Computationally derived anatomic subtypes of behavioral variant frontotemporal dementia show temporal stability and divergent patterns of longitudinal atrophy. Issue 1 (12th July 2021)

• Record Type: Journal Article
• Authors: Ranasinghe, Kamalini G.; Toller, Gianina; Cobigo, Yann; Chiang, Kevin; Callahan, Patrick; Eliazer, Caleb; Kramer, Joel H.; Rosen, Howard J.; Miller, Bruce L.; Rankin, Katherine P.
• Is Part Of: Alzheimer's & dementia. Volume 13:Issue 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-07-12
• Subjects: anatomic subtypes -- behavioral variant frontotemporal dementia -- disease progression -- gray matter atrophy -- longitudinal magnetic resonance imaging -- neurodegenerative disease; Alzheimer's disease -- Periodicals; Alzheimer's disease -- Diagnosis -- Periodicals; Dementia -- Periodicals; Dementia -- Diagnosis -- Periodicals; 616.831
• DOI: 10.1002/dad2.12183
• Languages: English
• ISSNs: 2352-8729

Abstract

Abstract: Introduction: Behavioral variant frontotemporal dementia (bvFTD) can be computationally divided into four distinct anatomic subtypes based on patterns of frontotemporal and subcortical atrophy. To more precisely predict disease trajectories of individual patients, the temporal stability of each subtype must be characterized. Methods: We investigated the longitudinal stability of the four previously identified anatomic subtypes in 72 bvFTD patients. We also applied a voxel‐wise mixed effects model to examine subtype differences in atrophy patterns across multiple timepoints. Results: Our results demonstrate the stability of the anatomic subtypes at baseline and over time. While they had common salience network atrophy, each subtype showed distinctive baseline and longitudinal atrophy patterns. Discussion: Recognizing these anatomically heterogeneous subtypes and their different patterns of atrophy progression in early bvFTD will improve disease course prediction in individual patients. Longitudinal volumetric predictions based on these anatomic subtypes may be used as a more accurate endpoint in treatment trials.