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Arefin TM, Lee CH, Liang Z, Rallapalli H, Wadghiri YZ, Turnbull DH, Zhang J. Towards reliable reconstruction of the mouse brain corticothalamic connectivity using diffusion MRI. Neuroimage 2023; 273:120111. [PMID: 37060936 PMCID: PMC10149621 DOI: 10.1016/j.neuroimage.2023.120111] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 03/29/2023] [Accepted: 04/12/2023] [Indexed: 04/17/2023] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography has yielded intriguing insights into brain circuits and their relationship to behavior in response to gene mutations or neurological diseases across a number of species. Still, existing tractography approaches suffer from limited sensitivity and specificity, leading to uncertain interpretation of the reconstructed connections. Hence, in this study, we aimed to optimize the imaging and computational pipeline to achieve the best possible spatial overlaps between the tractography and tracer-based axonal projection maps within the mouse brain corticothalamic network. We developed a dMRI-based atlas of the mouse forebrain with structural labels imported from the Allen Mouse Brain Atlas (AMBA). Using the atlas and dMRI tractography, we first reconstructed detailed node-to-node mouse brain corticothalamic structural connectivity matrices using different imaging and tractography parameters. We then investigated the effects of each condition for accurate reconstruction of the corticothalamic projections by quantifying the similarities between the tractography and the tracer data from the Allen Mouse Brain Connectivity Atlas (AMBCA). Our results suggest that these parameters significantly affect tractography outcomes and our atlas can be used to investigate macroscopic structural connectivity in the mouse brain. Furthermore, tractography in mouse brain gray matter still face challenges and need improved imaging and tractography methods.
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Affiliation(s)
- Tanzil Mahmud Arefin
- Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States; Center for Neurotechnology in Mental Health Research, Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, United States
| | - Choong Heon Lee
- Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States
| | - Zifei Liang
- Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States
| | - Harikrishna Rallapalli
- Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States
| | - Youssef Z Wadghiri
- Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States
| | - Daniel H Turnbull
- Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States
| | - Jiangyang Zhang
- Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States.
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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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Faskowitz J, Puxeddu MG, van den Heuvel MP, Mišić B, Yovel Y, Assaf Y, Betzel RF, Sporns O. Connectome topology of mammalian brains and its relationship to taxonomy and phylogeny. Front Neurosci 2023; 16:1044372. [PMID: 36711139 PMCID: PMC9874302 DOI: 10.3389/fnins.2022.1044372] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 12/12/2022] [Indexed: 01/12/2023] Open
Abstract
Network models of anatomical connections allow for the extraction of quantitative features describing brain organization, and their comparison across brains from different species. Such comparisons can inform our understanding of between-species differences in brain architecture and can be compared to existing taxonomies and phylogenies. Here we performed a quantitative comparative analysis using the MaMI database (Tel Aviv University), a collection of brain networks reconstructed from ex vivo diffusion MRI spanning 125 species and 12 taxonomic orders or superorders. We used a broad range of metrics to measure between-mammal distances and compare these estimates to the separation of species as derived from taxonomy and phylogeny. We found that within-taxonomy order network distances are significantly closer than between-taxonomy network distances, and this relation holds for several measures of network distance. Furthermore, to estimate the evolutionary divergence between species, we obtained phylogenetic distances across 10,000 plausible phylogenetic trees. The anatomical network distances were rank-correlated with phylogenetic distances 10,000 times, creating a distribution of coefficients that demonstrate significantly positive correlations between network and phylogenetic distances. Collectively, these analyses demonstrate species-level organization across scales and informational sources: we relate brain networks distances, derived from MRI, with evolutionary distances, derived from genotyping data.
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Affiliation(s)
- Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, United States
| | - Maria Grazia Puxeddu
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, United States
| | - Martijn P. van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Bratislav Mišić
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Yossi Yovel
- School of Neurobiology, Biochemistry and Biophysics, Tel Aviv University, Tel Aviv-Yafo, Israel
| | - Yaniv Assaf
- School of Neurobiology, Biochemistry and Biophysics, Tel Aviv University, Tel Aviv-Yafo, Israel
| | - Richard F. Betzel
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, United States
- Program in Neuroscience, Indiana University Bloomington, Bloomington, IN, United States
- Program in Cognitive Science, Indiana University Bloomington, Bloomington, IN, United States
- Indiana University Network Science Institute, Indiana University Bloomington, Bloomington, IN, United States
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, United States
- Program in Neuroscience, Indiana University Bloomington, Bloomington, IN, United States
- Program in Cognitive Science, Indiana University Bloomington, Bloomington, IN, United States
- Indiana University Network Science Institute, Indiana University Bloomington, Bloomington, IN, United States
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Im SJ, Suh JY, Shim JH, Baek HM. Deterministic Tractography Analysis of Rat Brain Using SIGMA Atlas in 9.4T MRI. Brain Sci 2021; 11:brainsci11121656. [PMID: 34942958 PMCID: PMC8699268 DOI: 10.3390/brainsci11121656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 12/14/2021] [Accepted: 12/16/2021] [Indexed: 11/28/2022] Open
Abstract
Preclinical studies using rodents have been the choice for many neuroscience researchers due totheir close reflection of human biology. In particular, research involving rodents has utilized MRI to accurately identify brain regions and characteristics by acquiring high resolution cavity images with different contrasts non-invasively, and this has resulted in high reproducibility and throughput. In addition, tractographic analysis using diffusion tensor imaging to obtain information on the neural structure of white matter has emerged as a major methodology in the field of neuroscience due to its contribution in discovering significant correlations between altered neural connections and various neurological and psychiatric diseases. However, unlike image analysis studies with human subjects where a myriad of human image analysis programs and procedures have been thoroughly developed and validated, methods for analyzing rat image data using MRI in preclinical research settings have seen significantly less developed. Therefore, in this study, we present a deterministic tractographic analysis pipeline using the SIGMA atlas for a detailed structural segmentation and structural connectivity analysis of the rat brain’s structural connectivity. In addition, the structural connectivity analysis pipeline presented in this study was preliminarily tested on normal and stroke rat models for initial observation.
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Affiliation(s)
- Sang-Jin Im
- Department of Core Facility for Cell to In-Vivo Imaging, Lee Gil Ya Cancer and Diabetes Institute, Gachon University, Incheon 21999, Korea; (S.-J.I.); (J.-Y.S.)
| | - Ji-Yeon Suh
- Department of Core Facility for Cell to In-Vivo Imaging, Lee Gil Ya Cancer and Diabetes Institute, Gachon University, Incheon 21999, Korea; (S.-J.I.); (J.-Y.S.)
| | - Jae-Hyuk Shim
- Department of BioMedical Science, Lee Gil Ya Cancer and Diabetes Institute, Gachon University, Incheon 21999, Korea;
| | - Hyeon-Man Baek
- Department of Core Facility for Cell to In-Vivo Imaging, Lee Gil Ya Cancer and Diabetes Institute, Gachon University, Incheon 21999, Korea; (S.-J.I.); (J.-Y.S.)
- Department of Molecular Medicine, Lee Gil Ya Cancer and Diabetes Institute, Gachon University, Incheon 21999, Korea
- Correspondence: ; Tel.: +82-32-899-6678
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