Multi-scale fMRI time series analysis for understanding neurodegeneration in MCI
This work addresses early detection of neurodegeneration for patients with MCI, but it is incremental as it combines existing methods like graph analysis and recurrence plots in a novel multi-scale framework.
The study tackled neurodegeneration in Mild Cognitive Impairment (MCI) by analyzing resting-state fMRI data from 50 healthy controls and 50 MCI subjects using a multi-scale deep learning approach, finding reduced activity in some brain regions and greater predictability in time-series for MCI.
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.