Harmonization of multi-center diffusion tensor tractography in neonates with congenital heart disease: Optimizing post-processing and application of ComBat
Advanced brain imaging of neonatal macrostructure and microstructure, which has prognosticating importance, is more frequently being incorporated into multi-center trials of neonatal neuroprotection. Multicenter neuroimaging studies, designed to overcome small sample sized clinical cohorts, are esse...
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Elsevier
2022-09-01
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| Series: | NeuroImage: Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666956022000381 |
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| author | Benjamin Meyers Vincent K. Lee Lauren Dennis Julia Wallace Vanessa Schmithorst Jodie K. Votava-Smith Vidya Rajagopalan Elizabeth Herrup Tracy Baust Nhu N. Tran Jill V. Hunter Daniel J. Licht J. William Gaynor Dean B. Andropoulos Ashok Panigrahy Rafael Ceschin |
| author_facet | Benjamin Meyers Vincent K. Lee Lauren Dennis Julia Wallace Vanessa Schmithorst Jodie K. Votava-Smith Vidya Rajagopalan Elizabeth Herrup Tracy Baust Nhu N. Tran Jill V. Hunter Daniel J. Licht J. William Gaynor Dean B. Andropoulos Ashok Panigrahy Rafael Ceschin |
| author_sort | Benjamin Meyers |
| collection | DOAJ |
| description | Advanced brain imaging of neonatal macrostructure and microstructure, which has prognosticating importance, is more frequently being incorporated into multi-center trials of neonatal neuroprotection. Multicenter neuroimaging studies, designed to overcome small sample sized clinical cohorts, are essential but lead to increased technical variability. Few harmonization techniques have been developed for neonatal brain microstructural (diffusion tensor) analysis. The work presented here aims to remedy two common problems that exist with the current state of the art approaches: 1) variance in scanner and protocol in data collection can limit the researcher's ability to harmonize data acquired under different conditions or using different clinical populations. 2) The general lack of objective guidelines for dealing with anatomically abnormal anatomy and pathology. Often, subjects are excluded due to subjective criteria, or due to pathology that could be informative to the final analysis, leading to the loss of reproducibility and statistical power. This proves to be a barrier in the analysis of large multi-center studies and is a particularly salient problem given the relative scarcity of neonatal imaging data. We provide an objective, data-driven, and semi-automated neonatal processing pipeline designed to harmonize compartmentalized variant data acquired under different parameters. This is done by first implementing a search space reduction step of extracting the along-tract diffusivity values along each tract of interest, rather than performing whole-brain harmonization. This is followed by a data-driven outlier detection step, with the purpose of removing unwanted noise and outliers from the final harmonization. We then use an empirical Bayes harmonization algorithm performed at the along-tract level, with the output being a lower dimensional space but still spatially informative. After applying our pipeline to this large multi-site dataset of neonates and infants with congenital heart disease (n = 398 subjects recruited across 4 centers, with a total of n = 763 MRI pre-operative/post-operative time points), we show that infants with single ventricle cardiac physiology demonstrate greater white matter microstructural alterations compared to infants with bi-ventricular heart disease, supporting what has previously been shown in literature. Our method is an open-source pipeline for delineating white matter tracts in subject space but provides the necessary modular components for performing atlas space analysis. As such, we validate and introduce Diffusion Imaging of Neonates by Group Organization (DINGO), a high-level, semi-automated framework that can facilitate harmonization of subject-space tractography generated from diffusion tensor imaging acquired across varying scanners, institutions, and clinical populations. Datasets acquired using varying protocols or cohorts are compartmentalized into subsets, where a cohort-specific template is generated, allowing for the propagation of the tractography mask set with higher spatial specificity. Taken together, this pipeline can reduce multi-scanner technical variability which can confound important biological variability in relation to neonatal brain microstructure. |
| format | Article |
| id | doaj-art-06ba43d0f35a454cbc33689a8ab8dd29 |
| institution | DOAJ |
| issn | 2666-9560 |
| language | English |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
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| series | NeuroImage: Reports |
| spelling | doaj-art-06ba43d0f35a454cbc33689a8ab8dd292025-08-20T03:17:32ZengElsevierNeuroImage: Reports2666-95602022-09-012310011410.1016/j.ynirp.2022.100114Harmonization of multi-center diffusion tensor tractography in neonates with congenital heart disease: Optimizing post-processing and application of ComBatBenjamin Meyers0Vincent K. Lee1Lauren Dennis2Julia Wallace3Vanessa Schmithorst4Jodie K. Votava-Smith5Vidya Rajagopalan6Elizabeth Herrup7Tracy Baust8Nhu N. Tran9Jill V. Hunter10Daniel J. Licht11J. William Gaynor12Dean B. Andropoulos13Ashok Panigrahy14Rafael Ceschin15Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USADepartment of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USADepartment of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USADepartment of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USADepartment of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USADivision of Cardiology, Department of Pediatrics, Children's Hospital of Los Angeles and Keck School of Medicine University of Southern California, Los Angeles, CA, USADepartment of Radiology, Children's Hospital of Los Angeles and Keck School of Medicine, University of Southern California, Los Angeles, CA, USADepartment of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USADepartment of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USADivision of Cardiology, Department of Pediatrics, Children's Hospital of Los Angeles and Keck School of Medicine University of Southern California, Los Angeles, CA, USADepartment of Radiology, Texas Children's Hospital, Houston, TX, USADepartment of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USADepartment of Surgery, Children's Hospital of Philadelphia, Philadelphia, PA, USADepartment of Anesthesiology, Texas Children's Hospital, Houston, TX, USADepartment of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USADepartment of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Corresponding author. Department of Radiology Children's Hospital of Pittsburgh of UPMC 4401 Penn Ave. Pittsburgh, PA, 15224, USA.Advanced brain imaging of neonatal macrostructure and microstructure, which has prognosticating importance, is more frequently being incorporated into multi-center trials of neonatal neuroprotection. Multicenter neuroimaging studies, designed to overcome small sample sized clinical cohorts, are essential but lead to increased technical variability. Few harmonization techniques have been developed for neonatal brain microstructural (diffusion tensor) analysis. The work presented here aims to remedy two common problems that exist with the current state of the art approaches: 1) variance in scanner and protocol in data collection can limit the researcher's ability to harmonize data acquired under different conditions or using different clinical populations. 2) The general lack of objective guidelines for dealing with anatomically abnormal anatomy and pathology. Often, subjects are excluded due to subjective criteria, or due to pathology that could be informative to the final analysis, leading to the loss of reproducibility and statistical power. This proves to be a barrier in the analysis of large multi-center studies and is a particularly salient problem given the relative scarcity of neonatal imaging data. We provide an objective, data-driven, and semi-automated neonatal processing pipeline designed to harmonize compartmentalized variant data acquired under different parameters. This is done by first implementing a search space reduction step of extracting the along-tract diffusivity values along each tract of interest, rather than performing whole-brain harmonization. This is followed by a data-driven outlier detection step, with the purpose of removing unwanted noise and outliers from the final harmonization. We then use an empirical Bayes harmonization algorithm performed at the along-tract level, with the output being a lower dimensional space but still spatially informative. After applying our pipeline to this large multi-site dataset of neonates and infants with congenital heart disease (n = 398 subjects recruited across 4 centers, with a total of n = 763 MRI pre-operative/post-operative time points), we show that infants with single ventricle cardiac physiology demonstrate greater white matter microstructural alterations compared to infants with bi-ventricular heart disease, supporting what has previously been shown in literature. Our method is an open-source pipeline for delineating white matter tracts in subject space but provides the necessary modular components for performing atlas space analysis. As such, we validate and introduce Diffusion Imaging of Neonates by Group Organization (DINGO), a high-level, semi-automated framework that can facilitate harmonization of subject-space tractography generated from diffusion tensor imaging acquired across varying scanners, institutions, and clinical populations. Datasets acquired using varying protocols or cohorts are compartmentalized into subsets, where a cohort-specific template is generated, allowing for the propagation of the tractography mask set with higher spatial specificity. Taken together, this pipeline can reduce multi-scanner technical variability which can confound important biological variability in relation to neonatal brain microstructure.http://www.sciencedirect.com/science/article/pii/S2666956022000381Diffusion tensor imagingNeonatal imagingCongenital heart disease |
| spellingShingle | Benjamin Meyers Vincent K. Lee Lauren Dennis Julia Wallace Vanessa Schmithorst Jodie K. Votava-Smith Vidya Rajagopalan Elizabeth Herrup Tracy Baust Nhu N. Tran Jill V. Hunter Daniel J. Licht J. William Gaynor Dean B. Andropoulos Ashok Panigrahy Rafael Ceschin Harmonization of multi-center diffusion tensor tractography in neonates with congenital heart disease: Optimizing post-processing and application of ComBat NeuroImage: Reports Diffusion tensor imaging Neonatal imaging Congenital heart disease |
| title | Harmonization of multi-center diffusion tensor tractography in neonates with congenital heart disease: Optimizing post-processing and application of ComBat |
| title_full | Harmonization of multi-center diffusion tensor tractography in neonates with congenital heart disease: Optimizing post-processing and application of ComBat |
| title_fullStr | Harmonization of multi-center diffusion tensor tractography in neonates with congenital heart disease: Optimizing post-processing and application of ComBat |
| title_full_unstemmed | Harmonization of multi-center diffusion tensor tractography in neonates with congenital heart disease: Optimizing post-processing and application of ComBat |
| title_short | Harmonization of multi-center diffusion tensor tractography in neonates with congenital heart disease: Optimizing post-processing and application of ComBat |
| title_sort | harmonization of multi center diffusion tensor tractography in neonates with congenital heart disease optimizing post processing and application of combat |
| topic | Diffusion tensor imaging Neonatal imaging Congenital heart disease |
| url | http://www.sciencedirect.com/science/article/pii/S2666956022000381 |
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