DeepRest

Resting-state functional MRI (rs-fMRI) has the potential to be a valuable tool for early diagnosis of certain brain pathologies. However, its accuracy is limited by the existence of a few confounding factors as drowsiness. Recent studies suggest that drowsiness can significantly affect brain connectivity measurements. Furthermore, the use of different correction techniques can result in varying cognitive messages. Unfortunately, the conditions of rs-fMRI examination can induce drowsiness, making it challenging to use this test for personalised medicine. Following our work, the DeepRest project aims to develop a new brain imaging toolbox based on a machine learning approach for detecting drowsiness during rs-fMRI to improve its sensitivity and specificity. This tool will be developed in collaboration with four partners, evaluated on a large cohort of patients, particularly for medical follow-up purposes, and eventually integrated into the SWOMed solution created by the industrial partner.