# Community-Aware Transformer for Autism Prediction in fMRI Connectome

arXiv: https://arxiv.org/abs/2307.10181 · HTML: https://mlsanity.com/paper/2307.10181/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 24 Jun 2023.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Brain Network Transformer](https://mlsanity.com/paper/2210.06681/) (2022)
- [FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation](https://mlsanity.com/paper/2205.12465/) (2022)
- [Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis](https://mlsanity.com/paper/2207.00813/) (2022)

## Built on by (most cited first, 28 of 28)

- [Towards a general-purpose foundation model for fMRI analysis](https://mlsanity.com/paper/2506.11167/) (2025)
- [Graph Neural Networks for Brain Graph Learning: A Survey](https://mlsanity.com/paper/2406.02594/) (2024)
- [Learning High-Order Relationships of Brain Regions](https://mlsanity.com/paper/2312.02203/) (2023)
- [Biologically Plausible Brain Graph Transformer](https://mlsanity.com/paper/2502.08958/) (2025)
- [Rethinking Functional Brain Connectome Analysis: Do Graph Deep Learning Models Help](https://mlsanity.com/paper/2501.17207/) (2025)
- [Multi-Atlas Brain Network Classification through Consistency Distillation and Complementary Information Fusion](https://mlsanity.com/paper/2410.08228/) (2024)
- [Core-Periphery Principle Guided State Space Model for Functional Connectome Classification](https://mlsanity.com/paper/2503.14655/) (2025)
- [MHNet: Multi-view High-order Network for Diagnosing Neurodevelopmental Disorders Using Resting-state fMRI](https://mlsanity.com/paper/2407.03217/) (2024)
- [Prompt Your Brain: Scaffold Prompt Tuning for Efficient Adaptation of fMRI Pre-trained Model](https://mlsanity.com/paper/2408.10567/) (2024)
- [RTGMFF: Enhanced fMRI-based Brain Disorder Diagnosis via ROI-driven Text Generation and Multimodal Feature Fusion](https://mlsanity.com/paper/2509.03214/) (2025)
- [GCAN: Generative Counterfactual Attention-guided Network for Explainable Cognitive Decline Diagnostics based on fMRI Functional Connectivity](https://mlsanity.com/paper/2403.01758/) (2024)
- [Self-Supervised Pre-training Tasks for an fMRI Time-series Transformer in Autism Detection](https://mlsanity.com/paper/2409.12304/) (2024)
- [STARFormer: A Novel Spatio-Temporal Aggregation Reorganization Transformer of FMRI for Brain Disorder Diagnosis](https://mlsanity.com/paper/2501.00378/) (2024)
- [BrainNetMLP: An Efficient and Effective Baseline for Functional Brain Network Classification](https://mlsanity.com/paper/2505.11538/) (2025)
- [ASDFormer: A Transformer with Mixtures of Pooling-Classifier Experts for Robust Autism Diagnosis and Biomarker Discovery](https://mlsanity.com/paper/2508.14005/) (2025)
- [BrainHGT: A Hierarchical Graph Transformer for Interpretable Brain Network Analysis](https://mlsanity.com/paper/2511.17604/) (2025)
- [Learnable Community-Aware Transformer for Brain Connectome Analysis with Token Clustering](https://mlsanity.com/paper/2403.08203/) (2024)
- [A Brain-to-Population Graph Learning Framework for Diagnosing Brain Disorders](https://mlsanity.com/paper/2506.16096/) (2025)
- [Learning the Hierarchical Organization in Brain Network for Brain Disorder Diagnosis](https://mlsanity.com/paper/2603.09606/) (2026)
- [Multimodal Connectome Fusion via Cross-Attention for Autism Spectrum Disorder Classification Using Graph Learning](https://mlsanity.com/paper/2603.15168/) (2026)
- [Exploring Subnetwork Interactions in Heterogeneous Brain Network via Prior-Informed Graph Learning](https://mlsanity.com/paper/2603.19307/) (2026)
- [Toward a Multi-View Brain Network Foundation Model: Cross-View Consistency Learning Across Arbitrary Atlases](https://mlsanity.com/paper/2603.20348/) (2026)
- [Progressive Self-Supervised Learning with Individualized Community Assignment for Brain Network Analysis](https://mlsanity.com/paper/2606.29695/) (2026)
- [SABER: A Semantic-Aligned Brain Network Analysis Framework via Multi-scale Hypergraphs](https://mlsanity.com/paper/2607.01901/) (2026)
- [Navigating Hierarchy: Hyperbolic Learning on Brain Graphs for Disorder Diagnosis](https://mlsanity.com/paper/2607.07077/) (2026)
- [MPP-GNN: Subject-Adaptive Community Detection for fMRI-Based Alzheimer's Disease Classification](https://mlsanity.com/paper/2607.28681/) (2026)
- [Information Bottleneck-Guided Adaptive Hypergraph Transformer for Brain Disease Diagnosis](https://mlsanity.com/paper/2609.37220/) (2026)
- [fMRI-TAMCL: Text-Anchored Supervised Multimodal Contrastive Learning for fMRI-Based Brain Disorder Classification](https://mlsanity.com/paper/2610.05880/) (2026)

## Abstract (arXiv)

Autism spectrum disorder(ASD) is a lifelong neurodevelopmental condition that affects social communication and behavior. Investigating functional magnetic resonance imaging (fMRI)-based brain functional connectome can aid in the understanding and diagnosis of ASD, leading to more effective treatments. The brain is modeled as a network of brain Regions of Interest (ROIs), and ROIs form communities and knowledge of these communities is crucial for ASD diagnosis. On the one hand, Transformer-based models have proven to be highly effective across several tasks, including fMRI connectome analysis to learn useful representations of ROIs. On the other hand, existing transformer-based models treat all ROIs equally and overlook the impact of community-specific associations when learning node embeddings. To fill this gap, we propose a novel method, Com-BrainTF, a hierarchical local-global transformer architecture that learns intra and inter-community aware node embeddings for ASD prediction task. Furthermore, we avoid over-parameterization by sharing the local transformer parameters for different communities but optimize unique learnable prompt tokens for each community. Our model outperforms state-of-the-art (SOTA) architecture on ABIDE dataset and has high interpretability, evident from the attention module. Our code is available at https://github.com/ubc-tea/Com-BrainTF.