{"paper_id":"22dfc6a4-92bd-4a77-b6f3-7baea7c8bc06","body_text":"Running head: NEURAL SIGNATURE OF EMOTION REGULATION CAPABILITIES \n1 \n \n \n \n \nThe elusive neural signature of emotion regulation capabilities: \nevidence from a large-scale consortium \n \nMaurizio Sicorello¹,²,*, Jenny Zaehringer¹, Lena Paschke³, Rosa Steimke⁴, Christine Stelzel⁵, \nPeter J. Gianaros⁶, Kevin S. LaBar⁷, John L. Graner⁷, Sang H. Kim⁸, Michèle Wessa⁹,¹⁰,¹¹,², \nMagdalena Sandner¹², Franziska Weinmar¹³, Birgit Derntl¹³,¹⁴, Thomas E. Kraynak¹⁵, Nathan \nT.M. Huneke¹⁶,¹⁷, Harry Fagan¹⁶,¹⁷, Nils Kohn¹⁸, Guillén Fernández¹⁸, Linlin Yan¹⁸, Agar \nMarín-Morales¹⁹,²⁰, Juan Verdejo-Román²⁰, Trevor Steward²¹, Ben J. Harrison²¹, Christopher \nG. Davey²¹, Denise Dörfel²²,²³, Henrik Walter³, Maital Neta²⁴,²⁵, Jordan Pierce²⁴,²⁵, David S. \nStolz²⁶, Johanna Kissler²⁷, Anissa Benzait²⁷, Susanne Erk³, Stella Berboth²⁸, Carien M. van \nReekum²⁹, Emma Tupitsa²⁹, Satja Mulej Bratec³⁰,³¹, Christian Sorg³¹,³²,³³, Laura Müller-\nPinzler²⁶, Andrzej Sokołowski³⁴, Wojciech Ł. Dragan³⁵, Monika Folkierska-Żukowska³⁶, \nValerie L. Jentsch³⁷, Christian J. Merz³⁷, Christoph Scheffel²³, Kersten Diers²³, Kaoru \nNashiro³⁸, Steve Heinke³⁹,⁴⁰, Jungwon Min³⁸, Mara Mather³⁸, Anne Gärtner²³, Kateri McRae⁴¹, \nJohn P. Powers⁴², Nick Doren⁴³, Silvia U. Maier⁴³, Stephan Nebe⁴³, Isabel Dziobek⁴⁴,⁴⁵, \nMichael Gaebler⁴⁶,⁴⁷, Judith K. Daniels⁴⁸,⁴⁹, Matthias Burghart⁵⁰,⁵¹, Stephanie N. L. Schmidt⁵⁰, \nLena Hofhansel⁵², Ute Habel⁵², Carmen Morawetz⁵³ \n \n*Correspondence should be addressed to Maurizio Sicorello, C4, 11, 68159 Mannheim; e-\nmail: maurizio.sicorello@zi-mannheim.de \n  \n  \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.670843doi: bioRxiv preprint \n\nRunning head: NEURAL SIGNATURE OF EMOTION REGULATION CAPABILITIES \n2 \n \n¹ Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental \nHealth, Medical Faculty Mannheim, Heidelberg University, Germany \n² German Center for Mental Health (DZPG), Partner Site Mannheim-Heidelberg-Ulm \n³ Charité - Universitätsmedizin Berlin, Department of Psychiatry and Neurosciences, \nDivision Mind and Brain, Berlin, Germany \n⁴ Department of Psychology, Division of Clinical Psychology and Psychotherapy in Adults, \nUniversity of Bremen, Bremen, Germany \n⁵ International Psychonanalytic University Berlin, Berlin, Germany \n⁶ University of Pittsburgh, Department of Psychology, Center for Mind-Body Science and \nHealth \n⁷ Center for Cognitive Neuroscience, Duke University, Durham, NC USA \n⁸ Department of Brain and Cognitive Engineering, Korea University \n⁹ Central Institute of Mental Health, Department of Neuropsychology and Psychological \nResilience Research, Mannheim, Germany \n¹⁰ DKFZ Hector Cancer Institute at the University Medical Center Mannheim, Germany \n¹¹ German Cancer Research Center (DKFZ) Heidelberg, Division Cancer Survivorship and \nPsychological Resilience, Germany \n¹² Johannes Gutenberg-University Mainz, Department of Clinical Psychology and \nNeuropsychology, Mainz, Germany \n¹³ Department of Psychiatry and Psychotherapy, Women’s Mental Health and Brain Function, \nTübingen Center for Mental Health (TüCMH), University of Tübingen, Tübingen, Germany. \n¹⁴ German Center for Mental Health (DZPG), partner site Tübingen, Germany \n¹⁵ Department of Psychiatry, University of Pittsburgh \n¹⁶ University Department of Psychiatry, Faculty of Medicine, University of Southampton, UK \n¹⁷ Hampshire and Isle of Wight Healthcare National Health Service Foundation Trust, \nSouthampton, UK \n¹⁸ Radboud University Medical Center, Department of Medical Neuroscience, Donders \nInstitute for Brain, Cognition and Behaviour, Nijmegen, Netherlands \n¹⁹ Department of Social, Developmental and Educational Psychology, University of Huelva, \nSpain \n²⁰ Department of Personality, Evaluation and Psychological Treatment; Mind Brain and \nBehavior Research Center (CIMCYC-UGR), University of Granada \n²¹ The University of Melbourne, Victoria, Australia \n²² TUD Dresden University of Technology, Center for Interdisciplinary Digital Sciences, \nDresden, Germany \n²³ TUD Dresden University of Technology, Faculty of Psychology, Chair of Differential and \nPersonality Psychology, Dresden, Germany \n²⁴ Department of Psychology, University of Nebraska-Lincoln \n²⁵ Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln \n²⁶ University of Lübeck, Department of Psychiatry and Psychotherapy, Social Neuroscience \nLab \n²⁷ Bielefeld University, Department of Psychology, Bielefeld, Germany \n²⁸ Charité Universitätsmedizin Berlin, Department of Psychiatry and Neurosciences, Berlin, \nGermany \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.670843doi: bioRxiv preprint \n\nRunning head: NEURAL SIGNATURE OF EMOTION REGULATION CAPABILITIES \n3 \n \n²⁹ Centre for Integrative Neuroscience and Neurodynamics, School of Psychology and \nClinical Language Sciences, University of Reading, Reading, UK \n³⁰ Department of Psychology, Faculty of Arts, University of Maribor, Slovenia \n³¹ Department of Neuroradiology, Technische Universität München, Munich, Germany \n³² Department of Psychiatry, Technische Universität München, Munich, Germany \n³³ TUM-Neuroimaging Center, Technische Universität München, Munich, Germany \n³⁴ Stanford University, Stanford, USA \n³⁵ Department of Psychology, Jagiellonian University, Kraków, Poland \n³⁶ University of Toronto Mississauga, Mississauga, Canada \n³⁷ Ruhr University Bochum, Institute of Cognitive Neuroscience, Department of Cognitive \nPsychology \n³⁸  Leonard Davis School of Gerontology, University of Southern California, Los Angeles, \nUSA \n³⁹ Human-IST Institute, University of Fribourg, Fribourg, Switzerland \n⁴⁰ Center of Economic Psychology, University of Basel, Basel, Switzerland \n⁴¹ University of Denver, Department of Psychology \n⁴² University of North Carolina at Chapel Hill, North Carolina Translational and Clinical \nSciences Institute \n⁴³ Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, \nZurich, Switzerland \n⁴⁴ Clinical Psychology of Social Interaction, Humboldt-Universität zu Berlin \n⁴⁵ German Center of Mental Health (DZPG), partner site Berlin-Potsdam, Germany \n⁴⁶ Max Planck Institute for Human Cognitive and Brain Sciences, Neurology Department, \nLeipzig, Germany \n⁴⁷ Charité - Universitätsmedizin Berlin \n⁴⁸ Department of Clinical Psychology, University of Groningen, The Netherlands \n⁴⁹ Traumacentrum Beilen, GGZ Drenthe, The Netherlands \n⁵⁰ University of Konstanz, Department of Clinical Psychology and Psychotherapy, Konstanz, \nGermany \n⁵¹ Max Planck Institute for the Study of Crime, Security and Law, Freiburg im Breisgau, \nGermany \n⁵² Department of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen University \n⁵³ Department of Psychology, University of Innsbruck, Austria \n \n \n \n  \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.670843doi: bioRxiv preprint \n\nRunning head: NEURAL SIGNATURE OF EMOTION REGULATION CAPABILITIES \n4 \n \nAbstract \nCognitive reappraisal is a fundamental emotion regulation strategy for mental and \nphysical well-being, but how its neural mechanisms relate to individual differences remains \npoorly understood. In a consortium effort analyzing 40 fMRI datasets (N=2,175), we examined \nthe relationship between neural activation during reappraisal tasks and three core individual \ndifference indices of reappraisal capabilities: (1) trait questionnaires, (2) task-based affective \nratings, and (3) amygdala down-regulation. Strikingly, there was no shared overlap across these \nthree common indices. Only a very weak correlation emerged between amygdala down-\nregulation and task-based affective ratings. Whole-brain analyses revealed no reliable neural \nassociations with trait questionnaires, and associations with task-based affective ratings fell \noutside canonical emotion regulation networks (e.g., prefrontal circuitry). Moreover, amygdala \ndown-regulation, often interpreted as a stable individual marker, was confounded by person-\nspecific whole-brain responses — a limitation extending to fMRI research beyond the emotion \nregulation domain. These findings challenge the assumption that an individual’s prefrontal \nactivity is a valid indicator of their reappraisal capabilities and suggest that common trait, \nbehavioral, and neural measures might capture distinct facets of emotion regulation. More \nbroadly, our results highlight concrete methodological challenges for fMRI research on \nindividual differences, with implications extending beyond emotion regulation to the \nneuroscience of personality, psychopathology, and general well-being. \n \n \n  \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.670843doi: bioRxiv preprint \n\nRunning head: NEURAL SIGNATURE OF EMOTION REGULATION CAPABILITIES \n5 \n \nIntroduction  \nEmotion regulation is a core determinant of mental and physical health (Sheppes et al., \n2015) (Sheppes et al., 2015). It encompasses the strategies people use—intentionally or \nautomatically—to influence their emotional experience (Braunstein et al., 2017; Thompson, \n1994). Among these, cognitive reappraisal—changing the interpretation of a situation to alter \nits emotional impact—has received particular attention in psychology, psychiatry, and the \nbroader public. It is central to cognitive-behavioral therapy and linked to resilience, lower \nsymptom burden, and improved daily affect (D’Agostino et al., 2017).  \n Neuroimaging studies have extensively mapped the neural basis of reappraisal. \nFunctional Magnetic Resonance Imaging (fMRI) consistently implicates a fronto-parietal \nnetwork, including lateral and medial prefrontal regions, often along with reduced amygdala \nactivation during reappraisal of negative stimuli (Buhle et al., 2014; Min et al., 2022; Morawetz \net al., 2017; Powers & LaBar, 2019). These findings are based primarily on within-person \ndesigns that contrast experimental conditions like “reappraise” versus “permit emotion” in the \nsame individuals. Yet, many studies — and broader theories — extend these neural within-\nperson mechanisms to explain between-person individual differences in well-being or \npsychopathology (Picó-Pérez et al., 2017; Sicorello & Schmahl, 2021), which is not necessarily \nvalid. \nInferences from within- to between-person levels often fall prey to the ecological \nfallacy (Kievit et al., 2013). This common phenomenon occurs when within- and between-\nperson associations do not have the same causal structure (Rohrer & Murayama, 2023). For \ninstance, people who habitually use reappraisal tend to report better mental health than other \npeople (between-person level; comparison between individuals). Yet in everyday life, \nreappraisal often occurs in response to situational stressors and may therefore coincide with a \nworse mood in that moment (within-person level; comparison between conditions). Similarly, \npsychologically meaningful individual differences in neurovascular factors — such as fitness \nor age — can influence the strength of global fMRI responses, potentially leading to ecological \nfallacies and complicating comparisons between people (Fabiani et al., 2014; Sicorello et al., \n2025). \nCompounding this issue, regions activated during reappraisal also subserve other \nfunctions, making it unclear whether they reflect regulation capabilities per se (Kragel et al., \n2018; Poldrack, 2011).In most studies, their relation to self-reported emotion regulation \ncapabilities is not tested directly, even on a within-person level (though see: Bo et al., 2024; \nOchsner et al., 2002; Wager et al., 2008). Moreover, task designs may not mirror naturalistic \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.670843doi: bioRxiv preprint \n\nRunning head: NEURAL SIGNATURE OF EMOTION REGULATION CAPABILITIES \n6 \n \nreal-life regulation, raising questions about their validity and practical relevance for important \nindividual differences (Enkavi & Poldrack, 2020; McDermott et al., 2018; Powers & LaBar, \n2019; Sicorello et al., 2025). Meta-analyses on reappraisal in mental disorders have yielded \nmixed results, with inconsistent findings for commonly implicated regulatory regions like the \ndorsolateral prefrontal cortex (dlPFC; Khodadadifar et al., 2022; Morawetz et al., 2025; \nSicorello & Schmahl, 2021). A recent systematic review found partial convergence in the \nhypothesized fronto-limbic circuits, but studies were underpowered (median N = 25), \nheterogeneous, and too few for formal meta-analysis (Morawetz & Basten, 2024). \nTo address the uncertainty surrounding the neural correlates of individual differences \nin reappraisal capabilities, we conducted a pre-registered, well-powered analysis of 40 task-\nbased fMRI datasets (N = 2,175; Table S1), aggregated by the newly founded Neurobiology of \nIndividual Differences in Emotion Regulation (NIDER) consortium. We examined three \nwidely used indices of reappraisal capability (Dörfel et al., 2020): (1) self-reported habitual \nreappraisal ( trait questionnaires ), (2) task-based reductions in negative affect ( task-based \naffective ratings ), and (3) task-based reductions in amygdala activation ( amygdala down-\nregulation). This enabled us to ask whether these indices reflect a shared underlying core \nconstruct—and whether individual differences in reappraisal capability, as indexed by each \nmeasure, are systematically associated with neural activity in a priori defined regulatory neural \nnetworks. \n \nResults \nConvergence between reappraisal capabilities from trait questionnaires, task-based \naffective ratings and amygdala down-regulation \nIn a first step, we aimed to assess the overlap between the three main outcome measures \nof cognitive reappraisal capabilities. If these three measures represent a similar core construct \nthey should show a meaningful association. There was a small statistically significant \nassociation between amygdala down-regulation and task-based affective ratings (Table 1). As \nhypothesized, participants with stronger down-regulation of the amygdala via cognitive \nreappraisal also showed a better down-regulation of task-based affective ratings. Yet, the effect \nsize was comparatively small. To detect this effect size with a statistical power of 80%, a single \nstudy would need to test 1105 participants. There was no significant association between trait \nquestionnaires and either outcome, i.e., amygdala down-regulation or task-based affective \nratings. The overlap (shared variance) between the three outcome measures is shown in Figure \n1, which indicates no meaningful overlap between all three outcomes.  \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.670843doi: bioRxiv preprint \n\nRunning head: NEURAL SIGNATURE OF EMOTION REGULATION CAPABILITIES \n7 \n \nBetween-study heterogeneity was not statistically significant for all three tests and was \nparticularly small for the non-significant correlations, indicating these null findings are \nunlikely to be explained by differences in study design. Forest plots are shown in Figures S1-\nS3.  \n \nTable 1             \nMeta-analytic associations between task-based affective ratings, trait questionnaires, and amygdala \ndown-regulation \n Effects  Heterogeneity  Sample \n r 95% CI p  τ 95% \nCI p  k N \nTask-based \naffective \nratings & \nAmygdala \n0.08 [0.02, \n0.15] 0.010 \n \n0.07 [0.00, \n0.18] 0.172 \n \n26 1377 \nTask-based \naffective \nratings & \nQuestionnaire \n0.05 [-0.00, \n0.10] 0.067 \n \n0.00 [0.00, \n0.10] 0.835 \n \n28 1397 \nAmygdala & \nTrait \nQuestionnaires \n0.01 [-0.05, \n0.06] 0.807 \n \n0.06 [0.00, \n0.15] 0.203 \n \n33 1833 \nNote. Results from pair-wise random effects meta-analyses on correlations. k = number of studies \nincluded in the individual meta-analysis. \n \nAssociations with regulatory neural networks \nTrait questionnaires \nThere was no statistically significant association between trait questionnaires and \nneural activity during reappraisal. The correlations with average activity in the two predefined \nnetworks were negligibly small (both |r| ≤ .04, p > .18, uncorrected; k = 37, N = 2091) and no \nvoxels survived the correction for multiple comparisons in the voxel-wise network of interest \nor the whole-brain approach. All brain-wide effect sizes were smaller than |r| ≤ .11. These null \nresults were stable when using a jackknife procedure, i.e., repeating the meta-analysis while \nleaving out one study at a time. For separate analyses on single studies, only three had \nsignificant voxels after FDR correction, with relatively low numbers, which is still expected \nby chance with the current number of studies (Gianaros et al. [2020]: 2 voxels; Dörfel et al. \n[2014]: 9 voxels; Sandner et al. [2021]: 22 voxels).  \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.670843doi: bioRxiv preprint \n\nRunning head: NEURAL SIGNATURE OF EMOTION REGULATION CAPABILITIES \n8 \n \n \n \nFigure 1. Overlap (shared variance) between the three outcome measures together with the \ncorresponding meta-analytic correlations between pairs of outcomes. \n \nTask-based affective ratings \nAs for trait questionnaires, there was no statistically significant association between \naverage down-regulation of task-based affective ratings and average neural activity during \nreappraisal in the preregistered regulatory networks (both |r| < .03, p > .22; k = 31, N = 1958). \nNo voxels within these networks survived correction for multiple comparisons in voxel-wise \nanalyses. Nevertheless, outside of the regulatory network there was a significant negative \nassociation in a cluster of 86 voxels. The study by Brehl and colleagues (2021) strongly \ninfluenced the results according to the jackknife procedure (Figure S4). Its exclusion led to a \nsubstantial increase to 586 significant voxels, mainly covering regions in the somatomotor and \ndorsal attention network, but also smaller clusters in the visual cortex, caudate nucleus and \nbrainstem (Figure 2C, Table S2). The association between these brain regions and task-based \naffective ratings was negative, indicating better down-regulation of negative emotions was \nassociated with decreased activations in these areas (average r = -.11, range = -.16–-.09). \nNeurosynth decoding suggested the meta-analytic activation map is most similar to language \nprocessing and most dissimilar to motoric activations.  \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.670843doi: bioRxiv preprint \n\nRunning head: NEURAL SIGNATURE OF EMOTION REGULATION CAPABILITIES \n9 \n \nWe repeated the whole-brain analyses separately for each single study. There were 151 \nsignificant voxels in Brehl et al. (2021) and 276 significant voxels in Gianaros et al. (2020). \nWhile it is still within chance-based expectations to have two out of 28 studies show significant \nresults, it is still noteworthy that these were the two studies with the largest sample sizes (242 \nand 176 respectively). Moreover, these results included many regions which are part of \nnetworks implicated in cognitive reappraisal and emotion processing, such as the amygdala, \nthe hypothalamus, the ventral and dorsal attention networks as well as a fronto-parietal \nnetwork, but also the somatosensory cortex and the default mode network (Bo et al., 2024). \n \nAmygdala down-regulation \nA person’s ability to either down-regulate their amygdala or up-regulate regulatory \nregions is often interpreted as an indicator of interindividual reappraisal capability. Such \nindices have been criticized as there is evidence that people differ in their global brain \nresponses, likely due to biological confounders (Fabiani et al., 2014), which might dominate \nany specific regional effects and dilute their usefulness as a marker of an individual’s emotion \nregulation capability (Sicorello et al., 2025). In the case of reappraisal, this might be \nparticularly problematic as opposite effects are expected for regulatory regions and emotion \ngenerating regions like the amygdala. \n In support of this criticism, across studies, participants with stronger amygdala \nresponses during reappraisal also had substantially stronger responses in the rest of the brain \nin comparison to other participants (Figure S5; mean r = .27, range =-.08–.68). This included \nthe two regulatory networks (first network: r < .29, p < .001; second network: r < .24, p < .001; \nk = 35, N = 2088) and generally most parts of the brain, with significant positive correlations \nfor over 170,000 voxels versus only one voxel with a negative correlation (Figure 2D). Hence, \na person is unlikely to simultaneously show both lower amygdala responses and higher \nregulatory network responses (e.g., in the dlPFC) when compared to other people using fMRI. \nThis limits the interpretational validity of single specific brain regions for interindividual \ndifferences in reappraisal capabilities, especially when hypothesizing effects in different \ndirections, as is the case for the amygdala and regulatory regions. \n \n \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.670843doi: bioRxiv preprint \n\nRunning head: NEURAL SIGNATURE OF EMOTION REGULATION CAPABILITIES \n10 \n \n \nFigure 2. (A) A priori emotion regulation networks of interest based on a previous meta-\nanalysis (Morawetz et al., 2020). (B-D) Significant brain-wide associations between \nactivation in the contrast [reappraise-view] and the three main outcomes for reappraisal \ncapabilities. All significant correlations had negative signs. Therefore, brighter colours \ncorrespond to larger negative associations. MNI coordinates for slices were: (A) x = 4, y = 0, \nz = 0; (B) x = 4, y = 0, z = 0; (C) x = -44, y = 7, z = 4; (D) x = 0, y = 0, z = 0. \n \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.670843doi: bioRxiv preprint \n\nRunning head: NEURAL SIGNATURE OF EMOTION REGULATION CAPABILITIES \n11 \n \nIn an attempt to test the specificity of any relevant target regions, we tested whether this \nfinding generalizes to other brain regions beyond the amygdala by calculating the average \npairwise between-person correlation for neural responses during reappraisal in 470 anatomical \nregions (Wager, 2024) of a publicly available dataset (N = 33; Wager et al., 2008) . This showed \nthat responses of any two regions across the brain are generally highly correlated on a between-\nperson level ( r = .58, SD = .18), limiting their spatial specificity and the inferential value of \nthis approach. Hence, if a person has a stronger fMRI response in one brain region than other \npeople, that person is likely to have a stronger fMRI response in any other region as well. This \nrepresents a phenomenon that has been previously noted (Jabakhanji et al., 2022), yet \ncommonly overlooked in the study of individual differences, including most research in the \ndomain of well-being and psychopathology.  \nWe further tested whether these high between-person correlations of different brain \nregions are specific to cognitive reappraisal or generalize across other psychological domains. \nWe again calculated the average pairwise between-person correlation for activity in the 470 \nanatomical regions in an openly available multi-task dataset (Kragel et al., 2018). This dataset \ncombines 18 studies across the domains of negative emotion, pain, and cognitive control, each \nrepresented by three subdomains, in turn each represented by two studies ( n = 15 per study, \ntotal N = 270]). Overall, we found the same pattern of overly large bivariate between-person \ncorrelations across the whole brain (mean correlation across domains: r = .37; Figure 3). \nIn sum, this suggests that interindividual differences in specific regional brain \nresponses, like the amygdala or the dlPFC, are strongly confounded by interindividual \ndifferences in global signal responses with implications beyond emotion regulation. \n \n \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.670843doi: bioRxiv preprint \n\nRunning head: NEURAL SIGNATURE OF EMOTION REGULATION CAPABILITIES \n12 \n \n \nFigure 3. Between-person correlations for pairs of regions based on an open dataset with \nthree domains, three subdomains per domain, and two studies per subdomain (n = 15 per \nstudy, total N = 270). Each estimate represents the average correlation between all possible \npairs of 470 regions. Whiskers represent the standard deviation.  \n \n \n \nStatistical power \nWe computed statistical power for a range of correlation effect sizes as a function \nofbetween-study heterogeneity (𝜏), the number of voxels corrected for with the FDR procedure \n(two networks, four networks, and whole-brain), and the number of voxels assumed to be true \npositives (corrected for spatial dependence, for methodological details and validation of the \nprocedure see supplements). Sample sizes were fixed to the values used for the whole-brain \ncorrelations with questionnaires. Results are shown in Figure 4. Given a conservative minimum \nnumber of 100 true positive voxels, we had sufficient statistical power to detect small \ncorrelations between 0.05-0.2, depending on the heterogeneity between studies. Heterogeneity \nwas overall very low in all conducted analyses, indicating the statistical power is likely \nsufficient for the lower end of these effect sizes. \n \n \n \n \n \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.670843doi: bioRxiv preprint \n\nRunning head: NEURAL SIGNATURE OF EMOTION REGULATION CAPABILITIES \n13 \n \n \n \n \n \nFigure 4. Power analyses for a range of hypothetical parameters. Blue lines indicate 80% \npower. Rows show results for different values of between-study heterogeneity (𝜏); columns \nshow the number of tests corrected based on the two preregistered networks of interest, an \nexpanded network of interest using the full results of Morawetz et al. (2020), and whole-brain \ndata after masking for gray matter and a minimum number of 20 studies with valid data. The \nlegend shows the number of voxels assumed to be true positives. Each of these numbers \ncorresponds to a smaller (analytically derived) number of effective (i.e., independent) tests, \nwhich were used as input in the power analysis (1=1, 10=7, 100=34, 1000=45, 10000=52). \n \n \n \n \n \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.670843doi: bioRxiv preprint \n\nRunning head: NEURAL SIGNATURE OF EMOTION REGULATION CAPABILITIES \n14 \n \nDiscussion \n Despite extensive research on the neural basis of cognitive reappraisal, the association \nbetween these neural measures and individual differences in psychologically assessed \nreappraisal capability remain inconsistent. Studies on well-being and psychopathology often \ninterpret reduced prefrontal activation (e.g., in the dlPFC) as reflecting impairments in \ncognitive reappraisal, yet recent meta-analyses show little convergence of such group \ndifferences across or within disorders in frontal regulatory regions (Khodadadifar et al., 2022; \nMorawetz et al., 2025; Sicorello & Schmahl, 2021). Dimensional approaches—linking brain \nfunction to measures of reappraisal capabilities—remain rare and typically underpowered. To \novercome this, we conducted a large-scale, preregistered consortium analysis with a sample \napproximately 20 times larger than the largest sample reported in a recent systematic review \n(Morawetz & Basten, 2024). By pooling whole-brain data from diverse studies, most of which \nhad not previously performed the targeted analyses, we aimed to identify robust neural markers \nof reappraisal capabilities and contribute to a better understanding of neurobiological \nindividual differences. \nThere was a striking lack of convergence between the three indices commonly used to \nindicate reappraisal capabilities: trait questionnaires, task-based affective ratings, and \namygdala down-regulation. Only the association between the latter two—simultaneously \nmeasured task-based affective ratings and amygdala down-regulation—was statistically \nsignificant. The small effect size indicates thatmore than 1000 participants are needed for \nsufficient power, which ismore than 10 times larger than those usually seen in the current \nliterature (Morawetz & Basten, 2024). The low between-study heterogeneity indicates that \nthese results are not due to differences in the included study designs. These findings suggest \nthat the three common indices of reappraisal capability capture qualitatively different facets of \nreappraisal, despite often being interpreted interchangeably. The absence of a common core \namong these indicators challenges the assumption that they reflect a shared underlying \ncapacity. \nSimilarly, there were no brain-behaviour associations between either trait \nquestionnaires or task-based affective ratings and neural activity in established emotion \nregulation networks. However, for task-based affective ratings, we observed significant \nassociations with lower activity in left sensorimotor and dorsal attention networks—regions \nnot commonly found in reappraisal research. The lower responses in dorsal attention networks \nmight relate to a lowered focus on negative external stimuli (Vossel et al., 2014), while the \nlowered sensorimotor responses might relate to the embodied aspects of emotional experiences \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.670843doi: bioRxiv preprint \n\nRunning head: NEURAL SIGNATURE OF EMOTION REGULATION CAPABILITIES \n15 \n \n(Reddan et al., 2024). Neurosynth decoding also indicated an increased language-related \nactivation, consistent with the verbal and cognitive nature of reappraisal strategies. Together, \nthese results suggest that regions outside canonical process-based emotion regulation \nnetworks—including the prefrontal cortex—may better reflect individual differences in \nregulation capability, albeit with still very small effect sizes. \nThe disconnect between trait questionnaires and task-based affective ratings offers a \npartial plausible explanation for the null finding for the association between trait questionnaires \nand task related brain activity. If questionnaires are not matched well to tasks on a behavioral \nlevel, meaningful associations between trait questionnaires and neural activity during these \ntasks seem less likely. The studies aggregated here predominantly used the most common \nreappraisal task (Ochsner et al., 2002) and the two most common reappraisal questionnaires \n(Emotion Regulation Questionnaire [ERQ; Gross & John, 2003]; and Cognitive Emotion \nRegulation Questionnaire [CERQ; Jermann et al., 2006]). Therefore, these results have \nimplications for a large body of research. Both questionnaires measure habitual tendencies, \nrather than ability. This habitual tendency or frequency of using reappraisal is only moderately \ncorrelated with reappraisal abilities in naturalistic daily life settings (Koval et al., 2023). \nCorrelations of these trait questionnaires with measures from experimental settings are even \nlower (Ford et al., 2017), as we have shown here. Regarding tasks, there has been criticism \nthat, for example, distancing oneself from pictorial stimuli by imagining it as not real or being \nviewed from a larger distance (common strategy instructions), might lack ecological validity \nfor reappraisal outside the laboratory (Powers & LaBar, 2019). Hence, more attention should \nbe given to the exact aspect of emotion regulation operationalized in a task or a questionnaire, \nwith a pressing need for novel task designs and more naturalistic stimuli. This is not restricted \nto emotion regulation, but has also been recently shown for emotion generation and a large \nrange of related trait questionnaires measuring negative affectivity (Sicorello et al., 2025). \nImportantly, a mismatch between tasks and questionnaires does not explain the very \nsmall associations between task-based ratings and neural measures. In principle, within-person \nassociations between brain responses and task-based self-reports can be very robust (Chang et \nal., 2015; Zhou et al., 2021). In contrast, between-person associations for person-wise averages \nof the same self-reports appear to have a ceiling around r = .20-.30, even with machine learning \napproaches (Gianaros et al., 2022; Han et al., 2022; Sicorello et al., 2025). One reason may be \nthat standard tasks were designed for process-based research and do not sufficiently elicit \nrelevant individual differences, for example, because they are not sufficiently naturalistic \n(Hedge et al., 2018). Another reason is that people appear to have very different average whole-\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.670843doi: bioRxiv preprint \n\nRunning head: NEURAL SIGNATURE OF EMOTION REGULATION CAPABILITIES \n16 \n \nbrain responses to tasks, related to biological confounds like neurovascularity or noise sources \nsuch as MR-system related fluctuations and subject movement (Fabiani et al., 2014; Huber et \nal., 2024; Liu, 2016; Sicorello et al., 2025; Ward et al., 2020). We found that fMRI responses \nduring reappraisal were highly correlated across regions on a between-person level, including \nthe amygdala and prefrontal cortex. This pattern extended beyond reappraisal to multiple task \ndomains, suggesting that person-level brain-wide responsivity severely limits the \ninterpretational validity of specific regional effects. Accounting for this dependency in the \nfMRI signal for such brain-wide responses is likely a crucial analytic step towards larger and \nmore valid effects (Davis et al., 1998; Sicorello et al., 2025). Yet, as the lively debate around \nglobal signal regression in resting state fMRI research has made clear, such approaches are \nanything but universally accepted (Murphy & Fox, 2017). \nOur study focused on non-clinical samples, as these most often consist of case-control \ndesigns with complex groups concerning their symptom and medication profiles. A \nrepresentative sample would be expected to have 1.3-1.5 times the standard deviation in the \nERQ compared to those studies we included (Preece et al., 2020). While a representative \nsample with this range would lead to only slightly increased effect sizes, the focus on \ntransdiagnostic samples that cover large parts of clinical dimensions could provide important \nextensions of our study. Moreover, most studies in the literature use the same questionnaires, \nwhich was reflected in our study. Questionnaires focusing on reappraisal ability, rather than \nhabitual use, might lead to slightly larger associations. \nWe provide a reusable implementation of image-based meta-analyses together with \naccessible power analyses tools, which might aid similar projects to consolidate the large body \nof fMRI research beyond emotion regulation. Most fMRI studies employ a large range of \nquestionnaires for individual differences, which are usually not tested for brain-wide \nassociations and therefore represent an extremely valuable data resource. Using a meta-analytic \napproach has similar statistical properties as random effects mega-analysis on individual-level \ndata (Eisenhauer, 2021; Koile & Cristia, 2021) and provides an efficient tool to retain and \nquantify the heterogeneity between studies, leading to more generalizable results. Nonetheless, \nstatistical tools alone are insufficient. Our findings challenge widespread assumptions about \nthe neural correlates of individual differences in cognitive reappraisal and call for a more \nconscious and explicit alignment between constructs, measurements, and analytic levels. \nIn summary, our findings challenge prevailing assumptions about the neural correlates \nof interindividual capabilities in cognitive reappraisal and raise fundamental questions about \nhow emotion regulation is measured across levels of analysis. The weak convergence between \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.670843doi: bioRxiv preprint \n\nRunning head: NEURAL SIGNATURE OF EMOTION REGULATION CAPABILITIES \n17 \n \ncommon outcome measures and their inconsistent associations with canonical reappraisal \nnetworks underscore the need for better-aligned behavioral, trait-based, and neural constructs \nfor them to capture a shared core of cognitive reappraisal. This is especially relevant given that \nextremely large-scale resting-state efforts have failed to reveal replicable neural correlates of \nemotion-related traits (Marek et al., 2022; Schulz et al., 2024). Employing tasks that are well-\ndesigned to elicit the individual differences of interest is a crucial strategy for a better \nneurobiological understanding of emotional well-being (Sicorello et al., 2025). Still, we \ndemonstrated that interindividual differences in whole-brain responses during tasks are a \nmethodological issue that likely affects the vast majority of the task-based fMRI literature, \nbeyond emotion regulation. Ultimately, identifying robust and generalizable neural markers of \nself-regulation requires both methodological progress and conceptual clarity in defining what \nwe seek to measure (Brandt & Mueller, 2022; Sicorello et al., 2025). Until then, we should \nrefrain from strong statements concerning a person’s emotion regulation capabilities based on \ntheir prefrontal cortex, amygdala, or any other specific brain region or network. \n \n \nMethods \nConsortium procedures \nStudies were eligible for inclusion if they measured (1) task-based fMRI (2) during a \ncognitive reappraisal task (3) using negative stimuli (4) to compare a down-regulation \ncondition with a maintaining/permitting condition (5) and included a trait-like questionnaire \nmeasure for cognitive reappraisal (6) in a healthy sample. The latter criterion was used to avoid \nexcessive heterogeneity in disorder presentation and medications (see the discussion section). \nContributing research groups were invited using two strategies. First, we conducted a \nsystematic literature search, contacting 64 authors via e-mail or Researchgate. Second, we used \nmailing lists of academic societies to advertise the consortium. In total, this led to 32 \ncontributing research teams with 40 datasets comprising up to 2,175 participants. For an \noverview of study characteristics see Table S1. Nine studies were unpublished and none of the \nstudies performed the relevant analyses for previous publications. Most papers used the ERQ \nto measure reappraisal, while six used the CERQ and only one study used a different \nquestionnaire (FEEL-E). The majority of studies used pictorial stimuli, two studies used videos, \none study used autobiographical memory cues, and one study used Cyberball. \n \n \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.670843doi: bioRxiv preprint \n\nRunning head: NEURAL SIGNATURE OF EMOTION REGULATION CAPABILITIES \n18 \n \nLiterature search \nWe conducted a systematic literature search of articles using the PubMed, Web of \nScience, and PsychINFO databases. We searched for the following keywords: “ emotion \nregulation” cross referenced with “ fMRI” or “ neuroimaging” or “ functional magnetic \nresonance imaging”  or “ functional MRI” . This search process yielded a total of 2,633 \npotentially relevant articles on July 7, 2021 (after duplicates were removed). Two independent \nreviewers (JZ, MS) systematically examined titles and relevant abstracts using the Rayyan \nwebsite (Ouzzani et al., 2016) to determine whether an article fulfills the criteria to be screened. \nThe following criteria were applied: The study included original empirical results, was written \nin English or German, included adult healthy participants, and assessed an explicit emotion \nregulation paradigm during fMRI measurement. In a next step, for all remaining studies full \ntexts were screened by three independent reviewers (JZ, MB, DL) and they were included if \n(1) participants were told to use reappraisal strategies to modulate a negative emotion, (2) an \nemotion regulation questionnaire was assessed (e.g., ERQ), (3) a control condition was \nincluded in which participants were confronted with emotional stimuli but did not regulate their \nemotions, and (4) negative stimuli were used in the emotion regulation task. Finally, a total of \nk = 61 studies fulfilled all inclusion criteria. Relevant articles in the authors’ library were also \nreviewed for titles that might have been missed by our literature search. Studies identified in \nthis manner ( k = 3) were collected for inclusion. Of the k = 64 studies, k = 17 reported the \ncorrelation between fMRI BOLD activation (‘reappraise-view’) and emotion regulation \nquestionnaire data. The authors of all 64 studies were contacted. See Figure S6 for a flow chart \ndepiction of the screening and selection of studies via literature search.  \nData extraction \n Research groups prepared up to three unthresholded group-level statistical images, \ncontaining voxel-wise t-statistics for the between-person correlation of the whole-brain neural \nresponse in the [reappraise - view] contrast and the three outcomes of interest. These outcomes \nwere (1) trait questionnaires of cognitive reappraisal, (2) the person-wise averaged decrease of \ntask-based affective ratings during reappraisal versus view conditions, and (3) and person-wise \naveraged decrease of amygdala responses during reappraisal versus view conditions.  \nA homepage with detailed instructions and a Matlab example analysis script was \nprovided to support analyses. Contributors were instructed to compute the three measures of \nreappraisal capabilities so that higher values correspond to better reappraisal capabilities. For \ntask-based affective ratings, this entailed (re-)coding for higher values to correspond to a more \nnegative emotional response and calculating person-wise averaged down-regulation of affect \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.670843doi: bioRxiv preprint \n\nRunning head: NEURAL SIGNATURE OF EMOTION REGULATION CAPABILITIES \n19 \n \nfrom the contrast [view - reappraise]; hence, a greater reduction in negative affect via \nreappraisal leads to higher values. Similarly, for amygdala down-regulation, average activity \nwas extracted from the [view - reappraise] contrast, as higher values correspond to a better \ndown-regulation of the amygdala for sign congruence with the other two outcomes. A \nstandardized amygdala-mask was provided for all research groups based on the intersection of \nan anatomical amygdala mask (SPM anatomy toolbox v2.2b) and a functional emotion \ngenerating network from an emotion regulation meta-analysis (Morawetz et al., 2020), which \nensures the anatomical and functional specificity of the amygdala region of interest. \n Furthermore, contributors provided direct bivariate correlations between the three \nreappraisal outcomes (e.g., trait questionnaires and task-based affective ratings) as well as \ndemographic and other study information (Table S1). \n Images were quality controlled individually by plotting t-values on a brain template, \nhistograms, and performing brain-wide thresholding at FDR = .05 to check for plausibility. \nCollectively, quality was controlled by checking for outlier studies within the same emotion \nregulation outcome category (e.g., mean whole-brain activation, Mahalanobis distance, and \ncovariance matrices). Two images were removed from whole-brain analyses due to insufficient \ncoverage (Berboth et al., 2021; Morawetz et al., 2016). Effective sample sizes for tests are \nreported in the results section. \n \nStatistical Analyses \nFirst, we assessed the concordance between the three outcome measures (trait \nquestionnaires, task-based ratings, amygdala down-regulation) using random effects meta-\nanalysis implemented in the R package meta using the DerSimonian and Laird approach to \nestimate between-study heterogeneity (Balduzzi et al., 2019). \nSecond, we tested associations between the three outcomes and two neural emotion \nregulation networks based on a recent process-based meta-analysis on emotion regulation \n(Morawetz et al., 2020; networks 1 and 2 ; Figure 2). These networks are thought to be \npredominantly implicated in the cognitive control (versus emotion generating) aspects of \nemotion regulation and mostly include regions in the prefrontal, parietal, and temporal cortex. \nImages were resampled to the same spatial resolution of 2x2x2 mm³. The statistical core \nprocedure was done in three pre-registered stages. In the first stage, we tested the correlation \nbetween average activity in the two networks of interest, respectively, and reappraisal \ncapabilities, with Bonferroni corrections for two tests. In the second stage, we performed voxel-\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.670843doi: bioRxiv preprint \n\nRunning head: NEURAL SIGNATURE OF EMOTION REGULATION CAPABILITIES \n20 \n \nwise tests in the combined two networks with FDR correction based on the Benjamini-\nHochberg procedure (FDR = 0.05, ≈10,000 voxels). In a third stage, we repeated the second-\nstage analysis on the whole brain (≈180,000 voxels). Between-study heterogeneity was tested \nfor statistical significance based on whole-brain data and corrected with the same FDR \nprocedure. \nTo perform the image-based fMRI meta-analysis, we developed custom Matlab code \nwhich interfaces with the fMRI-specific functionality of the CanlabCore toolbox (Wager, \n2024). Our procedure converts study-wise unthresholded t-maps to correlation maps and \ntherefore, together with sample size information, offers voxel-wise estimates of effect size, \nstatistical significance, and between-study heterogeneity via the DerSimonian and Laird \napproach (Borenstein et al., 2009; Bossier et al., 2019). The code was validated by testing \ncongruence with meta-analytic packages in R as well as performing a small meta-analysis on \nsix openly available group-level images of physical pain, which detected typically involved \nbrain regions and was successfully decoded as “pain” using neurosynth (see supplement \n“Testing the custom meta-analytic functions”, Figure S7 for details). \n \nOpen Science Practices \nAims, literature search strategy, and statistical analyses were pre-registered on \nPROSPERO: https://www.crd.york.ac.uk/PROSPERO/view/CRD42021243155. Group-level \nmaps and code are openly provided on https://github.com/MaurizioSicorello/NIDER_project. \nWe provide reusable functions to conduct image-based fMRI meta-analyses in Matlab \n(v2023b).  \nA notable error in the preregistration was the statement that only studies using pictures \nas stimuli are included. Rather, the meta-analysis included all types of stimuli, which we stated \ncorrectly on the accompanying homepage and the mailing list advertisements. Regardless of \nthis error, the overwhelming majority of studies used pictures as stimuli (88%) and the low \nstatistically non-significant between-study heterogeneities as well as the jackknife procedures \nindicate that stimulus type does not influence the results. \n  \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. 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