Brain Network Diffusion-Driven fMRI Connectivity Augmentation for Enhanced Autism Spectrum Disorder Diagnosis
This work addresses the challenge of small fMRI datasets for diagnosing autism spectrum disorder, representing an incremental improvement in data augmentation methods for neuroimaging.
The authors tackled the problem of limited fMRI data for autism spectrum disorder diagnosis by developing a transformer-based latent diffusion model to generate functional connectivity data, demonstrating its effectiveness as an augmentation tool to enhance recognition model performance.
Functional magnetic resonance imaging (fMRI) is an emerging neuroimaging modality that is commonly modeled as networks of Regions of Interest (ROIs) and their connections, named functional connectivity, for understanding the brain functions and mental disorders. However, due to the high cost of fMRI data acquisition and labeling, the amount of fMRI data is usually small, which largely limits the performance of recognition models. With the rise of generative models, especially diffusion models, the ability to generate realistic samples close to the real data distribution has been widely used for data augmentations. In this work, we present a transformer-based latent diffusion model for functional connectivity generation and demonstrate the effectiveness of the diffusion model as an augmentation tool for fMRI functional connectivity. Furthermore, extended experiments are conducted to provide detailed analysis of the generation quality and interpretations for the learned feature pattern. Our code will be made public upon acceptance.