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Otomo K, Omura T, Nozawa Y, Edwards SJ, Sato Y, Saito Y, Yagishita S, Uchida H, Watakabe Y, Naitou K, Yanai R, Sahara N, Takagi S, Katayama R, Iwata Y, Shiokawa T, Hayakawa Y, Otsuka K, Watanabe-Takano H, Haneda Y, Fukuhara S, Fujiwara M, Nii T, Meno C, Takeshita N, Yashiro K, Rosales Rocabado JM, Kaku M, Yamada T, Oishi Y, Koike H, Cheng Y, Sekine K, Koga JI, Sugiyama K, Kimura K, Karube F, Kim H, Manabe I, Nemoto T, Tainaka K, Hamada A, Brismar H, Susaki EA. descSPIM: an affordable and easy-to-build light-sheet microscope optimized for tissue clearing techniques. Nat Commun 2024; 15:4941. [PMID: 38866781 PMCID: PMC11169475 DOI: 10.1038/s41467-024-49131-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 05/24/2024] [Indexed: 06/14/2024] Open
Abstract
Despite widespread adoption of tissue clearing techniques in recent years, poor access to suitable light-sheet fluorescence microscopes remains a major obstacle for biomedical end-users. Here, we present descSPIM (desktop-equipped SPIM for cleared specimens), a low-cost ($20,000-50,000), low-expertise (one-day installation by a non-expert), yet practical do-it-yourself light-sheet microscope as a solution for this bottleneck. Even the most fundamental configuration of descSPIM enables multi-color imaging of whole mouse brains and a cancer cell line-derived xenograft tumor mass for the visualization of neurocircuitry, assessment of drug distribution, and pathological examination by false-colored hematoxylin and eosin staining in a three-dimensional manner. Academically open-sourced ( https://github.com/dbsb-juntendo/descSPIM ), descSPIM allows routine three-dimensional imaging of cleared samples in minutes. Thus, the dissemination of descSPIM will accelerate biomedical discoveries driven by tissue clearing technologies.
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Affiliation(s)
- Kohei Otomo
- Department of Biochemistry and Systems Biomedicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Biochemistry II, Juntendo University School of Medicine, Tokyo, Japan
- Nakatani Biomedical Spatialomics Hub, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Division of Biophotonics, National Institute for Physiological Sciences, National Institutes of Natural Sciences, Okazaki, Japan
- Biophotonics Research Group, Exploratory Research Center on Life and Living Systems, National Institutes of Natural Sciences, Okazaki, Japan
| | - Takaki Omura
- Department of Biochemistry and Systems Biomedicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Nakatani Biomedical Spatialomics Hub, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Neurosurgery, University of Tokyo, Tokyo, Japan
- Department of Neurosurgery and Neuro-Oncology, National Cancer Center Hospital, Tokyo, Japan
| | - Yuki Nozawa
- Biochemistry II, Juntendo University School of Medicine, Tokyo, Japan
| | - Steven J Edwards
- Science for Life Laboratory, Department of Applied Physics, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Yukihiko Sato
- Department of Biochemistry and Systems Biomedicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Nakatani Biomedical Spatialomics Hub, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yuri Saito
- Department of Biochemistry and Systems Biomedicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Nakatani Biomedical Spatialomics Hub, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Shigehiro Yagishita
- Department of Pharmacology and Therapeutics, Fundamental Innovative Oncology Core, National Cancer Center Research Institute, Tokyo, Japan
- Division of Molecular Pharmacology, National Cancer Center Research Institute, Tokyo, Japan
| | - Hitoshi Uchida
- Department of System Pathology for Neurological Disorders, Brain Research Institute, Niigata University, Niigata, Japan
| | - Yuki Watakabe
- Division of Biophotonics, National Institute for Physiological Sciences, National Institutes of Natural Sciences, Okazaki, Japan
- Biophotonics Research Group, Exploratory Research Center on Life and Living Systems, National Institutes of Natural Sciences, Okazaki, Japan
| | - Kiyotada Naitou
- Department of Basic Veterinary Science, Joint Faculty of Veterinary Medicine, Kagoshima University, Kagoshima, Japan
| | - Rin Yanai
- Advanced Neuroimaging Center, Institute for Quantum Medical Sciences, National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Naruhiko Sahara
- Advanced Neuroimaging Center, Institute for Quantum Medical Sciences, National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Satoshi Takagi
- Division of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Ryohei Katayama
- Division of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Yusuke Iwata
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Toshiro Shiokawa
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yoku Hayakawa
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kensuke Otsuka
- Biology and Environmental Chemistry Division, Sustainable System Research Laboratory, Central Research Institute of Electric Power Industry, Chiba, Japan
| | - Haruko Watanabe-Takano
- Department of Molecular Pathophysiology, Institute for Advanced Medical Sciences, Nippon Medical School, Tokyo, Japan
| | - Yuka Haneda
- Department of Molecular Pathophysiology, Institute for Advanced Medical Sciences, Nippon Medical School, Tokyo, Japan
| | - Shigetomo Fukuhara
- Department of Molecular Pathophysiology, Institute for Advanced Medical Sciences, Nippon Medical School, Tokyo, Japan
| | - Miku Fujiwara
- Department of Developmental Biology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Takenobu Nii
- Department of Developmental Biology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Chikara Meno
- Department of Developmental Biology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Naoki Takeshita
- Anatomy and Developmental Biology, Kyoto Prefectural University of Medicine, Kyoto, Japan
- Department of Pediatrics, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Kenta Yashiro
- Anatomy and Developmental Biology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Juan Marcelo Rosales Rocabado
- Division of Bio-prosthodontics, Faculty of Dentistry & Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan
| | - Masaru Kaku
- Division of Bio-prosthodontics, Faculty of Dentistry & Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan
| | - Tatsuya Yamada
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, USA
| | - Yumiko Oishi
- Department of Meidical Biochemistry, Tokyo Medical and Dental University, Tokyo, Japan
| | - Hiroyuki Koike
- Department of Meidical Biochemistry, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yinglan Cheng
- Department of Meidical Biochemistry, Tokyo Medical and Dental University, Tokyo, Japan
| | - Keisuke Sekine
- Laboratory of Cancer Cell Systems, National Cancer Center Research Institute, Tokyo, Japan
| | - Jun-Ichiro Koga
- The Second Department of Internal Medicine, University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Kaori Sugiyama
- Institute for Advanced Research of Biosystem Dynamics, Research Institute for Science and Engineering, Waseda University, Tokyo, Japan
| | - Kenichi Kimura
- Life Science Center for Survival Dynamics, Tsukuba Advanced Research Alliance (TARA), University of Tsukuba, Tsukuba, Japan
| | - Fuyuki Karube
- Lab of Histology and Cytology, Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Hyeree Kim
- Department of Systems Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Ichiro Manabe
- Department of Systems Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Tomomi Nemoto
- Division of Biophotonics, National Institute for Physiological Sciences, National Institutes of Natural Sciences, Okazaki, Japan
- Biophotonics Research Group, Exploratory Research Center on Life and Living Systems, National Institutes of Natural Sciences, Okazaki, Japan
| | - Kazuki Tainaka
- Department of System Pathology for Neurological Disorders, Brain Research Institute, Niigata University, Niigata, Japan
| | - Akinobu Hamada
- Department of Pharmacology and Therapeutics, Fundamental Innovative Oncology Core, National Cancer Center Research Institute, Tokyo, Japan
- Division of Molecular Pharmacology, National Cancer Center Research Institute, Tokyo, Japan
| | - Hjalmar Brismar
- Science for Life Laboratory, Department of Applied Physics, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Etsuo A Susaki
- Department of Biochemistry and Systems Biomedicine, Juntendo University Graduate School of Medicine, Tokyo, Japan.
- Biochemistry II, Juntendo University School of Medicine, Tokyo, Japan.
- Nakatani Biomedical Spatialomics Hub, Juntendo University Graduate School of Medicine, Tokyo, Japan.
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2
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Choi YK, Feng L, Jeong WK, Kim J. Connecto-informatics at the mesoscale: current advances in image processing and analysis for mapping the brain connectivity. Brain Inform 2024; 11:15. [PMID: 38833195 DOI: 10.1186/s40708-024-00228-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Accepted: 05/08/2024] [Indexed: 06/06/2024] Open
Abstract
Mapping neural connections within the brain has been a fundamental goal in neuroscience to understand better its functions and changes that follow aging and diseases. Developments in imaging technology, such as microscopy and labeling tools, have allowed researchers to visualize this connectivity through high-resolution brain-wide imaging. With this, image processing and analysis have become more crucial. However, despite the wealth of neural images generated, access to an integrated image processing and analysis pipeline to process these data is challenging due to scattered information on available tools and methods. To map the neural connections, registration to atlases and feature extraction through segmentation and signal detection are necessary. In this review, our goal is to provide an updated overview of recent advances in these image-processing methods, with a particular focus on fluorescent images of the mouse brain. Our goal is to outline a pathway toward an integrated image-processing pipeline tailored for connecto-informatics. An integrated workflow of these image processing will facilitate researchers' approach to mapping brain connectivity to better understand complex brain networks and their underlying brain functions. By highlighting the image-processing tools available for fluroscent imaging of the mouse brain, this review will contribute to a deeper grasp of connecto-informatics, paving the way for better comprehension of brain connectivity and its implications.
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Affiliation(s)
- Yoon Kyoung Choi
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, South Korea
- Department of Computer Science and Engineering, Korea University, Seoul, South Korea
| | | | - Won-Ki Jeong
- Department of Computer Science and Engineering, Korea University, Seoul, South Korea
| | - Jinhyun Kim
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, South Korea.
- Department of Computer Science and Engineering, Korea University, Seoul, South Korea.
- KIST-SKKU Brain Research Center, SKKU Institute for Convergence, Sungkyunkwan University, Suwon, South Korea.
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Bao LX, Luo ZM, Zhu XL, Xu YY. Automated identification of protein expression intensity and classification of protein cellular locations in mouse brain regions from immunofluorescence images. Med Biol Eng Comput 2024; 62:1105-1119. [PMID: 38150111 DOI: 10.1007/s11517-023-02985-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 11/28/2023] [Indexed: 12/28/2023]
Abstract
Knowledge of protein expression in mammalian brains at regional and cellular levels can facilitate understanding of protein functions and associated diseases. As the mouse brain is a typical mammalian brain considering cell type and structure, several studies have been conducted to analyze protein expression in mouse brains. However, labeling protein expression using biotechnology is costly and time-consuming. Therefore, automated models that can accurately recognize protein expression are needed. Here, we constructed machine learning models to automatically annotate the protein expression intensity and cellular location in different mouse brain regions from immunofluorescence images. The brain regions and sub-regions were segmented through learning image features using an autoencoder and then performing K-means clustering and registration to align with the anatomical references. The protein expression intensities for those segmented structures were computed on the basis of the statistics of the image pixels, and patch-based weakly supervised methods and multi-instance learning were used to classify the cellular locations. Results demonstrated that the models achieved high accuracy in the expression intensity estimation, and the F1 score of the cellular location prediction was 74.5%. This work established an automated pipeline for analyzing mouse brain images and provided a foundation for further study of protein expression and functions.
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Affiliation(s)
- Lin-Xia Bao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
- Guangdong Provincial Key Laboratory of Medical Imaging Processing, Southern Medical University, Guangzhou, 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510623, China
| | - Zhuo-Ming Luo
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
- Guangdong Provincial Key Laboratory of Medical Imaging Processing, Southern Medical University, Guangzhou, 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510623, China
| | - Xi-Liang Zhu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
- Guangdong Provincial Key Laboratory of Medical Imaging Processing, Southern Medical University, Guangzhou, 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510623, China
| | - Ying-Ying Xu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
- Guangdong Provincial Key Laboratory of Medical Imaging Processing, Southern Medical University, Guangzhou, 510515, China.
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510623, China.
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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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Ma X, Xing Y, Zhai R, Du Y, Yan H. Development and advancements in rodent MRI-based brain atlases. Heliyon 2024; 10:e27421. [PMID: 38510053 PMCID: PMC10950579 DOI: 10.1016/j.heliyon.2024.e27421] [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: 01/25/2023] [Revised: 02/15/2024] [Accepted: 02/28/2024] [Indexed: 03/22/2024] Open
Abstract
Rodents, particularly mice and rats, are extensively utilized in fundamental neuroscience research. Brain atlases have played a pivotal role in this field, evolving from traditional printed histology atlases to digital atlases incorporating diverse imaging datasets. Magnetic resonance imaging (MRI)-based brain atlases, also known as brain maps, have been employed in specific studies. However, the existence of numerous versions of MRI-based brain atlases has impeded their standardized application and widespread use, despite the consensus within the academic community regarding their significance in mice and rats. Furthermore, there is a dearth of comprehensive and systematic reviews on MRI-based brain atlases for rodents. This review aims to bridge this gap by providing a comprehensive overview of the advancements in MRI-based brain atlases for rodents, with a specific focus on mice and rats. It seeks to explore the advantages and disadvantages of histologically printed brain atlases in comparison to MRI brain atlases, delineate the standardized methods for creating MRI brain atlases, and summarize their primary applications in neuroscience research. Additionally, this review aims to assist researchers in selecting appropriate versions of MRI brain atlases for their studies or refining existing MRI brain atlas resources, thereby facilitating the development and widespread adoption of standardized MRI-based brain atlases in rodents.
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Affiliation(s)
- Xiaoyi Ma
- Department of Geriatrics, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Yao Xing
- School of Information Science and Technology, Fudan University, Shanghai, 200433, China
- Wuhan United Imaging Life Science Instrument Co., Ltd., Wuhan, 430071, China
| | - Renkuan Zhai
- Wuhan United Imaging Life Science Instrument Co., Ltd., Wuhan, 430071, China
| | - Yingying Du
- Wuhan United Imaging Life Science Instrument Co., Ltd., Wuhan, 430071, China
| | - Huanhuan Yan
- Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, 518048, China
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
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Willekens SMA, Morini F, Mediavilla T, Nilsson E, Orädd G, Hahn M, Chotiwan N, Visa M, Berggren PO, Ilegems E, Överby AK, Ahlgren U, Marcellino D. An MR-based brain template and atlas for optical projection tomography and light sheet fluorescence microscopy in neuroscience. Front Neurosci 2024; 18:1328815. [PMID: 38601090 PMCID: PMC11004350 DOI: 10.3389/fnins.2024.1328815] [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: 10/27/2023] [Accepted: 03/11/2024] [Indexed: 04/12/2024] Open
Abstract
Introduction Optical Projection Tomography (OPT) and light sheet fluorescence microscopy (LSFM) are high resolution optical imaging techniques, ideally suited for ex vivo 3D whole mouse brain imaging. Although they exhibit high specificity for their targets, the anatomical detail provided by tissue autofluorescence remains limited. Methods T1-weighted images were acquired from 19 BABB or DBE cleared brains to create an MR template using serial longitudinal registration. Afterwards, fluorescent OPT and LSFM images were coregistered/normalized to the MR template to create fusion images. Results Volumetric calculations revealed a significant difference between BABB and DBE cleared brains, leading to develop two optimized templates, with associated tissue priors and brain atlas, for BABB (OCUM) and DBE (iOCUM). By creating fusion images, we identified virus infected brain regions, mapped dopamine transporter and translocator protein expression, and traced innervation from the eye along the optic tract to the thalamus and superior colliculus using cholera toxin B. Fusion images allowed for precise anatomical identification of fluorescent signal in the detailed anatomical context provided by MR. Discussion The possibility to anatomically map fluorescent signals on magnetic resonance (MR) images, widely used in clinical and preclinical neuroscience, would greatly benefit applications of optical imaging of mouse brain. These specific MR templates for cleared brains enable a broad range of neuroscientific applications integrating 3D optical brain imaging.
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Affiliation(s)
- Stefanie M. A. Willekens
- Department of Clinical Microbiology, Umeå University, Umeå, Sweden
- The Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå University, Umeå, Sweden
- Department of Medical and Translational Biology, Umeå University, Umeå, Sweden
| | - Federico Morini
- Department of Medical and Translational Biology, Umeå University, Umeå, Sweden
| | - Tomas Mediavilla
- Department of Medical and Translational Biology, Umeå University, Umeå, Sweden
| | - Emma Nilsson
- Department of Clinical Microbiology, Umeå University, Umeå, Sweden
- The Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå University, Umeå, Sweden
| | - Greger Orädd
- Department of Medical and Translational Biology, Umeå University, Umeå, Sweden
| | - Max Hahn
- Department of Medical and Translational Biology, Umeå University, Umeå, Sweden
| | - Nunya Chotiwan
- Department of Clinical Microbiology, Umeå University, Umeå, Sweden
- The Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå University, Umeå, Sweden
| | - Montse Visa
- The Rolf Luft Research Centre for Diabetes and Endocrinology, Karolinska Institutet, Stockholm, Sweden
| | - Per-Olof Berggren
- The Rolf Luft Research Centre for Diabetes and Endocrinology, Karolinska Institutet, Stockholm, Sweden
| | - Erwin Ilegems
- The Rolf Luft Research Centre for Diabetes and Endocrinology, Karolinska Institutet, Stockholm, Sweden
| | - Anna K. Överby
- Department of Clinical Microbiology, Umeå University, Umeå, Sweden
- The Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå University, Umeå, Sweden
| | - Ulf Ahlgren
- Department of Medical and Translational Biology, Umeå University, Umeå, Sweden
| | - Daniel Marcellino
- Department of Medical and Translational Biology, Umeå University, Umeå, Sweden
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Koukalova L, Chmelova M, Amlerova Z, Vargova L. Out of the core: the impact of focal ischemia in regions beyond the penumbra. Front Cell Neurosci 2024; 18:1336886. [PMID: 38504666 PMCID: PMC10948541 DOI: 10.3389/fncel.2024.1336886] [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: 11/11/2023] [Accepted: 02/08/2024] [Indexed: 03/21/2024] Open
Abstract
The changes in the necrotic core and the penumbra following induction of focal ischemia have been the focus of attention for some time. However, evidence shows, that ischemic injury is not confined to the primarily affected structures and may influence the remote areas as well. Yet many studies fail to probe into the structures beyond the penumbra, and possibly do not even find any significant results due to their short-term design, as secondary damage occurs later. This slower reaction can be perceived as a therapeutic opportunity, in contrast to the ischemic core defined as irreversibly damaged tissue, where the window for salvation is comparatively short. The pathologies in remote structures occur relatively frequently and are clearly linked to the post-stroke neurological outcome. In order to develop efficient therapies, a deeper understanding of what exactly happens in the exo-focal regions is necessary. The mechanisms of glia contribution to the ischemic damage in core/penumbra are relatively well described and include impaired ion homeostasis, excessive cell swelling, glutamate excitotoxic mechanism, release of pro-inflammatory cytokines and phagocytosis or damage propagation via astrocytic syncytia. However, little is known about glia involvement in post-ischemic processes in remote areas. In this literature review, we discuss the definitions of the terms "ischemic core", "penumbra" and "remote areas." Furthermore, we present evidence showing the array of structural and functional changes in the more remote regions from the primary site of focal ischemia, with a special focus on glia and the extracellular matrix. The collected information is compared with the processes commonly occurring in the ischemic core or in the penumbra. Moreover, the possible causes of this phenomenon and the approaches for investigation are described, and finally, we evaluate the efficacy of therapies, which have been studied for their anti-ischemic effect in remote areas in recent years.
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Affiliation(s)
- Ludmila Koukalova
- Department of Neuroscience, Second Faculty of Medicine, Charles University, Prague, Czechia
| | - Martina Chmelova
- Department of Neuroscience, Second Faculty of Medicine, Charles University, Prague, Czechia
- Department of Cellular Neurophysiology, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czechia
| | - Zuzana Amlerova
- Department of Neuroscience, Second Faculty of Medicine, Charles University, Prague, Czechia
| | - Lydia Vargova
- Department of Neuroscience, Second Faculty of Medicine, Charles University, Prague, Czechia
- Department of Cellular Neurophysiology, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czechia
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Caznok Silveira AC, Antunes ASLM, Athié MCP, da Silva BF, Ribeiro dos Santos JV, Canateli C, Fontoura MA, Pinto A, Pimentel-Silva LR, Avansini SH, de Carvalho M. Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders. Front Neurosci 2024; 18:1340345. [PMID: 38445254 PMCID: PMC10912403 DOI: 10.3389/fnins.2024.1340345] [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: 11/17/2023] [Accepted: 01/29/2024] [Indexed: 03/07/2024] Open
Abstract
The study of brain connectivity has been a cornerstone in understanding the complexities of neurological and psychiatric disorders. It has provided invaluable insights into the functional architecture of the brain and how it is perturbed in disorders. However, a persistent challenge has been achieving the proper spatial resolution, and developing computational algorithms to address biological questions at the multi-cellular level, a scale often referred to as the mesoscale. Historically, neuroimaging studies of brain connectivity have predominantly focused on the macroscale, providing insights into inter-regional brain connections but often falling short of resolving the intricacies of neural circuitry at the cellular or mesoscale level. This limitation has hindered our ability to fully comprehend the underlying mechanisms of neurological and psychiatric disorders and to develop targeted interventions. In light of this issue, our review manuscript seeks to bridge this critical gap by delving into the domain of mesoscale neuroimaging. We aim to provide a comprehensive overview of conditions affected by aberrant neural connections, image acquisition techniques, feature extraction, and data analysis methods that are specifically tailored to the mesoscale. We further delineate the potential of brain connectivity research to elucidate complex biological questions, with a particular focus on schizophrenia and epilepsy. This review encompasses topics such as dendritic spine quantification, single neuron morphology, and brain region connectivity. We aim to showcase the applicability and significance of mesoscale neuroimaging techniques in the field of neuroscience, highlighting their potential for gaining insights into the complexities of neurological and psychiatric disorders.
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Affiliation(s)
- Ana Clara Caznok Silveira
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
- School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil
| | | | - Maria Carolina Pedro Athié
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | - Bárbara Filomena da Silva
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | | | - Camila Canateli
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | - Marina Alves Fontoura
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | - Allan Pinto
- Brazilian Synchrotron Light Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | | | - Simoni Helena Avansini
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | - Murilo de Carvalho
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
- Brazilian Synchrotron Light Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
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9
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Tang W, Li Z, Zou Y, Liao J, Li B. A multimodal pipeline for image correction and registration of mass spectrometry imaging with microscopy. Anal Chim Acta 2023; 1283:341969. [PMID: 37977791 DOI: 10.1016/j.aca.2023.341969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 10/12/2023] [Accepted: 10/26/2023] [Indexed: 11/19/2023]
Abstract
The integration of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) and histology plays a pivotal role in advancing our understanding of complex heterogeneous tissues, which provides a comprehensive description of biological tissue with both wide molecule coverage and high lateral resolution. Herein, we proposed a novel strategy for the correction and registration of MALDI MSI data with hematoxylin & eosin (H&E) staining images. To overcome the challenges of discrepancies in spatial resolution towards the unification of the two imaging modalities, a deep learning-based interpolation algorithm for MALDI MSI data was constructed, which enables spatial coherence and the following orientation matching between images. Coupled with the affine transformation (AT) and the subsequent moving least squares algorithm, the two types of images from one rat brain tissue section were aligned automatically with high accuracy. Moreover, we demonstrated the practicality of the developed pipeline by projecting it to a rat cerebral ischemia-reperfusion injury model, which would help decipher the link between molecular metabolism and pathological interpretation towards microregion. This new approach offers the chance for other types of bioimaging to boost the field of multimodal image fusion.
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Affiliation(s)
- Weiwei Tang
- State Key Laboratory of Natural Medicines and School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, 210009, China
| | - Zhen Li
- School of Science, China Pharmaceutical University, Nanjing, 211198, China
| | - Yuchen Zou
- State Key Laboratory of Natural Medicines and School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, 210009, China
| | - Jun Liao
- School of Science, China Pharmaceutical University, Nanjing, 211198, China.
| | - Bin Li
- State Key Laboratory of Natural Medicines and School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, 210009, China.
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10
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Li X, Zhang Y, Liu Y, Li P, Hu H, Wang L, He P, Dong Y, Zhang C. A high-throughput big-data orchestration and processing system for the High Energy Photon Source. JOURNAL OF SYNCHROTRON RADIATION 2023; 30:1086-1091. [PMID: 37729071 PMCID: PMC10624036 DOI: 10.1107/s1600577523006951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 08/06/2023] [Indexed: 09/22/2023]
Abstract
High-data-throughput and multimodal-acquisition experiments will prevail in next-generation synchrotron beamlines. Orchestrating dataflow pipelines connecting the data acquisition, processing, visualization and storage ends are becoming increasingly complex and essential for enhancing beamline performance. Mamba Data Worker (MDW) has been developed to address the data challenges for the forthcoming High Energy Photon Source (HEPS). It is an important component of the Mamba experimental control and data acquisition software ecosystem, which enables fast data acquisition and transmission, dynamic configuration of data processing pipelines, data multiplex in streaming, and customized data and metadata assembly. This paper presents the architecture and development plan of MDW, outlines the essential technologies involved, and illustrates its current application at the Beijing Synchrotron Radiation Facility (BSRF).
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Affiliation(s)
- Xiang Li
- Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
| | - Yi Zhang
- Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
| | - Yu Liu
- Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
| | - Pengcheng Li
- Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
| | - Hao Hu
- Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
| | - Liwen Wang
- Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
| | - Ping He
- Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
| | - Yuhui Dong
- Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
| | - Chenglong Zhang
- Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
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11
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Rijsketic DR, Casey AB, Barbosa DAN, Zhang X, Hietamies TM, Ramirez-Ovalle G, Pomrenze MB, Halpern CH, Williams LM, Malenka RC, Heifets BD. UNRAVELing the synergistic effects of psilocybin and environment on brain-wide immediate early gene expression in mice. Neuropsychopharmacology 2023; 48:1798-1807. [PMID: 37248402 PMCID: PMC10579391 DOI: 10.1038/s41386-023-01613-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 04/25/2023] [Accepted: 05/15/2023] [Indexed: 05/31/2023]
Abstract
The effects of context on the subjective experience of serotonergic psychedelics have not been fully examined in human neuroimaging studies, partly due to limitations of the imaging environment. Here, we administered saline or psilocybin to mice in their home cage or an enriched environment, immunofluorescently-labeled brain-wide c-Fos, and imaged iDISCO+ cleared tissue with light sheet fluorescence microscopy (LSFM) to examine the impact of environmental context on psilocybin-elicited neural activity at cellular resolution. Voxel-wise analysis of c-Fos-immunofluorescence revealed clusters of neural activity associated with main effects of context and psilocybin-treatment, which were validated with c-Fos+ cell density measurements. Psilocybin increased c-Fos expression in subregions of the neocortex, caudoputamen, central amygdala, and parasubthalamic nucleus while it decreased c-Fos in the hypothalamus, cortical amygdala, striatum, and pallidum in a predominantly context-independent manner. To gauge feasibility of future mechanistic studies on ensembles activated by psilocybin, we confirmed activity- and Cre-dependent genetic labeling in a subset of these neurons using TRAP2+/-;Ai14+ mice. Network analyses treating each psilocybin-sensitive cluster as a node indicated that psilocybin disrupted co-activity between highly correlated regions, reduced brain modularity, and dramatically attenuated intermodular co-activity. Overall, our results indicate that main effects of context and psilocybin were robust, widespread, and reorganized network architecture, whereas context×psilocybin interactions were surprisingly sparse.
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Affiliation(s)
- Daniel Ryskamp Rijsketic
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Austen B Casey
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Daniel A N Barbosa
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Xue Zhang
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94305, USA
| | - Tuuli M Hietamies
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Grecia Ramirez-Ovalle
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Matthew B Pomrenze
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94305, USA
- Nancy Pritzker Laboratory, Stanford University, Stanford, CA, 94305, USA
| | - Casey H Halpern
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94305, USA
- Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC) Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Robert C Malenka
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94305, USA
- Nancy Pritzker Laboratory, Stanford University, Stanford, CA, 94305, USA
| | - Boris D Heifets
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA.
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94305, USA.
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12
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Li Z, Shang Z, Liu J, Zhen H, Zhu E, Zhong S, Sturgess RN, Zhou Y, Hu X, Zhao X, Wu Y, Li P, Lin R, Ren J. D-LMBmap: a fully automated deep-learning pipeline for whole-brain profiling of neural circuitry. Nat Methods 2023; 20:1593-1604. [PMID: 37770711 PMCID: PMC10555838 DOI: 10.1038/s41592-023-01998-6] [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: 10/20/2022] [Accepted: 08/02/2023] [Indexed: 09/30/2023]
Abstract
Recent proliferation and integration of tissue-clearing methods and light-sheet fluorescence microscopy has created new opportunities to achieve mesoscale three-dimensional whole-brain connectivity mapping with exceptionally high throughput. With the rapid generation of large, high-quality imaging datasets, downstream analysis is becoming the major technical bottleneck for mesoscale connectomics. Current computational solutions are labor intensive with limited applications because of the exhaustive manual annotation and heavily customized training. Meanwhile, whole-brain data analysis always requires combining multiple packages and secondary development by users. To address these challenges, we developed D-LMBmap, an end-to-end package providing an integrated workflow containing three modules based on deep-learning algorithms for whole-brain connectivity mapping: axon segmentation, brain region segmentation and whole-brain registration. D-LMBmap does not require manual annotation for axon segmentation and achieves quantitative analysis of whole-brain projectome in a single workflow with superior accuracy for multiple cell types in all of the modalities tested.
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Affiliation(s)
- Zhongyu Li
- Division of Neurobiology, MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Zengyi Shang
- School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Jingyi Liu
- School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Haotian Zhen
- School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Entao Zhu
- School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Shilin Zhong
- National Institute of Biological Sciences (NIBS), Beijing, China
| | - Robyn N Sturgess
- Division of Neurobiology, MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Yitian Zhou
- School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Xuemeng Hu
- School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Xingyue Zhao
- School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Yi Wu
- School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Peiqi Li
- School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Rui Lin
- National Institute of Biological Sciences (NIBS), Beijing, China
| | - Jing Ren
- Division of Neurobiology, MRC Laboratory of Molecular Biology, Cambridge, UK.
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13
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Mahani FSN, Kalantari A, Fink GR, Hoehn M, Aswendt M. A systematic review of the relationship between magnetic resonance imaging based resting-state and structural networks in the rodent brain. Front Neurosci 2023; 17:1194630. [PMID: 37554291 PMCID: PMC10405456 DOI: 10.3389/fnins.2023.1194630] [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: 03/27/2023] [Accepted: 07/05/2023] [Indexed: 08/10/2023] Open
Abstract
Recent developments in rodent brain imaging have enabled translational characterization of functional and structural connectivity at the whole brain level in vivo. Nevertheless, fundamental questions about the link between structural and functional networks remain unsolved. In this review, we systematically searched for experimental studies in rodents investigating both structural and functional network measures, including studies correlating functional connectivity using resting-state functional MRI with diffusion tensor imaging or viral tracing data. We aimed to answer whether functional networks reflect the architecture of the structural connectome, how this reciprocal relationship changes throughout a disease, how structural and functional changes relate to each other, and whether changes follow the same timeline. We present the knowledge derived exclusively from studies that included in vivo imaging of functional and structural networks. The limited number of available reports makes it difficult to draw general conclusions besides finding a spatial and temporal decoupling between structural and functional networks during brain disease. Data suggest that when overcoming the currently limited evidence through future studies with combined imaging in various disease models, it will be possible to explore the interaction between both network systems as a disease or recovery biomarker.
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Affiliation(s)
- Fatemeh S. N. Mahani
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Aref Kalantari
- Department of Neurology, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Gereon R. Fink
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Mathias Hoehn
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany
| | - Markus Aswendt
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
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14
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Liang Z, Zhang J. Mouse brain MR super-resolution using a deep learning network trained with optical imaging data. FRONTIERS IN RADIOLOGY 2023; 3:1155866. [PMID: 37492378 PMCID: PMC10365285 DOI: 10.3389/fradi.2023.1155866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 04/28/2023] [Indexed: 07/27/2023]
Abstract
Introduction The resolution of magnetic resonance imaging is often limited at the millimeter level due to its inherent signal-to-noise disadvantage compared to other imaging modalities. Super-resolution (SR) of MRI data aims to enhance its resolution and diagnostic value. While deep learning-based SR has shown potential, its applications in MRI remain limited, especially for preclinical MRI, where large high-resolution MRI datasets for training are often lacking. Methods In this study, we first used high-resolution mouse brain auto-fluorescence (AF) data acquired using serial two-photon tomography (STPT) to examine the performance of deep learning-based SR for mouse brain images. Results We found that the best SR performance was obtained when the resolutions of training and target data were matched. We then applied the network trained using AF data to MRI data of the mouse brain, and found that the performance of the SR network depended on the tissue contrast presented in the MRI data. Using transfer learning and a limited set of high-resolution mouse brain MRI data, we were able to fine-tune the initial network trained using AF to enhance the resolution of MRI data. Discussion Our results suggest that deep learning SR networks trained using high-resolution data of a different modality can be applied to MRI data after transfer learning.
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15
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Menzel M, Gräßel D, Rajkovic I, Zeineh MM, Georgiadis M. Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI. eLife 2023; 12:e84024. [PMID: 37166005 PMCID: PMC10259419 DOI: 10.7554/elife.84024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 05/02/2023] [Indexed: 05/12/2023] Open
Abstract
Disentangling human brain connectivity requires an accurate description of nerve fiber trajectories, unveiled via detailed mapping of axonal orientations. However, this is challenging because axons can cross one another on a micrometer scale. Diffusion magnetic resonance imaging (dMRI) can be used to infer axonal connectivity because it is sensitive to axonal alignment, but it has limited spatial resolution and specificity. Scattered light imaging (SLI) and small-angle X-ray scattering (SAXS) reveal axonal orientations with microscopic resolution and high specificity, respectively. Here, we apply both scattering techniques on the same samples and cross-validate them, laying the groundwork for ground-truth axonal orientation imaging and validating dMRI. We evaluate brain regions that include unidirectional and crossing fibers in human and vervet monkey brain sections. SLI and SAXS quantitatively agree regarding in-plane fiber orientations including crossings, while dMRI agrees in the majority of voxels with small discrepancies. We further use SAXS and dMRI to confirm theoretical predictions regarding SLI determination of through-plane fiber orientations. Scattered light and X-ray imaging can provide quantitative micrometer 3D fiber orientations with high resolution and specificity, facilitating detailed investigations of complex fiber architecture in the animal and human brain.
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Affiliation(s)
- Miriam Menzel
- Department of Imaging Physics, Faculty of Applied Sciences, Delft University of TechnologyDelftNetherlands
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbHJülichGermany
| | - David Gräßel
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbHJülichGermany
| | - Ivan Rajkovic
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator LaboratoryStandfordUnited States
| | - Michael M Zeineh
- Department of Radiology, Stanford School of MedicineStanfordUnited States
| | - Marios Georgiadis
- Department of Radiology, Stanford School of MedicineStanfordUnited States
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16
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Ho A, Orton R, Tayler R, Asamaphan P, Herder V, Davis C, Tong L, Smollett K, Manali M, Allan J, Rawlik K, McDonald SE, Vink E, Pollock L, Gannon L, Evans C, McMenamin J, Roy K, Marsh K, Divala T, Holden MTG, Lockhart M, Yirrell D, Currie S, O'Leary M, Henderson D, Shepherd SJ, Jackson C, Gunson R, MacLean A, McInnes N, Bradley-Stewart A, Battle R, Hollenbach JA, Henderson P, Odam M, Chikowore P, Oosthuyzen W, Chand M, Hamilton MS, Estrada-Rivadeneyra D, Levin M, Avramidis N, Pairo-Castineira E, Vitart V, Wilkie C, Palmarini M, Ray S, Robertson DL, da Silva Filipe A, Willett BJ, Breuer J, Semple MG, Turner D, Baillie JK, Thomson EC. Adeno-associated virus 2 infection in children with non-A-E hepatitis. Nature 2023; 617:555-563. [PMID: 36996873 DOI: 10.1038/s41586-023-05948-2] [Citation(s) in RCA: 50] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 03/10/2023] [Indexed: 04/01/2023]
Abstract
An outbreak of acute hepatitis of unknown aetiology in children was reported in Scotland1 in April 2022 and has now been identified in 35 countries2. Several recent studies have suggested an association with human adenovirus with this outbreak, a virus not commonly associated with hepatitis. Here we report a detailed case-control investigation and find an association between adeno-associated virus 2 (AAV2) infection and host genetics in disease susceptibility. Using next-generation sequencing, PCR with reverse transcription, serology and in situ hybridization, we detected recent infection with AAV2 in plasma and liver samples in 26 out of 32 (81%) cases of hepatitis compared with 5 out of 74 (7%) of samples from unaffected individuals. Furthermore, AAV2 was detected within ballooned hepatocytes alongside a prominent T cell infiltrate in liver biopsy samples. In keeping with a CD4+ T-cell-mediated immune pathology, the human leukocyte antigen (HLA) class II HLA-DRB1*04:01 allele was identified in 25 out of 27 cases (93%) compared with a background frequency of 10 out of 64 (16%; P = 5.49 × 10-12). In summary, we report an outbreak of acute paediatric hepatitis associated with AAV2 infection (most likely acquired as a co-infection with human adenovirus that is usually required as a 'helper virus' to support AAV2 replication) and disease susceptibility related to HLA class II status.
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Affiliation(s)
- Antonia Ho
- Medical Research Council-University of Glasgow Centre for Virus Research, Glasgow, UK
| | - Richard Orton
- Medical Research Council-University of Glasgow Centre for Virus Research, Glasgow, UK
| | - Rachel Tayler
- Department of Paediatrics, Royal Hospital for Children, Glasgow, UK
| | - Patawee Asamaphan
- Medical Research Council-University of Glasgow Centre for Virus Research, Glasgow, UK
| | - Vanessa Herder
- Medical Research Council-University of Glasgow Centre for Virus Research, Glasgow, UK
| | - Chris Davis
- Medical Research Council-University of Glasgow Centre for Virus Research, Glasgow, UK
| | - Lily Tong
- Medical Research Council-University of Glasgow Centre for Virus Research, Glasgow, UK
| | - Katherine Smollett
- Medical Research Council-University of Glasgow Centre for Virus Research, Glasgow, UK
| | - Maria Manali
- Medical Research Council-University of Glasgow Centre for Virus Research, Glasgow, UK
| | - Jay Allan
- Medical Research Council-University of Glasgow Centre for Virus Research, Glasgow, UK
| | - Konrad Rawlik
- Pandemic Science Hub, Centre for Inflammation Research and Roslin Institute, University of Edinburgh, Edinburgh, UK
| | - Sarah E McDonald
- Medical Research Council-University of Glasgow Centre for Virus Research, Glasgow, UK
| | - Elen Vink
- Medical Research Council-University of Glasgow Centre for Virus Research, Glasgow, UK
| | - Louisa Pollock
- Medical Research Council-University of Glasgow Centre for Virus Research, Glasgow, UK
- Department of Paediatrics, Royal Hospital for Children, Glasgow, UK
| | | | - Clair Evans
- Department of Pathology, Queen Elizabeth University Hospital, Glasgow, UK
| | | | | | | | | | | | | | | | | | | | | | | | - Celia Jackson
- West of Scotland Specialist Virology Centre, Glasgow, UK
| | - Rory Gunson
- West of Scotland Specialist Virology Centre, Glasgow, UK
| | | | - Neil McInnes
- West of Scotland Specialist Virology Centre, Glasgow, UK
| | | | - Richard Battle
- Histocompatibility and Immunogenetics (H&I) Laboratory, Scottish National Blood Transfusion Service, Edinburgh Royal Infirmary, Edinburgh, UK
| | - Jill A Hollenbach
- Department of Neurology and Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
| | - Paul Henderson
- Child Life and Health, University of Edinburgh, Edinburgh, UK
| | - Miranda Odam
- Pandemic Science Hub, Centre for Inflammation Research and Roslin Institute, University of Edinburgh, Edinburgh, UK
| | - Primrose Chikowore
- Pandemic Science Hub, Centre for Inflammation Research and Roslin Institute, University of Edinburgh, Edinburgh, UK
| | - Wilna Oosthuyzen
- Pandemic Science Hub, Centre for Inflammation Research and Roslin Institute, University of Edinburgh, Edinburgh, UK
| | | | - Melissa Shea Hamilton
- Section of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK
| | - Diego Estrada-Rivadeneyra
- Section of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK
| | - Michael Levin
- Section of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK
| | - Nikos Avramidis
- Pandemic Science Hub, Centre for Inflammation Research and Roslin Institute, University of Edinburgh, Edinburgh, UK
| | - Erola Pairo-Castineira
- Pandemic Science Hub, Centre for Inflammation Research and Roslin Institute, University of Edinburgh, Edinburgh, UK
| | - Veronique Vitart
- Pandemic Science Hub, Centre for Inflammation Research and Roslin Institute, University of Edinburgh, Edinburgh, UK
- MRC Human Genetics Unit, Institute for Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Craig Wilkie
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - Massimo Palmarini
- Medical Research Council-University of Glasgow Centre for Virus Research, Glasgow, UK
| | - Surajit Ray
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - David L Robertson
- Medical Research Council-University of Glasgow Centre for Virus Research, Glasgow, UK
| | - Ana da Silva Filipe
- Medical Research Council-University of Glasgow Centre for Virus Research, Glasgow, UK
| | - Brian J Willett
- Medical Research Council-University of Glasgow Centre for Virus Research, Glasgow, UK
| | | | | | - David Turner
- Histocompatibility and Immunogenetics (H&I) Laboratory, Scottish National Blood Transfusion Service, Edinburgh Royal Infirmary, Edinburgh, UK
| | - J Kenneth Baillie
- Pandemic Science Hub, Centre for Inflammation Research and Roslin Institute, University of Edinburgh, Edinburgh, UK
- MRC Human Genetics Unit, Institute for Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Emma C Thomson
- Medical Research Council-University of Glasgow Centre for Virus Research, Glasgow, UK.
- Department of Clinical Research, London School of Hygiene and Tropical Medicine, London, UK.
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17
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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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18
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Wehrli P, Ge J, Michno W, Koutarapu S, Dreos A, Jha D, Zetterberg H, Blennow K, Hanrieder J. Correlative Chemical Imaging and Spatial Chemometrics Delineate Alzheimer Plaque Heterogeneity at High Spatial Resolution. JACS AU 2023; 3:762-774. [PMID: 37006756 PMCID: PMC10052239 DOI: 10.1021/jacsau.2c00492] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 06/19/2023]
Abstract
We present a novel, correlative chemical imaging strategy based on multimodal matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI), hyperspectral microscopy, and spatial chemometrics. Our workflow overcomes challenges associated with correlative MSI data acquisition and alignment by implementing 1 + 1-evolutionary image registration for precise geometric alignment of multimodal imaging data and their integration in a common, truly multimodal imaging data matrix with maintained MSI resolution (10 μm). This enabled multivariate statistical modeling of multimodal imaging data using a novel multiblock orthogonal component analysis approach to identify covariations of biochemical signatures between and within imaging modalities at MSI pixel resolution. We demonstrate the method's potential through its application toward delineating chemical traits of Alzheimer's disease (AD) pathology. Here, trimodal MALDI MSI of transgenic AD mouse brain delineates beta-amyloid (Aβ) plaque-associated co-localization of lipids and Aβ peptides. Finally, we establish an improved image fusion approach for correlative MSI and functional fluorescence microscopy. This allowed for high spatial resolution (300 nm) prediction of correlative, multimodal MSI signatures toward distinct amyloid structures within single plaque features critically implicated in Aβ pathogenicity.
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Affiliation(s)
- Patrick
M. Wehrli
- Department
of Psychiatry and Neurochemistry, Institute
of Neuroscience and Physiology, Sahlgrenska Academy, University of
Gothenburg, Mölndal 431 80, Sweden
| | - Junyue Ge
- Clinical
Neurochemistry Laboratory, Sahlgrenska University
Hospital Mölndal, Mölndal 431 80, Sweden
| | - Wojciech Michno
- Department
of Psychiatry and Neurochemistry, Institute
of Neuroscience and Physiology, Sahlgrenska Academy, University of
Gothenburg, Mölndal 431 80, Sweden
| | - Srinivas Koutarapu
- Department
of Psychiatry and Neurochemistry, Institute
of Neuroscience and Physiology, Sahlgrenska Academy, University of
Gothenburg, Mölndal 431 80, Sweden
| | - Ambra Dreos
- Department
of Psychiatry and Neurochemistry, Institute
of Neuroscience and Physiology, Sahlgrenska Academy, University of
Gothenburg, Mölndal 431 80, Sweden
| | - Durga Jha
- Department
of Psychiatry and Neurochemistry, Institute
of Neuroscience and Physiology, Sahlgrenska Academy, University of
Gothenburg, Mölndal 431 80, Sweden
| | - Henrik Zetterberg
- Department
of Psychiatry and Neurochemistry, Institute
of Neuroscience and Physiology, Sahlgrenska Academy, University of
Gothenburg, Mölndal 431 80, Sweden
- Clinical
Neurochemistry Laboratory, Sahlgrenska University
Hospital Mölndal, Mölndal 431 80, Sweden
- Department
of Neurodegenerative Disease, Queen Square Institute of Neurology, University College London, London WC1N 3BG, U.K.
- U.
K. Dementia Research Institute at University College London, London WC1N 3BG, U.K.
- Hong
Kong Center for Neurodegenerative Diseases, Sha Tin, N.T. 1512-1518, Hong Kong, China
| | - Kaj Blennow
- Department
of Psychiatry and Neurochemistry, Institute
of Neuroscience and Physiology, Sahlgrenska Academy, University of
Gothenburg, Mölndal 431 80, Sweden
- Clinical
Neurochemistry Laboratory, Sahlgrenska University
Hospital Mölndal, Mölndal 431 80, Sweden
| | - Jörg Hanrieder
- Department
of Psychiatry and Neurochemistry, Institute
of Neuroscience and Physiology, Sahlgrenska Academy, University of
Gothenburg, Mölndal 431 80, Sweden
- Clinical
Neurochemistry Laboratory, Sahlgrenska University
Hospital Mölndal, Mölndal 431 80, Sweden
- Department
of Neurodegenerative Disease, Queen Square Institute of Neurology, University College London, London WC1N 3BG, U.K.
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19
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Hsueh B, Chen R, Jo Y, Tang D, Raffiee M, Kim YS, Inoue M, Randles S, Ramakrishnan C, Patel S, Kim DK, Liu TX, Kim SH, Tan L, Mortazavi L, Cordero A, Shi J, Zhao M, Ho TT, Crow A, Yoo ACW, Raja C, Evans K, Bernstein D, Zeineh M, Goubran M, Deisseroth K. Cardiogenic control of affective behavioural state. Nature 2023; 615:292-299. [PMID: 36859543 PMCID: PMC9995271 DOI: 10.1038/s41586-023-05748-8] [Citation(s) in RCA: 74] [Impact Index Per Article: 74.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 01/20/2023] [Indexed: 03/03/2023]
Abstract
Emotional states influence bodily physiology, as exemplified in the top-down process by which anxiety causes faster beating of the heart1-3. However, whether an increased heart rate might itself induce anxiety or fear responses is unclear3-8. Physiological theories of emotion, proposed over a century ago, have considered that in general, there could be an important and even dominant flow of information from the body to the brain9. Here, to formally test this idea, we developed a noninvasive optogenetic pacemaker for precise, cell-type-specific control of cardiac rhythms of up to 900 beats per minute in freely moving mice, enabled by a wearable micro-LED harness and the systemic viral delivery of a potent pump-like channelrhodopsin. We found that optically evoked tachycardia potently enhanced anxiety-like behaviour, but crucially only in risky contexts, indicating that both central (brain) and peripheral (body) processes may be involved in the development of emotional states. To identify potential mechanisms, we used whole-brain activity screening and electrophysiology to find brain regions that were activated by imposed cardiac rhythms. We identified the posterior insular cortex as a potential mediator of bottom-up cardiac interoceptive processing, and found that optogenetic inhibition of this brain region attenuated the anxiety-like behaviour that was induced by optical cardiac pacing. Together, these findings reveal that cells of both the body and the brain must be considered together to understand the origins of emotional or affective states. More broadly, our results define a generalizable approach for noninvasive, temporally precise functional investigations of joint organism-wide interactions among targeted cells during behaviour.
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Affiliation(s)
- Brian Hsueh
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Ritchie Chen
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - YoungJu Jo
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Daniel Tang
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Misha Raffiee
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Yoon Seok Kim
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Masatoshi Inoue
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Sawyer Randles
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | | | - Sneha Patel
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Doo Kyung Kim
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Tony X Liu
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Soo Hyun Kim
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Longzhi Tan
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Leili Mortazavi
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Arjay Cordero
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Jenny Shi
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Mingming Zhao
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Theodore T Ho
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Ailey Crow
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Ai-Chi Wang Yoo
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Cephra Raja
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Kathryn Evans
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Daniel Bernstein
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Michael Zeineh
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Maged Goubran
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Karl Deisseroth
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
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20
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Rijsketic DR, Casey AB, Barbosa DA, Zhang X, Hietamies TM, Ramirez-Ovalle G, Pomrenze M, Halpern CH, Williams LM, Malenka RC, Heifets BD. UNRAVELing the synergistic effects of psilocybin and environment on brain-wide immediate early gene expression in mice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.19.528997. [PMID: 36865251 PMCID: PMC9980055 DOI: 10.1101/2023.02.19.528997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
The effects of context on the subjective experience of serotonergic psychedelics have not been fully examined in human neuroimaging studies, partly due to limitations of the imaging environment. Here, we administered saline or psilocybin to mice in their home cage or an enriched environment, immunofluorescently-labeled brain-wide c-Fos, and imaged cleared tissue with light sheet microscopy to examine the impact of context on psilocybin-elicited neural activity at cellular resolution. Voxel-wise analysis of c-Fos-immunofluorescence revealed differential neural activity, which we validated with c-Fos + cell density measurements. Psilocybin increased c-Fos expression in the neocortex, caudoputamen, central amygdala, and parasubthalamic nucleus and decreased c-Fos in the hypothalamus, cortical amygdala, striatum, and pallidum. Main effects of context and psilocybin-treatment were robust, widespread, and spatially distinct, whereas interactions were surprisingly sparse.
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Affiliation(s)
- Daniel Ryskamp Rijsketic
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Austen B. Casey
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Daniel A.N. Barbosa
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Xue Zhang
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA
| | - Tuuli M. Hietamies
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Grecia Ramirez-Ovalle
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Matthew Pomrenze
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA
- Nancy Pritzker Laboratory, Stanford University, Stanford, CA 94305, USA
| | - Casey H. Halpern
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Leanne M. Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA
- Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC) Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Robert C. Malenka
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA
- Nancy Pritzker Laboratory, Stanford University, Stanford, CA 94305, USA
| | - Boris D. Heifets
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA
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21
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Perens J, Salinas CG, Roostalu U, Skytte JL, Gundlach C, Hecksher-Sørensen J, Dahl AB, Dyrby TB. Multimodal 3D Mouse Brain Atlas Framework with the Skull-Derived Coordinate System. Neuroinformatics 2023; 21:269-286. [PMID: 36809643 DOI: 10.1007/s12021-023-09623-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/01/2023] [Indexed: 02/23/2023]
Abstract
Magnetic resonance imaging (MRI) and light-sheet fluorescence microscopy (LSFM) are technologies that enable non-disruptive 3-dimensional imaging of whole mouse brains. A combination of complementary information from both modalities is desirable for studying neuroscience in general, disease progression and drug efficacy. Although both technologies rely on atlas mapping for quantitative analyses, the translation of LSFM recorded data to MRI templates has been complicated by the morphological changes inflicted by tissue clearing and the enormous size of the raw data sets. Consequently, there is an unmet need for tools that will facilitate fast and accurate translation of LSFM recorded brains to in vivo, non-distorted templates. In this study, we have developed a bidirectional multimodal atlas framework that includes brain templates based on both imaging modalities, region delineations from the Allen's Common Coordinate Framework, and a skull-derived stereotaxic coordinate system. The framework also provides algorithms for bidirectional transformation of results obtained using either MR or LSFM (iDISCO cleared) mouse brain imaging while the coordinate system enables users to easily assign in vivo coordinates across the different brain templates.
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Affiliation(s)
- Johanna Perens
- Gubra ApS, Hørsholm, Denmark.,Section for Visual Computing, Department of Applied Mathematics and Computer Science, Technical University Denmark, Kongens Lyngby, Denmark.,Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark
| | | | | | | | - Carsten Gundlach
- Neutrons and X-rays for Materials Physics, Department of Physics, Technical University Denmark, Kongens Lyngby, Denmark
| | | | - Anders Bjorholm Dahl
- Section for Visual Computing, Department of Applied Mathematics and Computer Science, Technical University Denmark, Kongens Lyngby, Denmark
| | - Tim B Dyrby
- Section for Visual Computing, Department of Applied Mathematics and Computer Science, Technical University Denmark, Kongens Lyngby, Denmark.,Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark
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22
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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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23
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Stouffer KM, Witter MP, Tward DJ, Miller MI. Projective Diffeomorphic Mapping of Molecular Digital Pathology with Tissue MRI. COMMUNICATIONS ENGINEERING 2022; 1:44. [PMID: 37284027 PMCID: PMC10243734 DOI: 10.1038/s44172-022-00044-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 11/28/2022] [Indexed: 06/08/2023]
Abstract
Reconstructing dense 3D anatomical coordinates from 2D projective measurements has become a central problem in digital pathology for both animal models and human studies. Here we describe Projective Large Deformation Diffeomorphic Metric Mapping (LDDMM), a technique which projects diffeomorphic mappings of dense human magnetic resonance imaging (MRI) atlases at tissue scales onto sparse measurements at micrometre scales associated with histological and more general optical imaging modalities. We solve the problem of dense mapping surjectively onto histological sections by incorporating technologies for crossing modalities that use nonlinear scattering transforms to represent multiple radiomic-like textures at micron scales, together with a Gaussian mixture-model framework for modelling tears and distortions associated to each section. We highlight the significance of our method through incorporation of neuropathological measures and MRI, of relevance to the development of biomarkers for Alzheimer's disease and one instance of the integration of imaging data across the scales of clinical imaging and digital pathology.
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Affiliation(s)
- Kaitlin M. Stouffer
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA
| | - Menno P. Witter
- Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, Trondheim, Torgarden Norway
| | - Daniel J. Tward
- Departments of Computational Medicine and Neurology, University of California, Los Angeles, CA USA
| | - Michael I. Miller
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA
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24
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Monte B, Constantinou S, Koundal S, Lee H, Dai F, Gursky Z, Van Nostrand WE, Darbinyan A, Zlokovic BV, Wardlaw J, Benveniste H. Characterization of perivascular space pathology in a rat model of cerebral small vessel disease by in vivo magnetic resonance imaging. J Cereb Blood Flow Metab 2022; 42:1813-1826. [PMID: 35673963 PMCID: PMC9536121 DOI: 10.1177/0271678x221105668] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 05/09/2022] [Accepted: 05/13/2022] [Indexed: 11/17/2022]
Abstract
One of the most common causes of dementia is cerebral small vessel disease (SVD), which is associated with enlarged perivascular spaces (PVS). Clinically, PVS are visible as hyperintensities on T2-weighted (T2w) magnetic resonance images (MRI). While rodent SVD models exhibit arteriolosclerosis, PVS have not been robustly documented by MRI casting doubts on their clinical relevance. Here we established that the severity of SVD in spontaneously hypertensive stroke prone (SHRSP) rats correlated to 'moderate' SVD in human post-mortem tissue. We then developed two approaches for detecting PVS in SHRSP rats: 1) T2w imaging and 2) T1-weighted imaging with administration of gadoteric acid into cerebrospinal fluid. We applied the two protocols to six Wistar-Kyoto (WKY) control rats and thirteen SHRSP rats at ∼12 month of age. The primary endpoint was the number of hyperintense lesions. We found more hyperintensities on T2w MRI in the SHRSP compared to WKY rats (p-value = 0.023). CSF enhancement with gadoteric acid increased the visibility of PVS-like lesions in SHRSP rats. In some of the SHRSP rats, the MRI hyperintensities corresponded to enlarged PVS on histopathology. The finding of PVS-like hyperintensities on T2w MRI support the SHRSP rat's clinical relevance for studying the underlying pathophysiology of SVD.
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Affiliation(s)
- Brittany Monte
- Department of Anesthesiology, Yale School of Medicine, New Haven, CT, USA
| | | | - Sunil Koundal
- Department of Anesthesiology, Yale School of Medicine, New Haven, CT, USA
| | - Hedok Lee
- Department of Anesthesiology, Yale School of Medicine, New Haven, CT, USA
| | - Feng Dai
- Yale Center for Analytical Sciences, Yale School of Public Health, New Haven, CT, USA
| | - Zachary Gursky
- Department of Anesthesiology, Yale School of Medicine, New Haven, CT, USA
| | - William E Van Nostrand
- George and Anne Ryan Institute for Neuroscience and the Department of Biomedical and Pharmaceutical Sciences, University of Rhode Island, Kingston, RI, USA
| | - Armine Darbinyan
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Berislav V Zlokovic
- Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, USA
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, USA
| | - Joanna Wardlaw
- Division of Neuroimaging Sciences, Centre for Clinical Brain Sciences; UK Dementia Research Institute Centre at the University of Edinburgh; and Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Helene Benveniste
- Department of Anesthesiology, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale School of Medicine New Haven, CT, USA
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25
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Li Y, Wu J, Lu D, Xu C, Zheng Y, Peng H, Qu L. mBrainAligner-Web: a web server for cross-modal coherent registration of whole mouse brains. Bioinformatics 2022; 38:4654-4655. [PMID: 35951750 DOI: 10.1093/bioinformatics/btac549] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 07/16/2022] [Accepted: 08/07/2022] [Indexed: 12/24/2022] Open
Abstract
SUMMARY Recent whole-brain mapping projects are collecting increasingly larger sets of high-resolution brain images using a variety of imaging, labeling and sample preparation techniques. Both mining and analysis of these data require reliable and robust cross-modal registration tools. We recently developed the mBrainAligner, a pipeline for performing cross-modal registration of the whole mouse brain. However, using this tool requires scripting or command-line skills to assemble and configure the different modules of mBrainAligner for accommodating different registration requirements and platform settings. In this application note, we present mBrainAligner-Web, a web server with a user-friendly interface that allows to configure and run mBrainAligner locally or remotely across platforms. AVAILABILITY AND IMPLEMENTATION mBrainAligner-Web is available at http://mbrainaligner.ahu.edu.cn/ with source code at https://github.com/reaneyli/mBrainAligner-web. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yuanyuan Li
- Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, Anhui 230000, China
| | - Jun Wu
- Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, Anhui 230000, China
| | - Donghuan Lu
- Tencent Jarvis Lab, Shenzhen, Guangdong 518020, China
| | - Chao Xu
- Tencent Jarvis Lab, Shenzhen, Guangdong 518020, China
| | - Yefeng Zheng
- Tencent Jarvis Lab, Shenzhen, Guangdong 518020, China.,SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 211170, China
| | - Hanchuan Peng
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 211170, China
| | - Lei Qu
- Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, Anhui 230000, China.,SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 211170, China
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26
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Sun R, Wu J, Miao Y, Ouyang L, Qu L. Progressive 3D biomedical image registration network based on deep self-calibration. Front Neuroinform 2022; 16:932879. [PMID: 36213548 PMCID: PMC9532554 DOI: 10.3389/fninf.2022.932879] [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: 04/30/2022] [Accepted: 08/29/2022] [Indexed: 11/25/2022] Open
Abstract
Three dimensional deformable image registration (DIR) is a key enabling technique in building digital neuronal atlases of the brain, which can model the local non-linear deformation between a pair of biomedical images and align the anatomical structures of different samples into one spatial coordinate system. And thus, the DIR is always conducted following a preprocessing of global linear registration to remove the large global deformations. However, imperfect preprocessing may leave some large non-linear deformations that cannot be handled well by existing DIR methods. The recently proposed cascaded registration network gives a primary solution to deal with such large non-linear deformations, but still suffers from loss of image details caused by continuous interpolation (information loss problem). In this article, a progressive image registration strategy based on deep self-calibration is proposed to deal with the large non-linear deformations without causing information loss and introducing additional parameters. More importantly, we also propose a novel hierarchical registration strategy to quickly achieve accurate multi-scale progressive registration. In addition, our method can implicitly and reasonably implement dynamic dataset augmentation. We have evaluated the proposed method on both optical and MRI image datasets with obtaining promising results, which demonstrate the superior performance of the proposed method over several other state-of-the-art approaches for deformable image registration.
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Affiliation(s)
- Rui Sun
- Ministry of Education Key Laboratory of Intelligent Computing and Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, China
| | - Jun Wu
- Ministry of Education Key Laboratory of Intelligent Computing and Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, China
- *Correspondence: Jun Wu
| | - Yongchun Miao
- Ministry of Education Key Laboratory of Intelligent Computing and Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, China
| | - Lei Ouyang
- Ministry of Education Key Laboratory of Intelligent Computing and Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, China
| | - Lei Qu
- Ministry of Education Key Laboratory of Intelligent Computing and Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, China
- Hefei Comprehensive National Science Center, Institute of Artificial Intelligence, Hefei, China
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
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27
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Huang R, Zhou X, Chen G, Su L, Liu Z, Zhou P, Weng J, Min Y. Advances of functional nanomaterials for magnetic resonance imaging and biomedical engineering applications. WILEY INTERDISCIPLINARY REVIEWS. NANOMEDICINE AND NANOBIOTECHNOLOGY 2022; 14:e1800. [PMID: 35445588 DOI: 10.1002/wnan.1800] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 03/21/2022] [Accepted: 03/25/2022] [Indexed: 11/12/2022]
Abstract
Functional nanomaterials have been widely used in biomedical fields due to their good biocompatibility, excellent physicochemical properties, easy surface modification, and easy regulation of size and morphology. Functional nanomaterials for magnetic resonance imaging (MRI) can target specific sites in vivo and more easily detect disease-related specific biomarkers at the molecular and cellular levels than traditional contrast agents, achieving a broad application prospect in MRI. This review focuses on the basic principles of MRI, the classification, synthesis and surface modification methods of contrast agents, and their clinical applications to provide guidance for designing novel contrast agents and optimizing the contrast effect. Furthermore, the latest biomedical advances of functional nanomaterials in medical diagnosis and disease detection, disease treatment, the combination of diagnosis and treatment (theranostics), multi-model imaging and nanozyme are also summarized and discussed. Finally, the bright application prospects of functional nanomaterials in biomedicine are emphasized and the urgent need to achieve significant breakthroughs in the industrial transformation and the clinical translation is proposed. This article is categorized under: Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Diagnostic Tools > Diagnostic Nanodevices Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease.
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Affiliation(s)
- Ruijie Huang
- Department of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.,Department of Chemistry, University of Science and Technology of China, Hefei, China
| | - Xingyu Zhou
- Department of Chemistry, University of Science and Technology of China, Hefei, China
| | - Guiyuan Chen
- Department of Chemistry, University of Science and Technology of China, Hefei, China
| | - Lanhong Su
- Department of Chemistry, University of Science and Technology of China, Hefei, China
| | - Zhaoji Liu
- Department of Chemistry, University of Science and Technology of China, Hefei, China
| | - Peijie Zhou
- Department of Radiation Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Jianping Weng
- Department of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Yuanzeng Min
- Department of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.,Department of Chemistry, University of Science and Technology of China, Hefei, China
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28
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Tran MQ, Do T, Tran H, Tjiputra E, Tran QD, Nguyen A. Light-Weight Deformable Registration Using Adversarial Learning With Distilling Knowledge. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:1443-1453. [PMID: 34990354 DOI: 10.1109/tmi.2022.3141013] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Deformable registration is a crucial step in many medical procedures such as image-guided surgery and radiation therapy. Most recent learning-based methods focus on improving the accuracy by optimizing the non-linear spatial correspondence between the input images. Therefore, these methods are computationally expensive and require modern graphic cards for real-time deployment. In this paper, we introduce a new Light-weight Deformable Registration network that significantly reduces the computational cost while achieving competitive accuracy. In particular, we propose a new adversarial learning with distilling knowledge algorithm that successfully leverages meaningful information from the effective but expensive teacher network to the student network. We design the student network such as it is light-weight and well suitable for deployment on a typical CPU. The extensively experimental results on different public datasets show that our proposed method achieves state-of-the-art accuracy while significantly faster than recent methods. We further show that the use of our adversarial learning algorithm is essential for a time-efficiency deformable registration method. Finally, our source code and trained models are available at https://github.com/aioz-ai/LDR_ALDK.
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29
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The brainstem connectome database. Sci Data 2022; 9:168. [PMID: 35414055 PMCID: PMC9005652 DOI: 10.1038/s41597-022-01219-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 02/25/2022] [Indexed: 11/29/2022] Open
Abstract
Connectivity data of the nervous system and subdivisions, such as the brainstem, cerebral cortex and subcortical nuclei, are necessary to understand connectional structures, predict effects of connectional disorders and simulate network dynamics. For that purpose, a database was built and analyzed which comprises all known directed and weighted connections within the rat brainstem. A longterm metastudy of original research publications describing tract tracing results form the foundation of the brainstem connectome (BC) database which can be analyzed directly in the framework neuroVIISAS. The BC database can be accessed directly by connectivity tables, a web-based tool and the framework. Analysis of global and local network properties, a motif analysis, and a community analysis of the brainstem connectome provides insight into its network organization. For example, we found that BC is a scale-free network with a small-world connectivity. The Louvain modularity and weighted stochastic block matching resulted in partially matching of functions and connectivity. BC modeling was performed to demonstrate signal propagation through the somatosensory pathway which is affected in Multiple sclerosis. Measurement(s) | brainstem | Technology Type(s) | tract tracing metastudy | Factor Type(s) | brain region | Sample Characteristic - Organism | Rattus rattus | Sample Characteristic - Environment | Experimental setup | Sample Characteristic - Location | Germany |
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30
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Mittal S, Kim J, Bhargava R. Statistical Considerations and Tools to Improve Histopathologic Protocols with Spectroscopic Imaging. APPLIED SPECTROSCOPY 2022; 76:428-438. [PMID: 35296146 PMCID: PMC9202564 DOI: 10.1177/00037028211066327] [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] [Indexed: 06/14/2023]
Abstract
Advances in infrared (IR) spectroscopic imaging instrumentation and data science now present unique opportunities for large validation studies of the concept of histopathology using spectral data. In this study, we examine the discrimination potential of IR metrics for different histologic classes to estimate the sample size needed for designing validation studies to achieve a given statistical power and statistical significance. Next, we present an automated annotation transfer tool that can allow large-scale training/validation, overcoming the limitations of sparse ground truth data with current manual approaches by providing a tool to transfer pathologist annotations from stained images to IR images across diagnostic categories. Finally, the results of a combination of supervised and unsupervised analysis provide a scheme to identify diagnostic groups/patterns and isolating pure chemical pixels for each class to better train complex histopathological models. Together, these methods provide essential tools to take advantage of the emerging capabilities to record and utilize large spectroscopic imaging datasets.
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Affiliation(s)
- Shachi Mittal
- Department of Bioengineering and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana–Champaign, Urbana, IL, USA
- Department of Chemical Engineering, University of Washington, Seattle, WA, USA
| | - Jonathan Kim
- Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Rohit Bhargava
- Department of Bioengineering and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana–Champaign, Urbana, IL, USA
- Departments of Mechanical Science and Engineering, Electrical and Computer Engineering, Chemical and Biomolecular Engineering, and Chemistry, University of Illinois at Urbana–Champaign, Urbana, IL, USA
- Cancer Center at Illinois, University of Illinois at Urbana–Champaign, Urbana, IL, USA
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31
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Hoffmann M, Billot B, Greve DN, Iglesias JE, Fischl B, Dalca AV. SynthMorph: Learning Contrast-Invariant Registration Without Acquired Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:543-558. [PMID: 34587005 PMCID: PMC8891043 DOI: 10.1109/tmi.2021.3116879] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
We introduce a strategy for learning image registration without acquired imaging data, producing powerful networks agnostic to contrast introduced by magnetic resonance imaging (MRI). While classical registration methods accurately estimate the spatial correspondence between images, they solve an optimization problem for every new image pair. Learning-based techniques are fast at test time but limited to registering images with contrasts and geometric content similar to those seen during training. We propose to remove this dependency on training data by leveraging a generative strategy for diverse synthetic label maps and images that exposes networks to a wide range of variability, forcing them to learn more invariant features. This approach results in powerful networks that accurately generalize to a broad array of MRI contrasts. We present extensive experiments with a focus on 3D neuroimaging, showing that this strategy enables robust and accurate registration of arbitrary MRI contrasts even if the target contrast is not seen by the networks during training. We demonstrate registration accuracy surpassing the state of the art both within and across contrasts, using a single model. Critically, training on arbitrary shapes synthesized from noise distributions results in competitive performance, removing the dependency on acquired data of any kind. Additionally, since anatomical label maps are often available for the anatomy of interest, we show that synthesizing images from these dramatically boosts performance, while still avoiding the need for real intensity images. Our code is available at doic https://w3id.org/synthmorph.
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32
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Newmaster KT, Kronman FA, Wu YT, Kim Y. Seeing the Forest and Its Trees Together: Implementing 3D Light Microscopy Pipelines for Cell Type Mapping in the Mouse Brain. Front Neuroanat 2022; 15:787601. [PMID: 35095432 PMCID: PMC8794814 DOI: 10.3389/fnana.2021.787601] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 12/02/2021] [Indexed: 12/14/2022] Open
Abstract
The brain is composed of diverse neuronal and non-neuronal cell types with complex regional connectivity patterns that create the anatomical infrastructure underlying cognition. Remarkable advances in neuroscience techniques enable labeling and imaging of these individual cell types and their interactions throughout intact mammalian brains at a cellular resolution allowing neuroscientists to examine microscopic details in macroscopic brain circuits. Nevertheless, implementing these tools is fraught with many technical and analytical challenges with a need for high-level data analysis. Here we review key technical considerations for implementing a brain mapping pipeline using the mouse brain as a primary model system. Specifically, we provide practical details for choosing methods including cell type specific labeling, sample preparation (e.g., tissue clearing), microscopy modalities, image processing, and data analysis (e.g., image registration to standard atlases). We also highlight the need to develop better 3D atlases with standardized anatomical labels and nomenclature across species and developmental time points to extend the mapping to other species including humans and to facilitate data sharing, confederation, and integrative analysis. In summary, this review provides key elements and currently available resources to consider while developing and implementing high-resolution mapping methods.
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Affiliation(s)
- Kyra T Newmaster
- Department of Neural and Behavioral Sciences, The Pennsylvania State University, Hershey, PA, United States
| | - Fae A Kronman
- Department of Neural and Behavioral Sciences, The Pennsylvania State University, Hershey, PA, United States
| | - Yuan-Ting Wu
- Department of Neural and Behavioral Sciences, The Pennsylvania State University, Hershey, PA, United States
| | - Yongsoo Kim
- Department of Neural and Behavioral Sciences, The Pennsylvania State University, Hershey, PA, United States
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33
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Ruthig P, Schönwiesner M. Common principles in the lateralisation of auditory cortex structure and function for vocal communication in primates and rodents. Eur J Neurosci 2022; 55:827-845. [PMID: 34984748 DOI: 10.1111/ejn.15590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 12/24/2021] [Indexed: 11/27/2022]
Abstract
This review summarises recent findings on the lateralisation of communicative sound processing in the auditory cortex (AC) of humans, non-human primates, and rodents. Functional imaging in humans has demonstrated a left hemispheric preference for some acoustic features of speech, but it is unclear to which degree this is caused by bottom-up acoustic feature selectivity or top-down modulation from language areas. Although non-human primates show a less pronounced functional lateralisation in AC, the properties of AC fields and behavioral asymmetries are qualitatively similar. Rodent studies demonstrate microstructural circuits that might underlie bottom-up acoustic feature selectivity in both hemispheres. Functionally, the left AC in the mouse appears to be specifically tuned to communication calls, whereas the right AC may have a more 'generalist' role. Rodents also show anatomical AC lateralisation, such as differences in size and connectivity. Several of these functional and anatomical characteristics are also lateralized in human AC. Thus, complex vocal communication processing shares common features among rodents and primates. We argue that a synthesis of results from humans, non-human primates, and rodents is necessary to identify the neural circuitry of vocal communication processing. However, data from different species and methods are often difficult to compare. Recent advances may enable better integration of methods across species. Efforts to standardise data formats and analysis tools would benefit comparative research and enable synergies between psychological and biological research in the area of vocal communication processing.
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Affiliation(s)
- Philip Ruthig
- Faculty of Life Sciences, Leipzig University, Leipzig, Sachsen.,Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig
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34
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Tyson AL, Margrie TW. Mesoscale microscopy and image analysis tools for understanding the brain. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2022; 168:81-93. [PMID: 34216639 PMCID: PMC8786668 DOI: 10.1016/j.pbiomolbio.2021.06.013] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 06/09/2021] [Accepted: 06/29/2021] [Indexed: 12/12/2022]
Abstract
Over the last ten years, developments in whole-brain microscopy now allow for high-resolution imaging of intact brains of small animals such as mice. These complex images contain a wealth of information, but many neuroscience laboratories do not have all of the computational knowledge and tools needed to process these data. We review recent open source tools for registration of images to atlases, and the segmentation, visualisation and analysis of brain regions and labelled structures such as neurons. Since the field lacks fully integrated analysis pipelines for all types of whole-brain microscopy analysis, we propose a pathway for tool developers to work together to meet this challenge.
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Affiliation(s)
- Adam L Tyson
- Sainsbury Wellcome Centre, University College London, 25 Howland Street, London, W1T 4JG, United Kingdom
| | - Troy W Margrie
- Sainsbury Wellcome Centre, University College London, 25 Howland Street, London, W1T 4JG, United Kingdom.
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35
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Qu L, Li Y, Xie P, Liu L, Wang Y, Wu J, Liu Y, Wang T, Li L, Guo K, Wan W, Ouyang L, Xiong F, Kolstad AC, Wu Z, Xu F, Zheng Y, Gong H, Luo Q, Bi G, Dong H, Hawrylycz M, Zeng H, Peng H. Cross-modal coherent registration of whole mouse brains. Nat Methods 2022; 19:111-118. [PMID: 34887551 DOI: 10.1038/s41592-021-01334-w] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 10/28/2021] [Indexed: 01/04/2023]
Abstract
Recent whole-brain mapping projects are collecting large-scale three-dimensional images using modalities such as serial two-photon tomography, fluorescence micro-optical sectioning tomography, light-sheet fluorescence microscopy, volumetric imaging with synchronous on-the-fly scan and readout or magnetic resonance imaging. Registration of these multi-dimensional whole-brain images onto a standard atlas is essential for characterizing neuron types and constructing brain wiring diagrams. However, cross-modal image registration is challenging due to intrinsic variations of brain anatomy and artifacts resulting from different sample preparation methods and imaging modalities. We introduce a cross-modal registration method, mBrainAligner, which uses coherent landmark mapping and deep neural networks to align whole mouse brain images to the standard Allen Common Coordinate Framework atlas. We build a brain atlas for the fluorescence micro-optical sectioning tomography modality to facilitate single-cell mapping, and used our method to generate a whole-brain map of three-dimensional single-neuron morphology and neuron cell types.
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Affiliation(s)
- Lei Qu
- Ministry of Education Key Laboratory of Intelligent Computation & Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, China
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
| | - Yuanyuan Li
- Ministry of Education Key Laboratory of Intelligent Computation & Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, China
| | - Peng Xie
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Lijuan Liu
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
- Ministry of Education Key Laboratory of Developmental Genes and Human Disease, School of Life Science and Technology, Southeast University, Nanjing, China
| | - Yimin Wang
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Jun Wu
- Ministry of Education Key Laboratory of Intelligent Computation & Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, China
| | - Yu Liu
- Ministry of Education Key Laboratory of Intelligent Computation & Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, China
| | - Tao Wang
- Ministry of Education Key Laboratory of Intelligent Computation & Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, China
| | - Longfei Li
- Ministry of Education Key Laboratory of Intelligent Computation & Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, China
| | - Kaixuan Guo
- Ministry of Education Key Laboratory of Intelligent Computation & Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, China
| | - Wan Wan
- Ministry of Education Key Laboratory of Intelligent Computation & Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, China
| | - Lei Ouyang
- Ministry of Education Key Laboratory of Intelligent Computation & Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, China
| | - Feng Xiong
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Anna C Kolstad
- Department of Cell, Developmental & Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Zhuhao Wu
- Department of Cell, Developmental & Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Fang Xu
- CAS Key Laboratory of Brain Connectome and Manipulation, Interdisciplinary Center for Brain Information, The Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
| | | | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Science, Shanghai, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Science, Shanghai, China
- School of Biomedical Engineering, Hainan University, Haikou, China
| | - Guoqiang Bi
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
- CAS Key Laboratory of Brain Connectome and Manipulation, Interdisciplinary Center for Brain Information, The Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
- Center for Integrative Imaging, Hefei National Laboratory for Physical Sciences at the Microscale, and School of Life Sciences, University of Science and Technology of China, Hefei, China
| | - Hongwei Dong
- Center for Integrative Connectomics, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | | | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Hanchuan Peng
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China.
- Allen Institute for Brain Science, Seattle, WA, USA.
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36
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Shafique A, Babaie M, Sajadi M, Batten A, Skdar S, Tizhoosh HR. Automatic Multi-Stain Registration of Whole Slide Images in Histopathology. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3622-3625. [PMID: 34892022 DOI: 10.1109/embc46164.2021.9629970] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Joint analysis of multiple biomarker images and tissue morphology is important for disease diagnosis, treatment planning and drug development. It requires cross-staining comparison among Whole Slide Images (WSIs) of immune-histochemical and hematoxylin and eosin (H&E) microscopic slides. However, automatic, and fast cross-staining alignment of enormous gigapixel WSIs at single-cell precision is challenging. In addition to morphological deformations introduced during slide preparation, there are large variations in cell appearance and tissue morphology across different staining. In this paper, we propose a two-step automatic feature-based cross-staining WSI alignment to assist localization of even tiny metastatic foci in the assessment of lymph node. Image pairs were aligned allowing for translation, rotation, and scaling. The registration was performed automatically by first detecting landmarks in both images, using the scale-invariant image transform (SIFT), followed by the fast sample consensus (FSC) protocol for finding point correspondences and finally aligned the images. The Registration results were evaluated using both visual and quantitative criteria using the Jaccard index. The average Jaccard similarity index of the results produced by the proposed system is 0.942 when compared with the manual registration.
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37
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Yang JYM, Yeh CH, Poupon C, Calamante F. Diffusion MRI tractography for neurosurgery: the basics, current state, technical reliability and challenges. Phys Med Biol 2021; 66. [PMID: 34157706 DOI: 10.1088/1361-6560/ac0d90] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 06/22/2021] [Indexed: 01/20/2023]
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography is currently the only imaging technique that allows for non-invasive delineation and visualisation of white matter (WM) tractsin vivo,prompting rapid advances in related fields of brain MRI research in recent years. One of its major clinical applications is for pre-surgical planning and intraoperative image guidance in neurosurgery, where knowledge about the location of WM tracts nearby the surgical target can be helpful to guide surgical resection and optimise post-surgical outcomes. Surgical injuries to these WM tracts can lead to permanent neurological and functional deficits, making the accuracy of tractography reconstructions paramount. The quality of dMRI tractography is influenced by many modifiable factors, ranging from MRI data acquisition through to the post-processing of tractography output, with the potential of error propagation based on decisions made at each and subsequent processing steps. Research over the last 25 years has significantly improved the anatomical accuracy of tractography. An updated review about tractography methodology in the context of neurosurgery is now timely given the thriving research activities in dMRI, to ensure more appropriate applications in the clinical neurosurgical realm. This article aims to review the dMRI physics, and tractography methodologies, highlighting recent advances to provide the key concepts of tractography-informed neurosurgery, with a focus on the general considerations, the current state of practice, technical challenges, potential advances, and future demands to this field.
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Affiliation(s)
- Joseph Yuan-Mou Yang
- Department of Neurosurgery, The Royal Children's Hospital, Melbourne, Australia.,Neuroscience Research, Murdoch Children's Research Institute, Melbourne, Australia.,Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, Australia
| | - Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital, Taoyuan, Taiwan.,Department of Child and Adolescent Psychiatry, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
| | - Cyril Poupon
- NeuroSpin, Frédéric Joliot Life Sciences Institute, CEA, CNRS, Paris-Saclay University, Gif-sur-Yvette, France
| | - Fernando Calamante
- The University of Sydney, Sydney Imaging, Sydney, Australia.,The University of Sydney, School of Biomedical Engineering, Sydney, Australia
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38
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Giampetraglia M, Weigelin B. Recent advances in intravital microscopy for preclinical research. Curr Opin Chem Biol 2021; 63:200-208. [PMID: 34274700 DOI: 10.1016/j.cbpa.2021.05.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 05/08/2021] [Accepted: 05/14/2021] [Indexed: 12/14/2022]
Abstract
Intravital microscopy (IVM) has revolutionized our understanding of single-cell behavior in complex tissues by enabling real-time observation of molecular and cellular processes in their natural environment. In preclinical research, IVM has emerged as a standard tool for mechanistic studies of therapy response and the rational design of new treatment strategies. Technological developments keep expanding the imaging depth and quality that can be achieved in living tissue, and the maturation of imaging modalities such as fluorescence and phosphorescence lifetime imaging facilitates co-registration of individual cell dynamics with metabolic tissue states. Correlation of IVM with mesoscopic and macroscopic imaging modalities further promotes the translation of mechanistic insights gained by IVM into clinically relevant information. This review highlights some of the recent advances in IVM that have made the transition from experimental optical techniques to practical applications in basic and preclinical research.
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Affiliation(s)
- Martina Giampetraglia
- David H. Koch Center for Applied Research of Genitourinary Cancers, Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Bettina Weigelin
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tübingen, Tübingen, Germany; Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Germany.
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39
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Chandrashekhar V, Tward DJ, Crowley D, Crow AK, Wright MA, Hsueh BY, Gore F, Machado TA, Branch A, Rosenblum JS, Deisseroth K, Vogelstein JT. CloudReg: automatic terabyte-scale cross-modal brain volume registration. Nat Methods 2021; 18:845-846. [PMID: 34253927 DOI: 10.1038/s41592-021-01218-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
| | - Daniel J Tward
- Department of Computational Medicine and Neurology, University of California Los Angeles, Los Angeles, CA, USA
| | - Devin Crowley
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA
| | - Ailey K Crow
- CNC Program, Stanford University, Stanford, CA, USA
| | - Matthew A Wright
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Brian Y Hsueh
- CNC Program, Stanford University, Stanford, CA, USA.,Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Felicity Gore
- CNC Program, Stanford University, Stanford, CA, USA.,Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Timothy A Machado
- CNC Program, Stanford University, Stanford, CA, USA.,Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Audrey Branch
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Jared S Rosenblum
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Karl Deisseroth
- CNC Program, Stanford University, Stanford, CA, USA.,Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
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40
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Xhima K, McMahon D, Ntiri E, Goubran M, Hynynen K, Aubert I. Intravenous and Non-invasive Drug Delivery to the Mouse Basal ForebrainUsing MRI-guided Focused Ultrasound. Bio Protoc 2021; 11:e4056. [PMID: 34262999 PMCID: PMC8260260 DOI: 10.21769/bioprotoc.4056] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 02/26/2021] [Accepted: 03/14/2021] [Indexed: 11/02/2022] Open
Abstract
Basal forebrain cholinergic neurons (BFCNs) regulate circuit dynamics underlying cognitive processing, including attention, memory, and cognitive flexibility. In Alzheimer's disease and related neurodegenerative conditions, the degeneration of BFCNs has long been considered a key player in cognitive decline. The cholinergic system thus represents a key therapeutic target. A long-standing obstacle for the development of effective cholinergic-based therapies is not only the production of biologically active compounds but also a platform for safe and efficient drug delivery to the basal forebrain. The blood-brain barrier (BBB) presents a significant challenge for drug delivery to the brain, excluding approximately 98% of small-molecule biologics and nearly 100% of large-molecule therapeutic agents from entry into the brain parenchyma. Current modalities to achieve effective drug delivery to deep brain structures, such as the basal forebrain, are particularly limited. Direct intracranial injection via a needle or catheter carries risks associated with invasive neurosurgery. Intra-arterial injection of hyperosmotic solutions or therapeutics modified to penetrate the BBB using endogenous transport systems lack regional specificity, which may not always be desirable. Intranasal, intrathecal, and intraventricular administration have limited drug distribution beyond the brain surface. Here, we present a protocol for non-invasively, locally, and transiently increasing BBB permeability using MRI-guided focused ultrasound (MRIgFUS) in the murine basal forebrain for delivery of therapeutic agents targeting the cholinergic system. Ongoing work in preclinical models and clinical trials supports the safety and feasibility of MRIgFUS-mediated BBB modulation as a promising drug delivery modality for the treatment of debilitating neurological diseases.
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Affiliation(s)
- Kristiana Xhima
- Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Canada
- Biological Sciences, Sunnybrook Research Institute, Toronto, Canada
- Department of Laboratory Medicine and Pathobiology, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Dallan McMahon
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
- Department of Medical Biophysics, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Edward Ntiri
- Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Maged Goubran
- Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
- Department of Medical Biophysics, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Kullervo Hynynen
- Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
- Department of Medical Biophysics, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Isabelle Aubert
- Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Canada
- Biological Sciences, Sunnybrook Research Institute, Toronto, Canada
- Department of Laboratory Medicine and Pathobiology, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
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41
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A deep learning algorithm for 3D cell detection in whole mouse brain image datasets. PLoS Comput Biol 2021; 17:e1009074. [PMID: 34048426 PMCID: PMC8191998 DOI: 10.1371/journal.pcbi.1009074] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 06/10/2021] [Accepted: 05/12/2021] [Indexed: 12/29/2022] Open
Abstract
Understanding the function of the nervous system necessitates mapping the spatial distributions of its constituent cells defined by function, anatomy or gene expression. Recently, developments in tissue preparation and microscopy allow cellular populations to be imaged throughout the entire rodent brain. However, mapping these neurons manually is prone to bias and is often impractically time consuming. Here we present an open-source algorithm for fully automated 3D detection of neuronal somata in mouse whole-brain microscopy images using standard desktop computer hardware. We demonstrate the applicability and power of our approach by mapping the brain-wide locations of large populations of cells labeled with cytoplasmic fluorescent proteins expressed via retrograde trans-synaptic viral infection.
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Malovani C, Friedman N, Ben-Eliezer N, Tavor I. Tissue Probability Based Registration of Diffusion-Weighted Magnetic Resonance Imaging. J Magn Reson Imaging 2021; 54:1066-1076. [PMID: 33894095 DOI: 10.1002/jmri.27654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 04/01/2021] [Accepted: 04/02/2021] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Current registration methods for diffusion-MRI (dMRI) data mostly focus on white matter (WM) areas. Recently, dMRI has been employed for the characterization of gray matter (GM) microstructure, emphasizing the need for registration methods that consider all tissue types. PURPOSE To develop a dMRI registration method based on GM, WM, and cerebrospinal fluid (CSF) tissue probability maps (TPMs). STUDY TYPE Retrospective longitudinal study. POPULATION Thirty-two healthy participants were scanned twice (legacy data), divided into a training-set (n = 16) and a test-set (n = 16), and 35 randomly-selected participants from the Human Connectome Project. FIELD STRENGTH/SEQUENCE 3.0T, diffusion-weighted spin-echo echo-planar sequence; T1-weighted spoiled gradient-recalled echo (SPGR) sequence. ASSESSMENT A joint segmentation-registration approach was implemented: Diffusion tensor imaging (DTI) maps were classified into TPMs using machine-learning approaches. The resulting GM, WM, and CSF probability maps were employed as features for image alignment. Validation was performed on the test dataset and the HCP dataset. Registration performance was compared with current mainstream registration tools. STATISTICAL TESTS Classifiers used for segmentation were evaluated using leave-one-out cross-validation and scored using Dice-index. Registration success was evaluated by voxel-wise variance, normalized cross-correlation of registered DTI maps, intra- and inter-subject similarity of the registered TPMs, and region-based intra-subject similarity using an anatomical atlas. One-way ANOVAs were performed to compare between our method and other registration tools. RESULTS The proposed method outperformed mainstream registration tools as indicated by lower voxel-wise variance of registered DTI maps (SD decrease of 10%) and higher similarity between registered TPMs within and across participants, for all tissue types (Dice increase of 0.1-0.2; P < 0.05). DATA CONCLUSION A joint segmentation-registration approach based on diffusion-driven TPMs provides a more accurate registration of dMRI data, outperforming other registration tools. Our method offers a "translation" of diffusion data into structural information in the form of TPMs, allowing to directly align diffusion and structural images. LEVEL OF EVIDENCE 1 Technical Efficacy Stage: 1.
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Affiliation(s)
- Cfir Malovani
- School of Electrical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Naama Friedman
- Department of Anatomy and Anthropology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Noam Ben-Eliezer
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.,Department of Bio-Medical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel.,Center for Advanced Imaging Innovation and Research (CAI2R), New-York University Langone Medical Center, New York, New York, USA
| | - Ido Tavor
- Department of Anatomy and Anthropology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.,Strauss Center for Computational Neuroimaging, Tel Aviv University, Tel Aviv, Israel
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Liu B, Hu Y, Ma G, Xiao Y, Zhang B, Liang Y, Zhong P, Zeng X, Lin Z, Kong H, Wu G, Du Z, Fang Y, Huang M, Wang L, Yang X, Yu H. Reduced Retinal Microvascular Perfusion in Patients With Stroke Detected by Optical Coherence Tomography Angiography. Front Aging Neurosci 2021; 13:628336. [PMID: 33927607 PMCID: PMC8078175 DOI: 10.3389/fnagi.2021.628336] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 02/16/2021] [Indexed: 12/20/2022] Open
Abstract
Currently there is a shortage of biomarkers for stroke, one of the leading causes of death and disability in aging populations. Retinal vessels offer a unique and accessible “window” to study the microvasculature in vivo. However, the relationship between the retinal microvasculature and stroke is not entirely clear. To investigate the retinal microvascular characteristics in stroke, we recruited patients with stroke and age-matched control subjects from a tertiary hospital in China. The macular vessel density (VD) in the superficial capillary plexus (SCP) and deep capillary plexus (DCP), foveal avascular zone (FAZ) metrics, and optical coherence tomography angiography (OCTA) measured optic disc VD were recorded for analysis. A total of 189 patients with stroke and 195 control subjects were included. After adjusting for sex, visual acuity, systolic and diastolic blood pressure, a history of smoking, levels of hemoglobulin (HbA1c), cholesterol, and high-density lipoprotein (HDL), the macular VD of SCP and DCP in all sectors was decreased in patients with stroke. In the stroke group, the VD around the FAZ and the VD of the optic disk were lower. Logistic regression found the parafovea-superior-hemi VD of DCP > 54.53% [odds ratio (OR): 0.169] as a protective factor of stroke. Using the integration of all OCTA parameters and traditional risk factors, the area under the receiver operating characteristic (AUC) curve of distinguishing patients with stroke was 0.962, with a sensitivity of 0.944 and a specificity of 0.871. Our study demonstrates that the retinal VD is decreased in patients with stroke independently of the traditional risk factors of stroke, which may shed light on the monitoring of stroke using the retinal microvascular parameters.
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Affiliation(s)
- Baoyi Liu
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Yijun Hu
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.,Refractive Surgery Center, Aier Institute of Refractive Surgery, Guangzhou Aier Eye Hospital, Guangzhou, China.,Aier School of Ophthalmology, Central South University, Changsha, China
| | - Guixian Ma
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yu Xiao
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Bin Zhang
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yingying Liang
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Pingting Zhong
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Xiaomin Zeng
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Zhanjie Lin
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Huiqian Kong
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Guanrong Wu
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Zijing Du
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Ying Fang
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Manqing Huang
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Lijuan Wang
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Xiaohong Yang
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Honghua Yu
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
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Cabeen RP, Toga AW, Allman JM. Frontoinsular cortical microstructure is linked to life satisfaction in young adulthood. Brain Imaging Behav 2021; 15:2775-2789. [PMID: 33825124 DOI: 10.1007/s11682-021-00467-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/01/2021] [Indexed: 10/21/2022]
Abstract
Life satisfaction is a component of subjective well-being that reflects a global judgement of the quality of life according to an individual's own needs and expectations. As a psychological construct, it has attracted attention due to its relationship to mental health, resilience to stress, and other factors. Neuroimaging studies have identified neurobiological correlates of life satisfaction; however, they are limited to functional connectivity and gray matter morphometry. We explored features of gray matter microstructure obtained through compartmental modeling of multi-shell diffusion MRI data, and we examined cortical microstructure in frontoinsular cortex in a cohort of 807 typical young adults scanned as part of the Human Connectome Project. Our experiments identified the orientation dispersion index (ODI), and analogously fractional anisotropy (FA), of frontoinsular cortex as a robust set of anatomically-specific lateralized diffusion MRI microstructure features that are linked to life satisfaction, independent of other demographic, socioeconomic, and behavioral factors. We further validated our findings in a secondary test-retest dataset and found high reliability of our imaging metrics and reproducibility of outcomes. In our analysis of twin and non-twin siblings, we found basic microstructure in frontoinsular cortex to be strongly genetically determined. We also found a more moderate but still very significant genetic role in determining microstructure as it relates to life satisfaction in frontoinsular cortex. Our findings suggest a potential linkage between well-being and microscopic features of frontoinsular cortex, which may reflect cellular morphology and architecture and may more broadly implicate the integrity of the homeostatic processing performed by frontoinsular cortex as an important component of an individual's judgements of life satisfaction.
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Affiliation(s)
- Ryan P Cabeen
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
| | - Arthur W Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - John M Allman
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
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Claudi F, Tyson AL, Petrucco L, Margrie TW, Portugues R, Branco T. Visualizing anatomically registered data with brainrender. eLife 2021; 10:e65751. [PMID: 33739286 PMCID: PMC8079143 DOI: 10.7554/elife.65751] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 03/17/2021] [Indexed: 11/13/2022] Open
Abstract
Three-dimensional (3D) digital brain atlases and high-throughput brain-wide imaging techniques generate large multidimensional datasets that can be registered to a common reference frame. Generating insights from such datasets depends critically on visualization and interactive data exploration, but this a challenging task. Currently available software is dedicated to single atlases, model species or data types, and generating 3D renderings that merge anatomically registered data from diverse sources requires extensive development and programming skills. Here, we present brainrender: an open-source Python package for interactive visualization of multidimensional datasets registered to brain atlases. Brainrender facilitates the creation of complex renderings with different data types in the same visualization and enables seamless use of different atlas sources. High-quality visualizations can be used interactively and exported as high-resolution figures and animated videos. By facilitating the visualization of anatomically registered data, brainrender should accelerate the analysis, interpretation, and dissemination of brain-wide multidimensional data.
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Affiliation(s)
| | - Adam L Tyson
- UCL Sainsbury Wellcome CentreLondonUnited Kingdom
| | - Luigi Petrucco
- Institute of Neuroscience, Technical University of MunichMunichGermany
- Max Planck Institute of Neurobiology, Research Group of Sensorimotor ControlMartinsriedGermany
| | | | - Ruben Portugues
- Institute of Neuroscience, Technical University of MunichMunichGermany
- Max Planck Institute of Neurobiology, Research Group of Sensorimotor ControlMartinsriedGermany
- Munich Cluster for Systems Neurology (SyNergy)MunichGermany
| | - Tiago Branco
- UCL Sainsbury Wellcome CentreLondonUnited Kingdom
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46
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Takata N, Sato N, Komaki Y, Okano H, Tanaka KF. Flexible annotation atlas of the mouse brain: combining and dividing brain structures of the Allen Brain Atlas while maintaining anatomical hierarchy. Sci Rep 2021; 11:6234. [PMID: 33737651 PMCID: PMC7973786 DOI: 10.1038/s41598-021-85807-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 03/04/2021] [Indexed: 11/13/2022] Open
Abstract
A brain atlas is necessary for analyzing structure and function in neuroimaging research. Although various annotation volumes (AVs) for the mouse brain have been proposed, it is common in magnetic resonance imaging (MRI) of the mouse brain that regions-of-interest (ROIs) for brain structures (nodes) are created arbitrarily according to each researcher's necessity, leading to inconsistent ROIs among studies. One reason for such a situation is the fact that earlier AVs were fixed, i.e. combination and division of nodes were not implemented. This report presents a pipeline for constructing a flexible annotation atlas (FAA) of the mouse brain by leveraging public resources of the Allen Institute for Brain Science on brain structure, gene expression, and axonal projection. A mere two-step procedure with user-specified, text-based information and Python codes constructs FAA with nodes which can be combined or divided objectively while maintaining anatomical hierarchy of brain structures. Four FAAs with total node count of 4, 101, 866, and 1381 were demonstrated. Unique characteristics of FAA realized analysis of resting-state functional connectivity (FC) across the anatomical hierarchy and among cortical layers, which were thin but large brain structures. FAA can improve the consistency of whole brain ROI definition among laboratories by fulfilling various requests from researchers with its flexibility and reproducibility.
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Affiliation(s)
- Norio Takata
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, 160-8582, Japan.
- Central Institute for Experimental Animals (CIEA), 3-25-12, Tonomachi, Kawasaki, Kanagawa, 210-0821, Japan.
| | - Nobuhiko Sato
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, 160-8582, Japan
| | - Yuji Komaki
- Central Institute for Experimental Animals (CIEA), 3-25-12, Tonomachi, Kawasaki, Kanagawa, 210-0821, Japan
| | - Hideyuki Okano
- Department of Physiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, 160-8582, Japan
| | - Kenji F Tanaka
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, 160-8582, Japan
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Wang X, Zeng W, Yang X, Zhang Y, Fang C, Zeng S, Han Y, Fei P. Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain. eLife 2021; 10:e63455. [PMID: 33459255 PMCID: PMC7840180 DOI: 10.7554/elife.63455] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 12/27/2020] [Indexed: 12/21/2022] Open
Abstract
We have developed an open-source software called bi-channel image registration and deep-learning segmentation (BIRDS) for the mapping and analysis of 3D microscopy data and applied this to the mouse brain. The BIRDS pipeline includes image preprocessing, bi-channel registration, automatic annotation, creation of a 3D digital frame, high-resolution visualization, and expandable quantitative analysis. This new bi-channel registration algorithm is adaptive to various types of whole-brain data from different microscopy platforms and shows dramatically improved registration accuracy. Additionally, as this platform combines registration with neural networks, its improved function relative to the other platforms lies in the fact that the registration procedure can readily provide training data for network construction, while the trained neural network can efficiently segment-incomplete/defective brain data that is otherwise difficult to register. Our software is thus optimized to enable either minute-timescale registration-based segmentation of cross-modality, whole-brain datasets or real-time inference-based image segmentation of various brain regions of interest. Jobs can be easily submitted and implemented via a Fiji plugin that can be adapted to most computing environments.
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Affiliation(s)
- Xuechun Wang
- School of Optical and Electronic Information- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologyWuhanChina
| | - Weilin Zeng
- School of Optical and Electronic Information- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologyWuhanChina
| | - Xiaodan Yang
- School of Basic Medicine, Tongji Medical College, Huazhong University of Science and TechnologyWuhanChina
| | - Yongsheng Zhang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologyWuhanChina
| | - Chunyu Fang
- School of Optical and Electronic Information- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologyWuhanChina
| | - Shaoqun Zeng
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologyWuhanChina
| | - Yunyun Han
- School of Basic Medicine, Tongji Medical College, Huazhong University of Science and TechnologyWuhanChina
| | - Peng Fei
- School of Optical and Electronic Information- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologyWuhanChina
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Wieters F, Aswendt M. Structural integrity and remodeling underlying functional recovery after stroke. Neural Regen Res 2021; 16:1423-1424. [PMID: 33318437 PMCID: PMC8284267 DOI: 10.4103/1673-5374.301004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Affiliation(s)
- Frederique Wieters
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Cologne, Germany
| | - Markus Aswendt
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Cologne; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Germany
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Garcia-Saldivar P, Garimella A, Garza-Villarreal EA, Mendez FA, Concha L, Merchant H. PREEMACS: Pipeline for preprocessing and extraction of the macaque brain surface. Neuroimage 2020; 227:117671. [PMID: 33359348 DOI: 10.1016/j.neuroimage.2020.117671] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 12/04/2020] [Accepted: 12/16/2020] [Indexed: 01/18/2023] Open
Abstract
Accurate extraction of the cortical brain surface is critical for cortical thickness estimation and a key element to perform multimodal imaging analysis, where different metrics are integrated and compared in a common space. While brain surface extraction has become widespread practice in human studies, several challenges unique to neuroimaging of non-human primates (NHP) have hindered its adoption for the study of macaques. Although, some of these difficulties can be addressed at the acquisition stage, several common artifacts can be minimized through image preprocessing. Likewise, there are several image analysis pipelines for human MRIs, but very few automated methods for extraction of cortical surfaces have been reported for NHPs and none have been tested on data from diverse sources. We present PREEMACS, a pipeline that standardizes the preprocessing of structural MRI images (T1- and T2-weighted) and carries out an automatic surface extraction of the macaque brain. Building upon and extending pre-existing tools, the first module performs volume orientation, image cropping, intensity non-uniformity correction, and volume averaging, before skull-stripping through a convolutional neural network. The second module performs quality control using an adaptation of MRIqc method to extract objective quality metrics that are then used to determine the likelihood of accurate brain surface estimation. The third and final module estimates the white matter (wm) and pial surfaces from the T1-weighted volume (T1w) using an NHP customized version of FreeSurfer aided by the T2-weighted volumes (T2w). To evaluate the generalizability of PREEMACS, we tested the pipeline using 57 T1w/T2w NHP volumes acquired at 11 different sites from the PRIME-DE public dataset. Results showed an accurate and robust automatic brain surface extraction from images that passed the quality control segment of our pipeline. This work offers a robust, efficient and generalizable pipeline for the automatic standardization of MRI surface analysis on NHP.
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Affiliation(s)
- Pamela Garcia-Saldivar
- Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla. Blvd. Juriquilla, 3001 Querétaro, Querétaro, México
| | - Arun Garimella
- Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla. Blvd. Juriquilla, 3001 Querétaro, Querétaro, México; International Institute of Information Technology, Hyderabad, India
| | - Eduardo A Garza-Villarreal
- Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla. Blvd. Juriquilla, 3001 Querétaro, Querétaro, México
| | - Felipe A Mendez
- Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla. Blvd. Juriquilla, 3001 Querétaro, Querétaro, México
| | - Luis Concha
- Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla. Blvd. Juriquilla, 3001 Querétaro, Querétaro, México.
| | - Hugo Merchant
- Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla. Blvd. Juriquilla, 3001 Querétaro, Querétaro, México.
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Moldovanu S, Toporaș LP, Biswas A, Moraru L. Combining Sparse and Dense Features to Improve Multi-Modal Registration for Brain DTI Images. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E1299. [PMID: 33287067 PMCID: PMC7711905 DOI: 10.3390/e22111299] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 11/09/2020] [Accepted: 11/12/2020] [Indexed: 12/13/2022]
Abstract
A new solution to overcome the constraints of multimodality medical intra-subject image registration is proposed, using the mutual information (MI) of image histogram-oriented gradients as a new matching criterion. We present a rigid, multi-modal image registration algorithm based on linear transformation and oriented gradients for the alignment of T2-weighted (T2w) images (as a fixed reference) and diffusion tensor imaging (DTI) (b-values of 500 and 1250 s/mm2) as floating images of three patients to compensate for the motion during the acquisition process. Diffusion MRI is very sensitive to motion, especially when the intensity and duration of the gradient pulses (characterized by the b-value) increases. The proposed method relies on the whole brain surface and addresses the variability of anatomical features into an image stack. The sparse features refer to corners detected using the Harris corner detector operator, while dense features use all image pixels through the image histogram of oriented gradients (HOG) as a measure of the degree of statistical dependence between a pair of registered images. HOG as a dense feature is focused on the structure and extracts the oriented gradient image in the x and y directions. MI is used as an objective function for the optimization process. The entropy functions and joint entropy function are determined using the HOGs data. To determine the best image transformation, the fiducial registration error (FRE) measure is used. We compare the results against the MI-based intensities results computed using a statistical intensity relationship between corresponding pixels in source and target images. Our approach, which is devoted to the whole brain, shows improved registration accuracy, robustness, and computational cost compared with the registration algorithms, which use anatomical features or regions of interest areas with specific neuroanatomy. Despite the supplementary HOG computation task, the computation time is comparable for MI-based intensities and MI-based HOG methods.
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Affiliation(s)
- Simona Moldovanu
- Department of Computer Science and Information Technology, Faculty of Automation, Computers, Electrical Engineering and Electronics, Dunarea de Jos University of Galati, Galati 47 Domneasca Str., 800008 Galati, Romania;
- The Modelling & Simulation Laboratory, Dunarea de Jos University of Galati, Galati 47 Domneasca Str., 800008 Galati, Romania;
| | - Lenuta Pană Toporaș
- The Modelling & Simulation Laboratory, Dunarea de Jos University of Galati, Galati 47 Domneasca Str., 800008 Galati, Romania;
- Department of Chemistry, Physics & Environment, Faculty of Sciences and Environment, Dunarea de Jos University of Galati, 47 Domneasca Str., 800008 Galati, Romania
| | - Anjan Biswas
- Department of Physics, Chemistry and Mathematics, Alabama A&M University, Normal, AL 35762-4900, USA;
- Department of Mathematics, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Department of Applied Mathematics, National Research Nuclear University, 31 Kashirskoe Hwy, 115409 Moscow, Russia
| | - Luminita Moraru
- The Modelling & Simulation Laboratory, Dunarea de Jos University of Galati, Galati 47 Domneasca Str., 800008 Galati, Romania;
- Department of Chemistry, Physics & Environment, Faculty of Sciences and Environment, Dunarea de Jos University of Galati, 47 Domneasca Str., 800008 Galati, Romania
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