Multimodal analysis of functional and structural disconnection in Alzheimer's disease using multiple kernel SVM

• Published in: Human brain mapping Vol. 36; no. 6; pp. 2118 - 2131
• Main Authors: Dyrba, Martin, Grothe, Michel, Kirste, Thomas, Teipel, Stefan J.
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.06.2015; John Wiley & Sons, Inc; John Wiley and Sons Inc
• Subjects: Aged; Alzheimer Disease - pathology; Alzheimer Disease - physiopathology; Alzheimer's disease; Area Under Curve; Brain - pathology; Brain - physiopathology; Brain Mapping - methods; diffusion tensor imaging; Diffusion Tensor Imaging - methods; Female; Gray Matter - pathology; Gray Matter - physiopathology; Humans; magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Multimodal Imaging - methods; multiple kernel support vector machine; Pattern Recognition, Automated - methods; Rest; resting-state functional magnetic resonance imaging; Support Vector Machine; resting-state functional magnetic resonance imaging; diffusion tensor imaging; magnetic resonance imaging; multiple kernel support vector machine; Alzheimer's disease
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.22759

Alzheimer's disease (AD) patients exhibit alterations in the functional connectivity between spatially segregated brain regions which may be related to both local gray matter (GM) atrophy as well as a decline in the fiber integrity of the underlying white matter tracts. Machine learning algorithms are able to automatically detect the patterns of the disease in image data, and therefore, constitute a suitable basis for automated image diagnostic systems. The question of which magnetic resonance imaging (MRI) modalities are most useful in a clinical context is as yet unresolved. We examined multimodal MRI data acquired from 28 subjects with clinically probable AD and 25 healthy controls. Specifically, we used fiber tract integrity as measured by diffusion tensor imaging (DTI), GM volume derived from structural MRI, and the graph‐theoretical measures ‘local clustering coefficient’ and ‘shortest path length’ derived from resting‐state functional MRI (rs‐fMRI) to evaluate the utility of the three imaging methods in automated multimodal image diagnostics, to assess their individual performance, and the level of concordance between them. We ran the support vector machine (SVM) algorithm and validated the results using leave‐one‐out cross‐validation. For the single imaging modalities, we obtained an area under the curve (AUC) of 80% for rs‐fMRI, 87% for DTI, and 86% for GM volume. When it came to the multimodal SVM, we obtained an AUC of 82% using all three modalities, and 89% using only DTI measures and GM volume. Combined multimodal imaging data did not significantly improve classification accuracy compared to the best single measures alone. Hum Brain Mapp 36:2118–2131, 2015. © 2015 Wiley Periodicals, Inc.