Increased power by harmonizing structural MRI site differences with the ComBat batch method in ENIGMA

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023001%3A_____%2F20%3A00079967" target="_blank" >RIV/00023001:_____/20:00079967 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21230/20:00345952

Výsledek na webu

<a href="https://reader.elsevier.com/reader/sd/pii/S1053811920304420?token=8D4792AE38534068CAC843B09705B00560796751D8DD7F9203716189AF538F21E8FA30D58D6C9B954C1F199FABF5CA4B" target="_blank" >https://reader.elsevier.com/reader/sd/pii/S1053811920304420?token=8D4792AE38534068CAC843B09705B00560796751D8DD7F9203716189AF538F21E8FA30D58D6C9B954C1F199FABF5CA4B</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.neuroimage.2020.116956" target="_blank" >10.1016/j.neuroimage.2020.116956</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Increased power by harmonizing structural MRI site differences with the ComBat batch method in ENIGMA

Popis výsledku v původním jazyce

A common limitation of neuroimaging studies is their small sample sizes. To overcome this hurdle, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines neuroimaging data from many institutions worldwide. However, this introduces heterogeneity due to different scanning devices and sequences. ENIGMA projects commonly address this heterogeneity with random-effects meta-analysis or mixed-effects mega -analysis. Here we tested whether the batch adjustment method, ComBat, can further reduce site-related het-erogeneity and thus increase statistical power. We conducted random-effects meta-analyses, mixed-effects mega -analyses and ComBat mega-analyses to compare cortical thickness, surface area and subcortical volumes between 2897 individuals with a diagnosis of schizophrenia and 3141 healthy controls from 33 sites. Specifically, we compared the imaging data between individuals with schizophrenia and healthy controls, covarying for age and sex. The use of ComBat substantially increased the statistical significance of the findings as compared to random - effects meta-analyses. The findings were more similar when comparing ComBat with mixed-effects mega-analysis, although ComBat still slightly increased the statistical significance. ComBat also showed increased statistical power when we repeated the analyses with fewer sites. Results were nearly identical when we applied the ComBat harmonization separately for cortical thickness, cortical surface area and subcortical volumes. Therefore, we recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work. We provide easy-to-use functions in R that work even if imaging data are partially missing in some brain regions, and they can be trained with one data set and then applied to another (a requirement for some analyses such as machine learning).

Název v anglickém jazyce

Increased power by harmonizing structural MRI site differences with the ComBat batch method in ENIGMA

Popis výsledku anglicky

A common limitation of neuroimaging studies is their small sample sizes. To overcome this hurdle, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines neuroimaging data from many institutions worldwide. However, this introduces heterogeneity due to different scanning devices and sequences. ENIGMA projects commonly address this heterogeneity with random-effects meta-analysis or mixed-effects mega -analysis. Here we tested whether the batch adjustment method, ComBat, can further reduce site-related het-erogeneity and thus increase statistical power. We conducted random-effects meta-analyses, mixed-effects mega -analyses and ComBat mega-analyses to compare cortical thickness, surface area and subcortical volumes between 2897 individuals with a diagnosis of schizophrenia and 3141 healthy controls from 33 sites. Specifically, we compared the imaging data between individuals with schizophrenia and healthy controls, covarying for age and sex. The use of ComBat substantially increased the statistical significance of the findings as compared to random - effects meta-analyses. The findings were more similar when comparing ComBat with mixed-effects mega-analysis, although ComBat still slightly increased the statistical significance. ComBat also showed increased statistical power when we repeated the analyses with fewer sites. Results were nearly identical when we applied the ComBat harmonization separately for cortical thickness, cortical surface area and subcortical volumes. Therefore, we recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work. We provide easy-to-use functions in R that work even if imaging data are partially missing in some brain regions, and they can be trained with one data set and then applied to another (a requirement for some analyses such as machine learning).

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30103 - Neurosciences (including psychophysiology)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

NeuroImage

ISSN

1053-8119

e-ISSN

—

Svazek periodika

218

Číslo periodika v rámci svazku

September

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

"art. no. 116956"

Kód UT WoS článku

000555460300009

EID výsledku v databázi Scopus

2-s2.0-85086881765