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.

帕金森病中记录到的节律性神经元活动异常同步化的发现 患者被DBS抑制的概念已经提出， 病理活动可用作生物标记物以控制DBS治疗的递送。试点 帕金森病中aDBS的研究报告了触发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， β频率范围（13 - 35 Hz）。然而，这种方法有重要的局限性。最 重要的是，从DBS电极导线记录的β功率受到包括PD在内的运动的抑制 震颤，其检测高度依赖于电极导线位置和所需的记录蒙太奇， 刺激过程中的记录与定向电流导引不兼容，定向电流导引是最近的一项创新 采用分段的刺激触点。增加参数的固有复杂性 通过DBS创新的空间还可简化标准编程技术。最后， 使用另外的生物标记信号（例如，（从皮层记录）可能会提高 适应性控制DBS治疗以复杂多维脑血管疾病为特征的疾病 例如PD。目前的建议将确立克服这些限制的方法， 从ECoG记录中开发多特征分类技术，使用先进的 机器学习算法。拟议的研究建立在广泛和独特的 Charité患者DBS电极导线的多日、术中记录经验 来自美国大学患者的医院和术中ECoG和DBS记录 匹兹堡，以开发计算方法，以推进闭环，自适应DBS （aDBS）运动障碍的策略。