发表机构
Princeton University; Dartmouth College; Icahn School of Medicine at Mount Sinai; Harvard University; University of Texas at Dallas; Medical AI Research Center (MedARC)（普林斯顿大学; 达特茅斯学院; 西奈山伊坎医学院; 哈佛大学; 德克萨斯大学达拉斯分校; 医学人工智能研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究基于fMRI的实时闭环神经反馈分析方法落后，提出实时兼容的先进管道改编版，利用RT-Cloud平台实时扫描解码视觉感知，通过模拟分析记录性能变化因素，证明实时部署强大解码管道可行，为脑机接口应用铺路。
AI 中文摘要
基于功能磁共振成像（fMRI）的实时闭环神经反馈已取得重要科学和临床进展。然而，实时功能磁共振成像分析方法的复杂性落后于功能磁共振成像解码的当前水平，主要是计算因素所致。本文提出一种计算密集型的最先进管道（MindEye2）的实时兼容改编版，证明在此设置下仍可实现可靠的细粒度解码。利用开源、可扩展的基于云的平台RT-Cloud进行实时扫描，在向参与者展示图像后几秒内解码单次试验的视觉感知。最后，用模拟分析记录性能从离线到实时分析变化的驱动因素。这项工作证明在实时分析中部署这些强大的功能磁共振成像解码管道是可行的，为其在脑机接口用于科学发现和临床治疗铺平道路。
英文摘要
Real-time closed-loop neurofeedback based on functional magnetic resonance imaging (fMRI) has led to important scientific and clinical advances. However, the sophistication of the analysis methods used in real-time fMRI lags behind the state-of-the-art in fMRI decoding, largely due to computational factors: Most advanced decoding pipelines do not fit within the envelope of real-time processing, where the analysis needs to be conducted in a matter of seconds and without leveraging data acquired later in the session. Here, we present a real-time compatible adaptation of a computationally intensive state-of-the-art pipeline for reconstructing perceived natural images (MindEye2), and we demonstrate that reliable fine-grained decoding is still achievable in this setting. Using RT-Cloud, an open-source, scalable cloud-based platform, we performed a real-time scan where we decoded single-trial visual perception within seconds after an image was shown to the participant. Finally, we use simulated analyses to document the factors driving changes in performance from offline to real-time analysis. This work serves as a proof-of-concept that it is feasible to deploy these powerful fMRI decoding pipelines in real-time analysis, paving the way for their use in brain-computer interfaces for scientific discovery and clinical treatment.
