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.

In functional magnetic resonance imaging (MRI) research, interactions between brain regions are commonly modeled using graph-based representations.

However, they can also be conveniently incorporated into other platforms. Firstly, the high dimensionality of fMRI data presents a significant hurdle. Furthermore, the interpretation and analysis of fMRI data can be time-consuming and subjective. The graphical representation of fMRI data offers a plethora of opportunities to tackle these challenges. Several initiatives have been undertaken in the past decade to assemble comprehensive fMRI datasets.

However, it did not produce improvements in performance or other notable benefits, see Appendix 1. For the Exphormer, we experimented with different numbers of layers, dropout rates for the network and attention mechanism, and numbers of attention heads. The final configuration used dropout probability of 0.1, attention dropout of 0.3, 2 layers, and 4 attention heads. All experiments used learning rate decay starting at 0.001, decaying by 1e 5, over a total of 100 epochs with 5 warmup epochs. We used three different seeds for both the Exphormer and ResidualGCN and assessed the alignment with the results in Said et al. (2023), which only included one run for each experiment. Apart from evaluating performance, we investigated potential advantages of using attention-based models. Our hypothesis was that the attention mechanism could enhance robustness to data noise, particularly in scenarios where certain graph structure components, such as edges, are missing. To verify that the graph structure, nodes and edges taken together, convey meaningful information for prediction, it is important to compare models under noisy or incomplete data settings. We simulate noisy incomplete data by removing edges based on a pre-specified probability of edge removal.

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.

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.