Functional decoding of emotion regulation strategies in dynamic naturalistic contexts
Poster No:
598
Submission Type:
Abstract Submission
Authors:
Heng Jiang1, Jingxian He1, Kaeli Zimmermann2, Xinqi Zhou3, Xianyang Gan1, Stefania Ferraro1, Lan Wang1, Bo Zhou4, Keith Kendrick1, Feng Zhou5, Benjamin Becker6
Institutions:
1University of Electronic Science and Technology of China, Chengdu, China, 2Independent Researcher, Cologne, Germany (DE), 3Sichuan Normal University, Chengdu, China, 4Sichuan Provincial People's Hospital, Chengdu, China, 5University of Electronic Science and Technology of China, Chongqing, China, 6The University of Hong Kong, Hong Kong, China
First Author:
Co-Author(s):
Introduction:
Adaptive emotion regulation (ER) is essential for mental health (Troy et al., 2023) and ER dysfunctions represent transdiagnostic markers for mental disorders (Aldao et al., 2010; Fernandez et al., 2016). Cognitive reappraisal and acceptance are two ER strategies that form the basis for therapeutic interventions and while both strategies successfully facilitate the regulation of negative affect in experimental and clinical settings their common and distinct neural representations remain unclear (e.g., Kober et al., 2019; Monachesi et al., 2023). Previous neuroimaging studies combined the presentation of isolated static affective stimuli with mass univariate analyses, which limits the translation of the findings into dynamic real-life settings and comprehensive brain models. The present fMRI study aims to precisely determine common and distinct neural representations of reappraisal and acceptance during naturalistic emotional processing using machine learning based neural decoding.
Methods:
We trained support vector machine classification decoders by applying leave-one-subject-out cross-validation methods using subject-level whole-brain images from a discovery cohort (n = 59). Performance of the naturalistic ER signature-reappraisal (NERS-R), naturalistic ER signature-acceptance (NERS-A), and naturalistic negative emotion signature (NNES) were evaluated in a validation cohort (n = 33). Brain regions that robustly contributed were determined through bootstrapping (Kohoutová et al., 2020) and computation of model encoding maps (Haufe et al., 2014). We also further investigated the roles of isolated regions or functional networks and compared those two strategies. The generalizability of the decoders was tested on an independent large healthy participant dataset (generalization cohort 1, n = 358) and translation into application was tested using a clinical dataset examining ER in heavy cannabis users (CU) vs. healthy controls (HC) (generalization cohort 2, total nHC = 48, nMU = 49).
Results:
The NERS-A, NERS-R, and NNES (Fig.1a) could accurately predict the corresponding mental processes vs. the respective conditions across discovery, validation, and generalization cohorts 1 (all P < 0.0001, or < 0.05, respectively). On generalization cohort 2, NERS-R could accurately discriminate distancing vs. viewing negative pictures in the HC group (P < 0.05), but not the CU group. NNES could accurately discriminate view negative vs. neutral pictures in both groups (both P < 0.0001). Further permutation analysis indicated the NNES and NERS-R have significantly different pattern expressions between the two groups (both P < 0.0001), providing a neural marker for ER deficits in CU.
The model encoding map (Fig.1b) and spatial similarity analyses (Fig.2a) in healthy controls further indicated that both ER strategies engaged common and distinct frontal and limbic regions. While common regions included limbic networks, distinguishable contributions were observed in somatomotor and attention networks, or the frontoparietal control network, respectively (Fig2.a, b, and c). Results were further confirmed by voxel-level spatial similarity analyses (Fig.2d).
The model encoding map (Fig.1b) and spatial similarity analyses (Fig.2a) in healthy controls further indicated that both ER strategies engaged common and distinct frontal and limbic regions. While common regions included limbic networks, distinguishable contributions were observed in somatomotor and attention networks, or the frontoparietal control network, respectively (Fig2.a, b, and c). Results were further confirmed by voxel-level spatial similarity analyses (Fig.2d).
Conclusions:
We developed three sensitive neural signatures for ER under the dynamic naturalistic environment and demonstrated common and distinct neural representations of these signatures. The developed signatures may serve as precise markers for different ER strategies, with the clinical data underscoring a high translational potential to measure ER deficits in mental disorders.
Emotion, Motivation and Social Neuroscience:
Self Processes 2
Emotion and Motivation Other 1
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Other - acceptance; reappraisal; neural decoding; fMRI; multivariate pattern analysis; emotion regulation; neural signature
1|2Indicates the priority used for review
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