# Harmonization of Multi-Site Neuroimaging Data from Complex Study Designs

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2021 · $603,870

## Abstract

PROJECT SUMMARY
Over the past decade, the number of large multi-center neuroimaging studies has skyrocketed due to growing
investments by federal governments and private entities interested in brain development, aging, and pathology.
This has led to the accumulation of vast amounts of magnetic resonance imaging (MRI) data which have been
acquired with varying amounts of technical harmonization. Such efforts, which have focused on protocol
harmonization and comparisons with imaging phantoms, have shown great strides toward reducing inter-
scanner differences in imaging features extracted for further study. Unfortunately, MRI show inter-instrument
biases even in the most carefully controlled studies. Our group, among many others, has shown that these
differences often dwarf biological differences of interest measured using both structural and functional MRI.
 To address this, the field has rapidly been developing tools for the harmonization of imaging data after
acquisition. We have proposed several such tools, and our work has often focused on the adaptation of
methods used in genomic studies for batch effect correction. Our most recent such work involved the ComBat
method, which uses empirical Bayesian estimation to correct for site effects in both means and variances of
imaging features under study. To date, these tools have been successfully applied in studies of cortical
thickness, white matter microstructure, and functional connectivity. However, there are unfortunately several
key limitations to the ComBat method for imaging studies that stem from its original conception for gene
expression studies.
 ComBat was designed for the study of inter-scanner differences in cross-sectionally acquired data.
While cross-sectional studies are of great interest and exceedingly common, much focus in the context of
healthy brain development and aging has shifted to measuring longitudinal trajectories. In such cases, the
naïve application of ComBat is flawed and methodological research is necessary for appropriate harmonization
tools to be developed. Furthermore, more complex nested study design in which multiple scanners are used
per institution, or a subset of subjects are imaged on multiple scanners for harmonization purposes, are
increasingly common. Another key area of interest in modern neuroimaging studies is to focus on inter-region
structural or functional connectivity and uses multivariate pattern analysis (MVPA) to improve our
understanding of phenotypic associations as well as for personalized predictions. Unfortunately, the current
state-of-the-art in image harmonization ignores correlation structure between measurements, and thus inter-
scanner differences often persist.
 In this project, we propose a new generation of techniques that are applicable under complex study
designs and harmonize appropriately for studies involving applications of MVPA. In our final aim of this
proposal, we will apply the methods developed for more complex study des...

## Key facts

- **NIH application ID:** 10188649
- **Project number:** 5R01MH123550-02
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Russell Takeshi Shinohara
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $603,870
- **Award type:** 5
- **Project period:** 2020-06-10 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10188649

## Citation

> US National Institutes of Health, RePORTER application 10188649, Harmonization of Multi-Site Neuroimaging Data from Complex Study Designs (5R01MH123550-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10188649. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*