# Multi-Atlas Brain Network Classification through Consistency Distillation and Complementary Information Fusion

arXiv: https://arxiv.org/abs/2410.08228 · HTML: https://mlsanity.com/paper/2410.08228/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 89 references that are in the map.
- First posted on arXiv 28 Sep 2024.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Graph Contrastive Learning with Augmentations](https://mlsanity.com/paper/2010.13902/) (2020)
- [Vision Transformers Need Registers](https://mlsanity.com/paper/2309.16588/) (2023)
- [Hierarchical Graph Representation Learning with Differentiable Pooling](https://mlsanity.com/paper/1806.08804/) (2018)
- [Recipe for a General, Powerful, Scalable Graph Transformer](https://mlsanity.com/paper/2205.12454/) (2022)
- [Benchmarking Graph Neural Networks](https://mlsanity.com/paper/2003.00982/) (2020)
- [A Generalization of Transformer Networks to Graphs](https://mlsanity.com/paper/2012.09699/) (2020)
- [Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks](https://mlsanity.com/paper/1909.01315/) (2019)
- [Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks](https://mlsanity.com/paper/2203.00199/) (2022)
- [Brain Network Transformer](https://mlsanity.com/paper/2210.06681/) (2022)
- [Memory Transformer](https://mlsanity.com/paper/2006.11527/) (2020)
- [Machine Learning for Neuroimaging with Scikit-Learn](https://mlsanity.com/paper/1412.3919/) (2014)
- [Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks](https://mlsanity.com/paper/1703.02161/) (2017)
- [Community-Aware Transformer for Autism Prediction in fMRI Connectome](https://mlsanity.com/paper/2307.10181/) (2023)
- [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)
- [Contrasformer: A Brain Network Contrastive Transformer for Neurodegenerative Condition Identification](https://mlsanity.com/paper/2409.10944/) (2024)
- [Data-Driven Network Neuroscience: On Data Collection and Benchmark](https://mlsanity.com/paper/2211.12421/) (2022)
- [Integrated Brain Connectivity Analysis with fMRI, DTI, and sMRI Powered by Interpretable Graph Neural Networks](https://mlsanity.com/paper/2408.14254/) (2024)
- [A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression with MEG Brain Networks](https://mlsanity.com/paper/2005.05784/) (2020)
- [Multi-modal Graph Neural Network for Early Diagnosis of Alzheimer's Disease from sMRI and PET Scans](https://mlsanity.com/paper/2307.16366/) (2023)
- [Hierarchical Graph Convolutional Network Built by Multiscale Atlases for Brain Disorder Diagnosis Using Functional Connectivity](https://mlsanity.com/paper/2209.11232/) (2022)
- [Transformer-Based Hierarchical Clustering for Brain Network Analysis](https://mlsanity.com/paper/2305.04142/) (2023)
- [Dynamic Brain Transformer with Multi-level Attention for Functional Brain Network Analysis](https://mlsanity.com/paper/2309.01941/) (2023)
- [Pretraining is All You Need: A Multi-Atlas Enhanced Transformer Framework for Autism Spectrum Disorder Classification](https://mlsanity.com/paper/2307.01759/) (2023)

## Built on by (most cited first, 6 of 6)

- [BrainOOD: Out-of-distribution Generalizable Brain Network Analysis](https://mlsanity.com/paper/2502.01688/) (2025)
- [BrainPrompt: Multi-Level Brain Prompt Enhancement for Neurological Condition Identification](https://mlsanity.com/paper/2504.16096/) (2025)
- [Moving Beyond Functional Connectivity: Time-Series Modeling for fMRI-Based Brain Disorder Classification](https://mlsanity.com/paper/2602.08262/) (2026)
- [Improving Brain Disorder Diagnosis with Advanced Brain Function Representation and Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2504.03923/) (2025)
- [Toward a Multi-View Brain Network Foundation Model: Cross-View Consistency Learning Across Arbitrary Atlases](https://mlsanity.com/paper/2603.20348/) (2026)
- [Learning Cross-Atlas Consistent Brain Disorder Representations via Disentangled Multi-Atlas Functional Connectivity Learning](https://mlsanity.com/paper/2605.07026/) (2026)

## Abstract (arXiv)

In the realm of neuroscience, identifying distinctive patterns associated with neurological disorders via brain networks is crucial. Resting-state functional magnetic resonance imaging (fMRI) serves as a primary tool for mapping these networks by correlating blood-oxygen-level-dependent (BOLD) signals across different brain regions, defined as regions of interest (ROIs). Constructing these brain networks involves using atlases to parcellate the brain into ROIs based on various hypotheses of brain division. However, there is no standard atlas for brain network classification, leading to limitations in detecting abnormalities in disorders. Some recent methods have proposed utilizing multiple atlases, but they neglect consistency across atlases and lack ROI-level information exchange. To tackle these limitations, we propose an Atlas-Integrated Distillation and Fusion network (AIDFusion) to improve brain network classification using fMRI data. AIDFusion addresses the challenge of utilizing multiple atlases by employing a disentangle Transformer to filter out inconsistent atlas-specific information and distill distinguishable connections across atlases. It also incorporates subject- and population-level consistency constraints to enhance cross-atlas consistency. Additionally, AIDFusion employs an inter-atlas message-passing mechanism to fuse complementary information across brain regions. Experimental results on four datasets of different diseases demonstrate the effectiveness and efficiency of AIDFusion compared to state-of-the-art methods. A case study illustrates AIDFusion extract patterns that are both interpretable and consistent with established neuroscience findings.