機械学習による脳からの自発的思考の解読(Decoding Spontaneous Thoughts from the Brain via Machine Learning)

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2024-04-11 韓国基礎科学研究院(IBS)

機械学習による脳からの自発的思考の解読(Decoding Spontaneous Thoughts from the Brain via Machine Learning)

Figure 1. Predictive modeling analysis pipeline

バイオサイエンス研究所(IBS)の神経科学イメージング研究センター(CNIR)のKIM Hong JiとWOO Choong-Wanらの研究チームは、ダートマス大学のEmily FINNと協力して、人々が物語を読んだり自由な思考状態にあるときの主観的感情を予測するために、機能的磁気共鳴画像法(fMRI)と機械学習アルゴリズムを使用する可能性を示しました。彼らは、個人的な物語とfMRIを組み合わせて、自発的な思考時の情動的な内容の予測モデルを開発し、その解釈により、自己関連性と感情価の予測に関わる脳の領域やネットワークを特定しました。これにより、日常的な思考状態での感情の解読に可能性を示しました。

<関連情報>

自発的思考の脳内デコーディング: パーソナル・ナラティブを用いた自己関連性と価性の予測モデリング Brain decoding of spontaneous thought: Predictive modeling of self-relevance and valence using personal narratives

Hong Ji Kim, Byeol Kim Lux, Eunjin Lee, +1, and Choong-Wan Woo
Proceedings of the National Academy of Sciences  Published:March 28, 2024
DOI:https://doi.org/10.1073/pnas.2401959121

Significance

Spontaneous thought provides valuable insights into our internal states and context, but assessing its contents and dynamics is challenging due to its unconstrained nature. We addressed this challenge by developing functional MRI-based predictive models for two crucial content dimensions (i.e., self-relevance and valence) of spontaneous thought. Using personalized narratives as stimuli, we evoked cognitive and affective responses resembling real-life experiences. Our models were able to predict the levels of self-relevance and valence ratings during story reading and resting state, contributing to brain-based daydream decoding. These results hold significant implications for understanding individual differences and assessing mental health, shedding light on the study of internal states and contexts that shape our subjective experiences.

Abstract

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.

生物工学一般
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