Connectome-scale assessments of structural and functional connectivity in MCI: Structural and Functional Connectivity in MCI

Mild cognitive impairment (MCI) has received increasing attention not only because of its potential as a precursor for Alzheimer's disease (AD), but also as a predictor of conversion to other neurodegenerative diseases. Although MCI has been defined clinically, accurate and efficient diagnosis is still challenging. While neuroimaging techniques hold promise, compared to commonly-used biomarkers including amyloid plaques, tau protein levels and brain tissue atrophy, neuroimaging biomarkers are less well validated. In the present paper, we propose a connectomes-scale assessment of structural and functional connectivity in MCI via two independent multimodal DTI/fMRI datasets. We first used DTI-derived structural profiles to explore and tailor the most common and consistent landmarks, then applied them in a whole-brain functional connectivity analysis. The next step fused the results from two independent datasets together and resulted in a set of functional connectomes with the most differentiation power, hence named as “connectome signatures”. Our results indicate that these “connectome signatures” have significantly high MCI-vs-controls classification accuracy, at more than 95%. Interestingly, through functional meta-analysis, we found that the majority of “connectome signatures” are mainly derived from the interactions among different functional networks, e.g., cognition-perception and cognition-action domains, rather than from within a single network. Our work provides support for using functional “connectome signatures” as neuroimaging biomarkers of MCI