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 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 https://anwar-said.github.io/anwarsaid/neurograph.html .