Imaging Predictors

影像预测器

• 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
• 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' 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

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COVID-19相关认知障碍的神经特征， 使用体积、基于表面和基于轨迹的度量的组对比度水平。总的假设是， COVID-19相关痴呆症将表现出独特的神经特征，可通过多模态 神经成像作为我们的第一次尝试，我们将采用组明智的分析策略测量：1）灰质 使用功能性MRI（fMRI）的功能改变; 2）使用结构性MRI（sMRI）的灰质萎缩;以及，3） 脑白质异常初步数据（de Erasquin等人，在回顾中）表明， 60岁以上的COVID-19后登记者将出现认知障碍，提供平衡的样本 （痴呆：非痴呆1：1）。众所周知，变化将是慢性的-持续至少6个月-但事实并非如此。 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：灰质功能签名和连接组学。将评价灰质功能改变 使用从BOLD fMRI时间序列计算的基于体素的生理（VBP）度量。基于BOLD的VBP 所有队列中的fMRI血流（BF）和葡萄糖代谢率的PET测量将补充这些指标 （MRglu），用于交叉验证。将按组独立评估连接体改变 成分分析（GICA）和结构方程模型（SEM）的T2* BOLD时间序列。 目标2：灰质结构特征。灰质结构改变（萎缩和肥大）将是 使用体积和基于表面的分析进行评估。 目标3：白质结构特征。白色物质完整性将使用基于道的体积 和病变计数分析。 目的4探索性分析。通过分组对比发现的特征（目标1-3）将被测试， 与已知图案重叠（例如，AD/MCI、健康老龄化、代谢综合征、免疫介导的等）。他们 还将在每个受试者水平上进行测试，作为群体成员关系（COVID +/-;认知障碍）的预测因素 +/-）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感染后的急性状态下，认知障碍患者会表现出 EON异常和可能的其他尚未确定的CNS COVID神经特征， 与未受损的COVID-19幸存者相比， 非COVID控制。 假设2：神经信号的强度将与严重程度相关，但与模式无关。 假设3：神经签名将是时不变的，而不是由于严重程度的变化。 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。