# MCDGLN: Masked Connection-based Dynamic Graph Learning Network for Autism Spectrum Disorder

arXiv: https://arxiv.org/abs/2409.06163 · HTML: https://mlsanity.com/paper/2409.06163/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 41 references that are in the map.
- First posted on arXiv 10 Sep 2024.

## Builds on (most cited first, 11 of 11)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Brain Network Transformer](https://mlsanity.com/paper/2210.06681/) (2022)
- [Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention](https://mlsanity.com/paper/2105.13495/) (2021)
- [GATE: Graph CCA for Temporal SElf-supervised Learning for Label-efficient fMRI Analysis](https://mlsanity.com/paper/2203.09034/) (2022)
- [Analyzing complex functional brain networks: fusing statistics and network science to understand the brain](https://mlsanity.com/paper/1302.5721/) (2013)
- [A deep learning model for data-driven discovery of functional connectivity](https://mlsanity.com/paper/2112.04013/) (2021)
- [Dynamic Adaptive Spatio-temporal Graph Convolution for fMRI Modelling](https://mlsanity.com/paper/2109.12517/) (2021)
- [Spatial-Temporal Convolutional Attention for Mapping Functional Brain Networks](https://mlsanity.com/paper/2211.02315/) (2022)
- [Spatial Temporal Graph Convolution with Graph Structure Self-learning for Early MCI Detection](https://mlsanity.com/paper/2211.06161/) (2022)

## Built on by (most cited first, 1 of 1)

- [BrainSTR: Spatio-Temporal Contrastive Learning for Interpretable Dynamic Brain Network Modeling](https://mlsanity.com/paper/2603.09825/) (2026)

## Abstract (arXiv)

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by complex physiological processes. Previous research has predominantly focused on static cerebral interactions, often neglecting the brain's dynamic nature and the challenges posed by network noise. To address these gaps, we introduce the Masked Connection-based Dynamic Graph Learning Network (MCDGLN). Our approach first segments BOLD signals using sliding temporal windows to capture dynamic brain characteristics. We then employ a specialized weighted edge aggregation (WEA) module, which uses the cross convolution with channel-wise element-wise convolutional kernel, to integrate dynamic functional connectivity and to isolating task-relevant connections. This is followed by topological feature extraction via a hierarchical graph convolutional network (HGCN), with key attributes highlighted by a self-attention module. Crucially, we refine static functional connections using a customized task-specific mask, reducing noise and pruning irrelevant links. The attention-based connection encoder (ACE) then enhances critical connections and compresses static features. The combined features are subsequently used for classification. Applied to the Autism Brain Imaging Data Exchange I (ABIDE I) dataset, our framework achieves a 73.3% classification accuracy between ASD and Typical Control (TC) groups among 1,035 subjects. The pivotal roles of WEA and ACE in refining connectivity and enhancing classification accuracy underscore their importance in capturing ASD-specific features, offering new insights into the disorder.