  In this study, we present a technique that spans multi-scale views (global
scale -- meaning brain network-level and local scale -- examining each
individual ROI that constitutes the network) applied to resting-state fMRI
volumes. Deep learning based classification is utilized in understanding
neurodegeneration. The novelty of the proposed approach lies in utilizing two
extreme scales of analysis. One branch considers the entire network within
graph-analysis framework. Concurrently, the second branch scrutinizes each ROI
within a network independently, focusing on evolution of dynamics. For each
subject, graph-based approach employs partial correlation to profile the
subject in a single graph where each ROI is a node, providing insights into
differences in levels of participation. In contrast, non-linear analysis
employs recurrence plots to profile a subject as a multichannel 2D image,
revealing distinctions in underlying dynamics. The proposed approach is
employed for classification of a cohort of 50 healthy control (HC) and 50 Mild
Cognitive Impairment (MCI), sourced from ADNI dataset. Results point to: (1)
reduced activity in ROIs such as PCC in MCI (2) greater activity in occipital
in MCI, which is not seen in HC (3) when analysed for dynamics, all ROIs in MCI
show greater predictability in time-series.
