Imaging Predictors

该项目的总体目标是在以下位置发现新冠肺炎相关认知障碍的神经特征 使用体积、基于曲面和基于区域的度量的组对比度级别。总体假设是 新冠肺炎相关痴呆症将显示出可通过多种模式发现的独特神经特征 神经成像。作为我们的第一次尝试，我们将采用群体分析策略来衡量：1)灰质 使用功能磁共振成像(FMRI)的功能改变；2)使用结构磁共振成像(SMRI)的灰质萎缩；以及，3) 使用sMRI检查脑白质异常。初步数据(de Erasquin等人，在审查中)表明，~50%的 60岁以上的新冠肺炎后参与者将有认知障碍，提供一个平衡的样本 (痴呆：非痴呆∷1：1)。众所周知，变化将是长期的--至少持续6个月--但事实并非如此 目前尚不清楚认知障碍是恢复性的、进行性的还是混合性的。 目标1：灰质函数签名和连接学。灰质功能改变将被评估 使用从BOLD fMRI时间序列计算的基于体素的生理(VBP)度量。基于粗体的VBP 所有队列中的功能磁共振血流量(BF)和葡萄糖代谢率的PET测量将补充这些指标 (MRglu)在德克萨斯州队列中，用于交叉验证。连接性改变将由独立于团体的组织进行评估 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。灰质结构改变(萎缩和肥大)将是 使用体积分析和基于曲面的分析进行评估。 目标3：白质结构特征。白质完整性将使用基于束的、体积的 以及病变计数分析。 目的4探索性分析。将对通过分组对比(目标1-3)发现的特征进行测试 与已知模式(例如，AD/MCI、健康老龄化、代谢综合征、免疫调节等)重叠。他们 还将在每个受试者层面上进行测试，作为小组成员资格的预测因子(COVID+/-；认知障碍 +/-)，并作为定量生物标记物和内表型与项目1和2共同分析。 假设1：在新冠肺炎感染的急性后状态，认知障碍的人将表现出 EON中的异常和其他可能尚未确定的CNS COVID神经特征分别定义 通过上述神经成像指标与未受损的新冠肺炎幸存者或 到非冠状病毒控制组。 假设2：神经信号的强度将与症状和严重程度相关，而不是模式。 假设3：神经信号将是时间不变的，而不是由于症状-严重程度的变化。 假设4：神经特征将是队列不变的，而不是由于症状-严重程度的变化。

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.