Voxel-based morphometry and a deep learning model for the diagnosis of early Alzheimer’s disease based on cerebral gray matter changes
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This study aimed to analyse cerebral grey matter changes in mild cognitive impairment (MCI) using voxel-based morphometry and to diagnose early Alzheimer's disease using deep learning methods based on convolutional neural networks (CNNs) evaluating these changes. Participants (111 MCI, 73 normal cognition) underwent 3-T structural magnetic resonance imaging. The obtained images were assessed using voxel-based morphometry, including extraction of cerebral grey matter, analyses of statistical differences, and correlation analyses between cerebral grey matter and clinical cognitive scores in MCI. The CNN-based deep learning method was used to extract features of cerebral grey matter images. Compared to subjects with normal cognition, participants with MCI had grey matter atrophy mainly in the entorhinal cortex, frontal cortex, and bilateral frontotemporal lobes (p < 0.0001). This atrophy was significantly correlated with the decline in cognitive scores (p < 0.01). The accuracy, sensitivity, and specificity of the CNN model for identifying participants with MCI were 80.9%, 88.9%, and 75%, respectively. The area under the curve of the model was 0.891. These findings demonstrate that research based on brain morphology can provide an effective way for the clinical, non-invasive, objective evaluation and identification of early Alzheimer's disease.
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At the preclinical stage of AD, parahippocampal gyrus atrophy has been detected in patients with mild cognitive impairment.
“At the preclinical stage of AD, parahippocampal gyrus atrophy has also been detected in patients with mild cognitive impairment (Huang et al., 2023).”
From Multimodal MRI combined with RNA sequencing reveals pathological signatures in the 9-month-old 3×Tg-AD mouse brain (2026) · cites this paper as Huang et al., 2023
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- Authors
- Huang, H., Zheng, S., Yang, Z., Wu, Y., Li, Y., Qiu, J., Cheng, Y., Lin, P., Lin, Y., Guan, J., Mikulis, D. J., Zhou, T., & Wu, R.
- Affiliations
- Shantou University (CN)
- Southern Medical University Shenzhen Hospital (CN)
- Sun Yat-sen University (CN)
- Quanzhou Medical College (CN)
- University Health Network (CA)
- Corresponding author
- Zhou, Teng
- Wu, Renhua
- ORCID
- Venue
- Cerebral Cortex
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-03-17
- Volume
- 33
- Issue
- 3
- Pages
- 754-763
- ISSN
- 1047-3211 (ISSN-L)
- 1460-2199
- PMC
- PMC9890469
- OpenAlex
- W4212877785
- Cited by
- 57
- References
- 59
- Citations by year
- 2026: 9 · 2025: 11 · 2024: 16 · 2023: 14 · 2022: 7
- FWCI
- 7.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cerebral Cortex (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- Natural Science Foundation of China (82020108016, 31870981, 61902232)
- Li Ka Shing Foundation Cross Disciplinary Research (2020LKSFBME06, 2020LKSFG05D)
- Key Disciplinary Project of Clinical Medicine under the Guangdong High-Level University Development Program (002-18120302)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- early Alzheimer's disease, mild cognitive impairment, voxel-based morphometry, convolutional neural networks, deep learning, entorhinal cortex, frontal cortex, frontotemporal lobe, cognitive decline, brain structure, disease diagnosis
- MeSH terms
- Deep Learning, Alzheimer Disease, Atrophy, Humans, Magnetic Resonance Imaging, Cognitive Dysfunction, Gray Matter
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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