Basque Center on Cognition, Brain, and Language (BCBL)（巴斯克认知、大脑与语言中心（BCBL））；Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School（哈佛医学院 Athinoula A. Martinos 生物医学成像中心）；Ikerbasque, Basque Foundation for Science（伊克尔巴斯克基金会（巴斯克科学基金会））；CSAIL, Massachusetts Institute of Technology (MIT)（麻省理工学院计算机科学与人工智能实验室（CSAIL））
Fernando Pérez-Bueno, Hongwei Bran Li, Matthew S. Rosen, Shahin Nasr, Cesar Caballero-Gaudes, Juan Eugenio Iglesias
While functional Magnetic Resonance Imaging (fMRI) offers valuable insights into cognitive processes, its inherent spatial limitations pose challenges for detailed analysis of the fine-grained functional architecture of the brain. More specifically, MRI scanner and sequence specifications impose a trade-off between temporal resolution, spatial resolution, signal-to-noise ratio, and scan time. Deep Learning (DL) Super-Resolution (SR) methods have emerged as a promising solution to enhance fMRI resolution, generating high-resolution (HR) images from low-resolution (LR) images typically acquired with lower scanning times. However, most existing SR approaches depend on supervised DL techniques, which require training ground truth (GT) HR data, which is often difficult to acquire and simultaneously sets a bound for how far SR can go. In this paper, we introduce a novel self-supervised DL SR model that combines a DL network with an analytical approach and Total Variation (TV) regularization. Our method eliminates the need for external GT images, achieving competitive performance compared to supervised DL techniques and preserving the functional maps.
Resolution- and Stimulus-agnostic Super-Resolution of Ultra-High-Field Functional MRI: Application to Visual Studies
Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School（哈佛医学院Athinoula A.马蒂诺斯生物医学影像中心）；CSAIL, Massachusetts Institute of Technology (MIT)（麻省理工学院计算机科学与人工智能实验室）
NeuroGAN-3D: Enhancing Intrinsic Functional Brain Networks via High-Fidelity 3D Generative Super-Resolution
Georgia State University（佐治亚州立大学）；Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)（神经影像与数据科学转化研究三机构中心（TReNDS））；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）
SST-DUNet: Automated preclinical functional MRI skull stripping using Smart Swin Transformer and Dense UNet
School of Information Technology, Carleton University（卡尔顿大学信息技术学院）；Center for Translational NeuroImaging (CTNI), Northeastern University（东北大学转化神经影像学中心）；College of Science and Humanity Studies, Prince Sattam Bin Abdulaziz University（萨塔姆·本·阿卜杜勒阿齐兹王子大学科学与人文研究学院）；Tessellis Ltd.（泰塞利斯有限公司）
20-fold Accelerated 7T fMRI Using Referenceless Self-Supervised Deep Learning Reconstruction
Department of Electrical and Computer Engineering（电气与计算机工程系）；Center for Magnetic Resonance Research（磁共振研究中心）；Department of Neurosurgery, University of Minnesota（明尼苏达大学神经外科系）；University of Minnesota（明尼苏达大学）；Minneapolis, MN, USA（美国明尼苏达州明尼阿波利斯市（注：这部分按要求不应提取，实际是地址信息，此处仅为说明））
Whole-brain Transferable Representations from Large-Scale fMRI Data Improve Task-Evoked Brain Activity Decoding
Gamania Digital Entertainment Co., Ltd.（Gamania数字娱乐有限公司）；Institute of Information Science, Academia Sinica（Academia Sinica信息科学研究所）；Sony Computer Science Laboratories, Inc.（索尼计算机科学实验室有限公司）
Spec2VolCAMU-Net: A Spectrogram-to-Volume Model for EEG-to-fMRI Reconstruction based on Multi-directional Time-Frequency Convolutional Attention Encoder and Vision-Mamba U-Net
School of Artificial Intelligence, Chongqing University of Technology（人工智能学院，重庆理工大学）
Progress Towards Decoding Visual Imagery via fNIRS
McGill University（麦吉尔大学）；Princeton Neuroscience Institute（普林斯顿神经科学研究所）；Medical AI Research Center (MedARC)（医学人工智能研究中心）；Stability AI；University of Waterloo（滑铁卢大学）
Deep reinforcement learning for fMRI prediction of Autism Spectrum Disorder
Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；New York University Langone Medical Center（纽约大学朗格尼医学中心）；Indian Institute of Technology, Madras（印度理工学院马德拉斯分校）
目的：由于功能性MRI（fMRI）数据集一般都很小，我们寻求一种数据有效的方法来静息状态fMRI分类自闭症谱系障碍（ASD）与神经型（NT）控制。我们假设深度强化学习（DRL）分类器可以在小的fMRI训练集上有效地学习。研究方法：我们在自闭症脑成像数据交换（ABIDE）数据库中的100个图形标签对上训练了深度强化学习（DRL）分类器。为了进行比较，我们在相同的训练集上训练了一个监督深度学习（SDL）分类器。结果：DRL显著优于SDL，p值为2.4 x 10^（-7）。DRL在各种分类器性能指标方面都取得了优异的结果，包括F1得分76，而SDL为67。虽然SDL快速过拟合训练数据，但DRL以渐进的方式学习，并推广到单独的测试集。结论：DRL可以学习以数据有效的方式对ASD与NT进行分类，这样做是为了一个小的训练集。未来的工作将涉及优化神经网络的数据效率，并将该方法应用于其他功能磁共振成像数据集，即脑癌患者。
NSD-Imagery: A benchmark dataset for extending fMRI vision decoding methods to mental imagery
University of Minnesota（明尼苏达大学）；Alljoined；Princeton Neuroscience Institute（普林斯顿神经科学研究所）；Stability AI/Medical AI Research Center (MedARC)（Stability AI/医疗AI研究中心（MedARC））
Data-Efficient Psychiatric Disorder Detection via Self-supervised Learning on Frequency-enhanced Brain Networks
Institute of Innovation, Science and Sustainability（创新、科学与可持续发展研究院）；Federation University Australia（联邦大学澳大利亚分校）；Zhejiang Gongshang University（浙江工商大学）；The University of Melbourne（墨尔本大学）；RMIT University（皇家墨尔本理工学院）
