Self-Supervised Learning of Brain Dynamics from Broad Neuroimaging DataDownload PDF

Published: 31 Oct 2022, Last Modified: 13 Jan 2023NeurIPS 2022 AcceptReaders: Everyone
Keywords: pre-training, self-supervised learning, neuroimaging, mental state decoding, natural language processing, language modelling
Abstract: Self-supervised learning techniques are celebrating immense success in natural language processing (NLP) by enabling models to learn from broad language data at unprecedented scales. Here, we aim to leverage the success of these techniques for mental state decoding, where researchers aim to identify specific mental states (e.g., the experience of anger or joy) from brain activity. To this end, we devise a set of novel self-supervised learning frameworks for neuroimaging data inspired by prominent learning frameworks in NLP. At their core, these frameworks learn the dynamics of brain activity by modeling sequences of activity akin to how sequences of text are modeled in NLP. We evaluate the frameworks by pre-training models on a broad neuroimaging dataset spanning functional Magnetic Resonance Imaging data from 11,980 experimental runs of 1,726 individuals across 34 datasets, and subsequently adapting the pre-trained models to benchmark mental state decoding datasets. The pre-trained models transfer well, generally outperforming baseline models trained from scratch, while models trained in a learning framework based on causal language modeling clearly outperform the others.
TL;DR: We devise and evaluate novel self-supervised learning techniques for neuroimaging data, inspired by prominent learning frameworks in natural language processing, using one of the broadest neuroimaging datasets used for pre-training to date.
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