Towards Subject and Diagnostic Identifiability in the Alzheimer's Disease Spectrum based on Functional Connectomes

TL;DR

This paper introduces a novel principal component-based framework to improve the reliability and diagnostic separation of functional connectomes in Alzheimer's disease, aiding early detection and understanding of disease progression.

Contribution

The study presents a new identifiability framework that enhances test-retest reliability and diagnostic separation in functional connectivity data for Alzheimer's disease.

Findings

Increases test-retest correspondence of functional connectivity.

Improves separation of diagnostic groups in connectivity data.

Associates connectivity modes with neurocognitive outcomes.

Abstract

Alzheimer's disease (AD) is the only major cause of mortality in the world without an effective disease modifying treatment. Evidence supporting the so called disconnection hypothesis suggests that functional connectivity biomarkers may have clinical potential for early detection of AD. However, known issues with low test-retest reliability and signal to noise in functional connectivity may prevent accuracy and subsequent predictive capacity. We validate the utility of a novel principal component based diagnostic identifiability framework to increase separation in functional connectivity across the Alzheimer's spectrum by identifying and reconstructing FC using only AD sensitive components or connectivity modes. We show that this framework (1) increases test-retest correspondence and (2) allows for better separation, in functional connectivity, of diagnostic groups both at the whole brain and individual resting state network level. Finally, we evaluate a posteriori the association between connectivity mode weights with longitudinal neurocognitive outcomes.