Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery

TL;DR

This paper introduces MMKGL, a novel multi-modal graph learning method that adaptively constructs and fuses graphs from different modalities to improve autism prediction and identify relevant brain biomarkers.

Contribution

The paper proposes a multi-modal graph embedding and multi-kernel learning framework that adaptively constructs separate graphs for each modality and extracts heterogeneous information for disease prediction.

Findings

Outperforms state-of-the-art methods on ABIDE dataset

Identifies discriminative brain regions associated with autism

Effectively handles negative modality impact in multi-modal data

Abstract

Due to its complexity, graph learning-based multi-modal integration and classification is one of the most challenging obstacles for disease prediction. To effectively offset the negative impact between modalities in the process of multi-modal integration and extract heterogeneous information from graphs, we propose a novel method called MMKGL (Multi-modal Multi-Kernel Graph Learning). For the problem of negative impact between modalities, we propose a multi-modal graph embedding module to construct a multi-modal graph. Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision graph are introduced for optimization during the multi-graph fusion embedding process. We then propose a multi-kernel graph learning module to extract heterogeneous information from the multi-modal graph. The information in the multi-modal graph at different levels is aggregated by convolutional kernels with different receptive field sizes, followed by generating a cross-kernel discovery tensor for disease prediction. Our method is evaluated on the benchmark Autism Brain Imaging Data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology.