Getting personal: brain decoding of spontaneous thought using personal narratives

The contents of spontaneous thought and their dynamics are important factors for ones personality traits and mental health. However, they are difficult to assess because spontaneous thought occurs voluntarily without conscious constraints. Here, we aimed to decode two important content dimensions of spontaneous thought--self-relevance and valence--directly from functional Magnetic Resonance Imaging (fMRI) signals. To train brain decoders, we induced a wide range of levels of self-relevance and emotional valence using individually generated personal stories as well as stories written by others to mimic narrative-like spontaneous thoughts (n = 49). We then tested the brain decoders on two resting-state fMRI datasets (n = 49 and 90) with and without intermittent thought sampling, achieving significant predictions. The default mode and ventral attention networks were important contributors to the predictions. Overall, this study paves the way for the brain decoding of spontaneous thought and its use for clinical applications.