神经图:脑连接组学中图机器学习的基准。

ArXiv Pub Date : 2024-11-22
Anwar Said, Roza G Bayrak, Tyler Derr, Mudassir Shabbir, Daniel Moyer, Catie Chang, Xenofon Koutsoukos
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引用次数: 0

摘要

机器学习为分析高维功能神经成像数据提供了一个有价值的工具,并且在预测各种神经系统疾病、精神疾病和认知模式方面被证明是有效的。在功能磁共振成像(MRI)研究中,大脑区域之间的相互作用通常使用基于图的表示来建模。图机器学习方法的潜力已经在无数领域建立起来,标志着数据解释和预测建模的变革一步。然而,尽管这些技术前景光明,但由于潜在的预处理管道数量庞大,以及基于图的数据集构建的大参数搜索空间,将这些技术转移到神经成像领域一直具有挑战性。在本文中,我们介绍了NeuroGraph,一个基于图的神经成像数据集的集合,并展示了它在预测多种行为和认知特征方面的效用。我们通过制作包含静态和动态大脑连接的35个数据集,深入研究数据集生成搜索空间,运行超过15种基准方法进行基准测试。此外,我们还提供了用于静态和动态图学习的通用框架。我们大量的实验得出了几个关键的观察结果。值得注意的是,使用相关向量作为节点特征,合并更多感兴趣的区域,以及使用更稀疏的图可以提高性能。为了促进基于图的数据驱动神经成像分析的进一步发展,我们提供了一个全面的开源Python包,其中包括基准数据集、基线实现、模型训练和标准评估。
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NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics.

Machine learning provides a valuable tool for analyzing high-dimensional functional neuroimaging data, and is proving effective in predicting various neurological conditions, psychiatric disorders, and cognitive patterns. In functional magnetic resonance imaging (MRI) research, interactions between brain regions are commonly modeled using graph-based representations. The potency of graph machine learning methods has been established across myriad domains, marking a transformative step in data interpretation and predictive modeling. Yet, despite their promise, the transposition of these techniques to the neuroimaging domain has been challenging due to the expansive number of potential preprocessing pipelines and the large parameter search space for graph-based dataset construction. In this paper, we introduce NeuroGraph, a collection of graph-based neuroimaging datasets, and demonstrated its utility for predicting multiple categories of behavioral and cognitive traits. We delve deeply into the dataset generation search space by crafting 35 datasets that encompass static and dynamic brain connectivity, running in excess of 15 baseline methods for benchmarking. Additionally, we provide generic frameworks for learning on both static and dynamic graphs. Our extensive experiments lead to several key observations. Notably, using correlation vectors as node features, incorporating larger number of regions of interest, and employing sparser graphs lead to improved performance. To foster further advancements in graph-based data driven neuroimaging analysis, we offer a comprehensive open-source Python package that includes the benchmark datasets, baseline implementations, model training, and standard evaluation.

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