Although it has been used in MR radiomic studies [20,21,22,23], it has never been validated in that highly challenging context. This is an empirical study to investigate the impact of scanner effects when using machine learning on multi-site neuroimaging data. (2019) Harmonization of Infant Cortical Thickness Using Surface-to-Surface Cycle-Consistent Adversarial Networks. Radiomics faces the critical issue of a lack of reproducibility that still hampers the successful translation of radiomic model discovery into better diagnosis, patient classification, or monitoring radiomics-based tools. Harmonization of cortical thickness measurements across scanners and sites. Harmonization of cortical thickness measurements across scanners and sites Jean-Philippe Fortin 1,* , Nicholas Cullen 2,3,* , Yvette I. Sheline 3,4,5 , Warren D. Taylor 6 , Irem In this paper, we introduce multi-task learning (MTL) to data harmonization (DH); where we aim to harmonize images across different acquisition platforms and sites. Neuroimage. 167: 104 . Effectively, cortical thickness represents a totality of numerous microscopic properties; shown to be a relatively stable measure over the lifespan (Storsve et al., 2014; Hogstrom et al., 2013), it appears likely that any subtle structural characteristics predictive of the extent of short-term response to pharmacotherapy, if they exist, are not . Install pip install pycombat Usage. "Harmonization of cortical thickness measurements across scanners and sites." Neuroimage 167: 104-120. Funded grants include: 1) "Dimensional connectomics of anxious misery" across multiple sites have faced the necessity of harmonization. Data were harmonized across scanners and sites and statistically . Go to reference in article Crossref Google Scholar 63. . Preliminary results in 30 TS demonstrated cortical thickness, myelin, functional connectivity measures are comparable across 5 scanners, suggesting sensitivity to subject-specific connectome. We compared regional cortical thickness and surface area and measures of subcortical, lateral ventricular, Usage stats for A Comparison of Methods to Harmonize Cortical Thickness Measurements Across Scanners and Sites from the website that helps you find the preprints people are talking about. Harmonization of cortical thickness measurements across scanners and sites. van Zijl, Wilfred W. Lam and 3 more Open Access March 2018 Volume 168, Pages 222-241. Fortin J-P, Cullen N, Sheline YI et al (2018) Harmonization of • multicenter study cortical thickness measurements across scanners and sites. Biostatistics, 8(1):118-127, 2007. Fortin JP, Cullen N, Sheline YI et al. Neuroimage 167 , 104-120 (2018). We investigated classification performance using training and independent test sets drawn from two sources using both pre-harmonization and post-harmonization features. similar to scanner effects but observed across multiple batches in microarray experiments. Using longitudinal cortical thickness data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we demonstrate the presence of scanner-specific location and scale effects. NeuroImage, 167, 104-120, 2018: Link: Original ComBat paper for gene expression array: W. Evan Johnson and Cheng Li, Adjusting batch effects in microarray expression data using empirical Bayes methods. Neuroimage 167:104-120 20. PubMed Article PubMed Central Google Scholar 23. For up-to-dateinformation,seewww.adni-info.org. These classes of . Specifically, we introduce the Multi Stage Prediction (MSP) Network, a . We propose a set of tools for visualizing and identifying scanner effects that are generalizable to other modalities. METHODS: Here, we pooled data from 18 international cohorts with neuroimaging and clinical measurements in 18,925 participants (12,477 healthy control subjects and 6448 people with depression, of whom 694 had attempted suicide). ComBat has been utilized to harmonize multi-site diffusion tensor imaging data [43] and also cortical thickness measurements in 2 large multi-site studies across 11 scanners [44]. Harmonization of cortical thickness measurements across scanners and sites. 10.1016/j.neuroimage.2017.11.024 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ] NeuroImage 167:104-120. It is likely that additional non-structural MRI features are needed to further improve the obtained . ISMRM 2017: 1447. Harmonization of cortical thickness measurements across scanners and sites JP Fortin, N Cullen, YI Sheline, WD Taylor, I Aselcioglu, PA Cook, . ∙ 22 ∙ share . Harmonization for cortical thickness across sites in multi-center MRI study [abstract]. This leads to those methods that seek to harmonise measurements across sites by directly accounting for such covariates as scanner and site bias, and sequence contrast variabilities, e.g. Neuroimage 167, 104-120 , 2018 A total of 75 TS and more than two thousand patients with various psychiatric and neurological disorders are scheduled to participate in the project . . Fortin, J.P., et al. In the present study, we aim to compare results from three different harmonization approaches (1) LME, (2) ComBat, and (3) ComBat-GAM. Neuroimage 167, 104-120 , 2018 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. Here, we extend the ComBat approach to provide a harmonization procedure applicable to any radiomic feature. In this paper, we investigate scanner effects in two large multi-site studies on cortical thickness measurements, across a total of 11 scanners. Fortin, Jean-Philippe; Cullen, Nicholas; Sheline, Yvette I et al. Cortical thickness measurements were computed using cross-sectional FreeSurfer (version 5.1.0 . Harmonization of cortical thickness measurements across scanners and sites JP Fortin, N Cullen, YI Sheline, WD Taylor, I Aselcioglu, PA Cook, . A dataframe-friendly implementation of ComBat Harmonization which uses an empirical Bayesian framework to remove batch effects. Specifically, the intensity values of a single gradient direction of Johnson WE & Li C (2007) < doi:10.1093 . Harmonization of cortical thickness measurements across scanners and sites JP Fortin, N Cullen, YI Sheline, WD Taylor, I Aselcioglu, PA Cook, . Fortin J-P et al 2018 Harmonization of cortical thickness measurements across scanners and sites NeuroImage 167 104-20. scan. Peter C.M. Shinohara, R. T. (2018). Description of Research Expertise My overall research goal is to identify brain markers of depression and anxiety treatment response across disorders using structural and functional neuroimaging and to improve strategies for brain neuromodulation. In this paper, we investigate scanner effects in two large multi-site studies on cortical thickness measurements across a total of 11 scanners. . In this retrospective study, a database of thirty-two radiomic features, extracted from DCE-MR images of . Radiomics converts medical images into mineable data via a high-throughput extraction of quantitative features used for clinical decision support. View Article PubMed/NCBI Google Scholar 17. In department A, 18 F-FDG PET/CT images were acquired using a Gemini TF scanner (Philips) at 78 ± 9 min (mean ± SD; range, 59-108 min) after injection of 18 F-FDG (3 MBq/kg) at a rate of . Fortin J-P et al 2017 Harmonization of multi-site diffusion tensor imaging data NeuroImage 161 149-70. Preliminary results in 30 TS demonstrated cortical thickness, myelin, functional connectivity measures are comparable across 5 scanners, suggesting sensitivity to subject-specific connectome. A total of 75 TS and more than two thousand patients with various psychiatric and neurological disorders are scheduled to participate in the project . Fortin JP, Cullen N, Sheline YI, Taylor WD, Aselcioglu I, Cook PA, et al. ComBat has been utilized to harmonize multi-site diffusion tensor imaging data [43] and also cortical thickness measurements in 2 large multi-site studies across 11 scanners [44]. ComBat has been validated in MRI for the harmonization of cortical thickness measurements across scanners . Zhao F. et al. Link RESULTS: Systematic biases due to site differences in expert-traced lesion measurements were significant (P < .01 for both T1 and T2 lesion volumes), with site explaining >90% of the variation (range, 13.0-16.4 mL in T1 and 15.9-20.1 mL in T2) in lesion volumes. Harmonization of cortical thickness measurements across scanners and sites . Funded grants include: 1) "Dimensional connectomics of anxious misery" For each patient, the capillary blood glucose level was less than 8 mmol/L at the time of 18 F-FDG injection.. Among the available methods, ComBat, which was originally proposed to remove batch effects in genomics data (Johnson et al., 2007), has been recently adapted to diffusion tensor imaging data (Fortin et al., 2017), cortical thickness measurements (Fortin et al., 2018), and functional Johnson WE & Li C (2007) < doi:10.1093 . 2. . We observed a general decline in cortical thickness over age, in line with previous studies (13, 32). Harmonization of cortical thickness measurements across scanners and sites. However, as more datasets are becoming publicly available, there is a growing need for retrospective harmonization, pooling data from . Resting-state fMRI has the potential to help doctors detect abnormal behavior in brain activity and to diagnose patients with depression. M. H. Trivedi, M. M. Weissman, R. T. Shinohara, Harmonization of cortical thickness measurements across scanners and sites. (Harmonized cortical thickness was normally distributed.) Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects Ben Glocker 1, Robert Robinson , Daniel C. Castro , Qi Dou , Ender Konukoglu2 1 Biomedical Image Analysis Group, Imperial College London, UK 2 Computer Vision Laboratory, ETH Zurich, Zurich, Switzerland Abstract This is an empirical study to investigate the impact of scanner effects when us- Harmonization of cortical thickness measurements across scanners and sites Author links open overlay panel Jean-Philippe Fortin a 1 Nicholas Cullen b c 1 Yvette I. Sheline c d e Warren D. Taylor f Irem Aselcioglu c Philip A. Cook c d Phil Adams g Crystal Cooper h Maurizio Fava i Patrick J. McGrath g Melvin McInnis j Mary L. Phillips k Madhukar . Neuroimage 167, 104-120 , 2018 been recruited from over 50 sites across the U.S. and Canada. Biostatistics, 8(1):118-127, 2007. 10/10/2019 ∙ by Ben Glocker, et al. (2018) Harmonization of cortical thickness measurements across scanners and sites. NeuroImage, 167, 104-120, 2018: Link: Original ComBat paper for gene expression array: W. Evan Johnson and Cheng Li, Adjusting batch effects in microarray expression data using empirical Bayes methods. Neuroimage 167, 104-120 (2018 . Various investigations have assessed the reproducibility and validation of radiomic features across these discrepancies. Harmonization of cortical thickness measurements across scanners and sites . Magnetization Transfer Contrast and Chemical Exchange Saturation Transfer MRI. Recent studies have shown an improvement in segmentation with the combination of T1 + T2-FLAIR images. Harmonization techniques are required to remove scanner and site effects, while preserving the variability associated with biology. Indeed, radiomic feature … 2.2.2. 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. Output measures clustered according to . A dataframe-friendly implementation of ComBat Harmonization which uses an empirical Bayesian framework to remove batch effects. . large multi-site studies on cortical thickness measurements across a total of 11 scanners. Neuroimage 167 104-120. "Harmonization of cortical thickness measurements across scanners and sites." Neuroimage 167: 104-120. However, these radiomic features are susceptible to variation across scanners, acquisition protocols, and reconstruction settings. harmonization of multi-site longitudinal MRI data based on ComBat, a method originally developed for genomics and later adapted to cross-sectional MRI data.

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