Emmanuel Mignot on Muck Rack

Emmanuel Mignot

United States
As seen in: HuffPost, Nature, Wiley Online Library, Agenda (World Economic Forum), JAMA (The Journal of the American Medical Association), JAMA: Journal of the American Medical Association, The New England Journal of Medicine, The Lancet, Thrive Global, Digital Biomarkers and
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Articles

百年谜题终破解：发作性睡病病因与食欲素系统的发现及其临床转化

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By Emmanuel Mignot
| Tiger Sniff
2026-06-02 08:04 一种奇特疾病：突然睡去、清醒却不能动 这种奇特的疾病，其实早在19世纪末就进入了医学界的视野。 患者会在白天过度嗜睡，更吓人的是，他们可能在开车、工作，甚至交谈时，毫无征兆地、不可控制地睡去。另有约70%的患者会出现猝倒——当体验大笑或兴奋等强烈情绪时，全身或部分肌肉会突然失去张力，瞬间瘫软，但他们的意识是完全清醒的。这种肌肉失控可能持续几秒到几分钟。 1880年，一位法国神经病学家将该病命名为Narcolepsy，源自希腊语narkē（麻木）和lēpsis（攻击），意指麻木发作，中文译作发作性睡病。这种疾病分为两型：1型有猝倒症状，2型没有猝倒症状。 图1：猝倒（Cataplexy）的特征为由情绪诱发的、突发的肌肉无力发作，发作期间患者意识清醒。这类发作通常始于面部与颈部肌肉无力，随后逐渐蔓延，累及四肢与躯干肌肉。|图源：参考文献[11] 毫无疑问，这种疾病对生活的影响是灾难性的。在人群中，每2000人里就有1位患病。...
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The Brain Imaging and Neurophysiology Dataset of large-scale multimodal neural data

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By Charlotte Maschke, Peter N. Hadar, Yicheng Zhang, Jian Li, Gauri Ganjoo, Andrew Hoopes, Aditya Gupta, Manohar Ghanta, Christine Tsien Silvers, Bharath Gunapati, Robert Thomas, Jennifer Kim, Shibani S. Mukerji, Adrian Dalca, Sahar F. Zafar, Alice D. Lam, Emmanuel Mignot, Alessandro Guazzo, Bruce Nearing
| Nature
Abstract The Brain Imaging and Neurophysiology Dataset (BIND) represents one of the largest multi-institutional, multimodal, clinical neuroimaging repositories, comprising 1.8 million brain scans from 38,942 patients, linked to full-text reports and neurophysiological recordings. This comprehensive dataset addresses critical limitations in neuroimaging research by providing a rich and diverse set of large-scale multimodal data.
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A multimodal sleep foundation model for disease prediction - Nature Medicine

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By Magnus Ruud Kjaer, Emmanuel Mignot
| Nature
Abstract Sleep is a fundamental biological process with broad implications for physical and mental health, yet its complex relationship with disease remains poorly understood. Polysomnography (PSG)—the gold standard for sleep analysis—captures rich physiological signals but is underutilized due to challenges in standardization, generalizability and multimodal integration.
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