ArticleFrontiers in psychiatry2019
Classification of Early and Late Mild Cognitive Impairment Using Functional Brain Network of Resting-State fMRI.
Article in Frontiers in psychiatry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Classification of neurodegenerative diseases using brain effective connectivity and machine learning techniques: a systematic review.Frontiers in neurology · 2025Pooled it
- Long-term risk of late-life depression in widowed elderly: a five-year follow-up study.BMC geriatrics · 2025Article
- Distinguishing Early from Late Mild Cognitive Impairment Using Magnetic Resonance Free-Water Diffusion Tensor Imaging.NeuroSci · 2025Article
- Source-free collaborative domain adaptation via multi-perspective feature enrichment for functional MRI analysis.Pattern recognition · 2025Article
- InGSA: integrating generalized self-attention in CNN for Alzheimer's disease classification.Frontiers in artificial intelligence · 2025Article
- Classifying Unconscious, Psychedelic, and Neuropsychiatric Brain States with Functional Connectivity, Graph Theory, and Cortical Gradient Analysis.Brain sciences · 2024Article
- Random survival forest model for early prediction of Alzheimer's disease conversion in early and late Mild cognitive impairment stages.PloS one · 2024Article
- Normalized group activations based feature extraction technique using heterogeneous data for Alzheimer's disease classification.PeerJ. Computer science · 2024Article
- Machine learning-based prediction of mild cognitive impairment among individuals with normal cognitive function.Frontiers in neurology · 2024Article
- Epigenome-wide DNA methylation analysis of late-stage mild cognitive impairment.Frontiers in cell and developmental biology · 2024Article
- Subject classification and cross-time prediction based on functional connectivity and white matter microstructure features in a rat model of Alzheimer's using machine learning.Alzheimer's research & therapy · 2023Article
- Multimodal multitask learning for predicting MCI to AD conversion using stacked polynomial attention network and adaptive exponential decay.Scientific reports · 2023Article
- A deep learning framework for identifying Alzheimer's disease using fMRI-based brain network.Frontiers in neuroscience · 2023Article
- A Novel Neighborhood Rough Set-Based Feature Selection Method and Its Application to Biomarker Identification of Schizophrenia.IEEE journal of biomedical and health informatics · 2023Article
- Searching for optimal machine learning model to classify mild cognitive impairment (MCI) subtypes using multimodal MRI data.Scientific reports · 2022Article
- Individual-specific networks for prediction modelling - A scoping review of methods.BMC medical research methodology · 2022Article
- Alzheimer's Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield and Amygdala Volume of Structural MRI.Frontiers in aging neuroscience · 2022Article
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- Article
- Use of machine learning to identify functional connectivity changes in a clinical cohort of patients at risk for dementia.Frontiers in aging neuroscience · 2022Article
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6 authors.
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Abstract
Using the Pearson correlation coefficient to constructing functional brain network has been evidenced to be an effective means to diagnose different stages of mild cognitive impairment (MCI) disease. In this study, we investigated the efficacy of a classification framework to distinguish early mild cognitive impairment (EMCI) from late mild cognitive impairment (LMCI) by using the effective features derived from functional brain network of three frequency bands (full-band: 0.01-0.08 Hz; slow-4: 0.027-0.08 Hz; slow-5: 0.01-0.027 Hz) at Rest. Graphic theory was performed to calculate and analyze the relationship between changes in network connectivity. Subsequently, three different algorithms [minimal redundancy maximal relevance (mRMR), sparse linear regression feature selection algorithm based on stationary selection (SS-LR), and Fisher Score (FS)] were applied to select the features of network attributes, respectively. Finally, we used the support vector machine (SVM) with nested cross validation to classify the samples into two categories to obtain unbiased results. Our results showed that the global efficiency, the local efficiency, and the average clustering coefficient were significantly higher in the slow-5 band for the LMCI-EMCI comparison, while the characteristic path length was significantly longer under most threshold values. The classification results showed that the features selected by the mRMR algorithm have higher classification performance than those selected by the SS-LR and FS algorithms. The classification results obtained by using mRMR algorithm in slow-5 band are the best, with 83.87% accuracy (ACC), 86.21% sensitivity (SEN), 81.21% specificity (SPE), and the area under receiver operating characteristic curve (AUC) of 0.905. The present results suggest that the method we proposed could effectively help diagnose MCI disease in clinic and predict its conversion to Alzheimer's disease at an early stage.
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