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Lux, B. K.

Publications and source records attributed to Lux, B. K..

2 recordsLinked to original sources

Bodily Maps of Spontaneous Thought

AO_SCPLOWBSTRACTC_SCPLOWThe intricate relationship between the body and the mind has long been recognized, but the specific bodily representations of spontaneous thought remain elusive. Here, we developed and validated predictive models of spontaneous thought based on body maps using the emBODY and Free-Association Semantic tasks. Our valence and self-relevance models demonstrated robust prediction performances across three test datasets, with the valence model accurately decoding the bodily topography of emotions and feelings. Model weight patterns revealed the significance of peripheral limbs and heart area in predicting valence, while the head area played a crucial role in predicting self-relevance. Furthermore, we investigated the neurobiological underpinnings of body map representations using fMRI and ECG data and found evidence for the reflection of body map responses in central and autonomic nervous system activities. Overall, this study provides insights into the bodily representations of spontaneous thought, highlighting the interconnected relationships between the body and the mind.

neuroscience↗

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.

neuroscience↗