Abstract
Resting state (RS) functional magnetic resonance images (rsfMRI) were analyzed by spatial independent component analysis (sICA). Functional connectivity (FC) was further analyzed within the identified RS networks either by high dimension sICA or by local clustering. The latter approach permitted to drive a matched structural connectivity (SC) based on probabilistic tractography between the same clusters. Cortex segmentation tools ad diffusion MRI were used to correlate fiber and cortical damage. Methods and results are here compared concerning the translational fall-outs and the applicability in the evaluation and follow-up of neurodegenerative diseases. Emphasis is given to the integration of image, signal, and data processing methods.
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