Computation of personalized functional networks using self-supervised deep learning (Conference Presentation)

Yong Fan

doi:10.1117/12.2633803

4 October 2022 Computation of personalized functional networks using self-supervised deep learning (Conference Presentation)

Yong Fan

Author Affiliations +

Proceedings Volume PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022; PC122040Z (2022) https://doi.org/10.1117/12.2633803
Event: SPIE Nanoscience + Engineering, 2022, San Diego, California, United States

Abstract

A novel self-supervised deep learning (DL) method is developed to compute personalized brain functional networks (FNs) for characterizing brain functional neuroanatomy based on functional MRI (fMRI). Specifically, a DL model of convolutional neural networks with an encoder-decoder architecture is developed to compute personalized FNs directly from fMRI data. The DL model is trained to optimize functional homogeneity of the personalized FNs without utilizing any external supervision in an end-to-end fashion. We demonstrate that a DL model trained on fMRI scans from the Human Connectome Project can identify personalized FNs and generalizes well across four different datasets. We further demonstrate that the identified personalized FNs are informative for predicting individual differences in behavior, brain development, and schizophrenia status. Taken together, the self-supervised DL facilities rapid, generalizable computation of personalized FNs.

Conference Presentation

Citation Download Citation

Yong Fan "Computation of personalized functional networks using self-supervised deep learning (Conference Presentation)", Proc. SPIE PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022, PC122040Z (4 October 2022); https://doi.org/10.1117/12.2633803

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