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Neurosci."],"abstract":"<jats:p>We address the prediction of non-imaging variables based on structural brain connectivity derived from diffusion magnetic resonance images, using graph-based machine learning. We predict age and the mini-mental state examination (MMSE) score as examples of a demographic and a clinical variable. We propose a machine-learning model inspired by graph convolutional networks (GCNs), which takes a brain connectivity graph as input and processes the data separately through a parallel GCN mechanism with multiple branches. The novelty of our work lies in the model architecture, especially the Connectivity Attention Block, which learns an embedding representation of brain graphs while providing graph-level attention. We show experiments on publicly available datasets of PREVENT-AD and OASIS3. We validate our model by comparing it to existing methods and via ablations. This quantifies the degree to which the connectome varies depending on the task, which is important for improving our understanding of health and disease across the population. The proposed model generally demonstrates higher performance especially for age prediction compared to the existing machine-learning algorithms we tested, including classical methods and (graph and non-graph) deep learning.<\/jats:p>","DOI":"10.3389\/fnins.2026.1786355","type":"journal-article","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T05:47:08Z","timestamp":1779256028000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Structural connectome analysis using a graph-based deep model for prediction of non-imaging variables"],"prefix":"10.3389","volume":"20","author":[{"given":"Anees","family":"Kazi","sequence":"first","affiliation":[{"name":"Athinoula A. 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