Functional connectome vulnerability to Alzheimer's disease in alcohol use disorder: A preliminary study: Brain networks and connectivity. (7th December 2020)

• Record Type: Journal Article
• Authors: Joseph, Jane E; Flanagan, Julianne; Nowling, Duncan; Vaughan, Brandon; Warner, Graham; Lohnes, Laura; Ross‐Simmons, Shaquanda; Mintzer, Jacobo; Dean, Brian; Lawson, Andrew; Back, Sudie; Jensen, Jens H.; Benitez, Andreana
• Is Part Of: Alzheimer's & dementia. Volume 16(2020)Supplement 1
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-07
• 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.042226
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Alcohol Use Disorder (AUD) is a risk factor for Alzheimer's Disease (AD) and other dementias. Despite the emerging association between AUD and AD, there is a paucity of data available from mid‐life age ranges (e.g., 45 to 65 years) to assess AUD‐related risk for AD that might be present early in the disease trajectory. Method: Functional connectome measures (graph‐theory) were calculated from resting state fMRI scans in 118 healthy controls (HC), 25 subjects with AUD (45‐61 years of age), and 51 subjects with amnestic mild cognitive impairment (aMCI) or AD. Following dimension reduction of 282 network nodes into 10 components using principal components analysis, predictive modeling with best subsets was used to isolate network components that predicted age in HC. Network components that significantly predicted age in HC were further examined to determine whether the AUD group exhibited premature brain aging or showed similar pathologic profiles as AD/aMCI. Result: Predictive modeling with eigenvector centrality as a predictor of age yielded significant models (p's < .0001). A network component including occipital and frontal pole nodes was a significant predictor in all models. In the frontal pole, AUD age trajectories showed a baseline shift relative to age‐matched HC subjects (49‐61 years of age), indicating premature brain aging. However, these age trends were not significantly different when Fisher z‐transformations of age trends were compared with a t‐test (p = .5). Nevertheless, the ANOVA revealed a significant main effect of group (AUD, HC, aMCI; p < .0001): average eigenvector centrality of the frontal pole in AUD was significantly lower than in HC (p = 0.0001) and indistinguishable from that of AD/aMCI subjects (p = 1.0), according to Bonferroni post‐hoc tests. Conclusion: Premature brain aging in AUD may reflect vulnerability to AD‐like neurodegenerative processes that are manifest at the level of large‐scale network connectivity. Some data used in preparation of this abstract were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.