{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Buckley RF"],"funding":["NIBIB NIH HHS","NIA NIH HHS"],"pagination":["29-37"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC5496516"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["89(1)"],"pubmed_abstract":["<h4>Objective</h4>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).<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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."],"journal":["Neurology"],"pubmed_title":["Functional network integrity presages cognitive decline in preclinical Alzheimer disease."],"pmcid":["PMC5496516"],"funding_grant_id":["R01 AG026484","R01 AG034556","P50 AG005134","P01 AG036694","U19 AG010483","R01 AG037497","K23 EB019023","R01 EB014894","K01 AG040197","R21 AG038994","K23 AG049087","K24 AG035007","R01 AG027435"],"pubmed_authors":["Chhatwal JP","Hanseeuw BJ","Sepulcre J","Schultz AP","Hedden T","Smith EE","Johnson KA","Sperling RA","Buckley RF","Papp KV","Marshall G","Rentz DM"],"additional_accession":[]},"is_claimable":false,"name":"Functional network integrity presages cognitive decline in preclinical Alzheimer disease.","description":"<h4>Objective</h4>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).<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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.","dates":{"release":"2017-01-01T00:00:00Z","publication":"2017 Jul","modification":"2024-12-03T18:49:51.295Z","creation":"2019-03-26T23:44:01Z"},"accession":"S-EPMC5496516","cross_references":{"pubmed":["28592457"],"doi":["10.1212/WNL.0000000000004059","10.1212/wnl.0000000000004059"]}}