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 remains surprisingly under-explored due to the
expansive preprocessing pipeline and large parameter search space for
graph-based datasets construction. In this paper, we introduce NeuroGraph, a
collection of graph-based neuroimaging datasets that span multiple categories
of behavioral and cognitive traits. We delve deeply into the dataset generation
search space by crafting 35 datasets within both static and dynamic contexts,
running in excess of 15 baseline methods for benchmarking. Additionally, we
provide generic frameworks for learning on dynamic as well as static 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, we offer a
comprehensive open source Python package that includes the datasets, baseline
implementations, model training, and standard evaluation. The package is
publicly accessible at this https URL .

介绍了一种名为 NeuroGraph 的开源 Python 软件包，它包含了一系列基于图形的神经影像数据集以及基准的实现、模型训练和标准评估，可以用于机器学习研究高维功能性神经影像数据的分析和预测。