Scalable Biomarkers and Generative Digital Twins for Personalized Neurostimulation in Depression

项目概要/摘要 美国目前有超过1亿人表现出临床抑郁症的迹象， 是COVID-19危机爆发前的三倍目前，此类疾病的主要治疗方案 抑郁症患者包括药物和心理干预，急性和长期 其有效性明显有限：高达三分之一的患者出现治疗抵抗 萧条无创神经刺激治疗，如重复经颅磁刺激 （rTMS）--在大脑皮层上放置一个磁线圈，用于局部刺激大脑--最近 成为治疗难治性抑郁症的有前途的低风险干预措施。然而，机制和 对于这种治疗的适当参数仍然知之甚少。在最好的情况下，rTMS可以 戏剧性的效果，在几个小时内改变病人的生命进程。然而，在许多情况下， 可衡量的效果。这就提出了一个显而易见的问题：为什么目前的rTMS协议对某些人来说效果很好？ 个人而不是其他人？我们是否可以调整协议，使其对每个人都能很好地工作， 个性化治疗甚至是持久的治疗？越来越多的文献表明，如果我们学会剪裁， 治疗人类神经生理学的个体差异。在这里，我们提出了一个创新和独特的 精确精神病神经刺激的方法：使用 “数字双胞胎”。为了负担得起构建和扩展生成数字双胞胎，我们建议结合高 密度脑电图（HD-EEG）-这是非侵入性的，廉价的，易于部署- 在加速rTMS治疗方案期间测量脑连接的纵向变化， 萧条然后，使用可控的生成神经网络，允许详细的预测模拟， rTMS治疗的个体反应轨迹，我们可以开始预测治疗前的结果， 了解个体反应，个性化治疗参数。

Project Summary/Abstract More than 100 million people in the United States currently show signs of clinical depression, approximately three times more than before the onset of the COVID-19 crisis. Currently, the main treatment options for such depressed individuals include pharmacological and psychological interventions, the acute and long-term effectiveness of which are significantly limited: up to one-third of patients develop treatment-resistant depression. Noninvasive neurostimulation therapies such as repetitive Transcranial Magnetic Stimulation (rTMS)–where a magnetic coil placed over the cortex is used to focally stimulate the brain–have recently emerged as promising low-risk interventions for treatment-resistant depression. However, the mechanisms and appropriate parameters for this treatment remain poorly understood. In the best cases, rTMS can have dramatic effects, changing the course of a patient's life in hours. In many cases, however, it has little to no measurable effect. This raises the obvious question: why do current rTMS protocols work well for some individuals but not for others? Could we adapt protocols to work well for everyone, potentially providing reliable personalized treatment or even a lasting cure? A growing literature suggests this is possible if we learn to tailor treatment to individual differences in human neurophysiology. Here we propose an innovative and unique approach towards precision psychiatric neurostimulation: personalized modeling of treatment using “Generative Digital Twins”. To affordably build and scale Generative Digital Twins, we propose combining high density electroencephalography (HD-EEG)–which is non-invasive, inexpensive, and easily deployable–to measure longitudinal changes in brain connectivity during an accelerated rTMS treatment protocol for depression. Then, using controllable generative neural networks that allow detailed predictive simulations of individual response trajectories given rTMS treatment, we can begin to predict outcomes prior to treatment, understand individual responses, and personalize treatment parameters.