Altered brain functional connectivity and cardiovascular and metabolic risk factors in middle‐aged and older adults. (December 2021)

• Record Type: Journal Article
• Authors: Rashid, Barnaly; Schwab, Nadine A.; Carlyle, Becky C.; Terpstra, Melissa; Bookheimer, Susan Y.; Ances, Beau M.; Salat, David H.; Arnold, Steven E.
• Is Part Of: Alzheimer's & dementia. Volume 17(2021)Supplement 4
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.054205
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Metabolic and cardiovascular dysfunction accelerates biological aging and increases risk for many diseases, including Alzheimer's disease (AD) and AD‐related dementias (ADRD). The cumulative negative effects that the body and brain sustain due to the exposure to metabolic (e.g., insulin resistance, dyslipidemia), cardiovascular (e.g., blood pressure), and immune (e.g., C‐reactive protein) risk factors (i.e., MCI‐RFs) have been associated with increased vulnerability to cognitive decline later in life. This project examined whether the functional connectivity (FC) measures of core brain networks are altered due to MCI‐RFs, and how such alterations may relate to cognitive functioning. Method: Resting fMRI scans were collected in 217 participants (aged 36‐90+ years) of the Human Connectome Project Aging study, and preprocessed using FreeSurfer's standard stream. Using a seed‐based approach functional maps of the default mode network (DMN), amygdala, and hippocampus seeds were created. The vertex‐wise partial correlation (i.e., FC) measures to the seeds (i.e., correlation between mean seed time series and all other brain voxels' time series, later transformed onto cortical surface) were computed and used for further group‐level analyses. Connectomes were evaluated in relation to a MCI‐RF index (higher index score corresponds to greater risk) including measures of body mass index, insulin and glucose, lipids, blood pressure, renal function, nutritional parameters and C‐reactive protein. Cognition was assessed using a multi‐domain composite index (CCI) encompassing aspects of general cognition, memory, processing speed, and executive functioning. Result: With increasing age, all three networks showed reductions in network integrity with loss of both within‐network and between‐network functional connections in regions outside of the task‐positive network hubs. After correcting for age effects, associations between FC and MCI‐RF index were mostly observed in the DMN and amygdala. Increased MCI‐RF was associated with reduced negative connectivity (i.e., loss of between‐network connections) in amygdala map, suggesting less regulated networks. Associations with CCI were observed across all three networks' FC maps but mostly in the amygdala. Conclusion: Our findings suggest that MCI‐RFs impact brain network organization, and that connectivity in the amygdala network might be a key to the interface between metabolic, cardiovascular and immune dysfunction and cognition.