title: Predicting primary progressive aphasias with support vector machine approaches in structural MRI data.
abstract: Primary progressive aphasia (PPA) encompasses the three subtypes nonfluent/agrammatic variant PPA, semantic variant PPA, and the logopenic variant PPA, which are characterized by distinct patterns of language difficulties and regional brain atrophy. To validate the potential of structural magnetic resonance imaging data for early individual diagnosis, we used support vector machine classification on grey matter density maps obtained by voxel-based morphometry analysis to discriminate PPA subtypes (44 patients: 16 nonfluent/agrammatic variant PPA, 17 semantic variant PPA, 11 logopenic variant PPA) from 20 healthy controls (matched for sample size, age, and gender) in the cohort of the multi-center study of the German consortium for frontotemporal lobar degeneration. Here, we compared a whole-brain with a meta-analysis-based disease-specific regions-of-interest approach for support vector machine classification. We also used support vector machine classification to discriminate the three PPA subtypes from each other. Whole brain support vector machine classification enabled a very high accuracy between 91 and 97% for identifying specific PPA subtypes vs. healthy controls, and 78/95% for the discrimination between semantic variant vs. nonfluent/agrammatic or logopenic PPA variants. Only for the discrimination between nonfluent/agrammatic and logopenic PPA variants accuracy was low with 55%. Interestingly, the regions that contributed the most to the support vector machine classification of patients corresponded largely to the regions that were atrophic in these patients as revealed by group comparisons. Although the whole brain approach took also into account regions that were not covered in the regions-of-interest approach, both approaches showed similar accuracies due to the disease-specificity of the selected networks. Conclusion, support vector machine classification of multi-center structural magnetic resonance imaging data enables prediction of PPA subtypes with a very high accuracy paving the road for its application in clinical settings.
authors.affiliation: Max Planck Institute for Human Cognitive and Brain Sciences & Clinic for Cognitive Neurology, University Hospital Leipzig, Germany.
authors.forename: Sandrine
authors.initials: S
authors.lastname: Bisenius
authors.affiliation: Max Planck Institute for Human Cognitive and Brain Sciences & Clinic for Cognitive Neurology, University Hospital Leipzig, Germany.
authors.forename: Karsten
authors.initials: K
authors.lastname: Mueller
authors.affiliation: Clinic and Polyclinic for Psychiatry & Psychotherapy, Technical University Munich, Germany.
authors.forename: Janine
authors.initials: J
authors.lastname: Diehl-Schmid
authors.affiliation: Clinic and Polyclinic for Neurology, Saarland University Homburg, Germany.
authors.forename: Klaus
authors.initials: K
authors.lastname: Fassbender
authors.affiliation: Clinic and Polyclinic for Psychiatry & Psychotherapy, Technical University Munich, Germany.
authors.forename: Timo
authors.initials: T
authors.lastname: Grimmer
authors.affiliation: Clinic and Polyclinic for Psychiatry and Psychotherapy, University of Bonn, Germany.
authors.forename: Frank
authors.initials: F
authors.lastname: Jessen
authors.affiliation: Department of Neurology, University of Ulm, Germany.
authors.forename: Jan
authors.initials: J
authors.lastname: Kassubek
authors.affiliation: Clinic for Psychiatry and Psychotherapy, Friedrich-Alexander University Erlangen-Nuremberg, Germany.
authors.forename: Johannes
authors.initials: J
authors.lastname: Kornhuber
authors.affiliation: Department of Neurology, University of Ulm, Germany.
authors.forename: Bernhard
authors.initials: B
authors.lastname: Landwehrmeyer
authors.affiliation: Department of Neurology, University of Ulm, Germany.
authors.forename: Albert
authors.initials: A
authors.lastname: Ludolph
authors.affiliation: Clinic for Psychiatry and Psychotherapy, University of Goettingen, Germany.
authors.forename: Anja
authors.initials: A
authors.lastname: Schneider
authors.affiliation: Department of Neurology, University of Ulm, Germany.
authors.forename: Sarah
authors.initials: S
authors.lastname: Anderl-Straub
authors.affiliation: Max Planck Institute for Human Cognitive and Brain Sciences & Clinic for Cognitive Neurology, University Hospital Leipzig, Germany.
authors.forename: Katharina
authors.initials: K
authors.lastname: Stuke
authors.affiliation: Clinic of Neurology, Ludwig Maximilian University of Munich, Germany.
authors.forename: Adrian
authors.initials: A
authors.lastname: Danek
authors.affiliation: Department of Neurology, University of Ulm, Germany.
authors.forename: Markus
authors.initials: M
authors.lastname: Otto
authors.affiliation: Max Planck Institute for Human Cognitive and Brain Sciences & Clinic for Cognitive Neurology, University Hospital Leipzig, Germany.
authors.forename: Matthias L
authors.initials: ML
authors.lastname: Schroeter
mesh_terms: D000368:Aged; D018888:Aphasia, Primary Progressive / Q000000981:diagnostic imaging*; D001921:Brain / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D060388:Support Vector Machine
keywords: Grey matter; Multi-center; Primary progressive aphasia; Support vector machine classification; Whole brain approach