ArticleFrontiers in neurology2019
Identification of the Early Stage of Alzheimer's Disease Using Structural MRI and Resting-State fMRI.
Article in Frontiers in neurology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
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Who cites it
45 citing papers in PubMed, 1 synthesis or guideline pooled 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
- The Impact of Denoising Approaches on the Relationship Between Alzheimer's Disease Diagnostic Status and Network Topology Measures.Human brain mapping · 2026Article
- An interpretable cross-attentive multi-modal MRI fusion framework for schizophrenia identification.Neuroimage. Reports · 2026Article
- Striatal functional connectivity alterations in mild cognitive impairment subtypes defined by CSF A/T biomarkers.Frontiers in aging neuroscience · 2026Article
- Translating Molecular Psychiatry: From Biomarkers to Personalized Therapies-A Narrative Review.International journal of molecular sciences · 2025Review
- Utilizing structural MRI and unsupervised clustering to differentiate schizophrenia and Alzheimer's disease in late-onset psychosis.Behavioural brain research · 2025Article
- Integration of Neuroimaging and Molecular Biomarkers in the Diagnosis of Alzheimer's Disease and Frontotemporal Dementia: The Promise of fMRI.Current Alzheimer research · 2025Review
- An objective quantitative diagnosis of depression using a local-to-global multimodal fusion graph neural network.Patterns (New York, N.Y.) · 2024Article
- Structural disruption in subjective cognitive decline and mild cognitive impairment.Brain imaging and behavior · 2024Article
- Potential Benefits of Using Artificial Intelligence to Diagnose Alzheimer's Disease.Journal of clinical neurology (Seoul, Korea) · 2024Article
- The brain network hub degeneration in Alzheimer's disease.Biophysics reports · 2024Article
- Cortical similarities in psychiatric and mood disorders identified in federated VBM analysis via COINSTAC.Patterns (New York, N.Y.) · 2024Article
- Differences of individual gray matter networks between MCI patients who converted to AD within 3 Years and nonconverters.Heliyon · 2024Article
- Cognitive Decline: Current Intervention Strategies and Integrative Therapeutic Approaches for Alzheimer's Disease.Brain sciences · 2024Review
- Towards a multimodal neuroimaging-based risk score for mild cognitive impairment by combining clinical studies with a large (N>37000) population-based study.medRxiv : the preprint server for health sciences · 2024Article
- Prediction of cognitive performance differences in older age from multimodal neuroimaging data.GeroScience · 2024Article
- Seeing beyond the symptoms: biomarkers and brain regions linked to cognitive decline in Alzheimer's disease.Frontiers in aging neuroscience · 2024Article
- fMRI-based Alzheimer's disease detection via functional connectivity analysis: a systematic review.PeerJ. Computer science · 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
- Sex-Related Disparities in the Resting State Functional Connectivity of the Locus Coeruelus and Salience Network in Preclinical Alzheimer's Disease.International journal of molecular sciences · 2023Article
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3 authors.
Funding
Abstract
Accurate prediction of the early stage of Alzheimer's disease (AD) is important but very challenging. The goal of this study was to utilize predictors for diagnosis conversion to AD based on integrating resting-state functional MRI (rs-fMRI) connectivity analysis and structural MRI (sMRI). We included 177 subjects in this study and aimed at identifying patients with mild cognitive impairment (MCI) who progress to AD, MCI converter (MCI-C), patients with MCI who do not progress to AD, MCI non-converter (MCI-NC), patients with AD, and healthy controls (HC). The graph theory was used to characterize different aspects of the rs-fMRI brain network by calculating measures of integration and segregation. The cortical and subcortical measurements, e.g., cortical thickness, were extracted from sMRI data. The rs-fMRI graph measures were combined with the sMRI measures to construct input features of a support vector machine (SVM) and classify different groups of subjects. Two feature selection algorithms [i.e., the discriminant correlation analysis (DCA) and sequential feature collection (SFC)] were used for feature reduction and selecting a subset of optimal features. Maximum accuracy of 67 and 56% for three-group ("AD, MCI-C, and MCI-NC" or "MCI-C, MCI-NC, and HC") and four-group ("AD, MCI-C, MCI-NC, and HC") classification, respectively, were obtained with the SFC feature selection algorithm. We also identified hub nodes in the rs-fMRI brain network which were associated with the early stage of AD. Our results demonstrated the potential of the proposed method based on integration of the functional and structural MRI for identification of the early stage of AD.
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