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Differentiation of neurodegenerative parkinsonian syndromes by volumetric magnetic resonance imaging analysis and support vector machine classification
Author(s) -
Huppertz HansJürgen,
Möller Leona,
Südmeyer Martin,
Hilker Rüdiger,
Hattingen Elke,
Egger Karl,
Amtage Florian,
Respondek Gesine,
Stamelou Maria,
Schnitzler Alfons,
Pinkhardt Elmar H.,
Oertel Wolfgang H.,
Knake Susanne,
Kassubek Jan,
Höglinger Günter U.
Publication year - 2016
Publication title -
movement disorders
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.352
H-Index - 198
eISSN - 1531-8257
pISSN - 0885-3185
DOI - 10.1002/mds.26715
Subject(s) - putamen , cerebellum , progressive supranuclear palsy , atrophy , magnetic resonance imaging , basal ganglia , midbrain , pons , neuroscience , pathology , medicine , psychology , radiology , central nervous system
Background Clinical differentiation of parkinsonian syndromes is still challenging. Objectives A fully automated method for quantitative MRI analysis using atlas‐based volumetry combined with support vector machine classification was evaluated for differentiation of parkinsonian syndromes in a multicenter study. Methods Atlas‐based volumetry was performed on MRI data of healthy controls (n = 73) and patients with PD (204), PSP with Richardson's syndrome phenotype (106), MSA of the cerebellar type (21), and MSA of the Parkinsonian type (60), acquired on different scanners. Volumetric results were used as input for support vector machine classification of single subjects with leave‐one‐out cross‐validation. Results The largest atrophy compared to controls was found for PSP with Richardson's syndrome phenotype patients in midbrain (−15%), midsagittal midbrain tegmentum plane (−20%), and superior cerebellar peduncles (−13%), for MSA of the cerebellar type in pons (−33%), cerebellum (−23%), and middle cerebellar peduncles (−36%), and for MSA of the parkinsonian type in the putamen (−23%). The majority of binary support vector machine classifications between the groups resulted in balanced accuracies of >80%. With MSA of the cerebellar and parkinsonian type combined in one group, support vector machine classification of PD, PSP and MSA achieved sensitivities of 79% to 87% and specificities of 87% to 96%. Extraction of weighting factors confirmed that midbrain, basal ganglia, and cerebellar peduncles had the largest relevance for classification. Conclusions Brain volumetry combined with support vector machine classification allowed for reliable automated differentiation of parkinsonian syndromes on single‐patient level even for MRI acquired on different scanners. © 2016 International Parkinson and Movement Disorder Society