# 4D Spatio-Temporal Deep Learning with 4D fMRI Data for Autism Spectrum Disorder Classification

arXiv: https://arxiv.org/abs/2004.10165 · HTML: https://mlsanity.com/paper/2004.10165/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2020.

## Built on by (most cited first, 3 of 3)

- [Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review](https://mlsanity.com/paper/2206.11233/) (2022)
- [Efficient 4D fMRI ASD Classification using Spatial-Temporal-Omics-based Learning Framework](https://mlsanity.com/paper/2502.19386/) (2025)
- [Alzheimers Disease Classification in Functional MRI With 4D Joint Temporal-Spatial Kernels in Novel 4D CNN Model](https://mlsanity.com/paper/2506.02060/) (2025)

## Abstract (arXiv)

Autism spectrum disorder (ASD) is associated with behavioral and communication problems. Often, functional magnetic resonance imaging (fMRI) is used to detect and characterize brain changes related to the disorder. Recently, machine learning methods have been employed to reveal new patterns by trying to classify ASD from spatio-temporal fMRI images. Typically, these methods have either focused on temporal or spatial information processing. Instead, we propose a 4D spatio-temporal deep learning approach for ASD classification where we jointly learn from spatial and temporal data. We employ 4D convolutional neural networks and convolutional-recurrent models which outperform a previous approach with an F1-score of 0.71 compared to an F1-score of 0.65.