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

TL;DR

This paper introduces a novel 4D CNN model that captures both spatial and temporal features in fMRI data, improving Alzheimer's disease classification accuracy over traditional 3D models.

Contribution

The work develops a 4D convolutional network that extracts joint temporal-spatial kernels, advancing feature extraction for functional MRI analysis.

Findings

Enhanced spatial-temporal feature learning in fMRI data

Improved accuracy in Alzheimer's classification

Potential for earlier disease detection

Abstract

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.