Functional Magnetic Resonance Imaging
Functional Magnetic Resonance Imaging (fMRI) studies brain activity by measuring blood oxygenation levels, aiming to understand brain function and its relation to cognition and behavior. Current research heavily utilizes deep learning, including transformer networks, autoencoders, and diffusion models, to analyze high-dimensional fMRI data, improve spatial and temporal resolution, and decode cognitive states or even reconstruct visual imagery from brain activity. These advancements are improving diagnostic accuracy for neurological disorders like autism and Alzheimer's disease, and enabling novel applications such as personalized brain-computer interfaces and the development of more brain-like artificial intelligence models.
Papers
Looking through the mind's eye via multimodal encoder-decoder networks
Arman Afrasiyabi, Erica Busch, Rahul Singh, Dhananjay Bhaskar, Laurent Caplette, Nicholas Turk-Browne, Smita Krishnaswamy
Multi-modal Cross-domain Self-supervised Pre-training for fMRI and EEG Fusion
Xinxu Wei, Kanhao Zhao, Yong Jiao, Nancy B. Carlisle, Hua Xie, Gregory A. Fonzo, Yu Zhang