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

1 June 2025
Javier Salazar Cavazos
Scott Peltier
ArXiv (abs)PDFHTML
Main:1 Pages
5 Figures
1 Tables
Appendix:4 Pages
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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@article{cavazos2025_2506.02060,
  title={ Alzheimers Disease Classification in Functional MRI With 4D Joint Temporal-Spatial Kernels in Novel 4D CNN Model },
  author={ Javier Salazar Cavazos and Scott Peltier },
  journal={arXiv preprint arXiv:2506.02060},
  year={ 2025 }
}
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