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

Introduction: Frontotemporal dementia (FTD) encompasses heterogeneous clinical syndromes, and distinguishing its subtypes using imaging remains challenging. Methods: We 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. Results: Each 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. Discussion: This 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.