# Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional Connectivity

arXiv: https://arxiv.org/abs/2109.03115 · HTML: https://mlsanity.com/paper/2109.03115/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 7 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2003.14111/) (2020)
- [Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention](https://mlsanity.com/paper/2105.13495/) (2021)
- [Spatio-Temporal Graph Convolution for Resting-State fMRI Analysis](https://mlsanity.com/paper/2003.10613/) (2020)

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

- [NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics](https://mlsanity.com/paper/2306.06202/) (2023)
- [Spatio-Temporal Encoding of Brain Dynamics with Surface Masked Autoencoders](https://mlsanity.com/paper/2308.05474/) (2023)
- [BrainMAP: Learning Multiple Activation Pathways in Brain Networks](https://mlsanity.com/paper/2412.17404/) (2024)
- [A Deep Generative Model of Neonatal Cortical Surface Development](https://mlsanity.com/paper/2206.07542/) (2022)
- [DBGDGM: Dynamic Brain Graph Deep Generative Model](https://mlsanity.com/paper/2301.11408/) (2023)
- [Surface Vision Transformers: Flexible Attention-Based Modelling of Biomedical Surfaces](https://mlsanity.com/paper/2204.03408/) (2022)
- [Emotional Brain State Classification on fMRI Data Using Deep Residual and Convolutional Networks](https://mlsanity.com/paper/2210.17015/) (2022)
- [Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRI](https://mlsanity.com/paper/2311.10463/) (2023)
- [Defining and Benchmarking a Data-Centric Design Space for Brain Graph Construction](https://mlsanity.com/paper/2508.12533/) (2025)

## Abstract (arXiv)

The study of functional brain connectivity (FC) is important for understanding the underlying mechanisms of many psychiatric disorders. Many recent analyses adopt graph convolutional networks, to study non-linear interactions between functionally-correlated states. However, although patterns of brain activation are known to be hierarchically organised in both space and time, many methods have failed to extract powerful spatio-temporal features. To overcome those challenges, and improve understanding of long-range functional dynamics, we translate an approach, from the domain of skeleton-based action recognition, designed to model interactions across space and time. We evaluate this approach using the Human Connectome Project (HCP) dataset on sex classification and fluid intelligence prediction. To account for subject topographic variability of functional organisation, we modelled functional connectomes using multi-resolution dual-regressed (subject-specific) ICA nodes. Results show a prediction accuracy of 94.4% for sex classification (an increase of 6.2% compared to other methods), and an improvement of correlation with fluid intelligence of 0.325 vs 0.144, relative to a baseline model that encodes space and time separately. Results suggest that explicit encoding of spatio-temporal dynamics of brain functional activity may improve the precision with which behavioural and cognitive phenotypes may be predicted in the future.