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).

Figure 1

Download asset Open asset

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).

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.

The functional MRI, numerical data as well as the Matlab scripts used to generate the figures have been deposited on the figshare repository under accession code 11994498 (https://figshare.com/articles/Vicarious_pain_dataset/11994498) Statistical and pattern weight maps are available on the Neurovault repository under collection 6332 (https://neurovault.org/collections/6332/). Statistical and pattern weight images are available on Neurovault.

1. 1. Zhou F
   2. Li J
   3. Zhao W
   4. Xu L
   5. Zheng X
   6. Fu M
   7. Yao S
   8. Kendrick KM
   9. Wager TD
   10. Becker B

   (2020) figshare

   Vicarious pain dataset.

   https://doi.org/10.6084/m9.figshare.11994498
2. 1. Zhou F
   2. Li J
   3. Zhao W
   4. Xu L
   5. Zheng X
   6. Fu M
   7. Yao S
   8. Kendrick KM
   9. Wager TD
   10. Becker B

   (2020) NeuroVault

   ID 6332. Emotional contagion of pain across different social cues shares common and process-specific neural representations.

   https://neurovault.org/collections/6332/

1. 1. Afif A
   2. Minotti L
   3. Kahane P
   4. Hoffmann D

   (2010) Anatomofunctional organization of the insular cortex: a study using intracerebral electrical stimulation in epileptic patients

   Epilepsia 51:2305–2315.

   https://doi.org/10.1111/j.1528-1167.2010.02755.x

   • PubMed
   • Google Scholar
2. 1. Allman JM
   2. Tetreault NA
   3. Hakeem AY
   4. Manaye KF
   5. Semendeferi K
   6. Erwin JM
   7. Park S
   8. Goubert V
   9. Hof PR

   (2011) The Von Economo neurons in the frontoinsular and anterior cingulate cortex

   Annals of the New York Academy of Sciences 1225:59–71.

   https://doi.org/10.1111/j.1749-6632.2011.06011.x

   • PubMed
   • Google Scholar
3. 1. Amft M
   2. Bzdok D
   3. Laird AR
   4. Fox PT
   5. Schilbach L
   6. Eickhoff SB

   (2015) Definition and characterization of an extended social-affective default network

   Brain Structure and Function 220:1031–1049.

   https://doi.org/10.1007/s00429-013-0698-0

   • PubMed
   • Google Scholar
4. 1. Botvinick M
   2. Jha AP
   3. Bylsma LM
   4. Fabian SA
   5. Solomon PE
   6. Prkachin KM

   (2005) Viewing facial expressions of pain engages cortical Areas involved in the direct experience of pain

   NeuroImage 25:312–319.

   https://doi.org/10.1016/j.neuroimage.2004.11.043

   • PubMed
   • Google Scholar
5. Software

   1. Canlab

   (2020) Cognitive and Affective Neuroscience Laboratory - core tools

   GitHub.

   https://github.com/canlab/CanlabCore
6. 1. Carrillo M
   2. Han Y
   3. Migliorati F
   4. Liu M
   5. Gazzola V
   6. Keysers C

   (2019) Emotional mirror neurons in the rat's Anterior Cingulate Cortex

   Current Biology 29:1301–1312.

   https://doi.org/10.1016/j.cub.2019.03.024

   • PubMed
   • Google Scholar
7. 1. Cauda F
   2. Costa T
   3. Torta DM
   4. Sacco K
   5. D'Agata F
   6. Duca S
   7. Geminiani G
   8. Fox PT
   9. Vercelli A

   (2012) Meta-analytic clustering of the insular cortex: characterizing the meta-analytic connectivity of the insula when involved in active tasks

   NeuroImage 62:343–355.

   https://doi.org/10.1016/j.neuroimage.2012.04.012

   • PubMed
   • Google Scholar
8. 1. Chang LJ
   2. Gianaros PJ
   3. Manuck SB
   4. Krishnan A
   5. Wager TD

   (2015) A sensitive and specific neural signature for Picture-Induced negative affect

   PLOS Biology 13:e1002180.

   https://doi.org/10.1371/journal.pbio.1002180

   • PubMed
   • Google Scholar
9. 1. Chen LM

   (2018) Cortical representation of pain and touch: evidence from combined functional neuroimaging and electrophysiology in Non-human primates

   Neuroscience Bulletin 34:165–177.

   https://doi.org/10.1007/s12264-017-0133-2

   • PubMed
   • Google Scholar
10. 1. Chikazoe J
    2. Lee DH
    3. Kriegeskorte N
    4. Anderson AK

    (2014) Population coding of affect across stimuli, modalities and individuals

    Nature Neuroscience 17:1114–1122.

    https://doi.org/10.1038/nn.3749

    • PubMed
    • Google Scholar
11. 1. Corradi-Dell'Acqua C
    2. Hofstetter C
    3. Vuilleumier P

    (2011) Felt and seen pain evoke the same local patterns of cortical activity in insular and cingulate cortex

    Journal of Neuroscience 31:17996–18006.

    https://doi.org/10.1523/JNEUROSCI.2686-11.2011

    • PubMed
    • Google Scholar
12. 1. Corradi-Dell'Acqua C
    2. Tusche A
    3. Vuilleumier P
    4. Singer T

    (2016) Cross-modal representations of first-hand and vicarious pain, disgust and fairness in insular and cingulate cortex

    Nature Communications 7:10904.

    https://doi.org/10.1038/ncomms10904

    • PubMed
    • Google Scholar
13. 1. Craig AD
    2. Bushnell MC
    3. Zhang ET
    4. Blomqvist A

    (1994) A thalamic nucleus specific for pain and temperature sensation

    Nature 372:770–773.

    https://doi.org/10.1038/372770a0

    • PubMed
    • Google Scholar
14. 1. Craig AD
    2. Chen K
    3. Bandy D
    4. Reiman EM

    (2000) Thermosensory activation of insular cortex

    Nature Neuroscience 3:184–190.

    https://doi.org/10.1038/72131

    • PubMed
    • Google Scholar
15. 1. Craig AD

    (2009) How do you feel--now? the anterior insula and human awareness

    Nature Reviews Neuroscience 10:59–70.

    https://doi.org/10.1038/nrn2555

    • PubMed
    • Google Scholar
16. 1. Critchley HD
    2. Wiens S
    3. Rotshtein P
    4. Ohman A
    5. Dolan RJ

    (2004) Neural systems supporting interoceptive awareness

    Nature Neuroscience 7:189–195.

    https://doi.org/10.1038/nn1176

    • PubMed
    • Google Scholar
17. Book

    1. Decety JE
    2. Ickes WE

    (2009) The Social Neuroscience of Empathy

    MIT Press.

    https://doi.org/10.5167/uzh-25655

    • Google Scholar
18. 1. Etzel JA
    2. Zacks JM
    3. Braver TS

    (2013) Searchlight analysis: promise, pitfalls, and potential

    NeuroImage 78:261–269.

    https://doi.org/10.1016/j.neuroimage.2013.03.041

    • PubMed
    • Google Scholar
19. 1. Fan Y
    2. Duncan NW
    3. de Greck M
    4. Northoff G

    (2011) Is there a core neural network in empathy? an fMRI based quantitative meta-analysis

    Neuroscience & Biobehavioral Reviews 35:903–911.

    https://doi.org/10.1016/j.neubiorev.2010.10.009

    • PubMed
    • Google Scholar
20. 1. Gallo S
    2. Paracampo R
    3. Müller-Pinzler L
    4. Severo MC
    5. Blömer L
    6. Fernandes-Henriques C
    7. Henschel A
    8. Lammes BK
    9. Maskaljunas T
    10. Suttrup J
    11. Avenanti A
    12. Keysers C
    13. Gazzola V

    (2018) The causal role of the somatosensory cortex in prosocial behaviour

    eLife 7:e32740.

    https://doi.org/10.7554/eLife.32740

    • PubMed
    • Google Scholar
21. 1. Glasser MF
    2. Coalson TS
    3. Robinson EC
    4. Hacker CD
    5. Harwell J
    6. Yacoub E
    7. Ugurbil K
    8. Andersson J
    9. Beckmann CF
    10. Jenkinson M
    11. Smith SM
    12. Van Essen DC

    (2016) A multi-modal parcellation of human cerebral cortex

    Nature 536:171–178.

    https://doi.org/10.1038/nature18933

    • PubMed
    • Google Scholar
22. 1. Hadjistavropoulos T
    2. Craig KD
    3. Duck S
    4. Cano A
    5. Goubert L
    6. Jackson PL
    7. Mogil JS
    8. Rainville P
    9. Sullivan MJL
    10. Williams ACC
    11. Vervoort T
    12. Fitzgerald TD

    (2011) A biopsychosocial formulation of pain communication

    Psychological Bulletin 137:910–939.

    https://doi.org/10.1037/a0023876

    • PubMed
    • Google Scholar
23. 1. Haxby JV
    2. Connolly AC
    3. Guntupalli JS

    (2014) Decoding neural representational spaces using multivariate pattern analysis

    Annual Review of Neuroscience 37:435–456.

    https://doi.org/10.1146/annurev-neuro-062012-170325

    • PubMed
    • Google Scholar
24. 1. Haynes JD

    (2015) A primer on Pattern-Based approaches to fMRI: principles, pitfalls, and perspectives

    Neuron 87:257–270.

    https://doi.org/10.1016/j.neuron.2015.05.025

    • PubMed
    • Google Scholar
25. 1. Hong YW
    2. Yoo Y
    3. Han J
    4. Wager TD
    5. Woo CW

    (2019) False-positive neuroimaging: undisclosed flexibility in testing spatial hypotheses allows presenting anything as a replicated finding

    NeuroImage 195:384–395.

    https://doi.org/10.1016/j.neuroimage.2019.03.070

    • PubMed
    • Google Scholar
26. Book

    1. Hsu C-W
    2. Chang C-C
    3. Lin C-J

    (2003)

    A Practical Guide to Support Vector Classification

    National Taiwan University.

    • Google Scholar
27. 1. Iannetti GD
    2. Salomons TV
    3. Moayedi M
    4. Mouraux A
    5. Davis KD

    (2013) Beyond metaphor: contrasting mechanisms of social and physical pain

    Trends in Cognitive Sciences 17:371–378.

    https://doi.org/10.1016/j.tics.2013.06.002

    • Google Scholar
28. 1. Jauniaux J
    2. Khatibi A
    3. Rainville P
    4. Jackson PL

    (2019) A meta-analysis of neuroimaging studies on pain empathy: investigating the role of visual information and observers’ perspective

    Social Cognitive and Affective Neuroscience 14:789–813.

    https://doi.org/10.1093/scan/nsz055

    • Google Scholar
29. 1. Kamitani Y
    2. Tong F

    (2005) Decoding the visual and subjective contents of the human brain

    Nature Neuroscience 8:679–685.

    https://doi.org/10.1038/nn1444

    • PubMed
    • Google Scholar
30. 1. Koban L
    2. Jepma M
    3. López-Solà M
    4. Wager TD

    (2019) Different brain networks mediate the effects of social and conditioned expectations on pain

    Nature Communications 10:1–13.

    https://doi.org/10.1038/s41467-019-11934-y

    • Google Scholar
31. 1. Kragel PA
    2. Koban L
    3. Barrett LF
    4. Wager TD

    (2018) Representation, pattern information, and brain signatures: from neurons to neuroimaging

    Neuron 99:257–273.

    https://doi.org/10.1016/j.neuron.2018.06.009

    • PubMed
    • Google Scholar
32. 1. Kriegeskorte N
    2. Goebel R
    3. Bandettini P

    (2006) Information-based functional brain mapping

    PNAS 103:3863–3868.

    https://doi.org/10.1073/pnas.0600244103

    • PubMed
    • Google Scholar
33. 1. Krishnan A
    2. Woo CW
    3. Chang LJ
    4. Ruzic L
    5. Gu X
    6. López-Solà M
    7. Jackson PL
    8. Pujol J
    9. Fan J
    10. Wager TD

    (2016) Somatic and vicarious pain are represented by dissociable multivariate brain patterns

    eLife 5:e15166.

    https://doi.org/10.7554/eLife.15166

    • PubMed
    • Google Scholar
34. 1. Kvitsiani D
    2. Ranade S
    3. Hangya B
    4. Taniguchi H
    5. Huang JZ
    6. Kepecs A

    (2013) Distinct behavioural and network correlates of two interneuron types in prefrontal cortex

    Nature 498:363–366.

    https://doi.org/10.1038/nature12176

    • PubMed
    • Google Scholar
35. 1. Lamm C
    2. Decety J
    3. Singer T

    (2011) Meta-analytic evidence for common and distinct neural networks associated with directly experienced pain and empathy for pain

    NeuroImage 54:2492–2502.

    https://doi.org/10.1016/j.neuroimage.2010.10.014

    • PubMed
    • Google Scholar
36. 1. Leigh R
    2. Oishi K
    3. Hsu J
    4. Lindquist M
    5. Gottesman RF
    6. Jarso S
    7. Crainiceanu C
    8. Mori S
    9. Hillis AE

    (2013) Acute lesions that impair affective empathy

    Brain 136:2539–2549.

    https://doi.org/10.1093/brain/awt177

    • PubMed
    • Google Scholar
37. Preprint

    1. Li J
    2. Xu L
    3. Zheng X
    4. Fu M
    5. Zhou F
    6. Xu X
    7. Ma X
    8. Li K
    9. Keith K
    10. Becker B

    (2018) Common and dissociable contributions of alexithymia and autism to domain-specific interoceptive dysregulations-a dimensional neuroimaging approach

    bioRxiv.

    https://doi.org/10.1101/432971

    • Google Scholar
38. 1. Meng J
    2. Hu L
    3. Shen L
    4. Yang Z
    5. Chen H
    6. Huang X
    7. Jackson T

    (2012) Emotional primes modulate the responses to others' pain: an ERP study

    Experimental Brain Research 220:277–286.

    https://doi.org/10.1007/s00221-012-3136-2

    • PubMed
    • Google Scholar
39. 1. Mitchell JP

    (2009) Inferences about mental states

    Philosophical Transactions of the Royal Society B: Biological Sciences 364:1309–1316.

    https://doi.org/10.1098/rstb.2008.0318

    • Google Scholar
40. 1. Northoff G
    2. Heinzel A
    3. de Greck M
    4. Bermpohl F
    5. Dobrowolny H
    6. Panksepp J

    (2006) Self-referential processing in our brain--a meta-analysis of imaging studies on the self

    NeuroImage 31:440–457.

    https://doi.org/10.1016/j.neuroimage.2005.12.002

    • PubMed
    • Google Scholar
41. 1. Op de Beeck HP

    (2010) Against Hyperacuity in brain reading: spatial smoothing does not hurt multivariate fMRI analyses?

    NeuroImage 49:1943–1948.

    https://doi.org/10.1016/j.neuroimage.2009.02.047

    • PubMed
    • Google Scholar
42. 1. Peelen MV
    2. Wiggett AJ
    3. Downing PE

    (2006) Patterns of fMRI activity dissociate overlapping functional brain Areas that respond to biological motion

    Neuron 49:815–822.

    https://doi.org/10.1016/j.neuron.2006.02.004

    • PubMed
    • Google Scholar
43. 1. Peelen MV
    2. Downing PE

    (2007) Using multi-voxel pattern analysis of fMRI data to interpret overlapping functional activations

    Trends in Cognitive Sciences 11:4–5.

    https://doi.org/10.1016/j.tics.2006.10.009

    • PubMed
    • Google Scholar
44. 1. Rizzolatti G
    2. Craighero L

    (2004) The mirror-neuron system

    Annual Review of Neuroscience 27:169–192.

    https://doi.org/10.1146/annurev.neuro.27.070203.144230

    • PubMed
    • Google Scholar
45. 1. Rütgen M
    2. Seidel EM
    3. Silani G
    4. Riečanský I
    5. Hummer A
    6. Windischberger C
    7. Petrovic P
    8. Lamm C

    (2015) Placebo analgesia and its opioidergic regulation suggest that empathy for pain is grounded in self pain

    PNAS 112:E5638–E5646.

    https://doi.org/10.1073/pnas.1511269112

    • PubMed
    • Google Scholar
46. 1. Sakaguchi T
    2. Iwasaki S
    3. Okada M
    4. Okamoto K
    5. Ikegaya Y

    (2018) Ethanol facilitates socially evoked memory recall in mice by recruiting pain-sensitive anterior cingulate cortical neurons

    Nature Communications 9:3526.

    https://doi.org/10.1038/s41467-018-05894-y

    • PubMed
    • Google Scholar
47. 1. Shackman AJ
    2. Salomons TV
    3. Slagter HA
    4. Fox AS
    5. Winter JJ
    6. Davidson RJ

    (2011) The integration of negative affect, pain and cognitive control in the cingulate cortex

    Nature Reviews Neuroscience 12:154–167.

    https://doi.org/10.1038/nrn2994

    • PubMed
    • Google Scholar
48. 1. Shamay-Tsoory SG
    2. Aharon-Peretz J
    3. Perry D

    (2009) Two systems for empathy: a double dissociation between emotional and cognitive empathy in inferior frontal gyrus versus ventromedial prefrontal lesions

    Brain 132:617–627.

    https://doi.org/10.1093/brain/awn279

    • PubMed
    • Google Scholar
49. 1. Sheng F
    2. Han S

    (2012) Manipulations of cognitive strategies and intergroup relationships reduce the racial bias in empathic neural responses

    NeuroImage 61:786–797.

    https://doi.org/10.1016/j.neuroimage.2012.04.028

    • Google Scholar
50. 1. Shidara M
    2. Richmond BJ

    (2002) Anterior cingulate: single neuronal signals related to degree of reward expectancy

    Science 296:1709–1711.

    https://doi.org/10.1126/science.1069504

    • PubMed
    • Google Scholar
51. 1. Shmuel A
    2. Chaimow D
    3. Raddatz G
    4. Ugurbil K
    5. Yacoub E

    (2010) Mechanisms underlying decoding at 7 T: ocular dominance columns, broad structures, and macroscopic blood vessels in V1 convey information on the stimulated eye

    NeuroImage 49:1957–1964.

    https://doi.org/10.1016/j.neuroimage.2009.08.040

    • PubMed
    • Google Scholar
52. 1. Shura RD
    2. Hurley RA
    3. Taber KH

    (2014) Insular cortex: structural and functional neuroanatomy

    The Journal of Neuropsychiatry and Clinical Neurosciences 26:282.

    https://doi.org/10.1176/appi.neuropsych.260401

    • Google Scholar
53. 1. Sikes RW
    2. Vogt BA

    (1992) Nociceptive neurons in area 24 of rabbit cingulate cortex

    Journal of Neurophysiology 68:1720–1732.

    https://doi.org/10.1152/jn.1992.68.5.1720

    • PubMed
    • Google Scholar
54. 1. Singer T
    2. Critchley HD
    3. Preuschoff K

    (2009) A common role of insula in feelings, empathy and uncertainty

    Trends in Cognitive Sciences 13:334–340.

    https://doi.org/10.1016/j.tics.2009.05.001

    • PubMed
    • Google Scholar
55. 1. Timmers I
    2. Park AL
    3. Fischer MD
    4. Kronman CA
    5. Heathcote LC
    6. Hernandez JM
    7. Simons LE

    (2018) Is empathy for pain unique in its neural correlates? A Meta-Analysis of neuroimaging studies of empathy

    Frontiers in Behavioral Neuroscience 12:289.

    https://doi.org/10.3389/fnbeh.2018.00289

    • PubMed
    • Google Scholar
56. 1. Uddin LQ

    (2015) Salience processing and insular cortical function and dysfunction

    Nature Reviews Neuroscience 16:55–61.

    https://doi.org/10.1038/nrn3857

    • PubMed
    • Google Scholar
57. 1. Urien L
    2. Wang J

    (2019) Top-Down cortical control of acute and chronic pain

    Psychosomatic Medicine 81:851–858.

    https://doi.org/10.1097/PSY.0000000000000744

    • PubMed
    • Google Scholar
58. 1. Vachon-Presseau E
    2. Roy M
    3. Martel MO
    4. Albouy G
    5. Chen J
    6. Budell L
    7. Sullivan MJ
    8. Jackson PL
    9. Rainville P

    (2012) Neural processing of sensory and emotional-communicative information associated with the perception of vicarious pain

    NeuroImage 63:54–62.

    https://doi.org/10.1016/j.neuroimage.2012.06.030

    • PubMed
    • Google Scholar
59. 1. Valentini E
    2. Koch K

    (2012) Fine-grained analysis of shared neural circuits between perceived and observed pain: implications for the study of empathy for pain

    Journal of Neurophysiology 108:1805–1807.

    https://doi.org/10.1152/jn.00181.2012

    • PubMed
    • Google Scholar
60. 1. Varoquaux G

    (2018) Cross-validation failure: small sample sizes lead to large error bars

    NeuroImage 180:68–77.

    https://doi.org/10.1016/j.neuroimage.2017.06.061

    • PubMed
    • Google Scholar
61. 1. Wager TD
    2. Atlas LY
    3. Lindquist MA
    4. Roy M
    5. Woo CW
    6. Kross E

    (2013) An fMRI-based neurologic signature of physical pain

    New England Journal of Medicine 368:1388–1397.

    https://doi.org/10.1056/NEJMoa1204471

    • PubMed
    • Google Scholar
62. 1. Wager TD
    2. Atlas LY
    3. Botvinick MM
    4. Chang LJ
    5. Coghill RC
    6. Davis KD
    7. Iannetti GD
    8. Poldrack RA
    9. Shackman AJ
    10. Yarkoni T

    (2016) Pain in the ACC?

    PNAS 113:E2474–E2475.

    https://doi.org/10.1073/pnas.1600282113

    • PubMed
    • Google Scholar
63. 1. Woo CW
    2. Koban L
    3. Kross E
    4. Lindquist MA
    5. Banich MT
    6. Ruzic L
    7. Andrews-Hanna JR
    8. Wager TD

    (2014) Separate neural representations for physical pain and social rejection

    Nature Communications 5:5380.

    https://doi.org/10.1038/ncomms6380

    • PubMed
    • Google Scholar
64. 1. Woo CW
    2. Roy M
    3. Buhle JT
    4. Wager TD

    (2015) Distinct brain systems mediate the effects of nociceptive input and self-regulation on pain

    PLOS Biology 13:e1002036.

    https://doi.org/10.1371/journal.pbio.1002036

    • PubMed
    • Google Scholar
65. 1. Woo CW
    2. Chang LJ
    3. Lindquist MA
    4. Wager TD

    (2017) Building better biomarkers: brain models in translational neuroimaging

    Nature Neuroscience 20:365–377.

    https://doi.org/10.1038/nn.4478

    • PubMed
    • Google Scholar
66. 1. Xu L
    2. Bolt T
    3. Nomi JS
    4. Li J
    5. Zheng X
    6. Fu M
    7. Kendrick KM
    8. Becker B
    9. Uddin LQ

    (2020a) Inter-subject phase synchronization differentiates neural networks underlying physical pain empathy

    Social Cognitive and Affective Neuroscience 15:225–233.

    https://doi.org/10.1093/scan/nsaa025

    • PubMed
    • Google Scholar
67. 1. Xu X
    2. Dai J
    3. Liu C
    4. Chen Y
    5. Xin F
    6. Zhou F
    7. Zhou X
    8. Huang Y
    9. Wang J
    10. Zou Z
    11. Li J
    12. Ebstein RP
    13. Kendrick KM
    14. Zhou B
    15. Becker B

    (2020b) Common and Disorder-Specific neurofunctional markers of dysregulated empathic reactivity in major depression and generalized anxiety disorder

    Psychotherapy and Psychosomatics 89:114–116.

    https://doi.org/10.1159/000504180

    • PubMed
    • Google Scholar
68. 1. Yarkoni T
    2. Poldrack RA
    3. Nichols TE
    4. Van Essen DC
    5. Wager TD

    (2011) Large-scale automated synthesis of human functional neuroimaging data

    Nature Methods 8:665–670.

    https://doi.org/10.1038/nmeth.1635

    • PubMed
    • Google Scholar
69. 1. Yesudas EH
    2. Lee TMC

    (2015) The role of cingulate cortex in vicarious pain

    BioMed Research International 2015:1–10.

    https://doi.org/10.1155/2015/719615

    • Google Scholar
70. Preprint

    1. Yu H
    2. Koban L
    3. Chang LJ
    4. Wagner U
    5. Krishnan A
    6. Vuilleumier P
    7. Zhou X
    8. Wager TD

    (2019) A generalizable multivariate brain pattern for interpersonal guilt

    bioRxiv.

    https://doi.org/10.1101/835520

    • Google Scholar
71. 1. Zaki J
    2. Wager TD
    3. Singer T
    4. Keysers C
    5. Gazzola V

    (2016) The anatomy of suffering: understanding the relationship between nociceptive and empathic pain

    Trends in Cognitive Sciences 20:249–259.

    https://doi.org/10.1016/j.tics.2016.02.003

    • PubMed
    • Google Scholar
72. 1. Zhou F
    2. Zimmermann K
    3. Xin F
    4. Scheele D
    5. Dau W
    6. Banger M
    7. Weber B
    8. Hurlemann R
    9. Kendrick KM
    10. Becker B

    (2018) Shifted balance of dorsal versus ventral striatal communication with frontal reward and regulatory regions in cannabis-dependent males

    Human Brain Mapping 39:5062–5073.

    https://doi.org/10.1002/hbm.24345

    • PubMed
    • Google Scholar
73. 1. Zhou F
    2. Geng Y
    3. Xin F
    4. Li J
    5. Feng P
    6. Liu C
    7. Zhao W
    8. Feng T
    9. Guastella AJ
    10. Ebstein RP
    11. Kendrick KM
    12. Becker B

    (2019) Human extinction learning is accelerated by an angiotensin antagonist via ventromedial prefrontal cortex and its connections with basolateral amygdala

    Biological Psychiatry 86:910–920.

    https://doi.org/10.1016/j.biopsych.2019.07.007

    • PubMed
    • Google Scholar

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

   "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

   "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

    "This ORCID iD identifies the author of this article:" 0000-0002-9014-9671

• 5,819

  views

• 476

  downloads

• 84

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.