1
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Renton AI, Dao TT, Johnstone T, Civier O, Sullivan RP, White DJ, Lyons P, Slade BM, Abbott DF, Amos TJ, Bollmann S, Botting A, Campbell MEJ, Chang J, Close TG, Dörig M, Eckstein K, Egan GF, Evas S, Flandin G, Garner KG, Garrido MI, Ghosh SS, Grignard M, Halchenko YO, Hannan AJ, Heinsfeld AS, Huber L, Hughes ME, Kaczmarzyk JR, Kasper L, Kuhlmann L, Lou K, Mantilla-Ramos YJ, Mattingley JB, Meier ML, Morris J, Narayanan A, Pestilli F, Puce A, Ribeiro FL, Rogasch NC, Rorden C, Schira MM, Shaw TB, Sowman PF, Spitz G, Stewart AW, Ye X, Zhu JD, Narayanan A, Bollmann S. Neurodesk: an accessible, flexible and portable data analysis environment for reproducible neuroimaging. Nat Methods 2024; 21:804-808. [PMID: 38191935 PMCID: PMC11180540 DOI: 10.1038/s41592-023-02145-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 11/27/2023] [Indexed: 01/10/2024]
Abstract
Neuroimaging research requires purpose-built analysis software, which is challenging to install and may produce different results across computing environments. The community-oriented, open-source Neurodesk platform ( https://www.neurodesk.org/ ) harnesses a comprehensive and growing suite of neuroimaging software containers. Neurodesk includes a browser-accessible virtual desktop, command-line interface and computational notebook compatibility, allowing for accessible, flexible, portable and fully reproducible neuroimaging analysis on personal workstations, high-performance computers and the cloud.
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Affiliation(s)
- Angela I Renton
- The University of Queensland, Queensland Brain Institute, St Lucia, Brisbane, Queensland, Australia.
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia.
| | - Thuy T Dao
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Tom Johnstone
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Oren Civier
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Ryan P Sullivan
- The University of Sydney, School of Biomedical Engineering, Sydney, New South Wales, Australia
| | - David J White
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Paris Lyons
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Benjamin M Slade
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - David F Abbott
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Toluwani J Amos
- School of Life Science and Technology, University of Electronic Science and Technology, Chengdu, China
| | - Saskia Bollmann
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Andy Botting
- Australian Research Data Commons (ARDC), Sydney, New South Wales, Australia
| | - Megan E J Campbell
- School of Psychological Sciences, University of Newcastle, Newcastle, New South Wales, Australia
- Hunter Medical Research Institute Imaging Centre, Newcastle, New South Wales, Australia
| | - Jeryn Chang
- The University of Queensland, School of Biomedical Sciences, St Lucia, Brisbane, Queensland, Australia
| | - Thomas G Close
- The University of Sydney, School of Biomedical Engineering, Sydney, New South Wales, Australia
| | - Monika Dörig
- Integrative Spinal Research, Department of Chiropractic Medicine, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Korbinian Eckstein
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Gary F Egan
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
| | - Stefanie Evas
- School of Psychology, University of Adelaide, Adelaide, South Australia, Australia
- Human Health, Health & Biosecurity, CSIRO, Adelaide, South Australia, Australia
| | - Guillaume Flandin
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Kelly G Garner
- School of Psychology, University of New South Wales, Sydney, New South Wales, Australia
- The University of Queensland, School of Psychology, St Lucia, Brisbane, Queensland, Australia
| | - Marta I Garrido
- Melbourne School of Psychological Sciences, he University of Melbourne, Melbourne, Victoria, Australia
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia
| | - Satrajit S Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School, Boston, MA, USA
| | - Martin Grignard
- GIGA CRC In-Vivo Imaging, University of Liège, Liège, Belgium
| | - Yaroslav O Halchenko
- Center for Open Neuroscience, Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Anthony J Hannan
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Anibal S Heinsfeld
- Department of Psychology, Center for Perceptual Systems, Institute for Neuroscience, Center For Learning and Memory, The University of Texas at Austin, Austin, TX, USA
| | - Laurentius Huber
- National Institute of Mental Health (NIMH), National Institutes Health, Bethesda, MD, USA
| | - Matthew E Hughes
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Jakub R Kaczmarzyk
- Department of Biomedical Informatics, Stony Brook University, New York, NY, USA
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, New York, NY, USA
| | - Lars Kasper
- BRAIN-TO Lab, Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Levin Kuhlmann
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia
| | - Kexin Lou
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Yorguin-Jose Mantilla-Ramos
- Grupo Neuropsicología y Conducta (GRUNECO), Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia
| | - Jason B Mattingley
- The University of Queensland, Queensland Brain Institute, St Lucia, Brisbane, Queensland, Australia
- The University of Queensland, School of Psychology, St Lucia, Brisbane, Queensland, Australia
| | - Michael L Meier
- Integrative Spinal Research, Department of Chiropractic Medicine, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Jo Morris
- Australian Research Data Commons (ARDC), Sydney, New South Wales, Australia
| | - Akshaiy Narayanan
- School of Computer Science, The University of Auckland, Auckland, New Zealand
| | - Franco Pestilli
- Department of Psychology, Center for Perceptual Systems, Institute for Neuroscience, Center For Learning and Memory, The University of Texas at Austin, Austin, TX, USA
| | - Aina Puce
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Fernanda L Ribeiro
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Nigel C Rogasch
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia
- Hopwood Centre for Neurobiology, Lifelong Health Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, South Australia, Australia
| | - Chris Rorden
- McCausland Center for Brain Imaging, Department of Psychology, University of South Carolina, Columbia, SC, USA
| | - Mark M Schira
- School of Psychology, University of Wollongong, Wollongong, New South Wales, Australia
| | - Thomas B Shaw
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
- The University of Queensland, Centre for Advanced Imaging, St Lucia, Brisbane, Queensland, Australia
- Department of Neurology, Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia
| | - Paul F Sowman
- Macquarie University, School of Psychological Sciences, Sydney, New South Wales, Australia
| | - Gershon Spitz
- Department of Neuroscience, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia
- Monash-Epworth Rehabilitation Research Centre, Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Ashley W Stewart
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia
| | - Xincheng Ye
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Judy D Zhu
- Macquarie University, School of Psychological Sciences, Sydney, New South Wales, Australia
| | - Aswin Narayanan
- The University of Queensland, Centre for Advanced Imaging, St Lucia, Brisbane, Queensland, Australia
| | - Steffen Bollmann
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia.
- The University of Queensland, Centre for Advanced Imaging, St Lucia, Brisbane, Queensland, Australia.
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia.
- Queensland Digital Health Centre, The University of Queensland, Brisbane, Queensland, Australia.
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2
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Llambrich S, Tielemans B, Saliën E, Atzori M, Wouters K, Van Bulck V, Platt M, Vanherp L, Gallego Fernandez N, Grau de la Fuente L, Poptani H, Verlinden L, Himmelreich U, Croitor A, Attanasio C, Callaerts-Vegh Z, Gsell W, Martínez-Abadías N, Vande Velde G. Pleiotropic effects of trisomy and pharmacologic modulation on structural, functional, molecular, and genetic systems in a Down syndrome mouse model. eLife 2024; 12:RP89763. [PMID: 38497812 PMCID: PMC10948151 DOI: 10.7554/elife.89763] [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] [Indexed: 03/19/2024] Open
Abstract
Down syndrome (DS) is characterized by skeletal and brain structural malformations, cognitive impairment, altered hippocampal metabolite concentration and gene expression imbalance. These alterations were usually investigated separately, and the potential rescuing effects of green tea extracts enriched in epigallocatechin-3-gallate (GTE-EGCG) provided disparate results due to different experimental conditions. We overcame these limitations by conducting the first longitudinal controlled experiment evaluating genotype and GTE-EGCG prenatal chronic treatment effects before and after treatment discontinuation. Our findings revealed that the Ts65Dn mouse model reflected the pleiotropic nature of DS, exhibiting brachycephalic skull, ventriculomegaly, neurodevelopmental delay, hyperactivity, and impaired memory robustness with altered hippocampal metabolite concentration and gene expression. GTE-EGCG treatment modulated most systems simultaneously but did not rescue DS phenotypes. On the contrary, the treatment exacerbated trisomic phenotypes including body weight, tibia microarchitecture, neurodevelopment, adult cognition, and metabolite concentration, not supporting the therapeutic use of GTE-EGCG as a prenatal chronic treatment. Our results highlight the importance of longitudinal experiments assessing the co-modulation of multiple systems throughout development when characterizing preclinical models in complex disorders and evaluating the pleiotropic effects and general safety of pharmacological treatments.
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Affiliation(s)
- Sergi Llambrich
- Biomedical MRI, Department of Imaging and Pathology, KU LeuvenLeuvenBelgium
| | - Birger Tielemans
- Biomedical MRI, Department of Imaging and Pathology, KU LeuvenLeuvenBelgium
| | - Ellen Saliën
- Biomedical MRI, Department of Imaging and Pathology, KU LeuvenLeuvenBelgium
| | - Marta Atzori
- Department of Human Genetics, KU LeuvenLeuvenBelgium
| | - Kaat Wouters
- Laboratory of Biological Psychology, KU LeuvenLeuvenBelgium
| | | | - Mark Platt
- Centre for Preclinical Imaging, Department of Molecular and Clinical Cancer Medicine, University of LiverpoolLiverpoolUnited Kingdom
| | - Laure Vanherp
- Biomedical MRI, Department of Imaging and Pathology, KU LeuvenLeuvenBelgium
| | - Nuria Gallego Fernandez
- Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals (BEECA), Facultat de Biologia, Universitat de BarcelonaBarcelonaSpain
| | - Laura Grau de la Fuente
- Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals (BEECA), Facultat de Biologia, Universitat de BarcelonaBarcelonaSpain
| | - Harish Poptani
- Centre for Preclinical Imaging, Department of Molecular and Clinical Cancer Medicine, University of LiverpoolLiverpoolUnited Kingdom
| | - Lieve Verlinden
- Clinical and Experimental Endocrinology, KU LeuvenLeuvenBelgium
| | - Uwe Himmelreich
- Biomedical MRI, Department of Imaging and Pathology, KU LeuvenLeuvenBelgium
| | - Anca Croitor
- Biomedical MRI, Department of Imaging and Pathology, KU LeuvenLeuvenBelgium
| | | | | | - Willy Gsell
- Biomedical MRI, Department of Imaging and Pathology, KU LeuvenLeuvenBelgium
| | - Neus Martínez-Abadías
- Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals (BEECA), Facultat de Biologia, Universitat de BarcelonaBarcelonaSpain
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3
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Aljuraysi S, Platt M, Pulix M, Poptani H, Plagge A. Microcephaly with a disproportionate hippocampal reduction, stem cell loss and neuronal lipid droplet symptoms in Trappc9 KO mice. Neurobiol Dis 2024; 192:106431. [PMID: 38331351 DOI: 10.1016/j.nbd.2024.106431] [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: 12/04/2023] [Revised: 01/23/2024] [Accepted: 02/04/2024] [Indexed: 02/10/2024] Open
Abstract
Mutations of the human TRAFFICKING PROTEIN PARTICLE COMPLEX SUBUNIT 9 (TRAPPC9) cause a neurodevelopmental disorder characterised by microcephaly and intellectual disability. Trappc9 constitutes a subunit specific to the intracellular membrane-associated TrappII complex. The TrappII complex interacts with Rab11 and Rab18, the latter being specifically associated with lipid droplets (LDs). Here we used non-invasive imaging to characterise Trappc9 knock-out (KO) mice as a model of the human hereditary disorder. KOs developed postnatal microcephaly with many grey and white matter regions being affected. In vivo magnetic resonance imaging (MRI) identified a disproportionately stronger volume reduction in the hippocampus, which was associated with a significant loss of Sox2-positive neural stem and progenitor cells. Diffusion tensor imaging indicated a reduced organisation or integrity of white matter areas. Trappc9 KOs displayed behavioural abnormalities in several tests related to exploration, learning and memory. Trappc9-deficient primary hippocampal neurons accumulated a larger LD volume per cell following Oleic Acid stimulation, and the coating of LDs by Perilipin-2 was much reduced. Additionally, Trappc9 KOs developed obesity, which was significantly more severe in females than in males. Our findings indicate that, beyond previously reported Rab11-related vesicle transport defects, dysfunctions in LD homeostasis might contribute to the neurobiological symptoms of Trappc9 deficiency.
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Affiliation(s)
- Sultan Aljuraysi
- Department of Biochemistry, Cell and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; Department of Physiology, College of Medicine, King Saud University, Riyadh, Saudi Arabia
| | - Mark Platt
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; Centre for Preclinical Imaging, University of Liverpool, Liverpool, UK
| | - Michela Pulix
- Department of Biochemistry, Cell and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Harish Poptani
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; Centre for Preclinical Imaging, University of Liverpool, Liverpool, UK.
| | - Antonius Plagge
- Department of Biochemistry, Cell and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; Centre for Preclinical Imaging, University of Liverpool, Liverpool, UK.
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4
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Yamasaki E, Thakore P, Ali S, Solano AS, Wang X, Gao X, Labelle-Dumais C, Chaumeil MM, Gould DB, Earley S. Impaired intracellular Ca 2+ signaling contributes to age-related cerebral small vessel disease in Col4a1 mutant mice. Sci Signal 2023; 16:eadi3966. [PMID: 37963192 PMCID: PMC10726848 DOI: 10.1126/scisignal.adi3966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 10/25/2023] [Indexed: 11/16/2023]
Abstract
Humans and mice with mutations in COL4A1 and COL4A2 manifest hallmarks of cerebral small vessel disease (cSVD). Mice with a missense mutation in Col4a1 at amino acid 1344 (Col4a1+/G1344D) exhibit age-dependent intracerebral hemorrhages (ICHs) and brain lesions. Here, we report that this pathology was associated with the loss of myogenic vasoconstriction, an intrinsic vascular response essential for the autoregulation of cerebral blood flow. Electrophysiological analyses showed that the loss of myogenic constriction resulted from blunted pressure-induced smooth muscle cell (SMC) membrane depolarization. Furthermore, we found that dysregulation of membrane potential was associated with impaired Ca2+-dependent activation of large-conductance Ca2+-activated K+ (BK) and transient receptor potential melastatin 4 (TRPM4) cation channels linked to disruptions in sarcoplasmic reticulum (SR) Ca2+ signaling. Col4a1 mutations impair protein folding, which can cause SR stress. Treating Col4a1+/G1344D mice with 4-phenylbutyrate, a compound that promotes the trafficking of misfolded proteins and alleviates SR stress, restored SR Ca2+ signaling, maintained BK and TRPM4 channel activity, prevented loss of myogenic tone, and reduced ICHs. We conclude that alterations in SR Ca2+ handling that impair ion channel activity result in dysregulation of SMC membrane potential and loss of myogenic tone and contribute to age-related cSVD in Col4a1+/G1344D mice.
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Affiliation(s)
- Evan Yamasaki
- Department of Pharmacology, Center for Molecular and Cellular Signaling in the Cardiovascular System, University of Nevada, Reno School of Medicine, Reno, NV 89557-0318, USA
| | - Pratish Thakore
- Department of Pharmacology, Center for Molecular and Cellular Signaling in the Cardiovascular System, University of Nevada, Reno School of Medicine, Reno, NV 89557-0318, USA
| | - Sher Ali
- Department of Pharmacology, Center for Molecular and Cellular Signaling in the Cardiovascular System, University of Nevada, Reno School of Medicine, Reno, NV 89557-0318, USA
| | - Alfredo Sanchez Solano
- Department of Pharmacology, Center for Molecular and Cellular Signaling in the Cardiovascular System, University of Nevada, Reno School of Medicine, Reno, NV 89557-0318, USA
| | - Xiaowei Wang
- Department of Ophthalmology, UCSF School of Medicine, San Francisco, CA 94158, USA
| | - Xiao Gao
- Department of Physical Therapy and Rehabilitation Science, UCSF School of Medicine, San Francisco, CA 94143, USA
- Department of Radiology and Biomedical Imaging, UCSF School of Medicine, San Francisco, CA 94143, USA
| | | | - Myriam M. Chaumeil
- Department of Physical Therapy and Rehabilitation Science, UCSF School of Medicine, San Francisco, CA 94143, USA
- Department of Radiology and Biomedical Imaging, UCSF School of Medicine, San Francisco, CA 94143, USA
| | - Douglas B. Gould
- Department of Ophthalmology, UCSF School of Medicine, San Francisco, CA 94158, USA
- Department of Anatomy, Institute for Human Genetics, Cardiovascular Research Institute, Bakar Aging Research Institute, UCSF School of Medicine, San Francisco, CA 94158, USA
| | - Scott Earley
- Department of Pharmacology, Center for Molecular and Cellular Signaling in the Cardiovascular System, University of Nevada, Reno School of Medicine, Reno, NV 89557-0318, USA
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5
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Xu N, Zhang L, Larson S, Li Z, Anumba N, Daley L, Pan WJ, Chuang KH, Keilholz SD. Rodent Whole-Brain fMRI Data Preprocessing Toolbox. APERTURE NEURO 2023; 3:10.52294/001c.85075. [PMID: 37654427 PMCID: PMC10469192 DOI: 10.52294/001c.85075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Affiliation(s)
- Nan Xu
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Leo Zhang
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | | | - Zengmin Li
- Centre for Advanced Imaging and Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Nmachi Anumba
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Lauren Daley
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Wen-Ju Pan
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Kai-Hsiang Chuang
- Centre for Advanced Imaging and Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Shella D Keilholz
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
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6
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Kalantari A, Szczepanik M, Heunis S, Mönch C, Hanke M, Wachtler T, Aswendt M. How to establish and maintain a multimodal animal research dataset using DataLad. Sci Data 2023; 10:357. [PMID: 37277500 DOI: 10.1038/s41597-023-02242-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 05/15/2023] [Indexed: 06/07/2023] Open
Abstract
Sharing of data, processing tools, and workflows require open data hosting services and management tools. Despite FAIR guidelines and the increasing demand from funding agencies and publishers, only a few animal studies share all experimental data and processing tools. We present a step-by-step protocol to perform version control and remote collaboration for large multimodal datasets. A data management plan was introduced to ensure data security in addition to a homogeneous file and folder structure. Changes to the data were automatically tracked using DataLad and all data was shared on the research data platform GIN. This simple and cost-effective workflow facilitates the adoption of FAIR data logistics and processing workflows by making the raw and processed data available and providing the technical infrastructure to independently reproduce the data processing steps. It enables the community to collect heterogeneously acquired and stored datasets not limited to a specific category of data and serves as a technical infrastructure blueprint with rich potential to improve data handling at other sites and extend to other research areas.
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Affiliation(s)
- Aref Kalantari
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Cologne, Germany
| | - Michał Szczepanik
- Psychoinformatics Lab, Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Stephan Heunis
- Psychoinformatics Lab, Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Christian Mönch
- Psychoinformatics Lab, Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Michael Hanke
- Psychoinformatics Lab, Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Thomas Wachtler
- Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, München, Germany
| | - Markus Aswendt
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Cologne, Germany.
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Jülich, Jülich, Germany.
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7
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Renton AI, Dao TT, Johnstone T, Civier O, Sullivan RP, White DJ, Lyons P, Slade BM, Abbott DF, Amos TJ, Bollmann S, Botting A, Campbell MEJ, Chang J, Close TG, Eckstein K, Egan GF, Evas S, Flandin G, Garner KG, Garrido MI, Ghosh SS, Grignard M, Hannan AJ, Huber R, Kaczmarzyk JR, Kasper L, Kuhlmann L, Lou K, Mantilla-Ramos YJ, Mattingley JB, Morris J, Narayanan A, Pestilli F, Puce A, Ribeiro FL, Rogasch NC, Rorden C, Schira M, Shaw TB, Sowman PF, Spitz G, Stewart A, Ye X, Zhu JD, Hughes ME, Narayanan A, Bollmann S. Neurodesk: An accessible, flexible, and portable data analysis environment for reproducible neuroimaging. RESEARCH SQUARE 2023:rs.3.rs-2649734. [PMID: 36993557 PMCID: PMC10055538 DOI: 10.21203/rs.3.rs-2649734/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Neuroimaging data analysis often requires purpose-built software, which can be challenging to install and may produce different results across computing environments. Beyond being a roadblock to neuroscientists, these issues of accessibility and portability can hamper the reproducibility of neuroimaging data analysis pipelines. Here, we introduce the Neurodesk platform, which harnesses software containers to support a comprehensive and growing suite of neuroimaging software (https://www.neurodesk.org/). Neurodesk includes a browser-accessible virtual desktop environment and a command line interface, mediating access to containerized neuroimaging software libraries on various computing platforms, including personal and high-performance computers, cloud computing and Jupyter Notebooks. This community-oriented, open-source platform enables a paradigm shift for neuroimaging data analysis, allowing for accessible, flexible, fully reproducible, and portable data analysis pipelines.
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Affiliation(s)
- Angela I. Renton
- The University of Queensland, Queensland Brain Institute, St Lucia 4072, Australia
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Thuy T. Dao
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Tom Johnstone
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - Oren Civier
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - Ryan P. Sullivan
- The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| | - David J. White
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - Paris Lyons
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - Benjamin M. Slade
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - David F. Abbott
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Victoria, Australia
| | - Toluwani J. Amos
- School of Life Science and Technology, University of Electronic Science and Technology, China
| | - Saskia Bollmann
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Andy Botting
- Australian Research Data Commons (ARDC), Australia
| | - Megan E. J. Campbell
- School of Psychological Sciences, University of Newcastle, Australia
- Hunter Medical Research Institute Imaging Centre, Newcastle, Australia
| | - Jeryn Chang
- The University of Queensland, School of Biomedical Sciences, St Lucia 4072, Australia
| | - Thomas G. Close
- The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| | - Korbinian Eckstein
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Gary F. Egan
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Stefanie Evas
- School of Psychology, University of Adelaide, Adelaide, 5000, Australia
- Human Health, Health & Biosecurity, CSIRO, Adelaide, 5000, Australia
| | - Guillaume Flandin
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Kelly G. Garner
- The University of Queensland, Queensland Brain Institute, St Lucia 4072, Australia
- The University of Queensland, School of Psychology, St Lucia 4072, Australia
| | - Marta I. Garrido
- Melbourne School of Psychological Sciences, The University of Melbourne
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne
| | - Satrajit S. Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School, Boston, MA, USA
| | - Martin Grignard
- GIGA CRC In-Vivo Imaging, University of Liege, Liege, Belgium
| | - Anthony J. Hannan
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Victoria, Australia
| | - Renzo Huber
- Functional Magnetic Resonance Imaging Core Facility (FMRIF), National Institute of Mental Health (NIMH), USA
| | - Jakub R. Kaczmarzyk
- Medical Scientist Training Program, Stony Brook University, Stony Brook, NY, United States of America
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, United States of America
| | - Lars Kasper
- Techna Institute, University Health Network, Toronto, Canada
| | - Levin Kuhlmann
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia
| | - Kexin Lou
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | | | - Jason B. Mattingley
- The University of Queensland, Queensland Brain Institute, St Lucia 4072, Australia
- The University of Queensland, School of Psychology, St Lucia 4072, Australia
| | - Jo Morris
- Australian Research Data Commons (ARDC), Australia
| | | | - Franco Pestilli
- Department of Psychology, Center for Perceptual Systems, Center for Theoretical and Computational Neuroscience, Center on Aging and Population Sciences, Center for Learning and Memory, The University of Texas at Austin, 108 E Dean Keeton St, Austin, TX 78712, USA
| | - Aina Puce
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Fernanda L. Ribeiro
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Nigel C. Rogasch
- Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Australia
- Hopwood Centre for Neurobiology, Lifelong Health Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA, Australia
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Victoria, Australia
| | - Chris Rorden
- McCausland Center for Brain Imaging, Department of Psychology, University of South Carolina, Columbia SC, 29208, USA
| | - Mark Schira
- School of Psychology, University of Wollongong, Wollongong, 2522, Australia
| | - Thomas B. Shaw
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
- The University of Queensland, Centre for Advanced Imaging, St Lucia 4072, Australia
- Department of Neurology, Royal Brisbane and Women's Hospital, Brisbane, Australia
| | - Paul F. Sowman
- Macquarie University, School of Psychological Sciences, North Ryde 2112, Australia
| | - Gershon Spitz
- Department of Neuroscience, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Australia
- Monash-Epworth Rehabilitation Research Centre, Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, 3168, Australia
| | - Ashley Stewart
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Xincheng Ye
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Judy D. Zhu
- Macquarie University, School of Psychological Sciences, North Ryde 2112, Australia
| | - Matthew E. Hughes
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - Aswin Narayanan
- The University of Queensland, Centre for Advanced Imaging, St Lucia 4072, Australia
| | - Steffen Bollmann
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
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8
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Götz J, Wieters F, Fritz VJ, Käsgen O, Kalantari A, Fink GR, Aswendt M. Temporal and Spatial Gene Expression Profile of Stroke Recovery Genes in Mice. Genes (Basel) 2023; 14:454. [PMID: 36833381 PMCID: PMC9956317 DOI: 10.3390/genes14020454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 02/06/2023] [Accepted: 02/07/2023] [Indexed: 02/12/2023] Open
Abstract
Stroke patients show some degree of spontaneous functional recovery, but this is not sufficient to prevent long-term disability. One promising approach is to characterize the dynamics of stroke recovery genes in the lesion and distant areas. We induced sensorimotor cortex lesions in adult C57BL/6J mice using photothrombosis and performed qPCR on selected brain areas at 14, 28, and 56 days post-stroke (P14-56). Based on the grid walk and rotating beam test, the mice were classified into two groups. The expression of cAMP pathway genes Adora2a, Pde10a, and Drd2, was higher in poor- compared to well-recovered mice in contralesional primary motor cortex (cl-MOp) at P14&56 and cl-thalamus (cl-TH), but lower in cl-striatum (cl-Str) at P14 and cl-primary somatosensory cortex (cl-SSp) at P28. Plasticity and axonal sprouting genes, Lingo1 and BDNF, were decreased in cl-MOp at P14 and cl-Str at P28 and increased in cl-SSp at P28 and cl-Str at P14, respectively. In the cl-TH, Lingo1 was increased, and BDNF decreased at P14. Atrx, also involved in axonal sprouting, was only increased in poor-recovered mice in cl-MOp at P28. The results underline the gene expression dynamics and spatial variability and challenge existing theories of restricted neural plasticity.
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Affiliation(s)
- Jan Götz
- Faculty of Medicine, University of Cologne, 50923 Cologne, Germany
- Department of Neurology, University Hospital Cologne, 50931 Cologne, Germany
| | - Frederique Wieters
- Faculty of Medicine, University of Cologne, 50923 Cologne, Germany
- Department of Neurology, University Hospital Cologne, 50931 Cologne, Germany
| | - Veronika J. Fritz
- Faculty of Medicine, University of Cologne, 50923 Cologne, Germany
- Department of Neurology, University Hospital Cologne, 50931 Cologne, Germany
| | - Olivia Käsgen
- Faculty of Medicine, University of Cologne, 50923 Cologne, Germany
- Department of Neurology, University Hospital Cologne, 50931 Cologne, Germany
| | - Aref Kalantari
- Faculty of Medicine, University of Cologne, 50923 Cologne, Germany
- Department of Neurology, University Hospital Cologne, 50931 Cologne, Germany
| | - Gereon R. Fink
- Faculty of Medicine, University of Cologne, 50923 Cologne, Germany
- Department of Neurology, University Hospital Cologne, 50931 Cologne, Germany
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, 52425 Juelich, Germany
| | - Markus Aswendt
- Faculty of Medicine, University of Cologne, 50923 Cologne, Germany
- Department of Neurology, University Hospital Cologne, 50931 Cologne, Germany
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9
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Heller C, Kimmig ACS, Kubicki MR, Derntl B, Kikinis Z. Imaging the human brain on oral contraceptives: A review of structural imaging methods and implications for future research goals. Front Neuroendocrinol 2022; 67:101031. [PMID: 35998859 DOI: 10.1016/j.yfrne.2022.101031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 06/30/2022] [Accepted: 08/15/2022] [Indexed: 12/21/2022]
Abstract
Worldwide over 150 million women use oral contraceptives (OCs), which are the most prescribed form of contraception in both the United States and in European countries. Sex hormones, such as estradiol and progesterone, are important endogenous hormones known for shaping the brain across the life span. Synthetic hormones, which are present in OCs, interfere with the natural hormonal balance by reducing the endogenous hormone levels. Little is known how this affects the brain, especially during the most vulnerable times of brain maturation. Here, we review studies that investigate differences in brain gray and white matter in women using OCs in comparison to naturally cycling women. We focus on two neuroimaging methods used to quantify structural gray and white matter changes, namely structural MRI and diffusion MRI. Finally, we discuss the potential of these imaging techniques to advance knowledge about the effects of OCs on the brain and wellbeing in women.
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Affiliation(s)
- Carina Heller
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry and Psychotherapy, Jena University Hospital, Germany; Department of Clinical Psychology, Friedrich Schiller University Jena, Germany.
| | - Ann-Christin S Kimmig
- Department of Psychiatry and Psychotherapy, Innovative Neuroimaging, Tübingen Center for Mental Health (TüCMH), University of Tübingen, Tübingen, Germany; Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tübingen, Tübingen, Germany
| | - Marek R Kubicki
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Birgit Derntl
- Department of Psychiatry and Psychotherapy, Innovative Neuroimaging, Tübingen Center for Mental Health (TüCMH), University of Tübingen, Tübingen, Germany; Lead Graduate School, University of Tübingen, Tübingen, Germany
| | - Zora Kikinis
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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10
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Piekarski DJ, Zahr NM, Zhao Q, Sullivan EV, Pfefferbaum A. Alcohol's effects on the mouse brain are modulated by age and sex. Addict Biol 2022; 27:e13209. [PMID: 36001428 PMCID: PMC9539709 DOI: 10.1111/adb.13209] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 05/05/2022] [Accepted: 06/27/2022] [Indexed: 11/30/2022]
Abstract
Binge alcohol consumption is common among adolescents and may impair normal brain development. Emerging, longitudinal studies in adolescents suggest that the effects of binge alcohol exposure on brain structure differ between sexes. To test the hypothesis that the effects of binge alcohol exposure on developmental brain growth trajectories are influenced by age of exposure and sex, adolescent and adult, male and female C57Bl/6 mice (n = 32), were exposed to a binge‐like ethanol (EtOH) exposure paradigm (i.e., 5 cycles of 2 on/2 off days of 5 g/kg EtOH intraperitoneal) or served as saline controls. Longitudinal structural magnetic resonance imaging was acquired at baseline, following binge EtOH exposure, and after 2 weeks of recovery. Alcohol treatment showed interactions with age and sex in altering whole brain volume: adolescents of both sexes demonstrated inhibited whole brain growth relative to their control counterparts, although significance was only attained in female mice which showed a larger magnitude response to EtOH compared to male mice. In region of interest analyses, the somatosensory cortex and cerebellum showed inhibited growth in male and female adolescent mice exposed to EtOH, but the difference relative to controls did not reach multiple comparison‐corrected statistical significance. These data suggest that in mice exposed to binge EtOH treatment, adolescent age of exposure and female sex may confer a higher risk to the detrimental effects of EtOH on brain structure and reinforce the need for direct testing of both sexes.
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Affiliation(s)
| | - Natalie M. Zahr
- Department of Psychiatry and Behavioral Sciences Stanford University School of Medicine Stanford California USA
| | - Qingyu Zhao
- Department of Psychiatry and Behavioral Sciences Stanford University School of Medicine Stanford California USA
| | - Edith V. Sullivan
- Department of Psychiatry and Behavioral Sciences Stanford University School of Medicine Stanford California USA
| | - Adolf Pfefferbaum
- Neuroscience Program SRI International Menlo Park California USA
- Department of Psychiatry and Behavioral Sciences Stanford University School of Medicine Stanford California USA
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11
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Uselman TW, Medina CS, Gray HB, Jacobs RE, Bearer EL. Longitudinal manganese-enhanced magnetic resonance imaging of neural projections and activity. NMR IN BIOMEDICINE 2022; 35:e4675. [PMID: 35253280 PMCID: PMC11064873 DOI: 10.1002/nbm.4675] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 10/19/2021] [Accepted: 12/07/2021] [Indexed: 06/14/2023]
Abstract
Manganese-enhanced magnetic resonance imaging (MEMRI) holds exceptional promise for preclinical studies of brain-wide physiology in awake-behaving animals. The objectives of this review are to update the current information regarding MEMRI and to inform new investigators as to its potential. Mn(II) is a powerful contrast agent for two main reasons: (1) high signal intensity at low doses; and (2) biological interactions, such as projection tracing and neural activity mapping via entry into electrically active neurons in the living brain. High-spin Mn(II) reduces the relaxation time of water protons: at Mn(II) concentrations typically encountered in MEMRI, robust hyperintensity is obtained without adverse effects. By selectively entering neurons through voltage-gated calcium channels, Mn(II) highlights active neurons. Safe doses may be repeated over weeks to allow for longitudinal imaging of brain-wide dynamics in the same individual across time. When delivered by stereotactic intracerebral injection, Mn(II) enters active neurons at the injection site and then travels inside axons for long distances, tracing neuronal projection anatomy. Rates of axonal transport within the brain were measured for the first time in "time-lapse" MEMRI. When delivered systemically, Mn(II) enters active neurons throughout the brain via voltage-sensitive calcium channels and clears slowly. Thus behavior can be monitored during Mn(II) uptake and hyperintense signals due to Mn(II) uptake captured retrospectively, allowing pairing of behavior with neural activity maps for the first time. Here we review critical information gained from MEMRI projection mapping about human neuropsychological disorders. We then discuss results from neural activity mapping from systemic Mn(II) imaged longitudinally that have illuminated development of the tonotopic map in the inferior colliculus as well as brain-wide responses to acute threat and how it evolves over time. MEMRI posed specific challenges for image data analysis that have recently been transcended. We predict a bright future for longitudinal MEMRI in pursuit of solutions to the brain-behavior mystery.
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Affiliation(s)
- Taylor W. Uselman
- University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA
| | | | - Harry B. Gray
- Beckman Institute, California Institute of Technology, Pasadena, California, USA
| | - Russell E. Jacobs
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Elaine L. Bearer
- University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA
- Beckman Institute, California Institute of Technology, Pasadena, California, USA
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12
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Scharwächter L, Schmitt FJ, Pallast N, Fink GR, Aswendt M. Network analysis of neuroimaging in mice. Neuroimage 2022; 253:119110. [PMID: 35311664 DOI: 10.1016/j.neuroimage.2022.119110] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 03/01/2022] [Accepted: 03/15/2022] [Indexed: 10/18/2022] Open
Abstract
Graph theory allows assessing changes of neuronal connectivity and interactions of brain regions in response to local lesions, e.g., after stroke, and global perturbations, e.g., due to psychiatric dysfunctions or neurodegenerative disorders. Consequently, network analysis based on constructing graphs from structural and functional MRI connectivity matrices is increasingly used in clinical studies. In contrast, in mouse neuroimaging, the focus is mainly on basic connectivity parameters, i.e., the correlation coefficient or fiber counts, whereas more advanced network analyses remain rarely used. This review summarizes graph theoretical measures and their interpretation to describe networks derived from recent in vivo mouse brain studies. To facilitate the entry into the topic, we explain the related mathematical definitions, provide a dedicated software toolkit, and discuss practical considerations for the application to rs-fMRI and DTI. This way, we aim to foster cross-species comparisons and the application of standardized measures to classify and interpret network changes in translational brain disease studies.
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Affiliation(s)
- Leon Scharwächter
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany
| | - Felix J Schmitt
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany; University of Cologne, Institute of Zoology, Dept. of Computational Systems Neuroscience, Cologne, Germany
| | - Niklas Pallast
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany
| | - Gereon R Fink
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Germany
| | - Markus Aswendt
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Germany.
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13
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Systemic Administration of the TLR7/8 Agonist Resiquimod (R848) to Mice Is Associated with Transient, In Vivo-Detectable Brain Swelling. BIOLOGY 2022; 11:biology11020274. [PMID: 35205140 PMCID: PMC8869423 DOI: 10.3390/biology11020274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 01/24/2022] [Accepted: 01/26/2022] [Indexed: 11/17/2022]
Abstract
Peripheral administration of the E. coli endotoxin lipopolysaccharide (LPS) to rats promotes secretion of pro-inflammatory cytokines and in previous studies was associated with transient enlargement of cortical volumes. Here, resiquimod (R848) was administered to mice to stimulate peripheral immune activation, and the effects on brain volumes and neurometabolites determined. After baseline scans, 24 male, wild-type C57BL mice were triaged into three groups including R848 at low (50 μg) and high (100 μg) doses and saline controls. Animals were scanned again at 3 h and 24 h following treatment. Sickness indices of elevated temperature and body weight loss were observed in all R848 animals. Animals that received 50 μg R848 exhibited decreases in hippocampal N-acetylaspartate and phosphocreatine at the 3 h time point that returned to baseline levels at 24 h. Animals that received the 100 μg R848 dose demonstrated transient, localized, volume expansion (~5%) detectable at 3 h in motor, somatosensory, and olfactory cortices; and pons. A metabolic response evident at the lower dose and a volumetric change at the higher dose suggests a temporal evolution of the effect wherein the neurochemical change is demonstrable earlier than neurostructural change. Transient volume expansion in response to peripheral immune stimulation corresponds with previous results and is consistent with brain swelling that may reflect CNS edema.
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14
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Im SJ, Suh JY, Shim JH, Baek HM. Deterministic Tractography Analysis of Rat Brain Using SIGMA Atlas in 9.4T MRI. Brain Sci 2021; 11:brainsci11121656. [PMID: 34942958 PMCID: PMC8699268 DOI: 10.3390/brainsci11121656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 12/14/2021] [Accepted: 12/16/2021] [Indexed: 11/28/2022] Open
Abstract
Preclinical studies using rodents have been the choice for many neuroscience researchers due totheir close reflection of human biology. In particular, research involving rodents has utilized MRI to accurately identify brain regions and characteristics by acquiring high resolution cavity images with different contrasts non-invasively, and this has resulted in high reproducibility and throughput. In addition, tractographic analysis using diffusion tensor imaging to obtain information on the neural structure of white matter has emerged as a major methodology in the field of neuroscience due to its contribution in discovering significant correlations between altered neural connections and various neurological and psychiatric diseases. However, unlike image analysis studies with human subjects where a myriad of human image analysis programs and procedures have been thoroughly developed and validated, methods for analyzing rat image data using MRI in preclinical research settings have seen significantly less developed. Therefore, in this study, we present a deterministic tractographic analysis pipeline using the SIGMA atlas for a detailed structural segmentation and structural connectivity analysis of the rat brain’s structural connectivity. In addition, the structural connectivity analysis pipeline presented in this study was preliminarily tested on normal and stroke rat models for initial observation.
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Affiliation(s)
- Sang-Jin Im
- Department of Core Facility for Cell to In-Vivo Imaging, Lee Gil Ya Cancer and Diabetes Institute, Gachon University, Incheon 21999, Korea; (S.-J.I.); (J.-Y.S.)
| | - Ji-Yeon Suh
- Department of Core Facility for Cell to In-Vivo Imaging, Lee Gil Ya Cancer and Diabetes Institute, Gachon University, Incheon 21999, Korea; (S.-J.I.); (J.-Y.S.)
| | - Jae-Hyuk Shim
- Department of BioMedical Science, Lee Gil Ya Cancer and Diabetes Institute, Gachon University, Incheon 21999, Korea;
| | - Hyeon-Man Baek
- Department of Core Facility for Cell to In-Vivo Imaging, Lee Gil Ya Cancer and Diabetes Institute, Gachon University, Incheon 21999, Korea; (S.-J.I.); (J.-Y.S.)
- Department of Molecular Medicine, Lee Gil Ya Cancer and Diabetes Institute, Gachon University, Incheon 21999, Korea
- Correspondence: ; Tel.: +82-32-899-6678
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15
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Reactive Astrocytes Prevent Maladaptive Plasticity after Ischemic Stroke. Prog Neurobiol 2021; 209:102199. [PMID: 34921928 DOI: 10.1016/j.pneurobio.2021.102199] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 10/14/2021] [Accepted: 12/13/2021] [Indexed: 12/24/2022]
Abstract
Restoration of functional connectivity is a major contributor to functional recovery after stroke. We investigated the role of reactive astrocytes in functional connectivity and recovery after photothrombotic stroke in mice with attenuated reactive gliosis (GFAP-/-Vim-/-). Infarct volume and longitudinal functional connectivity changes were determined by in vivo T2-weighted magnetic resonance imaging (MRI) and resting-state functional MRI. Sensorimotor function was assessed with behavioral tests, and glial and neural plasticity responses were quantified in the peri-infarct region. Four weeks after stroke, GFAP-/-Vim-/- mice showed impaired recovery of sensorimotor function and aberrant restoration of global neuronal connectivity. These mice also exhibited maladaptive plasticity responses, shown by higher number of lost and newly formed functional connections between primary and secondary targets of cortical stroke regions and increased peri-infarct expression of the axonal plasticity marker Gap43. We conclude that reactive astrocytes modulate recovery-promoting plasticity responses after ischemic stroke.
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16
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Pursuit of precision medicine: Systems biology approaches in Alzheimer's disease mouse models. Neurobiol Dis 2021; 161:105558. [PMID: 34767943 PMCID: PMC10112395 DOI: 10.1016/j.nbd.2021.105558] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 11/05/2021] [Accepted: 11/08/2021] [Indexed: 12/12/2022] Open
Abstract
Alzheimer's disease (AD) is a complex disease that is mediated by numerous factors and manifests in various forms. A systems biology approach to studying AD involves analyses of various body systems, biological scales, environmental elements, and clinical outcomes to understand the genotype to phenotype relationship that potentially drives AD development. Currently, there are many research investigations probing how modifiable and nonmodifiable factors impact AD symptom presentation. This review specifically focuses on how imaging modalities can be integrated into systems biology approaches using model mouse populations to link brain level functional and structural changes to disease onset and progression. Combining imaging and omics data promotes the classification of AD into subtypes and paves the way for precision medicine solutions to prevent and treat AD.
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17
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Aswendt M, Green C, Sadler R, Llovera G, Dzikowski L, Heindl S, Gomez de Agüero M, Diedenhofen M, Vogel S, Wieters F, Wiedermann D, Liesz A, Hoehn M. The gut microbiota modulates brain network connectivity under physiological conditions and after acute brain ischemia. iScience 2021; 24:103095. [PMID: 34622150 PMCID: PMC8479691 DOI: 10.1016/j.isci.2021.103095] [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: 02/11/2021] [Revised: 07/14/2021] [Accepted: 09/02/2021] [Indexed: 12/13/2022] Open
Abstract
The gut microbiome has been implicated as a key regulator of brain function in health and disease. But the impact of gut microbiota on functional brain connectivity is unknown. We used resting-state functional magnetic resonance imaging in germ-free and normally colonized mice under naive conditions and after ischemic stroke. We observed a strong, brain-wide increase of functional connectivity in germ-free animals. Graph theoretical analysis revealed significant higher values in germ-free animals, indicating a stronger and denser global network but with less structural organization. Breakdown of network function after stroke equally affected germ-free and colonized mice. Results from histological analyses showed changes in dendritic spine densities, as well as an immature microglial phenotype, indicating impaired microglia-neuron interaction in germ-free mice as potential cause of this phenomenon. These results demonstrate the substantial impact of bacterial colonization on brain-wide function and extend our so far mainly (sub) cellular understanding of the gut-brain axis.
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Affiliation(s)
- Markus Aswendt
- Department of Neurology, University of Cologne, Faculty of Medicine and University Hospital, 50923 Cologne, Germany
| | - Claudia Green
- In-vivo-NMR Laboratory, Max Planck Institute for Metabolism Research, Gleuelerstrasse 50, 50931 Cologne, Germany
| | - Rebecca Sadler
- Institute for Stroke and Dementia Research (ISD), LMU Munich, Feodor-Lynen Strasse 17, 81377 Munich, Germany
| | - Gemma Llovera
- Institute for Stroke and Dementia Research (ISD), LMU Munich, Feodor-Lynen Strasse 17, 81377 Munich, Germany
| | - Lauren Dzikowski
- Institute for Stroke and Dementia Research (ISD), LMU Munich, Feodor-Lynen Strasse 17, 81377 Munich, Germany
| | - Steffanie Heindl
- Institute for Stroke and Dementia Research (ISD), LMU Munich, Feodor-Lynen Strasse 17, 81377 Munich, Germany
| | - Mercedes Gomez de Agüero
- Department for BioMedical Research (DBMR), University of Bern, 3012 Bern, Switzerland
- Institute of Systems Immunology, Julius-Maximilians University of Würzburg, 97070 Würzburg, Germany
| | - Michael Diedenhofen
- In-vivo-NMR Laboratory, Max Planck Institute for Metabolism Research, Gleuelerstrasse 50, 50931 Cologne, Germany
| | - Stefanie Vogel
- In-vivo-NMR Laboratory, Max Planck Institute for Metabolism Research, Gleuelerstrasse 50, 50931 Cologne, Germany
| | - Frederique Wieters
- Department of Neurology, University of Cologne, Faculty of Medicine and University Hospital, 50923 Cologne, Germany
| | - Dirk Wiedermann
- In-vivo-NMR Laboratory, Max Planck Institute for Metabolism Research, Gleuelerstrasse 50, 50931 Cologne, Germany
| | - Arthur Liesz
- Institute for Stroke and Dementia Research (ISD), LMU Munich, Feodor-Lynen Strasse 17, 81377 Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), 80807 Munich, Germany
| | - Mathias Hoehn
- In-vivo-NMR Laboratory, Max Planck Institute for Metabolism Research, Gleuelerstrasse 50, 50931 Cologne, Germany
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18
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Blaschke SJ, Hensel L, Minassian A, Vlachakis S, Tscherpel C, Vay SU, Rabenstein M, Schroeter M, Fink GR, Hoehn M, Grefkes C, Rueger MA. Translating Functional Connectivity After Stroke: Functional Magnetic Resonance Imaging Detects Comparable Network Changes in Mice and Humans. Stroke 2021; 52:2948-2960. [PMID: 34281374 DOI: 10.1161/strokeaha.120.032511] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
[Figure: see text].
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Affiliation(s)
- Stefan J Blaschke
- Faculty of Medicine and University Hospital, Department of Neurology, University of Cologne, Germany (S.J.B., L.H., S.V., C.T., S.U.V., M.R., M.S., G.R.F., C.G., M.A.R.)
- In-Vivo NMR Laboratory, Max Planck Institute for Metabolism Research, Cologne, Germany (S.J.B., A.M., S.V., M.R., M.S., M.H., C.G., M.A.R.)
- Cognitive Neuroscience Section, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Germany (S.J.B., L.H., C.T., M.S., G.R.F., M.H., C.G., M.A.R.)
| | - Lukas Hensel
- Faculty of Medicine and University Hospital, Department of Neurology, University of Cologne, Germany (S.J.B., L.H., S.V., C.T., S.U.V., M.R., M.S., G.R.F., C.G., M.A.R.)
- Cognitive Neuroscience Section, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Germany (S.J.B., L.H., C.T., M.S., G.R.F., M.H., C.G., M.A.R.)
| | - Anuka Minassian
- In-Vivo NMR Laboratory, Max Planck Institute for Metabolism Research, Cologne, Germany (S.J.B., A.M., S.V., M.R., M.S., M.H., C.G., M.A.R.)
| | - Susan Vlachakis
- Faculty of Medicine and University Hospital, Department of Neurology, University of Cologne, Germany (S.J.B., L.H., S.V., C.T., S.U.V., M.R., M.S., G.R.F., C.G., M.A.R.)
- In-Vivo NMR Laboratory, Max Planck Institute for Metabolism Research, Cologne, Germany (S.J.B., A.M., S.V., M.R., M.S., M.H., C.G., M.A.R.)
| | - Caroline Tscherpel
- Faculty of Medicine and University Hospital, Department of Neurology, University of Cologne, Germany (S.J.B., L.H., S.V., C.T., S.U.V., M.R., M.S., G.R.F., C.G., M.A.R.)
- Cognitive Neuroscience Section, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Germany (S.J.B., L.H., C.T., M.S., G.R.F., M.H., C.G., M.A.R.)
| | - Sabine U Vay
- Faculty of Medicine and University Hospital, Department of Neurology, University of Cologne, Germany (S.J.B., L.H., S.V., C.T., S.U.V., M.R., M.S., G.R.F., C.G., M.A.R.)
| | - Monika Rabenstein
- Faculty of Medicine and University Hospital, Department of Neurology, University of Cologne, Germany (S.J.B., L.H., S.V., C.T., S.U.V., M.R., M.S., G.R.F., C.G., M.A.R.)
- In-Vivo NMR Laboratory, Max Planck Institute for Metabolism Research, Cologne, Germany (S.J.B., A.M., S.V., M.R., M.S., M.H., C.G., M.A.R.)
| | - Michael Schroeter
- Faculty of Medicine and University Hospital, Department of Neurology, University of Cologne, Germany (S.J.B., L.H., S.V., C.T., S.U.V., M.R., M.S., G.R.F., C.G., M.A.R.)
- In-Vivo NMR Laboratory, Max Planck Institute for Metabolism Research, Cologne, Germany (S.J.B., A.M., S.V., M.R., M.S., M.H., C.G., M.A.R.)
- Cognitive Neuroscience Section, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Germany (S.J.B., L.H., C.T., M.S., G.R.F., M.H., C.G., M.A.R.)
| | - Gereon R Fink
- Faculty of Medicine and University Hospital, Department of Neurology, University of Cologne, Germany (S.J.B., L.H., S.V., C.T., S.U.V., M.R., M.S., G.R.F., C.G., M.A.R.)
- Cognitive Neuroscience Section, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Germany (S.J.B., L.H., C.T., M.S., G.R.F., M.H., C.G., M.A.R.)
| | - Mathias Hoehn
- Cognitive Neuroscience Section, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Germany (S.J.B., L.H., C.T., M.S., G.R.F., M.H., C.G., M.A.R.)
| | - Christian Grefkes
- Faculty of Medicine and University Hospital, Department of Neurology, University of Cologne, Germany (S.J.B., L.H., S.V., C.T., S.U.V., M.R., M.S., G.R.F., C.G., M.A.R.)
- In-Vivo NMR Laboratory, Max Planck Institute for Metabolism Research, Cologne, Germany (S.J.B., A.M., S.V., M.R., M.S., M.H., C.G., M.A.R.)
- Cognitive Neuroscience Section, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Germany (S.J.B., L.H., C.T., M.S., G.R.F., M.H., C.G., M.A.R.)
| | - Maria A Rueger
- Faculty of Medicine and University Hospital, Department of Neurology, University of Cologne, Germany (S.J.B., L.H., S.V., C.T., S.U.V., M.R., M.S., G.R.F., C.G., M.A.R.)
- In-Vivo NMR Laboratory, Max Planck Institute for Metabolism Research, Cologne, Germany (S.J.B., A.M., S.V., M.R., M.S., M.H., C.G., M.A.R.)
- Cognitive Neuroscience Section, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Germany (S.J.B., L.H., C.T., M.S., G.R.F., M.H., C.G., M.A.R.)
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19
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Brunner C, Grillet M, Urban A, Roska B, Montaldo G, Macé E. Whole-brain functional ultrasound imaging in awake head-fixed mice. Nat Protoc 2021; 16:3547-3571. [PMID: 34089019 DOI: 10.1038/s41596-021-00548-8] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 03/30/2021] [Indexed: 12/13/2022]
Abstract
Most brain functions engage a network of distributed regions. Full investigation of these functions thus requires assessment of whole brains; however, whole-brain functional imaging of behaving animals remains challenging. This protocol describes how to follow brain-wide activity in awake head-fixed mice using functional ultrasound imaging, a method that tracks cerebral blood volume dynamics. We describe how to set up a functional ultrasound imaging system with a provided acquisition software (miniScan), establish a chronic cranial window (timing surgery: ~3-4 h) and image brain-wide activity associated with a stimulus at high resolution (100 × 110 × 300 µm and 10 Hz per brain slice, which takes ~45 min per imaging session). We include codes that enable data to be registered to a reference atlas, production of 3D activity maps, extraction of the activity traces of ~250 brain regions and, finally, combination of data from multiple sessions (timing analysis averages ~2 h). This protocol enables neuroscientists to observe global brain processes in mice.
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Affiliation(s)
- Clément Brunner
- Neuro-Electronics Research Flanders, Leuven, Belgium
- VIB, Leuven, Belgium
- Imec, Leuven, Belgium
- Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Micheline Grillet
- Neuro-Electronics Research Flanders, Leuven, Belgium
- VIB, Leuven, Belgium
- Imec, Leuven, Belgium
- Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Alan Urban
- Neuro-Electronics Research Flanders, Leuven, Belgium
- VIB, Leuven, Belgium
- Imec, Leuven, Belgium
- Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Botond Roska
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
- University of Basel, Basel, Switzerland
- NCCR Molecular Systems Engineering, Basel, Switzerland
| | - Gabriel Montaldo
- Neuro-Electronics Research Flanders, Leuven, Belgium.
- VIB, Leuven, Belgium.
- Imec, Leuven, Belgium.
- Department of Neurosciences, KU Leuven, Leuven, Belgium.
| | - Emilie Macé
- Brain-Wide Circuits for Behavior Lab, Max Planck Institute of Neurobiology, Martinsried, Germany.
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20
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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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21
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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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22
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Scarpelli ML, Healey DR, Mehta S, Kodibagkar VD, Quarles CC. A practical method for multimodal registration and assessment of whole-brain disease burden using PET, MRI, and optical imaging. Sci Rep 2020; 10:17324. [PMID: 33057180 PMCID: PMC7560610 DOI: 10.1038/s41598-020-74459-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 09/30/2020] [Indexed: 11/16/2022] Open
Abstract
Many neurological diseases present with substantial genetic and phenotypic heterogeneity, making assessment of these diseases challenging. This has led to ineffective treatments, significant morbidity, and high mortality rates for patients with neurological diseases, including brain cancers and neurodegenerative disorders. Improved understanding of this heterogeneity is necessary if more effective treatments are to be developed. We describe a new method to measure phenotypic heterogeneity across the whole rodent brain at multiple spatial scales. The method involves co-registration and localized comparison of in vivo radiologic images (e.g. MRI, PET) with ex vivo optical reporter images (e.g. labeled cells, molecular targets, microvasculature) of optically cleared tissue slices. Ex vivo fluorescent images of optically cleared pathology slices are acquired with a preclinical in vivo optical imaging system across the entire rodent brain in under five minutes, making this methodology practical and feasible for most preclinical imaging labs. The methodology is applied in various examples demonstrating how it might be used to cross-validate and compare in vivo radiologic imaging with ex vivo optical imaging techniques for assessing hypoxia, microvasculature, and tumor growth.
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Affiliation(s)
- Matthew L Scarpelli
- Department of Neuroimaging, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, AZ, USA
| | - Debbie R Healey
- Department of Neuroimaging, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, AZ, USA
| | - Shwetal Mehta
- Department of Neurobiology, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, AZ, USA
| | - Vikram D Kodibagkar
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Christopher C Quarles
- Department of Neuroimaging, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, AZ, USA.
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23
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Young DM, Duhn C, Gilson M, Nojima M, Yuruk D, Kumar A, Yu W, Sanders SJ. Whole-Brain Image Analysis and Anatomical Atlas 3D Generation Using MagellanMapper. ACTA ACUST UNITED AC 2020; 94:e104. [PMID: 32981139 DOI: 10.1002/cpns.104] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
MagellanMapper is a software suite designed for visual inspection and end-to-end automated processing of large-volume, 3D brain imaging datasets in a memory-efficient manner. The rapidly growing number of large-volume, high-resolution datasets necessitates visualization of raw data at both macro- and microscopic levels to assess the quality of data, as well as automated processing to quantify data in an unbiased manner for comparison across a large number of samples. To facilitate these analyses, MagellanMapper provides both a graphical user interface for manual inspection and a command-line interface for automated image processing. At the macroscopic level, the graphical interface allows researchers to view full volumetric images simultaneously in each dimension and to annotate anatomical label placements. At the microscopic level, researchers can inspect regions of interest at high resolution to build ground truth data of cellular locations such as nuclei positions. Using the command-line interface, researchers can automate cell detection across volumetric images, refine anatomical atlas labels to fit underlying histology, register these atlases to sample images, and perform statistical analyses by anatomical region. MagellanMapper leverages established open-source computer vision libraries and is itself open source and freely available for download and extension. © 2020 Wiley Periodicals LLC. Basic Protocol 1: MagellanMapper installation Alternate Protocol: Alternative methods for MagellanMapper installation Basic Protocol 2: Import image files into MagellanMapper Basic Protocol 3: Region of interest visualization and annotation Basic Protocol 4: Explore an atlas along all three dimensions and register to a sample brain Basic Protocol 5: Automated 3D anatomical atlas construction Basic Protocol 6: Whole-tissue cell detection and quantification by anatomical label Support Protocol: Import a tiled microscopy image in proprietary format into MagellanMapper.
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Affiliation(s)
- David M Young
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California.,Institute for Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore
| | - Clif Duhn
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California
| | - Michael Gilson
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California
| | - Mai Nojima
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California
| | - Deniz Yuruk
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California
| | - Aparna Kumar
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California
| | - Weimiao Yu
- Institute for Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore
| | - Stephan J Sanders
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California
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24
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Celestine M, Nadkarni NA, Garin CM, Bougacha S, Dhenain M. Sammba-MRI: A Library for Processing SmAll-MaMmal BrAin MRI Data in Python. Front Neuroinform 2020; 14:24. [PMID: 32547380 PMCID: PMC7270712 DOI: 10.3389/fninf.2020.00024] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 04/23/2020] [Indexed: 11/23/2022] Open
Abstract
Small-mammal neuroimaging offers incredible opportunities to investigate structural and functional aspects of the brain. Many tools have been developed in the last decade to analyse small animal data, but current softwares are less mature than the available tools that process human brain data. The Python package Sammba-MRI (SmAll-MaMmal BrAin MRI in Python; http://sammba-mri.github.io) allows flexible and efficient use of existing methods and enables fluent scriptable analysis workflows, from raw data conversion to multimodal processing.
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Affiliation(s)
- Marina Celestine
- UMR9199 Laboratory of Neurodegenerative Diseases, Centre National de la Recherche Scientifique (CNRS), Fontenay-aux-Roses, France.,MIRCen, Institut de Biologie François Jacob, Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), Fontenay-aux-Roses, France
| | - Nachiket A Nadkarni
- UMR9199 Laboratory of Neurodegenerative Diseases, Centre National de la Recherche Scientifique (CNRS), Fontenay-aux-Roses, France.,MIRCen, Institut de Biologie François Jacob, Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), Fontenay-aux-Roses, France
| | - Clément M Garin
- UMR9199 Laboratory of Neurodegenerative Diseases, Centre National de la Recherche Scientifique (CNRS), Fontenay-aux-Roses, France.,MIRCen, Institut de Biologie François Jacob, Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), Fontenay-aux-Roses, France
| | - Salma Bougacha
- UMR9199 Laboratory of Neurodegenerative Diseases, Centre National de la Recherche Scientifique (CNRS), Fontenay-aux-Roses, France.,MIRCen, Institut de Biologie François Jacob, Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), Fontenay-aux-Roses, France.,UMR-S U1237 Physiopathologie et imagerie des troubles Neurologiques (PhIND), INSERM, Université de Caen-Normandie, GIP Cyceron, Caen, France.,Normandie Université, UNICAEN, PSL Research University, EPHE, Inserm, U1077, CHU de Caen, Neuropsychologie et Imagerie de la Mémoire Humaine, Caen, France
| | - Marc Dhenain
- UMR9199 Laboratory of Neurodegenerative Diseases, Centre National de la Recherche Scientifique (CNRS), Fontenay-aux-Roses, France.,MIRCen, Institut de Biologie François Jacob, Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), Fontenay-aux-Roses, France
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25
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Wang Q, Ding SL, Li Y, Royall J, Feng D, Lesnar P, Graddis N, Naeemi M, Facer B, Ho A, Dolbeare T, Blanchard B, Dee N, Wakeman W, Hirokawa KE, Szafer A, Sunkin SM, Oh SW, Bernard A, Phillips JW, Hawrylycz M, Koch C, Zeng H, Harris JA, Ng L. The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlas. Cell 2020; 181:936-953.e20. [PMID: 32386544 PMCID: PMC8152789 DOI: 10.1016/j.cell.2020.04.007] [Citation(s) in RCA: 498] [Impact Index Per Article: 124.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 12/12/2019] [Accepted: 04/03/2020] [Indexed: 01/25/2023]
Abstract
Recent large-scale collaborations are generating major surveys of cell types and connections in the mouse brain, collecting large amounts of data across modalities, spatial scales, and brain areas. Successful integration of these data requires a standard 3D reference atlas. Here, we present the Allen Mouse Brain Common Coordinate Framework (CCFv3) as such a resource. We constructed an average template brain at 10 μm voxel resolution by interpolating high resolution in-plane serial two-photon tomography images with 100 μm z-sampling from 1,675 young adult C57BL/6J mice. Then, using multimodal reference data, we parcellated the entire brain directly in 3D, labeling every voxel with a brain structure spanning 43 isocortical areas and their layers, 329 subcortical gray matter structures, 81 fiber tracts, and 8 ventricular structures. CCFv3 can be used to analyze, visualize, and integrate multimodal and multiscale datasets in 3D and is openly accessible (https://atlas.brain-map.org/).
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Affiliation(s)
- Quanxin Wang
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Song-Lin Ding
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Yang Li
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Josh Royall
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - David Feng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Phil Lesnar
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Nile Graddis
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Maitham Naeemi
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Benjamin Facer
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Anh Ho
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Tim Dolbeare
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Wayne Wakeman
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Aaron Szafer
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Susan M Sunkin
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Seung Wook Oh
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Amy Bernard
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Christof Koch
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Julie A Harris
- Allen Institute for Brain Science, Seattle, WA 98109, USA.
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA 98109, USA.
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26
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Pallast N, Wieters F, Nill M, Fink GR, Aswendt M. Graph theoretical quantification of white matter reorganization after cortical stroke in mice. Neuroimage 2020; 217:116873. [PMID: 32380139 DOI: 10.1016/j.neuroimage.2020.116873] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 04/11/2020] [Accepted: 04/21/2020] [Indexed: 02/08/2023] Open
Abstract
Stroke is a devastating disease leading to cell death and disconnection between neurons both locally and remote, often resulting in severe long-term disability. Spontaneous reorganization of areas and pathways not primarily affected by ischemia is, however, associated with albeit limited recovery of function. Quantitative mapping of whole-brain changes of structural connectivity concerning the ischemia-induced sensorimotor deficit and recovery thereof would help to target structural plasticity in order to improve rehabilitation. Currently, only in vivo diffusion MRI can extract the structural whole-brain connectome noninvasively. This approach is, however, used primarily in human studies. Here, we applied atlas-based MRI analysis and graph theory to DTI in wild-type mice with cortical stroke lesions. Using a DTI network approach and graph theory, we aimed at gaining insights into the dynamics of the spontaneous reorganization after stroke related to the recovery of function. We found evidence for altered structural integrity of connections of specific brain regions, including the breakdown of connections between brain regions directly affected by stroke as well as long-range rerouting of intra- and transhemispheric connections related to improved sensorimotor behavior.
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Affiliation(s)
- Niklas Pallast
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Germany
| | - Frederique Wieters
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Germany
| | - Marieke Nill
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Germany
| | - Gereon R Fink
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Germany
| | - Markus Aswendt
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Germany.
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27
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Aswendt M, Pallast N, Wieters F, Baues M, Hoehn M, Fink GR. Lesion Size- and Location-Dependent Recruitment of Contralesional Thalamus and Motor Cortex Facilitates Recovery after Stroke in Mice. Transl Stroke Res 2020; 12:87-97. [PMID: 32166716 PMCID: PMC7803721 DOI: 10.1007/s12975-020-00802-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 02/06/2020] [Accepted: 03/03/2020] [Indexed: 01/01/2023]
Abstract
Brain lesions caused by cerebral ischemia or hemorrhage lead to a local breakdown of energy homeostasis followed by irreversible cell death and long-term impairment. Importantly, local brain lesions also generate remote functional and structural disturbances, which contribute to the behavioral deficit but also impact the recovery of function. While spontaneous recovery has been associated with endogenous repair mechanisms at the vascular, neural, and immune cell levels, the impact of structural plasticity on sensory-motor dysfunction and recovery thereof remains to be elucidated by longitudinal imaging in a mouse model. Here, we applied behavioral assessments, in vivo fiber tracking, and histological validation in a photothrombotic stroke mouse model. Atlas-based whole-brain structural connectivity analysis and ex vivo histology revealed secondary neurodegeneration in the ipsilesional brain areas, mostly in the dorsal sensorimotor area of the thalamus. Furthermore, we describe for the first time a lesion size-dependent increase in structural connectivity between the contralesional primary motor cortex and thalamus with the ipsilesional cortex. The involvement of the contralesional hemisphere was associated with improved functional recovery relative to lesion size. This study highlights the importance of in vivo fiber tracking and the role of the contralesional hemisphere during spontaneous functional improvement as a potential novel stroke biomarker and therapeutic targets.
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Affiliation(s)
- Markus Aswendt
- Department of Neurology, Faculty of Medicine, University of Cologne and University Hospital Cologne, Kerpener Strasse, 62 50937, Cologne, Germany. .,Cognitive Neuroscience, Research Center Juelich, Institute of Neuroscience and Medicine (INM-3), Juelich, Germany.
| | - Niklas Pallast
- Department of Neurology, Faculty of Medicine, University of Cologne and University Hospital Cologne, Kerpener Strasse, 62 50937, Cologne, Germany
| | - Frederique Wieters
- Department of Neurology, Faculty of Medicine, University of Cologne and University Hospital Cologne, Kerpener Strasse, 62 50937, Cologne, Germany
| | - Mayan Baues
- Department of Neurology, Faculty of Medicine, University of Cologne and University Hospital Cologne, Kerpener Strasse, 62 50937, Cologne, Germany
| | - Mathias Hoehn
- Cognitive Neuroscience, Research Center Juelich, Institute of Neuroscience and Medicine (INM-3), Juelich, Germany.,Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
| | - Gereon R Fink
- Department of Neurology, Faculty of Medicine, University of Cologne and University Hospital Cologne, Kerpener Strasse, 62 50937, Cologne, Germany.,Cognitive Neuroscience, Research Center Juelich, Institute of Neuroscience and Medicine (INM-3), Juelich, Germany
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Chon U, Vanselow DJ, Cheng KC, Kim Y. Enhanced and unified anatomical labeling for a common mouse brain atlas. Nat Commun 2019; 10:5067. [PMID: 31699990 PMCID: PMC6838086 DOI: 10.1038/s41467-019-13057-w] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 10/17/2019] [Indexed: 01/13/2023] Open
Abstract
Anatomical atlases in standard coordinates are necessary for the interpretation and integration of research findings in a common spatial context. However, the two most-used mouse brain atlases, the Franklin-Paxinos (FP) and the common coordinate framework (CCF) from the Allen Institute for Brain Science, have accumulated inconsistencies in anatomical delineations and nomenclature, creating confusion among neuroscientists. To overcome these issues, we adopt here the FP labels into the CCF to merge the labels in the single atlas framework. We use cell type-specific transgenic mice and an MRI atlas to adjust and further segment our labels. Moreover, detailed segmentations are added to the dorsal striatum using cortico-striatal connectivity data. Lastly, we digitize our anatomical labels based on the Allen ontology, create a web-interface for visualization, and provide tools for comprehensive comparisons between the CCF and FP labels. Our open-source labels signify a key step towards a unified mouse brain atlas.
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Affiliation(s)
- Uree Chon
- Department of Neural and Behavioral Sciences, College of Medicine, Penn State University, Hershey, PA, USA
| | - Daniel J Vanselow
- Department of Pathology, College of Medicine, Penn State University, Hershey, PA, USA
| | - Keith C Cheng
- Department of Pathology, College of Medicine, Penn State University, Hershey, PA, USA
| | - Yongsoo Kim
- Department of Neural and Behavioral Sciences, College of Medicine, Penn State University, Hershey, PA, USA.
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Pallast N, Wieters F, Fink GR, Aswendt M. Atlas-based imaging data analysis tool for quantitative mouse brain histology (AIDAhisto). J Neurosci Methods 2019; 326:108394. [PMID: 31415844 DOI: 10.1016/j.jneumeth.2019.108394] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 08/02/2019] [Accepted: 08/05/2019] [Indexed: 02/01/2023]
Abstract
Cell counting in neuroscience is a routine method of utmost importance to support descriptive in vivo findings with quantitative data on the cellular level. Although known to be error- and bias-prone, manual cell counting of histological stained brain slices remains the gold standard in the field. While the manual approach is limited to small regions-of-interest in the brain, automated tools are needed to up-scale translational approaches and generate whole mouse brain counts in an atlas framework. Our goal was to develop an algorithm which requires no pre-training such as machine learning algorithms, only minimal user input, and adjustable variables to obtain reliable cell counting results for stitched mouse brain slices registered to a common atlas such as the Allen Mouse Brain atlas. We adapted filter banks to extract the maxima from round-shaped cell nuclei and various cell structures. In a qualitative as well as quantitative comparison to other tools and two expert raters, AIDAhisto provides accurate and fast results for cell nuclei as well as immunohistochemical stainings of various types of cells in the mouse brain.
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Affiliation(s)
- Niklas Pallast
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Frederique Wieters
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Gereon R Fink
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Juelich, Germany
| | - Markus Aswendt
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
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30
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Imaging in mice and men: Pathophysiological insights into multiple sclerosis from conventional and advanced MRI techniques. Prog Neurobiol 2019; 182:101663. [PMID: 31374243 DOI: 10.1016/j.pneurobio.2019.101663] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 06/17/2019] [Accepted: 07/17/2019] [Indexed: 01/16/2023]
Abstract
Magnetic resonance imaging (MRI) is the most important tool for diagnosing multiple sclerosis (MS). However, MRI is still unable to precisely quantify the specific pathophysiological processes that underlie imaging findings in MS. Because autopsy and biopsy samples of MS patients are rare and biased towards a chronic burnt-out end or fulminant acute early stage, the only available methods to identify human disease pathology are to apply MRI techniques in combination with subsequent histopathological examination to small animal models of MS and to transfer these insights to MS patients. This review summarizes the existing combined imaging and histopathological studies performed in MS mouse models and humans with MS (in vivo and ex vivo), to promote a better understanding of the pathophysiology that underlies conventional MRI, diffusion tensor and magnetization transfer imaging findings in MS patients. Moreover, it provides a critical view on imaging capabilities and results in MS patients and mouse models and for future studies recommends how to combine those particular MR sequences and parameters whose underlying pathophysiological basis could be partly clarified. Further combined longitudinal in vivo imaging and histopathological studies on rationally selected, appropriate mouse models are required.
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