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Neurobiol Aging. 2015 Jan;36 Suppl 1:S132-40. doi: 10.1016/j.neurobiolaging.2014.05.037. Epub 2014 Aug 27.

Diffusion weighted imaging-based maximum density path analysis and classification of Alzheimer's disease.

Author information

1
Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA.
2
Department of Neurology, UCLA School of Medicine, Los Angeles, CA, USA.
3
Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA.
4
Department of Radiology and Biomedical Imaging, UCSF School of Medicine, San Francisco, CA, USA.
5
Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA; Department of Neurology, University of Southern California, Los Angeles, CA, USA; Department of Psychiatry, University of Southern California, Los Angeles, CA, USA; Department of Radiology, University of Southern California, Los Angeles, CA, USA; Department of Engineering, University of Southern California, Los Angeles, CA, USA; Department of Pediatrics, University of Southern California, Los Angeles, CA, USA; Department of Ophthalmology, University of Southern California, Los Angeles, CA, USA. Electronic address: pthomp@usc.edu.

Abstract

Characterizing brain changes in Alzheimer's disease (AD) is important for patient prognosis and for assessing brain deterioration in clinical trials. In this diffusion weighted imaging study, we used a new fiber-tract modeling method to investigate white matter integrity in 50 elderly controls (CTL), 113 people with mild cognitive impairment, and 37 AD patients. After clustering tractography using a region-of-interest atlas, we used a shortest path graph search through each bundle's fiber density map to derive maximum density paths (MDPs), which we registered across subjects. We calculated the fractional anisotropy (FA) and mean diffusivity (MD) along all MDPs and found significant MD and FA differences between AD patients and CTL subjects, as well as MD differences between CTL and late mild cognitive impairment subjects. MD and FA were also associated with widely used clinical scores. As an MDP is a compact low-dimensional representation of white matter organization, we tested the utility of diffusion tensor imaging measures along these MDPs as features for support vector machine based classification of AD.

KEYWORDS:

ADNI; Classification; Connectivity; DTI; Fiber tract modeling; SVM; Tractography; White matter

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