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Larson KE, Oguz I. Synthetic Atrophy for Longitudinal Cortical Surface Analyses. FRONTIERS IN NEUROIMAGING 2022; 1:861687. [PMID: 37555187 PMCID: PMC10406236 DOI: 10.3389/fnimg.2022.861687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 04/22/2022] [Indexed: 08/10/2023]
Abstract
In the fields of longitudinal cortical segmentation and surface-based cortical thickness (CT) measurement, difficulty in assessing accuracy remains a substantial limitation due to the inability of experimental validation against ground truth. Although methods have been developed to create synthetic datasets for these purposes, none provide a robust mechanism for measuring exact thickness changes with surface-based approaches. This work presents a registration-based technique for inducing synthetic cortical atrophy to create a longitudinal ground truth dataset specifically designed to address this gap in surface-based accuracy validation techniques. Across the entire brain, our method can induce up to between 0.8 and 2.5 mm of localized cortical atrophy in a given gyrus depending on the region's original thickness. By calculating the image deformation to induce this atrophy at 400% of the original resolution in each direction, we can induce a sub-voxel resolution amount of atrophy while minimizing partial volume effects. We also show that cortical segmentations of synthetically atrophied images exhibit similar segmentation error to those obtained from images of naturally atrophied brains. Importantly, our method relies exclusively on publicly available software and datasets.
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Affiliation(s)
- Kathleen E. Larson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
| | - Ipek Oguz
- Department of Computer Science, Vanderbilt University, Nashville, TN, United States
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2
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Larson KE, Oguz I. Synthetic atrophy for longitudinal surface-based cortical thickness measurement. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2021; 11596:115963K. [PMID: 34873357 PMCID: PMC8643351 DOI: 10.1117/12.2580907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Difficulty in validating accuracy remains a substantial setback in the field of surface-based cortical thickness (CT) measurement due to the lack of experimental validation against ground truth. Although methods have been developed to create synthetic datasets for this purpose, none provide a robust mechanism for measuring exact thickness changes with surface-based approaches. This work presents a registration-based technique for inducing synthetic cortical atrophy to create a longitudinal, ground truth dataset specifically designed for accuracy validation of surface-based CT measurements. Across the entire brain, we show our method can induce up to between 0.6 and 2.6 mm of localized cortical atrophy in a given gyrus depending on the region's original thickness. By calculating the image deformation to induce this atrophy at 400% of the original resolution in each direction, we can induce a sub-voxel resolution amount of atrophy while minimizing partial volume effects. We also show that our method can be extended beyond application to CT measurements for the accuracy validation of longitudinal cortical segmentation and surface reconstruction pipelines when measuring accuracy against cortical landmarks. Importantly, our method relies exclusively on publicly available software and datasets.
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Affiliation(s)
| | - Ipek Oguz
- Vanderbilt University, Nashville, TN, USA
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3
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Wang Q, Ding SL, Li Y, Royall J, Feng D, Lesnar P, Graddis N, Naeemi M, Facer B, Ho A, Dolbeare T, Blanchard B, Dee N, Wakeman W, Hirokawa KE, Szafer A, Sunkin SM, Oh SW, Bernard A, Phillips JW, Hawrylycz M, Koch C, Zeng H, Harris JA, Ng L. The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlas. Cell 2020; 181:936-953.e20. [PMID: 32386544 PMCID: PMC8152789 DOI: 10.1016/j.cell.2020.04.007] [Citation(s) in RCA: 481] [Impact Index Per Article: 120.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 12/12/2019] [Accepted: 04/03/2020] [Indexed: 01/25/2023]
Abstract
Recent large-scale collaborations are generating major surveys of cell types and connections in the mouse brain, collecting large amounts of data across modalities, spatial scales, and brain areas. Successful integration of these data requires a standard 3D reference atlas. Here, we present the Allen Mouse Brain Common Coordinate Framework (CCFv3) as such a resource. We constructed an average template brain at 10 μm voxel resolution by interpolating high resolution in-plane serial two-photon tomography images with 100 μm z-sampling from 1,675 young adult C57BL/6J mice. Then, using multimodal reference data, we parcellated the entire brain directly in 3D, labeling every voxel with a brain structure spanning 43 isocortical areas and their layers, 329 subcortical gray matter structures, 81 fiber tracts, and 8 ventricular structures. CCFv3 can be used to analyze, visualize, and integrate multimodal and multiscale datasets in 3D and is openly accessible (https://atlas.brain-map.org/).
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Affiliation(s)
- Quanxin Wang
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Song-Lin Ding
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Yang Li
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Josh Royall
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - David Feng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Phil Lesnar
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Nile Graddis
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Maitham Naeemi
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Benjamin Facer
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Anh Ho
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Tim Dolbeare
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Wayne Wakeman
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Aaron Szafer
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Susan M Sunkin
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Seung Wook Oh
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Amy Bernard
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Christof Koch
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Julie A Harris
- Allen Institute for Brain Science, Seattle, WA 98109, USA.
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA 98109, USA.
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4
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Anderson RJ, Cook JJ, Delpratt N, Nouls JC, Gu B, McNamara JO, Avants BB, Johnson GA, Badea A. Small Animal Multivariate Brain Analysis (SAMBA) - a High Throughput Pipeline with a Validation Framework. Neuroinformatics 2020; 17:451-472. [PMID: 30565026 PMCID: PMC6584586 DOI: 10.1007/s12021-018-9410-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
While many neuroscience questions aim to understand the human brain, much current knowledge has been gained using animal models, which replicate genetic, structural, and connectivity aspects of the human brain. While voxel-based analysis (VBA) of preclinical magnetic resonance images is widely-used, a thorough examination of the statistical robustness, stability, and error rates is hindered by high computational demands of processing large arrays, and the many parameters involved therein. Thus, workflows are often based on intuition or experience, while preclinical validation studies remain scarce. To increase throughput and reproducibility of quantitative small animal brain studies, we have developed a publicly shared, high throughput VBA pipeline in a high-performance computing environment, called SAMBA. The increased computational efficiency allowed large multidimensional arrays to be processed in 1–3 days—a task that previously took ~1 month. To quantify the variability and reliability of preclinical VBA in rodent models, we propose a validation framework consisting of morphological phantoms, and four metrics. This addresses several sources that impact VBA results, including registration and template construction strategies. We have used this framework to inform the VBA workflow parameters in a VBA study for a mouse model of epilepsy. We also present initial efforts towards standardizing small animal neuroimaging data in a similar fashion with human neuroimaging. We conclude that verifying the accuracy of VBA merits attention, and should be the focus of a broader effort within the community. The proposed framework promotes consistent quality assurance of VBA in preclinical neuroimaging, thus facilitating the creation and communication of robust results.
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Affiliation(s)
- Robert J Anderson
- Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, NC, 27710, USA
| | - James J Cook
- Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, NC, 27710, USA
| | - Natalie Delpratt
- Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, NC, 27710, USA.,Department of Biomedical Engineering, Duke University Medical Center, 3302, Durham, NC, 27710, USA
| | - John C Nouls
- Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, NC, 27710, USA
| | - Bin Gu
- Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC, 27710, USA.,Department of Cell Biology and Physiology, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - James O McNamara
- Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC, 27710, USA.,Department of Neurobiology, Duke University Medical Center, Durham, NC, 27710, USA.,Department of Neurology, Duke University Medical Center, Durham, NC, 27710, USA
| | | | - G Allan Johnson
- Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, NC, 27710, USA.,Department of Biomedical Engineering, Duke University Medical Center, 3302, Durham, NC, 27710, USA
| | - Alexandra Badea
- Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, NC, 27710, USA. .,Department of Biomedical Engineering, Duke University Medical Center, 3302, Durham, NC, 27710, USA.
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5
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Vetreno RP, Yaxley R, Paniagua B, Johnson GA, Crews FT. Adult rat cortical thickness changes across age and following adolescent intermittent ethanol treatment. Addict Biol 2017; 22:712-723. [PMID: 26833865 PMCID: PMC4969224 DOI: 10.1111/adb.12364] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2015] [Revised: 11/02/2015] [Accepted: 12/11/2015] [Indexed: 12/27/2022]
Abstract
Human studies have established that adolescence is a period of brain maturation that parallels the development of adult behaviors. However, little is known regarding cortical development in the adult rat brain. We used magnetic resonance imaging (MRI) and histology to assess the impact of age on adult Wistar rat cortical thickness on postnatal day (P)80 and P220 as well as the effect of adolescent binge ethanol exposure on adult (P80) cortical thickness. MRI revealed changes in cortical thickness between P80 and P220 that differ across cortical region. The adult P220 rat prefrontal cortex increased in thickness whereas cortical thinning occurred in both the cingulate and parietal cortices relative to young adult P80 rats. Histological analysis confirmed the age-related cortical thinning. In the second series of experiments, an animal model of adolescent intermittent ethanol (AIE; 5.0 g/kg, intragastrically, 20 percent ethanol w/v, 2 days on/2 days off from P25 to P55) was used to assess the effects of alcohol on cortical thickness in young adult (P80) rats. MRI revealed that AIE resulted in region-specific cortical changes. A small region within the prefrontal cortex was significantly thinner whereas medial cortical regions were significantly thicker in young adult (P80) AIE-treated rats. The observed increase in cortical thickness was confirmed by histology. Thus, the rat cerebral cortex continues to undergo cortical thickness changes into adulthood, and adolescent alcohol exposure alters the young adult cortex that could contribute to brain dysfunction in adulthood.
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Affiliation(s)
- Ryan P. Vetreno
- Bowles Center for Alcohol Studies; Department of Psychiatry; University of North Carolina at Chapel Hill; Chapel Hill NC USA
| | - Richard Yaxley
- Bowles Center for Alcohol Studies; Department of Psychiatry; University of North Carolina at Chapel Hill; Chapel Hill NC USA
| | - Beatriz Paniagua
- Bowles Center for Alcohol Studies; Department of Psychiatry; University of North Carolina at Chapel Hill; Chapel Hill NC USA
| | - G. Allan Johnson
- Duke Center for In Vivo Microscopy; Duke University Medical Center; Durham NC USA
| | - Fulton T. Crews
- Bowles Center for Alcohol Studies; Department of Psychiatry; University of North Carolina at Chapel Hill; Chapel Hill NC USA
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6
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Lee J, Kim SH, Oguz I, Styner M. Enhanced Cortical Thickness Measurements for Rodent Brains via Lagrangian-based RK4 Streamline Computation. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2016; 9784. [PMID: 27065047 DOI: 10.1117/12.2216420] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
The cortical thickness of the mammalian brain is an important morphological characteristic that can be used to investigate and observe the brain's developmental changes that might be caused by biologically toxic substances such as ethanol or cocaine. Although various cortical thickness analysis methods have been proposed that are applicable for human brain and have developed into well-validated open-source software packages, cortical thickness analysis methods for rodent brains have not yet become as robust and accurate as those designed for human brains. Based on a previously proposed cortical thickness measurement pipeline for rodent brain analysis,1 we present an enhanced cortical thickness pipeline in terms of accuracy and anatomical consistency. First, we propose a Lagrangian-based computational approach in the thickness measurement step in order to minimize local truncation error using the fourth-order Runge-Kutta method. Second, by constructing a line object for each streamline of the thickness measurement, we can visualize the way the thickness is measured and achieve sub-voxel accuracy by performing geometric post-processing. Last, with emphasis on the importance of an anatomically consistent partial differential equation (PDE) boundary map, we propose an automatic PDE boundary map generation algorithm that is specific to rodent brain anatomy, which does not require manual labeling. The results show that the proposed cortical thickness pipeline can produce statistically significant regions that are not observed in the the previous cortical thickness analysis pipeline.
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Affiliation(s)
- Joohwi Lee
- University of North Carolina at Chapel Hill, Department of Computer Science, United States
| | - Sun Hyung Kim
- University of North Carolina at Chapel Hill, Department of Psychiatry, United States
| | - Ipek Oguz
- The University of Iowa, Department of Electrical and Computer Engineering, United States
| | - Martin Styner
- University of North Carolina at Chapel Hill, Department of Computer Science, United States; University of North Carolina at Chapel Hill, Department of Psychiatry, United States
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7
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Grauer M, Reynolds P, Hoogstoel M, Budin F, Styner MA, Oguz I. A midas plugin to enable construction of reproducible web-based image processing pipelines. Front Neuroinform 2013; 7:46. [PMID: 24416016 PMCID: PMC3875239 DOI: 10.3389/fninf.2013.00046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2013] [Accepted: 12/13/2013] [Indexed: 11/13/2022] Open
Abstract
Image processing is an important quantitative technique for neuroscience researchers, but difficult for those who lack experience in the field. In this paper we present a web-based platform that allows an expert to create a brain image processing pipeline, enabling execution of that pipeline even by those biomedical researchers with limited image processing knowledge. These tools are implemented as a plugin for Midas, an open-source toolkit for creating web based scientific data storage and processing platforms. Using this plugin, an image processing expert can construct a pipeline, create a web-based User Interface, manage jobs, and visualize intermediate results. Pipelines are executed on a grid computing platform using BatchMake and HTCondor. This represents a new capability for biomedical researchers and offers an innovative platform for scientific collaboration. Current tools work well, but can be inaccessible for those lacking image processing expertise. Using this plugin, researchers in collaboration with image processing experts can create workflows with reasonable default settings and streamlined user interfaces, and data can be processed easily from a lab environment without the need for a powerful desktop computer. This platform allows simplified troubleshooting, centralized maintenance, and easy data sharing with collaborators. These capabilities enable reproducible science by sharing datasets and processing pipelines between collaborators. In this paper, we present a description of this innovative Midas plugin, along with results obtained from building and executing several ITK based image processing workflows for diffusion weighted MRI (DW MRI) of rodent brain images, as well as recommendations for building automated image processing pipelines. Although the particular image processing pipelines developed were focused on rodent brain MRI, the presented plugin can be used to support any executable or script-based pipeline.
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Affiliation(s)
| | | | - Marion Hoogstoel
- Neuro Image Research and Analysis Laboratories, Department of Psychiatry, University of North Carolina Chapel Hill, NC, USA
| | - Francois Budin
- Neuro Image Research and Analysis Laboratories, Department of Psychiatry, University of North Carolina Chapel Hill, NC, USA
| | - Martin A Styner
- Neuro Image Research and Analysis Laboratories, Department of Psychiatry, University of North Carolina Chapel Hill, NC, USA
| | - Ipek Oguz
- Department of Electrical-Computer Engineering, University of Iowa Iowa City, IA, USA
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8
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Budin F, Hoogstoel M, Reynolds P, Grauer M, O'Leary-Moore SK, Oguz I. Fully automated rodent brain MR image processing pipeline on a Midas server: from acquired images to region-based statistics. Front Neuroinform 2013; 7:15. [PMID: 23964234 PMCID: PMC3741535 DOI: 10.3389/fninf.2013.00015] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2012] [Accepted: 07/23/2013] [Indexed: 11/13/2022] Open
Abstract
Magnetic resonance imaging (MRI) of rodent brains enables study of the development and the integrity of the brain under certain conditions (alcohol, drugs etc.). However, these images are difficult to analyze for biomedical researchers with limited image processing experience. In this paper we present an image processing pipeline running on a Midas server, a web-based data storage system. It is composed of the following steps: rigid registration, skull-stripping, average computation, average parcellation, parcellation propagation to individual subjects, and computation of region-based statistics on each image. The pipeline is easy to configure and requires very little image processing knowledge. We present results obtained by processing a data set using this pipeline and demonstrate how this pipeline can be used to find differences between populations.
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Affiliation(s)
- Francois Budin
- Neuro Image Research and Analysis Laboratories, Department of Psychiatry, University of North Carolina Chapel Hill, NC, USA
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Oguz I, Yaxley R, Budin F, Hoogstoel M, Lee J, Maltbie E, Liu W, Crews FT. Comparison of magnetic resonance imaging in live vs. post mortem rat brains. PLoS One 2013; 8:e71027. [PMID: 23967148 PMCID: PMC3742751 DOI: 10.1371/journal.pone.0071027] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2012] [Accepted: 06/29/2013] [Indexed: 11/19/2022] Open
Abstract
Magnetic Resonance Imaging (MRI) is an increasingly popular technique for examining neurobiology in rodents because it is both noninvasive and nondestructive. MRI scans can be acquired from either live or post mortem specimens. In vivo scans have a key advantage in that subjects can be scanned at multiple time-points in longitudinal studies. However, repeated exposure to anesthesia and stress may confound studies. In contrast, post mortem scans offer improved image quality and increased signal-to-noise ratio (SNR) due to several key advantages: First, the images are not disrupted by motion and pulsation artifacts. Second, they allow the brain tissue to be perfused with contrast agents, enhancing tissue contrast. Third, they allow longer image acquisition times, yielding higher resolution and/or improved SNR. Fourth, they allow assessment of groups of animals at the same age without scheduling complications. Despite these advantages, researchers are often skeptical of post mortem MRI scans because of uncertainty about whether the fixation process alters the MRI measurements. To address these concerns, we present a thorough comparative study of in vivo and post mortem MRI scans in healthy male Wistar rats at three age points throughout adolescence (postnatal days 28 through 80). For each subject, an in vivo scan was acquired, followed by perfusion and two post mortem scans at two different MRI facilities. The goal was to assess robustness of measurements, to detect any changes in volumetric measurements after fixation, and to investigate any differential bias that may exist between image acquisition techniques. We present this volumetric analysis for comparison of 22 anatomical structures between in vivo and post mortem scans. No significant changes in volumetric measurements were detected; however, as hypothesized, the image quality is dramatically improved in post mortem scans. These findings illustrate the validity and utility of using post mortem scans in volumetric neurobiological studies.
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Affiliation(s)
- Ipek Oguz
- University of North Carolina at Chapel Hill, Department of Psychiatry, Chapel Hill, North Carolina, USA.
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Oguz I, McMurray MS, Styner M, Johns JM. The translational role of diffusion tensor image analysis in animal models of developmental pathologies. Dev Neurosci 2012; 34:5-19. [PMID: 22627095 DOI: 10.1159/000336825] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2011] [Accepted: 01/24/2012] [Indexed: 12/31/2022] Open
Abstract
Diffusion tensor magnetic resonance imaging (DTI) has proven itself a powerful technique for clinical investigation of the neurobiological targets and mechanisms underlying developmental pathologies. The success of DTI in clinical studies has demonstrated its great potential for understanding translational animal models of clinical disorders, and preclinical animal researchers are beginning to embrace this new technology to study developmental pathologies. In animal models, genetics can be effectively controlled, drugs consistently administered, subject compliance ensured, and image acquisition times dramatically increased to reduce between-subject variability and improve image quality. When pairing these strengths with the many positive attributes of DTI, such as the ability to investigate microstructural brain organization and connectivity, it becomes possible to delve deeper into the study of both normal and abnormal development. The purpose of this review is to provide new preclinical investigators with an introductory source of information about the analysis of data resulting from small animal DTI studies to facilitate the translation of these studies to clinical data. In addition to an in-depth review of translational analysis techniques, we present a number of relevant clinical and animal studies using DTI to investigate developmental insults in order to further illustrate techniques and to highlight where small animal DTI could potentially provide a wealth of translational data to inform clinical researchers.
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Affiliation(s)
- Ipek Oguz
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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11
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Gerig G, Oguz I, Gouttard S, Lee J, An H, Lin W, McMurray M, Grewen K, Johns J, Styner MA. Synergy of image analysis for animal and human neuroimaging supports translational research on drug abuse. Front Psychiatry 2011; 2:53. [PMID: 22013425 PMCID: PMC3189614 DOI: 10.3389/fpsyt.2011.00053] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2011] [Accepted: 09/11/2011] [Indexed: 01/06/2023] Open
Abstract
The use of structural magnetic resonance imaging (sMRI) and diffusion tensor imaging (DTI) in animal models of neuropathology is of increasing interest to the neuroscience community. In this work, we present our approach to create optimal translational studies that include both animal and human neuroimaging data within the frameworks of a study of post-natal neuro-development in intra-uterine cocaine-exposure. We propose the use of non-invasive neuroimaging to study developmental brain structural and white matter pathway abnormalities via sMRI and DTI, as advanced MR imaging technology is readily available and automated image analysis methodology have recently been transferred from the human to animal imaging setting. For this purpose, we developed a synergistic, parallel approach to imaging and image analysis for the human and the rodent branch of our study. We propose an equivalent design in both the selection of the developmental assessment stage and the neuroimaging setup. This approach brings significant advantages to study neurobiological features of early brain development that are common to animals and humans but also preserve analysis capabilities only possible in animal research. This paper presents the main framework and individual methods for the proposed cross-species study design, as well as preliminary DTI cross-species comparative results in the intra-uterine cocaine-exposure study.
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Affiliation(s)
- Guido Gerig
- Scientific Computing and Imaging Institute, University of UtahSalt Lake City, UT, USA
| | - Ipek Oguz
- Department of Psychiatry, University of North CarolinaChapel Hill, NC, USA
| | - Sylvain Gouttard
- Scientific Computing and Imaging Institute, University of UtahSalt Lake City, UT, USA
| | - Joohwi Lee
- Department of Computer Science, University of North CarolinaChapel Hill, NC, USA
| | - Hongyu An
- Biomedical Research Imaging Center, University of North CarolinaChapel Hill, NC, USA
| | - Weili Lin
- Biomedical Research Imaging Center, University of North CarolinaChapel Hill, NC, USA
| | - Matthew McMurray
- Department of Psychology, University of North CarolinaChapel Hill, NC, USA
| | - Karen Grewen
- Department of Psychiatry, University of North CarolinaChapel Hill, NC, USA
| | - Josephine Johns
- Department of Psychiatry, University of North CarolinaChapel Hill, NC, USA
| | - Martin Andreas Styner
- Department of Psychiatry, University of North CarolinaChapel Hill, NC, USA
- Department of Computer Science, University of North CarolinaChapel Hill, NC, USA
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