A neural signature of affective arousal generalizes across valence and differs to autonomic arousal
Poster No:
612
Submission Type:
Abstract Submission
Authors:
Ran Zhang1, Xianyang Gan2, Ting Xu1, Fangwen Yu2, Lan Wang2, Xinwei Song2, Guojuan Jiao2, Xiqin LIU3, Feng Zhou1, Benjamin Becker4
Institutions:
1Southwest University, Chongqing, Chongqing, 2University of Electronic Science and Technology of China, Chengdu, Sichuan, 3West China Hospital of Sichuan University, Chengdu, Sichuan, 4The University of Hong Kong, Hong Kong, Hong Kong
First Author:
Co-Author(s):
Introduction:
The mental and neural topology of conscious affective experiences remains a topic of controversial debates(Harmon-Jones et al., 2017). Most current perspectives suggest that the core affect domains of valence and arousal critically separate affective experiences from other mental states(Anderson et al., 2014). While considerable progress has been made in developing comprehensive and precise brain models for the valence, corresponding models for the affective arousal in humans are currently lacking. Given the pivotal role of affective arousal in both conceptual frameworks and human experience, we combined naturalistic fMRI with predictive modeling to develop an accurate and generalizable brain affective arousal signature(BAAS) and in turn utilize it to enhance our comprehension of how the brain represents affective arousal, whether the presentation is separable from autonomic and wakefulness arousal and to refine the prediction specificity of neural signatures for affective experiences based on the arousal overshadowing theory.
Methods:
We conducted an immersive arousal experiment combining naturalistic fMRI with machine-learning-based neural decoding to develop and validate an neural signature for the subjective experience of affective arousal in humans(Fig. 1A; discovery cohort: n = 60, validation cohort: n = 36) and further determined which brain regions contribute to the whole-brain affective arousal prediction. Next, in a series of comprehensive analyses using a total 23 neuroimaging and meta-analytic datasets, we examined whether the BAAS showed high generalizability across contexts, socio-cultural diverse samples, states of consciousness or valence and paradigm classes but was not sensitive to cognitive effort, memorability, wakefulness or autonomic arousal. In addition, the voxel-level spatial similarity analysis and an exploratory conjunction analysis were conducted to evaluate whether the conscious experience of affective arousal and autonomic arousal are mediated by the same brain systems. Finally, we employed BAAS to recent decoding models for general negative affect and specific emotions(Ceko et al., 2022, Gan et al., 2024) and tested whether accounting for affective arousal can enhance the specificity of affective decoding models.
Results:
The results showed that the developed BAAS(Fig. 1B) accurately predicted subjective affective arousal, such that the averaged within-subject correlation between predicted and true arousal ratings was ≥0.80 in discovery and validation cohorts. The conscious experience of affective arousal is represented in distributed subcortical and cortical systems(Fig. 1B). We further demonstrated that the BAAS cuts across the valence and generalizes across highly arousing negative and positive experiences, whether induced by diverse external stimulus modalities (visual, auditory and tactile stimuli), arousal associated processes such as pain, or even when the events are only imagined during mental imagery(Fig. 1C) and does not capture other potential confounding factors(Fig. 2A). Furthermore, we identified that, although affective arousal and autonomic arousal exhibit distinguishable neural representations at the global level (i.e., whole-brain)(Fig. 2B), a set of subcortical(e.g., amygdala, thalamus) and insular regions encode both affective and autonomic arousal responses in a shared manner at the local level. Finally, we found that accounting for affective arousal(e.g., response of BAAS) considerably enhances the specificity of the brain emotion models(e.g., Fig. 2C).
·Fig. 1
·Fig. 2
Conclusions:
We established a comprehensive and accurate neural signature for affective arousal, which generalizes across valence, modalities, and populations. The neural basis of affective arousal is encoded in multiple distributed brain systems rather than isolated brain regions, and distinguishable from autonomic arousal or wakefulness. Last, we determined the substantial potential of BAAS in refining the specificity of affective neural signatures.
Emotion, Motivation and Social Neuroscience:
Emotional Perception 1
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Classification and Predictive Modeling 2
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
FUNCTIONAL MRI
Other - Affective arousal, Valence, Autonomic arousal, Multivariate pattern analysis, Naturalistic paradigm
1|2Indicates the priority used for review
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Provide references using APA citation style.
Čeko, M., Kragel, P. A., Woo, C. W., López-Solà, M., & Wager, T. D. (2022). Common and stimulus-type-specific brain representations of negative affect. Nature neuroscience, 25(6), 760-770.
Gan, X., Zhou, F., Xu, T., Liu, X., Zhang, R., Zheng, Z., ... & Becker, B. (2024). A neurofunctional signature of subjective disgust generalizes to oral distaste and socio-moral contexts. Nature human behaviour, 1-20.
Harmon-Jones, E., Harmon-Jones, C., & Summerell, E. (2017). On the importance of both dimensional and discrete models of emotion. Behavioral sciences, 7(4), 66.
Zhang, R., Gan, X., Xu, T., Yu, F., Wang, L., Song, X., ... & Becker, B. (2024). A neurofunctional signature of affective arousal generalizes across valence domains and distinguishes subjective experience from autonomic reactivity. bioRxiv, 2024-07.