# Recurrent Brain Graph Mapper for Predicting Time-Dependent Brain Graph Evaluation Trajectory

arXiv: https://arxiv.org/abs/2110.11237 · HTML: https://mlsanity.com/paper/2110.11237/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 6 Oct 2021.

## Builds on (most cited first, 8 of 8)

- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs](https://mlsanity.com/paper/1704.02901/) (2017)
- [Deep Graph Normalizer: A Geometric Deep Learning Approach for Estimating Connectional Brain Templates](https://mlsanity.com/paper/2012.14131/) (2020)
- [Foreseeing Brain Graph Evolution Over Time Using Deep Adversarial Network Normalizer](https://mlsanity.com/paper/2009.11166/) (2020)
- [Deep EvoGraphNet Architecture For Time-Dependent Brain Graph Data Synthesis From a Single Timepoint](https://mlsanity.com/paper/2009.13217/) (2020)
- [Language Modelling for Sound Event Detection with Teacher Forcing and Scheduled Sampling](https://mlsanity.com/paper/1907.08506/) (2019)
- [Image Evolution Trajectory Prediction and Classification from Baseline using Learning-based Patch Atlas Selection for Early Diagnosis](https://mlsanity.com/paper/1907.06064/) (2019)

## Built on by (most cited first, 4 of 4)

- [Predicting Brain Multigraph Population From a Single Graph Template for Boosting One-Shot Classification](https://mlsanity.com/paper/2209.06005/) (2022)
- [Predicting Infant Brain Connectivity with Federated Multi-Trajectory GNNs using Scarce Data](https://mlsanity.com/paper/2401.01383/) (2024)
- [Reservoir-Based Graph Convolutional Networks](https://mlsanity.com/paper/2603.24131/) (2026)
- [NeuroBRIDGE: Behavior-Conditioned Koopman Dynamics with Riemannian Alignment for Early Substance Use Initiation Prediction from Longitudinal Functional Connectome](https://mlsanity.com/paper/2603.29960/) (2026)

## Abstract (arXiv)

Several brain disorders can be detected by observing alterations in the brain's structural and functional connectivities. Neurological findings suggest that early diagnosis of brain disorders, such as mild cognitive impairment (MCI), can prevent and even reverse its development into Alzheimer's disease (AD). In this context, recent studies aimed to predict the evolution of brain connectivities over time by proposing machine learning models that work on brain images. However, such an approach is costly and time-consuming. Here, we propose to use brain connectivities as a more efficient alternative for time-dependent brain disorder diagnosis by regarding the brain as instead a large interconnected graph characterizing the interconnectivity scheme between several brain regions. We term our proposed method Recurrent Brain Graph Mapper (RBGM), a novel efficient edge-based recurrent graph neural network that predicts the time-dependent evaluation trajectory of a brain graph from a single baseline. Our RBGM contains a set of recurrent neural network-inspired mappers for each time point, where each mapper aims to project the ground-truth brain graph onto its next time point. We leverage the teacher forcing method to boost training and improve the evolved brain graph quality. To maintain the topological consistency between the predicted brain graphs and their corresponding ground-truth brain graphs at each time point, we further integrate a topological loss. We also use l1 loss to capture time-dependency and minimize the distance between the brain graph at consecutive time points for regularization. Benchmarks against several variants of RBGM and state-of-the-art methods prove that we can achieve the same accuracy in predicting brain graph evolution more efficiently, paving the way for novel graph neural network architecture and a highly efficient training scheme.