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.

在PD中记录的节奏神经元活动的异常同步的发现 DBS抑制患者已提出了与之相关的概念病理活性可以用作控制DBS治疗递送的生物标志物。飞行员 PD中ADB的研究报告了触发DBS模拟的有希望的临床结果当从DBS电极记录的信号显示出高水平的振荡能力 β频率范围（13 - 35 Hz）。但是，这种方法具有重要的局限性。最多重要的是，包括PD在内的运动抑制了从DBS铅记录的Beta功率 Treemor，其检测高度依赖于潜在客户位置和所需的记录单调刺激期间的记录与定向电流转向不相容，这是最近的创新采用分段的模拟触点。增加参数的继承复杂性通过DBS创新的空间也压倒了标准编程技术。最后，使用其他生物标志物信号（例如，从Cortex记录）可能会提高能力以复杂多维症状为标志的疾病的自适应控制DBS 例如PD。当前的提案将通过使用高级机器学习算法。拟议的研究基于广泛而独特的研究 Charité患者的DBS Lead的多天，术外记录的经验医院和术中ECOG和DBS记录的患者匹兹堡，为了开发计算方法来推进闭环，自适应DBS （ADB）运动障碍的策略。