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，有效的工具来诊断和区分帕金森氏病（PD）与具有相似临床特征的其他运动障碍，但预后和治疗不同，2）随着时间的推移，量化和跟踪PD的运动和认知症状，以及3）评估对症状控制的治疗变化的评估反应。目前用于这些目的的工具主要由临床量表和问卷组成；但是，结果可能是高度可变的。这是PD中准确且可行的生物标志物的迫切需要。我们提出了一种新颖的，神经生理学的机器学习方法来满足这种需求。我们已经开发了用于诊断PD（LEAD-PD）的EEG活性的线性预测编码（LPC）。 Lead-PD并没有专注于频带，而是使用<5分钟的静止数据捕获了PD患者功率谱的关键差异。初步结果表明，铅-PD在独立验证集中达到> 85％的灵敏度/特异性，超过了PD的其他潜在临床生物标志物。拟议的研究的总体目标是开发一种新型的，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。在此提案中，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。我们的具体目的是：目标1：确定脑电图在PD中的诊断作用。我们的工作假设是，铅-PD诊断指数将使PD患者与对照组，具有必需树的患者和帕金森氏症综合征（如多个系统萎缩（MSA）和渐进性上脑核麻痹（PSP），包括与阿尔茨海默氏症相关的痴呆症（ADRD），例如dlb fi dlb bodsies（dlb）。目标2：确定脑电图在预测PD关键症状的严重程度和进展中的作用。我们的工作假设是，铅-PD电机和认知指数将在2年内预测这些关键症状的基线严重程度和纵向担忧。目标3：确定脑电图在评估PD中对DBS治疗的运动反应中的作用。我们的工作可以为PD以及阿尔茨海默氏病和相关痴呆症（ADRD）的新生物标志物和实时应用，例如阿尔茨海默氏症的痴呆症（AD）和Lewy身体痴呆症（LBD），包括帕金森氏病（Parkinson）疾病痴呆症（PDD）和DLB。由于铅-PD指数可用于识别症状严重程度，包括在突触核生发病中连续疾病阶段的认知障碍和严重程度，因此我们的发现与LBD等ADRD直接相关。