Abstract #5658

Temporal-autoencoding neural network revealed the underlying functional dynamics of fMRI data: Evaluation using the Human Connectome Project data

Jong-Hwan Lee^1,2, Eric C. Wong³, and Peter Bandettini²

¹Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea, Republic of, ²Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States, ³Department of Radiology, University of California, San Diego, La Jolla, CA, United States

We proposed a novel approach based on a temporal autoencoding neural network (TANN) model to predict the fMRI volume in the next time point or repetition time (TR) based on the fMRI volume in the present TR. Using motor task data from the Human Connectome Project, our TANN model revealed the human motor cortex dynamics. The highly task-specific foot, hand, and tongue networks within the motor-related areas were clearly identified from the TANN weight features and the task-associated networks across the frontal, parietal, temporal, and visual areas were also clearly parcellated without any task information.

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