Alzheimers Disease Classification in Functional MRI With 4D Joint Temporal-Spatial Kernels in Novel 4D CNN Model
This work addresses earlier detection and better interventions for Alzheimer's disease patients using rs-fMRI data, but it appears incremental as it builds on existing 3D models by adding temporal dimensions.
The paper tackled the problem of suboptimal feature extraction in Alzheimer's disease classification from functional MRI data by developing a novel 4D CNN model with joint temporal-spatial kernels, resulting in improved diagnosis performance compared to 3D models.
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.