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
A Generative Self-Supervised Framework using Functional Connectivity in fMRI Data
Jungwon Choi, Seongho Keum, EungGu Yun, Byung-Hoon Kim, Juho Lee
Localizing and Assessing Node Significance in Default Mode Network using Sub-Community Detection in Mild Cognitive Impairment
Ameiy Acharya, Chakka Sai Pradeep, Neelam Sinha
Brainformer: Mimic Human Visual Brain Functions to Machine Vision Models via fMRI
Xuan-Bac Nguyen, Xin Li, Pawan Sinha, Samee U. Khan, Khoa Luu
A Compact Implicit Neural Representation for Efficient Storage of Massive 4D Functional Magnetic Resonance Imaging
Ruoran Li, Runzhao Yang, Wenxin Xiang, Yuxiao Cheng, Tingxiong Xiao, Jinli Suo
BLIS-Net: Classifying and Analyzing Signals on Graphs
Charles Xu, Laney Goldman, Valentina Guo, Benjamin Hollander-Bodie, Maedee Trank-Greene, Ian Adelstein, Edward De Brouwer, Rex Ying, Smita Krishnaswamy, Michael Perlmutter
Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Trees
Bryan Andrews, Joseph Ramsey, Ruben Sanchez-Romero, Jazmin Camchong, Erich Kummerfeld