Functional connectivity of white matter as a biomarker of cognitive decline in Alzheimer’s disease

• Published in: PloS one Vol. 15; no. 10; p. e0240513
• Main Authors: Gao, Yurui, Sengupta, Anirban, Li, Muwei, Zu, Zhongliang, Rogers, Baxter P., Anderson, Adam W., Ding, Zhaohua, Gore, John C.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 16.10.2020; Public Library of Science (PLoS)
• Subjects: Aged; Aged, 80 and over; Alzheimer Disease - diagnostic imaging; Alzheimer Disease - psychology; Alzheimer's disease; Biological markers; Biology and Life Sciences; Biomarkers; Biomedical engineering; Brain; Case-Control Studies; Cognition; Cognitive ability; Cognitive disorders; Cognitive Dysfunction - diagnostic imaging; Cognitive Dysfunction - physiopathology; Complications and side effects; Computer engineering; Correlation analysis; Dementia; Dementia disorders; Diagnosis; Disease Progression; Female; Functional magnetic resonance imaging; Funding; Generalized linear models; Health aspects; Humans; Learning algorithms; Machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical imaging; Medicine and Health Sciences; Memory; Neural circuitry; Neural networks; Neurodegenerative diseases; Neuropsychological Tests; Performance assessment; Physiological aspects; Population decline; Radiology; Research and Analysis Methods; Risk factors; Sensitivity and Specificity; Social Sciences; Substantia alba; Substantia grisea; Supervised Machine Learning; White Matter - diagnostic imaging; White Matter - physiopathology; United States; United States > US; Tennessee; Nashville Tennessee
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0240513

In vivo functional changes in white matter during the progression of Alzheimer's disease (AD) have not been previously reported. Our objectives are to measure changes in white matter functional connectivity (FC) in an elderly population undergoing cognitive decline as AD develops, to establish their relationship to neuropsychological scores of cognitive abilities, and to assess the performance in prediction of AD using white matter FC measures as features. Analyses were conducted using resting state functional MRI and neuropsychological data from 383 ADNI participants, including 136 cognitive normal (CN) controls, 46 with significant memory concern, 83 with early mild cognitive impairment (MCI), 37 with MCI, 46 with late MCI, and 35 with AD dementia. FC metrics between segregated white matter tracts and discrete gray matter volumes or between white matter tracts were quantitatively analyzed and characterized, along with their relationships to 6 cognitive measures. Finally, supervised machine learning was implemented on white matter FCs to classify the participants and performance of the classification was evaluated. Significant decreases in FC measures were found in white matter with prominent, specific, regional deficits appearing in late MCI and AD dementia patients from CN. These changes significantly correlated with neuropsychological measurements of impairments in cognition and memory. The sensitivity and specificity of distinguishing AD dementia and CN using white matter FCs were 0.83 and 0.81 respectively. The white matter FC decreased in late MCI and AD dementia patients compared to CN participants, and this decrease was correlated with cognitive measures. White matter FC is valuable in the prediction of AD. All these findings suggest that white matter FC may be a promising avenue for understanding functional impairments in white matter tracts during AD progression.