Optimizing differential identifiability improves connectome predictive modeling of cognitive deficits from functional connectivity in Alzheimer's disease
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 05, 2021
- Source ID
- 10.1002/hbm.25448
Entities
People
- Andrew J. Saykin
- Charanya Muralidharan
- David G. Clark
- Diana O Svaldi
- Enrico Amico
- Joaquín Goñi
- John D. West
- Kausar Abbas
- Liana G. Apostolova
- Mario Dzemidzic
- Shannon L. Risacher
Organizations
- AbbVie
- Alzheimer's Drug Discovery Foundation
- BioClinica
- Biogen
- Canadian Institutes of Health Research
- Chiron Corporation
- Eli Lilly and Company
- GE HealthCare
- Hoffmann-La Roche
- Indiana University
- National Institute of Biomedical Imaging and Bioengineering
- National Institute on Aging
- National Institutes of Health
- Northern California Institute for Research and Education
- Pfizer
- Purdue University
- Takeda Pharmaceutical Company
- United States Department of Defense