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Resting-State Functional Connectivity Predicts Cognitive Impairment Related to Alzheimer's Disease.
Front Aging Neurosci 2018; 10:94FA

Abstract

Resting-state functional connectivity (rs-FC) is a promising neuromarker for cognitive decline in aging population, based on its ability to reveal functional differences associated with cognitive impairment across individuals, and because rs-fMRI may be less taxing for participants than task-based fMRI or neuropsychological tests. Here, we employ an approach that uses rs-FC to predict the Alzheimer's Disease Assessment Scale (11 items; ADAS11) scores, which measure overall cognitive functioning, in novel individuals. We applied this technique, connectome-based predictive modeling, to a heterogeneous sample of 59 subjects from the Alzheimer's Disease Neuroimaging Initiative, including normal aging, mild cognitive impairment, and AD subjects. First, we built linear regression models to predict ADAS11 scores from rs-FC measured with Pearson's r correlation. The positive network model tested with leave-one-out cross validation (LOOCV) significantly predicted individual differences in cognitive function from rs-FC. In a second analysis, we considered other functional connectivity features, accordance and discordance, which disentangle the correlation and anticorrelation components of activity timecourses between brain areas. Using partial least square regression and LOOCV, we again built models to successfully predict ADAS11 scores in novel individuals. Our study provides promising evidence that rs-FC can reveal cognitive impairment in an aging population, although more development is needed for clinical application.

Authors+Show Affiliations

Department of Psychology, Yale University, New Haven, CT, United States.Department of Psychology, Yale University, New Haven, CT, United States.Department of Psychology, Yale University, New Haven, CT, United States.Department of Psychology, Yale University, New Haven, CT, United States.Department of Psychology, Yale University, New Haven, CT, United States.Department of Psychology, Yale University, New Haven, CT, United States. Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, United States. Department of Neuroscience, Yale School of Medicine, New Haven, CT, United States.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

29706883

Citation

Lin, Qi, et al. "Resting-State Functional Connectivity Predicts Cognitive Impairment Related to Alzheimer's Disease." Frontiers in Aging Neuroscience, vol. 10, 2018, p. 94.
Lin Q, Rosenberg MD, Yoo K, et al. Resting-State Functional Connectivity Predicts Cognitive Impairment Related to Alzheimer's Disease. Front Aging Neurosci. 2018;10:94.
Lin, Q., Rosenberg, M. D., Yoo, K., Hsu, T. W., O'Connell, T. P., & Chun, M. M. (2018). Resting-State Functional Connectivity Predicts Cognitive Impairment Related to Alzheimer's Disease. Frontiers in Aging Neuroscience, 10, p. 94. doi:10.3389/fnagi.2018.00094.
Lin Q, et al. Resting-State Functional Connectivity Predicts Cognitive Impairment Related to Alzheimer's Disease. Front Aging Neurosci. 2018;10:94. PubMed PMID: 29706883.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Resting-State Functional Connectivity Predicts Cognitive Impairment Related to Alzheimer's Disease. AU - Lin,Qi, AU - Rosenberg,Monica D, AU - Yoo,Kwangsun, AU - Hsu,Tiffany W, AU - O'Connell,Thomas P, AU - Chun,Marvin M, Y1 - 2018/04/13/ PY - 2017/11/22/received PY - 2018/03/19/accepted PY - 2018/5/1/entrez PY - 2018/5/1/pubmed PY - 2018/5/1/medline KW - Alzheimer's disease KW - aging KW - functional connectivity KW - mild cognitive impairment KW - resting state SP - 94 EP - 94 JF - Frontiers in aging neuroscience JO - Front Aging Neurosci VL - 10 N2 - Resting-state functional connectivity (rs-FC) is a promising neuromarker for cognitive decline in aging population, based on its ability to reveal functional differences associated with cognitive impairment across individuals, and because rs-fMRI may be less taxing for participants than task-based fMRI or neuropsychological tests. Here, we employ an approach that uses rs-FC to predict the Alzheimer's Disease Assessment Scale (11 items; ADAS11) scores, which measure overall cognitive functioning, in novel individuals. We applied this technique, connectome-based predictive modeling, to a heterogeneous sample of 59 subjects from the Alzheimer's Disease Neuroimaging Initiative, including normal aging, mild cognitive impairment, and AD subjects. First, we built linear regression models to predict ADAS11 scores from rs-FC measured with Pearson's r correlation. The positive network model tested with leave-one-out cross validation (LOOCV) significantly predicted individual differences in cognitive function from rs-FC. In a second analysis, we considered other functional connectivity features, accordance and discordance, which disentangle the correlation and anticorrelation components of activity timecourses between brain areas. Using partial least square regression and LOOCV, we again built models to successfully predict ADAS11 scores in novel individuals. Our study provides promising evidence that rs-FC can reveal cognitive impairment in an aging population, although more development is needed for clinical application. SN - 1663-4365 UR - https://www.unboundmedicine.com/medline/citation/29706883/Resting_State_Functional_Connectivity_Predicts_Cognitive_Impairment_Related_to_Alzheimer's_Disease_ L2 - https://dx.doi.org/10.3389/fnagi.2018.00094 DB - PRIME DP - Unbound Medicine ER -