# Data-Driven Network Neuroscience: On Data Collection and Benchmark

arXiv: https://arxiv.org/abs/2211.12421 · HTML: https://mlsanity.com/paper/2211.12421/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 72 references that are in the map.
- First posted on arXiv 11 Nov 2022.

## Builds on (most cited first, 16 of 16)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [TUDataset: A collection of benchmark datasets for learning with graphs](https://mlsanity.com/paper/2007.08663/) (2020)
- [Benchmarking Graph Neural Networks](https://mlsanity.com/paper/2003.00982/) (2020)
- [Residual Gated Graph ConvNets](https://mlsanity.com/paper/1711.07553/) (2017)
- [A Fair Comparison of Graph Neural Networks for Graph Classification](https://mlsanity.com/paper/1912.09893/) (2019)
- [Machine Learning for Neuroimaging with Scikit-Learn](https://mlsanity.com/paper/1412.3919/) (2014)
- [Spectral Graph Convolutions for Population-based Disease Prediction](https://mlsanity.com/paper/1703.03020/) (2017)
- [Explainable Classification of Brain Networks via Contrast Subgraphs](https://mlsanity.com/paper/2006.05176/) (2020)
- [Exploring the reproducibility of functional connectivity alterations in Parkinson's Disease](https://mlsanity.com/paper/1611.04794/) (2016)
- [Scikit-network: Graph Analysis in Python](https://mlsanity.com/paper/2009.07660/) (2020)
- [Machine Learning Methods for Brain Network Classification: Application to Autism Diagnosis using Cortical Morphological Networks](https://mlsanity.com/paper/2004.13321/) (2020)

## Built on by (most cited first, 12 of 12)

- [Contrasformer: A Brain Network Contrastive Transformer for Neurodegenerative Condition Identification](https://mlsanity.com/paper/2409.10944/) (2024)
- [BrainOOD: Out-of-distribution Generalizable Brain Network Analysis](https://mlsanity.com/paper/2502.01688/) (2025)
- [Multi-Atlas Brain Network Classification through Consistency Distillation and Complementary Information Fusion](https://mlsanity.com/paper/2410.08228/) (2024)
- [Contrastive Graph Pooling for Explainable Classification of Brain Networks](https://mlsanity.com/paper/2307.11133/) (2023)
- [BrainPrompt: Multi-Level Brain Prompt Enhancement for Neurological Condition Identification](https://mlsanity.com/paper/2504.16096/) (2025)
- [Ada-FCN: Adaptive Frequency-Coupled Network for fMRI-Based Brain Disorder Classification](https://mlsanity.com/paper/2511.04718/) (2025)
- [Machine Learning on Dynamic Functional Connectivity: Promise, Pitfalls, and Interpretations](https://mlsanity.com/paper/2409.11377/) (2024)
- [Moving Beyond Functional Connectivity: Time-Series Modeling for fMRI-Based Brain Disorder Classification](https://mlsanity.com/paper/2602.08262/) (2026)
- [Online Partitioned Local Depth for semi-supervised applications](https://mlsanity.com/paper/2512.15436/) (2025)
- [Toward a Multi-View Brain Network Foundation Model: Cross-View Consistency Learning Across Arbitrary Atlases](https://mlsanity.com/paper/2603.20348/) (2026)
- [Learning Multi-Scale Hypergraph for High-Order Brain Connectivity Analysis](https://mlsanity.com/paper/2606.03310/) (2026)
- [Interpretable Information-Decomposed Brain Graph Learning for fMRI-based Disease Diagnosis](https://mlsanity.com/paper/2608.20380/) (2026)

## Abstract (arXiv)

This paper presents a comprehensive and quality collection of functional human brain network data for potential research in the intersection of neuroscience, machine learning, and graph analytics. Anatomical and functional MRI images have been used to understand the functional connectivity of the human brain and are particularly important in identifying underlying neurodegenerative conditions such as Alzheimer's, Parkinson's, and Autism. Recently, the study of the brain in the form of brain networks using machine learning and graph analytics has become increasingly popular, especially to predict the early onset of these conditions. A brain network, represented as a graph, retains rich structural and positional information that traditional examination methods are unable to capture. However, the lack of publicly accessible brain network data prevents researchers from data-driven explorations. One of the main difficulties lies in the complicated domain-specific preprocessing steps and the exhaustive computation required to convert the data from MRI images into brain networks. We bridge this gap by collecting a large amount of MRI images from public databases and a private source, working with domain experts to make sensible design choices, and preprocessing the MRI images to produce a collection of brain network datasets. The datasets originate from 6 different sources, cover 4 brain conditions, and consist of a total of 2,702 subjects. We test our graph datasets on 12 machine learning models to provide baselines and validate the data quality on a recent graph analysis model. To lower the barrier to entry and promote the research in this interdisciplinary field, we release our brain network data and complete preprocessing details including codes at https://doi.org/10.17608/k6.auckland.21397377 and https://github.com/brainnetuoa/data_driven_network_neuroscience.