Harmonization Of Cortical Thickness Measurements Across Scanners And Sites
| Autor: | Myrna M. Weissman, Phil Adams, Melvin G. McInnis, Madhukar H. Trivedi, Patrick J. McGrath, Nicholas C. Cullen, Warren D. Taylor, Irem Aselcioglu, Mary L. Phillips, Crystal Cooper, Yvette I. Sheline, Ramin V. Parsey, Russell T. Shinohara, Maurizio Fava, Jean-Philippe Fortin |
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| Rok vydání: | 2018 |
| Předmět: |
Adult
Male Adolescent Computer science Cognitive Neuroscience Datasets as Topic computer.software_genre Imaging data Article 030218 nuclear medicine & medical imaging Young Adult 03 medical and health sciences 0302 clinical medicine Neuroimaging medicine Humans Multicenter Studies as Topic Set (psychology) Aged 030304 developmental biology Aged 80 and over Cerebral Cortex 0303 health sciences medicine.diagnostic_test business.industry Magnetic resonance imaging Pattern recognition Middle Aged Models Theoretical Magnetic Resonance Imaging medicine.anatomical_structure Neurology Cerebral cortex Data Interpretation Statistical Female Data mining Artificial intelligence business computer 030217 neurology & neurosurgery Diffusion MRI |
| DOI: | 10.7916/zxqt-tk59 |
| Popis: | With the proliferation of multi-site neuroimaging studies, there is a greater need for handling non-biological variance introduced by differences in MRI scanners and acquisition protocols. Such unwanted sources of variation, which we refer to as “scanner effects”, can hinder the detection of imaging features associated with clinical covariates of interest and cause spurious findings. In this paper, we investigate scanner effects in two large multi-site studies on cortical thickness measurements, across a total of 11 scanners. We propose a set of general tools for visualizing and identifying scanner effects that are generalizable to other modalities. We then propose to use ComBat, a technique adopted from the genomics literature and recently applied to diffusion tensor imaging data, to combine and harmonize cortical thickness values across scanners. We show that ComBat removes unwanted sources of scan variability while simultaneously increasing the power and reproducibility of subsequent statistical analyses. We also show that ComBat is useful for combining imaging data with the goal of studying life-span trajectories in the brain. |
| Databáze: | OpenAIRE |
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