3D tract‐specific local and global analysis of white matter integrity in Alzheimer's disease |
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| Authors: | Yan Jin Chao Huang Madelaine Daianu Liang Zhan Emily L. Dennis Robert I. Reid Clifford R. Jack Jr Hongtu Zhu Paul M. Thompson Alzheimer's Disease Neuroimaging Initiative |
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| Affiliation: | 1. Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California;2. Department of Biostatistics and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina;3. Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas;4. Computer Engineering Program, University of Wisconsin‐Stout, Menomonie, Wisconsin;5. Department of Information Technology, Mayo Clinic, Rochester, Minnesota;6. Department of Radiology, Mayo Clinic, Rochester, Minnesota;7. Departments of Neurology, Psychiatry, Pediatrics, Radiology, and Ophthalmology, Keck School of Medicine, University of Southern California, Los Angeles, California;8. Viterbi School of Engineering, University of Southern California, Los Angeles, California |
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| Abstract: | Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive decline in memory and other aspects of cognitive function. Diffusion‐weighted imaging (DWI) offers a non‐invasive approach to delineate the effects of AD on white matter (WM) integrity. Previous studies calculated either some summary statistics over regions of interest (ROI analysis) or some statistics along mean skeleton lines (Tract Based Spatial Statistic [TBSS]), so they cannot quantify subtle local WM alterations along major tracts. Here, a comprehensive WM analysis framework to map disease effects on 3D tracts both locally and globally, based on a study of 200 subjects: 49 healthy elderly normal controls, 110 with mild cognitive impairment, and 41 AD patients has been presented. 18 major WM tracts were extracted with our automated clustering algorithm—autoMATE (automated Multi‐Atlas Tract Extraction); we then extracted multiple DWI‐derived parameters of WM integrity along the WM tracts across all subjects. A novel statistical functional analysis method—FADTTS (Functional Analysis for Diffusion Tensor Tract Statistics) was applied to quantify degenerative patterns along WM tracts across different stages of AD. Gradually increasing WM alterations were found in all tracts in successive stages of AD. Among all 18 WM tracts, the fornix was most adversely affected. Among all the parameters, mean diffusivity (MD) was the most sensitive to WM alterations in AD. This study provides a systematic workflow to examine WM integrity across automatically computed 3D tracts in AD and may be useful in studying other neurological and psychiatric disorders. Hum Brain Mapp 38:1191–1207, 2017. © 2016 Wiley Periodicals, Inc. |
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| Keywords: | Alzheimer's disease diffusion‐weighted MRI functional statistical analysis tract‐specific analysis white matter |
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