Dataset Information

Predicting disease progression in behavioral variant frontotemporal dementia.

ABSTRACT:

Introduction

The behavioral variant of frontotemporal dementia (bvFTD) is a rare neurodegenerative disease. Reliable predictors of disease progression have not been sufficiently identified. We investigated multivariate magnetic resonance imaging (MRI) biomarker profiles for their predictive value of individual decline.

Methods

One hundred five bvFTD patients were recruited from the German frontotemporal lobar degeneration (FTLD) consortium study. After defining two groups ("fast progressors" vs. "slow progressors"), we investigated the predictive value of MR brain volumes for disease progression rates performing exhaustive screenings with multivariate classification models.

Results

We identified areas that predict disease progression rate within 1 year. Prediction measures revealed an overall accuracy of 80% across our 50 top classification models. Especially the pallidum, middle temporal gyrus, inferior frontal gyrus, cingulate gyrus, middle orbitofrontal gyrus, and insula occurred in these models.

Discussion

Based on the revealed marker combinations an individual prognosis seems to be feasible. This might be used in clinical studies on an individualized progression model.

SUBMITTER: Anderl-Straub S

PROVIDER: S-EPMC8719425 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

Predicting disease progression in behavioral variant frontotemporal dementia.

Anderl-Straub Sarah S Lausser Ludwig L Lombardi Jolina J Uttner Ingo I Fassbender Klaus K Fliessbach Klaus K Huppertz Hans-Jürgen HJ Jahn Holger H Kornhuber Johannes J Obrig Hellmuth H Schneider Anja A Semler Elisa E Synofzik Matthis M Danek Adrian A Prudlo Johannes J Kassubek Jan J Landwehrmeyer Bernhard B Lauer Martin M Volk Alexander E AE Wiltfang Jens J Diehl-Schmid Janine J Ludolph Albert C AC Schroeter Matthias L ML Kestler Hans A HA Otto Markus M

Alzheimer's & dementia (Amsterdam, Netherlands) 20211231 1

<h4>Introduction</h4>The behavioral variant of frontotemporal dementia (bvFTD) is a rare neurodegenerative disease. Reliable predictors of disease progression have not been sufficiently identified. We investigated multivariate magnetic resonance imaging (MRI) biomarker profiles for their predictive value of individual decline.<h4>Methods</h4>One hundred five bvFTD patients were recruited from the German frontotemporal lobar degeneration (FTLD) consortium study. After defining two groups ("fast p ...[more]

PMID: 35005196

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Project description:BackgroundAlzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) cause distinct atrophy and functional disruptions within two major intrinsic brain networks, namely the default network and the salience network, respectively. It remains unclear if inter-network relationships and whole-brain network topology are also altered and underpin cognitive and social-emotional functional deficits.MethodsIn total, 111 participants (50 AD, 14 bvFTD, and 47 age- and gender-matched healthy controls) underwent resting-state functional magnetic resonance imaging (fMRI) and neuropsychological assessments. Functional connectivity was derived among 144 brain regions of interest. Graph theoretical analysis was applied to characterize network integration, segregation, and module distinctiveness (degree centrality, nodal efficiency, within-module degree, and participation coefficient) in AD, bvFTD, and healthy participants. Group differences in graph theoretical measures and empirically derived network community structures, as well as the associations between these indices and cognitive performance and neuropsychiatric symptoms, were subject to general linear models, with age, gender, education, motion, and scanner type controlled.ResultsOur results suggested that AD had lower integration in the default and control networks, while bvFTD exhibited disrupted integration in the salience network. Interestingly, AD and bvFTD had the highest and lowest degree of integration in the thalamus, respectively. Such divergence in topological aberration was recapitulated in network segregation and module distinctiveness loss, with AD showing poorer modular structure between the default and control networks, and bvFTD having more fragmented modules in the salience network and subcortical regions. Importantly, aberrations in network topology were related to worse attention deficits and greater severity in neuropsychiatric symptoms across syndromes.ConclusionsOur findings underscore the reciprocal relationships between the default, control, and salience networks that may account for the cognitive decline and neuropsychiatric symptoms in dementia.

Dataset Information

Predicting disease progression in behavioral variant frontotemporal dementia.

Introduction

Methods

Results

Discussion

Publications

Predicting disease progression in behavioral variant frontotemporal dementia.

Similar Datasets

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