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September 17, 2026Machine Learning and Knowledge ExtractionOpen Access

Recurrent Graph Attention over Longitudinal Brain Networks Predicts Conversion from Mild Cognitive Impairment to Alzheimer’s Disease

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Authors

MAMedet AshimgaliyevАZАinur ZhumadillayevaMMMUSSABEK M.

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Cite This Study

Ashimgaliyev et al. (2026) studied this question.

synapsesocial.com/papers/6aabb7975f706d05830e6c17https://doi.org/10.3390/make8090285
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

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  2. 2Longitudinal Structural MRI-Based Deep Learning And Radiomics Features For Predicting Alzheimer\\\\\\\'s Disease Progression2026
  3. 3Predicting the Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Graph Frequency Bands and Functional Connectivity-Based Features2024 · 1 citations
  4. 4Interpretable temporal graph neural network for prognostic prediction of Alzheimer’s disease using longitudinal neuroimaging data2021 · 26 citations
  5. 5Utilizing graph convolutional networks for identification of mild cognitive impairment from single modal fMRI data: a multiconnection pattern combination approach2024 · 1 citations