Jiayu Chen1, Jingyu Liu2, Vince D Calhoun2, Alejandro Arias-Vasquez3, Marcel P Zwiers4, Cota Navin Gupta5, Barbara Franke6, Jessica A Turner7. 1. The Mind Research Network, Albuquerque, NM, USA. Electronic address: jchen@mrn.org. 2. The Mind Research Network, Albuquerque, NM, USA; Department of ECE, University of New Mexico, Albuquerque, NM, USA. 3. Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Departments of Human Genetics and Psychiatry, Nijmegen, The Netherlands; Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Department of Cognitive Neuroscience, Nijmegen, The Netherlands. 4. Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands. 5. The Mind Research Network, Albuquerque, NM, USA. 6. Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Departments of Human Genetics and Psychiatry, Nijmegen, The Netherlands. 7. The Mind Research Network, Albuquerque, NM, USA; Psychology Department and Neuroscience Institute, Georgia State University, Atlanta, GA, USA.
Abstract
BACKGROUND: Pooling of multi-site MRI data is often necessary when a large cohort is desired. However, different scanning platforms can introduce systematic differences which confound true effects of interest. One may reduce multi-site bias by calibrating pivotal scanning parameters, or include them as covariates to improve the data integrity. NEW METHOD: In the present study we use a source-based morphometry (SBM) model to explore scanning effects in multi-site sMRI studies and develop a data-driven correction. Specifically, independent components are extracted from the data and investigated for associations with scanning parameters to assess the influence. The identified scanning-related components can be eliminated from the original data for correction. RESULTS: A small set of SBM components captured most of the variance associated with the scanning differences. In a dataset of 1460 healthy subjects, pronounced and independent scanning effects were observed in brainstem and thalamus, associated with magnetic field strength-inversion time and RF-receiving coil. A second study with 110 schizophrenia patients and 124 healthy controls demonstrated that scanning effects can be effectively corrected with the SBM approach. COMPARISON WITH EXISTING METHOD(S): Both SBM and GLM correction appeared to effectively eliminate the scanning effects. Meanwhile, the SBM-corrected data yielded a more significant patient versus control group difference and less questionable findings. CONCLUSIONS: It is important to calibrate scanning settings and completely examine individual parameters for the control of confounding effects in multi-site sMRI studies. Both GLM and SBM correction can reduce scanning effects, though SBM's data-driven nature provides additional flexibility and is better able to handle collinear effects.
BACKGROUND: Pooling of multi-site MRI data is often necessary when a large cohort is desired. However, different scanning platforms can introduce systematic differences which confound true effects of interest. One may reduce multi-site bias by calibrating pivotal scanning parameters, or include them as covariates to improve the data integrity. NEW METHOD: In the present study we use a source-based morphometry (SBM) model to explore scanning effects in multi-site sMRI studies and develop a data-driven correction. Specifically, independent components are extracted from the data and investigated for associations with scanning parameters to assess the influence. The identified scanning-related components can be eliminated from the original data for correction. RESULTS: A small set of SBM components captured most of the variance associated with the scanning differences. In a dataset of 1460 healthy subjects, pronounced and independent scanning effects were observed in brainstem and thalamus, associated with magnetic field strength-inversion time and RF-receiving coil. A second study with 110 schizophrenia patients and 124 healthy controls demonstrated that scanning effects can be effectively corrected with the SBM approach. COMPARISON WITH EXISTING METHOD(S): Both SBM and GLM correction appeared to effectively eliminate the scanning effects. Meanwhile, the SBM-corrected data yielded a more significant patient versus control group difference and less questionable findings. CONCLUSIONS: It is important to calibrate scanning settings and completely examine individual parameters for the control of confounding effects in multi-site sMRI studies. Both GLM and SBM correction can reduce scanning effects, though SBM's data-driven nature provides additional flexibility and is better able to handle collinear effects.
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