CRCNS: Deep Neural Network Approaches for Closed-Loop Deep Brain Stimulation

CRCNS：用于闭环深部脑刺激的深度神经网络方法

基本信息

批准号：
10021999
负责人：
Robert Mark Richardson
金额：
$ 22.87万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-30 至 2021-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10021999
关键词：
3-Dimensional Affect Age Algorithms Artificial Intelligence Basal Ganglia Behavior Behavioral Berlin Biological Markers Bradykinesia Brain Brain region Cell Nucleus Characteristics Chronic Classification Clinical Complex Computing Methodologies Custom Data Data Collection Deep Brain Stimulation Detection Development Disease Electrocorticogram Electrodes Environment Epilepsy Evolution Frequencies Future Goals Hospitals Human Implant Intelligence Investigation Joints Knowledge Lead Life Location Machine Learning Maps Measures Medicine Methods Modeling Motor Movement Movement Disorders Neurons Neurosciences Noise Operative Surgical Procedures Parkinson Disease Parkinsonian Disorders Pathologic Patients Performance Periodicity Physiological Pilot Projects Population Procedures Recurrence Reporting Research Series Severities Signal Transduction Specificity Symptoms Techniques Testing Therapeutic Time Training Tremor Universities Validation Work artificial neural network base cohort computer framework deep learning deep neural network design experience implantation improved innovation interest learning strategy machine learning algorithm multidisciplinary neuropsychiatric disorder neurosurgery novel novel strategies reconstruction reduce symptoms remote sensor symptomatology translational neuroscience

项目摘要

The discovery that aberrant synchronization of rhythmic neuronal activity recorded in PD patients is suppressed by DBS has advanced the concept that measures associated with pathological activity may be used as biomarkers to control the delivery of DBS therapy. Pilot studies of aDBS in PD have reported promising clinical results from triggering DBS stimulation when the signal recorded from the DBS electrode showed a high level of oscillatory power in the beta frequency range (13 – 35 Hz). That approach, however, has important limitations. Most importantly, beta power recorded from the DBS lead is suppressed by movement including PD tremor, its detection is highly dependent on lead location and the recording montage needed to record during stimulation is incompatible with directional current steering, a recent innovation employing segmented stimulation contacts. The inherent complexity of the increased parameter space through DBS innovations also overwhelms standard programming techniques. Finally, use of additional biomarker signals (e.g., recorded from cortex) is likely to improve the ability to adaptively control DBS for disorders marked by complex multidimensional symptomatologies such as PD. The current proposal will establish methods for overcoming these limitations by developing techniques for multi-feature classification from ECoG recordings, using advanced machine learning algorithms. The proposed research builds upon the extensive and unique experiences with multi-day, extra-operative recording from DBS leads in patients at Charité Hospital and intraoperative ECoG and DBS recording from patients at the University of Pittsburgh, in order to develop computational methods to advance closed-loop, adaptive DBS (aDBS) strategies for movement disorders.

发现PD中记录的节律性神经活动异常同步患者受到 DBS 抑制，提出了以下概念：病理活动可用作生物标志物来控制 DBS 治疗的实施。 aDBS 在 PD 中的研究报告了触发 DBS 刺激的有希望的临床结果当 DBS 电极记录的信号显示出高水平的振荡功率时然而，这种方法有很大的局限性。重要的是，从 DBS 引线记录的 beta 功率受到包括 PD 在内的运动的抑制震颤，其检测高度依赖于引线位置和所需的记录蒙太奇刺激期间的记录与定向电流控制（最近的一项创新）不兼容采用分段刺激接触增加参数的固有复杂性。 DBS 创新带来的空间也压倒了标准编程技术。使用额外的生物标志物信号（例如，从皮层记录的）可能会提高针对以复杂多维症状为特征的疾病适应性控制 DBS 当前的提案将通过以下方式建立克服这些限制的方法。使用先进的技术，从 ECoG 记录中开发多特征分类技术所提出的研究建立在广泛且独特的基础上。 Charité 医院 DBS 导联对患者进行多天术外记录的经验大学患者的医院和术中 ECoG 和 DBS 记录匹兹堡，为了开发计算方法来推进闭环、自适应 DBS (aDBS) 运动障碍策略。