AUTHOR=Lim Kyoung Yoon , Yoon Soyeon , Park Seongbeom , Kim Seongmi , Kim Kyoungmin , Ahn Jehyun , Kim Jun Pyo , Kim Hee Jin , Na Duk L. , Seo Sang Won , Kwak Kichang 

TITLE=Regional CSF volume quantification using deep learning for comparative analysis of brain atrophy in frontotemporal dementia subtypes

JOURNAL=Frontiers in Aging Neuroscience

VOLUME=Volume 17 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2025.1631640

DOI=10.3389/fnagi.2025.1631640

ISSN=1663-4365

ABSTRACT=IntroductionFrontotemporal dementia (FTD) encompasses heterogeneous clinical syndromes, and distinguishing its subtypes using imaging remains challenging.MethodsWe developed a deep learning model to quantify brain atrophy by measuring cerebrospinal fluid (CSF) volumes in key regions of interest (RoIs) on standard MRI scans. In a retrospective study, we analyzed 3D T1-weighted MRI data from 1,854 individuals, including cognitively unimpaired (CU) controls, patients with dementia of the Alzheimer type (DAT), and FTD subtypes: behavioral variant FTD (bvFTD), nonfluent variant primary progressive aphasia (nfvPPA), and semantic variant PPA (svPPA). The model quantified CSF volumes in 14 clinically relevant RoIs and generated age- and sex-adjusted W-scores to express regional atrophy.ResultsEach FTD subtype exhibited a distinct, lateralized atrophy pattern: bvFTD showed widespread bilateral frontal and right-predominant parietal and temporal atrophy; nfvPPA showed left-predominant frontal and parietal atrophy; and svPPA exhibited marked left-lateralized temporal and hippocampal atrophy. All FTD subtypes demonstrated significantly greater CSF expansion in these characteristic regions compared to DAT and CU.DiscussionThis deep learning approach provides a simple, interpretable measure of brain atrophy that differentiates FTD subtypes, requiring only standard MRI with minimal preprocessing, and offers clinical utility.