Classification using fractional anisotropy predicts conversion in genetic frontotemporal dementia, a proof of concept. Issue 2 (11th June 2020)

• Record Type: Journal Article
• Authors: Feis, Rogier A; van der Grond, Jeroen; Bouts, Mark J R J; Panman, Jessica L; Poos, Jackie M; Schouten, Tijn M; de Vos, Frank; Jiskoot, Lize C; Dopper, Elise G P; van Buchem, Mark A; van Swieten, John C; Rombouts, Serge A R B
• Is Part Of: Brain communications. Volume 2:Issue 2(2020)
• Publication Date: 2020-06-11
• Subjects: frontotemporal dementia -- MAPT protein -- human -- GRN protein -- human -- multimodal MRI -- classification; 616
• DOI: 10.1093/braincomms/fcaa079
• Languages: English
• ISSNs: 2632-1297

Abstract

Abstract: Frontotemporal dementia is a highly heritable and devastating neurodegenerative disease. About 10–20% of all frontotemporal dementia is caused by known pathogenic mutations, but a reliable tool to predict clinical conversion in mutation carriers is lacking. In this retrospective proof-of-concept case-control study, we investigate whether MRI-based and cognition-based classifiers can predict which mutation carriers from genetic frontotemporal dementia families will develop symptoms ('convert') within 4 years. From genetic frontotemporal dementia families, we included 42 presymptomatic frontotemporal dementia mutation carriers. We acquired anatomical, diffusion-weighted imaging, and resting-state functional MRI, as well as neuropsychological data. After 4 years, seven mutation carriers had converted to frontotemporal dementia ('converters'), while 35 had not ('non-converters'). We trained regularized logistic regression models on baseline MRI and cognitive data to predict conversion to frontotemporal dementia within 4 years, and quantified prediction performance using area under the receiver operating characteristic curves. The prediction model based on fractional anisotropy, with highest contribution of the forceps minor, predicted conversion to frontotemporal dementia beyond chance level (0.81 area under the curve, family-wise error corrected P = 0.025 versus chance level). Other MRI-based and cognitive features did not outperform chance level. Even in a small sample, fractional anisotropy predicted conversion in presymptomatic frontotemporal dementia mutation carriers beyond chance level. After validation in larger data sets, conversion prediction in genetic frontotemporal dementia may facilitate early recruitment into clinical trials. Abstract : MRI-based classification combining anatomical, structural connectivity and functional connectivity measures may aid early frontotemporal dementia diagnosis. Feis et al. report that MRI-based classification using fractional anisotropy predicts frontotemporal dementia onset within 4 years beyond chance level in frontotemporal dementia mutation carriers. Graphical Abstract: