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Bimali, B.

Publications and source records attributed to Bimali, B..

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Amyloid-Tau PET Fusion Reveals Covarying Network Territories Linked to Disease Stage, Cognition, and APOE4 in Alzheimers Disease

BackgroundAmyloid-beta (A{beta}) and Tau are defining pathologies of Alzheimers disease, but their relationship varies substantially across the brain. Amyloid deposition is spatially widespread, whereas Tau shows greater regional heterogeneity and closer relationships with disease severity. Consequently, understanding the disease requires more than measuring the two pathologies independently or summarizing them within predefined regions. A major unresolved question is how Amyloid and Tau covary across the whole brain, where their spatial patterns converge or diverge, and whether their local combination carries distinct information about disease stage and cognition. MethodsWe analyzed paired florbetapir Amyloid PET and flortaucipir Tau PET from the Alzheimers Disease Neuroimaging Initiative (ADNI), comprising 378 paired imaging sessions from 320 participants spanning cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimers Dementia (AD). High-order joint independent component analysis was used to identify fine-grained, data-driven patterns of Amyloid-Tau covariance across individuals. The resulting Amyloid and Tau maps were characterized by their spatial similarity and correspondence with rsfMRI-derived intrinsic functional networks. We then used the same data-driven spatial regions to quantify, for each participant, the relative extent of Amyloid abnormality alone, Tau abnormality alone, and spatially overlapping Amyloid-Tau abnormality. ResultsNinety-six non-artifactual Amyloid-Tau components were identified, of which 73 showed appreciable spatial correspondence between their paired Amyloid and Tau maps, while 17 were Tau-localized and 6 Amyloid-localized. Amyloid component maps more frequently corresponded with rsfMRI-derived intrinsic network organization than Tau maps. Expression of the joint components differed across diagnosis groups, demonstrating spatially heterogeneous disease-stage patterns. Within the same data-driven regions, isolated Amyloid, isolated Tau, and overlapping Amyloid-Tau abnormality showed markedly different disease-stage profiles: Amyloid-only abnormality was more prominent in the earlier CN-to-MCI contrast, Tau-only abnormality in contrasts involving AD, whereas spatially overlapping Amyloid-Tau abnormality showed the broadest differences across disease stages and the largest mean CN-to-AD expansion. Overlapping abnormality was also associated with cognition across the greatest number of components and showed the strongest overall association with ADAS13. APOE4-related diagnosis-stage differences were considerably more widespread for pathology measures containing Amyloid than for Tau-only abnormality.

neuroscience↗

Multimodal Fusion Analysis of Florbetapir PET and Multiscale Functional Network Connectivity in Alzheimer's Disease

Accumulation of amyloid-beta plaques and disruption of intrinsic brain networks are two important characteristics of Alzheimers disease (AD), yet the relationship between amyloid accumulation and network dysfunction remains unclear. In this study, we integrated [18F]Florbetapir PET and resting-state fMRI (rsfMRI) derived Functional Network Connectivity (FNC) from 552 temporally matched longitudinal PET-rsfMRI sessions across 395 participants spanning Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and AD stages. With a model order of 11, joint Independent Component Analysis (jICA) was applied to the fused PET-FNC data, identifying 11 stable components, of which 9 PET-derived components corresponded to previously characterized brain regions or networks. The multimodal analysis revealed disease progression markers, including (1) a pattern of reduced subject loadings across clinical stages (CN > MCI > AD) in white matter and cerebellar regions, reflecting structural degeneration; (2) increased amyloid accumulation in affected individuals in grey matter regions, particularly in frontal, sensorimotor, extended hippocampal, and default mode network (DMN) regions, accompanied by functional connectivity alterations that reflected both compensatory and disruptive network dynamics. We identified PET-derived components that captured distinct stages of disease progression, with the DMN component emerging as a late-stage biomarker and a white matter component showing early-stage changes with limited progression thereafter. Additionally, several components showed significant variation in loadings between APOE{varepsilon} 4 carriers and non-carriers, linking the multimodal signatures to a well-established genetic risk factor for AD.

neuroscience↗