Dynamic mental illness indicators caught by advanced AI in brain imaging SciReports
enables downstream learning with minimal training data, making the direct investigation of system dynamics feasible. Our findings demonstrate that self-supervised pretraining on healthy adults dataset noticeably uplifts the downstream model’s performance on a disparate disorder dataset. These benefits generalize across datasets and disorders and thus alleviate the need to collect a massive amount of expensive data.
While the proposed framework is a stepping stone toward the direct study of signals, the proposed approach still needs to be improved to make it a clinically relevant. An interpretable model is essential to grasp better the difficult task of interpreting brain dynamics of mental disorders, and our approach demonstrably works quite well and provides a promising utility.
We find that interpreting DL models trained on fMRI signals to discriminate mental disorders from controls provides means to identify salient parts of brain dynamics. In particular, we show that one can capture sparse spatio-temporal signatures that encode information comparable with what is found via the traditional full data functional network connectivity analysis.
Belgique Dernières Nouvelles, Belgique Actualités
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