{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:44:21Z","timestamp":1759970661925,"version":"build-2065373602"},"reference-count":55,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T00:00:00Z","timestamp":1738108800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Cancer Center Support Grant","award":["P30 CA21765","R01 CA174794","R25 CA23944"],"award-info":[{"award-number":["P30 CA21765","R01 CA174794","R25 CA23944"]}]},{"name":"National Cancer Institute and ALSAC","award":["P30 CA21765","R01 CA174794","R25 CA23944"],"award-info":[{"award-number":["P30 CA21765","R01 CA174794","R25 CA23944"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Tomography"],"abstract":"<jats:p>Purpose: Sex classification is a major benchmark of previous work in learning on the structural connectome, a naturally occurring brain graph that has proven useful for studying cognitive function and impairment. While graph neural networks (GNNs), specifically graph convolutional networks (GCNs), have gained popularity lately for their effectiveness in learning on graph data, achieving strong performance in adult sex classification tasks, their application to pediatric populations remains unexplored. We seek to characterize the capacity for GNN models to learn connectomic patterns on pediatric data through an exploration of training techniques and architectural design choices. Methods: Two datasets comprising an adult BRIGHT dataset (N = 147 Hodgkin\u2019s lymphoma survivors and N = 162 age similar controls) and a pediatric Human Connectome Project in Development (HCP-D) dataset (N = 135 healthy subjects) were utilized. Two GNN models (GCN simple and GCN residual), a deep neural network (multi-layer perceptron), and two standard machine learning models (random forest and support vector machine) were trained. Architecture exploration experiments were conducted to evaluate the impact of network depth, pooling techniques, and skip connections on the ability of GNN models to capture connectomic patterns. Models were assessed across a range of metrics including accuracy, AUC score, and adversarial robustness. Results: GNNs outperformed other models across both populations. Notably, adult GNN models achieved 85.1% accuracy in sex classification on unseen adult participants, consistent with prior studies. The extension of the adult models to the pediatric dataset and training on the smaller pediatric dataset were sub-optimal in their performance. Using adult data to augment pediatric models, the best GNN achieved comparable accuracy across unseen pediatric (83.0%) and adult (81.3%) participants. Adversarial sensitivity experiments showed that the simple GCN remained the most robust to perturbations, followed by the multi-layer perceptron and the residual GCN. Conclusions: These findings underscore the potential of GNNs in advancing our understanding of sex-specific neurological development and disorders and highlight the importance of data augmentation in overcoming challenges associated with small pediatric datasets. Further, they highlight relevant tradeoffs in the design landscape of connectomic GNNs. For example, while the simpler GNN model tested exhibits marginally worse accuracy and AUC scores in comparison to the more complex residual GNN, it demonstrates a higher degree of adversarial robustness.<\/jats:p>","DOI":"10.3390\/tomography11020014","type":"journal-article","created":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T10:36:28Z","timestamp":1738146988000},"page":"14","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Graph Neural Network Learning on the Pediatric Structural Connectome"],"prefix":"10.3390","volume":"11","author":[{"given":"Anand","family":"Srinivasan","sequence":"first","affiliation":[{"name":"Departments of Radiology, St. Jude Children\u2019s Research Hospital, Memphis, TN 38105, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8452-858X","authenticated-orcid":false,"given":"Rajikha","family":"Raja","sequence":"additional","affiliation":[{"name":"Departments of Radiology, St. Jude Children\u2019s Research Hospital, Memphis, TN 38105, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9679-2054","authenticated-orcid":false,"given":"John O.","family":"Glass","sequence":"additional","affiliation":[{"name":"Departments of Radiology, St. Jude Children\u2019s Research Hospital, Memphis, TN 38105, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6984-2407","authenticated-orcid":false,"given":"Melissa M.","family":"Hudson","sequence":"additional","affiliation":[{"name":"Department of Oncology, St. Jude Children\u2019s Research Hospital, Memphis, TN 38105, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Noah D.","family":"Sabin","sequence":"additional","affiliation":[{"name":"Departments of Radiology, St. Jude Children\u2019s Research Hospital, Memphis, TN 38105, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0476-7001","authenticated-orcid":false,"given":"Kevin R.","family":"Krull","sequence":"additional","affiliation":[{"name":"Department of Psychology and Behavioral Sciences, St. Jude Children\u2019s Research Hospital, Memphis, TN 38105, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1054-1201","authenticated-orcid":false,"given":"Wilburn E.","family":"Reddick","sequence":"additional","affiliation":[{"name":"Departments of Radiology, St. Jude Children\u2019s Research Hospital, Memphis, TN 38105, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1016\/j.neuroimage.2013.04.056","article-title":"Structural connectomics in brain diseases","volume":"80","author":"Griffa","year":"2013","journal-title":"Neuroimage"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1016\/j.neuroimage.2012.09.004","article-title":"Development of brain structural connectivity between ages 12 and 30: A 4-Tesla diffusion imaging study in 439 adolescents and adults","volume":"64","author":"Dennis","year":"2013","journal-title":"Neuroimage"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1016\/j.neuroimage.2018.08.050","article-title":"The Lifespan Human Connectome Project in Development: A large-scale study of brain connectivity development in 5\u201321 year olds","volume":"183","author":"Somerville","year":"2018","journal-title":"Neuroimage"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2147","DOI":"10.1093\/cercor\/bhq291","article-title":"Graph-theoretical analysis reveals disrupted small-world organization of cortical thickness correlation networks in temporal lobe epilepsy","volume":"21","author":"Bernhardt","year":"2011","journal-title":"Cereb. 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