ArticleCNS neuroscience & therapeutics2023
Classification of Alzheimer's disease based on hippocampal multivariate morphometry statistics.
Article in CNS neuroscience & therapeutics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 18 citations in OpenAlex.
- Multimodal MRI insights into hippocampal pathological changes across the Alzheimer's disease continuum: advances and challenges.Japanese journal of radiology · 2026Review
- Hippocampal subfield thickness and shape analysis in examining the impact of TDP-43 in primary age-related tauopathy.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- MRI-Driven Longitudinal Studies of Hippocampal Alterations During the Initial Cognitive Decline.IEEE journal of translational engineering in health and medicine · 2025Article
- Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis.Journal of medical Internet research · 2024Article
- Altered Density of Resting-State Long- and Short-Range Functional Connectivity in Patients with Moderate-to-Severe Obstructive Sleep Apnea.Nature and science of sleep · 2024Article
- Bioinformatic analysis of hippocampal histopathology in Alzheimer's disease and the therapeutic effects of active components of traditional Chinese medicine.Frontiers in pharmacology · 2024Article
- Classification of Alzheimer's disease based on hippocampal multivariate morphometry statistics.CNS neuroscience & therapeutics · 2023Article
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlzheimer's disease (AD) is a neurodegenerative disease characterized by progressive cognitive decline, and mild cognitive impairment (MCI) is associated with a high risk of developing AD. Hippocampal morphometry analysis is believed to be the most robust magnetic resonance imaging (MRI) markers for AD and MCI. Multivariate morphometry statistics (MMS), a quantitative method of surface deformations analysis, is confirmed to have strong statistical power for evaluating hippocampus.
aimsWe aimed to test whether surface deformation features in hippocampus can be employed for early classification of AD, MCI, and healthy controls (HC).
methodsWe first explored the differences in hippocampus surface deformation among these three groups by using MMS analysis. Additionally, the hippocampal MMS features of selective patches and support vector machine (SVM) were used for the binary classification and triple classification.
resultsBy the results, we identified significant hippocampal deformation among the three groups, especially in hippocampal CA1. In addition, the binary classification of AD/HC, MCI/HC, AD/MCI showed good performances, and area under curve (AUC) of triple-classification model achieved 0.85. Finally, positive correlations were found between the hippocampus MMS features and cognitive performances.
conclusionsThe study revealed significant hippocampal deformation among AD, MCI, and HC. Additionally, we confirmed that hippocampal MMS can be used as a sensitive imaging biomarker for the early diagnosis of AD at the individual level.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.