Work

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

Year: 2025

Type: conference-paper

Abstract: 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.

Source: Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition

DOI: 10.58530/2025/3398

Authors: Javier Salazar Cavazos, Scott Peltier

Language: English

Topic: Functional Brain Connectivity Studies

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W4414235713

FWCI:

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