Common and distinct neurofunctional signatures of emotion regulation strategies and their clinical translation in dynamic naturalistic contexts

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Reappraisal and acceptance are effective ER strategies that form the basis of therapeutic interventions, yet their common and distinct neural signatures - and the translational potential of these signatures - remain unclear. Here, we combined naturalistic fMRI with multivariate predictive modeling to develop neurofunctional signatures that accurately and comprehensively characterize negative affect and its regulation via acceptance and reappraisal in dynamic, immersive contexts (n=59). These signatures demonstrated process-specificity and generalization across cohorts, cultures, and modalities (n=33, n=358, n=33). ER strategies were encoded in distributed, distinguishable neural representations, with shared contributions from the default mode network and strategy-specific contributions from the amygdala, somatomotor and attention (acceptance), and the frontoparietal control (reappraisal) networks. The neuromarkers precisely identified strategy-specific ER impairments in cannabis users (nhealthy_controls=48, ncannabis_users=49), underscoring their translational relevance. We provide comprehensive, clinically-relevant brain models of emotion regulation and dysregulation in naturalistic contexts. Biological sciences/Neuroscience/Emotion Biological sciences/Psychology emotion regulation affect fMRI multivariate pattern analysis addiction cannabis default mode network naturalistic Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Emotion regulation (ER) – the ability to modulate the experience and expression of emotions – is fundamental to adaptive functioning and mental health 1–6 . Impairments in ER represent a core transdiagnostic feature across major mental disorders (MDs) 5,7–9 . Regulation of negative affect can be achieved by several mental strategies, with cognitive reappraisal and acceptance being particularly effective 2,10–13 . Both are central building blocks of psychotherapeutic interventions 14–16 and have a high potential of being applied in everyday emotional challenges 17,18 . While both strategies efficiently regulate negative affect and focus on cognitive changes 13 (see process model of emotion regulation 1,2 ), they differ markedly in their operational and cognitive mechanisms. Reappraisal is based on reinterpreting the subjective meaning of the emotion-inducing event to control its affective impact 19,20 . Its successful implementation critically relies on an array of executive functions, including attention, working memory, and cognitive control, and represents an active and cognitively demanding process 21,22 . In contrast, acceptance involves a nonjudgmental awareness of emotional experiences without attempts to control them 23,24 . As such, it is considered a less cognitively demanding and more passive strategy relying on self-referential processes or metacognitive awareness 22 . Neurobiological models distinguish between brain systems involved in emotional reactivity – such as the amygdala and insula, and systems involved in regulation, including lateral prefrontal and parietal regions 25–27 . In line with the effectiveness of both strategies, fMRI meta-analyses indicate that both effectively modulate activity in subcortical emotion “reactivity” regions, such as the amygdala, 24,28 while enhancing the engagement of ventrolateral prefrontal regulatory regions (vlPFC) 29,30 . Notably, reappraisal has been strongly associated with engagement of the fronto-parietal control network 26,27,31 , reflecting its reliance on regulatory cognitive processes, while acceptance has been associated with an engagement of core regions of the posterior midline default mode network (DMN) 22,24,32–34 , suggesting a distinct neural signature reflective of self-related and introceptive processing. Despite extensive work using conventional neuroimaging and meta-analytic approaches several key questions remain unresolved, including (1) the common and distinct neural representations of reappraisal and acceptance remain controversially debated 22,35–37 , and, (2) the generalizability of the identified neural systems that have been extensively assessed using sparsely presented static stimuli (e.g. pictures) to naturalistic, dynamic emotional experiences which more closely reflect real-world affective experiences, as well as (3) the translational potential of these neural representations as precise neuromarkers for ER impairments in clinical populations. First, traditional mass-univariate analysis, widely used in previous studies, presents several limitations with respect to establishing comprehensive and accurate brain models for specific mental processes, including (1) voxel-wise independence assumptions that reduce sensitivity to comprehensively determine cognitive functions 38 and (2) averaging across voxels – each containing a massive amount of neurons (~5.5 million) – yields nonspecific signals 39,40 . Recent advances in multi-variate predictive models convergently suggest that emotional and cognitive processes are encoded in distributed neural representations rather than in isolated brain systems 28,41–43 . Second, few studies 22,33 have directly compared acceptance and reappraisal strategies using a within-subject design or under ecologically valid conditions. Most neurobiologically-informed models instead rely on meta-analyses 5,44,45 , which are limited by the univariate approach of the original studies and further constrained by variability indicated by preprocessing protocols 46 . Moreover, these studies used sparsely presented, static, and isolated stimuli (e.g., affective pictures), which limits ecological validity 47,48 , in particular in the context of emerging evidence indicating that neural systems that support affective experience differ substantially when engaged during dynamic naturalistic experiences 49–51 . Third, despite the recognition of impaired ER as a transdiagnostic impairment in MDs, including addiction 7,52,53 , findings have yet to be translated into clinically useful neuromarkers. While initial progress has been made in developing a neuromarker for cue reactivity, parallel progress in the domain of ER is lacking 54 . To address these critical limitations, we developed and extensively evaluated a comprehensive, ecologically valid, and clinically applicable brain model of ER under naturalistic conditions. We here capitalize on the combination of functional magnetic resonance imaging (fMRI) with machine-learning-based multivariate pattern analysis (MVPA) and naturalistic paradigms 55–58 . MVPA leverages information across multiple spatial scales 39,59 , allowing the establishment of comprehensive, accurate, and generalizable whole-brain models of emotional experiences (‘neuroaffective signatures’) with improved sensitivity and specificity and translating these into neuromarkers for MDs 59,60 . To further increase the ecological validity of these models, recent studies have employed naturalistic paradigms with dynamic videos, speech, and music as materials that are consistent with the typical sensory stimuli encountered in realistic daily life 47–49,61,62 . Against this background, we aimed here at precisely determining common and distinct neural representations of reappraisal and acceptance during naturalistic dynamic emotional processing combined with MVPA-based neural decoding. Briefly, 59 participants from a pre-registered discovery cohort and 33 participants from a pre-registered validation cohort underwent a dynamic naturalistic ER paradigm with concomitant fMRI (Fig.1a), with acceptance (NA), reappraisal (NR), or natural emotional experience to negative (NV) or neutral clips (NeutV). We next systematically tested (1) whether it is possible to develop precise and robust neural signatures to determine negative experiences (NNES) and their regulation via reappraisal and acceptance (NERS-R or NERS-A, respectively) across the discovery cohort and independent validation cohort (Fig.1b); (2) whether the developed decoders generalize to independent ER processing investigated using conventional static paradigms (e.g., picture) in a large dataset of healthy individuals (n=358) 28 and reappraisal pain induced by heat stimuli (n=33) 63 . Combing multiple analysis methods to determine (3) the distinct and common brain representations and brain systems that underly acceptance and reappraisal, utilizing e.g. Bayes Factor (BF) analysis which allows examining for both the null and alternative hypotheses 28,64,65 and spatial similarity analysis with encoding models and prefrontal systems involved in ER 66 as well as large-scale functional networks 67 (Fig.1c). Based on accumulating evidence for ER deficits in MDs, including excessive cannabis users (CU) 68 , we examined the clinical application potential of our decoders (4) to detect ER deficits in CU compared to healthy control (HC) participants (n HC =48, n CU =49) (Fig.1d). Results Assessment of the naturalistic ER paradigm Subjective experience and regulation of negative emotions . During fMRI, participants were instructed to react naturally to neutral or negative video clips or use corresponding ER strategies based on presented cues, then report their momentary negative affect on a 9-point Likert scale (9-very negative, 1-not negative at all). In both discovery (n=59) and validation (n=33) cohorts, significant main effects of condition (stimulus type: NV, NeutV; strategies: NV, NA, NR) were observed with repeated-measures analysis of variance (ANOVA) and further simple effects analysis, indicating that the negative clips induced considerable negative emotional experience (discovery cohort: P =9.96×10 -39 , =0.947; validation cohort: P =8.15×10 -17 , =0.947; Fig.2, details in supplements) and both strategies allowed participants to effectively attenuate the negative emotions(discovery cohort: P =3.18×10 -19 , =0.585; validation cohort: P =1.32×10 -12 , =0.639). Reappraisal, led to a stronger decrease in negative emotions as compared to acceptance (discovery cohort: P =2.02×10 -10 ; validation cohort: P =2.4×10 -5 ). Findings are further corroborated by success rates of ER (discovery cohort: 7.51±1.01, validation cohort: 7.53±0.98). Decoding emotional experience and ER strategies in dynamic naturalistic contexts Employing the support vector machine (SVM) with leave-one-subject-out cross-validation (LOSO-CV) allowed us to identify whole-brain fMRI activation predictive signatures of negative emotional experience (NNES), acceptance (NERS-A), and reappraisal (NERS-R) (Fig.3a, b, and c) during naturalistic emotion processing. Only the data from the discovery cohort were used to develop decoders. The performance of the decoders was validated using cross-validated classification in the discovery cohort and determining the reactivity of the decoders in an independent pre-registered validation cohort (n=33) that underwent a similar paradigm. NNES, NERS-A, and NERS-R could accurately discriminate the corresponding verse control condition, respectively (Fig.3d, Table 1). Testing the signatures with no further model fitting in the validation cohort revealed robust prediction performances in the independent datasets (Fig.3e). Table 1 . Classification performance on the discovery and validation cohort Condition Decoder ACC (%) Sens (%) Spec (%) Effect P SD CI CI size Discovery cohort (n=59) NV vs. NeutV NNES 94 2.2 98 95–100 90 81–97 3.01 <1 × 10 -50 NA vs. NV NERS-A 78 3.8 75 68–86 81 71–91 1.37 7.75 × 10 -10 NR vs. NV NERS-R 83 3.5 83 73–92 83 73–92 1.71 1.53 × 10 -13 Validation cohort (n=33) NV vs. NeutV NNES 100 0 100 100–100 100 100–100 3.55 2.33 × 10 -10 NA vs. NV NERS-A 70 8 70 53–86 70 53–85 0.47 0.0351 NR vs. NV NERS-R 88 5.7 88 75–97 88 76–97 1.42 1.10 × 10 -5 Generalization cohort 1 (n=358) NpV vs. NeutpV NNES 94 1.2 94 92–96 94 92–96 2.26 <1 × 10 -50 NpR vs. NpV NERS-A 69 2.5 69 64–74 69 64–74 0.45 1.14 × 10 -12 NpR vs. NpV NERS-R 81 2.1 81 77–85 81 78–85 1.21 <1 × 10 -50 Generalization cohort 2 (n=33) DP vs. EP NNES 52 8.7 52 35–69 52 33–69 -0.16 1 DP vs. EP NERS-A 61 8.5 61 44–77 61 44–77 0.26 0.2962 DP vs. EP NERS-R 76 7.5 76 60–90 76 60–90 0.94 0.0045 NV, view (react to) negative clips; NeutV, view (react to) neutral clips; NA, use acceptance strategy to negative clips; NR, use reappraisal strategy to negative clips; NNES, naturalistic negative emotion signature; NERS-A, naturalistic emotion regulation signature – acceptance; NERS-R, naturalistic emotion regulation signature – reappraisal; NpV, view negative picture; NeutpV, view neutral picture; NpR, regulate negative emotions induced by picture; DP, Decrease-pain; EP, experience-pain. Generalization to independent data Generalization across samples, MRI systems, culture, and stimuli (dynamic versus static or heat-induced pain) was first determined using data from a large independent cohort (n=358) 28 . NNES could accurately discriminate exposure to negative versus neutral pictures (Fig.3f, Table 1). In line with the strategy used by the participants (acceptance or reappraisal) 28 , NERS-A and NERS-R could accurately classify the ER vs. experience processing. After excluding the voxels from the visual network 67 (to control the interference of visual processing caused by different materials), NERS-R could accurately distinguish between using the reappraisal strategy to down-regulate vs. experiencing pain induced by heat stimuli, based on the data from the second independent cohort (n=33, Fig.3g). Yet NERS-A and NNES showed poor predictive performance of reappraisal. Together, these results underscore the robustness and specificity of the decoders for specific mental processes. Common and separate signatures of reappraisal, acceptance, and negative emotional experiences We next systematically evaluated which brain regions provided a consistent and robust contribution to negative affect and were regulated by the respective ER strategies. Bootstrap tests and encoding model estimation . We initially thresholded the multivariate patterns using bootstrapping (Fig.3a, b, and c) and – given that the decoding model features may not only reflect neurobiological processes but also noise components 70 – next transformed the bootstrapped within-subject weighted maps to reconstructed ‘activation pattern’ (encoding model) 71,72 and estimated the population-level reconstructed ‘activation pattern’ (FDR q <0.05, Fig. 4a). Aggressive negative emotions induced by watching the negative (versus neutral) clips were accompanied by a positive association with activity in subcortical regions engaged in emotional reactivity, such as the insular, thalamus, periaqueductal grey (PAG), as well as superior parietal (e.g. supramarginal), ventrolateral occipital and posterior mid-temporal regions involved in the integration of information and semantic content 41,73,74 ; negative association with activity in a widespread bilateral anterior temporal and frontal network, including the pre-supplementary motor area (SMA) as well as medial and lateral frontal regions. Compared to experiencing negative emotions, acceptance significantly attenuated activity in subcortical (e.g. fusiform and lingual gyrus), superior and inferior parietal, inferior temporal, and bilateral pars triangularis (IFSa) regions, while concomitantly associated with activity in the amygdala (extended to hippocampus), insular cortex, putamen, frontal regions including the prefrontal lobe (bilateral dorsolateral (dlPFC), dorsomedial (dmPFC), ventromedial (vmPFC), ventrolateral (vlPFC) prefrontal cortex, left rostral anterior cingulate cortex (rACC)), isthmus cingulate, precentral, lateral occipital cortex, SMA, and superior parietal regions including the precuneus (Fig. 4b). Reappraisal (versus experiencing negative emotion) is negatively associated with activity in subcortical regions, including the bilateral amygdala, insular, putamen, and thalamus involved in emotional experience, as well as lateral occipital, posterior frontal, and parietal regions, including the SMA and supramarginal gyrus. Reappraisal is concomitantly associated with activity in a bilateral frontal network encompassing medial, orbitofrontal, vlPFC, rACC, right dlPFC, as well as parietal regions including the precuneus, superior, and middle temporal regions (Fig. 4c). Univariate analyses additionally confirmed the observed effects (Extended Data Fig.1). Brain systems identified by BF analysis. BF analysis was used to determine the common and separate neural basis between acceptance and reappraisal. ‘Acceptance only’ brain regions were identified by activation during acceptance (BF>5) but not for reappraisal (BF5, acceptance BF5) and reappraisal (BF>5) 28,64 . Effective ER via both strategies robustly recruited cortical midline DMN regions including the bilateral dmPFC (extending to rACC), right inferior precuneus, left cuneus, middle temporal and lateral occipital regions (Fig.5a, see also conjunction analysis in Extended Data Fig.2). Acceptance and reappraisal specifically recruited distributed process-specific systems spanning several cortical systems, with the acceptance primarily engaging somatomotor (and to a lesser extend ventral attention and frontoparietal) large scale networks and subcortical systems (e.g., superior temporal lobe, dlPFC, vlPFC, insular, amygdala, and putamen), while reappraisal primarily engaged an extensive the frontoparietal and DMN regions (e.g., superior frontal and inferior parietal regions, dlPFC, vlPFC, supramarginal, and precuneus). Spatial similarity between stable decoding maps and interest networks, as well as crucial ROIs . We further estimated the spatial similarities between the reconstructed ‘activation pattern’ and prefrontal anatomical regions consistently involved in emotional experience and regulation (ROIs) 66 as well as large-scale functional networks 67 (Fig.5b, c). For each region, we calculated the ratio of importance for acceptance versus reappraisal versus experience negative affect (pie plot in Fig.5b, c; Supplementary Table 1), with importance defined as the percentage of the region encoded by each model 42,75 , while also directly comparing acceptance and reappraisal (Fig.5d). Except the vlPFC, which was engaged in all processes, prefrontal regions exhibited a stronger engagement during both regulation strategies as compared to experiencing the emotions. While the vmPFC, dmPFC, and vmPFC showed comparable contributions to both strategies, the ACC had stronger engagement for acceptance, whereas the dlPFC and orbitofrontal cortex (OFC) exhibited stronger engagement during reappraisal. On the network level, the visual networks exhibited equal importance across the three models, the experience of negative emotion may mostly encoded in the dorsal, ventral attention, and somatomotor networks. When comparing between ER strategies, the somatomotor and attention networks presented higher encoding importance for acceptance, while the frontoparietal networks had relative stronger contribution for reappraisal and the DMN exhibited similar importance for both strategies. To provide more comparative pictures of how acceptance and reappraisal are encoded, we analyzed the voxel-level spatial covariation between the unthresholded normalized (z-scored) encoding maps of NERS-A and NERS-R 76 . Fig.5e illustrates the joint distribution of the voxel weights of the encoding model of NERS-A and NERS-R. Octants with different colors indicate voxels of shared positive/negative, selective positive/negative weights, and voxel weights opposite for the two signatures. Stronger weights (sum of squared distances to the origin (SSDO)) were shown in the nonshared octants (5,7) as well as opposite octants (4,8), and fewer weights in the shared octant 6, further supporting that separable brain representations contribute to implementing acceptance and reappraisal strategies. Predictive performances between decoders. To determine whether our decoders reflected both the common and separate processing of naturalistic acceptance, reappraisal, and negative emotion, we also compare the decoders’ performances on different conditions from the validation cohort and add a neural signature for negative affect (PINES, picture-induced negative emotion signature 69 ). As shown in Extended Data Fig.2c, except for NERS-R, the other three decoders could distinguish between NV and NeutV, but NNES has the highest accuracy and effect size. NNES and PINES show low performance when predicting ER processing (acceptance and reappraisal). Only NERS-A could accurately classify between NA and NV. Both NERS-R and NERS-A could classify between NR and NV, but NERS-R is more accurate with a larger effect size. Together, these findings indicate that our decoders captured and further demonstrated the common and separate nature of the above three mental processes. Evaluation of predictive performances of local systems . Given the continuing debate about the specific systems underlying ER, including the dlPFC 27,28,77 , and the DMN 34,78,79 , we (1) employed network-based and searchlight-based analyses to estimate the predictive performances of local brain regions and (2) compared their performances with whole-brain decoders. To control for the potential effects of the number of voxels/features in prediction (i.e., the whole-brain model contains many more features), voxels were randomly selected (repeated 1,000 times) from a uniform distribution spanning the whole brain (Fig.6b, black), prefrontal cortex (light orange, which has been suggested play crucial roles in both generate and regulate emotions 26,27,31 ), or individual large-scale functional networks (averaged over 1,000 iterations) 41,42,80 . The asymptotic prediction when sampling from the whole-brain, as we trained decoders (black line in Fig.6b), was more accurate than the asymptotic prediction within individual networks (colored lines), especially with more available voxels (details in Supplementary). Moreover, model performance was optimized (i.e., reaching asymptote) when approximately 10,000 voxels were randomly sampled across the whole brain, further confirming that information about ER processing is contained in patterns of activity that span multiple systems. The above results can also be demonstrated by searchlight analysis, although statistically significant and thresholded brain regions are similar to the whole-brain decoders (Extended Data Fig.3), the effect sizes in terms of prediction–outcome accuracy were substantially smaller than those obtained from the developed decoders ( P <0.001). Together, findings underscore that ER and negative emotional experiences are encoded in distributed neural patterns that span multiple systems, rather than single brain regions or networks. Translation into a neuromarker for determining ER deficits in MDs To test whether our decoders can capture deficient ER processing in individuals with MDs 52,68,81 , we applied the decoders to extended data from our previous study 68 describing ER deficits in cannabis users (CU). For the present study, we incorporated additional unpublished data from individuals with CU disorder and matched controls who underwent a similar picture-based reappraisal paradigm with the concomitant fMRI system (details see ref. 68 , study protocol NCT02801214, clinical-trials.gov). A total of 48 HC participants and 49 CU were included in the extended dataset. In line with the findings in the initial study, behavioral analyses confirmed ER deficits in the CU (reappraisal success 68 , 0.46±0.46) as compared to HC (0.78±0.54) ( t =-3.072, p =0.003, Cohen’s d=0.50) in the extended sample. NERS-R could accurately predict neural activity during cognitive reappraisal (distancing) (NpD) vs. view (NpV) negative pictures in HC participants but not in CU (Fig.6, Table 2). NNES could accurately distinguish between NpD and view neutral pictures (NeupV) in both groups, underscoring impaired regulation rather than excessive emotion reactivity as a neurofunctional marker for CU. Further permutation tests revealed that the pattern expression of the two groups has significant differences for NERS-R and NNES (both P <0.0001, Fig.6). In contrast, NERS-A showed poor predictive performance. Table 2 . Classification performance on the clinical application cohort Condition Decoder ACC (%) Sens (%) Spec (%) Effect P SD CI CI size Clinical application cohort HC (n =48) NpV vs. NeutpV NNES 94 3.5 94 86–100 94 86–100 1.89 1.31 × 10 -10 NpD vs. NpV NERS-A 42 7.1 42 27–56 42 29–55 -0.11 0.3123 NpD vs. NpV NERS-R 69 6.7 69 56–82 49 56–82 0.74 0.0133 CU (n =49) NpV vs. NeutpV NNES 94 3.4 94 87–100 94 87–100 1.51 6.98 × 10 -11 NpD vs. NpV NERS-A 55 7.1 55 41–69 55 40–69 0.21 0.5681 NpD vs. NpV NERS-R 59 7 59 46–73 59 46–73 0.3 0.2528 HC, healthy control; CU, cannabis users; NpV, view negative pictures; NeutpV, view neutral pictures; NpD, distancing negative pictures. Discussion Identifying comprehensive and accurate brain models for the common and distinct brain representations that underly ER is critical for advancing mechanistic models of cognitive regulation as well as for developing accurate biomarkers for regulatory dysfunction in MDs. While reappraisal and acceptance are widely implemented in therapeutic contexts and have been debated from theoretical 1,3 , experimental 28 , and clinical 12,82 perspectives, the extent to which they rely on common and dissociable systems in ecologically valid dynamic contexts remains unresolved. Here, we combined immersive naturalistic neuroimaging with predictive modeling and BF analyses to systematically establish and validate three comprehensive, accurate, and sensitive brain-based models for negative affect (NNES) and its regulation via acceptance (NERS-A) and reappraisal (NERS-R). The developed decoders are generalizable across imaging systems, cultures, and paradigms. Next, through a series of systematic analyses, we uncovered the shared and separable neural representations of acceptance, reappraisal, and negative emotion, which indicated that these mental processes are encoded in distributed and distinguishable neural systems on the whole brain level, yet also show process-specific engagement of large-scale systems. Both acceptance and reappraisal engaged cortical midline regions of the core DMN, including the dmPFC and posterior parietal cortex, indicating a general contribution of this system to effective ER. Each strategy, furthermore, exhibited distinct profiles aligned with its cognitive demands. Acceptance was encoded in distributed regions spanning subcortical, somatomotor, and ventral attention networks, consistent with embodied awareness and experiential processing. Reappraisal, by contrast, relied on regions spanning the fronto-parietal control network, including lateral frontal and inferior parietal regions, reflecting its reliance on executive functions and semantic updating. While the distinction was further substantiated by BF analyses and spatial similarity mapping, decoding performance was substantially higher for whole-brain decoders as compared to regional models, underscoring the importance of brain-wide integration for capturing emotional experience and regulation. Finally, the neurofunctional decoders could detect reappraisal-specific ER deficits in a large set of CU, underscoring the sensitivity of the signatures to detect specific behavioral impairments on the neural level and clinical potential. Combining naturalistic fMRI with mass-univariate and multivariate predictive analyses, we established comprehensive and distributed brain models for negative emotional experiences and their cognitive regulation in dynamically close-to-real-life contexts. The affective videos induced immersive and strongly negative emotional states, which were accompanied by positive engagement of brain regions involved in emotional experience, physiological and affective arousal, including the insula, PAG, thalamus 26,27,74,75,83 , and negatively associated with the engagement of a widespread frontal and temporal network involved in cognitive and regulatory control. Spatial similarity and network-level analyses further confirmed the contribution of large-scale cortical networks, including the attention and visual networks (resembling previous studies 84 using dynamic stimuli). Importantly, both the developed (NNES) and an established signature for negative affect (PINES) poorly predict the ER strategies, supporting previous research indicating that emotion generation and regulation are neurally separable processes 25–27 . Both ER strategies could efficiently reduce negative emotional states, indicating that utilizing reappraisal and acceptance represents an efficient means to regulate negative emotions and the associated neural systems in dynamic, ecologically-valid contexts. In line with ref. 22 , reappraisal led to a greater reduction of subjective negativity compared to acceptance. On the neural level, ER via both strategies was predicted by neural regulation in distributed cortical and subcortical systems, with common and distinct signatures. BF and conjunction analyses demonstrated that effective ER by both strategies engaged core regions of the DMN that are strongly involved in the evaluation of the mental state of oneself and others (dmPFC) 19,26,31 , episodic autobiographical memory (inferior precuneus) 85,86 , rapid emotional conflict adaptation (rACC) 87 , self-awareness and interpersonal processing (temporal regions) 88,89 . Given that both ER strategies require the appraisal of the emotional state concerning the self, the dmPFC and other core DMN regions may support appraisal, metacognitive evaluation, and internal self-reference, enabling ER based on internal goals 90 . On the anatomical level, these functions of the DMN, in particular the dmPFC, may be supported by interconnections with the rostromedial prefrontal, lateral orbitofrontal (lOFC), and pregenual ACC, which may support its role as a central hub for the implementation of both ER strategies 31,90 . The effective regulation was accompanied by reduced engagement of a widespread bilateral network including bilateral superior parietal, inferior and precentral frontal, and lateral occipital regions, which may reflect attenuated motor readiness and expressive output, as well as attenuated visual processing of emotionally salient stimuli, and in turn may promote regulatory control across both strategies 91–93 . Distinguishable contributions were observed in somatomotor 94 , attention, or the frontoparietal control network 79,95 , respectively. In line with the notion that reappraisal represents an active and effortful ER strategy, effective implementation specifically engaged an extensive bilateral network of distributed regions within the fronto-parietal control network. These included lateral frontal regions involved in working memory (dlPFC) 77 , general and valence-specific inhibition (vlPFC, lOFC) 26,31,96,97 , and medial prefrontal regions involved in computation of value or safety (vmPFC, mOFC) 31,98–100 . Parietal regions were specifically recruited during reappraisal, including regions involved in self-referential processing (superior precuneus) 101–103 , attentional control (inferior precuneus) 104 , and conscious representation of affective semantic content (e.g., Griffiths et al., 105 ). In contrast, reappraisal specifically decreased engagement of brain regions traditionally linked to emotional “reactivity”, including subcortical systems such as the amygdala 26,27,98,104,106–109 and the anterior insular cortex 98 . Acceptance was also encoded in some distinguishable but rather focal prefrontal regions (e.g., dlPFC), yet showed stronger engagement of distributed regions spanning the somatomotor (e.g., superior parietal lobe, SMA, posterior insula) 110,111 and ventral attention networks (e.g., preSMA). Compared to reappraisal, enhanced engagement of these regions may reflect that effective acceptance relies on goal-oriented attentional deployment (superior parietal lobe 53,112,113 ), interoception (posterior insula 114,115 ), and embodied cognition-affect processing (SMA/preSMA 44 ). Notably, acceptance is specifically negatively associated with the brain systems involved in self-referential processing (superior precuneus) 101–103 and threat perception (fusiform) 116,117 , while it was positively associated with activation in subcortical systems traditionally linked to emotional reactivity, including the amygdala, hippocampus, and putamen. The stronger engagement of these systems may reflect the non-judgmental awareness of affective arousing and sensory experience and may promote hippocampus-dependent encoding and contextualization 118 , in turn facilitating adaptive integration of the emotional experience with autobiographical and situational contexts 22,118–121 . Comparative predictive analyses using the whole-brain decoders further substantiated common yet, on the whole-brain level, also clearly distinguishable neural representations, such that the decoders exhibited a considerably higher accuracy for the targeted ER strategy. The importance of individual brain regions or networks in the experience and regulation of negative emotions has been emphasized in previous frameworks 25–27 . While our findings support preferential engagement of specific networks by distinct strategies – the fronto-parietal control network during reappraisal and the somatomotor network during acceptance - our large-scale network- and searchlight-based predictive analyses further highlight that, under dynamic and naturalistic conditions, both strategies rely on the coordinated activity of distributed and distinguishable brain-wide representations. The distributed engagement was further supported by a system-specific analysis of the prefrontal cortex, commonly identified as systems promoting distinct ER strategies 25–27 . While most prefrontal systems contributed to both strategies, the orbitofrontal cortex showed a markedly higher engagement during reappraisal, which may be due to its extensive anatomical connections and functional coupling with the amygdala during reappraisal, and which is underscored as this region is critical for mediating effective reappraisal of negative emotions 92,122–124 . In general, the distributed signature of the whole-brain model showed the highest predictive performance, which is in line with decoders for other mental processes, e.g., digust 42 , fear 41 , or pain 80 . Importantly, our neurofunctional signatures for ER demonstrated a clinical translational potential, such that the decoders accurately tracked the ER strategy-specific neural representation of impaired reappraisal success. The NERS-R showed an accurate reappraisal prediction in HC but failed to distinguish reappraisal from negative experience in individuals with CU and reappraisal deficits on the behavioral level, with permutation tests indicating that NERS-R and NNES could capture the abnormal brain activation pattern among CU. Impaired ER and altered emotion reactivity have been widely demonstrated across multiple MDs, including CU 68,125,126 , opioid use disorders 81,127,128 , alcohol use disorders 129,130 , cocaine use disorder 131,132 , major depressive disorder 133,134 , anxiety disorders 135,136 , yet precise neuromarkers to track emotional deficits on the neurofunctional level are lacking. Given the critical and transdiagnostic role of ER deficits in MDs, the present multivariate predictive patterns may facilitate the development of brain-based diagnostic and treatment response markers. Our marker accurately tracks emotional reactivity and its effective modulation via cognitive strategies, the marker may also serve as a target state for behavioral as well as brain-based intervention (e.g., 108,137 ). Further studies are required to determine common and mental disorder-specific alterations in these markers to validate the decoders’ accuracy, clinical utility, and sensitivity. Limitations of the present study are inherent to technical limitations. While combining fMRI with MVPA gives a relatively precise way to investigate brain activity, and has been revealed to be consistent with the conventional univariate approach, limitations are inherent to fMRI (e.g., indirectly measuring neuronal activity, 3T fMRI may not be able to precisely localize to the midbrain and brainstem nuclei 75 ), and more precise measurement may support our understanding of the relationship between ER and brain activation. In conclusion, we developed and validated three robust, generalizable, and clinically translatable whole-brain neural signatures for negative emotional experience and its effective regulatory control via acceptance and reappraisal in dynamic naturalistic contexts. Our within-subject design enabled a systematic comparison of the common and distinct neural bases underlying these ER strategies and suggests that core DMN regions mediate effective regulation across strategies. On the whole brain level, the strategies were distinguishable, and the predictive neurofunctional signatures demonstrated high robustness across independent cohorts, experimental paradigms, and MRI systems. The findings emphasize that an effective ER is encoded not in isolated brain regions but in synergistic, distributed whole-brain activity patterns. Application to individuals with ER deficits underscored that the neural decoders offer substantial translational potential. Beyond accurately detecting ER deficits in individuals with MDs, future applications include: (a) serving as neural markers to assess intervention effects targeting acceptance and reappraisal; (b) characterizing ER-specific neural impairments across MDs; (c) delineating common and distinct neural architectures of various ER strategies; and (d) informing frameworks centered on the role of the DMN in ER. Together, this work provides a foundation for developing mechanistically-informed, brain-based targets and outcome measures for clinical interventions aimed at enhancing emotional resilience. Methods Participants Discovery cohort . Sixty-eight healthy participants were recruited from local universities. Exclusion criteria included left-handed, color blindness or weakness, and current physical or mental illnesses. Six participants were excluded due to excessive head motion (> 3 mm or 3°), and three due to failed post-check. Fifty-nine participants (19 male, 21.3±1.7 years old, mean±SD) were included in the final analysis. Validation cohort . Thirty-four healthy participants were recruited as the validation cohort (11 male, 20.3±1.4 years old, mean±SD), with the same exclusion criteria as the discovery cohort. One participant was excluded due to being detected as an outlier by the fmri_data.mahal function in the Canlabcore toolbox ( P <0.05 Bonferroni corrected). All participants provided written informed consent. All participants were compensated 140 RMB after the experiment. This pre-registered study (discovery cohort: https://osf.io/s3bp2, validation cohort: https://osf.io/68jkw) was approved by the ethics committee of the University of Electronic Science and Technology of China and in accordance with the Declaration of Helsinki. Stimuli and Paradigm Discovery cohort . A total of forty-eight silent video clips were used in the current ER paradigm, rated by one presented once by E-prime software (Version 3.0; Psychology Software Tools, Sharpsburg, PA). Twelve neutral clips include normal driving records, surveillance, vlogs, etc.; thirty-six negative clips include threatening animals, human fights, bullying, car accidents, horror movies, etc. Neutral and negative clips are matched by basic scenarios and characters. The experiments consisted of four conditions: react (view)-neutral (NeutV), react-negative (NV), reappraise-negative (NR), and accept-negative (NA), each with twelve clips, divided into four runs. To alleviate the potential cognitive overload caused by switching mindsets too often or the potential fatigue or boredom caused by using the same mindset for a long period, we combined “react” with “reappraisal” or “react” with “accept” in each run, and use ABBA method between participants to balance order effect (see Fig.1a). Participants were provided with written instructions (see supplements), asked to use a certain way of thinking (“react”, “reappraisal”, or “accept”) to subsequently video clip, and received ~20 mins training before scanning. Each trial began with a fixation cross (3–5s), followed by a 2s cue, then a fixation cross last 6–8s, after that a 25s video clip, followed by a fixation cross (1–1.5s), then a 9-point Likert scale (4s) for participants to report “How negative do you feel right now?” (9 - very negative, 1 - not negative at all). At the end of each run, participants will rate “On average, how successfully have you used the indicated mindset in this run?” (9 - very successful, 1 - not successful at all). Validation cohort . The stimuli, experiment conditions, and trial structure are identical to the discovery cohort. However, to control the potential influence of the “ER trial” on the “react trial” in the same run, we placed all 12 trials of each condition within one run (randomly presented), using the Latin square design to balance order effects between participants. MRI data acquisition and preprocessing Discovery cohor t. MRI data were collected on a 3.0 T scanner (Ingenia Elition; Philips Healthcare, Best, Netherlands) with a 32-channel head coil. Structural images were acquired using high-resolution three-dimensional T1-weighted images (repetition time (TR)=8.2 ms, echo time (TE)=3.8 ms, field of view (FOV)=243 mm, flip angle (FA)=8°, 243×243 matrix, 1×1×1 mm voxels, 180 sagittal slices, phase encoding anterior » posterior) and were used for anatomical localization and warping to the standard Montreal Neurological Institute (MNI) space only. Functional images were acquired with an echo planar imaging-free induction decay (EPI-FID) sequence (TR=2000 ms, TE=30 ms, FOV=240 mm, FA=80°, 80×80 matrix, 3×3×3.2 mm voxels, 34 interleaved ascending axial slices, phase encoding posterior » anterior). In total, four runs of each 268 measurements were acquired. Preprocessed was carried out using the Statistical Parametric Mapping (SPM 12, https://www.fil.ion.ucl.ac.uk/spm). The first five volumes of each run were removed, the different acquisition timing of each slice was corrected and realigned to the first volume, and nonlinear distortions related to the head motion were corrected by unwarping. The high-resolution anatomical image was segmented and co-registered with the functional images to the generated skull-stripped structural image. The functional images were normalized to Montreal Neurological Institute (MNI) space, interpolated to 2×2×2 mm 3 voxel size, smoothed by 8-mm full-width at half maximum (FWHM) Gaussian kernel, and bias field corrected. Image intensity outliers were identified based on meeting any of the following criteria: (a) signal intensity >3 standard deviations from the global mean, (b) signal intensity and Mahalanobis distances >10 mean absolute deviations based on moving averages with a full-width at half maximum (FWHM) of 20 image kernels. The time points marked as outliers were severed as separate nuisance covariates included in the first-level model. Additionally, the design matrix included negative feeling ratings (1−9) as a parametric modulator for the reaction or regulation period. Validation cohort . MRI data were collected on a 3.0 T scanner (Vida; Siemens Healthineers, Erlangen, Germany) with a 64-channel head coil. Structural images were acquired using high-resolution T1-weighted images by the original three-dimensional Single-shot TurboFLASH sequence from Siemens (TR=2300 ms, TE=2.32 ms, FOV=240 mm, FA=8°, 256×256 matrix, 0.9×0.9×0.9 mm voxels, 192 sagittal slices, phase encoding posterior » anterior) and were used for anatomical localization and warping to the standard Montreal Neurological Institute (MNI) space only. Functional images were acquired with an echo planar imaging-free induction decay (EPI-FID) sequence (TR=2000 ms, TE=29 ms, FOV=240 mm, FA=90°, 80×80 matrix, 3×3×3 mm voxels, 36 interleaved ascending axial slices, phase encoding posterior » anterior). In total, four runs of each 268 measurements were acquired. Using an identical pre-processing procedure to the discovery cohort (only modifying the parameters based on the sequence setting). Mass-univariate analysis Discovery cohort . Subject-level general linear model (GLM) analysis was conducted using SPM12. Twenty-four head motion parameters and indicator vectors of outlier time points were modeled as non-interested regressors 41 . The fixation-cross epochs served as the implicit baseline, the periods of cue presentation and rating were modeled to eliminate the extra effects on the baseline, and the clips presented period were used as the interest regressor. A high-pass filter of 180s was applied. Additionally, the subjective negativity ratings (1−9) for each clip were included as a parametric modulator for the reaction and regulation period. The primarily interesting contrasts are NR vs. NV, NA vs. NV, and NV vs. NeutV. To avoid the possibility that participants may subconsciously use the corresponding ER strategy at different runs, which may lead to confounding effects of another ER strategy when comparing NA or NR with NV, we modeled NV in the runs with NA and NR, respectively. The contrast beta images were submitted separately to the group-level analysis and used to perform MVPA analysis. We also conducted a single-trial analysis for MVPA analysis by specifying a GLM design matrix with separate interest regressors for each clip. Nuisance regressors, implicit baseline, and high-pass filters were identical to the above analysis. Validation cohort . The processing procedure is identical to the discovery cohort, except that the NV condition was modeled as a whole since all NV trials were in one run for the paradigm of the validation cohort. Multivariate pattern analysis Following the previous framework 138 , to obtain the robust brain activity patterns that can distinguish and predict between different contrasts mentioned in the mass-univariate analysis section, we trained the SVM classification (kernel linear C=1) decoders by applying LOSO-CV methods using subject-level whole-brain contrast images data (gray matter masked 41 ). Of note, only the data from the discovery cohort was used to develop the decoders. To assess the cross-validated performance of the decoders, we calculated the predictive accuracy, sensitivity, and specificity by comparing the prediction and true outcome (threshold=0). Additionally, to test the generalizability of the developed decoders, we applied them to other independent datasets, e.g., the validation cohort, and generalization cohort (see ‘Generalization cohort’ section below) to test the pattern response for each map and the classification capacity. Test generalizability with independent datasets One recent study 28 explored the ER process and provided subject-level univariate-beta maps from the Adult Health and Behavior project–phase 2 and the Pittsburgh Imaging Project 139 . Briefly, 358 participants performed ER tasks with picture material. Subject-level Reappraisal and acceptance strategies are reported to be used during the scanning 28 . To test decoders’ generalizability, NERS-A, and NERS-R were applied to predict ‘Regulate negative’ vs. ‘Look negative’ and NNES to predict ‘Look negative’ vs. ‘Look neutral’. We also test whether developed decoders could predict using the reappraisal strategy to “decrease” vs. “experience” the pain induced by heat stimuli, based on the data from the generalization cohort 2 (n=33) 63 . To control the interference of visual processing caused by different materials, we excluded the voxels from the visual network 67 . Identifying the neural basis of acceptance and reappraisal, as well as negative emotion response Bootstrap tests and encoding model estimate . To identify robust significant features, we first bootstrapped the discovery cohort of 10,000 samples (with replacement), calculated the two-tailed uncorrected P-value of the predictive weights, and used the False Discovery Rate (FDR) correction to control multiple comparisons (set FDR q<0.05). Given that the signatures of the multivariate backward/decoding model may capture the interested brain activation (e.g., ER) as well as the noise components in the data 70 . For interpretation, we transformed the bootstrapped within-subject pattern to ‘activation patterns’ (forward/encoding model), which is similar to the ‘structure coefficients’, and could indicate the direction of the relationship between the variable and the model without controlling for other variables – i.e., which voxels are positively or negatively related to ER strategies. The group-level significant brain regions were obtained by conducting a one-sample t -test (FDR q <0.05). Bayes Factor analysis. To identify the brain regions that are acceptance or reappraisal specific or the common regions of those two strategies, we utilized the BF approach to quantify evidence for alternative and null hypotheses 28,64 . Specifically, the BF value of a given voxel reflects the likelihood of the alternative and null (e.g., the neural activity is correlated with naturalistic acceptance or not). The BF values are calculated from t -values of a one-sample t -test of encoding maps from the discovery cohort, using a Jeffreys-Zellner-Siow (JZS) prior and formula (see below) provided by a previous study 140 . where N represents the sample size, t for the t -statistic , v denotes the degrees of freedom, and r is the scale factor. In the current study, r was set as 0.707, which suggested a moderate effect size 28,140 . Because the null is bound by t=0 and the current sample size (N=59), the BF cannot be smaller than .14 but can be infinitely large, a heuristic threshold of 5 was set for the alternative hypothesis and 1/5 for the null to attain moderate to strong evidence in favor of both hypotheses 64,141 . Note that in current datasets, the P value threshold of BF=5 is P =0.0067, which is smaller than the q <0.05 FDR-corrected threshold for the encoding model of NERS-R ( P =0.0190) but not for NERS-A ( P =0.0051), hence we further bound the alternative results by the corresponding FDR-corrected threshold, specifically as follows: ‘Acceptance only’ voxels – positive activation with BF>5 and P<0.0051 for acceptance, and BF5 and P5 and P5 and P<0.0051 for reappraisal, and BF<1/5 for acceptance. To verify the results of BF analysis, we also performed the conjunction analysis between the thresholded (FDR q<0.05) activation maps of those two strategies obtained from mass-univariate analysis and the reconstructed “activation pattern”, separately. Spatial similarity between stable decoding maps and interest networks, as well as crucial ROIs . The spatial similarity between stable encoding maps and seven large-scale resting-state networks, as well as crucial ROIs (dlPFC, vmPFC, dmPFC, and vlPFC), was illustrated by river plots. The spatial similarity was computed as cosine similarity between the networks or ROIs and the threshold NERS-A, NERS-R, and NNES (FDR q<0.05, positive values only). To further compare the underlying neural representations between NERS-A and NERS-R, we examined the voxel-level spatial covariation between the unthresholded weight maps for those two models (z-scored). Comparing the performance of developed models with PINES. To investigate whether developed decoders capture both the common and separate aspects of naturalistic acceptance, reappraisal, and reaction to negative emotion, we compared the decoders’ performances on data of different conditions from the validation cohort and along with a decoder provided by a previous study 69 – PINES – which have been demonstrated could track general negative emotion experiences induced by pictures. Evaluation of predictive performances of local systems . To investigate the contribution of a single resting-sate brain network (e.g., DMN) or individual regions to the ER or react processing, we first estimated the prediction performance of seven large-scale networks as well as the prefrontal cortex, which has been demonstrated to have crucial roles in both generate and regulate emotions 26,27,31 and compared the performance with developed models. Additionally, we employed a whole-brain searchlight-based analysis (three-voxel radius spheres) to test the predictive performance of individual regions. Together, we combined multiple analytic approaches to systematically interrogate the role of various brain regions or systems in acceptance and reappraisal. Clinical application potential ER and reaction abnormalities have been demonstrated in MDs, e.g., SUDs 52,68,81,126 . To examine whether our decoders could detect the emotion-related abnormality among SUD individuals, we applied NERS-A and NERS-R to classify the distancing (one of reappraisal) strategies with negative emotions induced by negative pictures and also applied NNES to predict negative vs. neutral emotions. The data were provided by a previous study 68 and newly collected data using a similar ER task paradigm and the identical scanner and sequence settings (see supplementary for details). Briefly, 48 health control participants (HC, 18 from our previous study 68 and 30 newly collected) and 49 cannabis users (CU, 23 from our previous study and 26 newly collected) were included in the final analysis. Declarations Data availability fMRI data used to train the signatures are available via figshare at https://doi.org/10.6084/m9.figshare.29179934.v1. Other authors provided the fMRI data in the generalization cohort 1 via NeuroVault (https://neurovault.org/collections/16266) 28 , and fMRI data in the generalization cohort 2 via the OpenfMRI (https://openfmri.org/dataset/ds000140) 63 . Code availability Code for analyzing data and generating figures is available via GitHub at https://github.com/canlab and https://github.com/h-psy/fMRI_studies/tree/main/NERS. Conflict of Interest The authors declare no competing interests. References Gross, J. J. The emerging field of emotion regulation: An integrative review. Rev. Gen. Psychol. 2 , 271–299 (1998). Gross, J. J. 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Behav. Res. Methods 51 , 1042–1058 (2019). Additional Declarations There is NO Competing Interest. Supplementary Files Supplementary.docx Comprehensive details concerning selected sections of the manuscript’s main body ExtendedData.docx Cite Share Download PDF Status: Published Journal Publication published 17 Mar, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6775990","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":463856361,"identity":"ae1a972b-691e-4af4-ac5f-0cece492e872","order_by":0,"name":"Benjamin 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11:16:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6775990/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6775990/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-026-70708-5","type":"published","date":"2026-03-17T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84375663,"identity":"57968571-4d7e-4860-bc6e-078d0ff41abd","added_by":"auto","created_at":"2025-06-11 08:19:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":844665,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTask paradigm\u003c/strong\u003e \u003cstrong\u003estructure, model evaluation, and general analytic workflow. a, \u003c/strong\u003eThe naturalistic ER paradigm used in the discovery and validation cohort. The cues were NNES, NERS-A, or NERS-R, indicating which strategy participants should use to view the neutral/negative video clips. At the end of each trial, participants rated their negative feelings. At the end of each run, participants rated their average success level in this run. Notably, the schematics were used only for illustrative purposes to avoid copyright issues and were not part of the original stimulus set. \u003cstrong\u003eb\u003c/strong\u003e, Feature estimation, model training, and evaluation. First-level contrast maps were used as features in the prediction analysis. The whole brain multivariate pattern predictive of the acceptance, reappraisal, or reaction to the naturalistic stimulus was trained on the discovery sample (n=59) using the support vector machine (SVM) and further evaluated in discovery (cross-validated), validation (n=33) cohorts. The generalizability of the developed signatures across samples, MRI systems, culture, and stimuli (dynamic versus static, n=358 or heat-induced pain, n=33). \u003cstrong\u003ec\u003c/strong\u003e, Systematically investigate the common and distinct neurobase of acceptance and reappraisal. Determining the contribution of specific brain systems by using univariate and multivariate methods. The performance of isolated brain regions or systems was tested by multiple prediction analyses. \u003cstrong\u003ed\u003c/strong\u003e, Testing the specificity of developed models in terms of distinguishing corresponding mental processing from other decoders. Subj, subject; NERS-A, naturalistic emotion regulation signature-acceptance; NERS-R, naturalistic emotion regulation signature-reappraisal; NNES, naturalistic negative emotion signature; PINES, picture-induced negative emotion signature from Chang et al.\u003csup\u003e69\u003c/sup\u003e; HC, healthy control; CU, cannabis users.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6775990/v1/ad88051a3abfc6682e752d3d.png"},{"id":84375664,"identity":"4e28758c-c121-4300-849d-a943b6ff68ca","added_by":"auto","created_at":"2025-06-11 08:19:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":215086,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNegative emotional state in the discovery and validation cohort. \u003c/strong\u003eAveraged subjective feeling ratings in the discovery (left) and validation cohort (right). Violin plots represent the value distribution, each dot represents the negative rating for individual participants averaged across trials in the corresponding condition. The dotted line in the center represents the median and the sides are the quartiles, the range of data points between Q1–1.5IQR and Q3 + 1.5IQR. ***\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001. NV, view (react to) negative clips; NeutV, view (react to) neutral clips; NA, use acceptance strategy to negative clips; NR, use reappraisal strategy to negative clips.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6775990/v1/f76a0596477fd5f4f6fc6c04.png"},{"id":84375665,"identity":"8dcec341-c6e1-4e3c-8a7f-f96119c33f29","added_by":"auto","created_at":"2025-06-11 08:19:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":959728,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDeveloped signatures and predictive performances.\u003c/strong\u003e NNES (thresholded at false discovery rate (FDR) \u003cem\u003eq\u003c/em\u003e\u0026lt; 0.05) (\u003cstrong\u003ea\u003c/strong\u003e), NERS-A (FDR \u003cem\u003eq\u003c/em\u003e\u0026lt;0.05 and unc. \u003cem\u003ep\u003c/em\u003e \u0026lt;0.005) (\u003cstrong\u003eb\u003c/strong\u003e), and NERS-R (FDR \u003cem\u003eq\u003c/em\u003e\u0026lt;0.05 and unc. \u003cem\u003ep\u003c/em\u003e \u0026lt;0.005) (\u003cstrong\u003ec\u003c/strong\u003e) pattern maps, based on a 10,000-sample bootstrapping (thresholded at FDR \u0026lt; 0.05). Hot color indicates positive weights, whereas cold color indicates negative weights. \u003cstrong\u003ed\u003c/strong\u003e, Receiver operating characteristic (ROC) plot illustrates the classification performances of the SVM models on the discovery cohort (n=59), using leave-one-subject-out cross-validation (LOSO-CV) with a misclassification threshold set to 0. The right three panels show the distributions of the decoders’ responses. The boxes are bounded at the first and third quartiles, the whiskers extend to the maximum and minimum values between the median±1.5 quartiles. Data points outside the whisker range are labeled as outliers. The yellow, green, and red dots beside the violin plots indicate correct classification, and the gray dots indicate misclassification. The classification performances of the developed decoders on the validation cohort (\u003cstrong\u003ee\u003c/strong\u003e, n=33) and generalization cohort (\u003cstrong\u003ef\u003c/strong\u003e, n=358, \u003cstrong\u003eg\u003c/strong\u003e, n=33). The red line indicates correct classification, and the blue line indicates misclassification. NNES, naturalistic negative emotion signature; NERS-A, naturalistic emotion regulation signature-acceptance; NERS-R, naturalistic emotion regulation signature-reappraisal; NA, negative (clips)-acceptance; NR, negative (clips)-reappraisal; NV, negative (clips)-view; NeutV, neutral (clips)-view; NpR, negative pictures-reappraisal; NpV, negative pictures-view; NeutpV, neutral pictures-view; ACC, accuracy.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6775990/v1/a842eaa09257a45a7d515f6a.png"},{"id":84375669,"identity":"04b4d56d-7883-47be-8b35-8adfca20bc9d","added_by":"auto","created_at":"2025-06-11 08:19:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":966875,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReconstructed ‘activation pattern’.\u003c/strong\u003e FDR corrected (\u003cem\u003eq\u003c/em\u003e\u0026lt;0.05) group-level reconstructed ‘activation pattern’ transformed from NNES (\u003cstrong\u003ea\u003c/strong\u003e), NERS-A (\u003cstrong\u003eb\u003c/strong\u003e), and NERS-R (\u003cstrong\u003ec\u003c/strong\u003e). The color indicates the direction of the relationship between the variable and the model without controlling for other variables – i.e., which voxels are positively (red) or negatively (blue) related to ER strategies. The hot color indicates positive associations, whereas the cold color indicates negative associations.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6775990/v1/29f7dc700c7686df1b9e7e20.png"},{"id":84375667,"identity":"4ae73c06-5ca2-4364-9132-21d23b2e64b5","added_by":"auto","created_at":"2025-06-11 08:19:58","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":949888,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparing acceptance, reappraisal, and negative emotion signatures. a, \u003c/strong\u003eBrain regions of the class of each voxel (Acceptance only, Reappraisal only, or Common).\u003cstrong\u003e \u003c/strong\u003eRiver plots illustrated the spatial similarity (cosine similarity) between reconstructed ‘activation pattern’ and anatomical parcellation of the prefrontal cortex\u003csup\u003e66\u003c/sup\u003e (\u003cstrong\u003eb\u003c/strong\u003e) as well as resting-state-based functional parcellation of cortical regions\u003csup\u003e67\u003c/sup\u003e (\u003cstrong\u003ec\u003c/strong\u003e). Reconstructed ‘Activation patterns’ were FDR q\u0026lt;0.05 thresholded and used positive voxels only for similarity calculation and explanation. In \u003cstrong\u003eb\u003c/strong\u003e and \u003cstrong\u003ec\u003c/strong\u003e, the thickness of the ribbons indicates the normalized maximum cosine between sets. The pie charts indicate the relative contributions of each pattern to each ROI (\u003cstrong\u003ea\u003c/strong\u003e) or network (\u003cstrong\u003eb\u003c/strong\u003e) (i.e., the percentage of voxels with the highest cosine similarity for each map). \u003cstrong\u003ed\u003c/strong\u003e, Regional importance scores for acceptance versus reappraisal. X-axis–reappraisal model importance (percentage of ROI or network occupied by the encoding model of reappraisal), y-axis–acceptance model importance (percentage of ROI or network occupied by the encoding model of acceptance). The gray line represents equal importance in both models; a greater distance between the points and the line indicates less equal importance in the two models. The region on the left side has a higher importance for acceptance than for reappraisal, and on the right side is the opposite. \u003cstrong\u003ee\u003c/strong\u003e, Voxel-level spatial similarity between the encoding model of NERS-A and NERS-R. Scatter plots illustrate normalized unthresholded voxel beta weights of the reconstructed ‘activation pattern’ of NERS-A (y-axis) and NERS-R (x-axis). Colored octants indicate voxels of shared positive (Octants 2) or shared negative (Octants 6), selective positive weights for NERS-A (Octant 1) or NERS-R (Octant 3), selective negative weights for NERS-A (Octant 5) or NERS-R (Octant 7), and voxel weights opposite for the two signatures (Octants 4 and 8). The right bars indicate the sum of squared distances from the origin (0, 0) for each octant, which integrates the number of voxels and combined weights.\u003cstrong\u003e f\u003c/strong\u003e, Regulating or experiencing naturalistic negative emotions is distributed across multiple systems. The model performance was evaluated as increasing numbers of voxels/features (\u003cem\u003ex\u003c/em\u003e-axis) were selected to predict experience naturalistic negative emotions (NV vs. NeutV), use acceptance (NA vs. NV), and reappraisal (NR vs. NV) strategy in different ROIs including the whole brain (black), prefrontal cortex (light orange), or individual large-scale cerebral networks (other colored lines). The \u003cem\u003ey\u003c/em\u003e-axis denotes the cross-validated classification accuracy. Colored dots indicate the mean correlation coefficients, solid lines indicate the mean parametric fit, and shaded regions indicate the s.d. dlPFC, dorsolateral prefrontal cortex; vlPFC, ventrolateral prefrontal cortex; dmPFC, dorsomedial prefrontal cortex; vmPFC, ventromedial prefrontal cortex; OFC, orbitofrontal cortex; ACC, anterior cingulate cortex; vAttention, ventral attention; dAttention, dorsal attention.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6775990/v1/80ee751632a57cac376bf370.png"},{"id":84375666,"identity":"16196c20-7452-457a-b295-4eb5429ce9df","added_by":"auto","created_at":"2025-06-11 08:19:58","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":289286,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTesting application potential. \u003c/strong\u003eThe NERS-R could accurately distinguish between distancing (NpD, another form of reappraisal) and viewing (NpV) negative pictures in health control (HC, n=48) participants but not for cannabis users (CU, n=49). Permutation tests reveal that the pattern expression of the two groups has significant differences (red bar chart). NNES could accurately distinguish between NpV and view neutral pictures (NeutpV); the pattern expressions of the two groups are also significantly different (green bar chart). *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, ***\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001, NS not significant (binomial test, two-sided, uncorrected).\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6775990/v1/02afa9a1d34f197b58a01d21.png"},{"id":109158083,"identity":"f53160ff-6748-4acc-bed3-af62288b3b13","added_by":"auto","created_at":"2026-05-13 07:07:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4878499,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6775990/v1/8839b7c0-b9eb-48d2-b659-5678e3e54cf4.pdf"},{"id":84375662,"identity":"65f56136-3e69-4969-b7e0-2c50f7604cac","added_by":"auto","created_at":"2025-06-11 08:19:57","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":67573,"visible":true,"origin":"","legend":"Comprehensive details concerning selected sections of the manuscript\u0026#x2019;s main body","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-6775990/v1/967fd2a558106704de8027f0.docx"},{"id":84375668,"identity":"b9995fd0-f06e-4b86-b370-6ea8fbf79a77","added_by":"auto","created_at":"2025-06-11 08:19:58","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":13672172,"visible":true,"origin":"","legend":"","description":"","filename":"ExtendedData.docx","url":"https://assets-eu.researchsquare.com/files/rs-6775990/v1/79ca194f01a679909e372de3.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Common and distinct neurofunctional signatures of emotion regulation strategies and their clinical translation in dynamic naturalistic contexts","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEmotion regulation (ER) \u0026ndash; the ability to modulate the experience and expression of emotions \u0026ndash; is fundamental to adaptive functioning and mental health\u003csup\u003e1\u0026ndash;6\u003c/sup\u003e. Impairments in ER represent a core transdiagnostic feature across major mental disorders (MDs)\u003csup\u003e5,7\u0026ndash;9\u003c/sup\u003e. Regulation of negative affect can be achieved by several mental strategies, with cognitive reappraisal and acceptance being particularly effective\u003csup\u003e2,10\u0026ndash;13\u003c/sup\u003e. Both are central building blocks of psychotherapeutic interventions\u003csup\u003e14\u0026ndash;16\u003c/sup\u003e and have a high potential of being applied in everyday emotional challenges\u003csup\u003e17,18\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWhile both strategies efficiently regulate negative affect and focus on cognitive changes\u003csup\u003e13\u003c/sup\u003e (see \u003cem\u003eprocess model of emotion regulation\u003c/em\u003e\u003csup\u003e1,2\u003c/sup\u003e),\u0026nbsp;they differ\u0026nbsp;markedly in their operational and cognitive mechanisms. Reappraisal is based on reinterpreting the subjective meaning of the emotion-inducing event to control its affective impact\u003csup\u003e19,20\u003c/sup\u003e. Its successful implementation critically relies on an array of executive functions, including attention, working memory, and cognitive control, and represents an active and cognitively demanding process\u003csup\u003e21,22\u003c/sup\u003e. In contrast, acceptance involves a nonjudgmental awareness of emotional experiences without attempts to control them\u003csup\u003e23,24\u003c/sup\u003e. As such, it is considered a less cognitively demanding and more passive strategy relying on self-referential processes or metacognitive awareness\u003csup\u003e22\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eNeurobiological models distinguish between brain systems involved in emotional reactivity\u0026nbsp;\u0026ndash; such as the amygdala and insula, and systems involved in regulation, including lateral prefrontal and parietal regions\u003csup\u003e25\u0026ndash;27\u003c/sup\u003e. In line with the effectiveness of both strategies, fMRI meta-analyses indicate that both effectively modulate activity in subcortical emotion \u0026ldquo;reactivity\u0026rdquo; regions, such as the\u0026nbsp;amygdala,\u003csup\u003e24,28\u003c/sup\u003e while enhancing the engagement of ventrolateral prefrontal regulatory regions (vlPFC)\u003csup\u003e29,30\u003c/sup\u003e. Notably, reappraisal has been strongly associated with engagement of the fronto-parietal control network\u003csup\u003e26,27,31\u003c/sup\u003e,\u0026nbsp;reflecting its reliance on regulatory cognitive processes, while acceptance has been associated with an engagement of core regions of the posterior midline default\u0026nbsp;mode network (DMN)\u003csup\u003e22,24,32\u0026ndash;34\u003c/sup\u003e, suggesting a distinct neural signature reflective of self-related and introceptive processing.\u003c/p\u003e\n\u003cp\u003eDespite extensive work using conventional neuroimaging and meta-analytic approaches several key questions remain unresolved, including (1) the common and distinct neural representations of reappraisal and acceptance remain controversially debated\u003csup\u003e22,35\u0026ndash;37\u003c/sup\u003e, and, (2) the generalizability of the identified neural systems that have been extensively assessed using sparsely presented static stimuli (e.g. pictures) to naturalistic, dynamic emotional experiences which more closely reflect real-world affective experiences, as well as (3) the translational potential of these neural representations as precise neuromarkers for ER impairments in clinical populations.\u003c/p\u003e\n\u003cp\u003eFirst, traditional mass-univariate analysis, widely used in previous studies, presents several limitations\u0026nbsp;with respect to\u0026nbsp;establishing comprehensive and accurate brain models for specific mental processes, including (1) voxel-wise independence assumptions that reduce sensitivity to comprehensively determine cognitive functions\u003csup\u003e38\u003c/sup\u003e and (2) averaging across voxels \u0026ndash; each containing a massive amount of neurons (~5.5 million) \u0026ndash; yields nonspecific signals\u003csup\u003e39,40\u003c/sup\u003e. Recent advances in multi-variate predictive models convergently suggest that emotional and cognitive processes are encoded in distributed neural representations rather than in isolated brain systems\u003csup\u003e28,41\u0026ndash;43\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSecond, few studies\u003csup\u003e22,33\u003c/sup\u003e have directly compared acceptance and reappraisal strategies using a within-subject design or under ecologically valid conditions. Most neurobiologically-informed models instead rely on meta-analyses\u003csup\u003e5,44,45\u003c/sup\u003e, which are limited by the univariate approach of the original studies and further constrained by variability indicated by preprocessing protocols\u003csup\u003e46\u003c/sup\u003e. Moreover, these studies used sparsely presented, static, and isolated stimuli (e.g., affective pictures), which limits ecological validity\u003csup\u003e47,48\u003c/sup\u003e, in particular in the context of emerging evidence indicating that neural systems that support affective experience differ substantially when engaged during dynamic naturalistic experiences\u003csup\u003e49\u0026ndash;51\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThird, despite the recognition of impaired ER as a transdiagnostic impairment in\u0026nbsp;MDs, including addiction\u003csup\u003e7,52,53\u003c/sup\u003e,\u0026nbsp;findings have yet to be translated into clinically useful neuromarkers. While initial progress has been made in developing a neuromarker for cue reactivity, parallel progress in the domain of ER is lacking\u003csup\u003e54\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo address these critical limitations, we developed and extensively evaluated a comprehensive, ecologically valid, and clinically applicable brain model of ER under naturalistic conditions. We here capitalize on the combination of functional magnetic resonance imaging (fMRI) with machine-learning-based multivariate pattern analysis (MVPA) and naturalistic paradigms\u003csup\u003e55\u0026ndash;58\u003c/sup\u003e. MVPA leverages information across multiple spatial scales\u003csup\u003e39,59\u003c/sup\u003e, allowing the establishment of\u0026nbsp;comprehensive, accurate, and generalizable whole-brain models of emotional experiences (\u0026lsquo;neuroaffective signatures\u0026rsquo;)\u0026nbsp;with improved sensitivity and specificity\u0026nbsp;and translating these into neuromarkers for\u0026nbsp;MDs\u003csup\u003e59,60\u003c/sup\u003e. To further increase the ecological validity of these models, recent studies have employed\u0026nbsp;naturalistic paradigms with dynamic videos, speech, and music as materials that are consistent with the typical sensory stimuli encountered in realistic daily life\u003csup\u003e47\u0026ndash;49,61,62\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAgainst this background, we aimed here at\u0026nbsp;precisely determining common and distinct neural representations of reappraisal and acceptance during naturalistic dynamic emotional processing combined with MVPA-based neural decoding. Briefly, 59 participants from a pre-registered discovery cohort and 33 participants from a pre-registered validation cohort underwent a dynamic naturalistic ER paradigm with concomitant fMRI (Fig.1a), with acceptance (NA), reappraisal (NR), or natural emotional experience to negative (NV) or neutral clips (NeutV). We next systematically tested (1) whether it is possible to develop precise and robust neural signatures to determine negative experiences (NNES) and their regulation via reappraisal and acceptance (NERS-R or NERS-A, respectively) across the discovery cohort and independent validation cohort (Fig.1b); (2) whether the developed decoders generalize to independent ER processing investigated using conventional static paradigms (e.g., picture) in a large dataset of healthy individuals (n=358)\u003csup\u003e28\u003c/sup\u003e and reappraisal pain induced by heat stimuli (n=33)\u003csup\u003e63\u003c/sup\u003e. Combing multiple analysis methods to determine (3) the distinct and common brain representations and brain systems that underly acceptance and reappraisal, utilizing e.g. Bayes Factor (BF) analysis which allows examining for both the null and alternative hypotheses \u003csup\u003e28,64,65\u003c/sup\u003e and spatial similarity analysis with encoding models and prefrontal systems involved in ER\u003csup\u003e66\u003c/sup\u003e as well as large-scale functional networks\u003csup\u003e67\u003c/sup\u003e (Fig.1c). Based on accumulating evidence for ER deficits in MDs, including excessive cannabis users (CU)\u003csup\u003e68\u003c/sup\u003e, we examined the clinical application potential of our decoders (4) to detect ER deficits in CU compared to healthy control (HC) participants (n\u003csub\u003eHC\u003c/sub\u003e=48, n\u003csub\u003eCU\u003c/sub\u003e=49) (Fig.1d).\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eAssessment of the naturalistic ER paradigm\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eSubjective experience and regulation of negative emotions\u003c/strong\u003e. During fMRI, participants were instructed to react naturally to neutral or negative video clips or use corresponding ER strategies based on presented cues, then report their momentary negative affect on a 9-point Likert scale (9-very negative, 1-not negative at all).\u0026nbsp;In both discovery (n=59) and validation (n=33) cohorts, significant main effects of condition (stimulus type: NV, NeutV; strategies: NV, NA, NR) were observed with\u0026nbsp;repeated-measures analysis of variance (ANOVA) and further simple effects analysis,\u0026nbsp;indicating that the negative clips induced considerable negative emotional experience\u0026nbsp;(discovery cohort:\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eP\u003c/em\u003e=9.96\u0026times;10\u003csup\u003e-39\u003c/sup\u003e, \u0026nbsp;=0.947; validation cohort:\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eP\u003c/em\u003e=8.15\u0026times;10\u003csup\u003e-17\u003c/sup\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003e =0.947; Fig.2, details in supplements) and both strategies allowed participants to effectively attenuate the negative emotions(discovery cohort: \u003cem\u003eP\u003c/em\u003e=3.18\u0026times;10\u003csup\u003e-19\u003c/sup\u003e, \u0026nbsp;=0.585; validation cohort: \u003cem\u003eP\u003c/em\u003e=1.32\u0026times;10\u003csup\u003e-12\u003c/sup\u003e, \u0026nbsp;=0.639). Reappraisal, led to a stronger decrease in negative emotions as compared to acceptance (discovery cohort: \u003cem\u003eP\u003c/em\u003e=2.02\u0026times;10\u003csup\u003e-10\u003c/sup\u003e; validation cohort: \u003cem\u003eP\u003c/em\u003e=2.4\u0026times;10\u003csup\u003e-5\u003c/sup\u003e). Findings are further corroborated by success rates of ER (discovery cohort: 7.51\u0026plusmn;1.01, validation cohort: 7.53\u0026plusmn;0.98).\u003c/p\u003e\n\u003ch2\u003eDecoding emotional experience and ER strategies in dynamic naturalistic contexts\u003c/h2\u003e\n\u003cp\u003eEmploying the support vector machine (SVM) with leave-one-subject-out cross-validation (LOSO-CV) allowed us to identify whole-brain fMRI activation predictive signatures of negative emotional experience (NNES), acceptance (NERS-A), and reappraisal (NERS-R) (Fig.3a, b, and c) during naturalistic emotion processing. Only the data from the discovery cohort were used to develop decoders. The performance of the decoders was validated using cross-validated classification in the discovery cohort and determining the reactivity of the decoders in an independent pre-registered validation cohort (n=33) that underwent a similar paradigm. NNES, NERS-A, and NERS-R could accurately discriminate the corresponding verse control condition, respectively (Fig.3d, Table 1). Testing the signatures with no further model fitting in the validation cohort revealed robust prediction performances in the independent datasets (Fig.3e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Classification performance on the discovery and validation cohort\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"737\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eCondition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eDecoder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003eACC (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003eSens (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003eSpec (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eEffect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003esize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" style=\"width: 737px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiscovery cohort\u003c/strong\u003e (n=59)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eNV vs. NeutV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eNNES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e95\u0026ndash;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e81\u0026ndash;97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;1\u003c/strong\u003e\u003cstrong\u003e\u0026times;\u003c/strong\u003e\u003cstrong\u003e10\u003csup\u003e-50\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eNA vs. NV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eNERS-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e68\u0026ndash;86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e71\u0026ndash;91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.75\u003c/strong\u003e\u003cstrong\u003e\u0026times;\u003c/strong\u003e\u003cstrong\u003e10\u003csup\u003e-10\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eNR vs. NV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eNERS-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e73\u0026ndash;92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e73\u0026ndash;92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.53\u003c/strong\u003e\u003cstrong\u003e\u0026times;\u003c/strong\u003e\u003cstrong\u003e10\u003csup\u003e-13\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" style=\"width: 737px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValidation cohort\u003c/strong\u003e (n=33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eNV vs. NeutV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eNNES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e100\u0026ndash;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e100\u0026ndash;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.33\u003c/strong\u003e\u003cstrong\u003e\u0026times;\u003c/strong\u003e\u003cstrong\u003e10\u003csup\u003e-10\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eNA vs. NV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eNERS-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e53\u0026ndash;86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e53\u0026ndash;85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0351\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eNR vs. NV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eNERS-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e75\u0026ndash;97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e76\u0026ndash;97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.10\u003c/strong\u003e\u003cstrong\u003e\u0026times;\u003c/strong\u003e\u003cstrong\u003e10\u003csup\u003e-5\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" style=\"width: 737px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeneralization cohort 1\u0026nbsp;\u003c/strong\u003e(n=358)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eNpV vs. NeutpV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eNNES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e92\u0026ndash;96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e92\u0026ndash;96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;1\u003c/strong\u003e\u003cstrong\u003e\u0026times;\u003c/strong\u003e\u003cstrong\u003e10\u003csup\u003e-50\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eNpR vs. NpV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eNERS-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e64\u0026ndash;74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e64\u0026ndash;74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.14\u003c/strong\u003e\u003cstrong\u003e\u0026times;\u003c/strong\u003e\u003cstrong\u003e10\u003csup\u003e-12\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eNpR vs. NpV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eNERS-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e77\u0026ndash;85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e78\u0026ndash;85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;1\u003c/strong\u003e\u003cstrong\u003e\u0026times;\u003c/strong\u003e\u003cstrong\u003e10\u003csup\u003e-50\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" style=\"width: 737px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeneralization cohort 2\u0026nbsp;\u003c/strong\u003e(n=33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eDP vs. EP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eNNES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e35\u0026ndash;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e33\u0026ndash;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eDP vs. EP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eNERS-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e44\u0026ndash;77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e44\u0026ndash;77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.2962\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eDP vs. EP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eNERS-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e60\u0026ndash;90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e60\u0026ndash;90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0045\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNV, view (react to) negative clips; NeutV, view (react to) neutral clips; NA, use acceptance strategy to negative clips; NR, use reappraisal strategy to negative clips; NNES, naturalistic negative emotion signature; NERS-A, naturalistic emotion regulation signature \u0026ndash; acceptance; NERS-R, naturalistic emotion regulation signature \u0026ndash; reappraisal; NpV, view negative picture; NeutpV, view neutral picture; NpR, regulate negative emotions induced by picture; DP, Decrease-pain; EP, experience-pain.\u003c/p\u003e\n\u003ch2\u003eGeneralization to independent data\u003c/h2\u003e\n\u003cp\u003eGeneralization across samples, MRI systems, culture, and stimuli (dynamic versus static or heat-induced pain) was first determined using data from a large independent cohort (n=358)\u003csup\u003e28\u003c/sup\u003e. NNES could accurately discriminate exposure to negative versus neutral pictures (Fig.3f, Table 1). In line with the strategy used by the participants (acceptance or reappraisal)\u003csup\u003e28\u003c/sup\u003e, NERS-A and NERS-R could accurately classify the ER vs. experience processing.\u003c/p\u003e\n\u003cp\u003eAfter excluding the voxels from the visual network\u003csup\u003e67\u003c/sup\u003e(to\u0026nbsp;control the interference of visual processing caused by different materials), NERS-R could accurately distinguish between using the reappraisal strategy to down-regulate vs. experiencing pain induced by heat stimuli, based on the data from the second independent cohort (n=33,\u0026nbsp;Fig.3g). Yet NERS-A and NNES showed poor predictive performance of reappraisal.\u003c/p\u003e\n\u003cp\u003eTogether, these results underscore the robustness and specificity of the decoders for specific mental processes.\u003c/p\u003e\n\u003ch2\u003eCommon and separate signatures of reappraisal, acceptance, and negative emotional experiences\u003c/h2\u003e\n\u003cp\u003eWe next systematically evaluated which brain regions provided a consistent and robust contribution to negative affect and were regulated by the respective ER strategies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBootstrap tests and encoding model estimation\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eWe initially thresholded the\u0026nbsp;multivariate patterns using bootstrapping (Fig.3a, b, and c) and \u0026ndash; given that the decoding model features may not only reflect neurobiological processes but also noise components\u003csup\u003e70\u003c/sup\u003e \u0026ndash; next transformed the bootstrapped within-subject weighted maps to reconstructed \u0026lsquo;activation pattern\u0026rsquo; (encoding model)\u003csup\u003e71,72\u003c/sup\u003e and estimated the population-level reconstructed \u0026lsquo;activation pattern\u0026rsquo; (FDR \u003cem\u003eq\u003c/em\u003e\u0026lt;0.05,\u0026nbsp;Fig. 4a).\u003c/p\u003e\n\u003cp\u003eAggressive negative emotions induced by watching the negative (versus neutral) clips were accompanied by a positive association with activity in subcortical regions engaged in emotional reactivity, such as the insular, thalamus, periaqueductal grey (PAG), as well as superior parietal (e.g. supramarginal), ventrolateral occipital and posterior mid-temporal regions involved in the integration of information and semantic content\u003csup\u003e41,73,74\u003c/sup\u003e; negative association with activity in a widespread bilateral anterior temporal and frontal network, including the pre-supplementary motor area (SMA) as well as medial and lateral frontal regions.\u003c/p\u003e\n\u003cp\u003eCompared to experiencing negative emotions, acceptance significantly attenuated activity in subcortical (e.g. fusiform and lingual gyrus), superior and inferior parietal, inferior temporal, and bilateral pars triangularis (IFSa) regions, while concomitantly associated with activity in the amygdala (extended to\u0026nbsp;hippocampus), insular cortex, putamen, frontal regions including the prefrontal lobe (bilateral dorsolateral (dlPFC), dorsomedial (dmPFC), ventromedial (vmPFC), ventrolateral (vlPFC) prefrontal cortex, left rostral anterior cingulate cortex (rACC)),\u0026nbsp;isthmus cingulate,\u0026nbsp;precentral,\u0026nbsp;lateral occipital cortex, SMA, and superior parietal regions including the precuneus (Fig. 4b).\u003c/p\u003e\n\u003cp\u003eReappraisal (versus experiencing negative emotion) is negatively associated with activity in subcortical regions, including the bilateral amygdala, insular, putamen, and thalamus involved in emotional experience, as well as\u0026nbsp;lateral occipital,\u0026nbsp;posterior frontal, and parietal regions, including the SMA and supramarginal gyrus. Reappraisal is concomitantly associated with activity in a bilateral frontal network encompassing medial, orbitofrontal, vlPFC, rACC, right dlPFC, as well as parietal regions including the precuneus, superior, and middle temporal regions (Fig. 4c).\u0026nbsp;Univariate analyses additionally confirmed the observed effects\u0026nbsp;(Extended Data Fig.1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBrain systems identified by BF analysis.\u0026nbsp;\u003c/strong\u003eBF analysis was used to determine the common and separate neural basis between acceptance and reappraisal. \u0026lsquo;Acceptance only\u0026rsquo; brain regions were identified by activation during acceptance (BF\u0026gt;5) but not for reappraisal (BF\u0026lt;1/5); reverse for \u0026lsquo;Reappraisal only\u0026rsquo; (reappraisal BF\u0026gt;5, acceptance BF\u0026lt;1/5); \u0026lsquo;Common\u0026rsquo; brain regions were identified by activation during both acceptance (BF\u0026gt;5) and reappraisal (BF\u0026gt;5)\u003csup\u003e28,64\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eEffective ER via both strategies robustly recruited cortical midline DMN regions including the bilateral dmPFC (extending to rACC), right inferior precuneus, left cuneus, middle temporal and lateral occipital regions (Fig.5a, see also conjunction analysis in Extended Data Fig.2). Acceptance and reappraisal specifically recruited distributed process-specific systems spanning several cortical systems, with the acceptance primarily engaging somatomotor (and to a lesser extend ventral attention and frontoparietal) large scale networks and subcortical systems (e.g., superior temporal lobe, dlPFC, vlPFC, insular, amygdala, and putamen), while reappraisal primarily engaged an extensive the frontoparietal and DMN regions (e.g., superior frontal and inferior parietal regions, dlPFC, vlPFC, supramarginal, and precuneus).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial similarity between stable decoding maps and interest networks, as well as crucial ROIs\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eWe further estimated the spatial similarities between the reconstructed \u0026lsquo;activation pattern\u0026rsquo; and prefrontal anatomical regions consistently involved in emotional experience and regulation (ROIs)\u003csup\u003e66\u003c/sup\u003e as well as large-scale functional networks\u003csup\u003e67\u003c/sup\u003e (Fig.5b, c). For each region, we calculated the ratio of importance for acceptance versus reappraisal versus experience negative affect (pie plot in Fig.5b, c; Supplementary Table 1), with importance defined as the percentage of the region encoded by each model\u003csup\u003e42,75\u003c/sup\u003e, while also directly comparing acceptance and reappraisal (Fig.5d). Except the vlPFC, which was engaged in all processes, prefrontal regions exhibited a stronger engagement during both regulation strategies as compared to experiencing the emotions. While the vmPFC, dmPFC, and vmPFC showed comparable contributions to both strategies, the ACC had stronger engagement for acceptance, whereas the dlPFC and orbitofrontal cortex (OFC) exhibited stronger engagement during reappraisal. On the network level, the visual networks exhibited equal importance across the three models, the experience of negative emotion may mostly encoded in the dorsal, ventral attention, and somatomotor networks. When comparing between ER strategies, the somatomotor and attention networks presented higher encoding importance for acceptance, while the frontoparietal networks had relative stronger contribution for reappraisal and the DMN exhibited similar importance for both strategies.\u003c/p\u003e\n\u003cp\u003eTo provide more comparative pictures of how acceptance and reappraisal are encoded, we analyzed the voxel-level spatial covariation between the unthresholded normalized (z-scored) encoding maps of NERS-A and NERS-R\u003csup\u003e76\u003c/sup\u003e. Fig.5e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eillustrates the joint distribution of the voxel weights of the encoding model of NERS-A and NERS-R. Octants with different colors indicate voxels of shared positive/negative, selective positive/negative weights, and voxel weights opposite for the two signatures. Stronger weights (sum of squared distances to the origin (SSDO)) were shown in the nonshared octants (5,7) as well as opposite octants (4,8), and fewer weights in the shared octant 6, further supporting that separable brain representations contribute to implementing acceptance and reappraisal strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictive performances between decoders.\u003c/strong\u003e To determine whether our decoders reflected both the common and separate processing of naturalistic acceptance, reappraisal, and negative emotion, we also compare the decoders\u0026rsquo; performances on different conditions from the validation cohort and add a neural signature for negative affect (PINES, picture-induced negative emotion signature\u003csup\u003e69\u003c/sup\u003e). As shown in Extended Data Fig.2c, except for NERS-R, the other three decoders could distinguish between NV and NeutV, but NNES has the highest accuracy and effect size. NNES and PINES show low performance when predicting ER processing (acceptance and reappraisal). Only NERS-A could accurately classify between NA and NV. Both NERS-R and NERS-A could classify between NR and NV, but NERS-R is more accurate with a larger effect size. Together, these findings indicate that our decoders captured and further demonstrated the common and separate nature of the above three mental processes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvaluation of predictive performances of local systems\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eGiven the continuing debate about the specific systems underlying ER, including the dlPFC\u003csup\u003e27,28,77\u003c/sup\u003e, and the\u0026nbsp;DMN\u003csup\u003e34,78,79\u003c/sup\u003e, we (1) employed network-based and searchlight-based analyses to estimate the predictive performances of local brain regions and (2) compared their performances with whole-brain decoders. To control for the potential effects of the number of voxels/features in prediction (i.e., the whole-brain model contains many more features), voxels were randomly selected (repeated 1,000 times) from a uniform distribution spanning the whole brain (Fig.6b, black), prefrontal cortex (light orange, which has been suggested play crucial roles in both generate and regulate emotions\u003csup\u003e26,27,31\u003c/sup\u003e), or individual large-scale functional networks (averaged over 1,000 iterations)\u003csup\u003e41,42,80\u003c/sup\u003e. The asymptotic prediction when sampling from the whole-brain, as we trained decoders (black line in Fig.6b), was more accurate than the asymptotic prediction within individual networks (colored lines), especially with more available voxels (details in Supplementary). Moreover, model performance was optimized (i.e., reaching asymptote) when approximately 10,000 voxels were randomly sampled across the whole brain, further confirming that information about ER processing is contained in patterns of activity that span multiple systems.\u0026nbsp;The above results can also be demonstrated by searchlight analysis, although statistically significant and thresholded brain regions are similar to the whole-brain decoders (Extended Data Fig.3), the effect sizes in terms of prediction\u0026ndash;outcome accuracy were substantially smaller than those obtained from the developed decoders (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001).\u0026nbsp;Together, findings underscore that ER and negative emotional experiences are encoded in distributed neural patterns that span multiple systems, rather than single brain regions or networks.\u003c/p\u003e\n\u003ch2\u003eTranslation into a neuromarker for determining ER deficits in\u0026nbsp;MDs\u003c/h2\u003e\n\u003cp\u003eTo test whether our decoders can capture deficient ER processing in individuals with\u0026nbsp;MDs\u003csup\u003e52,68,81\u003c/sup\u003e, we applied the decoders to extended data from our previous study\u003csup\u003e68\u003c/sup\u003e describing ER deficits in cannabis users (CU). For the present study, we incorporated additional unpublished data from individuals with CU disorder and matched controls who underwent a similar picture-based reappraisal paradigm with the concomitant fMRI system (details see ref.\u003csup\u003e68\u003c/sup\u003e, study protocol NCT02801214, clinical-trials.gov).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA total of 48 HC participants and 49 CU were included in the extended dataset. In line with the findings in the initial study, behavioral analyses confirmed ER deficits in the CU (reappraisal success\u003csup\u003e68\u003c/sup\u003e, 0.46\u0026plusmn;0.46) as compared to HC (0.78\u0026plusmn;0.54) (\u003cem\u003et\u003c/em\u003e=-3.072, \u003cem\u003ep\u003c/em\u003e=0.003, Cohen\u0026rsquo;s d=0.50) in the extended sample.\u003c/p\u003e\n\u003cp\u003eNERS-R could accurately predict neural activity during cognitive reappraisal (distancing) (NpD) vs. view (NpV) negative pictures in HC participants but not in CU (Fig.6, Table 2). NNES could accurately distinguish between NpD and view neutral pictures (NeupV) in both groups, underscoring impaired regulation rather than excessive emotion reactivity as a neurofunctional marker for CU. Further permutation tests revealed that the pattern expression of the two groups has significant differences for NERS-R and NNES (both \u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001, Fig.6). In contrast, NERS-A showed poor predictive performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Classification performance on the clinical application cohort\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"728\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eCondition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eDecoder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 94px;\"\u003e\n \u003cp\u003eACC (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003eSens (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 105px;\"\u003e\n \u003cp\u003eSpec (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eEffect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003esize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical application cohort\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHC\u003c/strong\u003e (n =48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eNpV vs. NeutpV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eNNES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e86\u0026ndash;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e86\u0026ndash;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.31\u003c/strong\u003e\u003cstrong\u003e\u0026times;\u003c/strong\u003e\u003cstrong\u003e10\u003csup\u003e-10\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eNpD vs. NpV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eNERS-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e27\u0026ndash;56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e29\u0026ndash;55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.3123\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eNpD vs. NpV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eNERS-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e56\u0026ndash;82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e56\u0026ndash;82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0133\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCU\u003c/strong\u003e (n =49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eNpV vs. NeutpV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eNNES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e87\u0026ndash;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e87\u0026ndash;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.98\u003c/strong\u003e\u003cstrong\u003e\u0026times;\u003c/strong\u003e\u003cstrong\u003e10\u003csup\u003e-11\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eNpD vs. NpV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eNERS-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e41\u0026ndash;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e40\u0026ndash;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.5681\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eNpD vs. NpV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eNERS-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e46\u0026ndash;73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e46\u0026ndash;73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.2528\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eHC, healthy control; CU, cannabis users; NpV, view negative pictures; NeutpV, view neutral pictures; NpD, distancing negative pictures.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIdentifying comprehensive and accurate brain models for the common and distinct brain representations that underly ER is critical for advancing mechanistic models of cognitive regulation as well as for developing accurate biomarkers for regulatory dysfunction in\u0026nbsp;MDs. While reappraisal and acceptance are widely implemented in therapeutic contexts and have been debated from theoretical\u003csup\u003e1,3\u003c/sup\u003e, experimental\u003csup\u003e28\u003c/sup\u003e, and clinical\u003csup\u003e12,82\u003c/sup\u003e perspectives, the extent to which they rely on common and dissociable systems in ecologically valid dynamic contexts remains unresolved.\u003c/p\u003e\n\u003cp\u003eHere, we combined immersive naturalistic neuroimaging with predictive modeling and BF analyses to systematically establish and validate three comprehensive, accurate, and sensitive brain-based models for negative affect (NNES) and its regulation via acceptance (NERS-A) and reappraisal (NERS-R). The developed decoders are generalizable across imaging systems, cultures, and paradigms. Next, through a series of systematic analyses, we uncovered the shared and separable neural representations of acceptance, reappraisal, and negative emotion, which indicated that these mental processes are encoded in distributed and distinguishable neural systems on the whole brain level, yet also show process-specific engagement of large-scale systems. Both acceptance and reappraisal engaged cortical midline regions of the core DMN, including the dmPFC and posterior parietal cortex, indicating a general contribution of this system to effective ER. Each strategy, furthermore, exhibited distinct profiles aligned with its cognitive demands. Acceptance was encoded in distributed regions spanning subcortical, somatomotor, and ventral attention networks, consistent with embodied awareness and experiential processing. Reappraisal, by contrast, relied on regions spanning the fronto-parietal control network, including lateral frontal and inferior parietal regions, reflecting its reliance on executive functions and semantic updating. While the distinction was further substantiated by BF analyses and spatial similarity mapping, decoding performance was substantially higher for whole-brain decoders as compared to regional models, underscoring the importance of brain-wide integration for capturing emotional experience and regulation. Finally, the neurofunctional decoders could detect reappraisal-specific ER deficits in a large set of CU, underscoring the sensitivity of the signatures to detect specific behavioral impairments on the neural level and clinical potential.\u003c/p\u003e\n\u003cp\u003eCombining naturalistic fMRI with\u0026nbsp;mass-univariate and multivariate predictive analyses,\u0026nbsp;we established comprehensive and distributed brain models for negative emotional experiences and their cognitive regulation in dynamically close-to-real-life contexts. The affective videos induced immersive and strongly negative emotional states, which were accompanied by positive engagement of brain regions involved in emotional experience, physiological and affective arousal, including the insula, PAG, thalamus\u003csup\u003e26,27,74,75,83\u003c/sup\u003e, and\u0026nbsp;negatively associated with the engagement of a\u0026nbsp;widespread frontal and temporal network involved in cognitive and regulatory control. Spatial\u0026nbsp;similarity and network-level analyses further confirmed the contribution of large-scale cortical networks, including the attention and visual networks (resembling previous studies\u003csup\u003e84\u003c/sup\u003e using dynamic stimuli). Importantly, both the developed (NNES) and an established signature for negative affect (PINES) poorly predict the ER strategies, supporting previous research indicating that emotion generation and regulation are neurally separable processes\u003csup\u003e25\u0026ndash;27\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eBoth ER strategies could efficiently reduce negative emotional states, indicating that utilizing reappraisal and acceptance represents an efficient means to regulate negative emotions and the associated neural systems in dynamic, ecologically-valid contexts.\u0026nbsp;In line with ref.\u003csup\u003e22\u003c/sup\u003e, reappraisal led to a greater reduction of subjective negativity compared to acceptance.\u003c/p\u003e\n\u003cp\u003eOn the neural level, ER via both strategies was predicted by neural regulation in distributed cortical and subcortical systems, with common and distinct signatures. BF and conjunction analyses demonstrated that effective ER by both strategies engaged core regions of the DMN that are strongly involved in the evaluation of the mental state of oneself and others (dmPFC)\u003csup\u003e19,26,31\u003c/sup\u003e, episodic autobiographical memory (inferior\u0026nbsp;precuneus)\u003csup\u003e85,86\u003c/sup\u003e, rapid emotional conflict adaptation (rACC)\u003csup\u003e87\u003c/sup\u003e, self-awareness and interpersonal processing (temporal regions)\u003csup\u003e88,89\u003c/sup\u003e. Given that both ER strategies require the appraisal of the emotional state concerning the self, the dmPFC and other core DMN regions may support appraisal, metacognitive evaluation, and internal self-reference, enabling ER based on internal goals\u003csup\u003e90\u003c/sup\u003e. On the anatomical level, these functions of the DMN, in particular the dmPFC, may be supported by interconnections with the rostromedial prefrontal, lateral orbitofrontal (lOFC), and pregenual ACC, which may support its role as a\u0026nbsp;central hub for the implementation of both ER strategies\u003csup\u003e31,90\u003c/sup\u003e. The effective regulation was accompanied by reduced engagement of a widespread bilateral\u0026nbsp;network including bilateral superior parietal, inferior and precentral frontal, and lateral occipital regions, which may reflect attenuated\u0026nbsp;motor readiness and expressive output, as well as attenuated visual processing of emotionally salient stimuli, and in turn may promote regulatory control across both strategies\u003csup\u003e91\u0026ndash;93\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eDistinguishable contributions were observed in somatomotor\u003csup\u003e94\u003c/sup\u003e, attention, or the frontoparietal control network\u003csup\u003e79,95\u003c/sup\u003e, respectively. In line with the notion that reappraisal represents an active and effortful ER strategy, effective implementation specifically engaged an extensive bilateral network of distributed regions within the fronto-parietal control network. These included\u0026nbsp;lateral frontal regions involved in working memory (dlPFC)\u003csup\u003e77\u003c/sup\u003e, general and valence-specific inhibition (vlPFC, lOFC)\u003csup\u003e26,31,96,97\u003c/sup\u003e, and medial prefrontal regions involved in computation of value or safety (vmPFC, mOFC)\u003csup\u003e31,98\u0026ndash;100\u003c/sup\u003e. Parietal regions were specifically recruited during reappraisal, including regions involved in self-referential processing (superior precuneus)\u003csup\u003e101\u0026ndash;103\u003c/sup\u003e, attentional control (inferior\u0026nbsp;precuneus)\u003csup\u003e104\u003c/sup\u003e, and conscious representation of affective semantic content (e.g., Griffiths et al.,\u003csup\u003e105\u003c/sup\u003e). In contrast, reappraisal specifically decreased engagement of brain regions traditionally linked to emotional \u0026ldquo;reactivity\u0026rdquo;, including subcortical systems such as the amygdala\u003csup\u003e26,27,98,104,106\u0026ndash;109\u003c/sup\u003e and the anterior insular cortex\u003csup\u003e98\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAcceptance was also encoded in some distinguishable but rather focal prefrontal regions (e.g., dlPFC), yet showed stronger engagement of distributed regions spanning the somatomotor (e.g.,\u0026nbsp;superior parietal lobe, SMA, posterior insula)\u003csup\u003e110,111\u003c/sup\u003e and ventral attention networks (e.g., preSMA). Compared to\u0026nbsp;reappraisal, enhanced engagement of these regions may reflect that effective acceptance relies on goal-oriented attentional deployment (superior parietal\u0026nbsp;lobe\u003csup\u003e53,112,113\u003c/sup\u003e), interoception (posterior insula\u003csup\u003e114,115\u003c/sup\u003e), and embodied cognition-affect processing (SMA/preSMA\u003csup\u003e44\u003c/sup\u003e). Notably, acceptance is specifically negatively associated with the brain systems involved in\u0026nbsp;self-referential processing (superior precuneus)\u003csup\u003e101\u0026ndash;103\u003c/sup\u003e and threat\u0026nbsp;perception (fusiform)\u003csup\u003e116,117\u003c/sup\u003e, while it was positively associated with activation in subcortical systems traditionally linked to emotional reactivity, including the amygdala, hippocampus, and putamen. The stronger engagement of these systems may reflect the non-judgmental awareness of affective arousing and sensory experience and may promote hippocampus-dependent encoding and contextualization\u003csup\u003e118\u003c/sup\u003e, in turn facilitating adaptive integration of the emotional experience with autobiographical and situational contexts\u003csup\u003e22,118\u0026ndash;121\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eComparative predictive analyses using the whole-brain decoders further substantiated common yet, on the whole-brain level, also clearly distinguishable neural representations, such that the decoders exhibited a considerably higher accuracy for the targeted ER strategy.\u0026nbsp;The importance of individual brain regions or networks in the experience and regulation of negative emotions has been emphasized in previous frameworks\u003csup\u003e25\u0026ndash;27\u003c/sup\u003e. While our findings support preferential engagement of specific networks by distinct strategies \u0026ndash; the fronto-parietal control network during reappraisal and the somatomotor network during acceptance - our large-scale network- and searchlight-based predictive analyses further highlight that, under dynamic and naturalistic conditions, both strategies rely on the coordinated activity of distributed and distinguishable brain-wide representations.\u0026nbsp;The distributed engagement was further supported by a system-specific analysis of the prefrontal cortex, commonly identified as systems promoting distinct ER strategies\u003csup\u003e25\u0026ndash;27\u003c/sup\u003e. While most prefrontal systems contributed to both strategies, the orbitofrontal cortex showed a markedly higher engagement during reappraisal, which may be due to its extensive anatomical connections and functional coupling with the amygdala during reappraisal, and which is underscored as this region is critical for mediating effective reappraisal of negative emotions\u003csup\u003e92,122\u0026ndash;124\u003c/sup\u003e. In general, the distributed signature of the whole-brain model showed the highest predictive performance, which is in line with decoders for other mental processes, e.g., digust\u003csup\u003e42\u003c/sup\u003e, fear\u003csup\u003e41\u003c/sup\u003e, or pain\u003csup\u003e80\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eImportantly, our neurofunctional signatures for ER demonstrated a clinical translational potential, such that the decoders accurately tracked the ER strategy-specific neural representation of impaired reappraisal success. The NERS-R showed an accurate reappraisal prediction in HC but failed to distinguish reappraisal from negative experience in individuals with CU and reappraisal deficits on the behavioral level, with permutation tests indicating that NERS-R and NNES could capture the abnormal brain activation pattern among CU. Impaired ER and altered emotion reactivity have been widely demonstrated across multiple\u0026nbsp;MDs, including CU\u003csup\u003e68,125,126\u003c/sup\u003e, opioid use disorders\u003csup\u003e81,127,128\u003c/sup\u003e, alcohol use disorders\u003csup\u003e129,130\u003c/sup\u003e, cocaine use disorder\u003csup\u003e131,132\u003c/sup\u003e, major depressive disorder\u003csup\u003e133,134\u003c/sup\u003e, anxiety disorders\u003csup\u003e135,136\u003c/sup\u003e, yet precise neuromarkers to track emotional deficits on the neurofunctional level are lacking. Given the critical and transdiagnostic role of ER deficits in\u0026nbsp;MDs, the present multivariate predictive patterns may facilitate the development of brain-based diagnostic and treatment response markers. Our marker accurately tracks emotional reactivity and its effective modulation via cognitive strategies, the marker may also serve as a target state for behavioral as well as brain-based intervention (e.g.,\u003csup\u003e108,137\u003c/sup\u003e). Further studies are required to determine common and mental disorder-specific alterations in these markers to validate the decoders\u0026rsquo; accuracy, clinical utility, and sensitivity.\u003c/p\u003e\n\u003cp\u003eLimitations of the present study are inherent to technical limitations. While combining fMRI with MVPA gives a relatively precise way to investigate brain activity, and has been revealed to be consistent with the conventional univariate approach, limitations are inherent to fMRI (e.g., indirectly measuring neuronal activity, 3T fMRI may not be able to precisely localize to the midbrain and brainstem nuclei\u003csup\u003e75\u003c/sup\u003e), and more precise measurement may support our understanding of the relationship between ER and brain activation.\u003c/p\u003e\n\u003cp\u003eIn conclusion, we developed and validated three robust, generalizable, and clinically translatable whole-brain neural signatures for negative emotional experience and its effective regulatory control via acceptance and reappraisal in dynamic naturalistic contexts. Our within-subject design enabled a systematic comparison of the common and distinct neural bases underlying these ER strategies and suggests that core DMN regions mediate effective regulation across strategies. On the whole brain level, the strategies were distinguishable, and the predictive neurofunctional signatures demonstrated high robustness across independent cohorts, experimental paradigms, and MRI systems. The findings emphasize that an effective ER is encoded not in isolated brain regions but in synergistic, distributed whole-brain activity patterns.\u003c/p\u003e\n\u003cp\u003eApplication to individuals with ER deficits underscored that the neural decoders offer substantial translational potential. Beyond accurately detecting ER deficits in individuals with MDs, future applications include: (a) serving as neural markers to assess intervention effects targeting acceptance and reappraisal; (b) characterizing ER-specific neural impairments across MDs; (c) delineating common and distinct neural architectures of various ER strategies; and (d) informing frameworks centered on the role of the DMN in ER. Together, this work provides a foundation for developing mechanistically-informed, brain-based targets and outcome measures for clinical interventions aimed at enhancing emotional resilience.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eParticipants\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eDiscovery cohort\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eSixty-eight healthy participants were recruited from local universities. Exclusion criteria included left-handed, color blindness or weakness, and current physical or mental illnesses. Six participants were excluded due to excessive head motion (\u0026gt; 3 mm or 3\u0026deg;), and three due to failed post-check. Fifty-nine participants (19 male, 21.3\u0026plusmn;1.7 years old, mean\u0026plusmn;SD) were included in the final analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation cohort\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eThirty-four healthy participants were recruited as the validation cohort (11 male, 20.3\u0026plusmn;1.4 years old, mean\u0026plusmn;SD), with the same exclusion criteria as the discovery cohort. One participant was excluded due to being detected as an outlier by the fmri_data.mahal function in the Canlabcore toolbox (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 Bonferroni corrected).\u003c/p\u003e\n\u003cp\u003eAll participants provided written informed consent. All participants were compensated 140 RMB after the experiment. This pre-registered study (discovery cohort: https://osf.io/s3bp2, validation cohort: https://osf.io/68jkw) was approved by the ethics committee of the University of Electronic Science and Technology of China and in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003ch2\u003eStimuli and Paradigm\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eDiscovery cohort\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eA total of forty-eight silent video clips were used in the current ER paradigm, rated by one presented once by E-prime software (Version 3.0; Psychology Software Tools, Sharpsburg, PA). Twelve neutral clips include normal driving records, surveillance, vlogs, etc.; thirty-six negative clips include threatening animals, human fights, bullying, car accidents, horror movies, etc. Neutral and negative clips are matched by basic scenarios and characters.\u003c/p\u003e\n\u003cp\u003eThe experiments consisted of four conditions: react (view)-neutral (NeutV), react-negative (NV), reappraise-negative (NR), and accept-negative (NA), each with twelve clips, divided into four runs. To alleviate the potential cognitive overload caused by switching mindsets too often or the potential fatigue or boredom caused by using the same mindset for a long period, we combined \u0026ldquo;react\u0026rdquo; with \u0026ldquo;reappraisal\u0026rdquo; or \u0026ldquo;react\u0026rdquo; with \u0026ldquo;accept\u0026rdquo; in each run, and use ABBA method between participants to balance order effect (see Fig.1a).\u003c/p\u003e\n\u003cp\u003eParticipants were provided with written instructions (see supplements), asked to use a certain way of thinking (\u0026ldquo;react\u0026rdquo;, \u0026ldquo;reappraisal\u0026rdquo;, or \u0026ldquo;accept\u0026rdquo;) to subsequently video clip, and received ~20 mins training before scanning. Each trial began with a fixation cross (3\u0026ndash;5s), followed by a 2s cue, then a fixation cross last 6\u0026ndash;8s, after that a 25s video clip, followed by a fixation cross (1\u0026ndash;1.5s), then a 9-point Likert scale (4s) for participants to report \u0026ldquo;How negative do you feel right now?\u0026rdquo; (9 - very negative, 1 - not negative at all). At the end of each run, participants will rate \u0026ldquo;On average, how successfully have you used the indicated mindset in this run?\u0026rdquo; (9 - very successful, 1 - not successful at all).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation cohort\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eThe stimuli, experiment conditions, and trial structure are identical to the discovery cohort. However, to control the potential influence of the \u0026ldquo;ER trial\u0026rdquo; on the \u0026ldquo;react trial\u0026rdquo; in the same run,\u0026nbsp;we placed all 12 trials of each condition within one run (randomly presented), using the Latin square design to balance order effects between participants.\u003c/p\u003e\n\u003ch2\u003eMRI data acquisition and preprocessing\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eDiscovery cohor\u003c/strong\u003e\u003cstrong\u003et.\u0026nbsp;\u003c/strong\u003eMRI data were collected on a 3.0 T scanner (Ingenia Elition; Philips Healthcare, Best, Netherlands) with a 32-channel head coil. Structural images were acquired using high-resolution three-dimensional\u0026nbsp;T1-weighted\u0026nbsp;images (repetition time (TR)=8.2 ms, echo time (TE)=3.8 ms, field of view (FOV)=243 mm, flip angle (FA)=8\u0026deg;, 243\u0026times;243 matrix, 1\u0026times;1\u0026times;1 mm voxels, 180 sagittal slices, phase encoding anterior \u0026raquo; posterior) and were used for anatomical localization and warping to the standard Montreal Neurological Institute (MNI) space only. Functional images were acquired with an echo planar imaging-free induction decay (EPI-FID) sequence (TR=2000 ms, TE=30 ms, FOV=240 mm, FA=80\u0026deg;, 80\u0026times;80 matrix, 3\u0026times;3\u0026times;3.2 mm voxels, 34 interleaved ascending axial slices, phase encoding posterior \u0026raquo; anterior). In total, four runs of each 268 measurements were acquired.\u0026nbsp;Preprocessed was carried out using the Statistical Parametric Mapping (SPM 12, https://www.fil.ion.ucl.ac.uk/spm). The first five volumes of each run were removed, the different acquisition timing of each slice was corrected and realigned to the first volume, and nonlinear distortions related to the head motion were corrected by unwarping. The high-resolution anatomical image was segmented and co-registered with the functional images to the generated skull-stripped structural image. The functional images were normalized to Montreal Neurological Institute (MNI) space, interpolated to 2\u0026times;2\u0026times;2 mm\u003csup\u003e3\u003c/sup\u003e voxel size, smoothed by 8-mm full-width at half maximum (FWHM) Gaussian kernel, and bias field corrected. Image intensity outliers were identified based on meeting any of the following criteria: (a) signal intensity \u0026gt;3 standard deviations from the global mean, (b) signal intensity and Mahalanobis distances \u0026gt;10 mean absolute deviations based on moving averages with a full-width at half maximum (FWHM) of 20 image kernels. The time points marked as outliers were severed as separate nuisance covariates included in the first-level model. Additionally, the design matrix included negative feeling ratings (1\u0026minus;9) as a parametric modulator for the reaction or regulation period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation cohort\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eMRI data were collected on a 3.0 T scanner (Vida; Siemens Healthineers, Erlangen, Germany) with a 64-channel head coil. Structural images were acquired using high-resolution\u0026nbsp;T1-weighted\u0026nbsp;images by the original three-dimensional\u0026nbsp;Single-shot TurboFLASH sequence from Siemens (TR=2300 ms, TE=2.32 ms, FOV=240 mm, FA=8\u0026deg;, 256\u0026times;256 matrix, 0.9\u0026times;0.9\u0026times;0.9 mm voxels, 192 sagittal slices, phase encoding posterior \u0026raquo; anterior) and were used for anatomical localization and warping to the standard Montreal Neurological Institute (MNI) space only. Functional images were acquired with an echo planar imaging-free induction decay (EPI-FID) sequence (TR=2000 ms, TE=29 ms, FOV=240 mm, FA=90\u0026deg;, 80\u0026times;80 matrix, 3\u0026times;3\u0026times;3 mm voxels, 36 interleaved ascending axial slices, phase encoding posterior \u0026raquo; anterior). In total, four runs of each 268 measurements were acquired.\u0026nbsp;Using an identical pre-processing procedure to the discovery cohort (only modifying the parameters based on the sequence setting).\u003c/p\u003e\n\u003ch2\u003eMass-univariate analysis\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eDiscovery cohort\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eSubject-level general linear model (GLM) analysis was conducted using SPM12. Twenty-four head motion parameters and indicator vectors of outlier time points were modeled as non-interested regressors \u003csup\u003e41\u003c/sup\u003e. The fixation-cross epochs served as the implicit baseline, the periods of cue presentation and rating were modeled to eliminate the extra effects on the baseline, and the clips presented period were used as the interest regressor. A high-pass filter of 180s was applied.\u0026nbsp;Additionally, the subjective negativity ratings (1\u0026minus;9) for each clip were included as a parametric modulator for the reaction and regulation period.\u0026nbsp;The primarily interesting contrasts are NR vs. NV, NA vs. NV, and\u0026nbsp;NV vs. NeutV. To avoid the possibility that participants may subconsciously use the corresponding ER strategy at different runs, which may lead to confounding effects of another ER strategy when comparing NA or NR with NV, we modeled NV in the runs with NA and NR, respectively. The contrast\u0026nbsp;beta images were\u0026nbsp;submitted separately to the group-level analysis\u0026nbsp;and used to perform MVPA analysis.\u003c/p\u003e\n\u003cp\u003eWe also conducted a single-trial analysis for MVPA analysis by specifying a GLM design matrix with separate interest regressors for each clip. Nuisance regressors,\u0026nbsp;implicit baseline,\u0026nbsp;and high-pass filters were identical to the above analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation cohort\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eThe processing procedure is identical to the discovery cohort, except that the NV condition was modeled as a whole since all NV trials were in one run for the paradigm of the validation cohort.\u003c/p\u003e\n\u003ch2\u003eMultivariate pattern analysis\u003c/h2\u003e\n\u003cp\u003eFollowing the previous framework\u003csup\u003e138\u003c/sup\u003e, to obtain the robust brain activity patterns that can distinguish and predict between different contrasts mentioned in the mass-univariate analysis section, we trained the SVM classification (kernel linear C=1) decoders by applying LOSO-CV methods using subject-level whole-brain contrast images data (gray matter masked\u003csup\u003e41\u003c/sup\u003e). Of note, only the data from the discovery cohort was used to develop the decoders. To assess the cross-validated performance of the decoders, we calculated the predictive accuracy, sensitivity, and specificity by comparing the prediction and true outcome (threshold=0). Additionally, to test the generalizability of the developed decoders, we applied them to other independent datasets, e.g., the validation cohort, and generalization cohort (see \u0026lsquo;Generalization cohort\u0026rsquo; section below) to test the pattern response for each map and the classification capacity.\u003c/p\u003e\n\u003ch2\u003eTest generalizability with independent datasets\u003c/h2\u003e\n\u003cp\u003eOne recent study\u003csup\u003e28\u003c/sup\u003e explored the ER process and provided subject-level univariate-beta maps from the Adult Health and Behavior project\u0026ndash;phase 2 and the Pittsburgh Imaging Project\u003csup\u003e139\u003c/sup\u003e. Briefly, 358 participants performed ER tasks with picture material. Subject-level Reappraisal and acceptance strategies are reported to be used during the scanning\u003csup\u003e28\u003c/sup\u003e. To test decoders\u0026rsquo; generalizability, NERS-A, and NERS-R were applied to predict \u0026lsquo;Regulate negative\u0026rsquo; vs. \u0026lsquo;Look negative\u0026rsquo; and NNES to predict \u0026lsquo;Look negative\u0026rsquo; vs. \u0026lsquo;Look neutral\u0026rsquo;.\u003c/p\u003e\n\u003cp\u003eWe also test whether developed decoders could predict using the reappraisal strategy to \u0026ldquo;decrease\u0026rdquo; vs. \u0026ldquo;experience\u0026rdquo; the pain induced by heat stimuli, based on the data from the generalization cohort 2 (n=33)\u003csup\u003e63\u003c/sup\u003e. To control the interference of visual processing caused by different materials, we excluded the voxels from the visual network\u003csup\u003e67\u003c/sup\u003e.\u003c/p\u003e\n\u003ch2\u003eIdentifying the neural basis of acceptance and reappraisal, as well as negative emotion response\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eBootstrap tests and encoding model estimate\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eTo identify robust significant features, we first bootstrapped the discovery cohort of 10,000 samples (with replacement), calculated the two-tailed uncorrected P-value of the predictive weights, and used the False Discovery Rate (FDR) correction to control multiple comparisons (set FDR q\u0026lt;0.05). Given that the signatures of the multivariate backward/decoding model may capture the interested brain activation (e.g., ER) as well as the noise components in the data\u003csup\u003e70\u003c/sup\u003e. For interpretation, we transformed the bootstrapped within-subject pattern to \u0026lsquo;activation patterns\u0026rsquo; (forward/encoding model), which is similar to the \u0026lsquo;structure coefficients\u0026rsquo;, and could indicate the direction of the relationship between the variable and the model without controlling for other variables \u0026ndash; i.e., which voxels are positively or negatively related to ER strategies. The group-level significant brain regions were obtained by conducting a one-sample \u003cem\u003et\u003c/em\u003e-test (FDR \u003cem\u003eq\u003c/em\u003e\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBayes Factor analysis.\u0026nbsp;\u003c/strong\u003eTo identify the brain regions that are acceptance or reappraisal specific or the common regions of those two strategies, we utilized the BF approach to quantify evidence for alternative and null hypotheses\u003csup\u003e28,64\u003c/sup\u003e. Specifically, the BF value of a given voxel reflects the likelihood of the alternative and null (e.g., the neural activity is correlated with naturalistic acceptance or not). The BF values are calculated from \u003cem\u003et\u003c/em\u003e-values of a one-sample \u003cem\u003et\u003c/em\u003e-test of encoding maps from the discovery cohort, using a Jeffreys-Zellner-Siow (JZS) prior and formula (see below) provided by a previous study\u003csup\u003e140\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cimg 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ts2oydMQdk9sLtz7hAkTjNo9yPbt20NNuLQRtC9JY05gIgi7n7imkmA+3blzJ3JtDUwSOEi7bS9mhjfffNO005QFGDdunPm0jBw50t8rT9Y+RA0rGKSpjGHwgk+dOmWmULmQjrCAfwIdcq24fPmyV5CgzT1xD5aCROvNnz/fP8oW/AxKxZdfuHCh99lnn/lH8XCFAmvrC4NFgErlL/dG58FCQULUAtoWlm0OCuG0Bdb+DO+//775/Pnnn81nUhYsWGCEXbct4xp0NMEtbsd58OBBc94gTNtGGLECWVLC7onNhXu/e/euqavuQMIOsMIGXmkHDrybsPuJO3CKyqcgtF3r16/3du3a5acU4flop3kv/AZ/maTCDVjBKO17iUIagwqhAYDgi+GYTilKiqwWCATEvIczZ86YT6DghUmVFCxGLkjLOPHQAVsnQr4jDQkeIcN1NLSQBpwDKTpM4MIxEMGEChCXGTNmeJs3bzbXwgk0CmaQlIL7qpaWRIgwcEBzNQVRPHjwwHwOHz7cfAIdsB29hm1u/aJtaW1tNddLCgK1vb6F+kl9CrZlcaJjlmpH4mDbmLD2ks7/jTfe8I+KuEJB1MCB9giBgnvi/Bwn5caNG4+1W2H51K9fv9DfIRQgAPJcbt4xgMOBlt+g9Qkb3Pzwww/+3iOC+YymNkzDw3lxfOS52bg2eRG8x0gKUlJDgu2Gx6/UNoNdqPBS/aNw+D7KnlcK/pfEdobNyjoK8WltXtjpsWUFwS6GnRMbHTY9NvLE2vStrY80fkNecT/2vPyf79wt7H5tXid5lriQR+Xy35L2HvhfHGweaMt+C4P0NPWq2lDX4twX9cvWV8CGTvl067Gtf9S1MLhOKdt2EFsX3c3C9YPtIcdRv7eUa0fiwHNwDv5n2x1L2PNZ27zdgvnNMb+x9Z1zxm0nLO752ey5wvIJOL/7nsgP9//2+jybzVfSuNewvAo+d1Q+h/le8F82ez/2OC4NLxjYl50WXmq5AmdffjlsASq1lToPhcsWElvwEApIozAFCass/Md16KJgkRZW+OKSVV6Hwf2Xy39LnHuw91puq8aziGTwHuLUq1oTp3xw3zTUbgdoCdbjMKHewnm4XhZE3U854rQjaeH5k75j22bZWQvcBx1qWGeehqh84p2HtbNByC9bPvjk3rhXF545mH9R+Rz8L2WHc7r3yP+SCGoyJWRAbPVMGQrvo8tWeJlmhTc3rZRaHHvjCy+8YPZRNxUKh1G3oX4Ki3zH712/A34HY8eONZ+AqYTz2PnDeYN5xFnlP2Dnc/ObDYJpaeyBQgDmN5wQsamHqc4J3GTjckSFs84aTBRhdvw4xGlH0oJzXlTcmChszBMi1uJ0jD8A66jEMe+Uo1Q+0SZg8kBtX6pNwqnw1VdfNeYA8g2/E3fxN8wimJeCPltR+RxcOI5YOzyre4+04+PHj/ePYlBo5BoSJDUe30puabEj81IgraUZ2fC/uPeHlBwcWSAhksZmVUqWsOfnekF1ExIwkmol2GulGY2UI8mIKe37jnv+IFyLvAtK9PUM5cyqUBm1uKMe3hXfBctiWrhGmnpVbbivqNEy7QX1zELZcJ/BjnYt1N1SI90k5b8axG1HagnXd/O4HgjL5zANAvA7t0zZMpKk/W14jQFOI5VgHfrcGQBBkB4ZIVQTpGRG9i44DBUaGrMfdCiyz22da5ByGX0jDbtkEW4Yr+tCA5dqNJIVVoK/ffu2+aw2jApXrFhhvJEbaSloyiHPW2hbjNf1zp07/W+KnuCMqBgB29FOWmx9cx338gJlPWwaJc/MtLtDhw75KUVnYRfyr9DY+0fF2UT9+/cvOQotdML+Xu2J247UklGjRpmVRG1+UReTOELmkbB8ppzRrlOucEK0kI5zKZBO35O4/S3KB40HUjgjGguSeyHzjGSVxBYDSG1hIxfS7TnZkKKTnJv/uxJiFFaaZAveB9eMsntxL/yH69jnd0cndvRS6YiXewjLnyxgZMY9Ro1CuXeej3dt84jjUqOwIPwnCbwPrhcloXPP3ENSyMOk+WifOVgG3LyoFuRx2P2SP5S1SjRItszHqR+1hrwOq3PUA5vv7mbziDJMuXH9efgPaVHllfcXVb9rRbl2pNZQrrgXm7/1orUL5rNt+4J9CnlPmeE98Juo8liKhhUMqIyuipxMpJGhUNkMjUu5jiAtFIBqNHw8W1hhopBl/Qxcq9JOoBRUEO67mh0E508C7y3MWZM8oMzZyp2UNIKBbTyC5ZP0apRZi33WKCoVFm15zSPVag+CcP5ql/1S1LIdEeVBsAy2K3Zwx7tKQkOZEqzjCDBHdNmyZWYfVUwhA43zCOqWQoOWKKIa/8OBBHVVlLovDTgnVcPJDfUuz2shX5jzWqjkmar7ydfly5ebJayrZUZAbV1ohFMHiYlDoZ74e+VBxR0W8AqYr7x69WqzOFStoBwXRgtmoSk3rsXff//9mINSVlAHwoK6uKQJeuVy7dq1blWhl4I6i4kxTbTPJOzYscO0VdVoI+JQq3ZExAMzlGtCoS2ifFBPooLPRdFQggFLKRPoh4APkydP7qxQdgU6S5rwxTSyH3/8sWn8K7WfVhs6Jio0XrHWM3bVqlUVhx52wbbFho9CtW3s2KytTa27iQp4Bdg6a+1vgACMXb+5udmEZbVQRsNmqtCoRAVGYUP4pcwQLIZz8B3HdAr2/1FBXVyCQa84lxtMC/gv5w47B+WqFt76acGP4KuvvuoMAJY1nJc8KCV8VZtatCMiPkRiRAggIBzvg33qJwPMxBQVB42NVUdbUI9VouYUtcXa1PIA5aacmSDOb8Lgf0nKJepcmy+YNijjVsVLeqFRN/suqPjZ7Hf2GKgX1BXuHTMBx/Ya9rzYMrmO3aKeE/U331s1OM9l07g2qk/eK+lB+yjf87u8243JE+6ffMoS8oPz2jwXImskGPjQuNEYUdmsv4HoOdj3193QYGclGPAb28FGbaXOQ35YO7/tTBEQ6FAp40HiBkbhv/a8aQkKBsD9kubCtYN1kfu0wooQInsU4MgHHwFWu0IN0512O5EOVJju1LjuxKrHKyW4IE5BoDCbm1ZKTfjtt992TjXFtEG53rdvn3fz5s1QNXycwCio+QtChvGVyBr8IQqCiH/06FrBuvjRRx9577zzjn8khMgaCQY+NIis6kVjKxtZzwP7Gp2YtXV3FwMHDjR+LHkAG7TrBDlv3jxzb0STC/MvwGly0KBB/lExAhtOi2GRMLPylRg2bJi/V1zYx/pmIFzh6xC0oWNb5/rUVyFEdZBgIOoCRrmEPd24caOf0j3ECXjFqnZ4D1cTBCRG264jLZ7JdKoIAGErbeKgWC4wSlYhesOCXrFiHYIJ9z537lyjHXG/R1hYt26dcfIVQlQPCQaibqDjY8TpRgGrNVwfdbiN1+5iPfxZTppOGy/8akVkmzBhgvmkg3Vh6iIdctisCYQqtAzc14ABA0xaUAjg+0ojYQIe+4sWLfKPinBPH3zwgVlGGK/+oFaC2Q78R2Y+IapLrw5050LUEaigV65cGdr51QJGvCyS8vvvv3cZ8VaKXXO+1EJaaUC7QZ65/gqkIUC0tbUlngNdDqY6Et/i/PnzsfOH/+AzgS+QEKK6SDAQogpgCycuPp1tlsJBNUDgINiQ7agRCnBUhIsXL5rPrCBewYwZM0zQq1rHdBBCxEOmBCGqQE8KeJVpYJQS1DLolRAiPdIYCCGEEKITaQyEEEII0YkEAyGEEEL4eN7/AWZSEI7MSVrAAAAAAElFTkSuQmCC\" width=\"518\" height=\"92\"\u003e\u003c/p\u003e\n\u003cp\u003ewhere \u003cem\u003eN\u003c/em\u003e represents the sample size,\u003cem\u003e\u0026nbsp;t\u003c/em\u003e for the\u003cem\u003e\u0026nbsp;t\u003c/em\u003e-statistic\u003cem\u003e, v\u0026nbsp;\u003c/em\u003edenotes the degrees of freedom,\u003cem\u003e\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;r\u003c/em\u003e is the scale factor. In the current study, r was set as 0.707, which suggested a moderate effect size\u003csup\u003e28,140\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eBecause the null is bound by t=0 and the current sample size (N=59), the BF cannot be smaller than .14 but can be infinitely large, a heuristic threshold of 5 was set for the alternative hypothesis and 1/5 for the null to attain moderate to strong evidence in favor of both hypotheses\u003csup\u003e64,141\u003c/sup\u003e. Note that in current datasets, the \u003cem\u003eP\u003c/em\u003e value threshold of BF=5 is \u003cem\u003eP\u003c/em\u003e=0.0067, which is smaller than the \u003cem\u003eq\u003c/em\u003e\u0026lt;0.05 FDR-corrected threshold for the encoding model of NERS-R (\u003cem\u003eP\u003c/em\u003e=0.0190) but not for NERS-A (\u003cem\u003eP\u003c/em\u003e=0.0051), hence we further bound the alternative results by the corresponding FDR-corrected threshold, specifically as follows:\u003c/p\u003e\n\u003col class=\"decimal_type\" start=\"50\"\u003e\n \u003cli\u003e\u0026lsquo;Acceptance only\u0026rsquo; voxels \u0026ndash; positive activation with BF\u0026gt;5 and P\u0026lt;0.0051 for acceptance, and BF\u0026lt;1/5 for reappraisal.\u003c/li\u003e\n \u003cli\u003e\u0026lsquo;Common\u0026rsquo; voxels \u0026ndash; positive activation with BF\u0026gt;5 and P\u0026lt;0.0051 for acceptance, and with BF\u0026gt;5 and P\u0026lt;0.0190 for reappraisal.\u003c/li\u003e\n \u003cli\u003e\u0026lsquo;Reappraisal only\u0026rsquo; voxels \u0026ndash; positive activation with BF\u0026gt;5 and P\u0026lt;0.0051 for reappraisal, and BF\u0026lt;1/5 for acceptance.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo verify the results of BF analysis, we also performed the conjunction analysis between the thresholded (FDR q\u0026lt;0.05) activation maps of those two strategies obtained from mass-univariate analysis and the reconstructed \u0026ldquo;activation pattern\u0026rdquo;, separately.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial similarity between stable decoding maps and interest networks, as well as crucial ROIs\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eThe spatial similarity between stable encoding maps and seven large-scale resting-state networks, as well as crucial ROIs (dlPFC, vmPFC, dmPFC, and vlPFC), was illustrated by river plots. The spatial similarity was computed as cosine similarity between the networks or ROIs and the threshold NERS-A, NERS-R, and NNES (FDR q\u0026lt;0.05, positive values only). To further compare the underlying neural representations between NERS-A and NERS-R, we examined the voxel-level spatial covariation between the unthresholded weight maps for those two models (z-scored).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparing the performance of developed models with PINES.\u0026nbsp;\u003c/strong\u003eTo investigate whether developed decoders capture both the common and separate aspects of naturalistic acceptance, reappraisal, and reaction to negative emotion, we compared the decoders\u0026rsquo; performances on data of different conditions from the validation cohort and along with a decoder provided by a previous study\u003csup\u003e69\u003c/sup\u003e \u0026ndash; PINES \u0026ndash; which have been demonstrated could track general negative emotion experiences induced by pictures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvaluation of predictive performances of local systems\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eTo investigate the contribution of a single resting-sate brain network (e.g., DMN) or individual regions to the ER or react processing, we first estimated the prediction performance of seven large-scale networks as well as the prefrontal cortex, which has been demonstrated to have crucial roles in both generate and regulate emotions\u003csup\u003e26,27,31\u003c/sup\u003e and compared the performance with developed models. Additionally, we employed a whole-brain searchlight-based analysis (three-voxel radius spheres) to test the predictive performance of individual regions.\u003c/p\u003e\n\u003cp\u003eTogether, we combined multiple analytic approaches to systematically interrogate the role of various brain regions or systems in acceptance and reappraisal.\u003c/p\u003e\n\u003ch2\u003eClinical application potential\u003c/h2\u003e\n\u003cp\u003eER and reaction abnormalities have been demonstrated in MDs, e.g., SUDs\u003csup\u003e52,68,81,126\u003c/sup\u003e. To examine whether our decoders could detect the emotion-related abnormality among SUD individuals, we applied NERS-A and NERS-R to classify the distancing (one of reappraisal) strategies with negative emotions induced by negative pictures and also applied NNES to predict negative vs. neutral emotions. The data were provided by a previous study\u003csup\u003e68\u003c/sup\u003e and newly collected data using a similar ER task paradigm and the identical scanner and sequence settings (see supplementary for details). Briefly, 48 health control participants (HC, 18 from our previous study\u003csup\u003e68\u003c/sup\u003e and 30 newly collected) and 49 cannabis users (CU, 23 from our previous study and 26 newly collected) were included in the final analysis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003efMRI data used to train the signatures are available via figshare at\u0026nbsp;https://doi.org/10.6084/m9.figshare.29179934.v1.\u0026nbsp;Other authors provided the fMRI data in the generalization cohort 1 via NeuroVault (https://neurovault.org/collections/16266)\u003csup\u003e28\u003c/sup\u003e, and fMRI data in the generalization cohort 2 via the OpenfMRI (https://openfmri.org/dataset/ds000140)\u003csup\u003e63\u003c/sup\u003e.\u003c/p\u003e\n\u003ch2\u003eCode availability\u003c/h2\u003e\n\u003cp\u003eCode for analyzing data and\u0026nbsp;generating figures\u0026nbsp;is available via GitHub at https://github.com/canlab and https://github.com/h-psy/fMRI_studies/tree/main/NERS.\u003c/p\u003e\n\u003ch2\u003eConflict of Interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGross, J. 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Methods\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, 1042\u0026ndash;1058 (2019).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"emotion regulation, affect, fMRI, multivariate pattern analysis, addiction, cannabis, default mode network, naturalistic ","lastPublishedDoi":"10.21203/rs.3.rs-6775990/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6775990/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Adaptive emotion regulation (ER) is essential for mental health. Reappraisal and acceptance are effective ER strategies that form the basis of therapeutic interventions, yet their common and distinct neural signatures - and the translational potential of these signatures - remain unclear. Here, we combined naturalistic fMRI with multivariate predictive modeling to develop neurofunctional signatures that accurately and comprehensively characterize negative affect and its regulation via acceptance and reappraisal in dynamic, immersive contexts (n=59). These signatures demonstrated process-specificity and generalization across cohorts, cultures, and modalities (n=33, n=358, n=33). ER strategies were encoded in distributed, distinguishable neural representations, with shared contributions from the default mode network and strategy-specific contributions from the amygdala, somatomotor and attention (acceptance), and the frontoparietal control (reappraisal) networks. The neuromarkers precisely identified strategy-specific ER impairments in cannabis users (nhealthy_controls=48, ncannabis_users=49), underscoring their translational relevance. We provide comprehensive, clinically-relevant brain models of emotion regulation and dysregulation in naturalistic contexts.","manuscriptTitle":"Common and distinct neurofunctional signatures of emotion regulation strategies and their clinical translation in dynamic naturalistic contexts","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-11 08:19:53","doi":"10.21203/rs.3.rs-6775990/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8f7ce20a-bb64-4725-84d1-07b3cceb91d4","owner":[],"postedDate":"June 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":49254766,"name":"Biological sciences/Neuroscience/Emotion"},{"id":49254767,"name":"Biological sciences/Psychology"}],"tags":[],"updatedAt":"2026-05-13T07:06:55+00:00","versionOfRecord":{"articleIdentity":"rs-6775990","link":"https://doi.org/10.1038/s41467-026-70708-5","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2026-03-17 04:00:00","publishedOnDateReadable":"March 17th, 2026"},"versionCreatedAt":"2025-06-11 08:19:53","video":"","vorDoi":"10.1038/s41467-026-70708-5","vorDoiUrl":"https://doi.org/10.1038/s41467-026-70708-5","workflowStages":[]},"version":"v1","identity":"rs-6775990","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6775990","identity":"rs-6775990","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}