# A Graph Neural Network Framework for Causal Inference in Brain Networks

arXiv: https://arxiv.org/abs/2010.07143 · HTML: https://mlsanity.com/paper/2010.07143/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2020.

## Built on by (most cited first, 16 of 16)

- [Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention](https://mlsanity.com/paper/2105.13495/) (2021)
- [Machine Learning and Deep Learning -- A review for Ecologists](https://mlsanity.com/paper/2204.05023/) (2022)
- [PTGB: Pre-Train Graph Neural Networks for Brain Network Analysis](https://mlsanity.com/paper/2305.14376/) (2023)
- [Exploring Causal Learning through Graph Neural Networks: An In-depth Review](https://mlsanity.com/paper/2311.14994/) (2023)
- [Heterogeneous Graph-Based Multimodal Brain Network Learning](https://mlsanity.com/paper/2110.08465/) (2021)
- [Transferability of coVariance Neural Networks and Application to Interpretable Brain Age Prediction using Anatomical Features](https://mlsanity.com/paper/2305.01807/) (2023)
- [Forecasting Brain Activity Based on Models of Spatio-Temporal Brain Dynamics: A Comparison of Graph Neural Network Architectures](https://mlsanity.com/paper/2112.04266/) (2021)
- [Brain Effective Connectome based on fMRI and DTI Data: Bayesian Causal Learning and Assessment](https://mlsanity.com/paper/2302.05451/) (2023)
- [Reimagining Urban Science: Scaling Causal Inference with Large Language Models](https://mlsanity.com/paper/2504.12345/) (2025)
- [There is more to graphs than meets the eye: Learning universal features with self-supervision](https://mlsanity.com/paper/2305.19871/) (2023)
- [Causal Graph Neural Networks for Healthcare](https://mlsanity.com/paper/2511.02531/) (2025)
- [Tau-BNO: Brain Neural Operator for Tau Transport Model](https://mlsanity.com/paper/2603.08108/) (2026)
- [Blood Glucose Control Via Pre-trained Counterfactual Invertible Neural Networks](https://mlsanity.com/paper/2405.17458/) (2024)
- [Brain Network Analysis Based on Fine-tuned Self-supervised Model for Brain Disease Diagnosis](https://mlsanity.com/paper/2506.11671/) (2025)
- [Integrating Causal Inference with Graph Neural Networks for Alzheimer's Disease Analysis](https://mlsanity.com/paper/2511.14922/) (2025)
- [A Counterfactual Graph Approach to Oppositional Narrative Analysis](https://mlsanity.com/paper/2603.06135/) (2026)

## Abstract (arXiv)

A central question in neuroscience is how self-organizing dynamic interactions in the brain emerge on their relatively static structural backbone. Due to the complexity of spatial and temporal dependencies between different brain areas, fully comprehending the interplay between structure and function is still challenging and an area of intense research. In this paper we present a graph neural network (GNN) framework, to describe functional interactions based on the structural anatomical layout. A GNN allows us to process graph-structured spatio-temporal signals, providing a possibility to combine structural information derived from diffusion tensor imaging (DTI) with temporal neural activity profiles, like observed in functional magnetic resonance imaging (fMRI). Moreover, dynamic interactions between different brain regions learned by this data-driven approach can provide a multi-modal measure of causal connectivity strength. We assess the proposed model's accuracy by evaluating its capabilities to replicate empirically observed neural activation profiles, and compare the performance to those of a vector auto regression (VAR), like typically used in Granger causality. We show that GNNs are able to capture long-term dependencies in data and also computationally scale up to the analysis of large-scale networks. Finally we confirm that features learned by a GNN can generalize across MRI scanner types and acquisition protocols, by demonstrating that the performance on small datasets can be improved by pre-training the GNN on data from an earlier and different study. We conclude that the proposed multi-modal GNN framework can provide a novel perspective on the structure-function relationship in the brain. Therewith this approach can be promising for the characterization of the information flow in brain networks.