# A Brain-to-Population Graph Learning Framework for Diagnosing Brain Disorders

arXiv: https://arxiv.org/abs/2506.16096 · HTML: https://mlsanity.com/paper/2506.16096/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 101 references that are in the map.
- First posted on arXiv 19 Jun 2025.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease](https://mlsanity.com/paper/1806.01738/) (2018)
- [Brain Network Transformer](https://mlsanity.com/paper/2210.06681/) (2022)
- [BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks](https://mlsanity.com/paper/2204.07054/) (2022)
- [Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model](https://mlsanity.com/paper/2304.11332/) (2023)
- [Sparse Graph Attention Networks](https://mlsanity.com/paper/1912.00552/) (2019)
- [Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models](https://mlsanity.com/paper/2310.03182/) (2023)
- [InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction](https://mlsanity.com/paper/1903.04233/) (2019)
- [Multi-modal Graph Learning for Disease Prediction](https://mlsanity.com/paper/2203.05880/) (2022)
- [Community-Aware Transformer for Autism Prediction in fMRI Connectome](https://mlsanity.com/paper/2307.10181/) (2023)
- [Edge-variational Graph Convolutional Networks for Uncertainty-aware Disease Prediction](https://mlsanity.com/paper/2009.02759/) (2020)
- [Fairness-Aware Graph Neural Networks: A Survey](https://mlsanity.com/paper/2307.03929/) (2023)
- [MMGPL: Multimodal Medical Data Analysis with Graph Prompt Learning](https://mlsanity.com/paper/2312.14574/) (2023)
- [Heterogeneous Graph-Based Multimodal Brain Network Learning](https://mlsanity.com/paper/2110.08465/) (2021)
- [DynDepNet: Learning Time-Varying Dependency Structures from fMRI Data via Dynamic Graph Structure Learning](https://mlsanity.com/paper/2209.13513/) (2022)
- [Hierarchical feature extraction on functional brain networks for autism spectrum disorder identification with resting-state fMRI data](https://mlsanity.com/paper/2412.02424/) (2024)

## Abstract (arXiv)

Recent developed graph-based methods for diagnosing brain disorders using functional connectivity highly rely on predefined brain atlases, but overlook the rich information embedded within atlases and the confounding effects of site and phenotype variability. To address these challenges, we propose a two-stage Brain-to-Population Graph Learning (B2P-GL) framework that integrates the semantic similarity of brain regions and condition-based population graph modeling. In the first stage, termed brain representation learning, we leverage brain atlas knowledge from GPT-4 to enrich the graph representation and refine the brain graph through an adaptive node reassignment graph attention network. In the second stage, termed population disorder diagnosis, phenotypic data is incorporated into population graph construction and feature fusion to mitigate confounding effects and enhance diagnosis performance. Experiments on the ABIDE I, ADHD-200, and Rest-meta-MDD datasets show that B2P-GL outperforms state-of-the-art methods in prediction accuracy while enhancing interpretability. Overall, our proposed framework offers a reliable and personalized approach to brain disorder diagnosis, advancing clinical applicability.