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Neurology. 2017 Jul 4;89(1):29-37. doi: 10.1212/WNL.0000000000004059. Epub 2017 Jun 7.

Functional network integrity presages cognitive decline in preclinical Alzheimer disease.

Author information

1
From the Florey Institutes of Neuroscience and Mental Health (R.F.B.), Melbourne; Melbourne School of Psychological Science (R.F.B.), University of Melbourne, Australia; Department of Neurology (R.F.B., A.P.S., K.V.P., B.J.H., G.M., D.M.R., K.A.J., R.A.S., J.P.C.), Athinoula A. Martinos Center for Biomedical Imaging (A.P.S., T.H., B.J.H., J.S., K.A.J.) and Gordon Center for Medical Imaging, Division of Nuclear Medicine and Molecular Imaging (J.S., K.A.J.), Department of Radiology, Massachusetts General Hospital; Harvard Medical School (R.F.B., A.P.S., T.H., K.V.P., B.J.H., G.M., D.M.R., K.A.J., R.A.S., J.P.C.); Center for Alzheimer Research and Treatment, Department of Neurology (K.V.P., G.M., D.M.R., K.A.J., R.A.S., J.P.C.), Brigham and Women's Hospital, Boston, MA: and Department of Psychiatry (E.E.S.), University of Texas Southwestern Medical Center, Dallas.
2
From the Florey Institutes of Neuroscience and Mental Health (R.F.B.), Melbourne; Melbourne School of Psychological Science (R.F.B.), University of Melbourne, Australia; Department of Neurology (R.F.B., A.P.S., K.V.P., B.J.H., G.M., D.M.R., K.A.J., R.A.S., J.P.C.), Athinoula A. Martinos Center for Biomedical Imaging (A.P.S., T.H., B.J.H., J.S., K.A.J.) and Gordon Center for Medical Imaging, Division of Nuclear Medicine and Molecular Imaging (J.S., K.A.J.), Department of Radiology, Massachusetts General Hospital; Harvard Medical School (R.F.B., A.P.S., T.H., K.V.P., B.J.H., G.M., D.M.R., K.A.J., R.A.S., J.P.C.); Center for Alzheimer Research and Treatment, Department of Neurology (K.V.P., G.M., D.M.R., K.A.J., R.A.S., J.P.C.), Brigham and Women's Hospital, Boston, MA: and Department of Psychiatry (E.E.S.), University of Texas Southwestern Medical Center, Dallas. reisa@bwh.harvard.edu.

Abstract

OBJECTIVE:

To examine the utility of resting-state functional connectivity MRI (rs-fcMRI) measurements of network integrity as a predictor of future cognitive decline in preclinical Alzheimer disease (AD).

METHODS:

A total of 237 clinically normal older adults (aged 63-90 years, Clinical Dementia Rating 0) underwent baseline β-amyloid (Aβ) imaging with Pittsburgh compound B PET and structural and rs-fcMRI. We identified 7 networks for analysis, including 4 cognitive networks (default, salience, dorsal attention, and frontoparietal control) and 3 noncognitive networks (primary visual, extrastriate visual, motor). Using linear and curvilinear mixed models, we used baseline connectivity in these networks to predict longitudinal changes in preclinical Alzheimer cognitive composite (PACC) performance, both alone and interacting with Aβ burden. Median neuropsychological follow-up was 3 years.

RESULTS:

Baseline connectivity in the default, salience, and control networks predicted longitudinal PACC decline, unlike connectivity in the dorsal attention and all noncognitive networks. Default, salience, and control network connectivity was also synergistic with Aβ burden in predicting decline, with combined higher Aβ and lower connectivity predicting the steepest curvilinear decline in PACC performance.

CONCLUSIONS:

In clinically normal older adults, lower functional connectivity predicted more rapid decline in PACC scores over time, particularly when coupled with increased Aβ burden. Among examined networks, default, salience, and control networks were the strongest predictors of rate of change in PACC scores, with the inflection point of greatest decline beyond the fourth year of follow-up. These results suggest that rs-fcMRI may be a useful predictor of early, AD-related cognitive decline in clinical research settings.

PMID:
28592457
PMCID:
PMC5496516
DOI:
10.1212/WNL.0000000000004059
[Indexed for MEDLINE]
Free PMC Article

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