Optimizing differential identifiability improves connectome predictive modeling of cognitive deficits from functional connectivity in Alzheimer's disease

• Main Authors: Abbas, K. (Author), Amico, E. (Author), Apostolova, L.G (Author), Clark, D.G (Author), Dzemidzic, M. (Author), Goñi, J. (Author), Muralidharan, C. (Author), Risacher, S.L (Author), Saykin, A.J (Author), Svaldi, D.O (Author), West, J.D (Author)
• Format: Article
• Language: English
• Published: John Wiley and Sons Inc 2021
• Subjects: AD; aged; Aged; Aged, 80 and over; Alzheimer disease; Alzheimer Disease; Alzheimer's disease; Article; cognition; cognitive defect; Cognitive Dysfunction; cohort analysis; connectome; Connectome; diagnostic imaging; female; Female; fMRI; functional connectivity; functional fingerprinting; functional magnetic resonance imaging; human; Humans; Magnetic Resonance Imaging; major clinical study; male; Male; model; nerve cell network; Nerve Net; nuclear magnetic resonance imaging; pathophysiology; prediction; predictive modeling; procedures; resting state; very elderly
• Online Access: View Fulltext in Publisher

 Functional connectivity, as estimated using resting state functional MRI, 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 (AD), we identify and characterize functional networks associated to specific cognitive deficits exhibited in AD. 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. © 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.