A recent study conducted by researchers at the Center for Neuroscience Imaging Research (CNIR) within the Institute for Basic Science (IBS), in collaboration with Dartmouth College, has utilized functional Magnetic Resonance Imaging (fMRI) and machine learning to predict subjective feelings during both story reading and spontaneous thinking.

Spontaneous thoughts occur continuously, even during rest or sleep, offering valuable insights into individuals’ emotional states. However, studying them is challenging due to their unconstrained nature. The team overcame this challenge by employing personalized narrative stimuli crafted through one-on-one interviews with participants, reflecting their past experiences and emotions.

By analyzing brain activity recorded during story reading inside the MRI scanner, the researchers identified key brain regions, such as the anterior insula and midcingulate cortex, crucial in predicting personal relevance and emotional tone. These findings suggest potential applications in understanding individual emotional experiences and improving mental health assessments.

The study’s predictive models demonstrated the ability to decode both self-relevance and valence not only during story reading but also during spontaneous, task-free thinking or resting. This breakthrough in decoding spontaneous thoughts holds promise for understanding individual differences in thoughts and emotions, thus aiding in the evaluation of mental well-being.

Dr. Woo Choong-Wan, associate director of IBS and lead researcher, emphasized the significance of this study in decoding intimate emotions within the natural flow of thoughts, potentially benefiting mental health. Kim Hongji, the study’s first author, highlighted its importance in decoding the emotional state associated with general thoughts, offering insights into subjective experiences and aiding mental well-being assessment.