Keywords: Diagnosis/Prediction, Analysis/Processing
Motivation: 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.
Goal(s): 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.
Approach: We apply our novel approach on the ADNI dataset with data augmentations such as circular time shifting to enforce time-invariant results.
Results: Experimental results show promising performance in capturing spatial-temporal data in functional MRI compared to 3D models.
Impact: The 4D CNN model improves Alzheimer’s 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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