Model-based neural control of brain stimulation for neuropsychiatric disorders

基于模型的神经精神疾病脑刺激神经控制

• 批准号: 10209839
• 负责人: Maryam Shanechi
• 金额: $ 62.7万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10209839
• 关键词: Acute; Address; Alternative Therapies; Biological Markers; Brain; Brain region; Clinical Trials; Controlled Clinical Trials; Data; Depressive disorder; Dissociation; Electric Stimulation; Electric Stimulation Therapy; Electrical Stimulation of the Brain; Electrodes; Electroencephalography; Elements; Epilepsy; Exhibits; Feedback; Frequencies; Future; Human; Implant; Implanted Electrodes; Individual; Knowledge; Learning; Machine Learning; Measures; Mental Depression; Modeling; Monitor; Moods; Output; Patient Self-Report; Patients; Pattern; Pharmaceutical Preparations; Psychotherapy; Public Health; Refractory; Resistance; Rest; Running; Signal Transduction; Site; Symptoms; System; Technology; Testing; Time; Training; Variant; base; comorbid depression; depressive symptoms; disability; experimental study; improved; mind control; mood symptom; neuropsychiatric disorder; neuropsychiatric symptom; neuropsychiatry; neuroregulation; novel; open label; predictive modeling; programs; real time model; reduce symptoms; relating to nervous system; response; success; therapeutic target; treatment-resistant depression

PROJECT SUMMARY Neuropsychiatric disorders are a leading cause of disability worldwide, with depressive disorders being the most disabling among them. Many patients are resistant to all current treatments. Invasive electrical brain stimulation for treatment-resistant depression showed early promise in open-label studies but has had variable efficacy in controlled clinical trials. To date, stimulation in neuropsychiatric disorders has been limited to an open-loop approach that applies a fixed pattern of continuous stimulation regardless of symptom levels. One limitation is that open-loop stimulation does not track the inter- and intra-subject variabilities in neuropsychiatric symptoms, which can change rapidly in an individual. Another limitation is the lack of an input-output model that can guide stimulation by predicting how ongoing stimulation input drives large-scale neural activity and the symptoms it underlies in an individual. We will address these limitations to enable precise invasive electrical brain stimulation for neuropsychiatric disorders by developing a novel real-time model-based neural control system. We will provide proof-of-concept demonstration for acute control of neural biomarkers of mood states related to depression symptoms in epilepsy patients with implanted intracranial electroencephalography (iEEG) electrodes, in whom we will obtain repeated mood self-reports and perform stimulation simultaneously with neural recording. The system will continuously adjust the stimulation parameters, for the first time, based on 2 elements: (i) Novel personalized input-output model learned on recorded brain network response while delivering a new stochastic stimulation waveform to excite network activity. (ii) Personalized decoder trained on multi-day continuous iEEG recordings and simultaneous mood self-reports to estimate mood state variations from neural activity as feedback. Combining these, we will build a real-time model-based closed-loop system to precisely drive the neurally-decoded mood state—the neural biomarker of mood—to a target level. Our system generalizes to any stimulation site. Here, we will demonstrate the system with orbitofrontal stimulation as we have shown it to acutely improve mood and modulate large-scale mood-relevant brain activity. We will run real-time closed-loop experiments in each patient. The system will estimate the neural biomarker from iEEG and adjust the stimulation amplitude and frequency in real-time based on the input-output model to drive the estimated biomarker to a target level. We will also develop model-free closed-loop on-off stimulation that turns stimulation on-off based on the neural biomarker. We will compare model-based, on-off and open-loop stimulations. Success of this program will enable precisely regulating a desired brain state by developing the first model-based closed- loop invasive brain stimulation system and advancing neuromodulation technology. It will also directly inform electrical stimulation therapy in future pivotal clinical trials of refractory depression. The developed system and gained knowledge will generalize across many neuropsychiatric disorders with broad public health impact.

项目摘要 神经精神疾病是全球残疾的主要原因，抑郁症是最大的 在他们之间禁用。许多患者对所有当前治疗都有抵抗力。侵入性电脑刺激 对于耐治疗的抑郁症在开放标签研究中表现出早期的希望，但效率的效率可变 对照临床试验。迄今为止，神经精神疾病的刺激仅限于开环 无论症状水平如何，都采用固定的连续刺激模式。一个限制是 这种开环刺激不会跟踪神经精神症状中的受试者间和受试者内的变化， 可以在个人中迅速改变。另一个限制是缺乏可以指导的投入输出模型 通过预测正在进行的刺激输入如何驱动大规模神经活动及其症状来刺激 个人的基础。我们将解决这些限制以实现精确的侵入性电脑刺激 通过开发一种新型的基于实时模型的神经控制系统，用于神经精神疾病。我们将 提供概念验证演示，以急性控制与情绪状态的神经元生物标志物 植入颅内脑电图（IEEG）电子癫痫患者的抑郁症状， 我们将在其中获得重复的情绪自我报告，并同时通过神经记录执行刺激。 该系统将基于2个要素首次不断调整刺激参数：（i）新颖 个性化的输入输出模型在记录的大脑网络响应中学到的同时提供了新的随机性 刺激波形到令人兴奋的网络活动。 （ii）在多天连续IEEG中训练的个性化解码器 记录和简单的情绪自我报告，以估计神经活动的情绪状态变化为 反馈。结合在一起，我们将建立一个基于实时模型的闭环系统，以精确推动 神经化的情绪状态（情绪的神经元生物标志物）达到目标水平。我们的系统概括到任何 刺激部位。在这里，我们将用轨道额刺激演示系统 急性改善情绪并调节与心情相关的大脑活动。我们将运行实时闭环 每个患者的实验。该系统将估算IEEG的神经生物标志物并调整刺激 基于输入输出模型实时实时的放大器和频率，以将估计的生物标记驱动到A 目标水平。我们还将开发无模型的闭环开关刺激 在神经生物标志物上。我们将比较基于模型的，开关和开环刺激。成功的成功 程序将通过开发第一个基于模型的闭合 - 循环侵入性大脑刺激系统和推进神经调节技术。它也将直接 在未来的耐火抑郁症关键临床试验中为电仿真疗法提供通知。开发的系统 并且获得知识将概括为具有广泛公共卫生影响的许多神经精神疾病。