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.

翻译：机器学习为分析高维功能神经成像数据提供了宝贵的工具，并在预测各种神经系统疾病、精神疾病和认知模式方面展现出有效性。在功能磁共振成像研究中，脑区之间的相互作用通常基于图表示进行建模。图机器学习方法已在众多领域确立其效力，标志着数据解释和预测建模的变革性进展。然而，尽管前景广阔，由于潜在预处理流程数量庞大以及基于图的数据集构建参数搜索空间广阔，这些技术向神经成像领域的迁移仍面临挑战。本文提出NeuroGraph——一个基于图的神经成像数据集集合，并展示了其在预测多类别行为和认知特征方面的实用性。通过构建涵盖静态与动态脑连接的35个数据集，我们深入探索了数据集生成的搜索空间，并运行了超过15种基线方法进行基准测试。此外，针对静态和动态图学习，我们提供了通用框架。大量实验得出了几项关键发现：值得注意的是，将相关性向量作为节点特征、纳入更多感兴趣区域以及采用更稀疏的图能提升性能。为促进基于图的神经成像数据分析进一步发展，我们提供了一个全面的开源Python软件包，包含基准数据集、基线实现、模型训练及标准评估流程。