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
Improving the Diagnosis of Psychiatric Disorders with Self-Supervised Graph State Space Models
Ahmed El Gazzar, Rajat Mani Thomas, Guido Van Wingen
Transfer learning to decode brain states reflecting the relationship between cognitive tasks
Youzhi Qu, Xinyao Jian, Wenxin Che, Penghui Du, Kai Fu, Quanying Liu
Motion Correction and Volumetric Reconstruction for Fetal Functional Magnetic Resonance Imaging Data
Daniel Sobotka, Michael Ebner, Ernst Schwartz, Karl-Heinz Nenning, Athena Taymourtash, Tom Vercauteren, Sebastien Ourselin, Gregor Kasprian, Daniela Prayer, Georg Langs, Roxane Licandro
Early Disease Stage Characterization in Parkinson's Disease from Resting-state fMRI Data Using a Long Short-term Memory Network
Xueqi Guo, Sule Tinaz, Nicha C. Dvornek