Individual classification of Alzheimer's disease with diffusion magnetic resonance imaging

• Published in: NeuroImage (Orlando, Fla.) Fla.), 2017-05, Vol.152, p.476-481
• Main Authors: Schouten, Tijn M., Koini, Marisa, Vos, Frank de, Seiler, Stephan, Rooij, Mark de, Lechner, Anita, Schmidt, Reinhold, Heuvel, Martijn van den, Grond, Jeroen van der, Rombouts, Serge A.R.B.
• Format: Article
• Language: English
• Subjects: Aged; Aged, 80 and over; Alzheimer Disease - classification; Alzheimer Disease - diagnostic imaging; Alzheimer Disease - pathology; Alzheimer's disease; Anisotropy; Artificial intelligence; Brain Mapping - methods; Classification; Dementia; Diffusion; Diffusion Magnetic Resonance Imaging; Diffusion Tensor Imaging; DTI; Female; Humans; Image Processing, Computer-Assisted; Information processing; Machine Learning; Magnetic resonance imaging; Male; Middle Aged; MRI; Neurodegenerative diseases; NMR; Nuclear magnetic resonance; Resonance; Statistical analysis; Stroke; Substantia alba; White Matter - diagnostic imaging; White Matter - pathology
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2017.03.025

Diffusion magnetic resonance imaging (MRI) is a powerful non-invasive method to study white matter integrity, and is sensitive to detect differences in Alzheimer's disease (AD) patients. Diffusion MRI may be able to contribute towards reliable diagnosis of AD. We used diffusion MRI to classify AD patients (N=77), and controls (N=173). We use different methods to extract information from the diffusion MRI data. First, we use the voxel-wise diffusion tensor measures that have been skeletonised using tract based spatial statistics. Second, we clustered the voxel-wise diffusion measures with independent component analysis (ICA), and extracted the mixing weights. Third, we determined structural connectivity between Harvard Oxford atlas regions with probabilistic tractography, as well as graph measures based on these structural connectivity graphs. Classification performance for voxel-wise measures ranged between an AUC of 0.888, and 0.902. The ICA-clustered measures ranged between an AUC of 0.893, and 0.920. The AUC for the structural connectivity graph was 0.900, while graph measures based upon this graph ranged between an AUC of 0.531, and 0.840. All measures combined with a sparse group lasso resulted in an AUC of 0.896. Overall, fractional anisotropy clustered into ICA components was the best performing measure. These findings may be useful for future incorporation of diffusion MRI into protocols for AD classification, or as a starting point for early detection of AD using diffusion MRI. •We use machine learning classification to classify Alzheimer's disease.•We use diffusion MRI based measures for classification.•Tract based diffusion tensor measures are excellent for classification.•Clustering fractional anisotropy into independent components can improve classification.