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、高效的工具来 1) 诊断帕金森病 (PD) 并将其与具有相似临床特征但预后和治疗不同的其他运动障碍区分开来；2) 量化和跟踪帕金森病随时间推移的运动和认知症状；3) 评估目前用于这些目的的工具包括临床量表和问卷；然而，结果可能存在很大差异，因此，我们迫切需要准确且可行的生物标志物。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，我们开发了用于诊断 PD 的脑电图活动线性预测编码 (LPC) (LEAD-PD)，而不是专注于频段。使用 <5 分钟静息数据的 PD 患者功率谱差异初步结果表明，LEAD-PD 在独立验证集中实现了 >85% 的敏感性/特异性，超过了其他潜在的 PD 临床生物标志物。拟议研究的目标是开发一种新颖、客观的生物标志物，用于诊断 PD 并跟踪其进展和治疗反应。在本提案中，我们将测试 LEAD-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： 目标 1：确定 EEG 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综合征（例如，帕金森综合征）。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)，例如路易体痴呆 (DLB) 目标 2：确定脑电图在预测关键症状的严重程度和进展方面的作用。我们的工作假设是，LEAD-PD 运动和认知指数将预测这些关键症状在 2 年内的基线严重程度和纵向恶化情况 目标 3：确定作用。我们的工作可以为 PD 和阿尔茨海默氏病及相关痴呆症 (ADRD)（如阿尔茨海默氏痴呆 (AD) 和路易体痴呆 (LBD)）提供新的生物标志物和实时应用。 ，包括帕金森病痴呆 (PDD) 和 DLB 因为 LEAD-PD 指数可用于辨别认知症状的严重程度，包括突触核蛋白病的连续疾病阶段的损伤和严重程度，我们的研究结果与 ADRD 直接相关，例如 LBD。