OBJECTIVE: The clinical diagnosis of corticobasal syndrome (CBS) represents a challenge for physicians and reliable diagnostic imaging biomarkers would support the diagnostic work-up. We aimed to investigate the neural signatures of CBS using multimodal T1-weighted and resting-state functional magnetic resonance imaging (MRI). METHODS: Nineteen patients with CBS (age 67.0 ± 6.0 years; mean±SD) and 19 matched controls (66.5 ± 6.0) were enrolled from the German Frontotemporal Lobar Degeneration Consortium. Changes in functional connectivity and structure were respectively assessed with eigenvector centrality mapping complemented by seed-based analysis and with voxel-based morphometry. In addition to mass-univariate statistics, multivariate support vector machine (SVM) classification tested the potential of multimodal MRI to differentiate patients and controls. External validity of SVM was assessed on independent CBS data from the 4RTNI database. RESULTS: A decrease in brain interconnectedness was observed in the right central operculum, middle temporal gyrus and posterior insula, while widespread connectivity increases were found in the anterior cingulum, medial superior-frontal gyrus and in the bilateral caudate nuclei. Severe and diffuse gray matter volume reduction, especially in the bilateral insula, putamen and thalamus, characterized CBS. SVM classification revealed that both connectivity (area under the curve 0.81) and structural abnormalities (0.80) distinguished CBS from controls, while their combination led to statistically non-significant improvement in discrimination power, questioning the additional value of functional connectivity over atrophy. SVM analyses based on structural MRI generalized moderately well to new data, which was decisively improved when guided by meta-analytically derived disease-specific regions-of-interest. CONCLUSIONS: Our data-driven results show impairment of functional connectivity and brain structure in CBS and explore their potential as imaging biomarkers.

Clinic for Neurology Saarland University Germany

Clinic for Psychiatry Psychosomatic medicine and Psychotherapy University Würzburg Germany

Department of Neurodegenerative Diseases and Geriatric Psychiatry University Bonn Germany

Department of Neurodegenerative Diseases Centre for Neurology and Hertie Institute for Clinical Brain Research University of Tübingen Germany; German Center for Neurodegenerative Diseases Tübingen Germany

Department of Neurology Charles University 1st Faculty of Medicine Prague Czech Republic

Department of Neurology University of Ulm Germany

Department of Psychiatry and Psychotherapy Technical University of Munich Germany

Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany

Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany; Clinic for Cognitive Neurology University Clinic Leipzig Germany

University Medical Center Göttingen Germany

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Structural parameters are superior to eigenvector centrality in detecting progressive supranuclear palsy with machine learning & multimodal MRI