ArticleBrain and behavior2019
Functional connectivity between white matter and gray matter based on fMRI for Alzheimer's disease classification.
Article in Brain and behavior, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning applications in vascular neuroimaging for the diagnosis and prognosis of cognitive impairment and dementia: a systematic review and meta-analysis.Alzheimer's research & therapy · 2025Pooled it
- Diagnostic power of resting-state fMRI for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: A systematic review.Human brain mapping · 2021Pooled it
- Mapping the functional connectome between grey matter and white matter.Communications biology · 2026Article
- Investigating White Matter Functional Network Connectivity Across the Alzheimer's Disease Spectrum Using Resting-State fMRI.bioRxiv : the preprint server for biology · 2026Article
- Investigating white matter functional network connectivity across the Alzheimer's disease spectrum using resting-state fMRI.Frontiers in neuroimaging · 2026Article
- Development and validation of a machine learning-based risk prediction model for post-stroke cognitive impairment.Scientific reports · 2025Article
- Machine-learning models for Alzheimer's disease diagnosis using neuroimaging data: survey, reproducibility, and generalizability evaluation.Brain informatics · 2025Review
- White matter functional connectome gradient dysfunction in major depressive disorder.Psychoradiology · 2025Article
- Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis.Journal of medical Internet research · 2024Article
- fMRI-based Alzheimer's disease detection via functional connectivity analysis: a systematic review.PeerJ. Computer science · 2024Article
- fMRI-Based Alzheimer's Disease Detection Using the SAS Method with Multi-Layer Perceptron Network.Brain sciences · 2023Article
- A Comparative Study of Correlation Methods in Functional Connectivity Analysis Using fMRI Data of Alzheimer's Patients.Journal of biomedical physics & engineering · 2023Article
- Effective Connectivity Evaluation of Resting-State Brain Networks in Alzheimer's Disease, Amnestic Mild Cognitive Impairment, and Normal Aging: An Exploratory Study.Brain sciences · 2023Article
- Hippocampus-based static functional connectivity mapping within white matter in mild cognitive impairment.Brain structure & function · 2022Article
- Classification and Interpretability of Mild Cognitive Impairment Based on Resting-State Functional Magnetic Resonance and Ensemble Learning.Computational intelligence and neuroscience · 2022Article
- Adiponectin Levels Are Associated with White Matter Lesions (WMLs) and Cognitive Impairment.BioMed research international · 2022Article
- Altered Homotopic Functional Connectivity Within White Matter in the Early Stages of Alzheimer's Disease.Frontiers in neuroscience · 2021Article
- Abnormal Functional Connectivity of the Amygdala in Mild Cognitive Impairment Patients With Depression Symptoms Revealed by Resting-State fMRI.Frontiers in psychiatry · 2021Article
- Functional connectivity between white matter and gray matter based on fMRI for Alzheimer's disease classification.Brain and behavior · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionAlzheimer's disease (AD) is a chronic neurodegenerative disease that generally starts slowly and leads to deterioration over time. Finding biomarkers more effective to predict AD transition is important for clinical medicine. And current research indicated that the lesion regions occur in both gray matter (GM) and white matter (WM).
methodsThis paper extracted BOLD time series from WM and GM, combined WM and GM together for analysis, constructed functional connectivity (FC) of static (sWGFC) and dynamic (dWGFC) between WM and GM, as well as static (sGFC) and dynamic (dGFC) FC within GM in order to evaluate the methods and areas most useful as feature sets for distinguishing NC from AD. These features will be evaluated using support vector machine (SVM) classifiers.
resultsThe FC constructed by WM BOLD time series based on fMRI showed widely differences between the AD group and NC group. In terms of the results of the classification, the performance of feature subsets selected from sWGFC was better than sGFC, and the performance of feature subsets selected from dWGFC was better than dGFC. Overall, the feature subsets selected from dWGFC was the best.
conclusionThese results indicated that there is a wide range of disconnection between WM and GM in AD, and association between WM and GM based on fMRI only is an effective strategy, and the FC between WM and GM could be a potential biomarker in the process of cognitive impairment and AD.
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