Sharing the distress of others is central to empathy. fMRI studies show that a number of brain regions involved in the direct experience of pain also increase their activity while participants perceive the pain of others, including the cingulate cortex, the insula, and the somatosensory cortices (Jauniaux et al., 2019; Keysers et al., 2010; Lamm et al., 2011; Timmers et al., 2018). Across humans, primates, and rodents, lesions in these regions impair the perception and the sharing of others’ emotions (Paradiso et al., 2021), providing evidence for their causal contribution to the perception or sharing of the emotions of others. A number of recent studies have used multivoxel pattern analysis to explore how these regions encode the pain of others using fMRI signals, with particular attention to the insula. Krishnan et al., 2016 showed participants’ images of hands or feet in painful or innocuous situations and found a pattern across voxels in the insula that could predict how much pain people reported they would feel in the depicted situations. Corradi-Dell’Acqua et al., 2016 also reported that the pattern of insula activity could discriminate between trials in which a cue signaled that someone else was receiving a shock from nonshock trials. Finally, Zhou et al., 2020 reanalyzed a dataset in which participants viewed photographs of hands in painful or nonpainful situations, or of painful and neutral facial expressions. They found that, in the insula, similar but dissociable patterns supported painfulness decoding for hands and faces: similar in that a pattern trained to discriminate painfulness from faces could do so from hands and vice versa, with the rostral insula contributing to both patterns; but dissociable in that many voxels contributed only to decoding of either faces or hands.

Directly recording electrical signals from these regions in humans would complement these more indirect fMRI measurements and sharpen our understanding of how these regions represent the intensity of other people’s pain for at least two reasons. First, fMRI records a mixed signal that includes synaptic input and local neural processing. Localizing blood-oxygen-level-dependent (BOLD) activity that encodes a particular stimulus property thus cannot ensure that neurons in that region actually have spiking activity that encodes that property (Boynton, 2011). For instance, BOLD signals in V1 fluctuate based on whether a stimulus is perceived or not in binocular rivalry (Boynton, 2011; Maier et al., 2008). In contrast, simultaneous electrical recordings in V1 show that broadband gamma activity, which is tightly coupled to spiking (Bartoli et al., 2019; Buzsáki et al., 2012; Miller et al., 2014), responds to a stimulus equally well whether it is perceived or suppressed. Only the slower components, <20 Hz, that are known to carry feedback synaptic input fluctuate with perception (Maier et al., 2008). Being able to record electrical activity, particularly in the broadband gamma range, would thus be critical to localize where in this circuitry neuronal spiking indeed represents the pain of others. Second, fMRI’s low temporal resolution makes it difficult to characterize the time course of responses.

For the anterior cingulate, we have intracranial recordings: Hutchison et al., 1999 documented a single neuron in epileptic patients that responded to the sight of a finger being pin-pricked with increased firing rate, and a recent rodent study revealed that cingulate neurons responding to pain experience have responses that increase with the intensity of the pain experienced by another rat (Carrillo et al., 2019). In contrast, although the insula is central in the neuroimaging literature on empathy, and shows increases of BOLD signal for watching painful compared to nonpainful social stimuli (Jabbi et al., 2007; Jauniaux et al., 2019; Lamm et al., 2011; Meffert et al., 2013; Singer et al., 2004; Timmers et al., 2018; Wicker et al., 2003), and shows patterns that encode painfulness (Corradi-Dell’Acqua et al., 2016; Krishnan et al., 2016; Zhou et al., 2020), we still lack insular intracranial recordings while individuals witness the pain of others. Intracranial electroencephalography (iEEG) has been recorded in the insula during the self-experience of pain (Bastuji et al., 2018; Bastuji et al., 2016; Liberati et al., 2020), and the insula and adjacent SII are the only cortical regions where iEEG electrode stimulation can induce painful sensations (Jobst et al., 2019; Mazzola et al., 2012), but to our knowledge there are no published studies recording from insular electrodes while patients witness the pain of others. The degree to which neuronal activity local to the insula, as opposed to feedback synaptic input from other regions such as the cingulate, encodes the intensity of other people’s pain therefore remains unclear and the time course of such neural activity remains undercharacterized.

To fill this gap and characterize the electrophysiological responses of the insula to the pain of others, we collected depth electrode recordings from seven epileptic patients during presurgical exploration, while they rated the different intensities of pain they perceived in another person in a video (Figure 1a and b). All these patients had macroelectrodes in their insulae that yielded local field potentials (LFPs) capable of measuring broadband gamma activity (circles in Figure 1c). Three patients, additionally, had microelectrodes at the tip of some macroelectrodes to record from isolated insular neurons (pluses in Figure 1c). Our stimuli also included two ways in which pain is perceived in others (Figure 1a). Half the stimuli (Faces) showed a female receiving electroshocks on the hand and expressing pain through facial expressions (furrowing eyebrows and tightening eyes). The other half (Hands) showed the protagonist’s hand slapped by a leather belt, and pain intensity had to be deduced from the movements of the belt and the hand. In both cases, movies, rather than static images, were chosen to provide richer and more ecological stimuli and provide information about the temporal dynamics with which such movies are represented in a field still dominated by the presentation of static images (Adolphs et al., 2003; Zinchenko et al., 2018). We used these two classes of stimuli because both tap into the visual perception of other people’s pain, and we start to understand that they do so through partially overlapping and partially dissociable routes (Jauniaux et al., 2019; Keysers et al., 2010; Timmers et al., 2018). For instance, the Hand stimuli depend on the hand region of the somatosensory cortex (Gallo et al., 2018), while facial expressions depend on both the ventral somatosensory cortex and the insula (Adolphs et al., 2000; Dal Monte et al., 2013; Mattavelli et al., 2019), whereby the insula appears to show partially overlapping and partially discriminable patterns encoding painfulness for these two types of stimuli (Zhou et al., 2020).

Figure 1

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Figure 1—source data 1

Mean MNI coordinates of recording sites.

In black, the MNI coordinates resulting from the average MNI coordinates of the two adjacent electrodes used for re-referencing for all macroelectrodes. In gray, the MNI coordinates of the microelectrodes. The color coding and patient identifiers reflect those used in Figure 1c.

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Our aim is to localize insular recording sites that do or do not encode the perceived intensity of others’ experiences from Face and/or Hand stimuli and to exploit the temporal resolution of iEEG to explore the timing of intensity coding and how it relates to the timing of intensity-relevant stimulus features. To provide evidence that a specific recording site does not encode intensity for a stimulus type, we supplement frequentist statistics with Bayesian statistics, which generate a Bayes factor (BF₁₀; Keysers et al., 2020). The Bayes factor indicates the probability of the data if there is intensity encoding (H₁) divided by that if there isn’t (H₀). A BF₁₀ < ⅓, which indicates the data is at least three times more likely under H₀ than H₁, is considered evidence for the absence of intensity coding. Importantly, in Bayesian statistics, whenever possible, it is advised to commit to a directional hypothesis (H+) that endows the BF₊₀ with higher sensitivity to detect evidence for both H+ and H₀ (Keysers et al., 2020; Wagenmakers et al., 2016). Given that fMRI experiments overwhelmingly report voxels with increases of BOLD signals for pain compared to no-pain stimuli, we therefore commit ourselves to a directional hypothesis and look for sites and neurons that increase their broadband activity or firing rate for stimuli that are perceived as more painful.

Here we characterize how the insula’s iEEG activity encodes the intensity of other people’s emotions using pain as an important category. LFPs indicate that neural activity in the insula within a broad range of frequencies, including the conventional theta, beta, and gamma frequency bands, scales with the perceived intensity of pain expressed by others. Interestingly, the insula only appeared to be recruited once the perceived pain level was at least moderate: activity was increased for moderate (5–6) compared to mild (3–4) and for severe (7–8) compared to moderate. However, activity for mild pain (3–4) was not significantly increased compared to minimal pain (1–2), or baseline activity. This echoes a recent finding that BBP activity in the insula is selectively increased only once thermal stimulation is consistently painful (Liberati et al., 2020). Furthermore, we isolate a small number of insular neurons increasing their firing with increases in the intensity of pain experienced by others.

As mentioned in the section ‘Introduction,’ BOLD signals can dissociate from neural spiking (Boynton, 2011; Maier et al., 2008). Just as V1 BOLD signals fluctuate with perception while spiking in V1 does not (Maier et al., 2008), the observation that BOLD signals in the insula fluctuate with perceived pain intensity alone cannot guarantee that neuronal spiking in the insula does. The insula’s BOLD signal could instead fluctuate with perceived intensity simply as a result of feedback synaptic input from other brain regions that encode perceived intensity (e.g., area 24 in the cingulate gyrus; Carrillo et al., 2019). The foremost impact of our broadband gamma and spiking data is thus to provide what is arguably the first evidence that the intensity of other people’s pain is indeed locally encoded in the activity of neurons in the insula.

The human insula has been in the focus of pain neuroscience as part of the pain matrix recruited by first-hand experience of pain (Ingvar, 1999). In this tradition, neuroimaging evidence for activation of the insula while witnessing pain experienced by others has led many to suggest it may represent a neural basis for empathy for pain (Bernhardt and Singer, 2012; Jauniaux et al., 2019; Lamm et al., 2011; Timmers et al., 2018). However, the insula is also recruited by a variety of tasks beyond nociception and pain empathy, including other affective states, sensorimotor functions, and decision-making under uncertainty (Craig, 2002; Craig, 2009; Uddin, 2015; Uddin et al., 2017). With so many tasks recruiting the insula, the idea has become popular that it may play a key role in attaching salience to behaviorally important stimuli (Legrain et al., 2011; Uddin, 2015; Uddin et al., 2017; Zaki et al., 2016). In this study, we do not intend to (and cannot) address the selectivity of the insula for the pain of others over other emotions, and we do not claim the neural responses we report are pain-specific. Instead, we characterize how the insula’s iEEG activity encodes the intensity of other people’s emotions, using pain as an important category, and use the agnostic terminology of ‘intensity coding’ rather than ‘pain intensity coding’ throughout the article to refer to recording sites. That the insula’s broadband activity correlated with motion energy in our Hand stimuli only while the motion was associated with pain (during the slapping), but not while motion was innocuous (during the initial belt lifting), shows that the effects we measured cannot be reduced to visual motion detection. That perceived intensity was mediating the association between motion and broadband activity further speaks against an interpretation of our results as reflecting unspecific motion processing. Future experiments using a wider gamut of control stimuli that are matched in motion but differ in emotions will be critical to address the specificity of the responses we describe, and hence, what state they could reliably signal (Zaki et al., 2016). For the Hand stimuli, this could include seeing hands experience a range of different salient affective experiences, such as a hand being hurt, being caressed, and being rejected in addition to a neutral handshake (Meffert et al., 2013), or the actor in our Hand movies could have been asked to reposition their hand after each slap. For the face, this could include disgusted, angry, fearful, and happy facial expressions matched for motion. In general, using multiple actors and painful hand interaction could help characterize how intensity coding in the insula is influenced by details of the stimuli that convey it, including the identity and gender of the actor, and how well our results generalize to other stimulus sets (Yarkoni, 2020).

An important and somewhat related question has been whether pain or salience cues are represented in a modality-specific or modality-general manner in the insula. fMRI studies have shown that the anterior insula is coactivated with different brain regions depending on whether the pain of others is conveyed via indirect cues or via the actual body part, such as the hand, that directly receives the noxious stimulation (Gallo et al., 2018; Jauniaux et al., 2019; Keysers et al., 2010; Lamm et al., 2011; Timmers et al., 2018). Here, we provide electrophysiological measures of neural activity that speak to that issue. We focus on the broadband gamma signal known to have comparatively high spatial specificity and be closest to neural spiking (Bartoli et al., 2019; Buzsáki et al., 2012; Miller et al., 2014), and find a mixed organization, consisting of modality-specific and -general locations in a partially intermixed layout. That is, we found locations with broadband activity and spiking associated with perceived intensity for the Hand, but not the Face; others associated with the Face, but not the Hand; and others still associated with perceived intensity for both. This echoes findings in recent fMRI studies that show that at the level of BOLD response within the insula some voxels encode intensity for both Hand and Face stimuli, while some only significantly contribute to intensity coding for one stimulus type (Zhou et al., 2020).

Leveraging our high temporal and spatial resolution, we found that locations showing intensity coding for the Hand stimuli have activity timing that echoes that of pain-related motion cues with very short lags (<80 ms). These optimal lags seem at odds with the much longer response onset latencies reported in the literature (Bastuji et al., 2018; Bastuji et al., 2016; Chen et al., 2009; Cornwell et al., 2008; Krolak-Salmon et al., 2003; Liberati et al., 2020; Liberati et al., 2016), but differ from response onset latencies in two ways. First, our BBP is calculated within time windows of 8× the inverse of the frequency. Hence, for the lowest broadband frequency of 20 Hz, power at time t includes signals that occur in a window starting 4 × 1/20 = 200 ms earlier and ending 200 ms later. The peak of BBP would thus still align with the peak of neural activity, but the onset of our BBP estimate may underestimate the onset of neural activity by up to 200 ms. Second, Figure 3i illustrates that our short lags do not reflect a mysterious lack of delay between the onset of motion information and the onset of its neural encoding, but rather, the close alignment of their peaks. The onset of motion information (~920 ms) actually precedes the peak neural intensity encoding (~1300 ms) by almost 400 ms, in line with typical response latencies. The close alignment of stimulus information and neural encoding peaks rather reflects a property of naturalistic dynamic stimuli in which information typically builds up progressively: in such situations, neural processing, perhaps through predictive coding, appears to overcome significant neural response latencies to synchronize its peak responses to the peak of the stimuli (Perrett et al., 2009; Reddy et al., 2015). Locations that show intensity coding for the Face appear to have activity echoing the timing of shape information, with lags in the 40–320 ms range. These lags are in a range similar to those found in other studies using facial expressions in the insula (Chen et al., 2009; Cornwell et al., 2008; Krolak-Salmon et al., 2003; Meeren et al., 2013). Using automated software to detect the level of activation of the facial action units 4 and 7 (i.e., lowering the eyebrows and tightening the eyelids), we find that this action unit information suffices to predict participants’ rating of the stimuli with high accuracy and follows the time course of the neural activity in the Face intensity encoding locations well enough to suggest that it provides traction on the analyses of dynamic pain-related facial expressions. On the other hand, the long neuronal lags we revealed for the facial motion information, with the earliest significant associations occurring in windows centered on 560 ms after the motion information, are unusually long for the start of responses to sensory stimuli in the insula as the onset latency for visual, auditory, tactile, or nociceptive responses is usually well below 560 ms (Bastuji et al., 2018; Bastuji et al., 2016; Chen et al., 2009; Cornwell et al., 2008; Krolak-Salmon et al., 2003; Liberati et al., 2020; Liberati et al., 2016), although insular activity can persist into such longer intervals (Meeren et al., 2013; Taniguchi et al., 2022). Together, this suggests that shape information is more likely than motion information to be the primary driver of the intensity coding to our facial stimuli.

An important consideration is whether preference for Face or Hand stimuli in specific locations could simply originate from some participants finding our facial stimuli more salient, and others the Hand stimuli, particularly given that our patients rated the Hand stimuli with slightly higher pain intensity than our Face stimuli. That we find Face and Hand preference side-by-side in simultaneously recorded locations and neurons in single patients suggests that this cannot suffice to explain our data. This is because if a patient were to find Hand stimuli more salient than Face stimuli, and the insula simply codes saliency, we would expect to find Hand-but-not-Face intensity coding in that patient’s insula, but we would not expect to find side-by-side locations with Hand-but-not-Face and Face-but-not-Hand coding. This also makes it unlikely that broadband activity simply reflects categorization uncertainty (Grinband et al., 2006). Indeed, it would be uneconomical for all insular neurons to redundantly encode the same exact signal of salience or categorization uncertainty. Our findings instead suggest that different locations might encode behaviorally relevant (and hence salient) information about the intensity of the pain of others with some degree of specialization for a particular type of information (Hand vs. Face). The insula intensity coding we measure could have a dual function: help perceive the intensity of other people’s emotions and tag intense emotions as salient, thereby reconciling the notion that it contributes to empathy and saliency. One peculiar observation in our data is that BBP for the Hand stimuli did not increase monotonically as a function of reported pain intensity, but in a J shape, with higher power for the lowest than second lowest rating. Some have argued that the insula may be part of an active inference circuitry in the brain that learns to predict emotionally relevant states and stimuli (Seth and Friston, 2016). In such a perspective, a J-shaped response curve could reflect a combination of underlying neurons representing the intensity of other people’s emotions (with a monotonic increase in activity) and others representing the prediction error (which for randomly presented intensities would have a U shape centered on the average intensity). Future experiments could contrast responses to a given pain intensity in blocks of high and blocks of low average presented intensity to disentangle the effect of observed and expected intensity on neural responses in the insula and shed further light on this predictive component.

On the other hand, we also found evidence that some locations and cells represent intensity coding for both the Hand and the Face stimuli. In addition, if we train a PLSR to predict perceived intensity from the activity pattern across all recorded locations, we find that training the decoder on Hand activity pattern and testing it on Face activity patterns (or vice versa) leads to above-chance decoding. This confirms that the insular representation of intensity can support stimulus-independent decoding – despite our PLSR not being biased to focus on signals that do generalize well across stimulus types. This provides an electrophysiological basis for recent fMRI studies that show that stimuli depicting situations in which others’ experience is painful or not can be discriminated using the same pattern across Hand and Face stimuli (Zhou et al., 2020).

In addition to the broadband results we report in detail, we find that theta power increases with perceived intensity. Given a growing animal literature establishing that interareal theta synchronization promotes learning about threats (Likhtik et al., 2014; Likhtik and Gordon, 2014; Taub et al., 2018; Tovote et al., 2015), examining the coherence in the theta range across iEEG electrodes in different brain regions during pain observation may in the future shed light on how humans learn about safety through others.

Spatially, we found that Hand intensity coding was enriched in the anterior dorsal insula, where we also found the largest proportion of locations encoding both Hand and Face intensity. This anterior bias was also observed in our BOLD signal for similar Hand stimuli. A recent meta-analysis identified that the most consistent BOLD activations when observing limbs in painful situations within the insula occur bilaterally around MNI coordinates y = 13 and z = 10 (Jauniaux et al., 2019), which closely matches where we find the highest density of Hand intensity coding (Figure 4a). Interestingly, locations with higher Hand intensity coding have increased connectivity at rest with extrainsular regions involved in processing two relevant stimulus dimensions. Connectivity was higher with cerebellar lobules VI, VII, and VIII, and with the inferior frontal gyrus, all of which are recruited by Abdelgabar et al., 2019; Caspers et al., 2010; Gazzola and Keysers, 2009 and necessary for (Abdelgabar et al., 2019; Keysers et al., 2018; Pobric and Hamilton, 2006) perceiving the very kinematics of hand actions we find to be good predictors of BBP activity in this study. Connectivity is also higher with the mid- and anterior cingulate cortex, which is associated with pain witnessing in humans (Jauniaux et al., 2019; Lamm et al., 2011; Timmers et al., 2018) and contains neurons with pain intensity coding in rats witnessing the pain of other rats (Carrillo et al., 2019).

With respect to Faces, somewhat surprisingly, we did not find a clear spatial clustering of intensity coding in our electrophysiological data. Overall, our ability to decode perceived intensity from our Face stimuli was also lower than that from the Hand stimuli. In that context, it is important to reflect on the fact that, despite our efforts to match the perceived intensity based on previous data (Gallo et al., 2018), patients (and to a lesser extent an age- and gender-matched control group) perceived the Hand stimuli as more intense than the Face stimuli. Given that responses were strongest for the highest rating, and that this rating was given more rarely for Faces than Hands, this difference could have contributed to making our Face results less consistent. At the same time, the variance in rating, a critical factor for determining the efficiency with which a regression can detect the presence of a rating–BBP relationship, did not differ across Hand and Face. Together, this difference in perceived intensity cautions us against overinterpreting the lack of detectability of a topographic organization of responses to Faces. Indeed, in our BOLD data, where decoding performance was similar for Hand and Face stimuli, Face intensity coding also had a clear spatial organization, being stronger more anteriorly, and meta-analyses show the left anterior insula to be reliably recruited by the observation of painful facial expressions (Jauniaux et al., 2019). However, that we found fewer locations and less reliable spatial organization for Face than Hand intensity coding dovetails with recent meta-analyses of the fMRI literature showing that the insula is more reliably recruited by the sight of limbs than by painful facial expressions (Jauniaux et al., 2019; Timmers et al., 2018). Indeed, that we find a macroscopic organization of intensity coding for the Hand, but not the Face, is echoed at the mesoscale: microwires with cells with Hand intensity coding also tend to show Hand intensity coding in the BBP signal that is thought to pool the spiking of many neighboring neurons, but the same is not true for the Face. In terms of lateralization, we find that our data is more likely if one assumes that both hemispheres have similar intensity coding than if one hemisphere were dominant. This echoes the fact that during noxious stimulation of the right hand both insulae show significant iEEG responses (although slightly stronger in the left insula; Liberati et al., 2020), and that fMRI fails to find robust lateralization of responses to empathy for pain (Jauniaux et al., 2019; Timmers et al., 2018).

The data presented in this work is publicly available at the Open Science Framework: https://osf.io/mcahz/.

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Author details

1. Efe Soyman

   1. Social Brain Lab, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Art and Sciences, Amsterdam, Netherlands
   2. Social Cognitive and Affective Neuroscience Lab, Koc University, Istanbul, Turkey

   Contribution

   Data curation, Software, Formal analysis, Validation, Visualization, Writing – original draft, Project administration, Writing – review and editing

   Contributed equally with

   Rune Bruls and Kalliopi Ioumpa

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0192-1541

2. Rune Bruls

   Social Brain Lab, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Art and Sciences, Amsterdam, Netherlands

   Contribution

   Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Writing – original draft, Project administration, Writing – review and editing

   Contributed equally with

   Efe Soyman and Kalliopi Ioumpa

   Competing interests

   No competing interests declared

3. Kalliopi Ioumpa

   Social Brain Lab, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Art and Sciences, Amsterdam, Netherlands

   Contribution

   Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Writing – original draft, Project administration, Writing – review and editing

   Contributed equally with

   Efe Soyman and Rune Bruls

   Competing interests

   No competing interests declared

4. Laura Müller-Pinzler

   Social Neuroscience Lab, Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany

   Contribution

   Software, Formal analysis, Validation, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5567-5430

5. Selene Gallo

   Social Brain Lab, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Art and Sciences, Amsterdam, Netherlands

   Present address

   Department of Psychiatry, Amsterdam University Medical Center, Amsterdam, Netherlands

   Contribution

   Resources, Software, Formal analysis, Validation, Writing – review and editing

   Competing interests

   No competing interests declared

6. Chaoyi Qin

   Social Brain Lab, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Art and Sciences, Amsterdam, Netherlands

   Contribution

   Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

7. Elisabeth CW van Straaten

   Department of Neurology and Clinical Neurophysiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

8. Matthew W Self

   Department of Vision and Cognition, Netherlands Institute of Neuroscience, Royal Netherlands Academy of Art and Sciences, Amsterdam, Netherlands

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5731-579X

9. Judith C Peters

   1. Department of Vision and Cognition, Netherlands Institute of Neuroscience, Royal Netherlands Academy of Art and Sciences, Amsterdam, Netherlands
   2. Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
   3. Maastricht Brain Imaging Center (M-BIC), Maastricht University, Maastricht, Netherlands

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

10. Jessy K Possel

    Department of Vision and Cognition, Netherlands Institute of Neuroscience, Royal Netherlands Academy of Art and Sciences, Amsterdam, Netherlands

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

11. Yoshiyuki Onuki

    Department of Neurosurgery, Jichi Medical University, Shimotsuke, Japan

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

12. Johannes C Baayen

    Department of Neurosurgery, VUmc, Amsterdam University Medical Center, Amsterdam, Netherlands

    Contribution

    Resources, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

13. Sander Idema

    Department of Neurosurgery, VUmc, Amsterdam University Medical Center, Amsterdam, Netherlands

    Contribution

    Resources, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

14. Christian Keysers

    1. Social Brain Lab, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Art and Sciences, Amsterdam, Netherlands
    2. Brain and Cognition, Department of Psychology, University of Amsterdam, Amsterdam, Netherlands

    Contribution

    Conceptualization, Resources, Software, Formal analysis, Supervision, Funding acquisition, Visualization, Methodology, Writing – original draft, Writing – review and editing

    For correspondence

    c.keysers@nin.knaw.nl

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-2845-5467

15. Valeria Gazzola

    1. Social Brain Lab, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Art and Sciences, Amsterdam, Netherlands
    2. Brain and Cognition, Department of Psychology, University of Amsterdam, Amsterdam, Netherlands

    Contribution

    Conceptualization, Resources, Software, Formal analysis, Supervision, Funding acquisition, Visualization, Methodology, Writing – original draft, Writing – review and editing

    For correspondence

    v.gazzola@nin.knaw.nl

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-0324-0619

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