Cognitive complaints in older adults at risk for Alzheimer's disease are associated with altered resting‐state networks. Issue 1 (22nd December 2016) - Contreras, Joey A.; Goñi, Joaquín; Risacher, Shannon L.; Amico, Enrico; Yoder, Karmen; Dzemidzic, Mario; West, John D.; McDonald, Brenna C.; Farlow, Martin R.; Sporns, Olaf; Saykin, Andrew J.
Abstract: Introduction: Pathophysiological changes that accompany early clinical symptoms in prodromal Alzheimer's disease (AD) may have a disruptive influence on brain networks. We investigated resting‐state functional magnetic resonance imaging (rsfMRI), combined with brain connectomics, to assess changes in whole‐brain functional connectivity (FC) in relation to neurocognitive variables. Methods: Participants included 58 older adults who underwent rsfMRI. Individual FC matrices were computed based on a 278‐region parcellation. FastICA decomposition was performed on a matrix combining all subjects' FC. Each FC pattern was then used as a response in a multilinear regression model including neurocognitive variables associated with AD (cognitive complaint index [CCI] scores from self and informant, an episodic memory score, and an executive function score). Results: Three connectivity independent component analysis (connICA) components (RSN, VIS, and FP‐DMN FC patterns) associated with neurocognitive variables were identified based on prespecified criteria. RSN‐pattern, characterized by increased FC within all resting‐state networks, was negatively associated with self CCI. VIS‐pattern, characterized by an increase in visual resting‐state network, was negatively associated with CCI self or informant scores. FP‐DMN‐pattern, characterized by an increased interaction of frontoparietal and default mode networks (DMN), was positively associated with verbal episodic memory. Discussion: Specific patterns of FC were differently associated with neurocognitive variables thought to change early in the course of AD. An integrative connectomics approach relating cognition to changes in FC may help identify preclinical and early prodromal stages of AD and help elucidate the complex relationship between subjective and objective indices of cognitive decline and differences in brain functional organization.