Linear predictive coding of EEG Activity for Diagnosis of Parkinson's Disease (LEAD-PD)

用于诊断帕金森病的脑电图活动的线性预测编码 (LEAD-PD)

• 批准号: 10659447
• 负责人: Soura Dasgupta
• 金额: $ 187.67万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10659447
• 关键词: 3-Dimensional; Alzheimer' s Disease; Alzheimer' s disease related dementia; American; Artificial Intelligence; Biological Markers; Brain; Clinic; Clinical; Clinical Data; Code; Cognition; Cognitive; Consumption; Data; Deep Brain Stimulation; Dementia with Lewy Bodies; Detection; Diagnosis; Diagnostic; Disease; Electrodes; Electroencephalography; Engineering; Epilepsy; Essential Tremor; Feedback; Frequencies; Gait; Impaired cognition; Impairment; Individual; Infrastructure; Lewy Body Dementia; Machine Learning; Measures; Mental Depression; Methods; Monitor; Moods; Motor; Movement Disorder Society Unified Parkinson' s Disease Rating Scale; Movement Disorders; Multiple System Atrophy; Nature; Nerve Degeneration; Neurobehavioral Manifestations; Neurodegenerative Disorders; Neurosciences; Noise; Operative Surgical Procedures; Parkinson Disease; Parkinson' s Dementia; Participant; Patients; Pharmaceutical Preparations; Prognosis; Progressive Supranuclear Palsy; Public Health; Questionnaires; Research; Rest; Role; Sampling; Scalp structure; Sensitivity and Specificity; Severities; Severity of illness; Signal Transduction; Societies; Symptoms; Syndrome; Technology; Testing; Therapeutic; Time; Validation; Work; accurate diagnosis; clinical biomarkers; cognitive testing; cohort; diagnostic tool; disease diagnosis; disease diagnostic; disorder control; geriatric depression; indexing; medical specialties; motor disorder; motor symptom; nervous system disorder; neurophysiology; non-motor symptom; novel; novel marker; precision medicine; recruit; response; signal processing; synergism; synucleinopathy; tool; treatment optimization; treatment response

Reliable and efficient tools are needed to 1) diagnose and differentiate Parkinson’s disease (PD) from other movement disorders with similar clinical features but with different prognosis and treatment, 2) quantify and track motor and cognitive symptoms of PD over time, and 3) assess response to treatment changes for optimization of symptom control. Current tools for these purposes mainly consist of clinical scales and questionnaires; however, the results can be highly variable. Thus, there is a critical need for accurate and feasible biomarkers in PD. We propose a novel, neurophysiological, machine-learning approach to fulfill this need. We have developed Linear predictive coding (LPC) of EEG Activity for the Diagnosis of PD (LEAD-PD). Rather than focusing on frequency bands, LEAD-PD captures critical differences in the power spectra of PD patients using <5 minutes of resting data. Preliminary results show that LEAD-PD achieves >85% sensitivity/specificity in independent validation sets, surpassing other potential clinical biomarkers for PD. The overall objective of the proposed research is to develop a novel, objective biomarker for diagnosing PD and tracking its progression and response to treatment. In this proposal, we will test the overall hypothesis that that LEAD-PD captures PD diagnosis and diversity/severity of clinical features. Our specific aims are: AIM 1: Determine the diagnostic role of EEG in PD. Our working hypothesis is that the LEAD-PD diagnostic index will distinguish patients with PD from controls, patients with essential tremor, and Parkinson-plus syndromes such as multiple system atrophy (MSA) and progressive supranuclear palsy (PSP), including Alzheimer-related dementias (ADRD) such as Dementia with Lewy Bodies (DLB). AIM 2: Determine the role of EEG in predicting the severity and progression of key symptoms of PD. Our working hypotheses are that the LEAD-PD motor and cognitive indices will predict both baseline severity and longitudinal worsening of these key symptoms over 2 years. AIM 3: Determine the role of EEG in assessing the motor response to DBS treatment in PD. Our work could contribute novel biomarkers and real-time applications for PD and for Alzheimer’s disease and related dementias (ADRD) such as Alzheimer’s dementia (AD) and Lewy Body Dementias (LBD), including Parkinson’s disease dementia (PDD) and DLB.  Because the LEAD-PD index may be utilized to discern symptom severity including cognitive impairment and severity across a continuous spectrum of disease stages in synucleinopathies, our findings are directly related to ADRD such as LBD.

需要可靠和有效的工具来1)诊断和区分帕金森病（PD）与其他具有相似临床特征但预后和治疗不同的运动障碍，2)量化和跟踪PD的运动和认知症状随时间的变化，以及3)评估对治疗变化的反应以优化症状控制。目前用于这些目的的工具主要包括临床量表和问卷；然而，结果可能是高度可变的。因此，迫切需要准确可行的PD生物标志物。我们提出了一种新颖的神经生理学机器学习方法来满足这一需求。我们开发了用于PD诊断的脑电活动线性预测编码（LPC） （LEAD-PD）。而不是专注于频段，铅-PD捕获PD患者功率谱的关键差异，使用<5分钟的静息数据。初步结果显示，在独立的验证集中，LEAD-PD的敏感性/特异性达到了85%，超过了其他潜在的PD临床生物标志物。该研究的总体目标是开发一种新的、客观的生物标志物来诊断PD并跟踪其进展和对治疗的反应。在本提案中，我们将测试铅-PD捕获PD诊断和临床特征的多样性/严重性的总体假设。我们的具体目标是：目的1：确定脑电图在PD中的诊断作用。我们的工作假设是，LEAD-PD诊断指数将区分PD患者与对照组、特发性震颤患者和帕金森加综合征，如多系统萎缩（MSA）和进行性核上性麻痹（PSP），包括阿尔茨海默相关痴呆（ADRD），如路易体痴呆（DLB）。目的2：确定脑电图在预测帕金森病主要症状的严重程度和进展中的作用。我们的工作假设是，铅- pd运动和认知指数将预测基线严重程度和这些关键症状在2年内的纵向恶化。目的3：确定脑电图在评估PD患者对DBS治疗的运动反应中的作用。我们的工作可以为PD和阿尔茨海默病及相关痴呆（ADRD）（如阿尔茨海默病痴呆（AD）和路易体痴呆（LBD），包括帕金森病痴呆（PDD）和DLB）提供新的生物标志物和实时应用。由于铅- pd指数可用于辨别症状的严重程度，包括认知障碍和在连续的疾病阶段的严重程度，我们的研究结果与ADRD（如LBD）直接相关。