Graph Neural Network Learning on the Pediatric Structural Connectome.

IF 2.2 4区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Anand Srinivasan, Rajikha Raja, John O Glass, Melissa M Hudson, Noah D Sabin, Kevin R Krull, Wilburn E Reddick
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引用次数: 0

Abstract

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's 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.

儿童结构连接体上的图神经网络学习。
目的:性别分类是先前结构连接体学习工作的主要基准,结构连接体是一种自然发生的脑图,已被证明对研究认知功能和损伤很有用。虽然图神经网络(gnn),特别是图卷积网络(GCNs)最近因其在图数据学习方面的有效性而受到欢迎,在成人性别分类任务中取得了出色的表现,但它们在儿科人群中的应用仍未得到探索。我们试图通过探索训练技术和架构设计选择来描述GNN模型在儿童数据上学习连接组模式的能力。方法:使用两个数据集,包括成人BRIGHT数据集(N = 147霍奇金淋巴瘤幸存者和N = 162年龄相仿的对照组)和儿童人类连接组发展项目(HCP-D)数据集(N = 135健康受试者)。训练了两个GNN模型(GCN简单模型和GCN残差模型)、一个深度神经网络(多层感知器)和两个标准机器学习模型(随机森林模型和支持向量机模型)。进行了架构探索实验,以评估网络深度、池化技术和跳过连接对GNN模型捕获连接组模式能力的影响。模型通过一系列指标进行评估,包括准确性、AUC评分和对抗稳健性。结果:gnn在这两个群体中都优于其他模型。值得注意的是,成人GNN模型对未见过的成年参与者的性别分类准确率达到85.1%,与先前的研究一致。将成人模型扩展到儿童数据集和在较小的儿童数据集上进行训练在性能上是次优的。使用成人数据来增强儿童模型,最佳GNN在未见过的儿童(83.0%)和成人(81.3%)参与者中实现了相当的准确性。对抗灵敏度实验表明,简单GCN对扰动的鲁棒性最强,其次是多层感知器和残差GCN。结论:这些发现强调了gnn在促进我们对性别特异性神经发育和疾病的理解方面的潜力,并强调了数据增强在克服小型儿科数据集相关挑战方面的重要性。此外,他们强调了连接体gnn设计景观中的相关权衡。例如,虽然与更复杂的残差GNN相比,测试的更简单的GNN模型显示出略差的准确性和AUC分数,但它显示出更高程度的对抗性鲁棒性。
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来源期刊
Tomography
Tomography Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
2.70
自引率
10.50%
发文量
222
期刊介绍: TomographyTM publishes basic (technical and pre-clinical) and clinical scientific articles which involve the advancement of imaging technologies. Tomography encompasses studies that use single or multiple imaging modalities including for example CT, US, PET, SPECT, MR and hyperpolarization technologies, as well as optical modalities (i.e. bioluminescence, photoacoustic, endomicroscopy, fiber optic imaging and optical computed tomography) in basic sciences, engineering, preclinical and clinical medicine. Tomography also welcomes studies involving exploration and refinement of contrast mechanisms and image-derived metrics within and across modalities toward the development of novel imaging probes for image-based feedback and intervention. The use of imaging in biology and medicine provides unparalleled opportunities to noninvasively interrogate tissues to obtain real-time dynamic and quantitative information required for diagnosis and response to interventions and to follow evolving pathological conditions. As multi-modal studies and the complexities of imaging technologies themselves are ever increasing to provide advanced information to scientists and clinicians. Tomography provides a unique publication venue allowing investigators the opportunity to more precisely communicate integrated findings related to the diverse and heterogeneous features associated with underlying anatomical, physiological, functional, metabolic and molecular genetic activities of normal and diseased tissue. Thus Tomography publishes peer-reviewed articles which involve the broad use of imaging of any tissue and disease type including both preclinical and clinical investigations. In addition, hardware/software along with chemical and molecular probe advances are welcome as they are deemed to significantly contribute towards the long-term goal of improving the overall impact of imaging on scientific and clinical discovery.
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