Journal
JOURNAL OF ALZHEIMERS DISEASE
Volume 86, Issue 4, Pages 1695-1710Publisher
IOS PRESS
DOI: 10.3233/JAD-215568
Keywords
Alzheimer's disease; classification; hippocampus; longitudinal study; mild cognitive impairment
Categories
Funding
- National Key Research and Development Program of China [2019YFA0706200]
- National Natural Science Foundation of China [61632014, 61627808]
- Natural Science Foundation of Gansu Province of China [20JR5RA292]
- Department of Education of Gansu Province: Innovation Star Project for Excellent Postgraduates [2021CXZX-121]
- Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health) [U01AG024904]
- DODADNI (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
- Biogen
- BristolMyers Squibb Company
- CereSpir, Inc.
- Cogstate
- Eisai Inc.
- Elan Pharmaceuticals, Inc.
- Eli Lilly and Company
- EuroImmun
- F. Hoffmann-La Roche Ltd.
- affiliated company Genentech, Inc.
- Fujirebio
- GE Healthcare
- IXICO Ltd.
- Janssen Alzheimer Immunotherapy Research & Development, LLC.
- Johnson & Johnson Pharmaceutical Research & Development LLC.
- Lumosity
- Merck Co., Inc.
- Meso Scale Diagnostics, LLC.
- NeuroRx Research
- Neurotrack Technologies
- Novartis Pharmaceuticals Corporation
- Pfizer Inc.
- Piramal Imaging
- Servier
- Takeda Pharmaceutical Company
- Transition Therapeutics
- ADNI clinical sites in Canada
- BioClinica, Inc.
- Lundbeck
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This study investigated the temporal evolution pattern of MCI and the conversion to AD using multivariate morphometry statistics as features. The atrophy of hippocampus in MCI patients showed a specific distribution pattern, with the introduction of longitudinal information significantly improving the accuracy of conversion prediction.
Background: Mild cognitive impairment (MCI), which is generally regarded as the prodromal stage of Alzheimer's disease (AD), is associated with morphological changes in brain structures, particularly the hippocampus. However, the indicators for characterizing the deformation of hippocampus in conventional methods are not precise enough and ignore the evolution information with the course of disease. Objective: The purpose of this study was to investigate the temporal evolution pattern of MCI and predict the conversion of MCI to AD by using the multivariate morphometry statistics (MMS) as fine features. Methods: First, we extracted MMS features from MRI scans of 64 MCI converters (MCIc), 81 MCI patients who remained stable (MCIs), and 90 healthy controls (HC). To make full use of the time information, the dynamic MMS (DMMS) features were defined. Then, the areas with significant differences between pairs of the three groups were analyzed using statistical methods and the atrophy/expansion were identified by comparing the metrics. In parallel, patch selection, sparse coding, dictionary learning and maximum pooling were used for the dimensionality reduction and the ensemble classifier GentleBoost was used to classify MCIc and MCIs. Results: The longitudinal analysis revealed that the atrophy of both MCIc and MCIs mainly distributed in dorsal CA1, then spread to subiculum and other regions gradually, while the atrophy area of MCIc was larger and more significant. And the introduction of longitudinal information promoted the accuracy to 91.76% for conversion prediction. Conclusion: The dynamic information of hippocampus holds a huge potential for understanding the pathology of MCI.
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