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Mansour H, Azrak R, Cook JJ, Hornburg KJ, Qi Y, Tian Y, Williams RW, Yeh FC, White LE, Johnson GA. An Open Resource: MR and light sheet microscopy stereotaxic atlas of the mouse brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.28.587246. [PMID: 38586051 PMCID: PMC10996689 DOI: 10.1101/2024.03.28.587246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
We have combined MR histology and light sheet microscopy (LSM) of five postmortem C57BL/6J mouse brains in a stereotaxic space based on micro-CT yielding a multimodal 3D atlas with the highest spatial and contrast resolution yet reported. Brains were imaged in situ with multi gradient echo (mGRE) and diffusion tensor imaging (DTI) at 15 μm resolution (∼ 2.4 million times that of clinical MRI). Scalar images derived from the average DTI and mGRE provide unprecedented contrast in 14 complementary 3D volumes, each highlighting distinct histologic features. The same tissues scanned with LSM and registered into the stereotaxic space provide 17 different molecular cell type stains. The common coordinate framework labels (CCFv3) complete the multimodal atlas. The atlas has been used to correct distortions in the Allen Brain Atlas and harmonize it with Franklin Paxinos. It provides a unique resource for stereotaxic labeling of mouse brain images from many sources.
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Moon HS, Mahzarnia A, Stout J, Anderson RJ, Strain M, Tremblay JT, Han ZY, Niculescu A, MacFarlane A, King J, Ashley-Koch A, Clark D, Lutz MW, Badea A. Multivariate investigation of aging in mouse models expressing the Alzheimer's protective APOE2 allele: integrating cognitive metrics, brain imaging, and blood transcriptomics. Brain Struct Funct 2024; 229:231-249. [PMID: 38091051 PMCID: PMC11082910 DOI: 10.1007/s00429-023-02731-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 11/03/2023] [Indexed: 01/31/2024]
Abstract
APOE allelic variation is critical in brain aging and Alzheimer's disease (AD). The APOE2 allele associated with cognitive resilience and neuroprotection against AD remains understudied. We employed a multipronged approach to characterize the transition from middle to old age in mice with APOE2 allele, using behavioral assessments, image-derived morphometry and diffusion metrics, structural connectomics, and blood transcriptomics. We used sparse multiple canonical correlation analyses (SMCCA) for integrative modeling, and graph neural network predictions. Our results revealed brain sub-networks associated with biological traits, cognitive markers, and gene expression. The cingulate cortex emerged as a critical region, demonstrating age-associated atrophy and diffusion changes, with higher fractional anisotropy in males and middle-aged subjects. Somatosensory and olfactory regions were consistently highlighted, indicating age-related atrophy and sex differences. The hippocampus exhibited significant volumetric changes with age, with differences between males and females in CA3 and CA1 regions. SMCCA underscored changes in the cingulate cortex, somatosensory cortex, olfactory regions, and hippocampus in relation to cognition and blood-based gene expression. Our integrative modeling in aging APOE2 carriers revealed a central role for changes in gene pathways involved in localization and the negative regulation of cellular processes. Our results support an important role of the immune system and response to stress. This integrative approach offers novel insights into the complex interplay among brain connectivity, aging, and sex. Our study provides a foundation for understanding the impact of APOE2 allele on brain aging, the potential for detecting associated changes in blood markers, and revealing novel therapeutic intervention targets.
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Affiliation(s)
- Hae Sol Moon
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Ali Mahzarnia
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Jacques Stout
- Brain Imaging and Analysis Center, Duke University School of Medicine, Durham, NC, USA
| | - Robert J Anderson
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Madison Strain
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, USA
| | - Jessica T Tremblay
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Zay Yar Han
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Andrei Niculescu
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Anna MacFarlane
- Department of Neuroscience, Duke University, Durham, NC, USA
| | - Jasmine King
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Allison Ashley-Koch
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, USA
| | - Darin Clark
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Michael W Lutz
- Department of Neurology, Duke University School of Medicine, Durham, NC, USA
| | - Alexandra Badea
- Department of Biomedical Engineering, Duke University, Durham, NC, USA.
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA.
- Brain Imaging and Analysis Center, Duke University School of Medicine, Durham, NC, USA.
- Department of Neurology, Duke University School of Medicine, Durham, NC, USA.
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Moon HS, Mahzarnia A, Stout J, Anderson RJ, Badea CT, Badea A. Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.13.571574. [PMID: 38168445 PMCID: PMC10760088 DOI: 10.1101/2023.12.13.571574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Alzheimer's disease (AD) remains one of the most extensively researched neurodegenerative disorders due to its widespread prevalence and complex risk factors. Age is a crucial risk factor for AD, which can be estimated by the disparity between physiological age and estimated brain age. To model AD risk more effectively, integrating biological, genetic, and cognitive markers is essential. Here, we utilized mouse models expressing the major APOE human alleles and human nitric oxide synthase 2 to replicate genetic risk for AD and a humanized innate immune response. We estimated brain age employing a multivariate dataset that includes brain connectomes, APOE genotype, subject traits such as age and sex, and behavioral data. Our methodology used Feature Attention Graph Neural Networks (FAGNN) for integrating different data types. Behavioral data were processed with a 2D Convolutional Neural Network (CNN), subject traits with a 1D CNN, brain connectomes through a Graph Neural Network using quadrant attention module. The model yielded a mean absolute error for age prediction of 31.85 days, with a root mean squared error of 41.84 days, outperforming other, reduced models. In addition, FAGNN identified key brain connections involved in the aging process. The highest weights were assigned to the connections between cingulum and corpus callosum, striatum, hippocampus, thalamus, hypothalamus, cerebellum, and piriform cortex. Our study demonstrates the feasibility of predicting brain age in models of aging and genetic risk for AD. To verify the validity of our findings, we compared Fractional Anisotropy (FA) along the tracts of regions with the highest connectivity, the Return-to-Origin Probability (RTOP), Return-to-Plane Probability (RTPP), and Return-to-Axis Probability (RTAP), which showed significant differences between young, middle-aged, and old age groups. Younger mice exhibited higher FA, RTOP, RTAP, and RTPP compared to older groups in the selected connections, suggesting that degradation of white matter tracts plays a critical role in aging and for FAGNN's selections. Our analysis suggests a potential neuroprotective role of APOE2, relative to APOE3 and APOE4, where APOE2 appears to mitigate age-related changes. Our findings highlighted a complex interplay of genetics and brain aging in the context of AD risk modeling.
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Affiliation(s)
- Hae Sol Moon
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Ali Mahzarnia
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Jacques Stout
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Robert J Anderson
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Cristian T. Badea
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Alexandra Badea
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
- Brain Imaging and Analysis Center, Duke University School of Medicine, Durham, NC, USA
- Department of Neurology, Duke University School of Medicine, Durham, NC, USA
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Tian Y, Johnson GA, Williams RW, White LE. A rapid workflow for neuron counting in combined light sheet microscopy and magnetic resonance histology. Front Neurosci 2023; 17:1223226. [PMID: 37841684 PMCID: PMC10569694 DOI: 10.3389/fnins.2023.1223226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 09/04/2023] [Indexed: 10/17/2023] Open
Abstract
Information on regional variation in cell numbers and densities in the CNS provides critical insight into structure, function, and the progression of CNS diseases. However, variability can be real or a consequence of methods that do not account for technical biases, including morphologic deformations, errors in the application of cell type labels and boundaries of regions, errors of counting rules and sampling sites. We address these issues in a mouse model by introducing a workflow that consists of the following steps: 1. Magnetic resonance histology (MRH) to establish the size, shape, and regional morphology of the mouse brain in situ. 2. Light-sheet microscopy (LSM) to selectively label neurons or other cells in the entire brain without sectioning artifacts. 3. Register LSM volumes to MRH volumes to correct for dissection errors and both global and regional deformations. 4. Implement stereological protocols for automated sampling and counting of cells in 3D LSM volumes. This workflow can analyze the cell densities of one brain region in less than 1 min and is highly replicable in cortical and subcortical gray matter regions and structures throughout the brain. This method demonstrates the advantage of not requiring an extensive amount of training data, achieving a F1 score of approximately 0.9 with just 20 training nuclei. We report deformation-corrected neuron (NeuN) counts and neuronal density in 13 representative regions in 5 C57BL/6J cases and 2 BXD strains. The data represent the variability among specimens for the same brain region and across regions within the specimen. Neuronal densities estimated with our workflow are within the range of values in previous classical stereological studies. We demonstrate the application of our workflow to a mouse model of aging. This workflow improves the accuracy of neuron counting and the assessment of neuronal density on a region-by-region basis, with broad applications for studies of how genetics, environment, and development across the lifespan impact cell numbers in the CNS.
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Affiliation(s)
- Yuqi Tian
- Duke Center for In Vivo Microscopy, Department of Radiology, Duke University, Durham, NC, United States
| | - G. Allan Johnson
- Duke Center for In Vivo Microscopy, Department of Radiology, Duke University, Durham, NC, United States
| | - Robert W. Williams
- Department of Genetics, Genomics and Informatics, The University of Tennessee Health Science Center, Memphis, TN, United States
| | - Leonard E. White
- Department of Neurology, Duke University, Durham, NC, United States
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de Souza DAR, Mathieu H, Deloulme JC, Barbier EL. Evaluation of kernel low-rank compressed sensing in preclinical diffusion magnetic resonance imaging. Front Neurosci 2023; 17:1172830. [PMID: 37332879 PMCID: PMC10272537 DOI: 10.3389/fnins.2023.1172830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 04/28/2023] [Indexed: 06/20/2023] Open
Abstract
Compressed sensing (CS) is widely used to accelerate clinical diffusion MRI acquisitions, but it is not widely used in preclinical settings yet. In this study, we optimized and compared several CS reconstruction methods for diffusion imaging. Different undersampling patterns and two reconstruction approaches were evaluated: conventional CS, based on Berkeley Advanced Reconstruction Toolbox (BART-CS) toolbox, and a new kernel low-rank (KLR)-CS, based on kernel principal component analysis and low-resolution-phase (LRP) maps. 3D CS acquisitions were performed at 9.4T using a 4-element cryocoil on mice (wild type and a MAP6 knockout). Comparison metrics were error and structural similarity index measure (SSIM) on fractional anisotropy (FA) and mean diffusivity (MD), as well as reconstructions of the anterior commissure and fornix. Acceleration factors (AF) up to 6 were considered. In the case of retrospective undersampling, the proposed KLR-CS outperformed BART-CS up to AF = 6 for FA and MD maps and tractography. For instance, for AF = 4, the maximum errors were, respectively, 8.0% for BART-CS and 4.9% for KLR-CS, considering both FA and MD in the corpus callosum. Regarding undersampled acquisitions, these maximum errors became, respectively, 10.5% for BART-CS and 7.0% for KLR-CS. This difference between simulations and acquisitions arose mainly from repetition noise, but also from differences in resonance frequency drift, signal-to-noise ratio, and in reconstruction noise. Despite this increased error, fully sampled and AF = 2 yielded comparable results for FA, MD and tractography, and AF = 4 showed minor faults. Altogether, KLR-CS based on LRP maps seems a robust approach to accelerate preclinical diffusion MRI and thereby limit the effect of the frequency drift.
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Affiliation(s)
| | - Hervé Mathieu
- Université Grenoble Alpes, INSERM, U1216, Grenoble Institut Neurosciences, Grenoble, France
- Université Grenoble Alpes, INSERM, US17, CNRS, UAR 3552, CHU Grenoble Alpes, Grenoble, France
| | | | - Emmanuel L. Barbier
- Université Grenoble Alpes, INSERM, U1216, Grenoble Institut Neurosciences, Grenoble, France
- Université Grenoble Alpes, INSERM, US17, CNRS, UAR 3552, CHU Grenoble Alpes, Grenoble, France
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6
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Tian Y, Johnson GA, Williams RW, White L. A rapid workflow for neuron counting in combined light sheet microscopy and magnetic resonance histology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.17.540884. [PMID: 37292796 PMCID: PMC10245654 DOI: 10.1101/2023.05.17.540884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Information on regional variation in cell numbers and densities in the CNS provides critical insight into structure, function, and the progression of CNS diseases. However, variability can be real or can be a consequence of methods that do not account for technical biases, including morphologic deformations, errors in the application of cell type labels and boundaries of regions, errors of counting rules and sampling sites. We address these issues of by introducing a workflow that consists of the following steps: 1. Magnetic resonance histology (MRH) to establish the size, shape, and regional morphology of the mouse brain in situ. 2. Light-sheet microscopy (LSM) to selectively label all neurons or other cells in the entire brain without sectioning artifacts. 3. Register LSM volumes to MRH volumes to correct for dissection errors and morphological deformations. 4. Implement novel protocol for automated sampling and counting of cells in 3D LSM volumes. This workflow can analyze the cells density of one brain region in less than 1 min and is highly replicable to cortical and subcortical gray matter regions and structures throughout the brain. We report deformation-corrected neuron (NeuN) counts and neuronal density in 13 representative regions in 5 C57B6/6J and 2 BXD strains. The data represent the variability among cases for the same brain region and across regions within case. Our data are consistent with previous studies. We demonstrate the application of our workflow to a mouse model of aging. This workflow improves the accuracy of neuron counting and the assessment of neuronal density on a region-by-region basis, with broad applications in how genetics, environment, and development across the lifespan impact brain structure.
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7
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Wang N, Maharjan S, Tsai AP, Lin PB, Qi Y, Wallace A, Jewett M, Liu F, Landreth GE, Oblak AL. Integrating multimodality magnetic resonance imaging to the Allen Mouse Brain Common Coordinate Framework. NMR IN BIOMEDICINE 2023; 36:e4887. [PMID: 36454009 PMCID: PMC10106385 DOI: 10.1002/nbm.4887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 11/28/2022] [Accepted: 11/30/2022] [Indexed: 05/07/2023]
Abstract
High-resolution magnetic resonance imaging (MRI) affords unique image contrasts to nondestructively probe the tissue microstructure; validation of MRI findings with conventional histology is essential to better understand the MRI contrasts. However, the dramatic difference in the spatial resolution and image contrast of these two techniques impedes accurate comparison between MRI metrics and traditional histology. To better validate various MRI metrics, we acquired whole mouse brain multigradient recalled-echo and multishell diffusion MRI datasets at 25-μm isotropic resolution. The recently developed Allen Mouse Brain Common Coordinate Framework (CCFv3) provides opportunities to integrate multimodal and multiscale datasets of the whole mouse brain in a common three-dimensional (3D) space. The T2*, quantitative susceptibility mapping, diffusion tensor imaging, and neurite orientation dispersion and density imaging parameters were compared with both serial two-photon tomography images and 3D Nissl staining images in the CCFv3 at the same spatial resolution. The correlation between MRI and Nissl staining strongly depends on different metrics and different regions of the brain. Integrating different imaging modalities to the same space may substantially improve our understanding of the complexity of the brain at different scales.
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Affiliation(s)
- Nian Wang
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, Indiana, USA
- Stark Neurosciences Research Institute, Indiana University, Indianapolis, Indiana, USA
| | - Surendra Maharjan
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, Indiana, USA
| | - Andy P. Tsai
- Stark Neurosciences Research Institute, Indiana University, Indianapolis, Indiana, USA
| | - Peter B. Lin
- Stark Neurosciences Research Institute, Indiana University, Indianapolis, Indiana, USA
| | - Yi Qi
- Center for In Vivo Microscopy, Department of Radiology, Duke University, Durham, North Carolina, USA
| | - Abigail Wallace
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, Indiana, USA
| | - Megan Jewett
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, Indiana, USA
| | - Fang Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
| | - Gary E. Landreth
- Stark Neurosciences Research Institute, Indiana University, Indianapolis, Indiana, USA
| | - Adrian L. Oblak
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, Indiana, USA
- Stark Neurosciences Research Institute, Indiana University, Indianapolis, Indiana, USA
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Johnson GA, Tian Y, Ashbrook DG, Cofer GP, Cook JJ, Gee JC, Hall A, Hornburg K, Qi Y, Yeh FC, Wang N, White LE, Williams RW. Merged magnetic resonance and light sheet microscopy of the whole mouse brain. Proc Natl Acad Sci U S A 2023; 120:e2218617120. [PMID: 37068254 PMCID: PMC10151475 DOI: 10.1073/pnas.2218617120] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 03/10/2023] [Indexed: 04/19/2023] Open
Abstract
We have developed workflows to align 3D magnetic resonance histology (MRH) of the mouse brain with light sheet microscopy (LSM) and 3D delineations of the same specimen. We start with MRH of the brain in the skull with gradient echo and diffusion tensor imaging (DTI) at 15 μm isotropic resolution which is ~ 1,000 times higher than that of most preclinical MRI. Connectomes are generated with superresolution tract density images of ~5 μm. Brains are cleared, stained for selected proteins, and imaged by LSM at 1.8 μm/pixel. LSM data are registered into the reference MRH space with labels derived from the ABA common coordinate framework. The result is a high-dimensional integrated volume with registration (HiDiver) with alignment precision better than 50 µm. Throughput is sufficiently high that HiDiver is being used in quantitative studies of the impact of gene variants and aging on mouse brain cytoarchitecture and connectomics.
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Affiliation(s)
| | - Yuqi Tian
- Center for In Vivo Microscopy, Duke University, Durham, NC27710
| | - David G. Ashbrook
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN38162
| | - Gary P. Cofer
- Center for In Vivo Microscopy, Duke University, Durham, NC27710
| | - James J. Cook
- Center for In Vivo Microscopy, Duke University, Durham, NC27710
| | - James C. Gee
- Department of Radiology, University of Pennsylvania, Philadelphia, PA19104
| | - Adam Hall
- LifeCanvas Technology, Cambridge, MA02141
| | | | - Yi Qi
- Center for In Vivo Microscopy, Duke University, Durham, NC27710
| | - Fang-Cheng Yeh
- Department of Neurologic Surgery, University of Pittsburgh, Pittsburgh, PA15260
| | - Nian Wang
- Department of Radiology, Indiana University, Bloomington, IN47401
| | | | - Robert W. Williams
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN38162
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Hornburg KJ, Slosky LM, Cofer G, Cook J, Qi Y, Porkka F, Clark NB, Pires A, Petrella JR, White LE, Wetsel WC, Barak L, Caron MG, Johnson GA. Prenatal heroin exposure alters brain morphology and connectivity in adolescent mice. NMR IN BIOMEDICINE 2023; 36:e4842. [PMID: 36259728 PMCID: PMC10483958 DOI: 10.1002/nbm.4842] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 09/30/2022] [Accepted: 10/05/2022] [Indexed: 06/16/2023]
Abstract
The United States is experiencing a dramatic increase in maternal opioid misuse and, consequently, the number of individuals exposed to opioids in utero. Prenatal opioid exposure has both acute and long-lasting effects on health and wellbeing. Effects on the brain, often identified at school age, manifest as cognitive impairment, attention deficit, and reduced scholastic achievement. The neurobiological basis for these effects is poorly understood. Here, we examine how in utero exposure to heroin affects brain development into early adolescence in a mouse model. Pregnant C57BL/6J mice received escalating doses of heroin twice daily on gestational days 4-18. The brains of offspring were assessed on postnatal day 28 using 9.4 T diffusion MRI of postmortem specimens at 36 μm resolution. Whole-brain volumes and the volumes of 166 bilateral regions were compared between heroin-exposed and control offspring. We identified a reduction in whole-brain volume in heroin-exposed offspring and heroin-associated volume changes in 29 regions after standardizing for whole-brain volume. Regions with bilaterally reduced standardized volumes in heroin-exposed offspring relative to controls include the ectorhinal and insular cortices. Regions with bilaterally increased standardized volumes in heroin-exposed offspring relative to controls include the periaqueductal gray, septal region, striatum, and hypothalamus. Leveraging microscopic resolution diffusion tensor imaging and precise regional parcellation, we generated whole-brain structural MRI diffusion connectomes. Using a dimension reduction approach with multivariate analysis of variance to assess group differences in the connectome, we found that in utero heroin exposure altered structure-based connectivity of the left septal region and the region that acts as a hub for limbic regulatory actions. Consistent with clinical evidence, our findings suggest that prenatal opioid exposure may have effects on brain morphology, connectivity, and, consequently, function that persist into adolescence. This work expands our understanding of the risks associated with opioid misuse during pregnancy and identifies biomarkers that may facilitate diagnosis and treatment.
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Affiliation(s)
- Kathryn J. Hornburg
- Department of Radiology, School of Medicine, Duke University; 311 Research Drive; Campus Box 3302; Durham, NC 27710 United States
| | - Lauren M. Slosky
- Department of Cell Biology, School of Medicine, Duke University; Campus Box 3709; Durham, NC 27710 United States
- Department of Pharmacology, University of Minnesota; 312 Church Street SE; 3-104 Nils Hasselmo Hall; Minneapolis, MN 55455 United States
| | - Gary Cofer
- Department of Radiology, School of Medicine, Duke University; 311 Research Drive; Campus Box 3302; Durham, NC 27710 United States
| | - James Cook
- Department of Radiology, School of Medicine, Duke University; 311 Research Drive; Campus Box 3302; Durham, NC 27710 United States
| | - Yi Qi
- Department of Radiology, School of Medicine, Duke University; 311 Research Drive; Campus Box 3302; Durham, NC 27710 United States
| | - Fiona Porkka
- Department of Cell Biology, School of Medicine, Duke University; Campus Box 3709; Durham, NC 27710 United States
| | - Nicholas B. Clark
- Department of Cell Biology, School of Medicine, Duke University; Campus Box 3709; Durham, NC 27710 United States
| | - Andrea Pires
- Department of Cell Biology, School of Medicine, Duke University; Campus Box 3709; Durham, NC 27710 United States
| | - Jeffrey R Petrella
- Department of Radiology, School of Medicine, Duke University; 311 Research Drive; Campus Box 3302; Durham, NC 27710 United States
| | - Leonard E. White
- Department of Neurology, School of Medicine, Duke University; Campus Box 2900; Durham, NC 27710 United States
| | - William C. Wetsel
- Department of Cell Biology, School of Medicine, Duke University; Campus Box 3709; Durham, NC 27710 United States
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Duke University; Campus Box 102508; Durham, NC 27710 United States
- Department of Neurology, School of Medicine, Duke University; Campus Box 2900; Durham, NC 27710 United States
| | - Lawrence Barak
- Department of Cell Biology, School of Medicine, Duke University; Campus Box 3709; Durham, NC 27710 United States
| | - Marc G. Caron
- Department of Cell Biology, School of Medicine, Duke University; Campus Box 3709; Durham, NC 27710 United States
- Department of Neurology, School of Medicine, Duke University; Campus Box 2900; Durham, NC 27710 United States
| | - G. Allan Johnson
- Department of Radiology, School of Medicine, Duke University; 311 Research Drive; Campus Box 3302; Durham, NC 27710 United States
- Department of Biomedical Engineering, Pratt School of Engineering, Duke University; Campus Box 90281; Durham, NC 27708-0281 United States
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Tian Y, Cook JJ, Johnson GA. Restoring morphology of light sheet microscopy data based on magnetic resonance histology. Front Neurosci 2023; 16:1011895. [PMID: 36685227 PMCID: PMC9846533 DOI: 10.3389/fnins.2022.1011895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 12/12/2022] [Indexed: 01/06/2023] Open
Abstract
The combination of cellular-resolution whole brain light sheet microscopy (LSM) images with an annotated atlas enables quantitation of cellular features in specific brain regions. However, most existing methods register LSM data with existing canonical atlases, e.g., The Allen Brain Atlas (ABA), which have been generated from tissue that has been distorted by removal from the skull, fixation and physical handling. This limits the accuracy of the regional morphologic measurement. Here, we present a method to combine LSM data with magnetic resonance histology (MRH) of the same specimen to restore the morphology of the LSM images to the in-skull geometry. Our registration pipeline which maps 3D LSM big data (terabyte per dataset) to MRH of the same mouse brain provides registration with low displacement error in ∼10 h with limited manual input. The registration pipeline is optimized using multiple stages of transformation at multiple resolution scales. A three-step procedure including pointset initialization, automated ANTs registration with multiple optimized transformation stages, and finalized application of the transforms on high-resolution LSM data has been integrated into a simple, structured, and robust workflow. Excellent agreement has been seen between registered LSM data and reference MRH data both locally and globally. This workflow has been applied to a collection of datasets with varied combinations of MRH contrasts from diffusion tensor images and LSM with varied immunohistochemistry, providing a routine method for streamlined registration of LSM images to MRH. Lastly, the method maps a reduced set of the common coordinate framework (CCFv3) labels from the Allen Brain Atlas onto the geometrically corrected full resolution LSM data. The pipeline maintains the individual brain morphology and allows more accurate regional annotations and measurements of volumes and cell density.
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Du EY, Ortega BK, Ninoyu Y, Williams RW, Cofer GP, Cook JJ, Hornburg KJ, Qi Y, Johnson GA, Friedman RA. Volumetric analysis of the aging auditory pathway using high resolution magnetic resonance histology. Front Aging Neurosci 2022; 14:1034073. [DOI: 10.3389/fnagi.2022.1034073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 10/27/2022] [Indexed: 11/12/2022] Open
Abstract
Numerous shown consequences of age-related hearing loss have been unveiled; however, the relationship of the cortical and subcortical structures of the auditory pathway with aging is not well known. Investigations into neural structure analysis remain sparse due to difficulties of doing so in animal models; however, recent technological advances have been able to achieve a resolution adequate to perform such studies even in the small mouse. We utilize 12 members of the BXD family of recombinant inbred mice and aged separate cohorts. Utilizing novel magnetic resonance histology imaging techniques, we imaged these mice and generated high spatial resolution three dimensional images which were then comprehensively labeled. We completed volumetric analysis of 12 separate regions of interest specific to the auditory pathway brainstem nuclei and cortical areas with focus on the effect of aging upon said structures. Our results showed significant interstrain variation in the age-related effect on structure volume supporting a genetic influence in this interaction. Through multivariable modeling, we observed heterogenous effects of aging between different structures. Six of the 12 regions of interests demonstrated a significant age-related effect. The auditory cortex and ventral cochlear nucleus were found to decrease in volume with age, while the medial division of the medial geniculate nucleus, lateral lemniscus and its nucleus, and the inferior colliculus increased in size with age. Additionally, no sex-based differences were noted, and we observed a negative relationship between auditory cortex volume and mouse weight. This study is one of the first to perform comprehensive magnetic resonance imaging and quantitative analysis in the mouse brain auditory pathway cytoarchitecture, offering both novel insights into the neuroanatomical basis of age-related changes in hearing as well as evidence toward a genetic influence in this interaction. High resonance magnetic resonance imaging provides a promising efficacious avenue in future mouse model hearing loss investigations.
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12
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Wang N, Wen Q, Maharjan S, Mirando AJ, Qi Y, Hilton MJ, Spritzer CE. Magic angle effect on diffusion tensor imaging in ligament and brain. Magn Reson Imaging 2022; 92:243-250. [PMID: 35777687 PMCID: PMC10155228 DOI: 10.1016/j.mri.2022.06.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 06/09/2022] [Accepted: 06/22/2022] [Indexed: 10/17/2022]
Abstract
PURPOSE To evaluate the magic angle effect on diffusion tensor imaging (DTI) measurements in rat ligaments and mouse brains. METHODS Three rat knee joints and three mouse brains were scanned at 9.4 T using a modified 3D diffusion-weighted spin echo pulse sequence with the isotropic spatial resolution of 45 μm. The b value was 1000 s/mm2 for rat knee and 4000 s/mm2 for mouse brain. DTI model was used to investigate the quantitative metrics at different orientations with respect to the main magnetic field. The collagen fiber structure of the ligament was validated with polarized light microscopy (PLM) imaging. RESULTS The signal intensity, signal-to-noise ratio (SNR), and DTI metrics in the ligament were strongly dependent on the collagen fiber orientation with respect to the main magnetic field from both simulation and actual MRI scans. The variation of fractional anisotropy (FA) was about ~32%, and the variation of mean diffusivity (MD) was ~11%. These findings were further validated with the numerical simulation at different SNRs (~10.0 to 86.0). Compared to the ligament, the DTI metrics showed little orientation dependence in mouse brains. CONCLUSION Magic angle effect plays an important role in DTI measurements in the highly ordered collagen-rich tissues, while MD showed less orientation dependence than FA.
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Affiliation(s)
- Nian Wang
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA; Indiana Center for Musculoskeletal Health, Indiana University, Indianapolis, IN, USA; Stark Neurosciences Research Institute, Indiana University, Indianapolis, IN, USA.
| | - Qiuting Wen
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA
| | - Surendra Maharjan
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA
| | - Anthony J Mirando
- Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Yi Qi
- Center for In Vivo Microscopy, Duke University School of Medicine, Durham, NC, USA
| | - Matthew J Hilton
- Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, USA; Department of Cell Biology, Duke University School of Medicine, Durham, NC, USA
| | - Charles E Spritzer
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA
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Raymond-Hayling H, Lu Y, Kadler KE, Shearer T. A fibre tracking algorithm for volumetric microstructural data - application to tendons. Acta Biomater 2022. [DOI: 10.1016/j.actbio.2022.10.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
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14
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Kim S, Oh H, Choi SH, Yoo YE, Noh YW, Cho Y, Im GH, Lee C, Oh Y, Yang E, Kim G, Chung WS, Kim H, Kang H, Bae Y, Kim SG, Kim E. Postnatal age-differential ASD-like transcriptomic, synaptic, and behavioral deficits in Myt1l-mutant mice. Cell Rep 2022; 40:111398. [PMID: 36130507 DOI: 10.1016/j.celrep.2022.111398] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 06/28/2022] [Accepted: 08/31/2022] [Indexed: 12/29/2022] Open
Abstract
Myelin transcription factor 1 like (Myt1l), a zinc-finger transcription factor, promotes neuronal differentiation and is implicated in autism spectrum disorder (ASD) and intellectual disability. However, it remains unclear whether Myt1l promotes neuronal differentiation in vivo and its deficiency in mice leads to disease-related phenotypes. Here, we report that Myt1l-heterozygous mutant (Myt1l-HT) mice display postnatal age-differential ASD-related phenotypes: newborn Myt1l-HT mice, with strong Myt1l expression, show ASD-like transcriptomic changes involving decreased synaptic gene expression and prefrontal excitatory synaptic transmission and altered righting reflex. Juvenile Myt1l-HT mice, with markedly decreased Myt1l expression, display reverse ASD-like transcriptomes, increased prefrontal excitatory transmission, and largely normal behaviors. Adult Myt1l-HT mice show ASD-like transcriptomes involving astrocytic and microglial gene upregulation, increased prefrontal inhibitory transmission, and behavioral deficits. Therefore, Myt1l haploinsufficiency leads to ASD-related phenotypes in newborn mice, which are temporarily normalized in juveniles but re-appear in adults, pointing to continuing phenotypic changes long after a marked decrease of Myt1l expression in juveniles.
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Affiliation(s)
- Seongbin Kim
- Department of Biological Sciences, Korea Advanced Institute for Science and Technology (KAIST), Daejeon 34141, Korea
| | - Hyoseon Oh
- Department of Biological Sciences, Korea Advanced Institute for Science and Technology (KAIST), Daejeon 34141, Korea
| | - Sang Han Choi
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon 16419, Korea; Department of Biomedical Engineering, Sungkyunkwan University, Suwon 16419, Korea
| | - Ye-Eun Yoo
- Center for Synaptic Brain Dysfunctions, Institute for Basic Science (IBS), Daejeon 34141, Korea
| | - Young Woo Noh
- Department of Biological Sciences, Korea Advanced Institute for Science and Technology (KAIST), Daejeon 34141, Korea
| | - Yisul Cho
- Department of Anatomy and Neurobiology, School of Dentistry, Kyungpook National University, Daegu 41940, Korea
| | - Geun Ho Im
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon 16419, Korea
| | - Chanhee Lee
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon 16419, Korea
| | - Yusang Oh
- Department of Bio and Brain Engineering, Korea Advanced Institute for Science and Technology (KAIST), Daejeon 34141, Korea
| | - Esther Yang
- Department of Anatomy and BK21 Graduate Program, Biomedical Sciences, College of Medicine, Korea University, Seoul 02841, Korea
| | - Gyuri Kim
- Department of Biological Sciences, Korea Advanced Institute for Science and Technology (KAIST), Daejeon 34141, Korea
| | - Won-Suk Chung
- Department of Biological Sciences, Korea Advanced Institute for Science and Technology (KAIST), Daejeon 34141, Korea
| | - Hyun Kim
- Department of Anatomy and BK21 Graduate Program, Biomedical Sciences, College of Medicine, Korea University, Seoul 02841, Korea
| | - Hyojin Kang
- Division of National Supercomputing, Korea Institute of Science and Technology Information (KISTI), Daejeon 34141, Korea
| | - Yongchul Bae
- Department of Anatomy and Neurobiology, School of Dentistry, Kyungpook National University, Daegu 41940, Korea
| | - Seong-Gi Kim
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon 16419, Korea; Department of Biomedical Engineering, Sungkyunkwan University, Suwon 16419, Korea
| | - Eunjoon Kim
- Department of Biological Sciences, Korea Advanced Institute for Science and Technology (KAIST), Daejeon 34141, Korea; Center for Synaptic Brain Dysfunctions, Institute for Basic Science (IBS), Daejeon 34141, Korea.
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Maharjan S, Tsai AP, Lin PB, Ingraham C, Jewett MR, Landreth GE, Oblak AL, Wang N. Age-dependent microstructure alterations in 5xFAD mice by high-resolution diffusion tensor imaging. Front Neurosci 2022; 16:964654. [PMID: 36061588 PMCID: PMC9428354 DOI: 10.3389/fnins.2022.964654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 07/18/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose To evaluate the age-dependent microstructure changes in 5xFAD mice using high-resolution diffusion tensor imaging (DTI). Methods The 5xFAD mice at 4, 7.5, and 12 months and the wild-type controls at 4 months were scanned at 9.4T using a 3D echo-planar imaging (EPI) pulse sequence with the isotropic spatial resolution of 100 μm. The b-value was 3000 s/mm2 for all the diffusion MRI scans. The samples were also acquired with a gradient echo pulse sequence at 50 μm isotropic resolution. The microstructure changes were quantified with DTI metrics, including fractional anisotropy (FA) and mean diffusivity (MD). The conventional histology was performed to validate with MRI findings. Results The FA values (p = 0.028) showed significant differences in the cortex between wild-type (WT) and 5xFAD mice at 4 months, while hippocampus, anterior commissure, corpus callosum, and fornix showed no significant differences for either FA and MD. FA values of 5xFAD mice gradually decreased in cortex (0.140 ± 0.007 at 4 months, 0.132 ± 0.008 at 7.5 months, 0.126 ± 0.013 at 12 months) and fornix (0.140 ± 0.007 at 4 months, 0.132 ± 0.008 at 7.5 months, 0.126 ± 0.013 at 12 months) with aging. Both FA (p = 0.029) and MD (p = 0.037) demonstrated significant differences in corpus callosum between 4 and 12 months age old. FA and MD were not significantly different in the hippocampus or anterior commissure. The age-dependent microstructure alterations were better captured by FA when compared to MD. Conclusion FA showed higher sensitivity to monitor amyloid deposition in 5xFAD mice. DTI may be utilized as a sensitive biomarker to monitor beta-amyloid progression for preclinical studies.
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Affiliation(s)
- Surendra Maharjan
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, United States
| | - Andy P. Tsai
- Stark Neurosciences Research Institute, Indiana University, Indianapolis, IN, United States
| | - Peter B. Lin
- Stark Neurosciences Research Institute, Indiana University, Indianapolis, IN, United States
| | - Cynthia Ingraham
- Stark Neurosciences Research Institute, Indiana University, Indianapolis, IN, United States
| | - Megan R. Jewett
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, United States
| | - Gary E. Landreth
- Stark Neurosciences Research Institute, Indiana University, Indianapolis, IN, United States
- Department of Anatomy, Cell Biology and Physiology, Indiana University, Indianapolis, IN, United States
| | - Adrian L. Oblak
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, United States
- Stark Neurosciences Research Institute, Indiana University, Indianapolis, IN, United States
| | - Nian Wang
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, United States
- Stark Neurosciences Research Institute, Indiana University, Indianapolis, IN, United States
- *Correspondence: Nian Wang,
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Badea A, Li D, Niculescu AR, Anderson RJ, Stout JA, Williams CL, Colton CA, Maeda N, Dunson DB. Absolute Winding Number Differentiates Mouse Spatial Navigation Strategies With Genetic Risk for Alzheimer's Disease. Front Neurosci 2022; 16:848654. [PMID: 35784847 PMCID: PMC9247395 DOI: 10.3389/fnins.2022.848654] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 04/29/2022] [Indexed: 11/13/2022] Open
Abstract
Spatial navigation and orientation are emerging as promising markers for altered cognition in prodromal Alzheimer's disease, and even in cognitively normal individuals at risk for Alzheimer's disease. The different APOE gene alleles confer various degrees of risk. The APOE2 allele is considered protective, APOE3 is seen as control, while APOE4 carriage is the major known genetic risk for Alzheimer's disease. We have used mouse models carrying the three humanized APOE alleles and tested them in a spatial memory task in the Morris water maze. We introduce a new metric, the absolute winding number, to characterize the spatial search strategy, through the shape of the swim path. We show that this metric is robust to noise, and works for small group samples. Moreover, the absolute winding number better differentiated APOE3 carriers, through their straighter swim paths relative to both APOE2 and APOE4 genotypes. Finally, this novel metric supported increased vulnerability in APOE4 females. We hypothesized differences in spatial memory and navigation strategies are linked to differences in brain networks, and showed that different genotypes have different reliance on the hippocampal and caudate putamen circuits, pointing to a role for white matter connections. Moreover, differences were most pronounced in females. This departure from a hippocampal centric to a brain network approach may open avenues for identifying regions linked to increased risk for Alzheimer's disease, before overt disease manifestation. Further exploration of novel biomarkers based on spatial navigation strategies may enlarge the windows of opportunity for interventions. The proposed framework will be significant in dissecting vulnerable circuits associated with cognitive changes in prodromal Alzheimer's disease.
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Affiliation(s)
- Alexandra Badea
- Department of Radiology, Duke University, Durham, NC, United States
- Department of Neurology, Duke University, Durham, NC, United States
- Brain Imaging and Analysis Center, Duke University, Durham, NC, United States
- Biomedical Engineering, Duke University, Durham, NC, United States
| | - Didong Li
- Department of Computer Science, Princeton University, Princeton, NJ, United States
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, United States
| | | | | | - Jacques A. Stout
- Brain Imaging and Analysis Center, Duke University, Durham, NC, United States
| | - Christina L. Williams
- Department of Psychology and Neuroscience, Duke University, Durham, NC, United States
| | - Carol A. Colton
- Department of Neurology, Duke University, Durham, NC, United States
| | - Nobuyo Maeda
- Department of Pathology and Laboratory Medicine, The University of North Carolina, Chapel Hill, Chapel Hill, NC, United States
| | - David B. Dunson
- Department of Statistical Science, Duke University, Durham, NC, United States
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Resolution and b value dependent Structural Connectome in ex vivo Mouse Brain. Neuroimage 2022; 255:119199. [PMID: 35417754 PMCID: PMC9195912 DOI: 10.1016/j.neuroimage.2022.119199] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Revised: 04/05/2022] [Accepted: 04/07/2022] [Indexed: 12/24/2022] Open
Abstract
Diffusion magnetic resonance imaging has been widely used in both clinical and preclinical studies to characterize tissue microstructure and structural connectivity. The diffusion MRI protocol for the Human Connectome Project (HCP) has been developed and optimized to obtain high-quality, high-resolution diffusion MRI (dMRI) datasets. However, such efforts have not been fully explored in preclinical studies, especially for rodents. In this study, high quality dMRI datasets of mouse brains were acquired at 9.4T system from two vendors. In particular, we acquired a high-spatial resolution dMRI dataset (25 μm isotropic with 126 diffusion encoding directions), which we believe to be the highest spatial resolution yet obtained; and a high-angular resolution dMRI dataset (50 μm isotropic with 384 diffusion encoding directions), which we believe to be the highest angular resolution compared to the dMRI datasets at the microscopic resolution. We systematically investigated the effects of three important parameters that affect the final outcome of the connectome: b value (1000s/mm2 to 8000 s/mm2), angular resolution (10 to 126), and spatial resolution (25 μm to 200 μm). The stability of tractography and connectome increase with the angular resolution, where more than 50 angles is necessary to achieve consistent results. The connectome and quantitative parameters derived from graph theory exhibit a linear relationship to the b value (R2 > 0.99); a single-shell acquisition with b value of 3000 s/mm2 shows comparable results to the multi-shell high angular resolution dataset. The dice coefficient decreases and both false positive rate and false negative rate gradually increase with coarser spatial resolution. Our study provides guidelines and foundations for exploration of tradeoffs among acquisition parameters for the structural connectome in ex vivo mouse brain.
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Vaish A, Rajwade A, Gupta A. TL-HARDI: Transform learning based accelerated reconstruction of HARDI data. Comput Biol Med 2022; 143:105212. [PMID: 35151154 DOI: 10.1016/j.compbiomed.2022.105212] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 12/17/2021] [Accepted: 01/02/2022] [Indexed: 11/03/2022]
Abstract
Diffusion magnetic resonance imaging (dMRI) is being extensively used to study the neural architecture of the brain. High angular resolution diffusion imaging (HARDI), a variant of diffusion MRI, measures the diffusion of water molecules along the angular gradient directions in the q-space. It provides better estimates of fiber orientations compared to the traditionally used diffusion tensor imaging (DTI). However, HARDI requires acquisition of relatively large number of samples leading to longer scanning times. Several approaches based on compressive sensing (CS) have been proposed to accelerate HARDI acquisition, leveraging on the sparse representation of the HARDI signal in a pre-specified sparsifying basis. In this paper, we propose to carry out reconstruction of compressively sensed HARDI data using an adaptively learned transform. The transform is learned (i) from the compressive measurements on-the-fly, thereby, eliminating the overhead of choosing fixed sparsifying transforms, and (ii) on overlapping patches of the data, thereby, capturing local image structure effectively. Experiments are conducted on multiple real HARDI data for varying sampling ratios and sampling schemes. The performance of the proposed "TL-HARDI" method is compared with the state-of-the-art methods on various known image quality metrics as well as on dMRI feature maps derived from the reconstructed images. The proposed method is observed to yield better reconstruction than the existing state-of-the-art methods in both quantitative and qualitative comparisons.
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19
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Xiao J, Hornburg KJ, Cofer G, Cook JJ, Pratson F, Qi Y, Johnson GA. A time-course study of actively stained mouse brains: Diffusion tensor imaging parameters and connectomic stability over 1 year. NMR IN BIOMEDICINE 2022; 35:e4611. [PMID: 34558744 PMCID: PMC10461792 DOI: 10.1002/nbm.4611] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 07/21/2021] [Accepted: 08/10/2021] [Indexed: 06/13/2023]
Abstract
While the application of diffusion tensor imaging (DTI), tractography, and connectomics to fixed tissue is a common practice today, there have been limited studies examining the effects of fixation on brain microstructure over extended periods. This mouse model time-course study reports the changes of regional brain volumes and diffusion scalar parameters, such as fractional anisotropy, across 12 representative brain regions as measures of brain structural stability. The scalar DTI parameters and regional volumes were highly variable over the first 2 weeks after fixation. The same parameters were consistent over a 2-8-week window after fixation, which means confounds from tissue stability over that scanning window were minimal. Quantitative connectomes were analyzed over the same time with extension out to 1 year. While there was some change in the scalar metrics at 1 year after fixation, these changes were sufficiently small, particularly in white matter, to support reproducible connectomes over a period ranging from 2-weeks to 1-year post-fixation. These findings delineate a scanning period, during which brain volumes, diffusion scalar metrics, and connectomes are remarkably consistent.
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Affiliation(s)
- Jaclyn Xiao
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - Kathryn J. Hornburg
- Duke Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Gary Cofer
- Duke Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - James J. Cook
- Duke Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Forrest Pratson
- Duke Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Yi Qi
- Duke Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - G. Allan Johnson
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
- Duke Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
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Zhao Q, Ridout RP, Shen J, Wang N. Effects of Angular Resolution and b Value on Diffusion Tensor Imaging in Knee Joint. Cartilage 2021; 13:295S-303S. [PMID: 33843284 PMCID: PMC8804734 DOI: 10.1177/19476035211007909] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
OBJECTIVE To investigate the influences of the diffusion gradient directions (angular resolution) and the strength of the diffusion gradient (b value) on diffusion tensor imaging (DTI) metrics and tractography of various connective tissues in knee joint. DESIGN Two rat knee joints were scanned on a preclinical 9.4-T system using a 3-dimensional diffusion-weighted spin echo pulse sequence. One protocol with b value of 500, 1500, and 2500 s/mm2 were acquired separately using 43 diffusion gradient directions. The other protocol with b value of 1000 s/mm2 was performed using 147 diffusion gradient directions. The in-plane resolution was 45 µm isotropic. Fractional anisotropy (FA) and mean diffusivity (MD) were compared at different angular resolution. Tractography was quantitatively evaluated at different b values and angular resolutions in cartilage, ligament, meniscus, and growth plate. RESULTS The ligament showed higher FA value compared with growth plate and cartilage. The FA values were largely overestimated at the angular resolution of 6. Compared with FA, MD showed less sensitivity to the angular resolution. The fiber tracking was failed at low angular resolution (6 diffusion gradient directions) or high b value (2500 s/mm2). The quantitative measurements of tract length and track volume were strongly dependent on angular resolution and b value. CONCLUSIONS To obtain consistent DTI outputs and tractography in knee joint, the scan may require a proper b value (ranging from 500 to 1500 s/mm2) and sufficient angular resolution (>14) with signal-to-noise ratio >10.
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Affiliation(s)
- Qi Zhao
- School of Psychology, Shanghai
University of Sport, Shanghai, China
| | - Rees P. Ridout
- Pratt School of Engineering, Duke
University, Durham, NC, USA
| | - Jikai Shen
- Pratt School of Engineering, Duke
University, Durham, NC, USA
| | - Nian Wang
- Department of Radiology, Duke
University School of Medicine, Durham, NC, USA,Department of Radiology and Imaging
Sciences, Indiana University School of Medicine, Indianapolis, IN, USA,Nian Wang, Department of Radiology and
Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202,
USA.
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Johnson GA, Laoprasert R, Anderson RJ, Cofer G, Cook J, Pratson F, White LE. A multicontrast MR atlas of the Wistar rat brain. Neuroimage 2021; 242:118470. [PMID: 34391877 PMCID: PMC8754086 DOI: 10.1016/j.neuroimage.2021.118470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 07/01/2021] [Accepted: 08/11/2021] [Indexed: 11/23/2022] Open
Abstract
We describe a multi-contrast, multi-dimensional atlas of the Wistar rat acquired at microscopic spatial resolution using magnetic resonance histology (MRH). Diffusion weighted images, and associated scalar images were acquired of a single specimen with a fully sampled Fourier reconstruction, 61 angles and b=3000 s/mm2 yielding 50 um isotropic spatial resolution. The higher angular sampling allows use of the GQI algorithm improving the angular invariance of the scalar images and yielding an orientation distribution function to assist in delineating subtle boundaries where there are crossing fibers and track density images providing insight into local fiber architecture. A multigradient echo image of the same specimen was acquired at 25 um isotropic spatial resolution. A quantitative susceptibility map enhances fiber architecture relative to the magnitude images. An accompanying multi-specimen atlas (n=6) was acquired with compressed sensing with the same diffusion protocol as used for the single specimen atlas. An average was created using diffeomorphic mapping. Scalar volumes from the diffusion data, a T2* weighted volume, a quantitative susceptibility map, and a track density volume, all registered to the same space provide multiple contrasts to assist in anatomic delineation. The new template provides significantly increased contrast in the scalar DTI images when compared to previous atlases. A compact interactive viewer based on 3D Slicer is provided to facilitate comparison among the contrasts in the multiple volumes. The single volume and average atlas with multiple 3D volumes provide an improved template for anatomic interrogation of the Wistar rat brain. The improved contrast to noise in the scalar DTI images and the addition of other volumes (eg. QA,QSM,TDI ) will facilitate automated label registration for MR histology and preclinical imaging.
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Affiliation(s)
- 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, Durham, NC 27710, USA.
| | - Rick Laoprasert
- Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA
| | - Robert J Anderson
- Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA
| | - Gary Cofer
- Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA
| | - James Cook
- Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA
| | - Forrest Pratson
- Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA
| | - Leonard E White
- Department of Neurology, Duke University Medical Center, Durham, NC 27710, USA
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23
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Blocker SJ, Cook J, Mowery YM, Everitt JI, Qi Y, Hornburg KJ, Cofer GP, Zapata F, Bassil AM, Badea CT, Kirsch DG, Johnson GA. Ex Vivo MR Histology and Cytometric Feature Mapping Connect Three-dimensional in Vivo MR Images to Two-dimensional Histopathologic Images of Murine Sarcomas. Radiol Imaging Cancer 2021; 3:e200103. [PMID: 34018846 DOI: 10.1148/rycan.2021200103] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Purpose To establish a platform for quantitative tissue-based interpretation of cytoarchitecture features from tumor MRI measurements. Materials and Methods In a pilot preclinical study, multicontrast in vivo MRI of murine soft-tissue sarcomas in 10 mice, followed by ex vivo MRI of fixed tissues (termed MR histology), was performed. Paraffin-embedded limb cross-sections were stained with hematoxylin-eosin, digitized, and registered with MRI. Registration was assessed by using binarized tumor maps and Dice similarity coefficients (DSCs). Quantitative cytometric feature maps from histologic slides were derived by using nuclear segmentation and compared with registered MRI, including apparent diffusion coefficients and transverse relaxation times as affected by magnetic field heterogeneity (T2* maps). Cytometric features were compared with each MR image individually by using simple linear regression analysis to identify the features of interest, and the goodness of fit was assessed on the basis of R2 values. Results Registration of MR images to histopathologic slide images resulted in mean DSCs of 0.912 for ex vivo MR histology and 0.881 for in vivo MRI. Triplicate repeats showed high registration repeatability (mean DSC, >0.9). Whole-slide nuclear segmentations were automated to detect nuclei on histopathologic slides (DSC = 0.8), and feature maps were generated for correlative analysis with MR images. Notable trends were observed between cell density and in vivo apparent diffusion coefficients (best line fit: R2 = 0.96, P < .001). Multiple cytoarchitectural features exhibited linear relationships with in vivo T2* maps, including nuclear circularity (best line fit: R2 = 0.99, P < .001) and variance in nuclear circularity (best line fit: R2 = 0.98, P < .001). Conclusion An infrastructure for registering and quantitatively comparing in vivo tumor MRI with traditional histologic analysis was successfully implemented in a preclinical pilot study of soft-tissue sarcomas. Keywords: MRI, Pathology, Animal Studies, Tissue Characterization Supplemental material is available for this article. © RSNA, 2021.
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Affiliation(s)
- Stephanie J Blocker
- From the Departments of Radiology (S.J.B., J.C., Y.Q., K.H., G.P.C., F.Z., C.T.B., G.A.J.), Radiation Oncology (Y.M.M., A.M.B., D.G.K.), and Pathology (J.I.E.), Duke University Medical Center, Center for In Vivo Microscopy, Bryan Research Building, 311 Research Dr, Durham, NC 27710
| | - James Cook
- From the Departments of Radiology (S.J.B., J.C., Y.Q., K.H., G.P.C., F.Z., C.T.B., G.A.J.), Radiation Oncology (Y.M.M., A.M.B., D.G.K.), and Pathology (J.I.E.), Duke University Medical Center, Center for In Vivo Microscopy, Bryan Research Building, 311 Research Dr, Durham, NC 27710
| | - Yvonne M Mowery
- From the Departments of Radiology (S.J.B., J.C., Y.Q., K.H., G.P.C., F.Z., C.T.B., G.A.J.), Radiation Oncology (Y.M.M., A.M.B., D.G.K.), and Pathology (J.I.E.), Duke University Medical Center, Center for In Vivo Microscopy, Bryan Research Building, 311 Research Dr, Durham, NC 27710
| | - Jeffrey I Everitt
- From the Departments of Radiology (S.J.B., J.C., Y.Q., K.H., G.P.C., F.Z., C.T.B., G.A.J.), Radiation Oncology (Y.M.M., A.M.B., D.G.K.), and Pathology (J.I.E.), Duke University Medical Center, Center for In Vivo Microscopy, Bryan Research Building, 311 Research Dr, Durham, NC 27710
| | - Yi Qi
- From the Departments of Radiology (S.J.B., J.C., Y.Q., K.H., G.P.C., F.Z., C.T.B., G.A.J.), Radiation Oncology (Y.M.M., A.M.B., D.G.K.), and Pathology (J.I.E.), Duke University Medical Center, Center for In Vivo Microscopy, Bryan Research Building, 311 Research Dr, Durham, NC 27710
| | - Kathryn J Hornburg
- From the Departments of Radiology (S.J.B., J.C., Y.Q., K.H., G.P.C., F.Z., C.T.B., G.A.J.), Radiation Oncology (Y.M.M., A.M.B., D.G.K.), and Pathology (J.I.E.), Duke University Medical Center, Center for In Vivo Microscopy, Bryan Research Building, 311 Research Dr, Durham, NC 27710
| | - Gary P Cofer
- From the Departments of Radiology (S.J.B., J.C., Y.Q., K.H., G.P.C., F.Z., C.T.B., G.A.J.), Radiation Oncology (Y.M.M., A.M.B., D.G.K.), and Pathology (J.I.E.), Duke University Medical Center, Center for In Vivo Microscopy, Bryan Research Building, 311 Research Dr, Durham, NC 27710
| | - Fernando Zapata
- From the Departments of Radiology (S.J.B., J.C., Y.Q., K.H., G.P.C., F.Z., C.T.B., G.A.J.), Radiation Oncology (Y.M.M., A.M.B., D.G.K.), and Pathology (J.I.E.), Duke University Medical Center, Center for In Vivo Microscopy, Bryan Research Building, 311 Research Dr, Durham, NC 27710
| | - Alex M Bassil
- From the Departments of Radiology (S.J.B., J.C., Y.Q., K.H., G.P.C., F.Z., C.T.B., G.A.J.), Radiation Oncology (Y.M.M., A.M.B., D.G.K.), and Pathology (J.I.E.), Duke University Medical Center, Center for In Vivo Microscopy, Bryan Research Building, 311 Research Dr, Durham, NC 27710
| | - Cristian T Badea
- From the Departments of Radiology (S.J.B., J.C., Y.Q., K.H., G.P.C., F.Z., C.T.B., G.A.J.), Radiation Oncology (Y.M.M., A.M.B., D.G.K.), and Pathology (J.I.E.), Duke University Medical Center, Center for In Vivo Microscopy, Bryan Research Building, 311 Research Dr, Durham, NC 27710
| | - David G Kirsch
- From the Departments of Radiology (S.J.B., J.C., Y.Q., K.H., G.P.C., F.Z., C.T.B., G.A.J.), Radiation Oncology (Y.M.M., A.M.B., D.G.K.), and Pathology (J.I.E.), Duke University Medical Center, Center for In Vivo Microscopy, Bryan Research Building, 311 Research Dr, Durham, NC 27710
| | - G Allan Johnson
- From the Departments of Radiology (S.J.B., J.C., Y.Q., K.H., G.P.C., F.Z., C.T.B., G.A.J.), Radiation Oncology (Y.M.M., A.M.B., D.G.K.), and Pathology (J.I.E.), Duke University Medical Center, Center for In Vivo Microscopy, Bryan Research Building, 311 Research Dr, Durham, NC 27710
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Badea A, Schmalzigaug R, Kim W, Bonner P, Ahmed U, Johnson GA, Cofer G, Foster M, Anderson RJ, Badea C, Premont RT. Microcephaly with altered cortical layering in GIT1 deficiency revealed by quantitative neuroimaging. Magn Reson Imaging 2021; 76:26-38. [PMID: 33010377 PMCID: PMC7802083 DOI: 10.1016/j.mri.2020.09.023] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 09/25/2020] [Accepted: 09/25/2020] [Indexed: 01/06/2023]
Abstract
G Protein-Coupled Receptor Kinase-Interacting Protein-1 (GIT1) regulates neuronal functions, including cell and axon migration and synapse formation and maintenance, and GIT1 knockout (KO) mice exhibit learning and memory deficits. We noted that male and female GIT1-KO mice exhibit neuroimaging phenotypes including microcephaly, and altered cortical layering, with a decrease in neuron density in cortical layer V. Micro-CT and magnetic resonance microscopy (MRM) were used to identify morphometric phenotypes for the skulls and throughout the GIT1-KO brains. High field MRM of actively-stained mouse brains from GIT1-KO and wild type (WT) controls (n = 6 per group) allowed segmenting 37 regions, based on co-registration to the Waxholm Space atlas. Overall brain size in GIT1-KO mice was ~32% smaller compared to WT controls. After correcting for brain size, several regions were significantly different in GIT1-KO mice relative to WT, including the gray matter of the ventral thalamic nuclei and the rest of the thalamus, the inferior colliculus, and pontine nuclei. GIT1-KO mice had reduced volume of white matter tracts, most notably in the anterior commissure (~26% smaller), but also in the cerebral peduncle, fornix, and spinal trigeminal tract. On the other hand, the basal ganglia appeared enlarged in GIT1-KO mice, including the globus pallidus, caudate putamen, and particularly the accumbens - supporting a possible vulnerability to addiction. Volume based morphometry based on high-resolution MRM (21.5 μm isotropic voxels) was effective in detecting overall, and local differences in brain volumes in GIT1-KO mice, including in white matter tracts. The reduced relative volume of specific brain regions suggests a critical, but not uniform, role for GIT1 in brain development, conducive to brain microcephaly, and aberrant connectivity.
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Affiliation(s)
- Alexandra Badea
- Department of Radiology, Duke University Medical Center, Durham, NC 27710, United States of America; Department of Neurology, Duke University Medical Center, Durham, NC 27710, United States of America; Departments of Biomedical Engineering, Duke University Medical Center, Durham, NC 27710, United States of America; Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, United States of America.
| | - Robert Schmalzigaug
- Department of Medicine, Duke University Medical Center, Durham, NC 27710, United States of America
| | - Woojoo Kim
- Department of Medicine, Duke University Medical Center, Durham, NC 27710, United States of America
| | - Pamela Bonner
- Department of Medicine, Duke University Medical Center, Durham, NC 27710, United States of America
| | - Umer Ahmed
- Department of Medicine, Duke University Medical Center, Durham, NC 27710, United States of America
| | - G Allan Johnson
- Department of Radiology, Duke University Medical Center, Durham, NC 27710, United States of America; Departments of Biomedical Engineering, Duke University Medical Center, Durham, NC 27710, United States of America
| | - Gary Cofer
- Department of Radiology, Duke University Medical Center, Durham, NC 27710, United States of America
| | - Mark Foster
- Department of Radiology, Duke University Medical Center, Durham, NC 27710, United States of America
| | - Robert J Anderson
- Department of Radiology, Duke University Medical Center, Durham, NC 27710, United States of America
| | - Cristian Badea
- Department of Radiology, Duke University Medical Center, Durham, NC 27710, United States of America; Departments of Biomedical Engineering, Duke University Medical Center, Durham, NC 27710, United States of America
| | - Richard T Premont
- Department of Medicine, Duke University Medical Center, Durham, NC 27710, United States of America.
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Wang N, Badar F, Xia Y. Resolution-dependent influences of compressed sensing in quantitative T2 mapping of articular cartilage. NMR IN BIOMEDICINE 2020; 33:e4260. [PMID: 32040226 PMCID: PMC7415577 DOI: 10.1002/nbm.4260] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 12/18/2019] [Accepted: 12/29/2019] [Indexed: 06/10/2023]
Abstract
This study evaluates the resolution-dependent influences of compressed sensing (CS) in MRI quantification of T2 mapping in articular cartilage with osteoarthritis (OA). T2-weighed 2D experiments of healthy and OA cartilage were fully sampled in k-space with five echo times at both 17.6 μm and 195.3 μm in-plane resolutions; termed as microscopic MRI (μMRI) and macroscopic MRI (mMRI) respectively. These fully sampled k-space data were under-sampled at various 2D CS accelerating factors (AF = 4-32). The under-sampled data were reconstructed individually into 2D images using nonlinear reconstruction, which were used to calculate the T2 maps. The bulk and zonal variations of T2 values in cartilage were evaluated at different AFs. The study finds that the T2 images at AFs up to 8 preserved major visual information and produced negligible artifacts for μMRI. The T2 values remained accurate for different sub-tissue zones at various AFs. The absolute difference between the CS (AF up to 32) and the Ground Truth (i.e., using 100% of the k-space data) of the mean T2 values through the whole tissue depth was higher in mMRI versus μMRI. For mMRI (where the resolution mimics the clinical MRI of human cartilage), the quantitative T2 mapping at AFs up to 4 showed negligible variations. This study demonstrates that both clinical MRI and μMRI can benefit from the use of CS in image acquisition, and μMRI benefits more from the use of CS by acquiring much less data, without losing significant accuracy in the quantification of T2 maps in osteoarthritic cartilage.
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Affiliation(s)
- Nian Wang
- Center for In Vivo Microscopy, Duke University School of Medicine, Durham, North Carolina, USA
- Department of Radiology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Farid Badar
- Department of Physics and Center for Biomedical Research, Oakland University, Rochester, MI 48309
| | - Yang Xia
- Department of Physics and Center for Biomedical Research, Oakland University, Rochester, MI 48309
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Wang N, Anderson RJ, Ashbrook DG, Gopalakrishnan V, Park Y, Priebe CE, Qi Y, Laoprasert R, Vogelstein JT, Williams RW, Johnson GA. Variability and heritability of mouse brain structure: Microscopic MRI atlases and connectomes for diverse strains. Neuroimage 2020; 222:117274. [PMID: 32818613 PMCID: PMC8442986 DOI: 10.1016/j.neuroimage.2020.117274] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 07/27/2020] [Accepted: 08/11/2020] [Indexed: 02/06/2023] Open
Abstract
Genome-wide association studies have demonstrated significant links between human brain structure and common DNA variants. Similar studies with rodents have been challenging because of smaller brain volumes. Using high field MRI (9.4 T) and compressed sensing, we have achieved microscopic resolution and sufficiently high throughput for rodent population studies. We generated whole brain structural MRI and diffusion connectomes for four diverse isogenic lines of mice (C57BL/6J, DBA/2J, CAST/EiJ, and BTBR) at spatial resolution 20,000 times higher than human connectomes. We measured narrow sense heritability (h2) I.e. the fraction of variance explained by strains in a simple ANOVA model for volumes and scalar diffusion metrics, and estimates of residual technical error for 166 regions in each hemisphere and connectivity between the regions. Volumes of discrete brain regions had the highest mean heritability (0.71 ± 0.23 SD, n = 332), followed by fractional anisotropy (0.54 ± 0.26), radial diffusivity (0.34 ± 0.022), and axial diffusivity (0.28 ± 0.19). Connection profiles were statistically different in 280 of 322 nodes across all four strains. Nearly 150 of the connection profiles were statistically different between the C57BL/6J, DBA/2J, and CAST/EiJ lines. Microscopic whole brain MRI/DTI has allowed us to identify significant heritable phenotypes in brain volume, scalar DTI metrics, and quantitative connectomes.
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Affiliation(s)
- Nian Wang
- Duke Center for In Vivo Microscopy, Department of Radiology, Duke University, Duke University Medical Center Box 3302, Durham, NC 27710, USA
| | - Robert J Anderson
- Duke Center for In Vivo Microscopy, Department of Radiology, Duke University, Duke University Medical Center Box 3302, Durham, NC 27710, USA
| | - David G Ashbrook
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Vivek Gopalakrishnan
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Youngser Park
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Carey E Priebe
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD 21287, USA; Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Yi Qi
- Duke Center for In Vivo Microscopy, Department of Radiology, Duke University, Duke University Medical Center Box 3302, Durham, NC 27710, USA
| | - Rick Laoprasert
- Duke Center for In Vivo Microscopy, Department of Radiology, Duke University, Duke University Medical Center Box 3302, Durham, NC 27710, USA
| | - Joshua T Vogelstein
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21287, USA; Center for Imaging Science, Johns Hopkins University, Baltimore, MD 21287, USA; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21287, USA; Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Robert W Williams
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - G Allan Johnson
- Duke Center for In Vivo Microscopy, Department of Radiology, Duke University, Duke University Medical Center Box 3302, Durham, NC 27710, USA; Department of Biomedical Engineering, Duke University, Durham, NC 27710, USA.
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Ly M, Foley L, Manivannan A, Hitchens TK, Richardson RM, Modo M. Mesoscale diffusion magnetic resonance imaging of the ex vivo human hippocampus. Hum Brain Mapp 2020; 41:4200-4218. [PMID: 32621364 PMCID: PMC7502840 DOI: 10.1002/hbm.25119] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 06/01/2020] [Accepted: 06/16/2020] [Indexed: 12/21/2022] Open
Abstract
Mesoscale diffusion magnetic resonance imaging (MRI) endeavors to bridge the gap between macroscopic white matter tractography and microscopic studies investigating the cytoarchitecture of human brain tissue. To ensure a robust measurement of diffusion at the mesoscale, acquisition parameters were arrayed to investigate their effects on scalar indices (mean, radial, axial diffusivity, and fractional anisotropy) and streamlines (i.e., graphical representation of axonal tracts) in hippocampal layers. A mesoscale resolution afforded segementation of the pyramidal cell layer (CA1-4), the dentate gyrus, as well as stratum moleculare, radiatum, and oriens. Using ex vivo samples, surgically excised from patients with intractable epilepsy (n = 3), we found that shorter diffusion times (23.7 ms) with a b-value of 4,000 s/mm2 were advantageous at the mesoscale, providing a compromise between mean diffusivity and fractional anisotropy measurements. Spatial resolution and sample orientation exerted a major effect on tractography, whereas the number of diffusion gradient encoding directions minimally affected scalar indices and streamline density. A sample temperature of 15°C provided a compromise between increasing signal-to-noise ratio and increasing the diffusion properties of the tissue. Optimization of the acquisition afforded a system's view of intra- and extra-hippocampal connections. Tractography reflected histological boundaries of hippocampal layers. Individual layer connectivity was visualized, as well as streamlines emanating from individual sub-fields. The perforant path, subiculum and angular bundle demonstrated extra-hippocampal connections. Histology of the samples confirmed individual cell layers corresponding to ROIs defined on MR images. We anticipate that this ex vivo mesoscale imaging will yield novel insights into human hippocampal connectivity.
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Affiliation(s)
- Maria Ly
- Department of RadiologyUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Lesley Foley
- Department of NeurobiologyUniversity of PittsburghPittsburghPennsylvaniaUSA
| | | | - T. Kevin Hitchens
- Department of NeurobiologyUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - R. Mark Richardson
- Department of Neurological SurgeryUniversity of PittsburghPittsburghPennsylvaniaUSA
- McGowan Institute for Regenerative MedicineUniversity of PittsburghPittsburghPennsylvaniaUSA
- Brain InstituteUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Michel Modo
- Department of RadiologyUniversity of PittsburghPittsburghPennsylvaniaUSA
- McGowan Institute for Regenerative MedicineUniversity of PittsburghPittsburghPennsylvaniaUSA
- Department of BioengineeringUniversity of PittsburghPittsburghPennsylvaniaUSA
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28
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Eekhoff JD, Lake SP. Three-dimensional computation of fibre orientation, diameter and branching in segmented image stacks of fibrous networks. J R Soc Interface 2020; 17:20200371. [PMID: 32752994 PMCID: PMC7482563 DOI: 10.1098/rsif.2020.0371] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 07/15/2020] [Indexed: 12/27/2022] Open
Abstract
Fibre topography of the extracellular matrix governs local mechanical properties and cellular behaviour including migration and gene expression. While quantifying properties of the fibrous network provides valuable data that could be used across a breadth of biomedical disciplines, most available techniques are limited to two dimensions and, therefore, do not fully capture the architecture of three-dimensional (3D) tissue. The currently available 3D techniques have limited accuracy and applicability and many are restricted to a specific imaging modality. To address this need, we developed a novel fibre analysis algorithm capable of determining fibre orientation, fibre diameter and fibre branching on a voxel-wise basis in image stacks with distinct fibre populations. The accuracy of the technique is demonstrated on computer-generated phantom image stacks spanning a range of features and complexities, as well as on two-photon microscopy image stacks of elastic fibres in bovine tendon and dermis. Additionally, we propose a measure of axial spherical variance which can be used to define the degree of fibre alignment in a distribution of 3D orientations. This method provides a useful tool to quantify orientation distributions and variance on image stacks with distinguishable fibres or fibre-like structures.
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Affiliation(s)
- Jeremy D. Eekhoff
- Department of Biomedical Engineering, Washington University in St Louis, St Louis, MO 63110, USA
| | - Spencer P. Lake
- Department of Biomedical Engineering, Washington University in St Louis, St Louis, MO 63110, USA
- Department of Mechanical Engineering and Materials Science, Washington University in St Louis, St Louis, MO 63110, USA
- Department of Orthopaedic Surgery, Washington University in St Louis, St Louis, MO 63110, USA
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29
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Ke J, Foley LM, Hitchens TK, Richardson RM, Modo M. Ex vivo mesoscopic diffusion MRI correlates with seizure frequency in patients with uncontrolled mesial temporal lobe epilepsy. Hum Brain Mapp 2020; 41:4529-4548. [PMID: 32691978 PMCID: PMC7555080 DOI: 10.1002/hbm.25139] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 06/08/2020] [Accepted: 07/05/2020] [Indexed: 12/28/2022] Open
Abstract
The role of hippocampal connectivity in mesial temporal lobe epilepsy (mTLE) remains poorly understood. The use of ex vivo hippocampal samples excised from patients with mTLE affords mesoscale diffusion magnetic resonance imaging (MRI) to identify individual cell layers, such as the pyramidal (PCL) and granule cell layers (GCL), which are thought to be impacted by seizure activity. Diffusion tensor imaging (DTI) of control (n = 3) and mTLE (n = 7) hippocampi on an 11.7 T MRI scanner allowed us to reveal intra‐hippocampal connectivity and evaluate how epilepsy affected mean (MD), axial (AD), and radial diffusivity (RD), as well as fractional anisotropy (FA). Regional measurements indicated a volume loss in the PCL of the cornu ammonis (CA) 1 subfield in mTLE patients compared to controls, which provided anatomical context. Diffusion measurements, as well as streamline density, were generally higher in mTLE patients compared to controls, potentially reflecting differences due to tissue fixation. mTLE measurements were more variable than controls. This variability was associated with disease severity, as indicated by a strong correlation (r = 0.87) between FA in the stratum radiatum and the frequency of seizures in patients. MD and RD of the PCL in subfields CA3 and CA4 also correlated strongly with disease severity. No correlation of MR measures with disease duration was evident. These results reveal the potential of mesoscale diffusion MRI to examine layer‐specific diffusion changes and connectivity to determine how these relate to clinical measures. Improving the visualization of intra‐hippocampal connectivity will advance the development of novel hypotheses about seizure networks.
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Affiliation(s)
- Justin Ke
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Lesley M Foley
- Animal Imaging Center, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - T Kevin Hitchens
- Animal Imaging Center, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Neurobiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - R Mark Richardson
- Centre for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Neurological Surgery, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Michel Modo
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Centre for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,McGowan Institute for Regenerative Medicine, Pittsburgh, Pennsylvania, USA
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30
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Enam SF, Kader SR, Bodkin N, Lyon JG, Calhoun M, Azrak C, Tiwari PM, Vanover D, Wang H, Santangelo PJ, Bellamkonda RV. Evaluation of M2-like macrophage enrichment after diffuse traumatic brain injury through transient interleukin-4 expression from engineered mesenchymal stromal cells. J Neuroinflammation 2020; 17:197. [PMID: 32563258 PMCID: PMC7306141 DOI: 10.1186/s12974-020-01860-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 05/29/2020] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Appropriately modulating inflammation after traumatic brain injury (TBI) may prevent disabilities for the millions of those inflicted annually. In TBI, cellular mediators of inflammation, including macrophages and microglia, possess a range of phenotypes relevant for an immunomodulatory therapeutic approach. It is thought that early phenotypic modulation of these cells will have a cascading healing effect. In fact, an anti-inflammatory, "M2-like" macrophage phenotype after TBI has been associated with neurogenesis, axonal regeneration, and improved white matter integrity (WMI). There already exist clinical trials seeking an M2-like bias through mesenchymal stem/stromal cells (MSCs). However, MSCs do not endogenously synthesize key signals that induce robust M2-like phenotypes such as interleukin-4 (IL-4). METHODS To enrich M2-like macrophages in a clinically relevant manner, we augmented MSCs with synthetic IL-4 mRNA to transiently express IL-4. These IL-4 expressing MSCs (IL-4 MSCs) were characterized for expression and functionality and then delivered in a modified mouse TBI model of closed head injury. Groups were assessed for functional deficits and MR imaging. Brain tissue was analyzed through flow cytometry, multi-plex ELISA, qPCR, histology, and RNA sequencing. RESULTS We observed that IL-4 MSCs indeed induce a robust M2-like macrophage phenotype and promote anti-inflammatory gene expression after TBI. However, here we demonstrate that acute enrichment of M2-like macrophages did not translate to improved functional or histological outcomes, or improvements in WMI on MR imaging. To further understand whether dysfunctional pathways underlie the lack of therapeutic effect, we report transcriptomic analysis of injured and treated brains. Through this, we discovered that inflammation persists despite acute enrichment of M2-like macrophages in the brain. CONCLUSION The results demonstrate that MSCs can be engineered to induce a stronger M2-like macrophage response in vivo. However, they also suggest that acute enrichment of only M2-like macrophages after diffuse TBI cannot orchestrate neurogenesis, axonal regeneration, or improve WMI. Here, we also discuss our modified TBI model and methods to assess severity, behavioral studies, and propose that IL-4 expressing MSCs may also have relevance in other cavitary diseases or in improving biomaterial integration into tissues.
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Affiliation(s)
- Syed Faaiz Enam
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | | | - Nicholas Bodkin
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Johnathan G Lyon
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Mark Calhoun
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Cesar Azrak
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Pooja Munnilal Tiwari
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Daryll Vanover
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Haichen Wang
- Department of Neurology, Duke University, Durham, NC, USA
| | - Philip J Santangelo
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
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Kain M, Bodin M, Loury S, Chi Y, Louis J, Simon M, Lamy J, Barillot C, Dojat M. Small Animal Shanoir (SAS) A Cloud-Based Solution for Managing Preclinical MR Brain Imaging Studies. Front Neuroinform 2020; 14:20. [PMID: 32508612 PMCID: PMC7248267 DOI: 10.3389/fninf.2020.00020] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 04/16/2020] [Indexed: 01/28/2023] Open
Abstract
Clinical multicenter imaging studies are frequent and rely on a wide range of existing tools for sharing data and processing pipelines. This is not the case for preclinical (small animal) studies. Animal population imaging is still in infancy, especially because a complete standardization and control of initial conditions in animal models across labs is still difficult and few studies aim at standardization of acquisition and post-processing techniques. Clearly, there is a need of appropriate tools for the management and sharing of data, post-processing and analysis methods dedicated to small animal imaging. Solutions developed for Human imaging studies cannot be directly applied to this specific domain. In this paper, we present the Small Animal Shanoir (SAS) solution for supporting animal population imaging using tools compatible with open data. The integration of automated workflow tools ensures accessibility and reproducibility of research outputs. By sharing data and imaging processing tools, hosted by SAS, we promote data preparation and tools for reproducibility and reuse, and participation in multicenter or replication "open science" studies contributing to the improvement of quality science in preclinical domain. SAS is a first step for promoting open science for small animal imaging and a contribution to the valorization of data and pipelines of reference.
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Affiliation(s)
- Michael Kain
- INRIA U1228, INSERM, Université de Rennes, Rennes, France
| | - Marjolaine Bodin
- INSERM U1216, Grenoble Institut des Neurosciences, Université Grenoble Alpes, CHU Grenoble Alpes, Grenoble, France
| | - Simon Loury
- INSERM U1216, Grenoble Institut des Neurosciences, Université Grenoble Alpes, CHU Grenoble Alpes, Grenoble, France
| | - Yao Chi
- INRIA U1228, INSERM, Université de Rennes, Rennes, France
| | - Julien Louis
- INRIA U1228, INSERM, Université de Rennes, Rennes, France
| | - Mathieu Simon
- INRIA U1228, INSERM, Université de Rennes, Rennes, France
| | - Julien Lamy
- ICube, University of Strasbourg-CNRS, Strasbourg, France
| | | | - Michel Dojat
- INSERM U1216, Grenoble Institut des Neurosciences, Université Grenoble Alpes, CHU Grenoble Alpes, Grenoble, France
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Wang N, White LE, Qi Y, Cofer G, Johnson GA. Cytoarchitecture of the mouse brain by high resolution diffusion magnetic resonance imaging. Neuroimage 2020; 216:116876. [PMID: 32344062 DOI: 10.1016/j.neuroimage.2020.116876] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 04/18/2020] [Accepted: 04/22/2020] [Indexed: 12/21/2022] Open
Abstract
MRI has been widely used to probe the neuroanatomy of the mouse brain, directly correlating MRI findings to histology is still challenging due to the limited spatial resolution and various image contrasts derived from water relaxation or diffusion properties. Magnetic resonance histology has the potential to become an indispensable research tool to mitigate such challenges. In the present study, we acquired high spatial resolution MRI datasets, including diffusion MRI (dMRI) at 25 μm isotropic resolution and quantitative susceptibility mapping (QSM) at 21.5 μm isotropic resolution to validate with conventional mouse brain histology. Diffusion weighted images (DWIs) show better delineation of cortical layers and glomeruli in the olfactory bulb than fractional anisotropy (FA) maps. However, among all the image contrasts, including quantitative susceptibility mapping (QSM), T1/T2∗ images and DTI metrics, FA maps highlight unique laminar architecture in sub-regions of the hippocampus, including the strata of the dentate gyrus and CA fields of the hippocampus. The mean diffusivity (MD) and axial diffusivity (AD) yield higher correlation with DAPI (0.62 and 0.71) and NeuN (0.78 and 0.74) than with NF-160 (-0.34 and -0.49). The correlations between FA and DAPI, NeuN, and NF-160 are 0.31, -0.01, and -0.49, respectively. Our findings demonstrate that MRI at microscopic resolution deliver a three-dimensional, non-invasive and non-destructive platform for characterization of fine structural detail in both gray matter and white matter of the mouse brain.
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Affiliation(s)
- Nian Wang
- Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA.
| | - Leonard E White
- Department of Neurology, Duke University Medical Center, Durham, NC 27710, USA
| | - Yi Qi
- Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA
| | - Gary Cofer
- Center for In Vivo Microscopy, Department of Radiology, 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, Durham, NC 27710, USA.
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Anderson RJ, Long CM, Calabrese ED, Robertson SH, Johnson GA, Cofer GP, O’Brien RJ, Badea A. Optimizing Diffusion Imaging Protocols for Structural Connectomics in Mouse Models of Neurological Conditions. FRONTIERS IN PHYSICS 2020; 8:88. [PMID: 33928076 PMCID: PMC8081353 DOI: 10.3389/fphy.2020.00088] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Network approaches provide sensitive biomarkers for neurological conditions, such as Alzheimer's disease (AD). Mouse models can help advance our understanding of underlying pathologies, by dissecting vulnerable circuits. While the mouse brain contains less white matter compared to the human brain, axonal diameters compare relatively well (e.g., ~0.6 μm in the mouse and ~0.65-1.05 μm in the human corpus callosum). This makes the mouse an attractive test bed for novel diffusion models and imaging protocols. Remaining questions on the accuracy and uncertainty of connectomes have prompted us to evaluate diffusion imaging protocols with various spatial and angular resolutions. We have derived structural connectomes by extracting gradient subsets from a high-spatial, high-angular resolution diffusion acquisition (120 directions, 43-μm-size voxels). We have simulated protocols with 12, 15, 20, 30, 45, 60, 80, 100, and 120 angles and at 43, 86, or 172-μm voxel sizes. The rotational stability of these schemes increased with angular resolution. The minimum condition number was achieved for 120 directions, followed by 60 and 45 directions. The percentage of voxels containing one dyad was exceeded by those with two dyads after 45 directions, and for the highest spatial resolution protocols. For the 86- or 172-μm resolutions, these ratios converged toward 55% for one and 39% for two dyads, respectively, with <7% from voxels with three dyads. Tractography errors, estimated through dyad dispersion, decreased most with angular resolution. Spatial resolution effects became noticeable at 172 μm. Smaller tracts, e.g., the fornix, were affected more than larger ones, e.g., the fimbria. We observed an inflection point for 45 directions, and an asymptotic behavior after 60 directions, corresponding to similar projection density maps. Spatially downsampling to 86 μm, while maintaining the angular resolution, achieved a subgraph similarity of 96% relative to the reference. Using 60 directions with 86- or 172-μm voxels resulted in 94% similarity. Node similarity metrics indicated that major white matter tracts were more robust to downsampling relative to cortical regions. Our study provides guidelines for new protocols in mouse models of neurological conditions, so as to achieve similar connectomes, while increasing efficiency.
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Affiliation(s)
| | | | - Evan D. Calabrese
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States
| | | | - G. Allan Johnson
- Department of Radiology, Duke University, Durham, CA, United States
| | - Gary P. Cofer
- Department of Radiology, Duke University, Durham, CA, United States
| | - Richard J. O’Brien
- Department of Neurology, School of Medicine, Duke University, Durham, CA, United States
| | - Alexandra Badea
- Department of Radiology, Duke University, Durham, CA, United States
- Department of Neurology, School of Medicine, Duke University, Durham, CA, United States
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34
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Liu C, Ye FQ, Newman JD, Szczupak D, Tian X, Yen CCC, Majka P, Glen D, Rosa MGP, Leopold DA, Silva AC. A resource for the detailed 3D mapping of white matter pathways in the marmoset brain. Nat Neurosci 2020; 23:271-280. [PMID: 31932765 PMCID: PMC7007400 DOI: 10.1038/s41593-019-0575-0] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Accepted: 12/10/2019] [Indexed: 12/19/2022]
Abstract
While the fundamental importance of the white matter in supporting neuronal communication is well known, existing publications of primate brains do not feature a detailed description of its complex anatomy. The main barrier to achieving this is that existing primate neuroimaging data have insufficient spatial resolution to resolve white matter pathways fully. Here we present a resource that allows detailed descriptions of white matter structures and trajectories of fiber pathways in the marmoset brain. The resource includes: (1) the highest-resolution diffusion-weighted MRI data available to date, which reveal white matter features not previously described; (2) a comprehensive three-dimensional white matter atlas depicting fiber pathways that were either omitted or misidentified in previous atlases; and (3) comprehensive fiber pathway maps of cortical connections combining diffusion-weighted MRI tractography and neuronal tracing data. The resource, which can be downloaded from marmosetbrainmapping.org, will facilitate studies of brain connectivity and the development of tractography algorithms in the primate brain.
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Affiliation(s)
- Cirong Liu
- Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.
- Department of Neurobiology, University of Pittsburgh Brain Institute, Pittsburgh, PA, USA.
| | - Frank Q Ye
- Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, and National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | - John D Newman
- Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
- Section on Quantitative Imaging and Tissue Sciences, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Diego Szczupak
- Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
- Department of Neurobiology, University of Pittsburgh Brain Institute, Pittsburgh, PA, USA
| | - Xiaoguang Tian
- Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
- Department of Neurobiology, University of Pittsburgh Brain Institute, Pittsburgh, PA, USA
| | - Cecil Chern-Chyi Yen
- Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Piotr Majka
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of Polish Academy of Sciences, Warsaw, Poland
- ARC Centre of Excellence for Integrative Brain Function, Clayton, Melbourne, Victoria, Australia
| | - Daniel Glen
- Scientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health (NIMH/NIH), Bethesda, MD, USA
| | - Marcello G P Rosa
- ARC Centre of Excellence for Integrative Brain Function, Clayton, Melbourne, Victoria, Australia
- Neuroscience Program, Monash Biomedicine Discovery Institute, Clayton, Melbourne, Victoria, Australia
| | - David A Leopold
- Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, and National Eye Institute, National Institutes of Health, Bethesda, MD, USA
- Section on Cognitive Neurophysiology and Imaging, Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Afonso C Silva
- Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.
- Department of Neurobiology, University of Pittsburgh Brain Institute, Pittsburgh, PA, USA.
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Zhang C, Arefin TM, Nakarmi U, Lee CH, Li H, Liang D, Zhang J, Ying L. Acceleration of three-dimensional diffusion magnetic resonance imaging using a kernel low-rank compressed sensing method. Neuroimage 2020; 210:116584. [PMID: 32004717 DOI: 10.1016/j.neuroimage.2020.116584] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2019] [Revised: 12/23/2019] [Accepted: 01/23/2020] [Indexed: 12/13/2022] Open
Abstract
Diffusion Magnetic Resonance Imaging (dMRI) has shown great potential in probing tissue microstructure and structural connectivity in the brain but is often limited by the lengthy scan time needed to sample the diffusion profile by acquiring multiple diffusion weighted images (DWIs). Although parallel imaging technique has improved the speed of dMRI acquisition, attaining high resolution three dimensional (3D) dMRI on preclinical MRI systems remained still time consuming. In this paper, kernel principal component analysis, a machine learning approach, was employed to estimate the correlation among DWIs. We demonstrated the feasibility of such correlation estimation from low-resolution training DWIs and used the correlation as a constraint to reconstruct high-resolution DWIs from highly under-sampled k-space data, which significantly reduced the scan time. Using full k-space 3D dMRI data of post-mortem mouse brains, we retrospectively compared the performance of the so-called kernel low rank (KLR) method with a conventional compressed sensing (CS) method in terms of image quality and ability to resolve complex fiber orientations and connectivity. The results demonstrated that the KLR-CS method outperformed the conventional CS method for acceleration factors up to 8 and was likely to enhance our ability to investigate brain microstructure and connectivity using high-resolution 3D dMRI.
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Affiliation(s)
- Chaoyi Zhang
- Electrical Engineering, University at Buffalo, State University of New York, Buffalo, NY, United States
| | - Tanzil Mahmud Arefin
- Radiology, New York University School of Medicine, New York City, NY, United States
| | - Ukash Nakarmi
- Radiology, Stanford University, Stanford, CA, United States
| | - Choong Heon Lee
- Radiology, New York University School of Medicine, New York City, NY, United States
| | - Hongyu Li
- Electrical Engineering, University at Buffalo, State University of New York, Buffalo, NY, United States
| | - Dong Liang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, People's Republic of China
| | - Jiangyang Zhang
- Radiology, New York University School of Medicine, New York City, NY, United States
| | - Leslie Ying
- Electrical Engineering, University at Buffalo, State University of New York, Buffalo, NY, United States; Biomedical Engineering, University at Buffalo, State University at New York, Buffalo, NY, United States.
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36
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Wang N, Mirando AJ, Cofer G, Qi Y, Hilton MJ, Johnson GA. Characterization complex collagen fiber architecture in knee joint using high-resolution diffusion imaging. Magn Reson Med 2020; 84:908-919. [PMID: 31962373 DOI: 10.1002/mrm.28181] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Revised: 01/02/2020] [Accepted: 01/03/2020] [Indexed: 12/26/2022]
Abstract
PURPOSE To evaluate the complex fiber orientations and 3D collagen fiber network of knee joint connective tissues, including ligaments, muscle, articular cartilage, and meniscus using high spatial and angular resolution diffusion imaging. METHODS Two rat knee joints were scanned using a modified 3D diffusion-weighted spin echo pulse sequence with the isotropic spatial resolution of 45 μm at 9.4T. The b values varied from 250 to 1250 s/mm2 with 31 diffusion encoding directions for 1 rat knee. The b value was fixed to 1000 s/mm2 with 147 diffusion encoding directions for the second knee. Both the diffusion tensor imaging (DTI) model and generalized Q-sampling imaging (GQI) method were used to investigate the fiber orientation distributions and tractography with the validation of polarized light microscopy. RESULTS To better resolve the crossing fibers, the b value should be great than or equal to 1000 s/mm2 . The tractography results were comparable between the DTI model and GQI method in ligament and muscle. However, the tractography exhibited apparent difference between DTI and GQI in connective tissues with more complex collagen fibers network, such as cartilage and meniscus. In articular cartilage, there were numerous crossing fibers found in superficial zone and transitional zone. Tractography generated with GQI also resulted in more intact tracts in articular cartilage than DTI. CONCLUSION High-resolution diffusion imaging with GQI method can trace the complex collagen fiber orientations and architectures of the knee joint at microscopic resolution.
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Affiliation(s)
- Nian Wang
- Center for In Vivo Microscopy, Duke University School of Medicine, Durham, North Carolina.,Department of Radiology, Duke University School of Medicine, Durham, North Carolina
| | - Anthony J Mirando
- Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, North Carolina
| | - Gary Cofer
- Center for In Vivo Microscopy, Duke University School of Medicine, Durham, North Carolina
| | - Yi Qi
- Center for In Vivo Microscopy, Duke University School of Medicine, Durham, North Carolina
| | - Matthew J Hilton
- Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, North Carolina.,Department of Cell Biology, Duke University School of Medicine, Durham, North Carolina
| | - G Allan Johnson
- Center for In Vivo Microscopy, Duke University School of Medicine, Durham, North Carolina.,Department of Radiology, Duke University School of Medicine, Durham, North Carolina
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Badea A, Wu W, Shuff J, Wang M, Anderson RJ, Qi Y, Johnson GA, Wilson JG, Koudoro S, Garyfallidis E, Colton CA, Dunson DB. Identifying Vulnerable Brain Networks in Mouse Models of Genetic Risk Factors for Late Onset Alzheimer's Disease. Front Neuroinform 2019; 13:72. [PMID: 31920610 PMCID: PMC6914731 DOI: 10.3389/fninf.2019.00072] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 11/13/2019] [Indexed: 12/18/2022] Open
Abstract
The major genetic risk for late onset Alzheimer’s disease has been associated with the presence of APOE4 alleles. However, the impact of different APOE alleles on the brain aging trajectory, and how they interact with the brain local environment in a sex specific manner is not entirely clear. We sought to identify vulnerable brain circuits in novel mouse models with homozygous targeted replacement of the mouse ApoE gene with either human APOE3 or APOE4 gene alleles. These genes are expressed in mice that also model the human immune response to age and disease-associated challenges by expressing the human NOS2 gene in place of the mouse mNos2 gene. These mice had impaired learning and memory when assessed with the Morris water maze (MWM) and novel object recognition (NOR) tests. Ex vivo MRI-DTI analyses revealed global and local atrophy, and areas of reduced fractional anisotropy (FA). Using tensor network principal component analyses for structural connectomes, we inferred the pairwise connections which best separate APOE4 from APOE3 carriers. These involved primarily interhemispheric connections among regions of olfactory areas, the hippocampus, and the cerebellum. Our results also suggest that pairwise connections may be subdivided and clustered spatially to reveal local changes on a finer scale. These analyses revealed not just genotype, but also sex specific differences. Identifying vulnerable networks may provide targets for interventions, and a means to stratify patients.
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Affiliation(s)
- Alexandra Badea
- Department of Radiology, Duke University, Durham, NC, United States.,Department of Neurology, Duke University School of Medicine, Durham, NC, United States.,Brain Imaging and Analysis Center, Duke University, Durham, NC, United States
| | - Wenlin Wu
- Pratt School of Engineering, Duke University, Durham, NC, United States
| | - Jordan Shuff
- Department of Biomedical Engineering, University of Delaware, Newark, NJ, United States
| | - Michele Wang
- Department of Psychology and Neuroscience, Trinity College of Arts & Sciences, Duke University, Durham, NC, United States
| | | | - Yi Qi
- Department of Radiology, Duke University, Durham, NC, United States
| | - G Allan Johnson
- Department of Radiology, Duke University, Durham, NC, United States
| | - Joan G Wilson
- Department of Neurology, Duke University School of Medicine, Durham, NC, United States
| | - Serge Koudoro
- School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, United States
| | - Eleftherios Garyfallidis
- School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, United States
| | - Carol A Colton
- Department of Neurology, Duke University School of Medicine, Durham, NC, United States
| | - David B Dunson
- Department of Statistical Science, Trinity College of Arts & Sciences, Duke University, Durham, NC, United States
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Wang N, Zhuang J, Wie H, Dibb R, Qi Y, Liu C. Probing demyelination and remyelination of the cuprizone mouse model using multimodality MRI. J Magn Reson Imaging 2019; 50:1852-1865. [PMID: 31012202 PMCID: PMC6810724 DOI: 10.1002/jmri.26758] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2018] [Revised: 04/07/2019] [Accepted: 04/08/2019] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Various studies by MRI exhibit that the corpus callosum (CC) is the most vulnerable to cuprizone administration, detecting the demyelination and remyelination process using different MRI parameters are, however, lacking. PURPOSE To investigate the sensitivity of multiparametric MRI both in vivo and ex vivo for demyelination and remyelination. STUDY TYPE Prospective. ANIMAL MODEL A cuprizone mice model with an age-matched control group (n = 5), 4-week cuprizone exposure group followed by 9-week on a normal diet (n = 6), and a 13-week cuprizone exposure group (n = 6). FIELD STRENGTH/SEQUENCE 3D gradient recalled echo, T2 -weighted, and diffusion tensor imaging (DTI) at 7.0T and 9.4T. ASSESSMENT Quantification of DTI metrics, quantitative susceptibility mapping (QSM), and T2 -weighted imaging intensity in major white matter bundles. STATISTICAL TESTS Nonparametric permutation tests were used with a cluster-forming threshold as 3.09 (equivalent to P = 0.001), and the significant level as P = 0.05 with family-wise correction. RESULTS In vivo susceptibility values increased from -11.7 to -0.7 ppb (P < 0.001) in CC and from -13.7 to -5.1 ppb (P < 0.001) in the anterior commissure (AC) after the 13-week cuprizone exposure. Ex vivo susceptibility values increased from -25.4 to 7.4 ppb (P < 0.001) in CC and from -41.6 to -15.8 ppb (P < 0.001) in AC. Susceptibility values showed high variations to demyelination for in vivo studies (94.0% in CC, 62.8% in AC). Susceptibility values exhibited higher variations than radial diffusivity for ex vivo studies (129.1% vs. 28.3% in CC, 62.0% vs. 25.0% in AC). In addition to the differential susceptibility variations in different white matter tracts, intraregional demyelination variation was also present not only in CC but also in the AC area by voxel-based analysis. DATA CONCLUSION QSM is sensitive to the demyelination process of cuprizone exposure, which can be a complementary technique to conventional T2 -weighted images and DTI metrics. LEVEL OF EVIDENCE 2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:1852-1865.
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Affiliation(s)
- Nian Wang
- Center for In Vivo Microscopy, Duke University, Durham, North Carolina, USA
- Brain Imaging and Analysis Center, Duke University, Durham, North Carolina, USA
| | - Jie Zhuang
- Brain Imaging and Analysis Center, Duke University, Durham, North Carolina, USA
| | - Hongjiang Wie
- Brain Imaging and Analysis Center, Duke University, Durham, North Carolina, USA
| | - Russell Dibb
- Center for In Vivo Microscopy, Duke University, Durham, North Carolina, USA
| | - Yi Qi
- Center for In Vivo Microscopy, Duke University, Durham, North Carolina, USA
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
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Sitek KR, Gulban OF, Calabrese E, Johnson GA, Lage-Castellanos A, Moerel M, Ghosh SS, De Martino F. Mapping the human subcortical auditory system using histology, postmortem MRI and in vivo MRI at 7T. eLife 2019; 8:e48932. [PMID: 31368891 PMCID: PMC6707786 DOI: 10.7554/elife.48932] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Accepted: 07/28/2019] [Indexed: 11/13/2022] Open
Abstract
Studying the human subcortical auditory system non-invasively is challenging due to its small, densely packed structures deep within the brain. Additionally, the elaborate three-dimensional (3-D) structure of the system can be difficult to understand based on currently available 2-D schematics and animal models. Wfe addressed these issues using a combination of histological data, post mortem magnetic resonance imaging (MRI), and in vivo MRI at 7 Tesla. We created anatomical atlases based on state-of-the-art human histology (BigBrain) and postmortem MRI (50 µm). We measured functional MRI (fMRI) responses to natural sounds and demonstrate that the functional localization of subcortical structures is reliable within individual participants who were scanned in two different experiments. Further, a group functional atlas derived from the functional data locates these structures with a median distance below 2 mm. Using diffusion MRI tractography, we revealed structural connectivity maps of the human subcortical auditory pathway both in vivo (1050 µm isotropic resolution) and post mortem (200 µm isotropic resolution). This work captures current MRI capabilities for investigating the human subcortical auditory system, describes challenges that remain, and contributes novel, openly available data, atlases, and tools for researching the human auditory system.
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Affiliation(s)
- Kevin R Sitek
- Massachusetts Institute of TechnologyCambridgeUnited States
- Harvard UniversityCambridgeUnited States
| | - Omer Faruk Gulban
- Department of Cognitive Neuroscience, Faculty of Psychology and NeuroscienceMaastricht UniversityMaastrichtNetherlands
| | | | | | - Agustin Lage-Castellanos
- Department of Cognitive Neuroscience, Faculty of Psychology and NeuroscienceMaastricht UniversityMaastrichtNetherlands
| | - Michelle Moerel
- Department of Cognitive Neuroscience, Faculty of Psychology and NeuroscienceMaastricht UniversityMaastrichtNetherlands
- Maastricht Centre for Systems Biology, Faculty of Science and EngineeringMaastricht UniversityMaastrichtNetherlands
| | - Satrajit S Ghosh
- Massachusetts Institute of TechnologyCambridgeUnited States
- Harvard UniversityCambridgeUnited States
| | - Federico De Martino
- Department of Cognitive Neuroscience, Faculty of Psychology and NeuroscienceMaastricht UniversityMaastrichtNetherlands
- Center for Magnetic Resonance ResearchUniversity of MinnesotaMinneapolisUnited States
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40
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Badea A, Ng KL, Anderson RJ, Zhang J, Miller MI, O’Brien RJ. Magnetic resonance imaging of mouse brain networks plasticity following motor learning. PLoS One 2019; 14:e0216596. [PMID: 31067263 PMCID: PMC6505950 DOI: 10.1371/journal.pone.0216596] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Accepted: 04/24/2019] [Indexed: 12/12/2022] Open
Abstract
We do not have a full understanding of the mechanisms underlying plasticity in the human brain. Mouse models have well controlled environments and genetics, and provide tools to help dissect the mechanisms underlying the observed responses to therapies devised for humans recovering from injury of ischemic nature or trauma. We aimed to detect plasticity following learning of a unilateral reaching movement, and relied on MRI performed with a rapid structural protocol suitable for in vivo brain imaging, and a longer diffusion tensor imaging (DTI) protocol executed ex vivo. In vivo MRI detected contralateral volume increases in trained animals (reachers), in circuits involved in motor control, sensory processing, and importantly, learning and memory. The temporal association area, parafascicular and mediodorsal thalamic nuclei were also enlarged. In vivo MRI allowed us to detect longitudinal effects over the ~25 days training period. The interaction between time and group (trained versus not trained) supported a role for the contralateral, but also the ipsilateral hemisphere. While ex vivo imaging was affected by shrinkage due to the fixation, it allowed for superior resolution and improved contrast to noise ratios, especially for subcortical structures. We examined microstructural changes based on DTI, and identified increased fractional anisotropy and decreased apparent diffusion coefficient, predominantly in the cerebellum and its connections. Cortical thickness differences did not survive multiple corrections, but uncorrected statistics supported the contralateral effects seen with voxel based volumetric analysis, showing thickening in the somatosensory, motor and visual cortices. In vivo and ex vivo analyses identified plasticity in circuits relevant to selecting actions in a sensory-motor context, through exploitation of learned association and decision making. By mapping a connectivity atlas into our ex vivo template we revealed that changes due to skilled motor learning occurred in a network of 35 regions, including the primary and secondary motor (M1, M2) and sensory cortices (S1, S2), the caudate putamen (CPu), visual (V1) and temporal association cortex. The significant clusters intersected tractography based networks seeded in M1, M2, S1, V1 and CPu at levels > 80%. We found that 89% of the significant cluster belonged to a network seeded in the contralateral M1, and 85% to one seeded in the contralateral M2. Moreover, 40% of the M1 and S1 cluster by network intersections were in the top 80th percentile of the tract densities for their respective networks. Our investigation may be relevant to studies of rehabilitation and recovery, and points to widespread network changes that accompany motor learning that may have potential applications to designing recovery strategies following brain injury.
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Affiliation(s)
- Alexandra Badea
- Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, NC, United States of America
- Department of Neurology, Duke University Medical Center, Durham, NC, United States of America
- Brain Imaging and Analysis Center, Duke University, Durham, NC, United States of America
- * E-mail:
| | - Kwan L. Ng
- Department of Neurology, UC Davis School of Medicine, Davis, CA, United States of America
| | - Robert J. Anderson
- Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, NC, United States of America
| | - Jiangyang Zhang
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, United States of America
| | - Michael I. Miller
- Center for Imaging Science, Johns Hopkins University; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States of America
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - Richard J. O’Brien
- Department of Neurology, Duke University Medical Center, Durham, NC, United States of America
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Wang N, Zhang J, Cofer G, Qi Y, Anderson RJ, White LE, Allan Johnson G. Neurite orientation dispersion and density imaging of mouse brain microstructure. Brain Struct Funct 2019; 224:1797-1813. [PMID: 31006072 DOI: 10.1007/s00429-019-01877-x] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Accepted: 04/11/2019] [Indexed: 12/14/2022]
Abstract
Advanced biophysical models like neurite orientation dispersion and density imaging (NODDI) have been developed to estimate the microstructural complexity of voxels enriched in dendrites and axons for both in vivo and ex vivo studies. NODDI metrics derived from high spatial and angular resolution diffusion MRI using the fixed mouse brain as a reference template have not yet been reported due in part to the extremely long scan time required. In this study, we modified the three-dimensional diffusion-weighted spin-echo pulse sequence for multi-shell and undersampling acquisition to reduce the scan time. This allowed us to acquire several exhaustive datasets that would otherwise not be attainable. NODDI metrics were derived from a complex 8-shell diffusion (1000-8000 s/mm2) dataset with 384 diffusion gradient-encoding directions at 50 µm isotropic resolution. These provided a foundation for exploration of tradeoffs among acquisition parameters. A three-shell acquisition strategy covering low, medium, and high b values with at least angular resolution of 64 is essential for ex vivo NODDI experiments. The good agreement between neurite density index (NDI) and the orientation dispersion index (ODI) with the subsequent histochemical analysis of myelin and neuronal density highlights that NODDI could provide new insight into the microstructure of the brain. Furthermore, we found that NDI is sensitive to microstructural variations in the corpus callosum using a well-established demyelination cuprizone model. The study lays the ground work for developing protocols for routine use of high-resolution NODDI method in characterizing brain microstructure in mouse models.
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Affiliation(s)
- Nian Wang
- Center for In Vivo Microscopy, Department of Radiology, Duke Medical Center, Duke University, 3302, Durham, NC, 27710, USA.
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA.
| | - Jieying Zhang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Gary Cofer
- Center for In Vivo Microscopy, Department of Radiology, Duke Medical Center, Duke University, 3302, Durham, NC, 27710, USA
| | - Yi Qi
- Center for In Vivo Microscopy, Department of Radiology, Duke Medical Center, Duke University, 3302, Durham, NC, 27710, USA
| | - Robert J Anderson
- Center for In Vivo Microscopy, Department of Radiology, Duke Medical Center, Duke University, 3302, Durham, NC, 27710, USA
| | - Leonard E White
- Department of Neurology, Duke University School of Medicine, Durham, NC, USA
| | - G Allan Johnson
- Center for In Vivo Microscopy, Department of Radiology, Duke Medical Center, Duke University, 3302, Durham, NC, 27710, USA.
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA.
- Department of Biomedical Engineering, Duke University, Durham, NC, USA.
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