4.7 Article

Disease specific and nonspecific metabolic brain networks in behavioral variant of frontotemporal dementia

Journal

HUMAN BRAIN MAPPING
Volume 44, Issue 3, Pages 1079-1093

Publisher

WILEY
DOI: 10.1002/hbm.26140

Keywords

behavioral variant of frontotemporal dementia; default mode network; FDG-PET; functional connectivity; network analysis; SSM; PCA

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Behavioral variant of frontotemporal dementia (bvFTD) is a common type of young-onset dementia. A specific multivariate metabolic brain pattern (bFDRP) has been identified in patients with bvFTD, and its evolution, internal structure, effect of atrophy, and relationship with other resting state networks have been explored. These findings have important implications for the diagnosis and prognosis of bvFTD.
Behavioral variant of frontotemporal dementia (bvFTD) is common among young-onset dementia patients. While bvFTD-specific multivariate metabolic brain pattern (bFDRP) has been identified previously, little is known about its temporal evolution, internal structure, effect of atrophy, and its relationship with nonspecific resting-state networks such as default mode network (DMN). In this multicenter study, we explored FDG-PET brain scans of 111 bvFTD, 26 Alzheimer's disease, 16 Creutzfeldt-Jakob's disease, 24 semantic variant primary progressive aphasia (PPA), 18 nonfluent variant PPA and 77 healthy control subjects (HC) from Slovenia, USA, and Germany. bFDRP was identified in a cohort of 20 bvFTD patients and age-matched HC using scaled subprofile model/principle component analysis and validated in three independent cohorts. It was characterized by hypometabolism in frontal cortex, insula, anterior/middle cingulate, caudate, thalamus, and temporal poles. Its expression in bvFTD patients was significantly higher compared to HC and other dementia syndromes (p < .0004), correlated with cognitive decline (p = .0001), and increased over time in longitudinal cohort (p = .0007). Analysis of internal network organization by graph-theory methods revealed prominent network disruption in bvFTD patients. We have further found a specific atrophy-related pattern grossly corresponding to bFDRP; however, its contribution to the metabolic pattern was minimal. Finally, despite the overlap between bFDRP and FDG-PET-derived DMN, we demonstrated a predominant role of the specific bFDRP. Taken together, we validated the bFDRP network as a diagnostic/prognostic biomarker specific for bvFTD, provided a unique insight into its highly reproducible internal structure, and proved that bFDRP is unaffected by structural atrophy and independent of normal resting state networks loss.

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