Using machine-learning to build predictive models linking inter-individual variability
at the neurobiological and phenotypical level is a rapidly expanding field with manifold
clinical applications. In fact, several studies have already demonstrated that connectome-based
prediction may allow individual inference on categorical and dimensional aspects of
psychiatric disorders. One of the major drawbacks of the standard approach, ie., the
use of the whole-brain functional connectome, though, is the high dimensionality of
the feature space even at coarse resolutions.
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© 2020 Published by Elsevier Inc.
