Flexible multivariate models for linking multi-scale connectome and genome data in Alzheimer's disease and related disorders

摘要 COVID-19正在世界各地产生重大影响，但我们仍在了解 这种疾病的机制和表现。有相当多的证据表明 COVID-19患者出现的症状。然而，这一影响及其与 年龄对大脑结构的影响迄今为止还没有研究过。我们建议使用多变量 从个体中提取共变脑模式的方法，以研究与 COVID-19以及与老年人年龄的潜在相互作用。我们将利用 正在制定的方法作为母公司奖励的一部分，但对其进行定制，以纳入 空间先验来解决缺血性病变。我们将评估COVID-19和年龄对这些的影响， 网络，并将其与从规范数据中提取的网络进行比较。我们将分享 通过用户友好的工具。研究结果有望为神经系统疾病的研究提供新的见解。 COVID-19的表现，包括年龄特异性影响。

Abstract COVID-19 is having a major impact around the world, however we are still learning about the mechanisms and manifestations of this illness. There is considerable evidence of neurological symptoms that occur in COVID-19 patients. However the impact of this, and its relationship with age, on brain structure have not been studies at all thus far. We propose to use multivariate approaches to extract covarying brain patterns from individuals to study changes associated with COVID-19 as well as potential interactions with age in older individuals. We will leverage the approaches being developed as part of the parent award, but customize them to incorporate spatial priors to address ischemic lesions. We will evaluate COVID-19 and age effects on these networks and compare them with networks extracted from normative data. We will share the methods via user friendly tools. Results are expected to provide insights into the neurological manifestations of COVID-19 including age specific effects.