Optimizing Differential Identifiability Improves Connectome Predictive Modeling of Cognitive Deficits in Alzheimer's Disease

TL;DR

This paper introduces an optimized method combining connectome predictive modeling and differential identifiability to improve the reliability and generalizability of predicting cognitive deficits in Alzheimer's disease from resting state fMRI data.

Contribution

It presents a novel framework that enhances individual connectome fingerprints, leading to better identification of cognitive-related networks and improved prediction accuracy.

Findings

Enhanced connectome fingerprinting improves cognitive outcome prediction.

Identified specific functional networks linked to Alzheimer's cognitive deficits.

Framework increases robustness and generalizability of connectome-based models.

Abstract

Functional connectivity, as estimated using resting state fMRI, has shown potential in bridging the gap between pathophysiology and cognition. However, clinical use of functional connectivity biomarkers is impeded by unreliable estimates of individual functional connectomes and lack of generalizability of models predicting cognitive outcomes from connectivity. To address these issues, we combine the frameworks of connectome predictive modeling and differential identifiability. Using the combined framework, we show that enhancing the individual fingerprint of resting state functional connectomes leads to robust identification of functional networks associated to cognitive outcomes and also improves prediction of cognitive outcomes from functional connectomes. Using a comprehensive spectrum of cognitive outcomes associated to Alzheimer's disease, we identify and characterize functional networks associated to specific cognitive deficits exhibited in Alzheimer's disease. This combined framework is an important step in making individual level predictions of cognition from resting state functional connectomes and in understanding the relationship between cognition and connectivity.