Shape and diffusion tensor imaging based integrative analysis of the hippocampus and the amygdala in Alzheimer's disease
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We analyzed, in an integrative fashion, the morphometry and structural integrity of the bilateral hippocampi and amygdalas in Alzheimer's disease (AD) using T1-weighted images and diffusion tensor images (DTIs). We detected significant hippocampal and amygdalar volumetric atrophies in AD relative to healthy controls (HCs). Shape analysis revealed significant region-specific atrophies with the hippocampal atrophy mainly being concentrated on the CA1 and CA2 while the amygdalar atrophy was concentrated on the basolateral and basomedial. In all structures, the structural integrity displayed a significantly decreased mean fractional anisotropy (FA) value and an increased mean trace value in AD. In addition to the inter-group comparisons, we systematically evaluated the discriminative power of our three types of features (volume, shape, and DTI), both individually and in their possible combinations, when differentiating between AD and HCs. We found the volume features to be redundant when the more sophisticated shape features were available. A combination of the shape and DTI features of the right hippocampus, with classification automatically performed by support vector machine, yielded the strongest classification result (overall accuracy, 94.6%; sensitivity, 95.5%; specificity, 93.3%).
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In AD, amyloid deposition spreads and the disease pathogenesis progressively involves a broad region of the brain.
“the amyloid deposition spreads, and the disease pathogenesis progressively involves a broad region of the brain”
From AAVrh.10-Mediated APOE2 Central Nervous System Gene Therapy for APOE4-Associated Alzheimer's Disease (2018) · cites this paper as [44]
The Introduction describes AD as a progressive neurodegenerative disorder, and the Results report abnormalities in the hippocampus and amygdala. But the paper’s passages do not say that amyloid deposition spreads or that pathology progressively extends across a broad region of the brain; the findings concern specific structures rather than brain-wide progression. (LLM, conf=0.82)
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References 73 · Cited by 64
- Authors
- Tang, X., Qin, Y., Wu, J., Zhang, M., Zhu, W., & Miller, M. I.
- Affiliations
- Sun Yat-sen University (CN)
- Tongji Hospital (CN)
- Johns Hopkins University (US)
- Corresponding author
- Tang, Xiaoying
- Venue
- Magnetic Resonance Imaging
- Publisher
- Elsevier BV
- Published
- 2016-10
- Volume
- 34
- Issue
- 8
- Pages
- 1087-1099
- ISSN
- 0730-725X (ISSN-L)
- PMC
- PMC6058972
- OpenAlex
- W2398671403
- Cited by
- 64
- References
- 73
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 6 · 2023: 4 · 2022: 8 · 2021: 8 · 2020: 9 · 2019: 4 · 2018: 5 · 2017: 5 · 2016: 2
- FWCI
- 4.74
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Magnetic Resonance Imaging (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Natural Science Foundation of China (81501546, 84101389, 81171308)
- SYSU-CMU Shunde International Joint Research Institute (20150306)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- Alzheimer's disease, hippocampus, amygdala, hippocampal atrophy, hippocampal subfields, CA1, CA2, basolateral amygdala, basomedial amygdala, fractional anisotropy, mean diffusivity, brain morphometry, shape analysis, diffusion tensor imaging, support vector machine classification, disease diagnosis
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Amygdala, Atrophy, Female, Hippocampus, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Sensitivity and Specificity, Diffusion Tensor Imaging
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