Efficient 4D fMRI ASD Classification using Spatial-Temporal-Omics-based Learning Framework

TL;DR

This paper introduces an efficient spatial-temporal-omics framework for classifying ASD using 4D fMRI data, effectively capturing detailed brain connectivity patterns while reducing computational costs.

Contribution

The study presents a novel framework that combines spatial-temporal-omics features from fMRI data, improving ASD classification accuracy and efficiency over previous methods.

Findings

Outperforms previous ASD classification methods on ABIDE dataset

Preserves full spatial resolution while capturing diverse statistical features

Maintains computational efficiency with extensive experiments

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder impacting social and behavioral development. Resting-state fMRI, a non-invasive tool for capturing brain connectivity patterns, aids in early ASD diagnosis and differentiation from typical controls (TC). However, previous methods, which rely on either mean time series or full 4D data, are limited by a lack of spatial information or by high computational costs. This underscores the need for an efficient solution that preserves both spatial and temporal information. In this paper, we propose a novel, simple, and efficient spatial-temporal-omics learning framework designed to efficiently extract spatio-temporal features from fMRI for ASD classification. Our approach addresses these limitations by utilizing 3D time-domain derivatives as the spatial-temporal inter-voxel omics, which preserve full spatial resolution while capturing diverse statistical characteristics of the time series at each voxel. Meanwhile, functional connectivity features serve as the spatial-temporal inter-regional omics, capturing correlations across brain regions. Extensive experiments and ablation studies on the ABIDE dataset demonstrate that our framework significantly outperforms previous methods while maintaining computational efficiency. We believe our research offers valuable insights that will inform and advance future ASD studies, particularly in the realm of spatial-temporal-omics-based learning.