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Hum Brain Mapp. 2018 Nov;39(11):4213-4227. doi: 10.1002/hbm.24241. Epub 2018 Jul 1.

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

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Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Department of Psychiatry, University of Pittsburgh School of Medicine, Philadelphia, Pennsylvania.
Department of Psychiatry, University of Michigan School of Medicine, Ann Arbor, Michigan.
Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts.
Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas.
Department of Psychiatry, Columbia University College of Physicians & Surgeons, New York, New York.
Division of Epidemiology, New York State Psychiatric Institute, New York, New York.
Mailman School of Public Health, Columbia University, New York, New York.
Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.


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.


ComBat; aging; atlas; fMRI; functional connectivity; graph theory; harmonization; multi-site; network efficiency

[Available on 2019-11-01]
[Indexed for MEDLINE]

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