ArticleCerebral cortex (New York, N.Y. : 1991)2023
Voxel-based morphometry and a deep learning model for the diagnosis of early Alzheimer's disease based on cerebral gray matter changes.
Article in Cerebral cortex (New York, N.Y. : 1991), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 55 citations in OpenAlex.
- Multimodal MRI combined with RNA sequencing reveals pathological signatures in the 9-month-old 3×Tg-AD mouse brain.Neural regeneration research · 2026Article
- Clinical pathways matter for multimodal deep learning in early Alzheimer's disease detection.Scientific reports · 2026Article
- Transcriptomic signatures of gray matter volume loss in cerebral small vessel disease.BMC neurology · 2026Article
- Geometric Fragility in Alzheimer's Disease: Probing the Loss of Hippocampal Hierarchical Abstraction via Contrastive Point Cloud Modeling.bioRxiv : the preprint server for biology · 2026Article
- A token efficient vision framework using patch residual transformer for Alzheimer's disease diagnosis.Scientific reports · 2026Article
- Comparing deep learning CNN method with traditional MRI-based hippocampal segmentation and volumetry for early Alzheimer's disease diagnosis across diverse populations.Scientific reports · 2025Article
- Current Clinical Applications of Structural MRI in Neurological Disorders.Journal of clinical neurology (Seoul, Korea) · 2025Review
- MRI Voxel Morphometry Shows Brain Volume Changes in Breast Cancer Survivors: Implications for Treatment.Pathophysiology : the official journal of the International Society for Pathophysiology · 2025Article
- Classification of the stages of Alzheimer's disease based on three-dimensional lightweight neural networks.PeerJ. Computer science · 2025Article
- Analysis of cognitive dysfunction in Parkinson's disease using voxel based morphometry and radiomics.Cognitive processing · 2024Article
- Intelligent prediction of Alzheimer's disease via improved multifeature squeeze-and-excitation-dilated residual network.Scientific reports · 2024Article
- Integrating Biomarkers From Virtual Reality and Magnetic Resonance Imaging for the Early Detection of Mild Cognitive Impairment Using a Multimodal Learning Approach: Validation Study.Journal of medical Internet research · 2024Article
- Removing outliers from the normative database improves regional atrophy detection in single-subject voxel-based morphometry.Neuroradiology · 2024Article
- Assessment of Gray Matter Microstructural Alterations in Alzheimer's Disease by Free Water Imaging.Journal of Alzheimer's disease : JAD · 2024Article
- Elevated circulating levels of GFAP associated with reduced volumes in hippocampal subregions linked to mild cognitive impairment among community-dwelling elderly individuals.Frontiers in aging neuroscience · 2024Article
- Sex dimorphic cortical brain volumes associated with antisocial behavior in young adults.Psychoradiology · 2023Article
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Authors and funding
13 authors at 5 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
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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