Brian Avants1, Phil Cook1, Lyle
Ungar1, James Gee1,
1University of
We
present a novel, unsupervised method, sparse canonical correlation analysis
for neuroimaging (SCCAN), that automatically locates correlated sets of voxels
in complementary imaging modalities. The method reveals significant and
syndrome-specific cortical thickness-diffusion tensor imaging networks in two
neurodegenerative diseases, AD and FTD. Subject diagnosis was confirmed by
autopsy or CSF-biomarker ratios. The SCCAN summary correlates, in AD, with
MMSE reduction and, in FTD, with reduced verbal fluency. Thus, SCCAN
identifies disease-specific networks of effects in white matter and cortical
thickness that appear in anatomy suspected to be involved in these diseases
and that relate specifically to impaired cognitive processes.