Ran Zhang, Xianyang Gan, Ting Xu, Fangwen Yu, Lan Wang, Xinwei Song, Guojuan Jiao, Xiqin Liu, Feng Zhou, Benjamin Becker
{"title":"A neurofunctional signature of affective arousal generalizes across valence domains and distinguishes subjective experience from autonomic reactivity","authors":"Ran Zhang, Xianyang Gan, Ting Xu, Fangwen Yu, Lan Wang, Xinwei Song, Guojuan Jiao, Xiqin Liu, Feng Zhou, Benjamin Becker","doi":"10.1038/s41467-025-61706-0","DOIUrl":null,"url":null,"abstract":"<p>Arousal is fundamental for affective experience and, together with valence, defines the core affective space. Precise brain models of affective arousal are lacking, leading to continuing debates of whether the neural systems generalize across valence domains and are separable from those underlying autonomic arousal or wakefulness. Here, we combine naturalistic fMRI with predictive modeling to develop a brain affective arousal signature (BAAS, discovery-validation design, <i>n</i> = 60, 36). We demonstrate its (1) sensitivity and generalizability across mental processes, valence, and stimulation modality and (2) neural distinction from autonomic arousal and wakefulness (24 studies, <i>n</i> = 868). Affective arousal is encoded in distributed cortical-subcortical (e.g., prefrontal, periaqueductal gray) systems with local similarities in thalamo-amygdala-insula systems between affective and autonomous arousal. We demonstrate application of the BAAS to improve specificity of established valence-specific neuromarkers. Our study provides a biologically plausible model for affective arousal that aligns with the affective space and has a high application potential.</p>","PeriodicalId":19066,"journal":{"name":"Nature Communications","volume":"1 1","pages":""},"PeriodicalIF":14.7000,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Communications","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41467-025-61706-0","RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Arousal is fundamental for affective experience and, together with valence, defines the core affective space. Precise brain models of affective arousal are lacking, leading to continuing debates of whether the neural systems generalize across valence domains and are separable from those underlying autonomic arousal or wakefulness. Here, we combine naturalistic fMRI with predictive modeling to develop a brain affective arousal signature (BAAS, discovery-validation design, n = 60, 36). We demonstrate its (1) sensitivity and generalizability across mental processes, valence, and stimulation modality and (2) neural distinction from autonomic arousal and wakefulness (24 studies, n = 868). Affective arousal is encoded in distributed cortical-subcortical (e.g., prefrontal, periaqueductal gray) systems with local similarities in thalamo-amygdala-insula systems between affective and autonomous arousal. We demonstrate application of the BAAS to improve specificity of established valence-specific neuromarkers. Our study provides a biologically plausible model for affective arousal that aligns with the affective space and has a high application potential.
期刊介绍:
Nature Communications, an open-access journal, publishes high-quality research spanning all areas of the natural sciences. Papers featured in the journal showcase significant advances relevant to specialists in each respective field. With a 2-year impact factor of 16.6 (2022) and a median time of 8 days from submission to the first editorial decision, Nature Communications is committed to rapid dissemination of research findings. As a multidisciplinary journal, it welcomes contributions from biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences, aiming to highlight important breakthroughs within each domain.