ArticleAlzheimer's research & therapy2024
A novel spatiotemporal graph convolutional network framework for functional connectivity biomarkers identification of Alzheimer's disease.
Article in Alzheimer's research & therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Long-Acting Naltrexone Restores Network Connectivity in Subjects With Comorbid Cannabis and Opioid Use Disorder.Addiction biology · 2026Article
- Clinical impact of the Alzheimer's Disease Neuroimaging Initiative: A review of studies using ADNI data (2023 to June 2025).Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Review
- Amygdala subregional atrophy across ATN-defined Mild Cognitive Impairment subgroups.Frontiers in neuroscience · 2026Article
- A systematic study on the integration of MRI connectivity metrics for Alzheimer's diagnosis, staging, and cognitive decline prediction.Frontiers in neuroimaging · 2026Article
- Long-acting naltrexone restores network connectivity in subjects with co-morbid cannabis and opioid use disorder.bioRxiv : the preprint server for biology · 2025Article
- Plasma biomarkers as core mediators in functional network abnormalities: Evidence from a two-cohort study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Graph neural networks in Alzheimer's disease diagnosis: a review of unimodal and multimodal advances.Frontiers in neuroscience · 2025Review
- Brain network dynamics in patients with single- and multiple-domain amnestic mild cognitive impairment.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Transcranial direct current stimulation and neuronal functional connectivity in MCI: role of individual factors associated to AD.Frontiers in psychiatry · 2024Article
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Abstract
backgroundFunctional connectivity (FC) biomarkers play a crucial role in the early diagnosis and mechanistic study of Alzheimer's disease (AD). However, the identification of effective FC biomarkers remains challenging. In this study, we introduce a novel approach, the spatiotemporal graph convolutional network (ST-GCN) combined with the gradient-based class activation mapping (Grad-CAM) model (STGC-GCAM), to effectively identify FC biomarkers for AD.
methodsThis multi-center cross-racial retrospective study involved 2,272 participants, including 1,105 cognitively normal (CN) subjects, 790 mild cognitive impairment (MCI) individuals, and 377 AD patients. All participants underwent functional magnetic resonance imaging (fMRI) and T1-weighted MRI scans. In this study, firstly, we optimized the STGC-GCAM model to enhance classification accuracy. Secondly, we identified novel AD-associated biomarkers using the optimized model. Thirdly, we validated the imaging biomarkers using Kaplan-Meier analysis. Lastly, we performed correlation analysis and causal mediation analysis to confirm the physiological significance of the identified biomarkers.
resultsThe STGC-GCAM model demonstrated great classification performance (The average area under the curve (AUC) values for different categories were: CN vs MCI = 0.98, CN vs AD = 0.95, MCI vs AD = 0.96, stable MCI vs progressive MCI = 0.79). Notably, the model identified specific brain regions, including the sensorimotor network (SMN), visual network (VN), and default mode network (DMN), as key differentiators between patients and CN individuals. These brain regions exhibited significant associations with the severity of cognitive impairment (p < 0.05). Moreover, the topological features of important brain regions demonstrated excellent predictive capability for the conversion from MCI to AD (Hazard ratio = 3.885, p < 0.001). Additionally, our findings revealed that the topological features of these brain regions mediated the impact of amyloid beta (Aβ) deposition (bootstrapped average causal mediation effect: β = -0.01 [-0.025, 0.00], p < 0.001) and brain glucose metabolism (bootstrapped average causal mediation effect: β = -0.02 [-0.04, -0.001], p < 0.001) on cognitive status.
conclusionsThis study presents the STGC-GCAM framework, which identifies FC biomarkers using a large multi-site fMRI dataset.
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