# Brain Networks and Intelligence: A Graph Neural Network Based Approach to Resting State fMRI Data

arXiv: https://arxiv.org/abs/2311.03520 · HTML: https://mlsanity.com/paper/2311.03520/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 66 references that are in the map.
- First posted on arXiv 6 Nov 2023.

## Builds on (most cited first, 18 of 18)

- [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)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [A Generalization of Transformer Networks to Graphs](https://mlsanity.com/paper/2012.09699/) (2020)
- [Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention](https://mlsanity.com/paper/2105.13495/) (2021)
- [Differentiable Graph Module (DGM) for Graph Convolutional Networks](https://mlsanity.com/paper/2002.04999/) (2020)
- [Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blocks](https://mlsanity.com/paper/1808.08127/) (2018)
- [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)
- [Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity Analysis](https://mlsanity.com/paper/2001.03690/) (2020)
- [Graph Saliency Maps through Spectral Convolutional Networks: Application to Sex Classification with Brain Connectivity](https://mlsanity.com/paper/1806.01764/) (2018)
- [IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction](https://mlsanity.com/paper/2103.15587/) (2021)
- [Accounting for Temporal Variability in Functional Magnetic Resonance Imaging Improves Prediction of Intelligence](https://mlsanity.com/paper/2211.07429/) (2022)

## Built on by (most cited first, 13 of 13)

- [DSAM: A Deep Learning Framework for Analyzing Temporal and Spatial Dynamics in Brain Networks](https://mlsanity.com/paper/2405.15805/) (2024)
- [DCA: Graph-Guided Deep Embedding Clustering for Brain Atlases](https://mlsanity.com/paper/2509.01426/) (2025)
- [Unified Cross-Modal Attention-Mixer Based Structural-Functional Connectomics Fusion for Neuropsychiatric Disorder Diagnosis](https://mlsanity.com/paper/2505.15139/) (2025)
- [Learning beyond Site Bias for OOD Generalization in Brain Networks](https://mlsanity.com/paper/2605.06050/) (2026)
- [MODL: Multilearner Online Deep Learning](https://mlsanity.com/paper/2405.18281/) (2024)
- [Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity](https://mlsanity.com/paper/2501.16345/) (2025)
- [Fast Clustering of Categorical Big Data](https://mlsanity.com/paper/2502.07081/) (2025)
- [Predicting Cognition from fMRI:A Comparative Study of Graph, Transformer, and Kernel Models Across Task and Rest Conditions](https://mlsanity.com/paper/2507.21016/) (2025)
- [A Dual-Attention Graph Network for fMRI Data Classification](https://mlsanity.com/paper/2508.13328/) (2025)
- [Geometry-Guided Generative Representation for Functional Brain Graphs](https://mlsanity.com/paper/2511.04539/) (2025)
- [Learning Structural-Functional Brain Representations through Multi-Scale Adaptive Graph Attention for Cognitive Insight](https://mlsanity.com/paper/2603.29967/) (2026)
- [Explainable Deep Learning of Resting-State Functional Connectomes Reveals Network Biomarkers of Adolescent Intelligence](https://mlsanity.com/paper/2609.33422/) (2026)
- [Temporal Geometry of Deep Networks: Hyperbolic Representations of Training Dynamics for Intrinsic Explainability](https://mlsanity.com/paper/2610.03000/) (2026)

## Abstract (arXiv)

Resting-state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli. In this paper, we present a novel modeling architecture called BrainRGIN for predicting intelligence (fluid, crystallized, and total intelligence) using graph neural networks on rsfMRI derived static functional network connectivity matrices. Extending from the existing graph convolution networks, our approach incorporates a clustering-based embedding and graph isomorphism network in the graph convolutional layer to reflect the nature of the brain sub-network organization and efficient network expression, in combination with TopK pooling and attention-based readout functions. We evaluated our proposed architecture on a large dataset, specifically the Adolescent Brain Cognitive Development Dataset, and demonstrated its effectiveness in predicting individual differences in intelligence. Our model achieved lower mean squared errors and higher correlation scores than existing relevant graph architectures and other traditional machine learning models for all of the intelligence prediction tasks. The middle frontal gyrus exhibited a significant contribution to both fluid and crystallized intelligence, suggesting their pivotal role in these cognitive processes. Total composite scores identified a diverse set of brain regions to be relevant which underscores the complex nature of total intelligence.