Journal
HUMAN BRAIN MAPPING
Volume 42, Issue 11, Pages 3500-3516Publisher
WILEY
DOI: 10.1002/hbm.25448
Keywords
AD; Alzheimer' s disease; cognition; fMRI; functional connectivity; functional fingerprinting; predictive modeling; resting state
Funding
- National Institute on Aging [NIA F32AG062157, NIA K02 AG048240, NIA P30 AG010133, NIA R01AG040770, NIA R01AG057739, NIA R56AG057195, NIA U01AG057195, NIA U01 AG02490]
- National Institutes of Health [NIH P60AA07611, NIH R01EB022574, NIH R01MH108467, NIH U01 AG024904]
- Baekgaard family
- Indiana Alcohol Research Center
- Northern California Institute for Research and Education
- Canadian Institutes of Health Research
- Transition Therapeutics
- Takeda Pharmaceutical Company
- Piramal Imaging
- Servier
- Pfizer Inc.
- Novartis Pharmaceuticals Corporation
- Neurotrack Technologies
- NeuroRx Research
- Meso Scale Diagnostics, LLC.
- Lumosity
- Lundbeck
- Merck Co., Inc.
- Johnson & Johnson Pharmaceutical Research & Development LLC.
- Janssen Alzheimer Immunotherapy Research & Development, LLC.
- IXICO Ltd.
- GE Healthcare
- Genentech, Inc.
- Fujirebio
- F. Hoffmann-La Roche Ltd
- EuroImmun
- Eli Lilly and Company
- Elan Pharmaceuticals, Inc.
- Cogstate
- Eisai Inc.
- CereSpir, Inc.
- Bristol-Myers Squibb Company
- Biogen
- BioClinica, Inc.
- Araclon Biotech
- Alzheimer's Drug Discovery Foundation
- AbbVie
- Alzheimer's Association
- National Institute of Biomedical Imaging and Bioengineering
- Department of Defense [W81XWH-12-2-0012]
- Alzheimer's Disease Neuroimaging Initiative (ADNI)
Ask authors/readers for more resources
The study combines connectome predictive modeling and differential identifiability frameworks to enhance individual fingerprints of resting state functional connectomes for more accurate identification of functional networks associated with cognitive outcomes and improve prediction accuracy of cognitive outcomes. By examining a broad spectrum of cognitive outcomes associated with Alzheimer's disease, specific functional networks related to cognitive deficits exhibited in AD were identified and characterized.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available