Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data

Meichen Yu, Kristin A. Linn, Philip A. Cook, Mary L. Phillips, Melvin Mcinnis, Maurizio Fava, Madhukar H. Trivedi, Myrna M. Weissman, Russell T. Shinohara, Yvette I. Sheline

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Acquiring resting-state functional magnetic resonance imaging (fMRI) datasets at multiple MRI scanners and clinical sites can improve statistical power and generalizability of results. However, multi-site neuroimaging studies have reported considerable nonbiological variability in fMRI measurements due to different scanner manufacturers and acquisition protocols. These undesirable sources of variability may limit power to detect effects of interest and may even result in erroneous findings. Until now, there has not been an approach that removes unwanted site effects. In this study, using a relatively large multi-site (4 sites) fMRI dataset, we investigated the impact of site effects on functional connectivity and network measures estimated by widely used connectivity metrics and brain parcellations. The protocols and image acquisition of the dataset used in this study had been homogenized using identical MRI phantom acquisitions from each of the neuroimaging sites; however, intersite acquisition effects were not completely eliminated. Indeed, in this study, we found that the magnitude of site effects depended on the choice of connectivity metric and brain atlas. Therefore, to further remove site effects, we applied ComBat, a harmonization technique previously shown to eliminate site effects in multi-site diffusion tensor imaging (DTI) and cortical thickness studies. In the current work, ComBat successfully removed site effects identified in connectivity and network measures and increased the power to detect age associations when using optimal combinations of connectivity metrics and brain atlases. Our proposed ComBat harmonization approach for fMRI-derived connectivity measures facilitates reliable and efficient analysis of retrospective and prospective multi-site fMRI neuroimaging studies.

Original languageEnglish (US)
JournalHuman Brain Mapping
DOIs
StateAccepted/In press - Jan 1 2018

Fingerprint

Magnetic Resonance Imaging
Neuroimaging
Atlases
Brain
Diffusion Tensor Imaging
Datasets

Keywords

  • Aging
  • Atlas
  • ComBat
  • FMRI
  • Functional connectivity
  • Graph theory
  • Harmonization
  • Multi-site
  • Network efficiency

ASJC Scopus subject areas

  • Anatomy
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology

Cite this

Yu, M., Linn, K. A., Cook, P. A., Phillips, M. L., Mcinnis, M., Fava, M., ... Sheline, Y. I. (Accepted/In press). Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data. Human Brain Mapping. https://doi.org/10.1002/hbm.24241

Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data. / Yu, Meichen; Linn, Kristin A.; Cook, Philip A.; Phillips, Mary L.; Mcinnis, Melvin; Fava, Maurizio; Trivedi, Madhukar H.; Weissman, Myrna M.; Shinohara, Russell T.; Sheline, Yvette I.

In: Human Brain Mapping, 01.01.2018.

Research output: Contribution to journalArticle

Yu, Meichen ; Linn, Kristin A. ; Cook, Philip A. ; Phillips, Mary L. ; Mcinnis, Melvin ; Fava, Maurizio ; Trivedi, Madhukar H. ; Weissman, Myrna M. ; Shinohara, Russell T. ; Sheline, Yvette I. / Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data. In: Human Brain Mapping. 2018.
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