ArticleQuantitative imaging in medicine and surgery2020
Identifying subtypes of mild cognitive impairment from healthy aging based on multiple cortical features combined with volumetric measurements of the hippocampal subfields.
Article in Quantitative imaging in medicine and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Developments in MRI radiomics research for vascular cognitive impairment.Insights into imaging · 2025Review
- Morphometric Measurement of Mean Cortical Curvature: Analysis of Alterations in Cognitive Impairment.Medicina (Kaunas, Lithuania) · 2025Article
- Prediction of future dementia among patients with mild cognitive impairment (MCI) by integrating multimodal clinical data.Heliyon · 2024Article
- Machine learning with multimodal neuroimaging data to classify stages of Alzheimer's disease: a systematic review and meta-analysis.Cognitive neurodynamics · 2024Review
- Article
- Age- and sex-related differences in cortical morphology and their relationships with cognitive performance in healthy middle-aged and older adults.Quantitative imaging in medicine and surgery · 2023Article
- Machine learning classifiers and associations of cognitive performance with hippocampal subfields in amnestic mild cognitive impairment.Frontiers in aging neuroscience · 2023Article
- Cognition and brain iron deposition in whole grey matter regions and hippocampal subfields.The European journal of neuroscience · 2022Article
- Improving segmentation reliability of multi-scanner brain images using a generative adversarial network.Quantitative imaging in medicine and surgery · 2022Article
- Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain.Neuroinformatics · 2022Article
- Aberrant cerebral perfusion pattern in amnestic mild cognitive impairment and Parkinson's disease with mild cognitive impairment: a comparative arterial spin labeling study.Quantitative imaging in medicine and surgery · 2021Article
- Analysis of Features of Alzheimer's Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network.Diagnostics (Basel, Switzerland) · 2021Article
- Prediction of Subtle Cognitive Decline in Normal Aging: Added Value of Quantitative MRI and PET Imaging.Frontiers in aging neuroscience · 2021Article
- Parametric estimation of reference signal intensity in the quantification of amyloid-beta deposition: anQuantitative imaging in medicine and surgery · 2021Article
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4 authors.
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Abstract
backgroundMild cognitive impairment (MCI) is subtle cognitive decline with an estimated 10-15% yearly conversion rate toward Alzheimer's disease (AD). It remains unexplored in brain cortical association areas in different lobes and its changes with progression and conversion of MCI.
methodsBrain structural magnetic resonance (MR) images were collected from 102 stable MCI (sMCI) patients. One hundred eleven were converted MCI (cMCI) patients, and 109 were normal control (NC). The cortical surface features and volumes of subcortical hippocampal subfields were calculated using the FreeSurfer software, followed by an analysis of variance (ANOVA) model, to reveal the differences between the NC-sMCI, NC-cMCI, and sMCI-cMCI groups. Afterward, the support vector machine-recursive feature elimination (SVM-RFE) method was applied to determine the differences between the groups.
resultsThe experimental results showed that there were progressive degradations in either range or degree of the brain structure from NC to sMCI, and then to cMCI. The SVM classifier obtained accuracies with 64.62%, 78.96%, and 70.33% in the sMCI-NC, cMCI-NC, and cMCI-sMCI groups, respectively, using the volumes of hippocampal subfields independently. The combination of the volumes from the hippocampal subfields and cortical measurements could significantly increase the performance to 71.86%, 84.64%, and 76.86% for the sMCI-NC, cMCI-NC, and cMCI-sMCI classifications, respectively. Also, the brain regions corresponding to the dominant features with strong discriminative power were widely located in the temporal, frontal, parietal, olfactory cortexes, and most of the hippocampal subfields, which were associated with cognitive decline, memory impairment, spatial navigation, and attention control.
conclusionsThe combination of cortical features with the volumes of hippocampal subfields could supply critical information for MCI detection and its conversion.
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