# Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum Disorder Classification

## TL;DR

This paper introduces a bootstrapped ensemble approach for graph convolutional neural networks to improve robustness and performance in Autism Spectrum Disorder classification using brain imaging data.

## Contribution

It proposes a novel bootstrapped G-CNN method that reduces sensitivity to graph construction choices and enhances robustness in ASD classification tasks.

## Key findings

- Improves classification accuracy on ABIDE dataset.
- Enhances robustness to noisy graph structures.
- Outperforms recent graph neural network methods.

## Abstract

Using predictive models to identify patterns that can act as biomarkers for different neuropathoglogical conditions is becoming highly prevalent. In this paper, we consider the problem of Autism Spectrum Disorder (ASD) classification where previous work has shown that it can be beneficial to incorporate a wide variety of meta features, such as socio-cultural traits, into predictive modeling. A graph-based approach naturally suits these scenarios, where a contextual graph captures traits that characterize a population, while the specific brain activity patterns are utilized as a multivariate signal at the nodes. Graph neural networks have shown improvements in inferencing with graph-structured data. Though the underlying graph strongly dictates the overall performance, there exists no systematic way of choosing an appropriate graph in practice, thus making predictive models non-robust. To address this, we propose a bootstrapped version of graph convolutional neural networks (G-CNNs) that utilizes an ensemble of weakly trained G-CNNs, and reduce the sensitivity of models on the choice of graph construction. We demonstrate its effectiveness on the challenging Autism Brain Imaging Data Exchange (ABIDE) dataset and show that our approach improves upon recently proposed graph-based neural networks. We also show that our method remains more robust to noisy graphs.