An automated tool that trains a multimodal classification model (EEG + fMRI) to predict phenotypic variables (sex, age, diagnosis) and assess whether fusing both modalities improves prediction compared to each modality alone.
This project investigates how resting-state functional connectivity differs between sleep and wake states using simultaneous EEG and fMRI data from the OpenNeuro “Simultaneous EEG and fMRI signals during sleep from humans” dataset. By comparing default mode network connectivity and EEG frequency-band connectivity across states, the project found minimal differences at the network level but a reliable increase in theta-band connectivity during sleep, highlighting the value of combining modalities to characterize state-dependent brain connectivity.
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