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Moon SW, Zhao L, Matloff W, Hobel S, Berger R, Kwon D, Kim J, Toga AW, Dinov ID. Brain structure and allelic associations in Alzheimer's disease. CNS Neurosci Ther 2023; 29:1034-1048. [PMID: 36575854 PMCID: PMC10018103 DOI: 10.1111/cns.14073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 12/06/2022] [Accepted: 12/11/2022] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Alzheimer's disease (AD), the most prevalent form of dementia, affects 6.5 million Americans and over 50 million people globally. Clinical, genetic, and phenotypic studies of dementia provide some insights of the observed progressive neurodegenerative processes, however, the mechanisms underlying AD onset remain enigmatic. AIMS This paper examines late-onset dementia-related cognitive impairment utilizing neuroimaging-genetics biomarker associations. MATERIALS AND METHODS The participants, ages 65-85, included 266 healthy controls (HC), 572 volunteers with mild cognitive impairment (MCI), and 188 Alzheimer's disease (AD) patients. Genotype dosage data for AD-associated single nucleotide polymorphisms (SNPs) were extracted from the imputed ADNI genetics archive using sample-major additive coding. Such 29 SNPs were selected, representing a subset of independent SNPs reported to be highly associated with AD in a recent AD meta-GWAS study by Jansen and colleagues. RESULTS We identified the significant correlations between the 29 genomic markers (GMs) and the 200 neuroimaging markers (NIMs). The odds ratios and relative risks for AD and MCI (relative to HC) were predicted using multinomial linear models. DISCUSSION In the HC and MCI cohorts, mainly cortical thickness measures were associated with GMs, whereas the AD cohort exhibited different GM-NIM relations. Network patterns within the HC and AD groups were distinct in cortical thickness, volume, and proportion of White to Gray Matter (pct), but not in the MCI cohort. Multinomial linear models of clinical diagnosis showed precisely the specific NIMs and GMs that were most impactful in discriminating between AD and HC, and between MCI and HC. CONCLUSION This study suggests that advanced analytics provide mechanisms for exploring the interrelations between morphometric indicators and GMs. The findings may facilitate further clinical investigations of phenotypic associations that support deep systematic understanding of AD pathogenesis.
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Affiliation(s)
- Seok Woo Moon
- Department of Neuropsychiatry, Research Institute of Medical ScienceKonkuk University School of MedicineSeoulKorea
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USCCaliforniaLos AngelesUSA
| | - Lu Zhao
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USCCaliforniaLos AngelesUSA
| | - William Matloff
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USCCaliforniaLos AngelesUSA
| | - Sam Hobel
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USCCaliforniaLos AngelesUSA
| | - Ryan Berger
- Microbiology & ImmunologyUniversity of MichiganAnn ArborMichiganUSA
| | - Daehong Kwon
- Department of Biomedical Science and EngineeringKonkuk UniversitySeoulKorea
| | - Jaebum Kim
- Department of Biomedical Science and EngineeringKonkuk UniversitySeoulKorea
| | - Arthur W. Toga
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USCCaliforniaLos AngelesUSA
| | - Ivo D. Dinov
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USCCaliforniaLos AngelesUSA
- Department of Health Behavior and Biological Sciences, Statistics Online Computational Resource (SOCR), Michigan Institute for Data Science (MIDAS)University of MichiganAnn ArborMichiganUSA
- Department of StatisticsUniversity of CaliforniaLos AngelesCaliforniaUSA
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Moon SW. Neuroimaging Genetics and Network Analysis in Alzheimer's Disease. Curr Alzheimer Res 2023; 20:526-538. [PMID: 37957920 DOI: 10.2174/0115672050265188231107072215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/22/2023] [Accepted: 08/13/2023] [Indexed: 11/15/2023]
Abstract
The issue of the genetics in brain imaging phenotypes serves as a crucial link between two distinct scientific fields: neuroimaging genetics (NG). The articles included here provide solid proof that this NG link has considerable synergy. There is a suitable collection of articles that offer a wide range of viewpoints on how genetic variations affect brain structure and function. They serve as illustrations of several study approaches used in contemporary genetics and neuroscience. Genome-wide association studies and candidate-gene association are two examples of genetic techniques. Cortical gray matter structural/volumetric measures from magnetic resonance imaging (MRI) are sources of information on brain phenotypes. Together, they show how various scientific disciplines have benefited from significant technological advances, such as the single-nucleotide polymorphism array in genetics and the development of increasingly higher-resolution MRI imaging. Moreover, we discuss NG's contribution to expanding our knowledge about the heterogeneity within Alzheimer's disease as well as the benefits of different network analyses.
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Affiliation(s)
- Seok Woo Moon
- Department of Psychiatry, Institute of Medical Science, Konkuk University School of Medicine, Chungju, Republic of Korea
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Karim MR, Michel A, Zappa A, Baranov P, Sahay R, Rebholz-Schuhmann D. Improving data workflow systems with cloud services and use of open data for bioinformatics research. Brief Bioinform 2019; 19:1035-1050. [PMID: 28419324 PMCID: PMC6169675 DOI: 10.1093/bib/bbx039] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Indexed: 11/22/2022] Open
Abstract
Data workflow systems (DWFSs) enable bioinformatics researchers to combine components for data access and data analytics, and to share the final data analytics approach with their collaborators. Increasingly, such systems have to cope with large-scale data, such as full genomes (about 200 GB each), public fact repositories (about 100 TB of data) and 3D imaging data at even larger scales. As moving the data becomes cumbersome, the DWFS needs to embed its processes into a cloud infrastructure, where the data are already hosted. As the standardized public data play an increasingly important role, the DWFS needs to comply with Semantic Web technologies. This advancement to DWFS would reduce overhead costs and accelerate the progress in bioinformatics research based on large-scale data and public resources, as researchers would require less specialized IT knowledge for the implementation. Furthermore, the high data growth rates in bioinformatics research drive the demand for parallel and distributed computing, which then imposes a need for scalability and high-throughput capabilities onto the DWFS. As a result, requirements for data sharing and access to public knowledge bases suggest that compliance of the DWFS with Semantic Web standards is necessary. In this article, we will analyze the existing DWFS with regard to their capabilities toward public open data use as well as large-scale computational and human interface requirements. We untangle the parameters for selecting a preferable solution for bioinformatics research with particular consideration to using cloud services and Semantic Web technologies. Our analysis leads to research guidelines and recommendations toward the development of future DWFS for the bioinformatics research community.
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Affiliation(s)
- Md Rezaul Karim
- Semantics in eHealth and Life Sciences (SeLS), Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland
| | - Audrey Michel
- School of Biochemistry and Cell Biology, University College Cork, Ireland
| | - Achille Zappa
- Insight Centre for Data Analytics, National University of Ireland Galway, Dangan, Galway, Ireland
| | - Pavel Baranov
- School of Biochemistry and Cell Biology, University College Cork, Ireland
| | - Ratnesh Sahay
- Semantics in eHealth and Life Sciences (SeLS), Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland
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Kalinin AA, Allyn-Feuer A, Ade A, Fon GV, Meixner W, Dilworth D, Husain SS, de Wet JR, Higgins GA, Zheng G, Creekmore A, Wiley JW, Verdone JE, Veltri RW, Pienta KJ, Coffey DS, Athey BD, Dinov ID. 3D Shape Modeling for Cell Nuclear Morphological Analysis and Classification. Sci Rep 2018; 8:13658. [PMID: 30209281 PMCID: PMC6135819 DOI: 10.1038/s41598-018-31924-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Accepted: 08/29/2018] [Indexed: 02/08/2023] Open
Abstract
Quantitative analysis of morphological changes in a cell nucleus is important for the understanding of nuclear architecture and its relationship with pathological conditions such as cancer. However, dimensionality of imaging data, together with a great variability of nuclear shapes, presents challenges for 3D morphological analysis. Thus, there is a compelling need for robust 3D nuclear morphometric techniques to carry out population-wide analysis. We propose a new approach that combines modeling, analysis, and interpretation of morphometric characteristics of cell nuclei and nucleoli in 3D. We used robust surface reconstruction that allows accurate approximation of 3D object boundary. Then, we computed geometric morphological measures characterizing the form of cell nuclei and nucleoli. Using these features, we compared over 450 nuclei with about 1,000 nucleoli of epithelial and mesenchymal prostate cancer cells, as well as 1,000 nuclei with over 2,000 nucleoli from serum-starved and proliferating fibroblast cells. Classification of sets of 9 and 15 cells achieved accuracy of 95.4% and 98%, respectively, for prostate cancer cells, and 95% and 98% for fibroblast cells. To our knowledge, this is the first attempt to combine these methods for 3D nuclear shape modeling and morphometry into a highly parallel pipeline workflow for morphometric analysis of thousands of nuclei and nucleoli in 3D.
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Affiliation(s)
- Alexandr A Kalinin
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.,Statistics Online Computational Resource (SOCR), Department of Health Behavior and Biological Sciences, University of Michigan School of Nursing, Ann Arbor, MI, USA
| | - Ari Allyn-Feuer
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Alex Ade
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Gordon-Victor Fon
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Walter Meixner
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - David Dilworth
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Syed S Husain
- Statistics Online Computational Resource (SOCR), Department of Health Behavior and Biological Sciences, University of Michigan School of Nursing, Ann Arbor, MI, USA
| | - Jeffrey R de Wet
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Gerald A Higgins
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Gen Zheng
- Division of Gastroenterology, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Amy Creekmore
- Division of Gastroenterology, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - John W Wiley
- Division of Gastroenterology, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - James E Verdone
- Department of Urology, James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Robert W Veltri
- Department of Urology, James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kenneth J Pienta
- Department of Urology, James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Donald S Coffey
- Department of Urology, James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Brian D Athey
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA. .,Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI, USA.
| | - Ivo D Dinov
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA. .,Statistics Online Computational Resource (SOCR), Department of Health Behavior and Biological Sciences, University of Michigan School of Nursing, Ann Arbor, MI, USA. .,Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI, USA.
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Leandrou S, Petroudi S, Kyriacou PA, Reyes-Aldasoro CC, Pattichis CS. Quantitative MRI Brain Studies in Mild Cognitive Impairment and Alzheimer's Disease: A Methodological Review. IEEE Rev Biomed Eng 2018; 11:97-111. [PMID: 29994606 DOI: 10.1109/rbme.2018.2796598] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Classifying and predicting Alzheimer's disease (AD) in individuals with memory disorders through clinical and psychometric assessment is challenging, especially in mild cognitive impairment (MCI) subjects. Quantitative structural magnetic resonance imaging acquisition methods in combination with computer-aided diagnosis are currently being used for the assessment of AD. These acquisitions methods include voxel-based morphometry, volumetric measurements in specific regions of interest (ROIs), cortical thickness measurements, shape analysis, and texture analysis. This review evaluates the aforementioned methods in the classification of cases into one of the following three groups: normal controls, MCI, and AD subjects. Furthermore, the performance of the methods is assessed on the prediction of conversion from MCI to AD. In parallel, it is also assessed which ROIs are preferred in both classification and prognosis through the different states of the disease. Structural changes in the early stages of the disease are more pronounced in the medial temporal lobe, especially in the entorhinal cortex, whereas with disease progression, both entorhinal cortex and hippocampus offer similar discriminative power. However, for the conversion from MCI subjects to AD, entorhinal cortex provides better predictive accuracies rather than other structures, such as the hippocampus.
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Wollmann T, Erfle H, Eils R, Rohr K, Gunkel M. Workflows for microscopy image analysis and cellular phenotyping. J Biotechnol 2017; 261:70-75. [PMID: 28757289 DOI: 10.1016/j.jbiotec.2017.07.019] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2017] [Revised: 07/18/2017] [Accepted: 07/21/2017] [Indexed: 10/19/2022]
Abstract
In large scale biological experiments, like high-throughput or high-content cellular screening, the amount and the complexity of images to be analyzed are steadily increasing. To handle and process these images, well defined image processing and analysis steps need to be performed by applying dedicated workflows. Multiple software tools have emerged with the aim to facilitate creation of such workflows by integrating existing methods, tools, and routines, and by adapting them to different applications and questions, as well as making them reusable and interchangeable. In this review, we describe workflow systems for the integration of microscopy image analysis techniques with focus on KNIME and Galaxy.
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Affiliation(s)
- Thomas Wollmann
- Dept. Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, University of Heidelberg, BioQuant, IPMB, and DKFZ Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany.
| | - Holger Erfle
- High-Content Analysis of the Cell (HiCell) and ViroQuant-CellNetworks RNAi Screening Facility, BioQuant, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
| | - Roland Eils
- Dept. Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, University of Heidelberg, BioQuant, IPMB, and DKFZ Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
| | - Karl Rohr
- Dept. Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, University of Heidelberg, BioQuant, IPMB, and DKFZ Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
| | - Manuel Gunkel
- High-Content Analysis of the Cell (HiCell) and ViroQuant-CellNetworks RNAi Screening Facility, BioQuant, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
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Moutsatsos IK, Parker CN. Recent advances in quantitative high throughput and high content data analysis. Expert Opin Drug Discov 2016; 11:415-23. [PMID: 26924521 DOI: 10.1517/17460441.2016.1154036] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
INTRODUCTION High throughput screening has become a basic technique with which to explore biological systems. Advances in technology, including increased screening capacity, as well as methods that generate multiparametric readouts, are driving the need for improvements in the analysis of data sets derived from such screens. AREAS COVERED This article covers the recent advances in the analysis of high throughput screening data sets from arrayed samples, as well as the recent advances in the analysis of cell-by-cell data sets derived from image or flow cytometry application. Screening multiple genomic reagents targeting any given gene creates additional challenges and so methods that prioritize individual gene targets have been developed. The article reviews many of the open source data analysis methods that are now available and which are helping to define a consensus on the best practices to use when analyzing screening data. EXPERT OPINION As data sets become larger, and more complex, the need for easily accessible data analysis tools will continue to grow. The presentation of such complex data sets, to facilitate quality control monitoring and interpretation of the results will require the development of novel visualizations. In addition, advanced statistical and machine learning algorithms that can help identify patterns, correlations and the best features in massive data sets will be required. The ease of use for these tools will be important, as they will need to be used iteratively by laboratory scientists to improve the outcomes of complex analyses.
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Affiliation(s)
- Ioannis K Moutsatsos
- a Novartis Institute of Biomedical Research , Novartis - Developmental and Molecular Pathways (DMP) , Basel , Switzerland
| | - Christian N Parker
- a Novartis Institute of Biomedical Research , Novartis - Developmental and Molecular Pathways (DMP) , Basel , Switzerland
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Automated retinofugal visual pathway reconstruction with multi-shell HARDI and FOD-based analysis. Neuroimage 2015; 125:767-779. [PMID: 26551261 DOI: 10.1016/j.neuroimage.2015.11.005] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2015] [Revised: 09/22/2015] [Accepted: 11/03/2015] [Indexed: 12/30/2022] Open
Abstract
Diffusion MRI tractography provides a non-invasive modality to examine the human retinofugal projection, which consists of the optic nerves, optic chiasm, optic tracts, the lateral geniculate nuclei (LGN) and the optic radiations. However, the pathway has several anatomic features that make it particularly challenging to study with tractography, including its location near blood vessels and bone-air interface at the base of the cerebrum, crossing fibers at the chiasm, somewhat-tortuous course around the temporal horn via Meyer's Loop, and multiple closely neighboring fiber bundles. To date, these unique complexities of the visual pathway have impeded the development of a robust and automated reconstruction method using tractography. To overcome these challenges, we develop a novel, fully automated system to reconstruct the retinofugal visual pathway from high-resolution diffusion imaging data. Using multi-shell, high angular resolution diffusion imaging (HARDI) data, we reconstruct precise fiber orientation distributions (FODs) with high order spherical harmonics (SPHARM) to resolve fiber crossings, which allows the tractography algorithm to successfully navigate the complicated anatomy surrounding the retinofugal pathway. We also develop automated algorithms for the identification of ROIs used for fiber bundle reconstruction. In particular, we develop a novel approach to extract the LGN region of interest (ROI) based on intrinsic shape analysis of a fiber bundle computed from a seed region at the optic chiasm to a target at the primary visual cortex. By combining automatically identified ROIs and FOD-based tractography, we obtain a fully automated system to compute the main components of the retinofugal pathway, including the optic tract and the optic radiation. We apply our method to the multi-shell HARDI data of 215 subjects from the Human Connectome Project (HCP). Through comparisons with post-mortem dissection measurements, we demonstrate the retinotopic organization of the optic radiation including a successful reconstruction of Meyer's loop. Then, using the reconstructed optic radiation bundle from the HCP cohort, we construct a probabilistic atlas and demonstrate its consistency with a post-mortem atlas. Finally, we generate a shape-based representation of the optic radiation for morphometry analysis.
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Moon SW, Dinov ID, Hobel S, Zamanyan A, Choi YC, Shi R, Thompson PM, Toga AW. Structural Brain Changes in Early-Onset Alzheimer's Disease Subjects Using the LONI Pipeline Environment. J Neuroimaging 2015; 25:728-37. [PMID: 25940587 PMCID: PMC4537660 DOI: 10.1111/jon.12252] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2014] [Revised: 03/20/2015] [Accepted: 03/22/2015] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND AND PURPOSE This study investigates 36 subjects aged 55-65 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to expand our knowledge of early-onset (EO) Alzheimer's Disease (EO-AD) using neuroimaging biomarkers. METHODS Nine of the subjects had EO-AD, and 27 had EO mild cognitive impairment (EO-MCI). The structural ADNI data were parcellated using BrainParser, and the 15 most discriminating neuroimaging markers between the two cohorts were extracted using the Global Shape Analysis (GSA) Pipeline workflow. Then the Local Shape Analysis (LSA) Pipeline workflow was used to conduct local (per-vertex) post-hoc statistical analyses of the shape differences based on the participants' diagnoses (EO-MCI+EO-AD). Tensor-based Morphometry (TBM) and multivariate regression models were used to identify the significance of the structural brain differences based on the participants' diagnoses. RESULTS The significant between-group regional differences using GSA were found in 15 neuroimaging markers. The results of the LSA analysis workflow were based on the subject diagnosis, age, years of education, apolipoprotein E (ε4), Mini-Mental State Examination, visiting times, and logical memory as regressors. All the variables had significant effects on the regional shape measures. Some of these effects survived the false discovery rate (FDR) correction. Similarly, the TBM analysis showed significant effects on the Jacobian displacement vector fields, but these effects were reduced after FDR correction. CONCLUSIONS These results may explain some of the differences between EO-AD and EO-MCI, and some of the characteristics of the EO cognitive impairment subjects.
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Affiliation(s)
- Seok Woo Moon
- Department of Psychiatry, Konkuk University School of Medicine, Seoul 143-701, Korea
| | - Ivo D. Dinov
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
- University of Michigan, School of Nursing, Ann Arbor, MI 48109, USA
| | - Sam Hobel
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
| | - Alen Zamanyan
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
| | - Young Chil Choi
- Department of Radiology, Konkuk University School of Medicine, Seoul 143-701, Korea
| | - Ran Shi
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
| | - Paul M. Thompson
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
| | - Arthur W. Toga
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
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MaPSeq, A Service-Oriented Architecture for Genomics Research within an Academic Biomedical Research Institution. INFORMATICS 2015. [DOI: 10.3390/informatics2030020] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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Moon SW, Dinov ID, Zamanyan A, Shi R, Genco A, Hobel S, Thompson PM, Toga AW. Gene interactions and structural brain change in early-onset Alzheimer's disease subjects using the pipeline environment. Psychiatry Investig 2015; 12:125-35. [PMID: 25670955 PMCID: PMC4310910 DOI: 10.4306/pi.2015.12.1.125] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2013] [Revised: 02/02/2014] [Accepted: 02/03/2014] [Indexed: 01/07/2023] Open
Abstract
OBJECTIVE This article investigates subjects aged 55 to 65 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to broaden our understanding of early-onset (EO) cognitive impairment using neuroimaging and genetics biomarkers. METHODS Nine of the subjects had EO-AD (Alzheimer's disease) and 27 had EO-MCI (mild cognitive impairment). The 15 most important neuroimaging markers were extracted with the Global Shape Analysis (GSA) Pipeline workflow. The 20 most significant single nucleotide polymorphisms (SNPs) were chosen and were associated with specific neuroimaging biomarkers. RESULTS We identified associations between the neuroimaging phenotypes and genotypes for a total of 36 subjects. Our results for all the subjects taken together showed the most significant associations between rs7718456 and L_hippocampus (volume), and between rs7718456 and R_hippocampus (volume). For the 27 MCI subjects, we found the most significant associations between rs6446443 and R_superior_frontal_gyrus (volume), and between rs17029131 and L_Precuneus (volume). For the nine AD subjects, we found the most significant associations between rs16964473 and L_rectus gyrus (surface area), and between rs12972537 and L_rectus_gyrus (surface area). CONCLUSION We observed significant correlations between the SNPs and the neuroimaging phenotypes in the 36 EO subjects in terms of neuroimaging genetics. However, larger sample sizes are needed to ensure that the effects will be detectable for a reasonable false-positive error rate using the GSA and Plink Pipeline workflows.
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Affiliation(s)
- Seok Woo Moon
- Department of Psychiatry, Konkuk University School of Medicine, Chungju, Republic of Korea
| | - Ivo D. Dinov
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
- Statistics Online Computational Resource, UMSM, University of Michigan, Ann Arbor, MI, USA
| | - Alen Zamanyan
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Ran Shi
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Alex Genco
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Sam Hobel
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Paul M. Thompson
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Arthur W. Toga
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
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Moon SW, Dinov ID, Kim J, Zamanyan A, Hobel S, Thompson PM, Toga AW. Structural Neuroimaging Genetics Interactions in Alzheimer's Disease. J Alzheimers Dis 2015; 48:1051-63. [PMID: 26444770 PMCID: PMC4730943 DOI: 10.3233/jad-150335] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
This article investigates late-onset cognitive impairment using neuroimaging and genetics biomarkers for Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. Eight-hundred and eight ADNI subjects were identified and divided into three groups: 200 subjects with Alzheimer's disease (AD), 383 subjects with mild cognitive impairment (MCI), and 225 asymptomatic normal controls (NC). Their structural magnetic resonance imaging (MRI) data were parcellated using BrainParser, and the 80 most important neuroimaging biomarkers were extracted using the global shape analysis Pipeline workflow. Using Plink via the Pipeline environment, we obtained 80 SNPs highly-associated with the imaging biomarkers. In the AD cohort, rs2137962 was significantly associated bilaterally with changes in the hippocampi and the parahippocampal gyri, and rs1498853, rs288503, and rs288496 were associated with the left and right hippocampi, the right parahippocampal gyrus, and the left inferior temporal gyrus. In the MCI cohort, rs17028008 and rs17027976 were significantly associated with the right caudate and right fusiform gyrus, rs2075650 (TOMM40) was associated with the right caudate, and rs1334496 and rs4829605 were significantly associated with the right inferior temporal gyrus. In the NC cohort, Chromosome 15 [rs734854 (STOML1), rs11072463 (PML), rs4886844 (PML), and rs1052242 (PML)] was significantly associated with both hippocampi and both insular cortices, and rs4899412 (RGS6) was significantly associated with the caudate. We observed significant correlations between genetic and neuroimaging phenotypes in the 808 ADNI subjects. These results suggest that differences between AD, MCI, and NC cohorts may be examined by using powerful joint models of morphometric, imaging and genotypic data.
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Affiliation(s)
- Seok Woo Moon
- Department of Psychiatry, Konkuk University School of Medicine, Seoul, Republic of Korea
| | - Ivo D. Dinov
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, United States of America
- University of Michigan, School of Nursing, Ann Arbor, Michigan, United States of America
| | - Jaebum Kim
- Department of Animal Biotechnology, Konkuk University, Seoul, Republic of Korea
| | - Alen Zamanyan
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, United States of America
| | - Sam Hobel
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, United States of America
| | - Paul M. Thompson
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, United States of America
| | - Arthur W. Toga
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, United States of America
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13
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Dinov ID, Petrosyan P, Liu Z, Eggert P, Zamanyan A, Torri F, Macciardi F, Hobel S, Moon SW, Sung YH, Jiang Z, Labus J, Kurth F, Ashe-McNalley C, Mayer E, Vespa PM, Van Horn JD, Toga AW. The perfect neuroimaging-genetics-computation storm: collision of petabytes of data, millions of hardware devices and thousands of software tools. Brain Imaging Behav 2014; 8:311-22. [PMID: 23975276 PMCID: PMC3933453 DOI: 10.1007/s11682-013-9248-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The volume, diversity and velocity of biomedical data are exponentially increasing providing petabytes of new neuroimaging and genetics data every year. At the same time, tens-of-thousands of computational algorithms are developed and reported in the literature along with thousands of software tools and services. Users demand intuitive, quick and platform-agnostic access to data, software tools, and infrastructure from millions of hardware devices. This explosion of information, scientific techniques, computational models, and technological advances leads to enormous challenges in data analysis, evidence-based biomedical inference and reproducibility of findings. The Pipeline workflow environment provides a crowd-based distributed solution for consistent management of these heterogeneous resources. The Pipeline allows multiple (local) clients and (remote) servers to connect, exchange protocols, control the execution, monitor the states of different tools or hardware, and share complete protocols as portable XML workflows. In this paper, we demonstrate several advanced computational neuroimaging and genetics case-studies, and end-to-end pipeline solutions. These are implemented as graphical workflow protocols in the context of analyzing imaging (sMRI, fMRI, DTI), phenotypic (demographic, clinical), and genetic (SNP) data.
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Affiliation(s)
- Ivo D Dinov
- Laboratory of Neuro Imaging (LONI), David Geffen School of Medicine at UCLA, University of California, Los Angeles, 635 S. Charles Young Drive, Suite 225, Los Angeles, CA, 90095-7334, USA,
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14
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Torri F, Dinov ID, Zamanyan A, Hobel S, Genco A, Petrosyan P, Clark AP, Liu Z, Eggert P, Pierce J, Knowles JA, Ames J, Kesselman C, Toga AW, Potkin SG, Vawter MP, Macciardi F. Next generation sequence analysis and computational genomics using graphical pipeline workflows. Genes (Basel) 2014; 3:545-75. [PMID: 23139896 PMCID: PMC3490498 DOI: 10.3390/genes3030545] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Whole-genome and exome sequencing have already proven to be essential and powerful methods to identify genes responsible for simple Mendelian inherited disorders. These methods can be applied to complex disorders as well, and have been adopted as one of the current mainstream approaches in population genetics. These achievements have been made possible by next generation sequencing (NGS) technologies, which require substantial bioinformatics resources to analyze the dense and complex sequence data. The huge analytical burden of data from genome sequencing might be seen as a bottleneck slowing the publication of NGS papers at this time, especially in psychiatric genetics. We review the existing methods for processing NGS data, to place into context the rationale for the design of a computational resource. We describe our method, the Graphical Pipeline for Computational Genomics (GPCG), to perform the computational steps required to analyze NGS data. The GPCG implements flexible workflows for basic sequence alignment, sequence data quality control, single nucleotide polymorphism analysis, copy number variant identification, annotation, and visualization of results. These workflows cover all the analytical steps required for NGS data, from processing the raw reads to variant calling and annotation. The current version of the pipeline is freely available at http://pipeline.loni.ucla.edu. These applications of NGS analysis may gain clinical utility in the near future (e.g., identifying miRNA signatures in diseases) when the bioinformatics approach is made feasible. Taken together, the annotation tools and strategies that have been developed to retrieve information and test hypotheses about the functional role of variants present in the human genome will help to pinpoint the genetic risk factors for psychiatric disorders.
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Affiliation(s)
- Federica Torri
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA 92617, USA; E-Mails: (F.T.); (S.G.P.)
- Biomedical Informatics Research Network (BIRN), Information Sciences Institute, University of Southern California, Los Angeles, CA 90292, USA; E-Mails: (I.D.D.); (J.A.); (C.K.); (A.W.T.)
| | - Ivo D. Dinov
- Biomedical Informatics Research Network (BIRN), Information Sciences Institute, University of Southern California, Los Angeles, CA 90292, USA; E-Mails: (I.D.D.); (J.A.); (C.K.); (A.W.T.)
- Laboratory of Neuro Imaging (LONI), University of California, Los Angeles, CA 90095, USA; E-Mails: (A.Z.); (S.H.); (A.G.); (P.P.); (Z.L.); (P.E.); (J.P.)
| | - Alen Zamanyan
- Laboratory of Neuro Imaging (LONI), University of California, Los Angeles, CA 90095, USA; E-Mails: (A.Z.); (S.H.); (A.G.); (P.P.); (Z.L.); (P.E.); (J.P.)
| | - Sam Hobel
- Laboratory of Neuro Imaging (LONI), University of California, Los Angeles, CA 90095, USA; E-Mails: (A.Z.); (S.H.); (A.G.); (P.P.); (Z.L.); (P.E.); (J.P.)
| | - Alex Genco
- Laboratory of Neuro Imaging (LONI), University of California, Los Angeles, CA 90095, USA; E-Mails: (A.Z.); (S.H.); (A.G.); (P.P.); (Z.L.); (P.E.); (J.P.)
| | - Petros Petrosyan
- Laboratory of Neuro Imaging (LONI), University of California, Los Angeles, CA 90095, USA; E-Mails: (A.Z.); (S.H.); (A.G.); (P.P.); (Z.L.); (P.E.); (J.P.)
| | - Andrew P. Clark
- Zilkha Neurogenetic Institute, USC Keck School of Medicine, Los Angeles, CA 90033, USA; E-Mails: (A.P.C.); (J.A.K.)
| | - Zhizhong Liu
- Laboratory of Neuro Imaging (LONI), University of California, Los Angeles, CA 90095, USA; E-Mails: (A.Z.); (S.H.); (A.G.); (P.P.); (Z.L.); (P.E.); (J.P.)
| | - Paul Eggert
- Laboratory of Neuro Imaging (LONI), University of California, Los Angeles, CA 90095, USA; E-Mails: (A.Z.); (S.H.); (A.G.); (P.P.); (Z.L.); (P.E.); (J.P.)
- Department of Computer Science, University of California, Los Angeles, CA 90095, USA
| | - Jonathan Pierce
- Laboratory of Neuro Imaging (LONI), University of California, Los Angeles, CA 90095, USA; E-Mails: (A.Z.); (S.H.); (A.G.); (P.P.); (Z.L.); (P.E.); (J.P.)
| | - James A. Knowles
- Zilkha Neurogenetic Institute, USC Keck School of Medicine, Los Angeles, CA 90033, USA; E-Mails: (A.P.C.); (J.A.K.)
| | - Joseph Ames
- Biomedical Informatics Research Network (BIRN), Information Sciences Institute, University of Southern California, Los Angeles, CA 90292, USA; E-Mails: (I.D.D.); (J.A.); (C.K.); (A.W.T.)
| | - Carl Kesselman
- Biomedical Informatics Research Network (BIRN), Information Sciences Institute, University of Southern California, Los Angeles, CA 90292, USA; E-Mails: (I.D.D.); (J.A.); (C.K.); (A.W.T.)
| | - Arthur W. Toga
- Biomedical Informatics Research Network (BIRN), Information Sciences Institute, University of Southern California, Los Angeles, CA 90292, USA; E-Mails: (I.D.D.); (J.A.); (C.K.); (A.W.T.)
- Laboratory of Neuro Imaging (LONI), University of California, Los Angeles, CA 90095, USA; E-Mails: (A.Z.); (S.H.); (A.G.); (P.P.); (Z.L.); (P.E.); (J.P.)
| | - Steven G. Potkin
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA 92617, USA; E-Mails: (F.T.); (S.G.P.)
- Biomedical Informatics Research Network (BIRN), Information Sciences Institute, University of Southern California, Los Angeles, CA 90292, USA; E-Mails: (I.D.D.); (J.A.); (C.K.); (A.W.T.)
| | - Marquis P. Vawter
- Functional Genomics Laboratory, Department of Psychiatry And Human Behavior, School of Medicine, University of California, Irvine, CA 92697, USA; E-Mail:
| | - Fabio Macciardi
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA 92617, USA; E-Mails: (F.T.); (S.G.P.)
- Biomedical Informatics Research Network (BIRN), Information Sciences Institute, University of Southern California, Los Angeles, CA 90292, USA; E-Mails: (I.D.D.); (J.A.); (C.K.); (A.W.T.)
- Author to whom correspondence should be addressed; E-Mail: ; Tel.: +1-949-824-4559; Fax: +1-949-824-2072
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Patel ZH, Kottyan LC, Lazaro S, Williams MS, Ledbetter DH, Tromp H, Rupert A, Kohram M, Wagner M, Husami A, Qian Y, Valencia CA, Zhang K, Hostetter MK, Harley JB, Kaufman KM. The struggle to find reliable results in exome sequencing data: filtering out Mendelian errors. Front Genet 2014; 5:16. [PMID: 24575121 PMCID: PMC3921572 DOI: 10.3389/fgene.2014.00016] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2013] [Accepted: 01/16/2014] [Indexed: 12/30/2022] Open
Abstract
Next Generation Sequencing studies generate a large quantity of genetic data in a relatively cost and time efficient manner and provide an unprecedented opportunity to identify candidate causative variants that lead to disease phenotypes. A challenge to these studies is the generation of sequencing artifacts by current technologies. To identify and characterize the properties that distinguish false positive variants from true variants, we sequenced a child and both parents (one trio) using DNA isolated from three sources (blood, buccal cells, and saliva). The trio strategy allowed us to identify variants in the proband that could not have been inherited from the parents (Mendelian errors) and would most likely indicate sequencing artifacts. Quality control measurements were examined and three measurements were found to identify the greatest number of Mendelian errors. These included read depth, genotype quality score, and alternate allele ratio. Filtering the variants on these measurements removed ~95% of the Mendelian errors while retaining 80% of the called variants. These filters were applied independently. After filtering, the concordance between identical samples isolated from different sources was 99.99% as compared to 87% before filtering. This high concordance suggests that different sources of DNA can be used in trio studies without affecting the ability to identify causative polymorphisms. To facilitate analysis of next generation sequencing data, we developed the Cincinnati Analytical Suite for Sequencing Informatics (CASSI) to store sequencing files, metadata (eg. relatedness information), file versioning, data filtering, variant annotation, and identify candidate causative polymorphisms that follow either de novo, rare recessive homozygous or compound heterozygous inheritance models. We conclude the data cleaning process improves the signal to noise ratio in terms of variants and facilitates the identification of candidate disease causative polymorphisms.
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Affiliation(s)
- Zubin H Patel
- Division of Rheumatology, Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati OH, USA ; Medical Scientist Training Program, University of Cincinnati College of Medicine, Cincinnati OH, USA
| | - Leah C Kottyan
- Division of Rheumatology, Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati OH, USA ; Department of Veterans Affairs, Veterans Affairs Medical Center - Cincinnati, Cincinnati OH, USA
| | - Sara Lazaro
- Division of Rheumatology, Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati OH, USA ; Department of Veterans Affairs, Veterans Affairs Medical Center - Cincinnati, Cincinnati OH, USA
| | - Marc S Williams
- Genomic Medicine Institute, Geisinger Health System, Danville PA, USA
| | - David H Ledbetter
- Genomic Medicine Institute, Geisinger Health System, Danville PA, USA
| | - Hbgerard Tromp
- Genomic Medicine Institute, Geisinger Health System, Danville PA, USA
| | - Andrew Rupert
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati OH, USA
| | - Mojtaba Kohram
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati OH, USA
| | - Michael Wagner
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati OH, USA
| | - Ammar Husami
- Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati OH, USA
| | - Yaping Qian
- Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati OH, USA
| | - C Alexander Valencia
- Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati OH, USA
| | - Kejian Zhang
- Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati OH, USA
| | - Margaret K Hostetter
- Division of Infectious Disease, Cincinnati Children's Hospital Medical Center, Cincinnati OH, USA
| | - John B Harley
- Division of Rheumatology, Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati OH, USA ; Department of Veterans Affairs, Veterans Affairs Medical Center - Cincinnati, Cincinnati OH, USA
| | - Kenneth M Kaufman
- Division of Rheumatology, Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati OH, USA ; Department of Veterans Affairs, Veterans Affairs Medical Center - Cincinnati, Cincinnati OH, USA
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16
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Regional neuroplastic brain changes in patients with chronic inflammatory and non-inflammatory visceral pain. PLoS One 2014; 9:e84564. [PMID: 24416245 PMCID: PMC3885578 DOI: 10.1371/journal.pone.0084564] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2013] [Accepted: 11/24/2013] [Indexed: 12/29/2022] Open
Abstract
Regional cortical thickness alterations have been reported in many chronic inflammatory and painful conditions, including inflammatory bowel diseases (IBD) and irritable bowel syndrome (IBS), even though the mechanisms underlying such neuroplastic changes remain poorly understood. In order to better understand the mechanisms contributing to grey matter changes, the current study sought to identify the differences in regional alterations in cortical thickness between healthy controls and two chronic visceral pain syndromes, with and without chronic gut inflammation. 41 healthy controls, 11 IBS subjects with diarrhea, and 16 subjects with ulcerative colitis (UC) underwent high-resolution T1-weighted magnetization-prepared rapid acquisition gradient echo scans. Structural image preprocessing and cortical thickness analysis within the region of interests were performed by using the Laboratory of Neuroimaging Pipeline. Group differences were determined using the general linear model and linear contrast analysis. The two disease groups differed significantly in several cortical regions. UC subjects showed greater cortical thickness in anterior cingulate cortical subregions, and in primary somatosensory cortex compared with both IBS and healthy subjects. Compared with healthy subjects, UC subjects showed lower cortical thickness in orbitofrontal cortex and in mid and posterior insula, while IBS subjects showed lower cortical thickness in the anterior insula. Large effects of correlations between symptom duration and thickness in the orbitofrontal cortex and postcentral gyrus were only observed in UC subjects. The findings suggest that the mechanisms underlying the observed gray matter changes in UC subjects represent a consequence of peripheral inflammation, while in IBS subjects central mechanisms may play a primary role.
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Gan Z, Stowe JC, Altintas I, McCulloch AD, Zambon AC. Using Kepler for Tool Integration in Microarray Analysis Workflows. PROCEDIA COMPUTER SCIENCE 2014; 29:2162-2167. [PMID: 26605000 PMCID: PMC4655117 DOI: 10.1016/j.procs.2014.05.201] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Increasing numbers of genomic technologies are leading to massive amounts of genomic data, all of which requires complex analysis. More and more bioinformatics analysis tools are being developed by scientist to simplify these analyses. However, different pipelines have been developed using different software environments. This makes integrations of these diverse bioinformatics tools difficult. Kepler provides an open source environment to integrate these disparate packages. Using Kepler, we integrated several external tools including Bioconductor packages, AltAnalyze, a python-based open source tool, and R-based comparison tool to build an automated workflow to meta-analyze both online and local microarray data. The automated workflow connects the integrated tools seamlessly, delivers data flow between the tools smoothly, and hence improves efficiency and accuracy of complex data analyses. Our workflow exemplifies the usage of Kepler as a scientific workflow platform for bioinformatics pipelines.
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Affiliation(s)
- Zhuohui Gan
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Jennifer C. Stowe
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Ilkay Altintas
- San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA, USA
| | - Andrew D. McCulloch
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Alexander C. Zambon
- Departments of Pharmacology, University of California, San Diego, La Jolla, CA, USA
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18
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Irritable bowel syndrome in female patients is associated with alterations in structural brain networks. Pain 2013; 155:137-149. [PMID: 24076048 DOI: 10.1016/j.pain.2013.09.020] [Citation(s) in RCA: 120] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2013] [Revised: 09/18/2013] [Accepted: 09/20/2013] [Indexed: 12/11/2022]
Abstract
Alterations in gray matter (GM) density/volume and cortical thickness (CT) have been demonstrated in small and heterogeneous samples of subjects with differing chronic pain syndromes, including irritable bowel syndrome (IBS). Aggregating across 7 structural neuroimaging studies conducted at University of California, Los Angeles, Los Angeles, CA, USA, between August 2006 and April 2011, we examined group differences in regional GM volume in 201 predominantly premenopausal female subjects (82 IBS, mean age: 32±10 SD, 119 healthy controls [HCs], 30±10 SD). Applying graph theoretical methods and controlling for total brain volume, global and regional properties of large-scale structural brain networks were compared between the group with IBS and the HC group. Relative to HCs, the IBS group had lower volumes in the bilateral superior frontal gyrus, bilateral insula, bilateral amygdala, bilateral hippocampus, bilateral middle orbital frontal gyrus, left cingulate, left gyrus rectus, brainstem, and left putamen. Higher volume was found in the left postcentral gyrus. Group differences were no longer significant for most regions when controlling for the Early Trauma Inventory global score, with the exception of the right amygdala and the left postcentral gyrus. No group differences were found for measures of global and local network organization. Compared to HCs, in patients with IBS, the right cingulate gyrus and right thalamus were identified as being significantly more critical for information flow. Regions involved in endogenous pain modulation and central sensory amplification were identified as network hubs in IBS. Overall, evidence for central alterations in patients with IBS was found in the form of regional GM volume differences and altered global and regional properties of brain volumetric networks.
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19
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Jiang Z, Dinov ID, Labus J, Shi Y, Zamanyan A, Gupta A, Ashe-McNalley C, Hong JY, Tillisch K, Toga AW, Mayer EA. Sex-related differences of cortical thickness in patients with chronic abdominal pain. PLoS One 2013; 8:e73932. [PMID: 24040118 PMCID: PMC3764047 DOI: 10.1371/journal.pone.0073932] [Citation(s) in RCA: 59] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2013] [Accepted: 07/24/2013] [Indexed: 12/11/2022] Open
Abstract
Background & Aims Regional reductions in gray matter (GM) have been reported in several chronic somatic and visceral pain conditions, including irritable bowel syndrome (IBS) and chronic pancreatitis. Reported GM reductions include insular and anterior cingulate cortices, even though subregions are generally not specified. The majority of published studies suffer from limited sample size, heterogeneity of populations, and lack of analyses for sex differences. We aimed to characterize regional changes in cortical thickness (CT) in a large number of well phenotyped IBS patients, taking into account the role of sex related differences. Methods Cortical GM thickness was determined in 266 subjects (90 IBS [70 predominantly premenopausal female] and 176 healthy controls (HC) [155 predominantly premenopausal female]) using the Laboratory of Neuro Imaging (LONI) Pipeline. A combined region of interest (ROI) and whole brain approach was used to detect any sub-regional and vertex-level differences after removing effects of age and total GM volume. Correlation analyses were performed on behavioral data. Results While IBS as a group did not show significant differences in CT compared to HCs, sex related differences were observed both within the IBS and the HC groups. When female IBS patients were compared to female HCs, whole brain analysis showed significant CT increase in somatosensory and primary motor cortex, as well as CT decrease in bilateral subgenual anterior cingulate cortex (sgACC). The ROI analysis showed significant regional CT decrease in bilateral subregions of insular cortex, while CT decrease in cingulate was limited to left sgACC, accounting for the effect of age and GM volume. Several measures of IBS symptom severity showed significant correlation with CT changes in female IBS patients. Conclusions Significant, sex related differences in CT are present in both HCs and in IBS patients. The biphasic neuroplastic changes in female IBS patients are related to symptom severity.
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Affiliation(s)
- Zhiguo Jiang
- Gail and Gerald Oppenheimer Family Center for Neurobiology of Stress and Pain and Interoception Network (PAIN) Repository, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
- Human Performance and Engineering Laboratory, Kessler Foundation Research Center, West Orange, New Jersey, United States of America
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey, United States of America
| | - Ivo D. Dinov
- Laboratory of NeuroImaging (LONI), University of California Los Angeles, Los Angeles, California, United States of America
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - Jennifer Labus
- Gail and Gerald Oppenheimer Family Center for Neurobiology of Stress and Pain and Interoception Network (PAIN) Repository, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
| | - Yonggang Shi
- Laboratory of NeuroImaging (LONI), University of California Los Angeles, Los Angeles, California, United States of America
| | - Alen Zamanyan
- Laboratory of NeuroImaging (LONI), University of California Los Angeles, Los Angeles, California, United States of America
| | - Arpana Gupta
- Gail and Gerald Oppenheimer Family Center for Neurobiology of Stress and Pain and Interoception Network (PAIN) Repository, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
| | - Cody Ashe-McNalley
- Gail and Gerald Oppenheimer Family Center for Neurobiology of Stress and Pain and Interoception Network (PAIN) Repository, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
| | - Jui-Yang Hong
- Gail and Gerald Oppenheimer Family Center for Neurobiology of Stress and Pain and Interoception Network (PAIN) Repository, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
| | - Kirsten Tillisch
- Gail and Gerald Oppenheimer Family Center for Neurobiology of Stress and Pain and Interoception Network (PAIN) Repository, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
| | - Arthur W. Toga
- Laboratory of NeuroImaging (LONI), University of California Los Angeles, Los Angeles, California, United States of America
| | - Emeran A. Mayer
- Gail and Gerald Oppenheimer Family Center for Neurobiology of Stress and Pain and Interoception Network (PAIN) Repository, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
- Ahmanson-Lovelace Brain Mapping Center, University of California Los Angeles, Los Angeles, California, United States of America
- * E-mail:
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20
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Zhan J, Dinov ID, Li J, Zhang Z, Hobel S, Shi Y, Lin X, Zamanyan A, Feng L, Teng G, Fang F, Tang Y, Zang F, Toga AW, Liu S. Spatial-temporal atlas of human fetal brain development during the early second trimester. Neuroimage 2013; 82:115-26. [PMID: 23727529 DOI: 10.1016/j.neuroimage.2013.05.063] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2013] [Revised: 05/15/2013] [Accepted: 05/16/2013] [Indexed: 01/29/2023] Open
Abstract
During the second trimester, the human fetal brain undergoes numerous changes that lead to substantial variation in the neonatal in terms of its morphology and tissue types. As fetal MRI is more and more widely used for studying the human brain development during this period, a spatiotemporal atlas becomes necessary for characterizing the dynamic structural changes. In this study, 34 postmortem human fetal brains with gestational ages ranging from 15 to 22 weeks were scanned using 7.0 T MR. We used automated morphometrics, tensor-based morphometry and surface modeling techniques to analyze the data. Spatiotemporal atlases of each week and the overall atlas covering the whole period with high resolution and contrast were created. These atlases were used for the analysis of age-specific shape changes during this period, including development of the cerebral wall, lateral ventricles, Sylvian fissure, and growth direction based on local surface measurements. Our findings indicate that growth of the subplate zone is especially striking and is the main cause for the lamination pattern changes. Changes in the cortex around Sylvian fissure demonstrate that cortical growth may be one of the mechanisms for gyration. Surface deformation mapping, revealed by local shape analysis, indicates that there is global anterior-posterior growth pattern, with frontal and temporal lobes developing relatively quickly during this period. Our results are valuable for understanding the normal brain development trajectories and anatomical characteristics. These week-by-week fetal brain atlases can be used as reference in in vivo studies, and may facilitate the quantification of fetal brain development across space and time.
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Affiliation(s)
- Jinfeng Zhan
- Research Center for Sectional and Imaging Anatomy, Shandong University School of Medicine, 44 Wen-hua Xi Road, 250012 Jinan, Shandong, China
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Fani N, Gutman D, Tone EB, Almli L, Mercer KB, Davis J, Glover E, Jovanovic T, Bradley B, Dinov ID, Zamanyan A, Toga AW, Binder EB, Ressler KJ. FKBP5 and attention bias for threat: associations with hippocampal function and shape. JAMA Psychiatry 2013; 70:392-400. [PMID: 23407841 PMCID: PMC3732315 DOI: 10.1001/2013.jamapsychiatry.210] [Citation(s) in RCA: 102] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
IMPORTANCE The FKBP5 gene product regulates glucocorticoid receptor (GR) sensitivity and hypothalamic-pituitary-adrenal axis functioning and has been associated with many stress-related psychiatric disorders. The study of intermediate phenotypes, such as emotion-processing biases and their neural substrates, provides a way to clarify the mechanisms by which FKBP5 dysregulation mediates risk for psychiatric disorders. OBJECTIVE To examine whether allelic variations for a putatively functional single-nucleotide polymorphism associated with FKBP5 gene regulation (rs1360780) would relate differentially to attention bias for threat. this was measured through behavioral response on a dot probe task and hippocampal activation during task performance. Morphologic substrates of differential hippocampal response were also measured. DESIGN Cross-sectional study conducted from 2010 to 2012 examining associations between genotype, behavioral response, and neural response (using functional magnetic resonance imaging [fMRI]) on the dot probe; voxel-based morphometry and global and local shape analyses were used to measure structural differences in hippocampi between genotype groups. SETTING Participants were recruited from primary care clinics of a publicly funded hospital in Atlanta, Georgia. PARTICIPANTS An African American cohort of adults (N = 103) was separated into 2 groups by genotype: one genotype group included carriers of the rs1360780 T allele, which has been associated with increased risk for posttraumatic stress disorder and affective disorders; the other group did not carry this allele. Behavioral data included both sexes (N = 103); the MRI cohort (n = 36) included only women. MAIN OUTCOME MEASURES Behavioral and fMRI (blood oxygen level-dependent) response, voxel-based morphometry, and shape analyses. RESULTS Carriers of the rs1360780 T allele showed an attention bias toward threat compared with individuals without this allele (F1,90 = 5.19, P = .02). Carriers of this allele demonstrated corresponding increases in hippocampal activation and differences in morphology; global and local shape analyses revealed alterations in hippocampal shape for TT/TC compared with CC genotype groups. CONCLUSION Genetic variants of FKBP5 may be associated with risk for stress-related psychiatric disorders via differential effects on hippocampal structure and function, resulting in altered attention response to perceived threat.
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Affiliation(s)
- Negar Fani
- Departments of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia 30329, USA
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Computational and bioinformatics frameworks for next-generation whole exome and genome sequencing. ScientificWorldJournal 2013; 2013:730210. [PMID: 23365548 PMCID: PMC3556895 DOI: 10.1155/2013/730210] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2012] [Accepted: 11/22/2012] [Indexed: 12/28/2022] Open
Abstract
It has become increasingly apparent that one of the major hurdles in the genomic age will be the bioinformatics challenges of next-generation sequencing. We provide an overview of a general framework of bioinformatics analysis. For each of the three stages of (1) alignment, (2) variant calling, and (3) filtering and annotation, we describe the analysis required and survey the different software packages that are used. Furthermore, we discuss possible future developments as data sources grow and highlight opportunities for new bioinformatics tools to be developed.
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Stropp T, McPhillips T, Ludäscher B, Bieda M. Workflows for microarray data processing in the Kepler environment. BMC Bioinformatics 2012; 13:102. [PMID: 22594911 PMCID: PMC3431220 DOI: 10.1186/1471-2105-13-102] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2011] [Accepted: 03/08/2012] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Microarray data analysis has been the subject of extensive and ongoing pipeline development due to its complexity, the availability of several options at each analysis step, and the development of new analysis demands, including integration with new data sources. Bioinformatics pipelines are usually custom built for different applications, making them typically difficult to modify, extend and repurpose. Scientific workflow systems are intended to address these issues by providing general-purpose frameworks in which to develop and execute such pipelines. The Kepler workflow environment is a well-established system under continual development that is employed in several areas of scientific research. Kepler provides a flexible graphical interface, featuring clear display of parameter values, for design and modification of workflows. It has capabilities for developing novel computational components in the R, Python, and Java programming languages, all of which are widely used for bioinformatics algorithm development, along with capabilities for invoking external applications and using web services. RESULTS We developed a series of fully functional bioinformatics pipelines addressing common tasks in microarray processing in the Kepler workflow environment. These pipelines consist of a set of tools for GFF file processing of NimbleGen chromatin immunoprecipitation on microarray (ChIP-chip) datasets and more comprehensive workflows for Affymetrix gene expression microarray bioinformatics and basic primer design for PCR experiments, which are often used to validate microarray results. Although functional in themselves, these workflows can be easily customized, extended, or repurposed to match the needs of specific projects and are designed to be a toolkit and starting point for specific applications. These workflows illustrate a workflow programming paradigm focusing on local resources (programs and data) and therefore are close to traditional shell scripting or R/BioConductor scripting approaches to pipeline design. Finally, we suggest that microarray data processing task workflows may provide a basis for future example-based comparison of different workflow systems. CONCLUSIONS We provide a set of tools and complete workflows for microarray data analysis in the Kepler environment, which has the advantages of offering graphical, clear display of conceptual steps and parameters and the ability to easily integrate other resources such as remote data and web services.
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Affiliation(s)
- Thomas Stropp
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada
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Lord E, Leclercq M, Boc A, Diallo AB, Makarenkov V. Armadillo 1.1: an original workflow platform for designing and conducting phylogenetic analysis and simulations. PLoS One 2012; 7:e29903. [PMID: 22253821 PMCID: PMC3256230 DOI: 10.1371/journal.pone.0029903] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2011] [Accepted: 12/08/2011] [Indexed: 11/30/2022] Open
Abstract
In this paper we introduce Armadillo v1.1, a novel workflow platform dedicated to designing and conducting phylogenetic studies, including comprehensive simulations. A number of important phylogenetic and general bioinformatics tools have been included in the first software release. As Armadillo is an open-source project, it allows scientists to develop their own modules as well as to integrate existing computer applications. Using our workflow platform, different complex phylogenetic tasks can be modeled and presented in a single workflow without any prior knowledge of programming techniques. The first version of Armadillo was successfully used by professors of bioinformatics at Université du Quebec à Montreal during graduate computational biology courses taught in 2010–11. The program and its source code are freely available at: <http://www.bioinfo.uqam.ca/armadillo>.
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Affiliation(s)
- Etienne Lord
- Département d'informatique, Université du Québec à Montréal, Montréal, Canada
| | - Mickael Leclercq
- Département d'informatique, Université du Québec à Montréal, Montréal, Canada
| | - Alix Boc
- Département de sciences biologiques, Université de Montréal, Montréal, Canada
| | | | - Vladimir Makarenkov
- Département d'informatique, Université du Québec à Montréal, Montréal, Canada
- * E-mail:
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Abstract
Neurological imaging represents a powerful paradigm for investigation of brain structure, physiology and function across different scales. The diverse phenotypes and significant normal and pathological brain variability demand reliable and efficient statistical methodologies to model, analyze and interpret raw neurological images and derived geometric information from these images. The validity, reproducibility and power of any statistical brain map require appropriate inference on large cohorts, significant community validation, and multidisciplinary collaborations between physicians, engineers and statisticians.
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Affiliation(s)
- Ivo D Dinov
- SOCR Resource and Laboratory of Neuro Imaging, UCLA Statistics, 8125 Mathematical Science Bldg, Los Angeles, CA 90095, USA, Tel.: +1 310 825 8430
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