarious pain predicted FE vicarious pain (and vice versa) more accurately as compared with the predictions using a negative emotion decoder, suggesting that the common vicarious pain representations do not simply reflect shared unspecific processes of negative affect or arousal.”

d) And, "the same limitation (about limited matching between conditions) applies to the comparison to the thermal pain condition, which is a very different condition in terms of trial structure, "immediateness" of experience for the participants, and visual stimulation (apart from the sample size)

With respect to the IAPS paradigm we partly agree with the reviewer (see comment above), however, we do not follow that “the same limitation applies to the comparison to the thermal pain condition”. Of note the vicarious pain decoders robustly predicted levels of self-experienced pain suggesting a generalizability (rather than difference) across different modalities of “pain” induction (for a similar view on the strategy of generalization approaches across contexts and modalities to determine the specificity of neural decoders for specific mental processes, see also Kragel et al., 2018). With respect to the different sample sizes please see also response and new analysis / results summarized under point #6a.

6) Need for a sober and more complete discussion of implications, and perhaps an additional analysis

a) A reviewer notes that "the vicarious pain pattern successfully distinguishes between different thermal pain levels, speaking for shared neural patterns between self-experienced pain and vicarious pain, which appears to be the most interesting (yet deemphasized) finding of the study. Yet, after repeatedly emphasizing the advantages of MVPA over mass-univariate techniques, the authors argue that "although the vicarious pain patterns could accurately predict the intensity of self-experienced pain this result does not imply that vicarious and somatic pain share common neural representations", without further elaboration. Dismissing this finding in such a way leaves the manuscript with the sole main finding that physical and affective vicarious pain show overlap also on a multivariate pattern level; thus what seems crucial is to carve out much better the discrepancy between the current results and the previous ones by Krishnan in eLife as well; as a reader, I am left wondering what is correct now – that there are, or there aren't "shared representations"? the interpretation "Krishnan et al. used different instructions" is of course one possible explanation, but what about sample size and power? Maybe Krishnan et al. was underpowered, and the shared patterns would have been found there as well? But then why should a passive observation paradigm be "better" in identifying shared patterns, if anything I would have expected the opposite as my own experience as well as prior work e.g. by Feng et al. (in NeuroImage) tell us that reduced attention if anything leads to less activation in insula, A/MCC, and the likes? (apart from the fact that a between sample prediction should also have less sensitivity). My proposal here would be to go much deeper into the discrepancy between the current and Krishnan's findings – that alone could be a very interesting paper, given the current sample size; one way to do that could be e.g. to "simulate" (e.g. by random drawing and permutation testing with N as in Krishnan) the lower sample size of Krishnan and to test whether or not the differences between studies remain."

We thank the reviewer for her/his helpful comments and suggestions. We agree with the reviewer that the sample size might play an important role. To this end we employed simulations with the NS vicarious pain and its control condition (which are similar to the stimuli used by Krishnan et al., 2016) with randomly select N=20, 40, 80, 120, 160 and 200 subjects (repeated 2,000 times). We found that with increasing sample size used to develop the decoder predictions (prediction-outcome correlation coefficient) of both,

Moreover, we found that when we randomly select 40 subjects – thus closely matching sample size and β images used in Krishnan et al., 2016, to train the predictive model we observed that 10.2% of the simulations failed to predict thermal pain levels and 20.1% of the simulations failed to predict subjective pain ratings (correlation coefficients were lower than 0.1395, corresponding to P = 0.05), which was, to some extent, consistent with results reported by Krishnan et al., 2016. However, as mentioned by the reviewer, other differences between the studies may additionally have contributed to the inconsistent findings, including different instructions or attention of the subjects. In contrast in Krishnan et al., 2016 paper the vicarious pain signature was developed based on the ratings of “how much pain they (subjects) might feel in the same situation as displayed in the picture”, which mainly captured more cognitive component of pain empathy, whereas in our study subjects have stronger engaged the affective pain empathy component while watching the vicarious pain stimuli implicitly (see above online rating results). As hypothesized by the reviewer the specific task instructions modulate the neural activation in pain empathy paradigms. A recent meta-analysis reported that – although both instructions engaged the core empathic networks – the mid-cingulate gyrus was more activated by cognitive/evaluative pain empathy paradigms, while the right inferior frontal gyrus and anterior insula were more activated by passive perceptual/affective pain empathy paradigms (Timmers et al., 2018).

Due to these differences a clear determination of the specific factors that may have led to the different findings between the studies is not possible, yet we agree with the reviewer that sample size may have been a contributing factor that should be discussed. We thank the reviewer for this comment and included the new analyses exploring the effects of sample size on the prediction in the reviewed manuscript and integrated the corresponding findings into the Discussion as follows:

“Moreover, we found that the present pattern could successfully predict pain experience during thermal heat stimulation while the VPS was not sensitive to self-experienced pain. The observed differences might be explained in terms of (1) the considerably larger sample size included in the present study and prediction accuracy (as reflected by prediction-outcome correlation) of self-experienced pain experience increased as a function of sample size used to develop the NS vicarious pain decoder (see additional analysis presented in Figure 7—figure supplement 2), and (2) differences between paradigms and instructions such that, for example, a recent meta-analysis of empathy for pain studies showed that the mid-cingulate gyrus was more activated by explicit cognitive/evaluative paradigms while the right inferior frontal gyrus and anterior insula were more activated by implicit perceptual/affective paradigms (Timmers et al., 2018).”

b) S/he notes that, "the discrepancies between the uni and multivariate analyses are another "gold mine" of this paper, but they are hardly addressed or explained. For example, the effect size (r) of the univariare analyses in Figure 1 is very low (0.171 -> ~4% common variance), whereas the MVPA analyses suggest much stronger, up to ~50%, correspondence. The authors need to seriously grapple with this in the Discussion.

We agree with the reviewer that the discrepancies between the univariate and multivariate analyses are very interesting and important. Please note that the correlation coefficient between NS and FE vicarious pain patterns (r=0.170, see Figure 3A) was similar to the univariate activation maps although the multivariate pattern expressions (i.e., repeated cross-validated NS and FE vicarious pain pattern expressions of NS and FE stimuli, respectively) were more similar (mean rs474 > 0.43). However, this finding does not necessarily imply similar correlations between local patterns and local activations (e.g., focusing on the mid-insula reveals also higher activation similarity in this study). Moreover, the current findings of the comparisons between univariate and multivariate analyses might not generalize to other stimuli or paradigms. We thank the reviewer for this excellent idea, however, given that in the current design corresponding conclusions would need to remain speculative we decided to not include an in-depth discussion on this point.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

The reviewers and I were enthusiastic about the revision:

– The authors have taken on board all of my comments and presented detailed responses and further analyses of the data. All of these provide assurances that the results are robust. The new figures and supplementary figures are great and illustrate the data and the robustness of the findings well.

– The authors have diligently responded to all my prior concerns and comments, and carried out the additional analyses I have suggested. I think the current version of this study/manuscript presents major advancement in the field both from theoretical and technical perspectives. I have no further comments.

– The authors have been very responsive to the reviewer comments, and I would like to thank them for that. The paper has much improved as a consequence… Again, congratulations on a fine and huge piece of work, that certainly will advance the field.

Nevertheless, they did have some suggestions for further enhancing the report.

"It would benefit the paper to a. delineate more clearly/accessibly, what the mid-insula entails (there is a reference to the GitHub and paper, which is already great, but not all readers will want to check), and how it "differs"/can be disambiguated from "anterior" and possibly also "posterior" IC (anterior has been often reported in empathy but also in salience research)"

We fully agree that a clearer description of the mid-insula mask that was used should be incorporated in the manuscript to facilitate transparency and comparison with previous research on this cytoarchitectonic and functional heterogenous region. We therefore incorporated the following information in the manuscript text and included a figure supplement to the corresponding figure (Figure 5):

“The mid-insula was defined based on the Human Connectome Project (HCP) multi-modal parcellation atlas (Glasser et al., 2016) (encompassing PoI2, FOP2, FOP3 and MI and available from the Cognitive and Affective Neuroscience Laboratory Github repository at https://github.com/canlab/Neuroimaging_Pattern_Masks; Figure 5—figure supplement 1 displays the mid-insula mask).”

I leave it to the authors to determine whether they wish to tackle the following additional analysis…

"It might be useful to also test whether these other divisions (AI, PI) show similar or distinct effects as the mid-insula."

Or, whether they would prefer to simply amend the manuscript to address this constructive suggestion:

"Basically, it would be good to know whether the mid-insula cluster includes previously reported anterior insula clusters (at least partially), and whether what we should take away really is a stronger focus on mid rather than anterior insula in future empathy/vicarious pain studies and models."

Again, I am comfortable with the authors eschewing additional analyses if they wish, but they need to address the substance of the reviewer's very reasonable suggestion prior to publication.

We fully agree with the reviewer and the editor that this reasonable and important suggestion should be addressed to incorporate our findings better into current models on the role of the insula in (pain) empathy. We hesitate to include additional analysis on the two other subregions of the insula because this would lead to a massive increase in the number of tests (i.e., four predictions were tested alone for the pain empathy paradigm, not including the additional predictions for the IAPS paradigm or the two pain experience measures in the independent dataset). We therefore decided to discuss the contribution of neural representations in the anterior versus mid-insula in the context of previous fMRI studies employing comparable multivariate methods (e.g., Corradi-Dell'Acqua et al., 2011; Corradi-Dell’Acqua et al., 2016). To determine the regional overlap of our findings with the previous reported MVPA results we mapped the peak voxels of the previous studies by Corradi-Dell’ Acqua and colleagues (Corradi-Dell'Acqua et al., 2011; Corradi-Dell’Acqua et al., 2016) on our results and found the previous reported peak voxels did neither overlap with our mid-insula mask nor the cluster exhibiting overlapping MVPA patterns on the whole-brain level. From our perspective particularly the latter is noteworthy given that it indicates that the inconsistent findings between the studies with respect to the identified insular region are not simply an artifact of the mid-insula mask. Of note, we did find overlapping clusters in the anterior and middle insula in univariate and searchlight-based decoding analyses. In contrast the whole-brain predictive models only converged on the middle but not the anterior insula. The whole-brain MVPA based predictive models are more sensitive and specific in predicting mental processes including (vicarious) pain and negative emotions (Chang et al., 2015; Kragel et al., 2018; Krishnan et al., 2016) and reliable predictive regions identified in the whole-brain MVPA are generally more “conservative” as compared with univariate activation and searchlight-based decoding analyses see Figure 2 and Figure 4 as well as previous studies (e.g., Chang et al., 2015). In line with our findings, a previous study employed the same whole-brain MVPA approach to predict NS vicarious pain induced by an evaluative paradigm also identified the bilateral mid-insula as reliable (q < 0.05, FDR corrected) predictive regions (Krishnan et al., 2016), further conforming that the mid-insula cortex is consistently predictive of vicarious pain across paradigms and samples. Nevertheless, based on the comments from the editor / reviewer we decided to further elaborate on the anterior versus middle insula in the context of the discussion of the Corradi-Dell’Acqua et al. findings as follows:

“Although overarching models of the neural basis and neuroimaging meta-analysis (Jauniaux et al., 2019; Timmers et al., 2018) emphasize the role of the anterior insula in pain empathy processing, accumulating evidence from studies examining shared and process-specific representations of vicarious pain suggest a specific role of the mid-insula in vicarious pain (Corradi-Dell'Acqua et al., 2011; Krishnan et al., 2016), whereas the (left) anterior insula also responded to negative stimuli in general (Corradi-Dell'Acqua et al., 2011) and across modalities (Corradi-Dell’Acqua et al., 2016). […] Together with the functional relevance of the mid-insula to predict objective and subjective pain experience in an independent sample and the contribution of this region to nociception as well as vicarious pain (Botvinick et al., 2005; Krishnan et al., 2016; Lamm et al., 2011; Timmers et al., 2018; Wager et al., 2013) our findings suggest that the shared representations in the mid-insula across vicarious pain induction procedures may specifically code the automatic pain sharing which resonates with embodies conceptualizations of vicarious pain (see e.g., Corradi-Dell'Acqua et al., 2011 for a convergent interpretation).”

References

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Decety, J. E., and Ickes, W. E. (2009). The social neuroscience of empathy. MIT Press.

Gilam, G., Gross, J. J., Wager, T. D., Keefe, F. J., and Mackey, S. C. (2020). What Is the Relationship between Pain and Emotion? Bridging Constructs and Communities. Neuron.

Jauniaux, J., Khatibi, A., Rainville, P., and Jackson, P. L. (2019). A meta-analysis of neuroimaging studies on pain empathy: investigating the role of visual information and observers’ perspective. Social cognitive and affective neuroscience, 14(8), 789-813.

Lamm, C., Rütgen, M., and Wagner, I. C. (2019). Imaging empathy and prosocial emotions. Neuroscience letters, 693, 49-53.

Smith, A. T., Kosillo, P., and Williams, A. L. (2011). The confounding effect of response amplitude on MVPA performance measures. Neuroimage, 56(2), 525-530.

Yao, S., Becker, B., Geng, Y., Zhao, Z., Xu, X., Zhao, W., Ren, P., and Kendrick, K. M. (2016). Voluntary control of anterior insula and its functional connections is feedback-independent and increases pain empathy. Neuroimage, 130, 230-240.

Zunhammer, M., Bingel, U., and Wager, T. D. (2018). Placebo effects on the neurologic pain signature: a meta-analysis of individual participant functional magnetic resonance imaging data. JAMA neurology, 75(11), 1321-1330.

https://doi.org/10.7554/eLife.56929.sa2

Article and author information

Author details

  1. Feng Zhou

    1. Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
    2. Department of Psychological and Brain Sciences, Dartmouth College, Hanover, United States
    Contribution
    Formal analysis, Visualization, Methodology, Writing - original draft, Writing - review and editing
    For correspondence
    zhou.feng@live.com
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4509-4026
  2. Jialin Li

    Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
    Contribution
    Data curation, Formal analysis, Writing - original draft
    Competing interests
    No competing interests declared
  3. Weihua Zhao

    Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
    Contribution
    Data curation, Project administration
    Competing interests
    No competing interests declared
  4. Lei Xu

    Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
    Contribution
    Data curation, Project administration
    Competing interests
    No competing interests declared
  5. Xiaoxiao Zheng

    Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
    Contribution
    Data curation, Project administration
    Competing interests
    No competing interests declared
  6. Meina Fu

    Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
    Contribution
    Data curation, Project administration
    Competing interests
    No competing interests declared
  7. Shuxia Yao

    Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
    Contribution
    Data curation, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
  8. Keith M Kendrick

    Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
    Contribution
    Conceptualization, Supervision, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0371-5904
  9. Tor D Wager

    Department of Psychological and Brain Sciences, Dartmouth College, Hanover, United States
    Contribution
    Resources, Software, Supervision, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  10. Benjamin Becker

    Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
    Contribution
    Conceptualization, Funding acquisition, Writing - original draft, Project administration, Writing - review and editing
    For correspondence
    ben_becker@gmx.de
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9014-9671

Funding

National Natural Science Foundation of China (91632117)

  • Benjamin Becker

National Natural Science Foundation of China (31700998)

  • Keith M Kendrick

National Natural Science Foundation of China (31530032)

  • Shuxia Yao

National Institute of Mental Health (R01 MH116026)

  • Tor D Wager

National Institute of Biomedical Imaging and Bioengineering (R01EB026549)

  • Tor D Wager

National Key Research and Development Program of China (2018YFA0701400)

  • Benjamin Becker

Fundamental Research Funds for Central Universities (ZYGX2015Z002)

  • Benjamin Becker

Science, Innovation and Technology Department of the Sichuan Province (2018JY0001)

  • Benjamin Becker

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

Acknowledgements

This work was supported by the National Key Research and Development Program of China (Grant No. 2018YFA0701400), National Natural Science Foundation of China (NSFC, No 91632117, 31700998, 31530032); Fundamental Research Funds for Central Universities (ZYGX2015Z002), Science, Innovation and Technology Department of the Sichuan Province (2018JY0001); National Institute of Mental Health (R01 MH116026) and National Institute of Biomedical Imaging and Bioengineering (R01EB026549).

Ethics

Human subjects: All participants provided written informed consent for study participation and consent to publish the data. The study and all procedures were approved by the local ethics committee at the University of Electronic Science and Technology of China (Approval ID: 298) and was in accordance with the most recent revision of the Declaration of Helsinki.

Senior Editor

  1. Christian Büchel, University Medical Center Hamburg-Eppendorf, Germany

Reviewing Editor

  1. Alexander Shackman, University of Maryland, United States

Reviewers

  1. Mitul Mehta
  2. Deniz Vatansever, University of York, United Kingdom

Publication history

  1. Received: March 14, 2020
  2. Accepted: September 5, 2020
  3. Accepted Manuscript published: September 7, 2020 (version 1)
  4. Version of Record published: September 21, 2020 (version 2)

Copyright

© 2020, Zhou 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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