# Alzheimers Disease Classification in Functional MRI With 4D Joint Temporal-Spatial Kernels in Novel 4D CNN Model

arXiv: https://arxiv.org/abs/2506.02060 · HTML: https://mlsanity.com/paper/2506.02060/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 1 Jun 2025.

## Builds on (most cited first, 3 of 3)

- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [4D CNN for semantic segmentation of cardiac volumetric sequences](https://mlsanity.com/paper/1906.07295/) (2019)
- [4D Spatio-Temporal Deep Learning with 4D fMRI Data for Autism Spectrum Disorder Classification](https://mlsanity.com/paper/2004.10165/) (2020)

## Abstract (arXiv)

Previous works in the literature apply 3D spatial-only models on 4D functional MRI data leading to possible sub-par feature extraction to be used for downstream tasks like classification. In this work, we aim to develop a novel 4D convolution network to extract 4D joint temporal-spatial kernels that not only learn spatial information but in addition also capture temporal dynamics. Experimental results show promising performance in capturing spatial-temporal data in functional MRI compared to 3D models. The 4D CNN model improves Alzheimers disease diagnosis for rs-fMRI data, enabling earlier detection and better interventions. Future research could explore task-based fMRI applications and regression tasks, enhancing understanding of cognitive performance and disease progression.