Towards Generating a Multimodal and Multivariate Classification Model from Imaging and Non-Imaging Measures for Accurate Diagnosis and Monitoring of Dementia in Parkinsons disease.

从影像学和非影像学测量中生成多模式和多变量分类模型，以准确诊断和监测帕金森病痴呆。

• 批准号: 10028103
• 负责人: Virendra R Mishra
• 金额: $ 76.25万
• 依托单位: CLEVELAND CLINIC LERNER COM-CWRU
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10028103
• 关键词: Age; Alzheimer' s Disease; Amyloid beta-Protein; Anatomy; Biological Markers; Brain; Classification; Clinical; Clinical Trials; Cognition; Complex; Data; Dementia; Diagnosis; Diffusion; Diffusion Magnetic Resonance Imaging; Disease; Education; Fiber; Functional Magnetic Resonance Imaging; Functional disorder; Gaussian model; Genetic; Goals; Image; Impaired cognition; LRRK2 gene; Light; Machine Learning; Magnetic Resonance Imaging; Measurement; Measures; Methods; Modality; Modeling; Monitor; Neurites; Neurodegenerative Disorders; Neuropsychology; Parkinson Disease; Parkinson' s Dementia; Pathologic; Patients; Pattern; Physiological Processes; Process; Public Health; Research; Resolution; Rest; Risk; Severity of illness; Statistical Models; Structure; Validation; Variant; Water; accurate diagnosis; alpha synuclein; base; cognitive impairment in Parkinson' s; cohort; data acquisition; demographics; density; disorder subtype; gray matter; high risk; illness length; machine learning algorithm; mild cognitive impairment; multimodality; neuroimaging; non-Gaussian model; novel; pre-clinical; predictive marker; prion-like; prognostic; progression marker; rapid eye movement; recruit; sex; tau Proteins; tool; transmission process; white matter; white matter change

PROJECT SUMMARY The goal of the proposed research is to identify the best predictive biomarkers of dementia in Parkinson’s disease (PDD) through a multimodal and multivariate statistical model utilizing both neuroimaging derived measures (diffusion-weighted MRI (dMRI), resting-state functional MRI (rsfMRI), and T1-weighted MRI measures) and non- imaging measures such as demographics (age, sex, years of education), clinical (disease duration and severity), genetics (LRRK2), and CSF-measures (Total Tau, β-Amyloid, α-synuclein). It is critical to identify biomarkers that can predict dementia in Parkinson’s disease (PD) as approximately 50-80% of PD patients develop PDD within twelve years of diagnosis. Identifying pathophysiology-based biomarkers that could identify PD patients at high risk for PDD reliably is critical for better prognostication, correct identification of PDD in its prodromal stage to recruit in new disease-modifying clinical trials, and better understanding the pathophysiological processes underlining PDD. The proposed project has two important components. The first component of the project is to understand the pathophysiological mechanism underlying PDD through sophisticated voxelwise dMRI-derived measures estimated using a multi-shell high angular and spatial resolution dMRI data acquisition, and understanding network-level white matter (WM)-derived structural connectivity and rsfMRI-derived functional connectivity in PDD. The second component of the project is to identify the biomarkers that predict PDD through multivariate statistical modelling by combining these sophisticated pathologically relevant neuroimaging measures with non-imaging measures (such as clinical, demographics, genetics, and CSF-measures). We will recruit demographically matched healthy controls (HC) along with demographically, disease duration, and disease severity matched PD patients with mild cognitive impairment (PD-MCI), PD-non-MCI (PD-nMCI), and PDD for this project. We will acquire multi-shell dMRI data at three b-values, namely 500s/mm2, 1000s/mm2, and 2500s/mm2 with a high angular and spatial resolution and estimate various unbiased free-water (fiso) corrected Gaussian dMRI-derived measures along with non-Gaussian dMRI-derived measures such as diffusion kurtosis measures, and neurite orientation dispersion and density imaging measures. We will further compare these measures between the groups to identify significant dMRI-derived measures separating the groups, and understanding the neuroanatomical correlates of these measures with various neuropsychological scores. Furthermore, we will estimate dMRI-derived structural connectivity and rsfMRI-derived functional connectivity to understand network-level discrepancies predicting PDD. These pathologically relevant neuroimaging measures will be further combined with various non-imaging measures through a novel machine learning algorithm to identify the comprehensive and best predictors of PDD. The tools developed in our proposal also has great potential for significantly advancing the understanding of other neurodegenerative disorders such as Alzheimer’s disease (AD) thereby helping to understand AD- and PD-specific neuroanatomical changes predicting dementia.

项目总结