Learning shared neural manifolds from multi-subject FMRI data. (arXiv:2201.00622v1 [q-bio.NC])

Functional magnetic resonance imaging (fMRI) is a notoriously noisy
measurement of brain activity because of the large variations between
individuals, signals marred by environmental differences during collection, and
spatiotemporal averaging required by the measurement resolution. In addition,
the data is extremely high dimensional, with the space of the activity
typically having much lower intrinsic dimension. In order to understand the
connection between stimuli of interest and brain activity, and analyze
differences and commonalities between subjects, it becomes important to learn …

arxiv bio data learning