NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics
- URL: http://arxiv.org/abs/2306.06202v3
- Date: Wed, 22 Nov 2023 00:57:54 GMT
- Title: NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics
- Authors: Anwar Said, Roza G. Bayrak, Tyler Derr, Mudassir Shabbir, Daniel
Moyer, Catie Chang, Xenofon Koutsoukos
- Abstract summary: We introduce NeuroGraph, a collection of graph-based neuroimaging datasets.
We demonstrate its utility for predicting multiple categories of behavioral and cognitive traits.
- Score: 10.294767093317404
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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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