# Rethinking Functional Brain Connectome Analysis: Do Graph Deep Learning Models Help

arXiv: https://arxiv.org/abs/2501.17207 · HTML: https://mlsanity.com/paper/2501.17207/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 98 references that are in the map.
- First posted on arXiv 28 Jan 2025.

## Builds on (most cited first, 25 of 25)

- [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)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1801.07606/) (2018)
- [Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View](https://mlsanity.com/paper/1909.03211/) (2019)
- [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)
- [FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation](https://mlsanity.com/paper/2205.12465/) (2022)
- [A Survey of Graph Neural Networks for Social Recommender Systems](https://mlsanity.com/paper/2212.04481/) (2022)
- [Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis](https://mlsanity.com/paper/2207.00813/) (2022)
- [NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics](https://mlsanity.com/paper/2306.06202/) (2023)
- [Community-Aware Transformer for Autism Prediction in fMRI Connectome](https://mlsanity.com/paper/2307.10181/) (2023)
- [Rank Collapse Causes Over-Smoothing and Over-Correlation in Graph Neural Networks](https://mlsanity.com/paper/2308.16800/) (2023)
- [Learning High-Order Relationships of Brain Regions](https://mlsanity.com/paper/2312.02203/) (2023)
- [Analyzing complex functional brain networks: fusing statistics and network science to understand the brain](https://mlsanity.com/paper/1302.5721/) (2013)
- [A deep learning model for data-driven discovery of functional connectivity](https://mlsanity.com/paper/2112.04013/) (2021)
- [A hierarchical independent component analysis model for longitudinal Neuroimaging studies](https://mlsanity.com/paper/1808.01557/) (2018)
- [A mathematical perspective on edge-centric brain functional connectivity](https://mlsanity.com/paper/2106.10631/) (2021)
- [Effective and Interpretable fMRI Analysis via Functional Brain Network Generation](https://mlsanity.com/paper/2107.11247/) (2021)
- [Centralized and distributed cognitive task processing in the human connectome](https://mlsanity.com/paper/1802.00334/) (2018)

## Built on by (most cited first, 9 of 9)

- [Measurement noise limits the advantage of nonlinear models over linear models in biomedical prediction](https://mlsanity.com/paper/2606.18420/) (2026)
- [On the Limits of Applying Graph Transformers for Brain Connectome Classification](https://mlsanity.com/paper/2503.15902/) (2025)
- [A Benchmark Analysis of Graph and Non-Graph Methods for Caenorhabditis Elegans Neuron Classification](https://mlsanity.com/paper/2603.02241/) (2026)
- [Brain-Atlas-Guided Generative Counterfactual Attention for Explainable Cognitive Decline Diagnosis Using Multimodal Connectomes](https://mlsanity.com/paper/2606.01237/) (2026)
- [BrainLinear: A Linear Model for Brain Network Analysis in Sparse Tangent Subspaces](https://mlsanity.com/paper/2608.15266/) (2026)
- [It's the Geometry, Not the Model: Effective Rank and Subspace Alignment in Functional Connectivity Classification](https://mlsanity.com/paper/2609.29932/) (2026)
- [MAC-Net: A Multi-Task Deep Learning Framework for Modeling Cognitive Function From Task-Based fMRI](https://mlsanity.com/paper/2609.33440/) (2026)
- [Beyond Site Agreement: Re-estimation for Brain Network Generalization](https://mlsanity.com/paper/2609.34611/) (2026)
- [Temporal Geometry of Deep Networks: Hyperbolic Representations of Training Dynamics for Intrinsic Explainability](https://mlsanity.com/paper/2610.03000/) (2026)

## Abstract (arXiv)

Graph deep learning models, a class of AI-driven approaches employing a message aggregation mechanism, have gained popularity for analyzing the functional brain connectome in neuroimaging. However, their actual effectiveness remains unclear. In this study, we re-examine graph deep learning versus classical machine learning models based on four large-scale neuroimaging studies. Surprisingly, we find that the message aggregation mechanism, a hallmark of graph deep learning models, does not help with predictive performance as typically assumed, but rather consistently degrades it. To address this issue, we propose a hybrid model combining a linear model with a graph attention network through dual pathways, achieving robust predictions and enhanced interpretability by revealing both localized and global neural connectivity patterns. Our findings urge caution in adopting complex deep learning models for functional brain connectome analysis, emphasizing the need for rigorous experimental designs to establish tangible performance gains and perhaps more importantly, to pursue improvements in model interpretability.