Individual classification of Alzheimer's disease with diffusion magnetic resonance imaging.
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.
Institute of Psychology, Leiden University, The Netherlands; Department of Radiology, Leiden University, The Netherlands; Leiden Institute for Brain and Cognition, The Netherlands. Electronic address: t.m.schouten@fsw.leidenuniv.nl.
Tijn M
TM
Schouten
Department of Neurology, Medical University of Graz, Austria.
Marisa
M
Koini
Institute of Psychology, Leiden University, The Netherlands; Department of Radiology, Leiden University, The Netherlands; Leiden Institute for Brain and Cognition, The Netherlands.
Frank de
F
Vos
Department of Neurology, Medical University of Graz, Austria.
Stephan
S
Seiler
Institute of Psychology, Leiden University, The Netherlands; Leiden Institute for Brain and Cognition, The Netherlands.
Mark de
M
Rooij
Department of Neurology, Medical University of Graz, Austria.
Anita
A
Lechner
Department of Neurology, Medical University of Graz, Austria.
Reinhold
R
Schmidt
Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, The Netherlands.
Martijn van den
MVD
Heuvel
Department of Radiology, Leiden University, The Netherlands.
Jeroen van der
JV
Grond
Institute of Psychology, Leiden University, The Netherlands; Department of Radiology, Leiden University, The Netherlands; Leiden Institute for Brain and Cognition, The Netherlands.
Serge A R B
SARB
Rombouts
D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000145:classification* / Q000000981:diagnostic imaging* / Q000473:pathology; D016880:Anisotropy; D001931:Brain Mapping / Q000379:methods*; D038524:Diffusion Magnetic Resonance Imaging; D056324:Diffusion Tensor Imaging; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D066127:White Matter / Q000000981:diagnostic imaging / Q000473:pathology
Alzheimer's disease; Classification; DTI; Diffusion; MRI