Brain Decoding of Spontaneous Thought: Predictive Modeling of Self-relevance and Valence Using Personal Narratives

Brain Decoding of Spontaneous Thought: Predictive Modeling of Self-relevance and Valence Using Personal Narratives

This repository contains the predictive models, data, and codes to generate the main figures for the following publication:

"Brain Decoding of Spontaneous Thought: Predictive Modeling of Self-relevance and Valence Using Personal Narratives"

Hong Ji Kim, Byeol Kim Lux, Eunjin Lee, Emily S. Finn, Choong-Wan Woo, 2023

Dependencies:
https://github.com/canlab/CanlabCore 
https://github.com/cocoanlab/cocoanCORE
https://github.com/spm/spm12
*Note that the current study used these repositories (which are also based on the SPM toolbox) for all fMRI data analyses.

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 fMRI-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.

Please see 'make_figures.m' for instructions and codes. If you have any questions, please contact Hong Ji Kim (hongji.eo.kim@gmail.com).