Accumulating and heterogeneous network‐knockout profiles in amnestic mild cognitive impairment and Alzheimer's disease dementia: Neuroimaging / New imaging methods. (7th December 2020)

• Record Type: Journal Article
• Authors: Nestor, Sean M; Bratislav, Misic; Ramirez, Joel; Graham, Simon J; Verhoeff, Nicolaas Paul L.G.; Stuss, Donald T; Masellis, Mario; Black, Sandra E
• Is Part Of: Alzheimer's & dementia. Volume 16(2020)Supplement 4
• 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.039184
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Large‐scale brain networks are disrupted in Alzheimer's disease (AD). We posit that network disruption may be explained by additive and multifactor knockout of multiple networks (functional and structural) as opposed to a single system or set of subsystems. Here we test this hypothesis using a data‐driven structural covariance network analysis. Methods: Demographic, imaging and available biomarker data were downloaded from 77 normal controls (NC) (age= 72±6, M/F=33/44), 106 persons with amnestic mild cognitive impairment (MCI) (age= 73±8, M/F=70/36) and 42 persons with Alzheimer's dementia (age=75±9, M/F=25/17) that participated in the Alzheimer's Disease Neuroimaging Initiative 2.0. We used a multivariate covariance technique to isolate large‐scale grey matter networks that relate fiber tract microstructure derived at different stages of injury to vertex‐based cortical thickness maps. Cortical thickness was computed from 3.0 Telsa T1‐weighted MRI and tract‐based white matter microstructural measures were performed using diffusion tensor imaging; these data were submitted to a multivariate data‐driven analysis. Measures of network integrity (NII) were computed for each significant covariance network for all participants. Univariate models assessed the relationship between biomarkers, demographic variables and NIIs for each network. Network knockouts were defined as an NII <1.5 SD below the NC group average for a given network. Knockout combinations were characterized across individuals and average knockout numbers were compared between groups. Results: Eight network ensembles related to tract‐based connectivity or recapitulated functional network architecture. Degradation of these systems was additively and differentially linked to increased age, APOE e4 genotype and unique combinations of white matter hyperintensity volume, CSF markers (CSF amyloid and Tau protein levels) and hippocampal volume in MCI. Knockouts exponentially and significantly increased across groups: NC>MCI>AD (P<0.01). There were 64 unique network knockout combinations across all participants. Conclusion: Heterogeneous pathological exposures may additively and differentially disrupt large‐scale networks, leading to many possible network knockout sequences that accumulate exponentially from MCI to AD. These findings may partially explain the endophenotypic heterogeneity in AD and should be assessed in other cohort samples.