Frontotemporal Dementia Subtyping using Machine Learning, Multivariate Statistics, and Neuroimaging

Abstract Frontotemporal Dementia (FTD) is a prevalent form of early-onset dementia characterized by progressive neurodegeneration. It encompasses a group of heterogeneous disorders, including behavioral variant frontotemporal dementia (bvFTD), nonfluent variant primary progressive aphasia (nfvPPA), and semantic variant primary progressive aphasia (svPPA). Due to disease heterogeneity and overlapping symptoms, diagnosis of FTD and its subtypes still poses a challenge. Magnetic-resonance imaging (MRI) is commonly used to support the diagnosis of FTD. Using machine learning and multivariate statistics, we tested whether brain atrophy patterns are associated with severity of cognitive impairment, whether this relationship differs between the phenotypic subtypes, and whether we could use these brain patterns to classify patients according to their FTD variant. A total of 136 patients (70 bvFTD, 36 svPPA, 30 nfvPPA) from the frontotemporal lobar degeneration neuroimaging initiative (FTLDNI) database underwent brain MRI and clinical and neuropsychological examination. Deformation-based morphometry (DBM), which offers increased sensitivity to subtle local differences in structural image contrasts was used to estimate regional cortical and subcortical atrophy. Atlas-based associations between DBM values and performance across different cognitive tests were assessed using partial least squares (PLS). We then applied linear regression models to discern the group differences regarding the relationship between atrophy and cognitive decline in the three FTD phenotypes. Lastly, we assessed whether the combination of neural and behavioral patterns in the latent variables identified in the PLS analysis could be used as features in a machine-learning model to predict FTD subtypes in patients. Results revealed four significant latent variables that combined accounted for 86% of the shared covariance between cognitive and brain atrophy measures. PLS-based atrophy and behavioral patterns predicted the FTD phenotypes with a cross-validated accuracy of 89.12%, with high specificity (91.46-97.15%) and sensitivity (84.19-93.56%). When using only MRI measures and two behavioral tests in the PLS and classification algorithm, ensuring clinical feasibility, our model was similarly precise (83.62%, specificity 86.38-93.51%, sensitivity 76.17-87.50%). Here, including only atrophy or behavior patterns in the analysis led to prediction accuracies of 69.76% and 76.38%, respectively, highlighting the increased value of combining MRI and clinical measures in subtype classification. We demonstrate that the combination of brain atrophy and clinical characteristics, and multivariate statistical methods can serve as an imaging biomarker for early disease phenotyping in FTD, whereby inclusion of DBM measures adds to the classification precision in the absence of extensive clinical testing.