Computing personalized brain functional networks from fMRI using self-supervised deep learning

Abstract

A novel self-supervised deep learning (DL) method is developed for computing bias-free, personalized brain functional networks (FNs) that provide unique opportunities to better understand brain function, behavior, and disease. Specifically, convolutional neural networks with an encoder-decoder architecture are employed to compute personalized FNs from resting-state fMRI data without utilizing any external supervision by optimizing functional homogeneity of personalized FNs in a self-supervised setting. We demonstrate that a DL model trained on fMRI scans from the Human Connectome Project can identify canonical 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, self-supervised DL allows for rapid, generalizable computation of personalized FNs.

Computation of transcranial magnetic stimulation electric fields using self-supervised deep learning

“…Inspired by self-supervised deep learning methods (Geneva and Zabaras, 2020;Guo et al, 2020;Li et al, 2021;Fan, 2018, 2020;Qin et al, 2019;Raissi et al, 2019;Rao et al, 2021;Tian et al, 2020;Winovich et al, 2019;Yang and Perdikaris, 2019;Zhu et al, 2019) and the pioneer deep learning based E-field computation methods (Xu et al, 2021;Yokota et al, 2019), we develop a novel selfsupervised deep learning based TMS E-field modeling method to obtain precise high-resolution E-fields.…”

“…Therefore, their accuracy would be bounded by the conventional numerical methods used to generate the training data. Inspired by self-supervised deep learning methods (Geneva and Zabaras, 2020; Guo et al, 2020; Li et al, 2021; Li and Fan, 2018, 2020; Qin et al, 2019; Raissi et al, 2019; Rao et al, 2021; Tian et al, 2020; Winovich et al, 2019; Yang and Perdikaris, 2019; Zhu et al, 2019) and the pioneer deep learning based E-field computation methods (Xu et al, 2021; Yokota et al, 2019), we develop a novel self-supervised deep learning based TMS E-field modeling method to obtain precise high-resolution E-fields. Specially, given a head model and the primary E-field generated by TMS coil, a DL model is built to generate the electric scalar potential by minimizing a loss function that measures how well the generated electric scalar potential fits the governing PDE, from which the E-field can be derived directly.…”