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

> **NIH NIH RF1** · GEORGIA STATE UNIVERSITY · 2020 · $143,254

## Abstract

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.

## Key facts

- **NIH application ID:** 10157432
- **Project number:** 3RF1AG063153-01A1S1
- **Recipient organization:** GEORGIA STATE UNIVERSITY
- **Principal Investigator:** VINCE D CALHOUN
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $143,254
- **Award type:** 3
- **Project period:** 2019-08-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10157432

## Citation

> US National Institutes of Health, RePORTER application 10157432, Flexible multivariate models for linking multi-scale connectome and genome data in Alzheimer's disease and related disorders (3RF1AG063153-01A1S1). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/10157432. Licensed CC0.

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