# Spatial-Temporal Convolutional Attention for Mapping Functional Brain Networks

arXiv: https://arxiv.org/abs/2211.02315 · HTML: https://mlsanity.com/paper/2211.02315/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 4 Nov 2022.

## Builds on (most cited first, 1 of 1)

- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)

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

- [Understanding LLMs: A Comprehensive Overview from Training to Inference](https://mlsanity.com/paper/2401.02038/) (2024)
- [Brain-Inspired Exploration of Functional Networks and Key Neurons in Large Language Models](https://mlsanity.com/paper/2502.20408/) (2025)
- [MCDGLN: Masked Connection-based Dynamic Graph Learning Network for Autism Spectrum Disorder](https://mlsanity.com/paper/2409.06163/) (2024)
- [FNF: Functional Network Fingerprint for Large Language Models](https://mlsanity.com/paper/2601.22692/) (2026)

## Abstract (arXiv)

Using functional magnetic resonance imaging (fMRI) and deep learning to explore functional brain networks (FBNs) has attracted many researchers. However, most of these studies are still based on the temporal correlation between the sources and voxel signals, and lack of researches on the dynamics of brain function. Due to the widespread local correlations in the volumes, FBNs can be generated directly in the spatial domain in a self-supervised manner by using spatial-wise attention (SA), and the resulting FBNs has a higher spatial similarity with templates compared to the classical method. Therefore, we proposed a novel Spatial-Temporal Convolutional Attention (STCA) model to discover the dynamic FBNs by using the sliding windows. To validate the performance of the proposed method, we evaluate the approach on HCP-rest dataset. The results indicate that STCA can be used to discover FBNs in a dynamic way which provide a novel approach to better understand human brain.