title: Automated MRI-based classification of primary progressive aphasia variants.
abstract: Degeneration of language regions in the dominant hemisphere can result in primary progressive aphasia (PPA), a clinical syndrome characterized by progressive deficits in speech and/or language function. Recent studies have identified three variants of PPA: progressive non-fluent aphasia (PNFA), semantic dementia (SD) and logopenic progressive aphasia (LPA). Each variant is associated with characteristic linguistic features, distinct patterns of brain atrophy, and different likelihoods of particular underlying pathogenic processes, which makes correct differential diagnosis highly clinically relevant. Evaluation of linguistic behavior can be challenging for non-specialists, and neuroimaging findings in single subjects are often difficult to evaluate by eye. We investigated the utility of automated structural MR image analysis to discriminate PPA variants (N=86) from each other and from normal controls (N=115). T1 images were preprocessed to obtain modulated grey matter (GM) images. Feature selection was performed with principal components analysis (PCA) on GM images as well as images of lateralized atrophy. PC coefficients were classified with linear support vector machines, and a cross-validation scheme was used to obtain accuracy rates for generalization to novel cases. The overall mean accuracy in discriminating between pairs of groups was 92.2%. For one pair of groups, PNFA and SD, we also investigated the utility of including several linguistic variables as features. Models with both imaging and linguistic features performed better than models with only imaging or only linguistic features. These results suggest that automated methods could assist in the differential diagnosis of PPA variants, enabling therapies to be targeted to likely underlying etiologies.
authors.affiliation: Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA. swilson@memory.ucsf.edu
authors.forename: Stephen M
authors.initials: SM
authors.lastname: Wilson
authors.forename: Jennifer M
authors.initials: JM
authors.lastname: Ogar
authors.forename: Victor
authors.initials: V
authors.lastname: Laluz
authors.forename: Matthew
authors.initials: M
authors.lastname: Growdon
authors.forename: Jung
authors.initials: J
authors.lastname: Jang
authors.forename: Shenly
authors.initials: S
authors.lastname: Glenn
authors.forename: Bruce L
authors.initials: BL
authors.lastname: Miller
authors.forename: Michael W
authors.initials: MW
authors.lastname: Weiner
authors.forename: Maria Luisa
authors.initials: ML
authors.lastname: Gorno-Tempini
mesh_terms: D000368:Aged; D000465:Algorithms; D001037:Aphasia / Q000145:classification* / Q000175:diagnosis*; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity