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Local white matter architecture is so unique and highly conserved within an individual that it can be considered a unique neural phenotype. Here we show that this phenotype can be quantified by measuring the density of microscopic water diffusion along major white matter fascicles and producing a high dimensional vector that can be used to compute the distance between two structural connectomes, i.e., a local connectome fingerprint. The distance between two local connectome fingerprints reflects a low dimen
Quantifying differences or similarities in connectomes has been a challenge due to the immense complexity of global brain networks. Here we introduce a noninvasive method that uses diffusion MRI to characterize whole-brain white matter architecture as a single local connectome fingerprint that allows for a direct comparison between structural connectomes. In four independently data sets with repeated scans (total N = 213), we show that the local connectome fingerprint is highly specific to an individual, al
TBSS results showing all 3 group comparisons for WMTI metrics of radial extra-axonal diffusivity, axial extra-axonal diffusivity, and axonal water fraction: Clusters of significantly different voxels ( &lt .05) are shown in red-orange and overlaid on the FMRIB FA template, together with the mean skeleton (green). Clusters of increased radial extra-axonal diffusivity are found both for NC versus MCI groups and for MCI versus AD groups. Clusters of increased axial extra-axonal diffusivity are found for NC versu