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Abstract #5115

Graph Neural Network Learning on the Pediatric Structural Connectome

Anand Srinivasan1, Rajikha Raja1, John O Glass1, Melissa M Hudson2, Noah D Sabin1, Kevin R Krull3, and Wilburn E Reddick1
1Radiology, St Jude Children's Research Hospital, Memphis, TN, United States, 2Oncology, St Jude Children's Research Hospital, Memphis, TN, United States, 3Psychology and Biobehavioral Sciences, St Jude Children's Research Hospital, Memphis, TN, United States

Synopsis

Keywords: Diagnosis/Prediction, Analysis/Processing

Motivation: The structural connectome is a naturally occurring brain connectivity graph useful for studying cognitive function, yet machine learning applications on the connectome remain largely unexplored in pediatric populations.

Goal(s): We aimed to train models for pediatric connectome sex classification, a clinically relevant benchmark for learning on the connectome.

Approach: We trained two graph neural networks (GNNs) and a multilayer perceptron (MLP) using data obtained from 135 pediatric patients. Pediatric data was enriched with connectomes from 309 adults to test the effect on model performance.

Results: Enriching the pediatric dataset with adult data improved model performance. The best GNN achieved 84.4% pediatric classification accuracy.

Impact: Our demonstrated 84.4% accuracy using GNNs to predict sex from pediatric structural connectomes underscored the capacity of GNNs to advance our understanding of sex-specific neurological development and highlighted the potential benefit of using adult connectomic data to enrich pediatric datasets.

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Keywords