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

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

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 gen...

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Bibliographic Details
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
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