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

• 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
• 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

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.

帕金森病患者节律性神经元活动异常同步化的发现 患者被星展银行抑制提出了与测量相关的概念 病理活动可作为控制DBS治疗给药的生物标志物。引航员 帕金森病患者的aDBS研究已经报道了触发DBS刺激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功率时 贝塔频率范围(13-35赫兹)。然而，这种方法有很大的局限性。多数 重要的是，从DBS导联记录的Beta功率被包括PD在内的移动所抑制 震颤，其检测高度依赖于铅的位置和所需的记录蒙太奇 刺激过程中的记录与最近的创新方向电流转向不兼容 采用分段刺激触点。增加的参数的内在复杂性 DBS创新的空间也压倒了标准的编程技术。最后， 使用额外的生物标记物信号(例如，从皮质记录)可能会提高 对以复杂多维症状为标志的疾病自适应控制DBS 比如警察局。目前的提案将通过以下方式确立克服这些限制的方法 开发ECoG记录的多特征分类技术，使用高级 机器学习算法。拟议的研究建立在广泛和独特的 Charité患者DBS导联多日术外记录的体会 华盛顿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)运动障碍的战略。