Precision Functional Mapping of Imagined and Experienced Pain

Precision Functional Mapping of Imagined and Experienced Pain | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Precision Functional Mapping of Imagined and Experienced Pain View ORCID Profile Michael Sun , View ORCID Profile Bogdan Petre , View ORCID Profile Ke Bo , View ORCID Profile Carmen I. Bango , Malka Hershkop , Sydney L. Shohan , View ORCID Profile Heejung Jung , View ORCID Profile Tor D. Wager doi: https://doi.org/10.1101/2025.08.06.668906 Michael Sun 1 Dartmouth College Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Michael Sun Bogdan Petre 1 Dartmouth College Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Bogdan Petre Ke Bo 1 Dartmouth College Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ke Bo Carmen I. Bango 2 Oregon Health and Science University Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Carmen I. Bango Malka Hershkop 3 Columbia University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sydney L. Shohan 1 Dartmouth College Find this author on Google Scholar Find this author on PubMed Search for this author on this site Heejung Jung 4 Stanford University Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Heejung Jung Tor D. Wager 1 Dartmouth College Ph.D Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tor D. Wager For correspondence: tor.d.wager{at}dartmouth.edu Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Imagination is a principal human capacity, enabling simulation of sensations and situations for planning, learning, and empathy. We examined neural representations of experienced and imagined pain across eight body sites using deep-phenotyping, precision functional MRI with over seven hours of scanning per participant (N = 9). Conventional mapping, pattern classification, and representational similarity analyses converged to show that both imagined and experienced pain activated nociceptive regions (e.g., dorsoposterior insula) and non-nociceptive regions (e.g., supplementary motor area). However, imagined pain did not reliably reactivate body site-selective nociceptive patterns in any region. Instead, imagined and experienced pain shared multivariate representations across widespread cortical and subcortical regions, particularly transmodal cortical areas including dorsomedial and lateral prefrontal cortex, hippocampus, and thalamus. These findings suggest that imagination generates distributed pain-like activity while preserving a neural distinction from verum pain. This distinction may explain the difficulty of vividly imagining pain and underlie its role in empathy, planning, and pain vulnerability. Author Note We are grateful for assistance from Terry Sackett, Maryam Amini, Melanie Kos, Stephanie Sun, Eilis Murphy, Samuel Bergerson, Kristoffer Mansson, Sreekar Kasturi, Jason Davis, and Charles Mazof for assistance in data collection for this study. We thank Byeol Kim, Ben Graul, Li-Bo Zhang, and Zhaoxing Wei for comments on the manuscript, and Luke Chang and Marianne Reddan for insightful discussion. This work was funded by a grant from the Hitchcock Foundation and grant R37MH076136 from the National Institutes of Mental Health. Code for analyses are available at https://github.com/canlab/ . Introduction "Reason is the natural organ of truth; but imagination is the organ of meaning." - Milton Friedman Imagination allows people to mentally simulate possible futures 1 , enabling them to savor potential pleasures and develop mastery by anticipating events and their consequences. It allows us to refine our performance through mental practice: Envisioning the thrill of hoisting a championship trophy or the pain of touching a burning ember can serve as reinforcers that shape decision-making even in the absence of physical action 2 – 5 . Yet, imagination can be a double-edged sword. Imagining pain helps individuals prepare for medical procedures 6 – 9 , but can also activate neurotransmitter systems that amplify anticipated pain and fear 10 , increase negative expectations, and produce aversive learning, components of placebo and nocebo effects in medical contexts 11 – 14 . Imagery is also central to how we understand and empathize with others 15 , and is a crucial factor in cognitive and social development 16 . In clinical settings, this capacity is harnessed through imaginal exposure therapies, which activate neural circuits involved in extinction learning 17 —a core mechanism underlying symptom reduction in posttraumatic stress disorder 18 , anxiety disorders (e.g., specific phobia and obsessive-compulsive disorder) 19 , and, increasingly, in chronic pain 20 , 21 . This raises some central questions: How does the brain represent imagined pain, a stimulus that must be veridical enough to guide behavior, yet not confused with the real thing? Does imagination activate early nociceptive representations, or are its effects instead mediated by higher-order cortical areas? Can the brain walk the fine line of engaging somatic and affective representations without fully triggering the alarm systems reserved for actual injury? Imagination has been a central topic in neuroscience. Early neuroimaging work in vision helped resolve a classic debate over whether or not imagination is depictive, resembling perception and involving sensory reactivation (e.g., visualizing an object reactivates early visual cortex) 22 , or propositional, whereby imagination encodes abstract, language-like descriptions rather than perceptual details 23 . More recently, studies have used multivariate pattern analyses to demonstrate re-activation of content-selective patterns in early perceptual cortices in vision 24 – 28 , olfaction 29 , 30 , taste 31 , and audition 32 – 39 , including activation of somatotopic regions in somatosensory cortices 40 . These findings suggest that imagery may activate early sensory representations. At the same time, early sensory activation is typically weaker 28 , 41 – 44 , and some recent studies have highlighted distinct, opposing activity patterns underlying visual perception and imagery 45 . The degree to which imagination can activate sensory representations, the conditions under which it does so, and alternative cortical substrates for imagination remain open questions. The effects of imagination on pain are even less well understood. Pain is particularly crucial for both empathy 46 – 50 and personal decision-making and learning 51 , and it involves distinct pathways 52 , representations 53 , 54 and multivariate fMRI patterns 55 , 56 from other forms of somatosensation. On one hand, a seminal study identified little activation in pain-related regions with imagination, with greater activation in the absence of stimulation after hypnotic suggestion 57 . This finding suggests that pain is particularly difficult to volitionally imagine in a veridical fashion, which perhaps underlies the so-called “empathy gap” in pain 58 . However, other studies have found overlapping activations in pain- related regions, including the anterior cingulate cortex (ACC), insula, and primary and secondary somatosensory cortices (S1, S2) 15 , 46 , 57 , 59 , as well as subcortical areas like the thalamus, amygdala, and parahippocampus 60 , 61 . Prefrontal and parietal cortices have also been found to be activated during pain imagery, providing a potential substrate for imagery generation 15 , 46 , 57 , 59 . A central issue is that all of these areas are activated by many processes beyond pain 55 , and may thus reflect changes in a variety of cognitive and affective processes, including language and emotion. The presence of overlapping activation does not imply shared representations of pain per se 62 . To identify common engagement of pain representations, shared activity patterns should both reliably encode pain and be selective for pain 62 . Previous studies of pain imagery have not identified representations with these properties. For example, Derbyshire et al. 57 do not quantify the relative magnitude of activation or representational pattern similarity for experienced and imagined pain, nor do they identify pain intensity-encoding nociceptive representations. Thus, how strongly imagination affects nociceptive representations, and what types of representations are shared across pain imagery and veridical pain experience, remains unknown. We address this question using precision functional mapping (PFM). This approach leverages high-resolution, high- volume individual-level fMRI data to reveal reliable and anatomically specific brain representations that are often obscured in group-averaged analyses. Unlike conventional group-averaged analyses, which often blur fine-grained functional topographies due to inter-individual variability, precision functional mapping (PFM) enables the discovery of individualized neural signatures with high reliability and reproducibility 63 , 64 , despite lower total group sample size 65 . PFM studies typically involve the study of “highly sampled individuals", collecting several hours of data per participant, allowing for the identification of distinct functional boundaries, networks, and representational patterns that are consistent across sessions and tasks. We analyzed 7+ hours of fMRI data during imagined and perceived heat pain on 8 distinct body sites ( Figure 1 ), allowing us to identify body site selective nociceptive representations in individual participants, and to test both imagined and perceived pain and their body site selectivity on new, independent scan days. This allowed us to test whether imagination activates individualized nociceptive representations in key areas targeted by ascending nociceptive pathways, including spinothalamic pathway ventral posterior lateral thalamus (VPL) and dorsal posterior insula (dpIns), medial pathway intralaminar thalamus and anterior midcingulate (aMCC). Download figure Open in new tab Figure 1: Study design and analytical approaches. (A) A within-participant experimental design had participants calibrate painful and non-painful stimulation temperatures at each of 8 body sites (chest, abdomen, and bilateral lower jaw, arms, and legs). In the fMRI scanner, participants underwent ten sessions of fMRI scanning, featuring pseudo-randomized trials of hot or warm stimulation, or instructions to imagine themselves receiving painful heat stimulation. (B) Bar plot depicting the length of time participants participated in the study and the lag between sessions. FMRI data are analyzed through the lens of (C) Univariate Activation Maps, (D) Precision Classification Pattern Tests, and (E) Representational Similarity Analyses. (C-E) show group results from each analysis, elaborated further in the text. PFM is often done with resting state functional connectivity, and not task-based multi-voxel activation and pattern analysis we do here. We can see things in individual voxel maps that can’t be seen in group analyses. For example, Gordon et al., 2023 discovered three intereffector regions (superior, medial, inferior) across primary somatomotor cortices, that support an Action Mode Network/Somato-cognitive Action Network, whereby the body is prepared to synchronize to act in response to a demand. These sites are functionally connected with SMA, thalamus, posterior putamen, postural cerebellum, dorsal anterior cingulate, parietal regions, and the insula. To identify other brain systems in which pain and imagined pain involve similar representations, we used Representational Similarity Analysis (RSA) in a series of regions spanning the brain and compared the distribution of findings to established maps of cortical hierarchies 66 . This allowed us to identify ‘pain-like’ representations across higher-level cortical systems. These approaches allowed us to identify and localize substrates for imagined pain in the human brain. Results Prior to neuroimaging, each participant underwent a calibration session using an Ascending Method of Limits procedure 67 to determine the temperature sensitivity, pain thresholds, and pain self-report on each body site ( Figure 2 and Methods). The maximum tolerable intensity (pain tolerance, “Hot” stimulation; 46-48.5°C; see Methods) and detection threshold plus 1°C (“Warm”) on each body site was used in fMRI. Thermal detection thresholds varied significantly by body site, F(7, 14.44) = 6.60, p = .001, partial η² = .76. Post-FDR 68 correction, the left arm had significantly lower thresholds (greater sensitivity) than the face (vs. left face p < .029; vs. right face p < .019), legs (vs. left leg: p < .001; vs. right leg: p = .013), chest (p = .002), abdomen (p = .015), and right leg (p = .013). The right arm also showed lower thresholds than the left leg (q < .001). Pain tolerance did not significantly vary by site, F(7, 14.69) = 11.37, p = .07. Body sites did not vary significantly in pain ratings, Valence: F(7, 18.24) = 1.44, p = .248, Intensity: F(7, 16.59) = 1.57, p = .213. Download figure Open in new tab Figure 2: Behavioral and Self-Report Results. Left: Average detection and pain tolerance temperatures for each body site and participant (dots with connecting lines). Baseline temperature was 32°C (blue dashed line), and the maximum temperature delivered was 49°C (red line). Right: Pain valence and intensity in response to high heat (tolerance intensity) during calibration. No significant differences in pain metrics were found between body sites post- calibration. Participant-level data is available in Supplementary Figure S1 and Supplementary Table 1. Group-level and individual participant activation maps To compare with previous studies, which have largely focused on group-level regional activation, we tested Experienced pain (Hot - Warm) and Imagined pain (Imagine - Warm) contrasts at the group-level and in individual participants (all FDR q < 0.05 corrected for multiple comparisons; Figure 3 , Supplementary Figures 1 and 2, Methods, and Supplementary Data 1 and 2). Group analyses indicated significant activation in a broad set of typical pain- processing regions ( Figure 3 , left). Many of these were activated in multiple individual participants as well ( Figure 3 ). Every individual participant showed pain-evoked responses (Hot - Warm) in core insular-opercular regions— spanning somatomotor and parietal operculum (BA43, OP4/PV; FOP1–4), posterior and middle insular cortex (PoI2; MI), and surrounding association cortex (TA2; PFop). The somatomotor region BA43 lies at the ventral aspect of the motor cortex adjacent to M1 (BA4) and overlaps with the inferior ‘inter-effector’ region in Gordon et al. 69 . It is also the inter-effector region most closely connected with the mid-insula and posterior insula, which are selectively engaged in thermal pain 70 , 71 , and surrounding operculum. In other words, A wider set of regions was activated in at least 22.22% (2 of 9) of individual participants, including anterior mid-cingulate cortex, medial and lateral thalamus, dorsolateral prefrontal cortex (dlPFC), and other subcortical forebrain cerebellar, and brainstem regions (Supplementary Figure S2 and Supplemental Table 2). Download figure Open in new tab Figure 3: Group-level and participant-level univariate contrast results for experienced pain (Hot–Warm) and imagined pain (Imagine–Warm). Shown on top are group-level and participant-level t-maps (yellow = activation, blue = deactivation). (A) Experienced pain activated thalamus, S2, insula, cingulate, dlPFC, vlPFC, cerebellum, and putamen. (C) Imagined pain activated thalamus, premotor cortex, SMA, S1, and amygdala. (B, D) participant-level maps show consistent activation in all participants’ experienced pain maps, and 6 of 9 participants’ imagined pain maps in the inferior intereffector region of the (E) Gordon et al., 2023 69 ‘action mode’ network, which is visible in previously published multivoxel pain signatures such as the SIIPS-1 72 and the Multisensory Fibromyalgia signature 73 . (F) Bar plots show individual and group mean beta values in select regions. Colored lines show means and standard errors for individual participants. Imagined pain (Imagine - Warm) activated a much more limited set of regions at the group level, with stronger evidence for inter-individual variability. Group findings include five regions activated in common with experienced pain, including left laterodorsal (LD) and intralaminar thalamic nuclei (IL), superior premotor cortex, right supplementary motor area (SMA), and anterior putamen ( Figure 3 , right). Interestingly, primary somatosensory cortex (S1) and amygdala were activated during imagined, but not experienced pain. In individual participants, all these regions were significant in at least 22.22% (2/9) of individual participants, except for left LD thalamus, which was not significantly active in any individual participant. The left superior premotor cortex and right SMA (6/9 participants) and S1 (4/9 participants) were the most consistent. Outside of these regions, we found consistent activation (5/9 participants) in the left ventral inter-effector region BA43) and paracentral lobule, bilateral dlPFC and premotor areas, and the cerebellum (Supplementary Figure S3 and Supplemental Table 2). We also calculated conjunction analyses across experienced and imagined pain in individual participants (Supplemental Figure S4 and Supplemental Table 2). The most consistent common activation (6/9) participants was found in the left ventral inter-effector region (BA43). Other regions activated in both experienced and imagined pain within individuals included other somatomotor regions, auditory and visual regions, temporoparietal regions, dlPFC, vlPFC, cingulate, insular cortex, and portions of thalamus, basal ganglia, pons, midbrain, and cerebellum (Supplementary Figure S4). Thus, different participants tended to show FDR-corrected overlap in different regions, with the most consistency in ventral somatomotor ‘action-mode network’. Analyses of body site selective nociceptive representations We next identified individualized, body site-selective nociceptive representations in key pain-processing regions (see Figure 5 , Supplementary Table S1). For each region, we trained participant-specific support vector machine (SVM) classifiers to distinguish hot from warm stimulation within each body site, which constituted potential nociceptive representations. Then, we tested effects of interest on data from independent sessions collected on different days (leave-one-session-out cross-validation; Figure 4 , Methods, and Supplementary Table S2). Nociceptive representations were defined as neural patterns with significant effects in test data for (1) [Hot - Rest] on the target body site, (2) [Hot - Warm] for the target body site, and (3) [target body site > other body sites] in the [Hot - Warm] contrast, to identify body-site selectivity. Nociceptive representations identified in this way were tested for [Imagine - Rest], [Imagine - Warm], and [Imagine - Warm] x [Target vs. Other body site] effects. For each putative nociceptive representation, we calculated the pattern expression on test data for each condition (Hot, Warm, Imagine; see Methods) and contrast across pattern expression scores (e.g., [Imagine - Warm]). We then tested these scores against zero at the group level to assess statistical significance treating participants as a random effect. Download figure Open in new tab Figure 4: SVM precision functional mapping. (A) Analysis pipeline for precision functional analysis of nociceptive representations. Within each pain-processing region of interest, body site-specific Hot vs. Warm conditions are discriminated using a leave-one-session (day)-out cross-validated support vector machine (SVM). The resulting participant- and body site-specific weight maps are tested on [Hot vs. Warm] and [Hot vs. Rest] comparisons from held-out sessions (days), and Hot, Warm, and Imagine conditions from other body sites across all days. (B) Example group-average SVM patterns in dorsal posterior insula (dpIns), with cartoon depiction of tests of pattern expression. Top: dpIns test data for the Hot (painful) condition for each participant. Bottom: SVM weight maps in dpIns for each participant. The pattern expression for each condition (Hot, Warm, Imagine) is calculated as the dot product of the weight map and test data for that condition. Pattern expression responses values are subsequently compared across conditions (e.g., [Imagine - Warm]) and tested against zero at the group level (i.e., participant as a random effect) to establish significance. Follow-up tests assess significance within individual participants across sessions. Download figure Open in new tab Figure 5. Nociception, body site-selectivity, and imagination effects in precision functional mapping of pain-processing regions of interest. Three regions – dpIns, S1, and S2– showed significant effects on individually defined nociceptive patterns (SVM weight maps trained on individual body sites) for [Hot - Rest], [Hot - Warm], and [Hot - Warm] x [Target vs. Other body sites]. Amygdala, thalamus, and parabrachial nuclei are also highlighted to show significant regions for four or more individual participants. The colors identify individual regions, which were defined bilaterally to allow for patterns to be differently lateralized for different body sites. Bar plots show individual and group pattern expression on independent test data for each of these regions. Colored lines show means and standard errors for individual participants. dpIns: dorsoposterior insula; S1: primary somatosensory cortex; S2: secondary somatosensory cortex; MD: Mediodorsal; IL: Intralaminar. Within the set of a priori pain-processing regions (Supplementary Figure S5), 12 of 14 regions showed significant [Hot-Rest] and [Hot - Warm] pattern expression for individualized patterns in test data, and 3 of those regions – dpIns, S1, and S2 – also showed body site selectivity, fulfilling all three criteria for body site-specific nociceptive regions. All three criteria were additionally significant at the single-participant level for a majority of participants in dpIns (8/9 participants), S1 (8/9 participants), and for at least 4/9 participants in S2, parabrachial nuclei, amygdala, and thalamus (IL and MD). Thus, multiple cortical and subcortical regions exhibited body site-selective nociceptive pattern representations, suggesting that fine-grained spatial encoding of painful stimuli is a robust and reliable feature of distributed pain-processing systems in the brain. None of the 14 regions were significant for [Imagine-Rest] and [Imagine - Warm], nor did any region show body site selective responses for imagination ( Figure 6 ). At the single participant level, 7/9 participants exhibited significant [Imagine-Rest] and [Imagine-Warm] pattern expression in at least one region, the most common of which was exhibited in S1 for 4/9 participants. Six of 9 subjects exhibited significant pattern expression in all three criteria including body site selectivity in at least one of 7 regions: parabrachial nuclei (3/9), hypothalamus (2/9), S1 (2/9), amygdala (1/9), periaqueductal gray (1/9), anterior insula (1/9), and middle insula (1/9). Thus, while some affective and subcortical regions were weakly engaged during imagined pain, the neural signatures lacked spatial specificity and robustness. No regions exhibited the consistent, body site-selective nociceptive representations seen during actual pain, suggesting that imagination does not evoke the same coding observed during verum heat pain stimulation. Download figure Open in new tab Figure 6. (A) Regions expressing significant Hot-Warm pattern expression shown in pink, and regions further expressing Target-Other Body Site specificity in expression shown in red. Regions shown include a priori pain pathways regions that are significant p < .05 and CANLab2024 regions after FDR correction. (B) shows an effect size map of the body site selective regions, and (C) shows the number of participants whereby hot-warm body site selectivity was significant after FDR correction at the single-participant level. (D) shows the body site selectivity at the group level for regions defined in (A) and (B) by computing t-value maps across body sites and taking a winner-take-all approach across body sites. The map shows clear contralateral lateralization in cortical representations for the left and right sides of the body, though midline body sites may preferentially activate ‘inter-effector’ regions. Note that this is a simplification and the body site-selective patterns analyzed do not require simple mapping from one region to one body site. In a broader search across 131 parcels covering the whole brain (see Supplementary Figure S5; Supplementary Table S3; Methods), 10 parcels fulfilled all three criteria for nociceptive representations after FDR correction (red in Figure 6A ). Regions included parcels covering the pain-processing regions above, A1, BA7, M1, SMA, and the dorsal intraparietal sulcus. 93 parcels showed significant [Hot – Rest] and [Hot – Warm] contrasts (pink in Figure 6A ). The relative degree of body site selectivity is shown in Figure 6B in units of effect size (Cohen’s d ). The dpIns (posterior granular insula), BA5, dorsal intraparietal cortex, M1, and SMA was significant in 9/9 individual participants, whereas a broader set of regions showed nociceptive representations in smaller numbers of participants ( Figure 6C ), pointing to potential individual differences in nociceptive representations. Note that the SVM patterns are agnostic to the spatial structure of body-site selective responses within each region, and reflect both large-scale classic somatotopy and more patchy or fine-grained patterns, including the possibility of multiple somatotopic maps contained within our ROIs. Among significant regions with nociceptive representations, several also showed imagination-related activation. Lateral amygdala, anterior IPL, and anterior operculum showed significant [Imagine-Rest] and [Imagine-Warm] pattern expression at the group level. However, no regions showed nociceptive representations with body-site selectivity for imagined pain. At the single-participant level, no participants exhibited significant [Imagine-Rest] pattern expression in the three regions mentioned above. However, for [Imagine-Warm], 5/9 participants exhibited significant lateral amygdala pattern expression, and 4/9 participants exhibited significant anterior IPL and anterior opercular pattern expression. Only one participant showed any evidence for activation of body site-selective nociceptive representations during imagined pain (in BA8, BA9, and anterior putamen). These results show that imagination does not systematically activate nociceptive representations, and in the few cases it does, the activation was not selective to the body site imagined. Representational Similarity Analysis Finally, we used representational similarity analysis (RSA 74 ) to analyze pattern similarity for experienced vs. imagined pain across the brain ( Figure 7 ; see Methods and Supplementary Data 3). First, we constructed whole-brain voxelwise representational similarity matrices (RSMs) by computing within-participant Spearman’s spatial correlations from stimulus-evoked activity patterns in each of the 24 study conditions (i.e., Hot, Warm, and Imagine x 8 body sites per participant), shown in Fig. 7A and 7B . We calculated contrasts in pattern similarity related to target vs. other body site ( Fig. 7C left), similarity within each condition ( Fig. 7C center), and similarity across (Hot, Warm) and (Hot, Imagine) conditions ( Fig. 7C , right). The main comparison of interest for imagination was whether patterns were more similar for (Hot, Imagine) than for (Hot, Warm), which would indicate ‘pain-like’ pattern activity. This analysis excluded images from the same day in calculating pattern correlations to avoid bias due to session effects (see Methods). Download figure Open in new tab Figure 7: Representational Similarity Within and Between Conditions. (A) Group-level and (B) participant-level RSMs show considerable interparticipant similarity of whole-brain representations. (C) Model RSMs for Body Site Similarity (left), Intra-Conditional Similarity (center), and Cross-Conditional Similarity (right). White = 1 and black = 0 in indicator matrices. (D) RSA revealed significantly stronger body site-specific representations of whole-brain activation across conditions, with a consistent effect direction across all participants. (E) The Hot and Imagine conditions exhibited significantly greater within-condition correlations compared to the Warm condition (Hot, Imagine) > (Hot, Warm), with consistent effect direction across all participants. (F) In whole-brain RSA, the Hot and Imagine conditions (Hot, Imagine) showed significantly greater cross-condition correlations compared to Hot and Warm conditions (Hot, Warm). (G) The surface maps show all Bonferroni-corrected regions where (Hot, Imagine) > (Hot, Warm). The insets show bar graphs of Fisher’s z-transformed correlations with individual participants’ data (lines) and group means +- s.e.m. (bars) for key regions. Most regions showed substantially more similar activity patterns for (Hot, Imagine) than (Hot, Warm), except for early sensory regions like S2 and posterior insula. * p < 0.05, ** p < 0.01 and *** p < .001. Download figure Open in new tab Figure 8. Study design for the deep phenotyping of real and imagined pain in eight body sites. The left depicts the experimental protocol for each trial of the pain calibration task. The right depicts the experimental protocol for each fMRI run targeting a single body site. Whole-brain RSA revealed that activity patterns were more similar within a body site than across body sites for all participants (M=0.4(.01), t=40.41, p=1.55e-10, d=19.86), indicating body site-selective activity patterns. Patterns evoked during Hot (M=0.09(.02), t=4.08, p=.004, d=1.36) and Imagine (M=0.08(.01), t=6.35, p=.0002, d=2.12) conditions were found to be significantly correlated across body sites, suggesting high consistency in the neural activation of these conditions. Warm patterns were also correlated across body sites (Warm: M=.01(.003), t=2.91, p=.02, d=0.97), but more weakly so (Hot-Warm: M=0.08(.02), t=3.91, p=.004, d=1.75; Imagine-Warm: M=0.07(.01), t=5.49, p=.0006, d=-2.65). Moreover, the correlation between Hot and Imagine conditions (i.e., (Hot, Imagine); M=0.09(.009), t=10.29, p=6.88e-6, d=3.43), was higher than the correlation between Hot and Warm conditions ((Hot, Warm); M=0.04(.009), t=4.96, p=.001, d=2.07) despite the presence of verum somatosensory stimulation in both Hot and Warm conditions. Thus, at the whole-brain level, activity patterns reflect both body-site selectivity and similarity in evoked responses within test conditions. Crucially, imagination produces ‘pain-like’ activity patterns. To test for neuroanatomical variability across brain systems in pattern similarity, we performed the same RSA analysis and contrasts for each of the 131 parcels spanning the brain (Supplemental Figure S5). We found significant body site-selective activity in most regions across the brain (Supplemental Figure S7), indicating that body site-selective representations are more widespread at the pattern level than previously appreciated. The Imagine/Hot correlations were greater than Hot/Warm correlations in 128 out of 131 regions across the brain after FDR-correction, and 61 out of 131 after Bonferroni correction ( Figure 7G ). Thus, in all these regions, imagination produced similar brain activity patterns to experienced pain, compared with nonpainful warmth. These regions including subcortical, brainstem, and limbic circuits, particularly those involved in nociceptive relay (thalamus), arousal (brainstem nuclei), emotion regulation (ventromedial prefrontal cortex [vmPFC], orbital frontal cortex), and memory (hippocampus). High-level frontal and temporal cortical areas also show similarity, consistent with top-down simulation or imagery of pain. Only three regions showed comparable similarity for (Hot, Warm) and (Hot, Imagine) with no significant differences: S2, OP4, and A1. We calculated the spatial similarity between regions with similar imagined and experienced pain patterns after Bonferroni correction with the principal gradient map of Margulies et al. 2016 66 , which defined a gradient from unimodal sensorimotor regions to transmodal regions that integrate information across modalities. The regions with imagined-experienced pain similarity strongly overlapped with transmodal regions (mean correlation with the principal gradient r = 0.56 (s.e. 0.003), t = 182.8, p < .001, d = 0.67). This widespread overlap suggests that imagining pain recruits many of the same cortical and subcortical systems involved in physically experiencing pain, and that cortical representations of imagined pain lie chiefly in transmodal association areas, rather than unimodal sensorimotor areas. Thus, imagining pain does not activate early nociceptive representations, but produces ‘pain- like’ activity patterns in high-level association areas and interconnected subcortical regions. Discussion The ability to imagine pain underlies several crucial forms of cognition: It allows us to imagine our own future pain and pain under hypothetical decision scenarios, supporting decision-making and learning 13 , 14 , and allows us to imagine others’ pain, supporting empathy 47 , 49 , 73 , 75 . This study characterizes the neural representations of experienced and imagined pain across eight body sites using a deep-phenotyping, precision functional mapping framework, using three complementary analytic approaches to investigate the neural bases of imagined pain and the degree to which imagination activates early nociceptive representations and pain representations throughout the cortex and subcortex. These analyses converge to show that while experienced pain elicits widespread, robust, and body site- selective activations, imagined pain does not activate early body site-specific nociceptive representations. Instead, it activates selected regions, including recently-identified ‘inter-effector’ motor regions associated with an ‘action- mode’ network 69 , and pain-like patterns throughout a broad set of cortical and subcortical regions, particularly transmodal cortical areas at the top of the cortical processing hierarchy 66 . These findings identify a new basis for pain imagination that both permits fictive activation of pain-related representations and preserves the distinction between imagination and experience. Idiosyncratic Pain Sensitivity and Neural Activation Individualized calibration revealed notable variation in thermal sensitivity and pain thresholds across body sites, consistent with prior reports of regional differences in cutaneous innervation and nociceptive fiber density 76 . Interestingly, the face—despite being highly innervated—exhibited the highest thermal pain thresholds (least sensitivity), whereas the arms and legs were most sensitive. Divergent and Overlapping Activation in Experienced and Imagined Pain Consistent with prior studies and meta-analyses, pain (compared with non-painful warmth) activated a broad set of canonical nociceptive regions, including the thalamus, insula, S2, and cingulate cortex, alongside higher-order areas implicated in transmodal cognitive function 77 , 78 . Many of these were activated in every or nearly every participant in individualized analyses across the multiple scanning days. In contrast, imagined pain elicited activity in a more restricted set of regions, with more diversity in activated areas across individuals. Group-level activation consistent across individuals was found in only a few regions, including the amygdala, SMA, medial thalamus, anterior putamen. Furthermore, the majority of participants activated an area consistent with the ventral inter-effector region 69 , a motor/premotor area with body-wide representation. Many individual participants also activated the middle inter- effector motor region and posterior somatosensory opercular regions. The bilateral SMA – an area linked to action policy and motivation 79 and self-regulation 80 – was the most consistently engaged region during imagined pain, aligning with its proposed role in motor imagery and simulation 81 . The inter-effector regions, SMA, anterior putamen, medial thalamus, and somatosensory operculum are all part of the recently characterized ‘action-mode network’, a cortical-subcortical network important for motivated action, autonomic regulation 82 , 83 , and pain control 84 . Thus, the action-mode network concept links nociception with motivated action, and its involvement in imagined pain may reflect motivated action planning common to both experienced and simulated affect. Precision mapping reveals body site-selective representations of experienced but not imagined pain in nociceptive regions While previous studies have investigated pain imagery 15 , 46 , 57 , 59 , 60 , 61 and related phenomena including placebo 85 , mindfulness 86 , and hypnosis 87 , most previous studies using pattern recognition to perceptual representations underlying mental imagery have focused on other domains, particularly vision 41 , 88 , audition 89 , and olfaction 90 . Thus, these findings represent the first attempt, to our knowledge, to assess whether mental imagery can activate individualized nociceptive representations. This is an important direction for future studies, as activation of ‘pain- related’ gross anatomical regions has been shown to reflect non-specific processes related to emotion, attention, motor function, and other processes 49 , 91 – 93 , whereas multivariate patterns are considerably more specific 55 , 94 . We identified individualized nociceptive representations that fulfilled three criteria: (1) activation during painful heat, (2) greater activation for heat than non-painful warmth, and (3) body site-selectivity, all established in independent test data from different scan days. These representations were located in early nociceptive regions that receive mono- or di-synaptic input from spinal nociceptive afferents 52 , 95 , 96 , including dpIns, S2, S1, ventrolateral and medial thalamus, and aMCC, but also in some transmodal association cortices and subcortical regions (e.g., thalamus, amygdala, basal ganglia) not directly associated with nociception. These findings add to a growing field identifying body site-selectivity in motor activity, touch, and pain beyond the somatomotor and insular cortices 97 – 101 , and suggest that pain somatotopy is supported by a broad, hierarchical network spanning sensory, affective, and integrative systems. While imagined pain activated some of the same broad areas as experienced pain, particularly in the ‘action-mode network’ (see above), imagination did not activate nociceptive representations, and did not show the pattern of body site-selectivity apparent during verum pain in any one region. In most cases this was also true at the individual- participant level. Some exceptions were that (1) six subjects activated body site-selective nociceptive patterns in at least one region among parabrachial nuclei, hypothalamus, S1, amygdala, periaqueductal gray, anterior insula, and middle insula, and (2) seven participants showed pain-pattern expression in functionally diverse but largely non-overlapping regions, with the highest overlap featured in S1, overlapping among four participants. Overall, the findings suggest that imagined pain does not rely on topographically organized representations in nociceptive regions, but may rely on distributed, higher-order representations integrating affective memory, visual imagery, and conceptual knowledge in an individualized fashion. Importantly, the selective and inconsistent recruitment of nociceptive regions during imagination—and during other non-somatosensory experiences such as viewing emotionally aversive images 102 , 103 —raises the possibility that such engagement of pain-related representations in affective or conceptual contexts may be one mechanism linking mental simulation to the future development of pain disorders in otherwise healthy individuals. This interpretation aligns with a growing body of work from multiple groups demonstrating the involvement of pain-related brain patterns in non-nociceptive tasks and their predictive relevance for clinical outcomes 13 , 86 , 104 – 107 . Representational similarity reveals pain-like patterns across transmodal cortical areas Representational Similarity Analysis (RSA) complements the other analyses presented here by providing a different lens on how pain-related and imagined states are encoded in the brain. Rather than isolating nociceptive representations per se, RSA assesses the similarity of multivoxel activity patterns evoked by painful, nonpainful, and imagined stimuli 74 , 108 . These patterns may partially reflect nociceptive input but are also shaped by a broader set of processes, including attention, affect, cognitive appraisal, and motivational state 109 – 111 . Importantly, RSA, like the individualized machine learning–based pattern recognition methods discussed above, enables the identification of idiographic neural representations that vary across individuals and conditions, without requiring shared anatomical topography across participants 112 , 113 . This makes RSA particularly well-suited for precision neuroimaging approaches that seek to model functional brain representations at the level of individual experience. The RSA analyses showed that patterns evoked during experienced pain were more similar to those evoked by pain imagination than nonpainful warmth across widespread areas of the cortex and subcortex. Remarkably, this pattern of similarity between pain and imagination was widespread, reflected in all but three – S2, OP4, and A1 – of the regions studied across the entire brain. Thus, imagined pain evokes a globally distributed pattern of ‘pain-like’ activity. The pain-imagined pain similarity was weakest in somatosensory/opercular regions that encode early somatosensory and nociceptive inputs, and strongest in transmodal areas 66 thought to underlie higher-order abstractions and conceptual maps 114 – 116 . Several areas with the strongest overlap in this study, including vmPFC, posterior temporal- parietal junction, and anterior temporal cortices have received substantial attention as substrates of encoding abstract conceptual relations (i.e., cognitive maps) and representations that integrate narrative meaning across long time scales 117 – 119 , as well as in predictive processing-related regulation of posterior sensory areas e.g., 120 , 121 . Thus, these results fit with the hypothesis that imagined pain does not re-activate early sensory representations, but produces pain-like activity patterns in higher-order maps of conceptual space, potentially reflecting embodied simulation 122 , 123 or predictive processes that facilitate disambiguating sensory input in favor of expected percepts 124 , 125 . This latter concept provides a link between imagination and placebo effects, which also activate the vmPFC and other transmodal areas like posterior temporoparietal junction 126 – 129 . Thus, imagination, like expectation, may shift internal models towards a pain-like state 130 , 131 , facilitating early detection and priming decision-making systems to respond quickly to actual nociceptive input. Limitations and future directions This study has several limitations that could be addressed in future studies. First, nociceptive representations could be defined based on criteria of specificity and generalizability in a more comprehensive fashion. This has been possible with more macroscopic, population-level patterns by testing across studies 55 , 56 , and there is a natural tension between testing multiple body sites and including many non-somatic conditions within the same individuals (i.e., imagining pain). Secondly, the lines between nociceptive sensory representations, pain perception, and other sequelae of pain such as action planning are unclear. There is perhaps no single study that can definitively disentangle them, and converging evidence across neuroscience methods (e.g., invasive electrophysiological recording 132 will be required to continue to understand the neuroscientific basis of pain imagery. In addition to these issues, future studies could address the ability to activate perceptual representations with training, in special populations with chronic pain – particularly pain engaged by non-nociceptive stimuli (e.g., Bogaerts et al., 2023 133 , in functional neurological disorder). In addition, the propensity to activate pain-like representations could be investigated as a neuromarker for vulnerability to chronic pain after injury or insult. This propensity may also confer the ability to empathize with others in pain, increasing either empathic care or empathic distress 62 , 134 . For example, some areas with the greatest pain - imagined pain similarity in this study have also been targeted with neurostimulation 135 , 136 to promote pain empathy, and lesions (e.g., of vmPFC) reduce empathy 137 , 138 . Conclusion By leveraging individualized calibration and precision functional mapping of densely sampled data over repeated fMRI sessions per participant, this study demonstrates that pain imagination engages pain-like patterns across a broad set of cortical and subcortical regions, including activation of multiple regions associated with the recently defined ‘action mode network’ and pain-like multivariate representations in transmodal cortical regions. It does not activate targeted, body site-specific nociceptive representations in early somatosensory regions. This provides a way for imagination to engage decision-making, planning, and potentially perceptual inference processes to simulate pain, while maintaining a distinction between real and imagined experience by virtue of selective activation of sensory representations by real experience. These findings provide a blueprint for identifying and studying how both early sensory/perceptual representations and transmodal pain-like patterns relate to empathy, predisposition to chronic pain, and other aspects of learning and decision-making. Methods This study was registered as part of a national clinical trial studying placebo effects on pain ( https://clinicaltrials.gov/study/NCT04653064 ), and a preregistration for this portion of the study is available on the Open Science Framework ( https://osf.io/zv4ec ) . Participants Nine participants (4 females, 5 males) were recruited from the Dartmouth Department of Psychological and Brain Sciences between the ages of 24-56 (M=35.63 years, SD=11.62 years). Written informed consent was obtained in accordance with the Declaration of Helsinki and the study was approved in full by the Dartmouth IRB. Preliminary eligibility of participants was determined through an MRI safety screening form. No significant abnormalities were detected in any participant’s structural scans. View this table: View inline View popup Download powerpoint Table 1: Participant Demographics Experimental Protocol Thermal Stimulation For thermal stimulation, a TSA2 Advanced Thermosensory Stimulator (TSA2 139 ; Medoc Advanced Medical Systems, Ramat Yishai, Israel) with a 16 mm 2 Peltier thermode end plate was used. This end plate delivered stimulation to, from top-to-bottom, the bilateral mandibular base (face), xiphoid process (chest), bilateral volar flexor below the cubital fossa (arms), umbilical surface of the abdomen below umbilicus (abdomen), and bilateral medial heads of the gastrocnemius below popliteal fossa (legs), and was affixed to each of the eight body sites with compression sleeves. Thermode stimulation induces a fast, sustained, and well-calibrated sensation at levels safely calibrated to each individual participant. Stimulus application was computer controlled and recorded with PsychoPy3 v2020.2.5 140 . Heat Calibration with the Ascending Method of Limits Prior to neuroimaging, each participant underwent a calibration session to determine the temperature sensitivity and pain thresholds of each bodysite to be used in the fMRI setting. Calibration was done using an Ascending Method of Limits procedure delivered using PsychoPy3. Each trial of the procedure targeted a randomly-selected body site, with the experimenter affixing the thermode at the beginning of each trial. After one trial each of eight body sites, the process was repeated three additional times for a total of 32 total trials per participant. Each trial begins with the participant indicating that they are ready to receive a thermal stimulus with the space-bar. Stimulation began after a 0-4s jitter period at 32 degrees Celsius, escalating a half-degree per second and continuing to escalate until the thermode reached a maximum of 49 degrees Celsius, after which the trial would terminate. Participants indicated with the spacebar at the moment they could detect a change in temperature, which indicated their detection threshold. A subsequent spacebar press terminated the stimulation and indicated their pain threshold. Following a subsequent randomized jitter period of 0-4s, a 13s stimulation pulse, peaking at the terminated temperature for 10s with a 1.5s ramp-up and ramp-down time, was delivered to a body site. The participants were then instructed to make unipolar responses to questions regarding the perceived stimulus intensity (‘How intense was that overall?’) and bipolar responses to valence (‘How pleasant was that overall?’) of that pulse using a mouse-pointer. Responses were made on a generalized Labeled Magnitude Scale (gLMS; 141 , 142 with anchors for No Sensation (0% of scale length), Barely Detectable (1.4% of scale length), Weak (6.1% of scale length), Moderate (17.2% of scale length), Strong (35.4% of scale length), Very Strong (53.3% of scale length), and Strongest Imaginable Sensation (100% of scale length). Neuroimaging Sessions During each neuroimaging session, participants were tasked to undergo runs (i.e., scan repetitions) consisting of 8.89 minutes of pseudo-randomized presentations of eight high-heat trials (bodysite-specific pain threshold temperature), eight low-heat ‘warm’ trials (bodysite-specific detection threshold + 1 degree celsius), or 24 no- stimulation trials, 12 of which were prepended with a cue to imagine high-heat stimulation. The Peltier thermode was manually positioned to stimulate each body site, randomized and then signaled to the experimenter by an instruction message prior to each run. Participants were presented with an imagination instruction (featured in 109 that they were asked to memorize prior to the first run. Each trial consisted of either a white fixation cross during which the individual would receive a 13 second thermal stimulation, or a 2-second picture cue of the designated body site with a text reminder instruction to imagine followed by 11 seconds of an orange fixation cross indicating the need to sustain the imagination. Upon completion of a run, participants were then instructed to use a slider to rate (1) the valence and (2) the stimulus intensity of the most painful thermal event, and (3) their overall bodily comfort after the run using a gLMS scale. The imagination instruction prior to the start of each fMRI session was as follows: “During this scan, you will occasionally see picture cues of a body part where the thermode is attached. When you see this, try to imagine as hard as you can that the thermal stimulations are more painful than they are. Try to focus on how unpleasant the pain is, for instance, how strongly you would like to remove yourself from it. Pay attention to the burning, stinging and shooting sensations. You can use your mind to turn up the dial of the pain, much like turning up the volume dial on a stereo. As you feel the pain rise in intensity, imagine it rising faster and faster and going higher and higher. Picture your skin being held up against a glowing hot metal or fire. Think of how disturbing it is to be burned, and visualize your skin sizzling, melting and bubbling as a result of the intense heat.” During fMRI data acquisition, imagination trials were prepended by the following reminder text: “Picture your [bodysite] being held up against a glowing hot metal or fire. Visualize the skin on your [bodysite] sizzling, melting and bubbling.” FMRI Data Acquisition All imaging data were recorded with a 3.0T Siemens PRISMA scanner (Siemens Medical Systems, Erlangen, Germany) at the Dartmouth Brain Imaging Center. Acquisition was conducted with a 32 channel headcoil at a TR of 460 milliseconds. 2.7mm isotropic (TE=27.2ms, flip angle 44, matrix 32 x 32, field of view 220mm). A high resolution 0.8mm isotropic T1-weighted structural MRI (TR=2s, TE=2.11ms, Flip angle 8 degrees, FOV: 256mm) was acquired for each participant. Participants’ heads were immobilized by inflatable cushions and foam pillows. Analysis Self-Report All self-report data were analyzed using Matlab (MATLAB, The MathWorks, Inc., Natick, Massachusetts, United States). For the Ascending Method of Limits Calibration, participants rated the intensity and valence of each heat- stimulation trial. For each fMRI run, participants rated the intensity and valence of the highest-pain sensation they felt after every fMRI run, and their overall body comfort to assess the safety of continued scanning. Preprocessing To mitigate the effects of magnetic disequilibrium at the start of each run, six TRs (2.76 seconds) were spent to allow the scanner to reach a steady state, and were discarded during subsequent analysis. Results included in this manuscript come from preprocessing performed using fMRIPrep 23.1.4 143 , 144 ; RRID:SCR_016216), which is based on Nipype 1.8.6 145 , 146 ; RRID:SCR_002502). Preprocessing of B0 inhomogeneity mappings. For each participant, a B0-nonuniformity map (or fieldmap) was estimated based on two (or more) echo-planar imaging (EPI) references with FSL topup 147 . Anatomical data preprocessing. All T1-weighted (T1w) images were corrected for intensity non-uniformity (INU) with N4BiasFieldCorrection 148 , distributed with ANTs 149 , RRID:SCR_004757). The T1w-reference was then skull-stripped with a Nipype implementation of the antsBrainExtraction.sh workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using FSL fast, RRID:SCR_002823 150 . An anatomical T1w-reference map was computed after registration of T1w images (after INU-correction) using mri_robust_template (FreeSurfer 7.3.2 151 ). Brain surfaces were reconstructed using recon-all (FreeSurfer 7.3.2, RRID:SCR_001847 152 , and the brain mask estimated previously was refined with a custom variation of the method to reconcile ANTs-derived and FreeSurfer- derived segmentations of the cortical gray-matter of Mindboggle (RRID:SCR_002438 153 ). Functional data preprocessing. For each of the BOLD runs found per participant (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated by aligning and averaging single-band references (SBRefs). Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using mcflirt (FSL, 154 . The estimated fieldmap was then aligned with rigid-registration to the target EPI (echo-planar imaging) reference run. The field coefficients were mapped on to the reference EPI using the transform. The BOLD reference was then co-registered to the T1w reference using bbregister (FreeSurfer) which implements boundary-based registration 155 . Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. First, a reference volume and its skull-stripped version were generated using a custom methodology of fMRIPrep. Several confounding time-series were calculated based on the preprocessed BOLD: framewise displacement (FD), DVARS and three region-wise global signals. FD was computed using two formulations following Power (absolute sum of relative motions, 156 and Jenkinson (relative root mean square displacement between affines 154 ) FD and DVARS are calculated for each functional run, both using their implementations in Nipype (following the definitions by 156 ). The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction (CompCor 157 ). Principal components are estimated after high-pass filtering the preprocessed BOLD time-series (using a discrete cosine filter with 128s cut-off) for the two CompCor variants: temporal (tCompCor) and anatomical (aCompCor). tCompCor components are then calculated from the top 2% variable voxels within the brain mask. For aCompCor, three probabilistic masks (CSF, WM and combined CSF+WM) are generated in anatomical space. The implementation differs from that of Behzadi et al. in that instead of eroding the masks by 2 pixels on BOLD space, a mask of pixels that likely contain a volume fraction of GM is subtracted from the aCompCor masks. This mask is obtained by dilating a GM mask extracted from the FreeSurfer’s aseg segmentation, and it ensures components are not extracted from voxels containing a minimal fraction of GM. Finally, these masks are resampled into BOLD space and binarized by thresholding at 0.99 (as in the original implementation). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the k components with the largest singular values are retained, such that the retained components’ time series are sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components are dropped from consideration. The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each 158 . Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardized DVARS were annotated as motion outliers. Additional nuisance timeseries are calculated by means of principal components analysis of the signal found within a thin band (crown) of voxels around the edge of the brain, as proposed by 159 . The BOLD time-series were resampled into standard space, generating a preprocessed BOLD run in MNI152NLin2009cAsym space. First, a reference volume and its skull-stripped version were generated using a custom methodology of fMRIPrep. The BOLD time-series were resampled onto the following surfaces (FreeSurfer reconstruction nomenclature): fsnative, fsaverage6. The BOLD time-series were resampled onto the left/right- symmetric template “fsLR” 160 . General Linear Modelling (GLM) Participant-Session Level GLM First-level GLM analyses were conducted in SPM12 at the level of each participant’s session, using SPM FAST to account for temporal autocorrelation 161 , 162 . The experimental runs for each session were concatenated and boxcar regressors, convolved with the canonical hemodynamic response function, were constructed to model periods for the 13s thermal stimulations (hot and warm), 2s imagination cue presentation, 11s imagination period, and post- trials rating periods. Resting fixation cross epochs were used as an implicit baseline. Each session was high-pass filtered at 180s. Nuisance covariates included 24 motion regressors, 5 temporal components (tCompCor), 5 anatomical components (aCompCor), and cerebrospinal fluid from fmriprep output. Spike regressors were also generated using the CANlab Tool outliers() that implements a number of spike detection methods and flags outlying images using Mahalanoubis distance. This procedure resulted in a beta image for each participant’s run. Participant-Level GLM Participant-level GLM analyses were conducted by first smoothing each participant’s run beta images with a 6mm Full-Width Half-Max smoothing kernel and subjecting these images to robust regression using the regress(‘robust’) command from CANlab Tools. The design matrix used Warm images as the intercept, and included dummy-coded condition regressors of interest (Hot vs. Warm and Imagine vs. Warm). Eight regressors were used to average body site effects (using the CANlab Tool create_orthogonal_contrast_set() ), and Session, Run, and a pain report metric for each run were centered and included as regressors of no interest. The pain metric was computed from each participant’s intensity and valence ratings, normalized by the pain threshold temperature at the respective body site as follows: The resulting contrasts of interest (Hot vs. Warm and Imagine vs. Warm) for each participant were thresholded with the false-discovery rate (FDR 68 , q < .05 to make inferences about whether a voxel was activated or deactivated for a participant’s contrast. Group-Level Univariate Analyses Group-level GLM analyses were conducted by taking the resulting participant-level contrast images of interest and performing robust parcel wise regression using 518 regions of interest (ROIs i.e., parcels) defined by the CANLab 2024 atlas and the robust_parcelwise() command from CANlab Tools. The results were thresholded with the false- discovery rate, q < .05 across ROIs to make inferences about whether an ROI was activated or deactivated on average. Precision Functional Mapping with Support Vector Machine Precision functional mapping was performed by training a support vector machine (SVM) experienced pain classifier, classifying hot from warm images for each body site in each participant. Using the xval_SVM() function from CANlab Tools, we trained SVM models on each participant’s unsmoothed, run-level replicate images for each body site. Patterns were extracted from CANlab 2024 atlas ROIs that were downsampled to their second level ( labels_2 ) and bilaterally merged to allow for adequate full-body pattern representation within 131 regions (Supplementary Figure S5). Classification was cross-validated using a leave-one-session-out approach, yielding a Hot-Warm SVM body site weight map for each fold of the data. To assess the consistency of body site-specific pain representations, pattern expression was computed with the dot product for each body site’s Hot-Warm weight maps against that participant’s unsmoothed, run-level images across all conditions (Hot, Warm, and Imagine) and all body sites. For each ROI, a mixed-effects model was fit to predict pattern expression, with the Warm condition at the same body site as the intercept. Regressors included dummy-coded terms for Condition (Hot vs. Warm, Imagine vs. Warm), Body Site Comparison (Target vs. Other Body Site), their interaction, and participant-level nesting with random slopes and intercepts. A priori contrasts were specified as follows: Conditional Averages Warm: Target Body Site (Intercept Model) Warm: Other Body Site – Warm: Target Body Site Hot: Target Body Site – Intercept Imagine: Target Body Site – Intercept Hot: Other Body Site – Hot: Target Body Site Imagine: Other Body Site – Imagine: TargetBody Site Tests of Body Site-Selectivity: Hot: Target Body Site – Hot: Other Body Site Imagine: Target Body Site – Imagine: Other Body Site Hot: Target Body Site – Warm: Other Body Site Imagine: Target Body Site – Warm: Other Body Site Tests of Pain Body Site-Selectivity: (Hot: Target Body Site – Hot: Other Body Site) – (Warm: Target Body Site – Warm: Other Body Site) (Imagine: Target Body Site – Imagine: Other Body Site) – (Warm: Target Body Site – Warm: Other Body Site) A brain region was considered body site-selective in experienced or imagined pain if (1) it exhibited significant, positive pattern expression in the Hot or Imagine condition and (2) expression was significantly greater for the target body site than other body sites. The involvement was considered pain-specific if it also significantly differentiated from the Warm condition. Statistical significance was determined using false discovery rate (FDR) correction across regions (q < .05). Body Site Mapping Trial-level gifti images for the cortex were first smoothed with a 6mm FWHM kernel, then subjected to robust regression to generate associated t-maps for each body site from each participant. The first hot-stimulation trial of each run was removed because of the outsized whole-brain activation intensity induced by these trials. The t-maps were averaged by body site within participant (∼5 session maps per body site per participant), before being averaged by body site across participants (9 participant maps per body site). The resulting eight body site t-maps were subjected to winner-take-all analysis, such that the highest positive t-value at each gifti vertex on the cortical surface across the eight maps took on the color of the respective body site. Representational Similarity Analysis Representational similarity matrices (RSMs) were computed using Spearman’s rho spatial correlations on unsmoothed run-level data for each participant. Images were averaged within each of the three conditions (Hot, Warm, and Imagine) across the eight body sites, resulting in 24 total conditions and a corresponding 24 × 24 RSM. This allowed us to assess representational similarity within each condition and across body sites, both within and between conditions, for each participant. To control for potential session-related confounds, cross-condition body site diagonals were recomputed while excluding sessions that contained the target body site, as Hot, Warm, and Imagine conditions for a given body site were always collected within the same run. Participant-level RSMs were then averaged to create a 24 × 24 group-level RSM. Separate RSMs were generated for the whole brain, as well as for each CANlab 2024 ROI (Supplementary Figure S5). To evaluate overall similarity within and across conditions, we conducted a series of one-tailed t -tests across participants after applying Fisher’s r -to-Z transformation to address the non-normality of the rho statistic. Tests were conducted for each of the main conditions (hot, warm, and imagine vs. 0) and cross-conditions (hot and warm, hot and imagine, imagine and warm) reflecting our a priori hypothesis that there are voxelwise patterns that reflects each condition across body sites, and that these patterns recapitulate themselves positively across conditions. We also tested condition contrasts (e.g., Hot vs. Warm) as well as cross-conditional contrasts (Hot and Imagine vs. Hot and Warm). Statistical significance was initially determined using false discovery rate (FDR) correction across regions (q < .05), and the stability of the results were further examined with a more stringent Bonferroni correction across regions. An ROI was determined to be related to Imagination if the cross-condition correlation between either the Imagine and Warm or the Imagine and Hot were significantly greater than that of the Hot and Warm. An ROI was determined to be related to pain imagination if cross-condition correlations between Imagination and Hot conditions were significantly greater across participants relative to cross-condition correlations between Imagination and Warm and/or Hot and Warm. Data and Code Availability We used the open-source software fMRIprep for image preprocessing and the CANLab Tools in Matlab to write custom code for data processing, which is available on GitHub at ( http://github.com/canlab ). Data can be provided upon request. Funder Information Declared National Institute of Mental Health, https://ror.org/04xeg9z08 , R37MH076136 Hitchcock Foundation References 1. ↵ Taylor , S. E. 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