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

点击翻译按钮获取中文摘要

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.