Brain decoding of spontaneous thought: Predictive modeling of self-relevance and valence using personal narratives.

Autor:	Kim HJ; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon 16419, South Korea.; Department of Biomedical Engineering, Sungkyunkwan University, Suwon 16419, South Korea.; Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon 16419, South Korea., Lux BK; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon 16419, South Korea.; Department of Biomedical Engineering, Sungkyunkwan University, Suwon 16419, South Korea.; Department of Psychological and Brain Sciences, Dartmouth College, NH 03755., Lee E; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon 16419, South Korea.; Department of Biomedical Engineering, Sungkyunkwan University, Suwon 16419, South Korea.; Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon 16419, South Korea., Finn ES; Department of Psychological and Brain Sciences, Dartmouth College, NH 03755., Woo CW; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon 16419, South Korea.; Department of Biomedical Engineering, Sungkyunkwan University, Suwon 16419, South Korea.; Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon 16419, South Korea.; Life-inspired Neural Network for Prediction and Optimization Research Group, Suwon 16419, South Korea.
Jazyk:	angličtina
Zdroj:	Proceedings of the National Academy of Sciences of the United States of America [Proc Natl Acad Sci U S A] 2024 Apr 02; Vol. 121 (14), pp. e2401959121. Date of Electronic Publication: 2024 Mar 28.
DOI:	10.1073/pnas.2401959121
Abstrakt:	The contents and dynamics of spontaneous thought are important factors for personality traits and mental health. However, assessing spontaneous thoughts is challenging due to their unconstrained nature, and directing participants' attention to report their thoughts may fundamentally alter them. Here, we aimed to decode two key content dimensions of spontaneous thought-self-relevance and valence-directly from brain activity. To train functional MRI-based predictive models, we used individually generated personal stories as stimuli in a story-reading task to mimic narrative-like spontaneous thoughts ( n = 49). We then tested these models on multiple test datasets (total n = 199). The default mode, ventral attention, and frontoparietal networks played key roles in the predictions, with the anterior insula and midcingulate cortex contributing to self-relevance prediction and the left temporoparietal junction and dorsomedial prefrontal cortex contributing to valence prediction. Overall, this study presents brain models of internal thoughts and emotions, highlighting the potential for the brain decoding of spontaneous thought. Competing Interests: Competing interests statement:The authors declare no competing interest.
Databáze:	MEDLINE
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