Introduces Off-log metric for correlation matrices and Grassmannian subspace distances to improve sensitivity and classification in fMRI brain network analysis across clinical and ageing datasets.
FreeSurfer.NeuroImage62,774–781 (2012)
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Riemannian geometry meets fMRI: the advantages of modeling correlation manifolds and eigenvector subspaces
Introduces Off-log metric for correlation matrices and Grassmannian subspace distances to improve sensitivity and classification in fMRI brain network analysis across clinical and ageing datasets.