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Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

Longitudinal ComBat: A method for harmonizing longitudinal multi-scanner imaging data.

Joanne C Beer¹, Nicholas J Tustison², Philip A Cook³

¹Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, United States.

|July 9, 2020

Summary

This summary is machine-generated.

This study introduces a new method to harmonize neuroimaging data from different scanners, improving the accuracy of Alzheimer's Disease Neuroimaging Initiative (ADNI) studies. The longitudinal ComBat method enhances detection of brain changes over time.

Keywords:

ADNI Alzheimer’s ComBat Cortical thickness Harmonization MRI

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Published on: September 24, 2017

Area of Science:

Neuroimaging
Biostatistics
Computational Neuroscience

Background:

Neuroimaging datasets are often aggregated across multiple sites and scanners to increase statistical power.
Systematic scanner effects introduce technical variability, potentially biasing biological variability estimates.
Harmonization is crucial for accurate analysis of multi-site, longitudinal neuroimaging data.

Purpose of the Study:

To propose and evaluate a novel method for harmonizing longitudinal multi-scanner neuroimaging data.
To adapt the ComBat algorithm for longitudinal data analysis, addressing scanner effects.
To improve the detection of biological changes in longitudinal studies while controlling for technical variability.

Main Methods:

Applied a longitudinal ComBat harmonization method to neuroimaging data.
Utilized longitudinal cortical thickness measurements from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (n=663).
Compared results from unharmonized data, cross-sectional ComBat, and longitudinal ComBat in association analyses and simulation studies.

Main Results:

Demonstrated the presence of both additive and multiplicative scanner effects in various brain regions.
Longitudinal ComBat showed greater power in detecting longitudinal changes compared to cross-sectional ComBat.
The proposed method effectively controlled type I error rates, outperforming unharmonized data with scanner as a covariate.

Conclusions:

The longitudinal ComBat method offers a robust approach for harmonizing multi-scanner longitudinal neuroimaging data.
This technique enhances the reliability of detecting disease-related changes in brain structure over time.
The method is applicable to diverse longitudinal datasets, including genomic data and neuroimaging studies of various neurological and psychiatric conditions.