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

• URL: http://arxiv.org/abs/2506.02060v1
• Date: Sun, 01 Jun 2025 15:57:53 GMT
• Title: Alzheimers Disease Classification in Functional MRI With 4D Joint Temporal-Spatial Kernels in Novel 4D CNN Model
• Authors: Javier Salazar Cavazos, Scott Peltier,
• Abstract summary: We develop a novel 4D convolution network to extract 4D joint temporal-spatial kernels.<n>The 4D CNN model improves Alzheimers disease diagnosis for rs-fMRI data, enabling earlier detection and better interventions.<n>Future research could explore task-based fMRI applications and regression tasks, enhancing understanding of cognitive performance and disease progression.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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