Journal
FRONTIERS IN AGING NEUROSCIENCE
Volume 13, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fnagi.2021.711579
Keywords
Alzheimer's disease; diffusion tensor imaging; support vector machine; mild cognitive impairment; automated fiber quantification; tractography; conversion; biomarker
Categories
Funding
- National Institute of General Medical Sciences (NIGMS) Centers of Biomedical Research Excellence (COBRE) Phase III: Multimodal Imaging of Neuropsychiatric Disorders (MIND) [P30GM122734]
- Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health) [U01 AG024904]
- DOD ADNI (Department of Defense) [W81XWH-12-2-0012]
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- AbbVie
- Alzheimer's Association
- Alzheimer's Drug Discovery Foundation
- Araclon Biotech
- BioClinica, Inc.
- Biogen
- BristolMyers Squibb Company
- CereSpir, Inc.
- Cogstate
- Eisai Inc.
- Elan Pharmaceuticals, Inc.
- Eli Lilly and Company
- EuroImmun
- F. Hoffmann-La Roche Ltd., and its affiliated company Genentech, Inc.
- Fujirebio
- GE Healthcare
- IXICO Ltd.
- Janssen Alzheimer Immunotherapy Research & Development, LLC.
- Johnson & Johnson Pharmaceutical Research & Development LLC.
- Lumosity
- Lundbeck
- Merck Co., Inc.
- Meso Scale Diagnostics, LLC.
- NeuroRx Research
- Neurotrack Technologies
- Novartis Pharmaceuticals Corporation
- Pfizer Inc.
- Piramal Imaging
- Servier
- Takeda Pharmaceutical Company
- Transition Therapeutics
- Canadian Institutes of Health Research
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The study found that diffusion measures from seven white matter tracts can predict the conversion to Alzheimer's Disease (AD), with axial diffusivity being the most predictive measure. Additional analyses revealed that specific white matter changes in certain brain regions are the best predictors of conversion from MCI to AD.
Identifying biomarkers that can assess the risk of developing Alzheimer's Disease (AD) remains a significant challenge. In this study, we investigated the integrity levels of brain white matter in 34 patients with mild cognitive impairment (MCI) who later converted to AD and 53 stable MCI patients. We used diffusion tensor imaging (DTI) and automated fiber quantification to obtain the diffusion properties of 20 major white matter tracts. To identify which tracts and diffusion measures are most relevant to AD conversion, we used support vector machines (SVMs) to classify the AD conversion and non-conversion MCI patients based on the diffusion properties of each tract individually. We found that diffusivity measures from seven white matter tracts were predictive of AD conversion with axial diffusivity being the most predictive diffusion measure. Additional analyses revealed that white matter changes in the central and parahippocampal terminal regions of the right cingulate hippocampal bundle, central regions of the right inferior frontal occipital fasciculus, and posterior and anterior regions of the left inferior longitudinal fasciculus were the best predictors of conversion from MCI to AD. An SVM based on these white matter tract regions achieved an accuracy of 0.75. These findings provide additional potential biomarkers of AD risk in MCI patients.
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