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
Machine Learning-based Estimation of Respiratory Fluctuations in a Healthy Adult Population using BOLD fMRI and Head Motion Parameters
Abdoljalil Addeh, Fernando Vega, Rebecca J. Williams, G. Bruce Pike, M. Ethan MacDonald
Neuro-Vision to Language: Enhancing Brain Recording-based Visual Reconstruction and Language Interaction
Guobin Shen, Dongcheng Zhao, Xiang He, Linghao Feng, Yiting Dong, Jihang Wang, Qian Zhang, Yi Zeng