Imaging Predictors

影像预测器

基本信息

批准号：
10628512
负责人：
PETER Thornton FOX
金额：
$ 52.5万
依托单位：
UNIVERSITY OF TEXAS HLTH SCIENCE CENTER
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-08-15 至 2028-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10628512
关键词：
Acute Age Years Alzheimer&apos s Disease Archives Atrophic Biological Markers Blood flow Brain Diseases COVID-19 COVID-19 survivors Characteristics Chronic Clinical Clinical Course of Disease Cognitive Data Data Analyses Dementia Disease Electronic Health Record Enrollment Equation Evaluation Exhibits Functional Magnetic Resonance Imaging Genetic Genetic Variation Glucose Goals Hypertrophy Image Immune Impaired cognition Impairment Individual Infection Investments Lesion Liquid substance Magnetic Resonance Imaging Measures Mediating Metabolic syndrome Modeling Participant Pattern Persons Phase Physiological Population Positron-Emission Tomography Post-Acute Sequelae of SARS-CoV-2 Infection Prospective, cohort study Quality Control Recording of previous events Reporting Research Research Personnel Reverse Transcriptase Polymerase Chain Reaction Risk SARS-CoV-2 infection Sampling Series Serum Severities Site Surface Survivors Symptoms Testing Texas Time Validation Variant Woman cohort comorbidity coronavirus disease data acquisition data management data standards demented dementia risk endophenotype experience follow-up gray matter healthy aging independent component analysis men metabolic rate multimodal neuroimaging multimodality neural neural patterning neuroimaging non-demented pandemic disease post-COVID-19 recruit statistics white matter

项目摘要

The overall goal of this project is to discover neural signatures of COVID-19-associated cognitive impairment at the group-contrast level using volumetric, surface-based, and tract-based metrics. The overall hypothesis is that COVID-19-associated dementia will exhibit unique neural signatures discoverable through multi-modality neuroimaging. As our first foray, we will employ group-wise analytic strategies measuring: 1) gray-matter functional alterations using functional MRI (fMRI); 2) gray-matter atrophy using structural MRI (sMRI); and, 3) white-matter abnormalities using sMRI. Preliminary data (de Erasquin et al., in review) indicate that ~50% of post-COVID-19 enrollees over 60 years of age will be cognitively impaired, providing a balanced sample (demented:non-demented∷1:1). It is known that changes will be chronic – lasting at least 6 months – but it is not yet known whether cognitive impairment will be recuperative, progressive, or mixed. Aim 1: Gray-matter Functional Signature & Connectomics. Gray-matter functional alterations will be evaluated using voxel-based physiological (VBP) metrics computed from BOLD fMRI times series. BOLD-based VBP metrics will be supplemented by fMRI blood flow (BF) in all cohorts and PET measures of glucose metabolic rate (MRglu) in the Texas cohort, for cross validation. Connectomic alterations will be assessed by group independent components analysis (GICA) and structural equation modeling (SEM) of T2* BOLD time series. Aim 2: Gray-matter Structural Signature. Gray-matter structural alterations (atrophy and hypertrophy) will be evaluated using both volumetric and surface-based analyses. Aim 3: White-matter Structural Signature. White matter integrity will be evaluated using tract-based, volumetric and lesion-counting analytics. Aim 4 Exploratory analyses. Features discovered through group-wise contrasts (Aims 1-3) will be tested for overlap with known patterns (e.g., AD/MCI, healthy aging, metabolic syndrome, immune mediated, etc.). They will also be tested at the per-subject level as predictors of group membership (COVID +/-; cognitive impairment +/-) and co-analyzed as quantitative biomarkers and endophenotypes with Projects 1 and 2. Hypotheses 1: In the post-acute state of COVID-19 infection, persons with cognitive impairment will exhibit abnormalities in an EON and likely other as-yet-undefined neural signatures of CNS COVID severally defined by the above-described neuroimaging measures when contrasted either to non-impaired COVID-19 survivors or to non-COVID controls. Hypothesis 2: The strength of the neural signatures will be symptom-severity correlated, but not the pattern. Hypothesis 3: The neural signatures will be time invariant, other than due to symptom-severity variation. Hypothesis 4: The neural signatures will be cohort invariant, other than due to symptom-severity variation.

该项目的总体目标是发现与 COVID-19 相关的认知障碍的神经特征使用基于体积、基于表面和基于区域的指标的组对比度水平总体假设是： COVID-19 相关痴呆症将表现出可通过多模式发现的独特神经特征作为我们的第一次尝试，我们将采用分组分析策略来测量：1）灰质。使用功能 MRI (fMRI) 进行功能改变；2) 使用结构 MRI (sMRI) 进行灰质萎缩；3) 使用 sMRI 检测白质异常的初步数据（de Erasquin 等人，评论中）表明，约 50% 的患者存在白质异常。 COVID-19 后 60 岁以上的参与者将出现认知障碍，提供了一个平衡的样本（痴呆：非痴呆∷1）众所周知，变化将是慢性的——至少持续 6 个月——但事实并非如此。目前还不知道认知障碍是恢复性的、进行性的还是混合性的。目标 1：将评估灰质功能特征和连接组学。使用根据 BOLD fMRI 时间序列计算的基于体素的生理 (VBP) 指标。所有队列中的 fMRI 血流 (BF) 和葡萄糖代谢率的 PET 测量将补充指标（MRglu）在德克萨斯队列中，用于交叉验证的连接组改变将由独立组进行评估。 T2* BOLD 时间序列的成分分析 (GICA) 和结构方程建模 (SEM)。目标 2：灰质结构特征改变（萎缩和肥大）。使用体积分析和基于表面的分析进行评估。目标 3：将使用基于束的体积来评估白质结构特征。和病灶计数分析。目标 4 将测试通过分组对比发现的特征（目标 1-3）。与已知模式重叠（例如 AD/MCI、健康老龄化、代谢综合征、免疫介导等）。还将在每个受试者水平上进行测试，作为群体成员资格的预测因素（COVID +/-；认知障碍 +/-) 并作为定量生物标志物和内表型与项目 1 和 2 共同分析。假设 1：在 COVID-19 感染的急性后状态下，有认知障碍的人会表现出 EON 异常以及中枢神经系统新冠病毒可能其他尚未定义的神经特征（分别定义）与未受损的 COVID-19 幸存者或到非新冠肺炎控制。假设 2：神经信号的强度与症状严重程度相关，但与模式无关。假设 3：除了症状严重程度的变化之外，神经特征将不随时间变化。假设 4：除了症状严重程度的变化之外，神经特征将是队列不变的。