Common and distinct neurofunctional signatures of dynamic naturalistic emotion regulation strategies

Domain: chemistry
Published: 2026-03-17
Editorial outcome: accept

Abstract
Adaptive emotion regulation is essential for mental health. Reappraisal and acceptance are effective yet cognitively distinct emotion regulation strategies. A key unanswered question is whether their neural implementations are supported by common overarching or distinct neurofunctional processes, especially under dynamic, naturalistic conditions that mirror real-life scenarios. 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 generalizability across cohorts, cultures, and modalities (n=33, 358, 45, and 33, respectively). Emotion regulation 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 male cannabis users (n =48, healthy_controls n =49), underscoring their potential clinical translational relevance. Collectively, these findings demonstrate shared and distinct neural signatures of reappraisal and acceptance, highlight the critical role of whole-brain integration in emotion regulation, and provide comprehensive, clinically relevant brain models of emotion regulation and dysregulation in naturalistic contexts.

Introduction
Emotion regulation (ER) – the ability to modulate the experience and expression of emotions – is fundamental to adaptive functioning and mental health1–6. Impairments in ER represent a core transdiagnostic feature across major mental disorders (MDs)5,7–11. Regulation of negative affect can be achieved by several mental strategies, with reappraisal and acceptance being particularly effective2,12–15. Both are central building blocks of psychotherapeutic interventions16–18 and highly suitable for managing everyday emotional challenges19,20.

While both strategies focus on cognitive changes15 (see process model of emotion regulation1,2), 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 impact21,22. It critically relies on multiple executive functions, including attention, working memory, and cognitive control, which represent an active and cognitively demanding process23,24. While reappraisal is the most extensively studied ER strategy, a critical question remains: what constitutes an effective and psychologically distinct alternative? Strategies like avoidance, suppression, or distraction have been commonly investigated; however, they are often maladaptive and associated with poor long-term outcomes6,7. In contrast, acceptance, a core component of mindfulness-based third-wave cognitive therapies, involves nonjudgmental awareness of emotional experiences without attempts to control them25,26. It has been recognized as both a potent and adaptive alternative, and is considered a less cognitively demanding and more passive strategy relying on self-referential processes and metacognitive awareness24. This distinction is crucial for both cognitive neuroscience and therapeutic applications. If reappraisal and acceptance simply recruit the same neural pathways, it would suggest a final common pathway for effective regulation. Conversely, dissociable neural patterns would provide strong evidence for multiple, distinct routes to ER and adaptive mental health, with significant implications for personalized interventions. Additionally, ongoing debates27–29 about the extent of neural overlap between emotion generation and regulation (as well as the overlap between different emotion regulation strategies30) further highlight the need for empirical work that initially carefully characterizes the neural basis of distinct emotion regulation processes. Directly comparing reappraisal and acceptance represents as such a critical step for characterizing strategy-specific neural substrates involved in the regulation of negative affect.

The prevailing neurobiological models of emotion distinguish between brain systems involved in emotional reactivity, e.g., the amygdala, insula, and systems involved in regulation, including lateral prefrontal and parietal regions31–33. fMRI meta-analyses indicate that both strategies effectively modulate activity in subcortical emotion “reactivity” regions, e.g., the amygdala26,34, while enhancing the engagement of ventrolateral prefrontal regulatory regions (vlPFC)30,35. Notably, reappraisal is strongly associated with engagement of the fronto-parietal control network32,33,36, reflecting its reliance on regulatory cognitive processes, while acceptance is associated with engagement of core regions of the posterior midline default mode network (DMN)24,26,37–39, suggesting a distinct neural signature reflective of self-related and introspective processing. Together, further research is required to better characterize and extend theoretical models describing the relationship between emotion generation and regulation. This is particularly important given the distinct cognitive mechanisms and implementation demands that underlie acceptance and reappraisal.

Despite extensive work using conventional neuroimaging and meta-analytic approaches several key questions remain unresolved, including (1) a precise and comprehensive mapping of the common and distinct whole-brain neural representations of reappraisal and acceptance; (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; (3) the extent to which ER relies on localized modules versus distributed neural computations across the whole brain; and (4) the translational potential of these neural representations as precise neuromarkers for ER impairments in clinical population.

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 functions40 and (2) the frequent use of ROIs-based averaging, in which activation signals are averaged across voxels (each containing a massive amount of neurons, ~5.5 million) for group-level inference, which may yield nonspecific signals41,42.

Second, few studies24,35,38 have directly compared acceptance and reappraisal using a within-subject design or under ecologically valid conditions. Most neurobiologically-informed models instead rely on meta-analyses5,43,44, which are limited by the univariate approach of the original studies and further constrained by variability indicated by preprocessing protocols45. Moreover, these studies used sparsely presented, static, and isolated stimuli (e.g., affective pictures), which limits ecological validity46,47, in particular in the context of emerging evidence indicating that neural systems that support affective experience differ substantially when engaged during dynamic naturalistic experiences48–51.

Third, conventional analytical approaches, which often rely on certain significance thresholds to identify activated brain regions, may have led many previous studies and theoretical frameworks to emphasize the contributions of specific brain areas or functional networks. However, emerging evidence from both human and animal research indicates that complex psychological processes (e.g., decision-making52, responses to disgust53, fear54, or pain55) involve distributed neural computations that integrate information from the entire brain. Whether acceptance and reappraisal are similarly encoded at a whole-brain level remains an open empirical question.

Fourth, despite the recognition of impaired ER as a transdiagnostic impairment in MDs, including addiction5,7–11, findings have yet to be translated into clinically useful neuromarkers. While initial progress has been made in developing a neuromarker for cue reactivity, in the domain of ER is lacking56.

To address these critical limitations, we developed and extensively evaluated three comprehensive and ecologically valid brain models under naturalistic conditions, capturing negative emotional experience, acceptance, and reappraisal, with substantial potential for clinical application. We here capitalize on the combination of functional magnetic resonance imaging (fMRI) with machine-learning-based multivariate pattern analysis (MVPA) and naturalistic paradigms57–60. MVPA leverages information across multiple spatial scales41,61 to detect distributed patterns across the entire brain, which is uniquely suited to quantifying the overlap and dissociation among psychological processes without relying on a priori assumptions regarding “emotion generation” versus “regulation” regions. It further enables the development of comprehensive, accurate, and generalizable whole-brain models of emotional experiences (‘neuroaffective signatures’) with improved sensitivity and specificity. Importantly, MVPA offers the opportunity to compare predictive performances across networks or regions of interest while controlling for confounds related to voxel number. These features collectively enhance the translation potential of MVPA-derived models as clinically useful neuromarkers for MDs56,61,62. 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 life46–48,50,51,63,64.

In this work, we aimed to precisely determine 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 fMRI ER paradigm (Fig.1a), with acceptance (NA), reappraisal (NR), or natural emotional experience to negative (NV) or neutral clips (NeutV). We systematically tested (1) whether it is possible to develop precise and robust neural signatures to determine negative experiences (naturalistic negative emotional signature, NNES) and their regulation via reappraisal (naturalistic emotion regulation signature-reappraisal, NERS-R) and acceptance (naturalistic emotion regulation signature-acceptance, 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 two independent dataset of healthy individuals (n=358, n=45)34,65 and reappraisal pain induced by heat stimuli (n=33)66. Combining multiple analysis methods to determine (3) the distinct and common brain representations and systems that underlie acceptance and reappraisal, utilizing, e.g., Bayes Factor (BF) analysis, which allows examining for both the null and alternative hypotheses34,67,68, and spatial similarity analysis between encoding models and prefrontal systems69 or large-scale functional networks70 (Fig.1c). (4) Utilize network-based and searchlight-based analysis to compare the predictive performance between local systems and whole-brain level. Based on accumulating evidence for ER deficits in MDs5,7–11, including excessive cannabis users (CU)71, we (5) examined the decoders’ clinical application potential to detect ER deficits in CU compared to healthy control (HC) participants (n =48, n =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 and negative video clips, and use corresponding ER strategies based on presented cues to negative clips, 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 indicated that negative clips induced considerable negative emotions (discovery cohort: F[1,58]=1039.434, P=9.96×10-39, 𝜂2 =0.947; validation cohort: F[1,32]=255.626, P=8.15×10-17, 𝜂2 =0.947; Fig.2, details in supplements). Both strategies effectively attenuate the negative emotions (discovery cohort: F[2,95]=81.668, P=3.18×10-19, 𝜂2 =0.585; validation cohort: F[2,52]=56.637, P=1.32×10-12, 𝜂2 =0.639). Reappraisal led to a stronger decrease compared to acceptance (discovery cohort: P=2.02×10-10; validation cohort: P=2.4×10-5). Findings are further corroborated by success rates (discovery cohort: 7.51±1.01, validation cohort: 7.53±0.98).

Decoding emotional experience and ER strategies in dynamic naturalistic contexts

Employing the SVM with leave-one-subject-out cross-validation (LOSO-CV) 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. Decoders were developed using data from the discovery cohort only. Their performance was validated using cross-validated classification in the discovery cohort and determining the reactivity in an independent 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 without further model fitting in an independent validation cohort revealed robust predictive performances (Fig.3e).

Generalization to independent data

To assess the generalizability of the decoders across samples, MRI systems, culture, and stimuli (dynamic versus static or heat-induced pain), we first tested them in a large independent cohort (n=358)34. NNES could accurately discriminate exposure to negative versus neutral pictures (Fig.3f left; Table 1). Consistent with the strategy used by the participants (acceptance or reappraisal)34, NERS-A and NERS-R could accurately classify the down-regulate vs. experiencing negative emotions. NNES could accurately distinguish between experiencing negative and neutral emotions. In another independent picture-based ER task (n=45)65, NERS-R reliably classified distancing (a form of reappraisal72) vs. experiencing negative emotions, NNES classified experiencing negative vs. neutral emotions (Fig.3f middle; Table 1). However, NERS-A showed poor performance.

When using the whole-brain data, the decoders exhibited poor predictive performance in distinguishing between reappraisal-associated down-regulating of heat-induced pain and its experience (n=33, Fig.S4c)66. One possible explanation refers to the different stimulus modalities (visual-video/somatosensory-heat) engaging distinct sensory processing pathways. After excluding voxels from the visual network70, the NERS-R could accurately discriminate between reappraising and experiencing heat-induced pain (Fig.3f right; Table 1). However, NERS-A and NNES still failed to discriminate accurately, underscoring the domain-specificity of the decoders. 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/networks provided a consistent and robust contribution to the corresponding mental processes.

Bootstrap tests and encoding model estimation. We initially threshold the multivariate patterns using bootstrapping (Fig.3a, b, and c). Since decoding model features may capture not only neurobiological processes of interest ( e.g., related to ER), but also noise73, we subsequently transformed the bootstrapped within-subject weighted maps into reconstructed ‘activation pattern’ (encoding model)74,75 and estimated them at the population-level (FDR q<0.05, Fig.4a). This pattern reflects the direction of the relationship between each voxel and the target variable, indicating which voxels are positively or negatively associated with predicted mental processes, e.g., ER strategy. Additional spatial similarity analysis showed that the decoding and encoding models exhibit high spatial similarities across the vast majority of voxels (Fig.S1).

The intense 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 content54,76,77; negative association with activity in a widespread bilateral anterior temporal and frontal network, including the pre-supplementary motor area (SMA)78 and medial/lateral frontal regions.

Acceptance (versus experiencing negative emotion) 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 increased 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, insula, 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).

The effects were confirmed by the reconstructed activation patterns across datasets and with univariate analyses (Figs.S2 and Figs.S3). Consistent patterns were observed under conditions where the decoders achieved significantly accurate predictions (e.g., NERS-R across all datasets). Some inconsistencies were observed in generalization cohorts 2 and 3 for NERS-A, reflecting lower predictive accuracy when participants performed a different strategy (reappraisal rather than acceptance). These results demonstrate that the decoders reliably capture the neural representations associated with the specific mental processes they were trained to predict.

Brain systems identified by BF analysis. BF analysis was used to determine the common and separate neuro-basis between acceptance and reappraisal. ‘Acceptance only’ brain regions were identified by activation during acceptance (BF>5) but not for reappraisal (BF<1/5); reverse for ‘Reappraisal only’ (reappraisal BF>5, acceptance BF<1/5); ‘Common’ brain regions were identified by activation during both acceptance (BF>5) and reappraisal (BF>5)34,67.

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, conjunction analysis in Fig.S4). 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/crucial ROIs. To provide a more quantitative measure of the extent to which specific prefrontal anatomical regions69 or large-scale networks70 are consistently involved in emotional experience and regulation, we estimated the spatial similarities between the reconstructed ‘activation pattern’ and anatomical and functional templates of frontal regions or large-scale networks, respectively (Fig.5b, c). For each region, we calculated the ratio of contribution for acceptance versus reappraisal versus experience negative affect (pie plot in Fig.5b, c; Table S1). The contribution is defined as the percentage of the region encoded by each model53,79, 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 contribution across the three models; the experience of negative emotion may be mostly encoded in the dorsal, ventral attention, and somatomotor networks. When comparing ER strategies, the somatomotor and attention networks presented higher contributions for acceptance, while the frontoparietal networks had relatively stronger contributions for reappraisal, and the DMN exhibited similar contributions for both strategies.

To provide a more precise, quantitative measure of how similar or different the acceptance and reappraisal are encoded across the entire brain, we additionally analyzed the voxel-level spatial covariation between the unthresholded normalized (z-scored) encoding maps of NERS-A and NERS-R80. 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 voxel weights of shared positive/negative, selective positive/negative weights, and opposite for the two models. A high proportion of weights (sum of squared distances to the origin) clustered in the nonshared octants (5,7) and opposite octants (4,8), while a comparably small proportion of weights clustered in the shared octant 6, further supporting that separable brain representations contribute to implementing acceptance and reappraisal strategies. This was further confirmed in the results of the decoding maps (Fig.S4d).

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 compared the decoders’ performances on different conditions from the validation cohort and added a neural signature for negative affect (PINES, picture-induced negative emotion signature81). Except for NERS-R, the other three decoders could distinguish between NV and NeutV, but NNES has the highest accuracy and effect size (Fig.S5 and Table S2). 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 based on whole-brain or local systems

Given the continuing debate about the specific systems underlying ER, e.g., the dlPFC33,34,82, DMN39,83,84, and emerging evidence that emphasizes the distributed neural computations at whole-brain-level52–55, we employed network-based and searchlight-based analyses to estimate the predictive performances of local brain regions/networks and 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.6, black), prefrontal cortex (light orange, which has been suggested play crucial roles in both generate and regulate emotions32,33,36), or individual large-scale functional networks (averaged over 1,000 iterations)53–55. The asymptotic prediction when sampling from the whole-brain, as we trained decoders (black line in Fig.6), 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 (Fig.S6), 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 MDs10,71,85, we applied the decoders to extended data from our previous study71 describing ER deficits in CU. 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 system71.

A total of 48 HC and 49 CU participants were included. Consistent with the initial study, behavioral analyses confirmed ER deficits in the CU (reappraisal success71, 0.46±0.46) as compared to HC (0.78±0.54) (t=-3.072, P=0.003, Cohen’s d=0.50).

NERS-R could accurately predict neural activity during reappraisal (distancing) (NpD) vs. viewing (NpV) negative pictures in HC but not in CU participants (Fig.7, Table 2), and exhibited marginally significant differences between groups in terms of the (NpD-NpV) pattern expression (accuracy=61±5%, P=0.053)56. 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), which are consistent with the reconstructed “activation patterns”. In contrast, NERS-A showed poor predictive performance.

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), 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 used the ABBA method between participants to balance the order effect (see Fig.1a).

Participants were provided with written instructions and trained to use a certain mental strategy (“react”, “reappraisal”, or “accept”) during the subsequent presentation of the video clip. Participants received ~20 minutes of training before scanning. The instructions and procedures were closely aligned with evaluated procedures from previous studies on emotion regulation via reappraisal and acceptance (e.g., ref.23,26). For “react”, participants were asked to react and feel naturally, and not try to mitigate their reaction in any way. For “reappraisal”, participants were asked to regulate their emotion via re-interpreting the content and context of the video, for instance, via imagining that there would be a positive outcome. For “accept”, participants were asked to experience their feelings within an “inaction” framework and not to try to judge, control, change, or fight against the emotions. 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 cohort. 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 using a gradient-recalled echo (GRE) echo-planar imaging (EPI) 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 generate the skull-stripped structural image. The functional images were normalized to MNI space, interpolated to 2×2×2 mm3 voxel size, and smoothed by an 8-mm full-width at half maximum (FWHM) Gaussian kernel. 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 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 regressors54. 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 framework152, 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 masked54). Of note, only the data from the discovery cohort were 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

To test our decoders’ generalizability across samples, MRI systems, culture, and stimuli (dynamic versus static or heat-induced pain), we test our decoders’ predictive performance among independent datasets. One recent study34 explored the ER process and provided subject-level univariate-beta maps from the Adult Health and Behavior project–phase 2 and the Pittsburgh Imaging Project153. Briefly, 358 participants performed ER tasks with picture material. Reappraisal and acceptance were both reported to be used during the scanning34. NERS-A and NERS-R were applied to predict ‘Regulate negative’ vs. ‘Look negative’, and NNES to predict ‘Look negative’ vs. ‘Look neutral’. Another independent picture-based ER research65 provided data from 45 participants while using the distancing strategy (a form of reappraisal72) to negative pictures, as well as viewing negative and neutral pictures. NERS-A and NERS-R were tested to classify distancing negative pictures vs. view negative pictures; NNES were tested to distinguish viewing negative and neutral pictures.

We also test whether developed decoders could predict using the reappraisal-like strategy to “decrease” vs. “experience” the pain induced by heat stimuli, based on the data from the generalization cohort 3 (n=33)66. To control the interference of visual processing caused by different materials, we excluded the voxels from the visual network70.

Identifying the neural basis of acceptance and reappraisal, as well as negative emotion response

Next, we combined multiple analytic approaches to systematically interrogate the role of various brain regions or systems in acceptance and reappraisal.

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). The weights of a multivariate backward/decoding model can be misleading if interpreted as reflecting brain activation of interest (e.g., related to ER). For example, a voxel that does not contain relevant information may still gain a large absolute weight because of its contribution to counteract shared noise with other voxels that do contain meaningful signals73,154. To address this issue and facilitate interpretation, we followed the approach proposed by Haufe and colleagues73 and transformed the bootstrapped within-subject pattern into ‘activation patterns’ (forward/encoding model) using the following formula:

A = cov(X) × W × cos(S)-1

where A is the reconstructed activation pattern, cov(X) is the covariance matrix of training data, W is the pattern weight vector, and cov(S) is the covariance matrix of the latent factors, which is defined as WT × X. This reconstructed activation obtained via the Haufe transformation is analogous to ‘structure coefficients’, providing interpretable predictive-feature values that reflect the direction of the association between voxel activity and the target variable, indicating whether higher activation predicts greater or lower engagement in the corresponding mental process (e.g., the use of emotion regulation strategies)54,73,155. It has been shown to improve both the interpretability and reliability of the predictive features155–158. The group-level significant brain regions were obtained by conducting a one-sample t-test (FDR q<0.05).

To provide a more comprehensive understanding of the relationship between the reconstructed activation patterns and the decoding weight maps, we also analyzed the voxel-wise spatial similarity between their normalized unthresholded maps for each strategy separately.

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 hypotheses34,67. 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 study159.

B = (1+t^2/v)^-(v+1)/2 / ∫ ∞ 0 (1+Ngr^2)^-1/2(1+t^2/v)^-(v+1)/2 (2π)^-1/2g^-3/2e^-1/(2g)dg / (1+Ngr^2)^v

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 size34,159.

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 hypotheses67,160. 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 BF<1/5 for reappraisal.
⚫ ‘Common’ voxels – positive activation with BF>5 and P<0.0051 for acceptance, and with BF>5 and P<0.0190 for reappraisal.
⚫ ‘Reappraisal only’ voxels – positive activation with BF>5 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 networks70, as well as crucial prefrontal ROIs69 (dlPFC, vmPFC, dmPFC, vlPFC, and ACC), 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), reflecting the concordance between each model and corresponding brain responses161. To further compare the underlying neural representations between NERS-A and NERS-R, we examined the voxel-level spatial covariation between their unthresholded reconstructed activation patterns and weight maps for those two models (z-scored), separately.

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 study81 – PINES – which has 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-state 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 generating and regulating emotions32,33,36 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.

Clinical application potential

ER and reaction abnormalities have been demonstrated in MDs, e.g., SUDs10,71,85,131, and may serve as a transdiagnostic structure5,7–11. 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 study71 and newly collected data using a similar ER task paradigm and the identical scanner and sequence settings (details see ref.71 and supplementary, study protocol NCT02801214, clinical-trials.gov). Briefly, 48 male health control participants (HC, 18 from our previous study71 and 30 newly collected, 24.3±5.0 years old) and 49 male cannabis users (CU, 23 from our previous study and 26 newly collected, 24.1±5.3 years old) were included in the final analysis. All participants provided written informed consent prior to participation. Both previously published and newly collected data were obtained under the same ethical approval granted by the Medical Faculty of the University of Bonn and conducted in accordance with the Declaration of Helsinki. We further used the permutation test to investigate whether the pattern expression of the decoders to the data of HC and CU has a significant group difference142,143. In this approach, the H0 assumes that the HC and CU groups have no significant differences. In each permutation, individual-level data were randomly sampled from the two groups, standardized using Z-score normalization to mitigate the influence of potential outliers, and subsequently subjected to forced-choice classification, with a total of 10,000 permutations performed. Finally, we tested whether the observed classification accuracy of the original group (e.g., HC) differed significantly (p<0.05) from the null distribution generated by permutation.

Data availability

The fMRI data generated in this study have been deposited in the figshare repository under DOI: https://doi.org/10.6084/m9.figshare.29179934. The fMRI data used in the generalization cohorts are available in the NeuroVault (https://neurovault.org/collections/16266)34, OpARA (https://opara.zih.tu-dresden.de/xmlui/handle/123456789/1951)65, and OpenfMRI (https://openfmri.org/dataset/ds000140)66. Source data are provided with this paper.

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. A version of the code used in this study is available via Zenodo (https://doi.org/10.5281/zenodo.18625297)162.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors have satisfactorily addressed many of the comments and suggestions I raised regarding the initial version of the manuscript. Thanks for that.\nFirst, as suggested, the authors compared the decoding model weights with the reconstructed activation patterns, as shown in Extended Data Fig. 1. While this analysis is informative, it would be helpful if the authors could further indicate which specific brain voxels drive the observed positive correlations—i.e., those corresponding to regions 1 and 6 in the scatter plots.\nSecond, the authors applied the Haufe transformation across the training, validation, and test datasets, with the results presented in Extended Data Fig. 2. This addition strengthens the study, but there is only a minimal description about the new results. A more detailed description and interpretation of the comparisons across these datasets would improve clarity and help readers better understand the implications of these results.", "reviewer_2": "The authors have addressed all my comments very well and I have no further concerns. I think this is excellent and impressive amount of work that should be published.", "reviewer_3": "The authors have revised and improved aspects of the paper in response to previous comments on the originally submitted version. These changes have clarified some previous issues and filled in some important missing information. However, the responses to some important issues that were raised do not appear to fully engage with the concerns and in one case no clear response was provided. My overall sense of the study is that it addresses a very worthwhile issue and makes some headway on each of the four issues the authors note as remaining unresolved (see line 74), but when these component advances are combined, the overall level of novelty and theoretical and applied significance falls short of what one would expect from the journal, and therefore the paper seems more appropriate for a more specialist journal.\n1. As an example of the incremental nature of the advances in the paper, the prior review comment stated: “For example, it is perhaps not surprising that two strategies that differ greatly in the cognitive processes they involve, one involving effortful, active, top-down regulation of emotion and the other involving passive meta-cognitive awareness and self-referential processes, have both overlapping and non-overlapping neural correlates.”. I couldn’t find a specific response to this concern, other than to reiterate that the strategies are “fundamentally contrasting emotion regulation strategies”. The authors’ response focuses on the limitations of other studies and approaches and the advantages of the application of multivariate machine-learning methods in the current study. It’s left unclear whether the authors agree with the comment’s implication that this concern, if valid, suggests that this major aspect of the study is relatively incremental.\n2. Regarding the point about the novelty and importance of using naturalistic stimuli, the authors acknowledge that they don’t have data directly showing this. Absent this, they point to the relatively low accuracy of PINES and evidence that picture-based fMRI signatures have shown limited generalizability to dynamic emotional stimuli. However, there are multiple reasons why PINES may exhibit lower prediction accuracy other than the static vs. naturalistic dimension (e.g., differences in the analytic pipeline and approach) so this claim (the basis of one of the 4 key unresolved issues noted by the authors as motivating the study) regarding the key importance of using naturalistic stimuli remains speculative rather than directly supported by evidence.\n3. The response to the point about the incremental nature of the clinical translational potential of the findings includes some additional new analyses, which is a welcome addition. Out of all their responses, I think this is most effective response from the authors. However, in the context of the current literature, much of which the authors cite in their response, even with these additional findings, it’s still not clear that crosses the line to being more than an incremental advance. The newly revised discussion (around line 627) also contributes to the sense that this advance is only one step on the road, with much more work required for validation and generalization.\n4. For their response to the comment about the challenges top-down models (e.g. Zhang), the authors point out that the paper had not been published when their paper had been submitted, but challenges to the currently dominant top-down models of emotion regulation have been around for several years, with some of these papers being referenced in that review article (see for example references 17-19 in Zhang et al.) – thus, the antecedent theoretical views that were elaborated in Zhang et al. have been available in the literature for years and should be addressed to an appropriate extent. The authors now briefly acknowledge this other major theoretical perspective, but don’t go on to discuss these other perspectives and implications for the interpretation of their findings (though the review comment requested discussion of the implications). This issue is relevant to the overall goal of not only providing predictive models, but just as importantly, explaining the overall findings in the context of current theoretical models. If one adopts the alternative perspective described by Zhang et al., the interpretation of the current findings would be quite different. Ideally, additional analyses could help establish the extent to which the findings conclusively support the authors’ theoretical claims."}, "author_response": "Dear Reviewers:\nWe thank the reviewers for their positive evaluation of our revision and the\nadditional comments that allowed us to further improve our manuscript. The comments\nallowed us to improve the clarity, rigor, and overall quality of the manuscript.\nFor your reference, we provide two versions of the manuscript: a clean version\nincorporating the reviewers’ comments, and a tracked-changes version highlighting\nall the revisions made in this round. Detailed responses to each point are provided below.\nWith sincere regards\nReviewers' Comments and Authors’ Responses:\nReviewer #1 (Remarks to the Author):\nThe authors have satisfactorily addressed many of the comments and suggestions\nI raised regarding the initial version of the manuscript. Thanks for that.\nResponse:\nWe thank the reviewer for the positive evaluation of the revised manuscript. We\nappreciate the reviewer’s constructive feedback and comments on the original version,\nwhich has greatly helped to improve the manuscript.\nFirst, as suggested, the authors compared the decoding model weights with the\nreconstructed activation patterns, as shown in Extended Data Fig. 1. While this analysis\nis informative, it would be helpful if the authors could further indicate which specific\nbrain voxels drive the observed positive correlations—i.e., those corresponding to\nregions 1 and 6 in the scatter plots.\nResponse: We thank the reviewer for this follow-up suggestion and fully agree\nthat further specifying the voxels that drive the positive association will be important\nto set the findings better into context. Based on the suggestion, we have revised\nExtended Data Fig. 1, which now explicitly shows which specific brain voxels drive\n1\nthe observed positive correlations between the encoding and decoding models. In the\nupdated figure, the regions highlighted in the scatter plots are now displayed on the\nstandard reference brain; for completeness, we have shown both the shared positive\nvoxels (Octants 2, shown in yellow) and the shared negative voxels (Octants 6, shown\nin blue). The resulting spatial patterns largely overlap with those obtained by the FDR-\ncorrected Haufe transformation and, as such, further confirm our findings. We have\nincluded the new information and interpretation as follows:\nThe related context after revision is as follows:\nExtended Data:\n(Line 923 in the tracked-changes version; the same applies below)\nencoding models. Voxel-level spatial similarity between the normalized unthresholded\ndecoding weighted map and encoding map of NERS-A (a), NERS-R (b), and NNES\n(c), separately. Scatter plots illustrate normalized unthresholded voxel beta weights of\nthe decoding map (y-axis) and encoding map (x-axis). Colored octants indicate voxels\nof shared positive (Octants 2) or shared negative (Octants 6), selective positive weights\nfor decoding map (Octant 1) or encoding map (Octant 3), selective negative weights for\ndecoding map (Octant 5) or encoding map (Octant 7), and voxel weights opposite for\nthe two maps (Octants 4 and 8). The middle bars indicate the sum of squared distances\n3\nfrom the origin (0, 0) for each octant, which integrates the number of voxels and\ncombined weights. Displayed on the right are the voxel distributions of the patterns\nwith consistent positive correlations, i.e., voxels originating from the shared positive\n(Octants 2, shown in yellow) and shared negative (Octants 6, shown in blue) sectors.\nThe resulting spatial patterns are largely consistent with and cover a broader spatial\nextent than those identified by the FDR-corrected Haufe transformation.\nSecond, the authors applied the Haufe transformation across the training,\nvalidation, and test datasets, with the results presented in Extended Data Fig. 2. This\naddition strengthens the study, but there is only a minimal description about the new\nresults. A more detailed description and interpretation of the comparisons across these\ndatasets would improve clarity and help readers better understand the implications of\nthese results.\nResponse:\nWe thank the reviewer for the constructive suggestion and agree that the results\nwill benefit from a better integration into the manuscript. We have therefore added more\ndetailed descriptions and interpretations, along with a brief discussion of the\ncorresponding results in the main text, to improve clarity and integration of the findings.\nThe related context after revision is as follows:\nResults section:\n(Line 306)The effects were confirmed by the reconstructed activation patterns\nacross datasets and with univariate analyses (Extended Data Figs. 2 and 3). Consistent\nreconstructed patterns were observed under conditions where the decoders achieved\nsignificant predictions (e.g., NERS-R across all datasets), demonstrating the robustness\nof our findings. For the NERS-A, the generalization to cohorts 2 and 3 remained\ninconsistent, likely reflecting that the Haufe-transformed patterns were derived from\ndata based on a different emotion regulation process than that used to train the\nrespective acceptance-tracking decoder (i.e., cohorts 2 and 3 employed emotion\nregulation via reappraisal rather than via acceptance – the strategy used in the NERS-\nA data). Together, these observations further support the robustness and, to a certain\nextent, process-specificity of the developed decoders.\nExtended Data:\n4\n(Line 941)\nExtended Data Fig.2 | Reconstructed ‘activation pattern’ from the validation\nand generalization cohorts. FDR corrected (q<0.05) group-level reconstructed\n‘activation pattern’ transformed from the validation cohort (a), generalization cohort 1\n(b), generalization cohort 2 (c), and generalization cohort 3 (d). The color indicates the\ndirection of the relationship between each voxel and the target variable – i.e., which\nvoxels are positively (red) or negatively (blue) related to the corresponding mental\nprocessing (e.g., emotion regulation strategies). Consistent reconstructed activation\npatterns were observed in datasets where the decoders achieved significantly accurate\npredictions (e.g., NERS-R across all datasets), indicating the robustness of the findings.\nSome inconsistencies were noted, for example, in generalization cohorts 2 and 3 for\ndata corresponding to a different mental process than that used to train the decoder (i.e.,\nreappraisal rather than acceptance).\nDiscussion section:\n(Line 489) The robustness of these observations is further supported by the\nconsistent reconstructed brain activation patterns across multiple datasets and\ntraditional univariate analyses. Of note, the NERS-A, trained on experimental data\nusing the acceptance strategy, did not generalize to experimental data obtained during\nemotion regulation via reappraisal (generalization cohorts 2 and 3), which underscores\n– to a certain extent – the process-specificity of the neural decoders.\nReviewer #2 (Remarks to the Author):\nThe authors have addressed all my comments very well and I have no further\nconcerns. I think this is excellent and impressive amount of work that should be\npublished.\nResponse:\nWe sincerely thank the reviewer for the positive evaluation and encouring\ncommnets. We appreciate the time, effort, and thoughtful evaluation of our work.\nReviewer #3 (Remarks to the Author):\nThe authors have revised and improved aspects of the paper in response to\nprevious comments on the originally submitted version. These changes have clarified\nsome previous issues and filled in some important missing information. However, the\nresponses to some important issues that were raised do not appear to fully engage with\nthe concerns and in one case no clear response was provided. My overall sense of the\nstudy is that it addresses a very worthwhile issue and makes some headway on each of\nthe four issues the authors note as remaining unresolved (see line 74), but when these\ncomponent advances are combined, the overall level of novelty and theoretical and\napplied significance falls short of what one would expect from the journal, and therefore\nthe paper seems more appropriate for a more specialist journal.\nWe thank the reviewer for acknowledging the improvements made to the\nmanuscript and for noting that the revisions have helped to clarify some previously\nunclear points and to address important missing information. We also apologize that\nsome of our earlier responses in revision 1 did not clearly indicate the corresponding\nchanges in the manuscript, and we have included further revisions based on the new\ncomments. For details, please refer to the detailed responses below, with clear\nindications of the corresponding revisions in the manuscript.\nMoreover, we appreciate the reviewer’s recognition that the study addresses an\nimportant open question and makes progress toward the unresolved issues outlined in\nour revised Introduction. With respect to the concern regarding overall novelty and\ntheoretical and applied significance, we would like to clarify that from our perspective,\nthe manuscript’s contribution lies in jointly addressing multiple unresolved questions\nwithin a single, coherent experimental and analytical framework. Specifically, the work\nintegrates several open issues in the literature through a unified line of investigation\nthat combines advanced multivariate analytic approaches with new experimental data\nfrom multiple independent samples, including healthy individuals (a preregistered\ndiscovery cohort, n = 59; and independent preregistered validation cohort, n = 33; other\nindependent generalization cohorts from other labs, n = 358, 45, and 33, respectively)\nas well as extended data from cannabis users (n = 49; healthy controls, n = 48). We\nbelieve that this combination of methodological rigor, cross-sample validation, and the\nresulting conceptual and applied implications of the findings, provides a level of\ncontribution that is appropriate for the scope and readership of Nature Communications.\n1. As an example of the incremental nature of the advances in the paper, the prior\nreview comment stated: “For example, it is perhaps not surprising that two strategies\nthat differ greatly in the cognitive processes they involve, one involving effortful, active,\ntop-down regulation of emotion and the other involving passive meta-cognitive\nawareness and self-referential processes, have both overlapping and non-overlapping\nneural correlates.”. I couldn’t find a specific response to this concern, other than to\nreiterate that the strategies are “fundamentally contrasting emotion regulation\nstrategies”. The authors’ response focuses on the limitations of other studies and\napproaches and the advantages of the application of multivariate machine-learning\nmethods in the current study. It’s left unclear whether the authors agree with the\ncomment’s implication that this concern, if valid, suggests that this major aspect of the\nstudy is relatively incremental.\nResponse:\nWe thank the reviewer for raising this point and apologize that our previous\nresponse did not clearly indicate how we addressed this specific example in the previous\nrevision. While we agree that, at a general level, it may not be surprising that two\nemotion regulation strategies with markedly different cognitive operations show both\noverlapping and non-overlapping neural correlates, we believe that a theoretical\nassumption alone is not sufficient. Crucially, as highlighted by the other reviewers, the\nsubstantial differences in the operational and cognitive mechanisms of reappraisal and\nacceptance make them particularly well-suited for a direct experimental comparison\nwithin the same sample. Such a comparison, within a pre-registered, advanced\nanalytical and replication–generalization design, is important for advancing our\nunderstanding of emotion regulation processes beyond intuition or post hoc inference.\nExtensive revisions in the second paragraph of the Introduction aimed at\naddressing this point raised during the previous revision from a conceptual and\ntheoretical perspective by clarifying why a direct, within-subject comparison of\nreappraisal and acceptance is informative and critical for determining competing\naccounts1,2. In addition, in response to Comment 4, we have now further expanded this\nsection to explicitly acknowledge recent challenges to traditional top–down and strong\nseparability models (e.g., Zhang et al.).\nIn summary, the contribution of the present work is not to demonstrate that overlap\nand distinction between emotion regulation strategies coexist, but to empirically clarify\nthe representational level at which strategy differences emerge under dynamic and\nnaturalistic contexts —an issue that cannot be resolved by theoretical intuition alone\n(or by univariate analyses of fMRI data or the corresponding meta-analyses based on\nunivariate results, see also for methodological discussions3,4). Together with existing\nempirical gaps and methodological constraints in the literature, these aspects position\nthe current study as addressing an important and timely open question. Against this\nbackground, we respectfully disagree that our work should be considered merely\n“relatively incremental.” We have revised corresponding parts in the manuscript to\noutline the novelty and relevance of our work as follows:\n(Line 34) While both strategies focus on cognitive changes5 (see process model of\nemotion regulation6,7), they differ markedly in their operational and cognitive\nmechanisms. Reappraisal is based on reinterpreting the subjective meaning of the\nemotion-inducing event to control its affective impact8,9. It critically relies on multiple\nexecutive functions, including attention, working memory, and cognitive control,\nwhich represent an active and cognitively demanding process1,10. While reappraisal is\nthe most extensively studied ER strategy, a critical question remains: what constitutes\nan effective and psychologically distinct alternative? Strategies like avoidance,\nsuppression, or distraction have been commonly investigated; however, they are often\nmaladaptive and associated with poor long-term outcomes11,12. In contrast, acceptance,\na core component of mindfulness-based third-wave cognitive therapies, involves\nnonjudgmental awareness of emotional experiences without attempts to control\nthem13,14. It has been recognized as both a potent and adaptive alternative, and is\nconsidered a less cognitively demanding and more passive strategy relying on self-\nreferential processes and metacognitive awareness1. This distinction is crucial for both\ncognitive neuroscience and therapeutic applications. If reappraisal and acceptance\nsimply recruit the same neural pathways, it would suggest a final common pathway for\neffective regulation. Conversely, dissociable neural patterns would provide strong\nevidence for multiple, distinct routes to ER and adaptive mental health, with significant\nimplications for personalized interventions. Additionally, ongoing debates15–17 about\nthe extent of neural overlap between emotion generation and regulation (as well as the\noverlap between different emotion regulation strategies18) further highlight the need for\nempirical work that initially carefully characterizes the neural basis of distinct emotion\nregulation processes. Directly comparing reappraisal and acceptance represents as such\na critical step for characterizing strategy-specific neural substrates involved in the\nregulation of negative affect.\n2. Regarding the point about the novelty and importance of using naturalistic\nstimuli, the authors acknowledge that they don’t have data directly showing this. Absent\nthis, they point to the relatively low accuracy of PINES and evidence that picture-based\nfMRI signatures have shown limited generalizability to dynamic emotional stimuli.\nHowever, there are multiple reasons why PINES may exhibit lower prediction accuracy\n9\nother than the static vs. naturalistic dimension (e.g., differences in the analytic pipeline\nand approach) so this claim (the basis of one of the 4 key unresolved issues noted by\nthe authors as motivating the study) regarding the key importance of using naturalistic\nstimuli remains speculative rather than directly supported by evidence.\nResponse:\nWe thank the reviewer for this comment. As noted in the manuscript (Line 123),\nnumerous recent reviews, perspectives, and empirical fMRI studies support that\nnaturalistic paradigms, with their dynamic nature, better approximate real-life\nexperiences, including affective experiences, and can increase the ecological validity\nof neural models19–26. Of note, understanding how brain systems supporting emotion\ngeneration and regulation operate in everyday life, in naturalistic settings, is highlighted\nas one of the outstanding questions in the recent Opinion paper mentioned by this\nreviewer (see Zhang et al.).\nHowever, on a methodological level, we also agree that other factors may\ninfluence the performance of decoders trained on static images (PINES), and we have\ntherefore included a carefully qualified statement in the Discussion (Line 521) to avoid\noverinterpretation of these findings. Nevertheless, our results demonstrate clear\nperformance differences between decoders trained on static images (PINES) and those\ntrained under naturalistic paradigms in the present study (NNES). Along with the results\nfrom the current study, findings of related studies from our team indicate that signatures\nderived from dynamic stimuli generalize well to data based on static stimuli27, whereas\nsignatures derived from data based on static stimuli show poor generalization to\ndynamic contexts24. While previous work20 has compared classical versus naturalistic\ncognitive neuroscience paradigms and highlighted the advantages of naturalistic\napproaches; a direct comparison of emotion regulation processes under dynamic versus\nstatic stimuli is beyond the primary focus of the present study. However, we also agree\nthat a detailed and ultimate comparison would require the acquisition of novel data with\nclosely aligned experimental instructions, acquisition parameters, etc. To provide the\nreader with a more balanced discussion of the findings and clearly underscore the\nlimitations regarding this point we have revised the corresponding discussion sections\nas follows:\nThe related context after revision is as follows:\n(Line 517) The relatively poor performance of PINES when predicting data based\non naturalistic dynamic stimuli may also underscore that the neural systems for\nprocessing emotional information in dynamic context may differ from those engaged\nby sparsely presented stimuli in conventional laboratory experiments (see ref.15,23,24).\nAlthough our results indicate differences between static and naturalistic paradigms, we\ndo not claim that naturalistic stimuli are inherently superior, as multiple factors may\ncontribute to these observed differences.\n(Line 642) While previous studies have demonstrated the advantages of\nnaturalistic paradigms in terms of ecological validity19–26, further research directly\ncomparing reappraisal and acceptance processes under naturalistic paradigms versus\nstatic-image-based paradigms are required to directly compare the neural processes\nunderlying different emotion regulation processes in response to static (e.g., pictures)\nand dynamic (e.g., movies) stimuli.\n3. The response to the point about the incremental nature of the clinical\ntranslational potential of the findings includes some additional new analyses, which is\na welcome addition. Out of all their responses, I think this is most effective response\nfrom the authors. However, in the context of the current literature, much of which the\nauthors cite in their response, even with these additional findings, it’s still not clear that\ncrosses the line to being more than an incremental advance. The newly revised\ndiscussion (around line 627) also contributes to the sense that this advance is only one\nstep on the road, with much more work required for validation and generalization.\nResponse:\nWe thank the reviewer for the positive evaluation of the additional analyses\naddressing the clinical translational potential of the findings. While further validation\nand generalization are indeed necessary to fully translate neuroimaging into application,\nthis does not imply limited relevance of the present work.\nAltered emotional reactivity and deficits in emotion regulation are well-\nestablished, transdiagnostic features across mental disorders, providing a strong\nconceptual basis for identifying neural markers of dysfunctional emotion regulation\nprocesses12,28–32. One recent comprehensive review33 highlights that machine-learning-\nbased approaches can yield mechanistically informative neuromarkers with clear\n11\nclinical relevance, a view supported by initial and innovative high-impact studies in\naddiction34 and depression35.\nImportantly, to our knowledge, the present study is the first to test the clinical\napplicability of a multivariate neuromarker for emotion regulation in a clinical\npopulation. The approach is based on a recent innovative study, which aimed at\ntranslating an fMRI MVPA-based neuromarkers for drug-cue reactivity into clinical\napplication34. The work and a flanking commentary36 – both published in Nature\nNeuroscience (include the commentary) – emphasize both the promise of distributed\nneural signatures as transdiagnostic biomarkers and the need for continued validation,\nan emphasis that reflects standard translational practice rather than a limitation unique\nto the present study.\nTogether, the innovative, comparative, and strategy-specific approach in the\ncurrent study extends the literature beyond incremental advances focused on single\nstrategies, and represents a substantive step toward clinically actionable and process-\nspecific neuromarkers of emotion regulation.\n4. For their response to the comment about the challenges top-down models (e.g.\nZhang), the authors point out that the paper had not been published when their paper\nhad been submitted, but challenges to the currently dominant top-down models of\nemotion regulation have been around for several years, with some of these papers being\nreferenced in that review article (see for example references 17-19 in Zhang et al.) –\nthus, the antecedent theoretical views that were elaborated in Zhang et al. have been\navailable in the literature for years and should be addressed to an appropriate extent.\nThe authors now briefly acknowledge this other major theoretical perspective, but don’t\ngo on to discuss these other perspectives and implications for the interpretation of their\nfindings (though the review comment requested discussion of the implications). This\nissue is relevant to the overall goal of not only providing predictive models, but just as\nimportantly, explaining the overall findings in the context of current theoretical models.\nIf one adopts the alternative perspective described by Zhang et al., the interpretation of\nthe current findings would be quite different. Ideally, additional analyses could help\nestablish the extent to which the findings conclusively support the authors’ theoretical\nclaims.\nWe thank the reviewer for emphasizing the importance of the alternative\nperspectives that challenge top-down models, including Zhang et al. and related earlier\nwork (references 17–19). We acknowledge that these perspectives warrant further\nconsideration and have expanded the Introduction and Discussion to situate our\nfindings within this broader framework.\nViewed through this lens, our results remain interpretable and informative. For\nexample, shared contributions of the default mode network across strategies align with\ncommon valuation- and meaning-related processes, while strategy-specific engagement\nof frontoparietal control networks during reappraisal and amygdala–somatomotor–\nattention systems during acceptance reflects preferential recruitment of distinct\nsubprocesses within a shared architecture. Thus, our findings do not rely on a strict\nneural separability assumption, but provide empirical support for shared-and-distinct\nsubprocess accounts (and in line with the conclusion of the Zhang et al paper regarding\none of the two key datasets the papers is based on – Bo et al., - suggesting that: “It is\nworth noting that while most brain regions show overlap between emotion regulation\nand emotion generation, this study [77] also identified a set of regions whose responses\nwere consistent with the neural separability assumption, at least in the context of\ndownregulation of responses to emotional images”).\nWe emphasize that the primary aim of the current study was to compare the neural\nrepresentations of reappraisal and acceptance under dynamic, naturalistic conditions,\nrather than to test overarching theories on emotion generation versus separation. Further\ndissociating shared versus distinct subprocesses of emotion generation and regulation\nis moreover not in line with our pre-registered analyses or the design of the study and\nwould as such require additional exploratory post hoc analyses on experimental data\nnot designed to address this question. As such, an extensive discussion is beyond the\ncurrent scope of our work. Nevertheless, by leveraging brain-wide multivariate\nmodeling and task contrasts, our study provides a robust, theory-agnostic\ncharacterization of common and strategy-specific neural processes in the domain of\nemotion regulation.\nBased on the comment from the reviewer, we have further expanded the\ncorresponding discussion – in addition to changes in the introduction that have been\nincluded in the last revision to acknowledge these accounts and the recent opinion paper\n- to clarify how the findings can be interpreted across frameworks while maintaining\n13\ntheir core empirical contributions. Changes in the related context are outlined below:\nThe related context after revision is as follows:\nIntroduction section:\n(Line 51) Additionally, ongoing debates15–17 about the extent of neural overlap\nbetween emotion generation and regulation (as well as the overlap between different\nemotion regulation strategies18) further highlight the need for empirical work that\ninitially carefully characterizes the neural basis of distinct emotion regulation processes.\nDiscussion section:\n(Line 596) Of note, our findings do not rely on a strict top–down or strong neural\nseparability account of emotion regulation. Recent theoretical perspectives have\nemphasized substantial neural overlap between emotion generation and emotion\nregulation, proposing that both processes draw on shared valuation-related systems\nwhile preferentially engaging distinct subprocesses depending on contextual demands\nand regulatory goals15,37,38. Interpreted within this perspective, the present results\nsuggest that in the context of emotion regulation, acceptance and reappraisal recruit\ncommon, distributed neural systems, in particular core regions of the DMN, while also\nrecruiting distinguishable engagement of frontoparietal, somatomotor, attentional, and\nsubcortical systems. The observed distinguishability may, to a certain extent, reflect\ndifferences in how shared neural resources are coordinated and weighted, rather than\nthe engagement of entirely separate neural modules.\n14"}]

Decision letter