# A Hybrid 3DCNN and 3DC-LSTM based model for 4D Spatio-temporal fMRI data: An ABIDE Autism Classification study

arXiv: https://arxiv.org/abs/2002.05981 · HTML: https://mlsanity.com/paper/2002.05981/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2020.

## Built on by (most cited first, 6 of 6)

- [Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review](https://mlsanity.com/paper/2206.11233/) (2022)
- [Improving the Diagnosis of Psychiatric Disorders with Self-Supervised Graph State Space Models](https://mlsanity.com/paper/2206.03331/) (2022)
- [Evaluation of augmentation methods in classifying autism spectrum disorders from fMRI data with 3D convolutional neural networks](https://mlsanity.com/paper/2110.10489/) (2021)
- [fMRI-S4: learning short- and long-range dynamic fMRI dependencies using 1D Convolutions and State Space Models](https://mlsanity.com/paper/2208.04166/) (2022)
- [Deep Learning in Healthcare: An In-Depth Analysis](https://mlsanity.com/paper/2302.10904/) (2023)
- [Efficient 4D fMRI ASD Classification using Spatial-Temporal-Omics-based Learning Framework](https://mlsanity.com/paper/2502.19386/) (2025)

## Abstract (arXiv)

Functional Magnetic Resonance Imaging (fMRI) captures the temporal dynamics of neural activity as a function of spatial location in the brain. Thus, fMRI scans are represented as 4-Dimensional (3-space + 1-time) tensors. And it is widely believed that the spatio-temporal patterns in fMRI manifests as behaviour and clinical symptoms. Because of the high dimensionality (~ 1 Million) of fMRI, and the added constraints of limited cardinality of data sets, extracting such patterns are challenging. A standard approach to overcome these hurdles is to reduce the dimensionality of the data by either summarizing activation over time or space at the expense of possible loss of useful information. Here, we introduce an end-to-end algorithm capable of extracting spatiotemporal features from the full 4-D data using 3-D CNNs and 3-D Convolutional LSTMs. We evaluate our proposed model on the publicly available ABIDE dataset to demonstrate the capability of our model to classify Autism Spectrum Disorder (ASD) from resting-state fMRI data. Our results show that the proposed model achieves state of the art results on single sites with F1-scores of 0.78 and 0.7 on NYU and UM sites, respectively.