Bodily Maps of Spontaneous Thought

Data and scripts for Shin, Lux, Kim, & Woo (2023) Bodily Maps of Spontaneous ThoughtHyemin Shin*, Byeol Kim Lux*, Hong Ji Kim, Choong-Wan Woo§  *co-first authors§corresponding author: Choong-Wan Woo (choongwan.woo@gmail.com) Preprint: biorxiv All scripts are written in Matlab and has been tested...

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Hauptverfasser: Kim Lux, Byeol, Shin, Hyemin, Kim, Hong Ji, Woo, Choong-Wan
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description Data and scripts for Shin, Lux, Kim, & Woo (2023) Bodily Maps of Spontaneous ThoughtHyemin Shin*, Byeol Kim Lux*, Hong Ji Kim, Choong-Wan Woo§  *co-first authors§corresponding author: Choong-Wan Woo (choongwan.woo@gmail.com) Preprint: biorxiv All scripts are written in Matlab and has been tested on Matlab version 2020b. Dependencies CanlabCore CocoanCORE Time Expected install time is approximately 5 minutes. Expected run time is around an hour in total. Main scripts The following are the major scripts that allow you to regenerate the results and figures of the manuscript. The order follows the results as they appeared in the manuscript. Additional external toolboxes and data shared from previous works have been included in the 'external' subfolder. The data shared from previous works were cited in the manuscript. bodymap_1_across_model.m bodymap_2_within_model.m bodymap_3_nummenmaa.m bodymap_4_heartcluster_1.m bodymap_4_heartcluster_2.m bodymap_5_somatotopy.m To use these scripts, download and unzip the provided zipfile. Open the code in Matlab and set the current folder to the unzipped folder. Add the path to the current folder and its subfolders. The necessary data and functions to run the scripts has been saved in the folders. You can also find some interim results that are already included. Abstract The 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.
doi_str_mv 10.5281/zenodo.10322287
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Dependencies CanlabCore CocoanCORE Time Expected install time is approximately 5 minutes. Expected run time is around an hour in total. Main scripts The following are the major scripts that allow you to regenerate the results and figures of the manuscript. The order follows the results as they appeared in the manuscript. Additional external toolboxes and data shared from previous works have been included in the 'external' subfolder. The data shared from previous works were cited in the manuscript. bodymap_1_across_model.m bodymap_2_within_model.m bodymap_3_nummenmaa.m bodymap_4_heartcluster_1.m bodymap_4_heartcluster_2.m bodymap_5_somatotopy.m To use these scripts, download and unzip the provided zipfile. Open the code in Matlab and set the current folder to the unzipped folder. Add the path to the current folder and its subfolders. The necessary data and functions to run the scripts has been saved in the folders. You can also find some interim results that are already included. Abstract The 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. 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Dependencies CanlabCore CocoanCORE Time Expected install time is approximately 5 minutes. Expected run time is around an hour in total. Main scripts The following are the major scripts that allow you to regenerate the results and figures of the manuscript. The order follows the results as they appeared in the manuscript. Additional external toolboxes and data shared from previous works have been included in the 'external' subfolder. The data shared from previous works were cited in the manuscript. bodymap_1_across_model.m bodymap_2_within_model.m bodymap_3_nummenmaa.m bodymap_4_heartcluster_1.m bodymap_4_heartcluster_2.m bodymap_5_somatotopy.m To use these scripts, download and unzip the provided zipfile. Open the code in Matlab and set the current folder to the unzipped folder. Add the path to the current folder and its subfolders. The necessary data and functions to run the scripts has been saved in the folders. You can also find some interim results that are already included. 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Abstract The 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. 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title Bodily Maps of Spontaneous Thought
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