1
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Li H, Ye Q, Wang D, Shi B, Xu W, Zhang S, Han X, Zhang XY, Thompson GJ. Resting State Brain Networks under Inverse Agonist versus Complete Knockout of the Cannabinoid Receptor 1. ACS Chem Neurosci 2024; 15:1669-1683. [PMID: 38575140 PMCID: PMC11027912 DOI: 10.1021/acschemneuro.3c00804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 03/14/2024] [Accepted: 03/14/2024] [Indexed: 04/06/2024] Open
Abstract
The cannabinoid receptor 1 (CB1) is famous as the target of Δ9-tetrahydrocannabinol (THC), which is the active ingredient of marijuana. Suppression of CB1 is frequently suggested as a drug target or gene therapy for many conditions (e.g., obesity, Parkinson's disease). However, brain networks affected by CB1 remain elusive, and unanticipated psychological effects in a clinical trial had dire consequences. To better understand the whole brain effects of CB1 suppression we performed in vivo imaging on mice under complete knockout of the gene for CB1 (cnr1-/-) and also under the CB1 inverse agonist rimonabant. We examined white matter structural changes and brain function (network activity and directional uniformity) in cnr1-/- mice. In cnr1-/- mice, white matter (in both sexes) and functional directional uniformity (in male mice) were altered across the brain but network activity was largely unaltered. Conversely, under rimonabant, functional directional uniformity was not altered but network activity was altered in cortical regions, primarily in networks known to be altered by THC (e.g., neocortex, hippocampal formation). However, rimonabant did not alter many brain regions found in both our cnr1-/- results and previous behavioral studies of cnr1-/- mice (e.g., thalamus, infralimbic area). This suggests that chronic loss of cnr1 is substantially different from short-term suppression, subtly rewiring the brain but largely maintaining the network activity. Our results help explain why pathological mutations in CB1 (e.g., chronic pain) do not always provide insight into the side effects of CB1 suppression (e.g., clinical depression), and thus urge more preclinical studies for any drugs that suppress CB1.
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Affiliation(s)
- Hui Li
- iHuman
Institute, ShanghaiTech University, Shanghai 201210, China
| | - Qiong Ye
- High
Magnetic Field Laboratory, Hefei Institutes
of Physical Science, Chinese Academy of Sciences, Hefei, Anhui 230031, China
| | - Da Wang
- iHuman
Institute, ShanghaiTech University, Shanghai 201210, China
- School
of Life Science and Technology, ShanghaiTech
University, Shanghai 201210, China
| | - Bowen Shi
- iHuman
Institute, ShanghaiTech University, Shanghai 201210, China
- School
of Life Science and Technology, ShanghaiTech
University, Shanghai 201210, China
| | - Wenjing Xu
- Institute
of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key
Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai 200433, China
| | - Shuning Zhang
- iHuman
Institute, ShanghaiTech University, Shanghai 201210, China
- School
of Life Science and Technology, ShanghaiTech
University, Shanghai 201210, China
| | - Xiaoyang Han
- Institute
of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key
Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai 200433, China
| | - Xiao-Yong Zhang
- Institute
of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key
Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai 200433, China
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2
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Zhang Y, Quan Z, Lou F, Fang Y, Thompson GJ, Chen G, Zhang X. A proton birdcage coil integrated with interchangeable single loops for multi-nuclear MRI/MRS. J Zhejiang Univ Sci B 2024; 25:168-180. [PMID: 38303499 PMCID: PMC10835210 DOI: 10.1631/jzus.b2300587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Energy metabolism is fundamental for life. It encompasses the utilization of carbohydrates, lipids, and proteins for internal processes, while aberrant energy metabolism is implicated in many diseases. In the present study, using three-dimensional (3D) printing from polycarbonate via fused deposition modeling, we propose a multi-nuclear radiofrequency (RF) coil design with integrated 1H birdcage and interchangeable X-nuclei (2H, 13C, 23Na, and 31P) single-loop coils for magnetic resonance imaging (MRI)/magnetic resonance spectroscopy (MRS). The single-loop coil for each nucleus attaches to an arc bracket that slides unrestrictedly along the birdcage coil inner surface, enabling convenient switching among various nuclei and animal handling. Compared to a commercial 1H birdcage coil, the proposed 1H birdcage coil exhibited superior signal-excitation homogeneity and imaging signal-to-noise ratio (SNR). For X-nuclei study, prominent peaks in spectroscopy for phantom solutions showed excellent SNR, and the static and dynamic peaks of in vivo spectroscopy validated the efficacy of the coil design in structural imaging and energy metabolism detection simultaneously.
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Affiliation(s)
- Yi Zhang
- Department of Neurosurgery, the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, China
- Key Laboratory of Precise Treatment and Clinical Translational Research of Neurological Diseases, Hangzhou 310009, China
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou 310058, China
| | - Zhiyan Quan
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou 310058, China
- Interdisciplinary Institute of Neuroscience and Technology, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, China
| | - Feiyang Lou
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou 310058, China
- Interdisciplinary Institute of Neuroscience and Technology, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, China
- School of Medicine, Zhejiang University, Hangzhou 310020, China
| | - Yujiao Fang
- iHuman Institute, School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Garth J Thompson
- iHuman Institute, School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Gao Chen
- Department of Neurosurgery, the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, China.
- Key Laboratory of Precise Treatment and Clinical Translational Research of Neurological Diseases, Hangzhou 310009, China.
| | - Xiaotong Zhang
- Department of Neurosurgery, the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, China. ,
- Key Laboratory of Precise Treatment and Clinical Translational Research of Neurological Diseases, Hangzhou 310009, China. ,
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou 310058, China. ,
- Interdisciplinary Institute of Neuroscience and Technology, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, China. ,
- School of Medicine, Zhejiang University, Hangzhou 310020, China. ,
- College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China. ,
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3
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Wang D, Li H, Xu M, Bo B, Pei M, Liang Z, Thompson GJ. Differential Effect of Global Signal Regression Between Awake and Anesthetized Conditions in Mice. Brain Connect 2024; 14:48-59. [PMID: 38063007 DOI: 10.1089/brain.2023.0032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2024] Open
Abstract
Introduction: In resting-state functional magnetic resonance imaging (rs-fMRI) studies, global signal regression (GSR) is a controversial preprocessing strategy. It effectively eliminates global noise driven by motion and respiration but also can introduce artifacts and remove functionally relevant metabolic information. Most preclinical rs-fMRI studies are performed in anesthetized animals, and anesthesia will alter both metabolic and neuronal activity. Methods: In this study, we explored the effect of GSR on rs-fMRI data collected under anesthetized and awake state in mice (n = 12). We measured global signal amplitude, and also functional connectivity (FC), functional connectivity density (FCD) maps, and brain modularity, all commonly used data-driven analysis methods to quantify connectivity patterns. Results: We found that global signal amplitude was similar between the awake and anesthetized states. However, GSR had a different impact on connectivity networks and brain modularity changes between states. We demonstrated that GSR had a more prominent impact on the anesthetized state, with a greater decrease in functional connectivity and increased brain modularity. We classified mice using the change in amplitude of brain modularity coefficient (ΔQ) before and after GSR processing. The results revealed that, when compared with the largest ΔQ group, the smallest ΔQ group had increased FCD in the cortex region in both the awake and anesthetized states. This suggests differences in individual mice may affect how GSR differentially affects awake versus anesthetized functional connectivity. Discussion: This study suggests that, for rs-fMRI studies which compare different physiological states, researchers should use GSR processing with caution.
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Affiliation(s)
- Da Wang
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Hui Li
- iHuman Institute, ShanghaiTech University, Shanghai, China
| | - Mengyang Xu
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Binshi Bo
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China
| | - Mengchao Pei
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China
| | - Zhifeng Liang
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China
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4
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Zou C, Ruan Y, Li H, Wan Q, Du F, Yuan J, Qin Q, Thompson GJ, Yang X, Li Y, Liu X, Zheng H. A new deuterium-labeled compound [2,3,4,6,6'- 2 H 5 ]-D-glucose for deuterium magnetic resonance metabolic imaging. NMR Biomed 2023; 36:e4890. [PMID: 36477944 DOI: 10.1002/nbm.4890] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 12/01/2022] [Accepted: 12/02/2022] [Indexed: 06/15/2023]
Abstract
Deuterium (2 H) magnetic resonance imaging is an emerging approach for noninvasively studying glucose metabolism in vivo, which is important for understanding pathogenesis and monitoring the progression of many diseases such as tumors, diabetes, and neurodegenerative diseases. However, the synthesis of 2 H-labeled glucose is costly because of the expensive raw substrates and the requirement for extreme reaction conditions, making the 2 H-labeled glucose rather expensive and unaffordable for clinic use. In this study, we present a new deuterated compound, [2,3,4,6,6'-2 H5 ]-D-glucose, with an approximate 10-fold reduction in production costs. The synthesis route uses cheaper raw substrate methyl-α-D-glucopyranoside, relies on mild reaction conditions (80°C), and has higher deuterium labeling efficiency. Magnetic resonance spectroscopy (MRS) and mass spectroscopy experiments confirmed the successful deuterium labeling in the compound. Animal studies demonstrated that the substrate could describe the glycolytic metabolism in a glioma rat model by quantifying the downstream metabolites through 2 H-MRS on an ultrahigh field system. Comparison of the glucose metabolism characteristics was carried out between [2,3,4,6,6'-2 H5 ]-D-glucose and commercial [6,6'-2 H2 ]-D-glucose in the animal studies. This cost-effective compound will help facilitate the clinical translation of deuterium magnetic resonance imaging, and enable this powerful metabolic imaging modality to be widely used in both preclinical and clinical research and applications.
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Affiliation(s)
- Chao Zou
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen, Guangdong, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yingheng Ruan
- Shenzhen Dingbang Bioscience Co., Ltd, Shenzhen, Guangdong, China
| | - Huanxi Li
- Shenzhen Dingbang Bioscience Co., Ltd, Shenzhen, Guangdong, China
| | - Qian Wan
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Feng Du
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Jiawen Yuan
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Qikai Qin
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | | | - Xiaojun Yang
- Shenzhen Dingbang Bioscience Co., Ltd, Shenzhen, Guangdong, China
| | - Ye Li
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen, Guangdong, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xin Liu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen, Guangdong, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- University of Chinese Academy of Sciences, Beijing, China
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5
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Grandjean J, Desrosiers-Gregoire G, Anckaerts C, Angeles-Valdez D, Ayad F, Barrière DA, Blockx I, Bortel A, Broadwater M, Cardoso BM, Célestine M, Chavez-Negrete JE, Choi S, Christiaen E, Clavijo P, Colon-Perez L, Cramer S, Daniele T, Dempsey E, Diao Y, Doelemeyer A, Dopfel D, Dvořáková L, Falfán-Melgoza C, Fernandes FF, Fowler CF, Fuentes-Ibañez A, Garin CM, Gelderman E, Golden CEM, Guo CCG, Henckens MJAG, Hennessy LA, Herman P, Hofwijks N, Horien C, Ionescu TM, Jones J, Kaesser J, Kim E, Lambers H, Lazari A, Lee SH, Lillywhite A, Liu Y, Liu YY, López-Castro A, López-Gil X, Ma Z, MacNicol E, Madularu D, Mandino F, Marciano S, McAuslan MJ, McCunn P, McIntosh A, Meng X, Meyer-Baese L, Missault S, Moro F, Naessens DMP, Nava-Gomez LJ, Nonaka H, Ortiz JJ, Paasonen J, Peeters LM, Pereira M, Perez PD, Pompilus M, Prior M, Rakhmatullin R, Reimann HM, Reinwald J, Del Rio RT, Rivera-Olvera A, Ruiz-Pérez D, Russo G, Rutten TJ, Ryoke R, Sack M, Salvan P, Sanganahalli BG, Schroeter A, Seewoo BJ, Selingue E, Seuwen A, Shi B, Sirmpilatze N, Smith JAB, Smith C, Sobczak F, Stenroos PJ, Straathof M, Strobelt S, Sumiyoshi A, Takahashi K, Torres-García ME, Tudela R, van den Berg M, van der Marel K, van Hout ATB, Vertullo R, Vidal B, Vrooman RM, Wang VX, Wank I, Watson DJG, Yin T, Zhang Y, Zurbruegg S, Achard S, Alcauter S, Auer DP, Barbier EL, Baudewig J, Beckmann CF, Beckmann N, Becq GJPC, Blezer ELA, Bolbos R, Boretius S, Bouvard S, Budinger E, Buxbaum JD, Cash D, Chapman V, Chuang KH, Ciobanu L, Coolen BF, Dalley JW, Dhenain M, Dijkhuizen RM, Esteban O, Faber C, Febo M, Feindel KW, Forloni G, Fouquet J, Garza-Villarreal EA, Gass N, Glennon JC, Gozzi A, Gröhn O, Harkin A, Heerschap A, Helluy X, Herfert K, Heuser A, Homberg JR, Houwing DJ, Hyder F, Ielacqua GD, Jelescu IO, Johansen-Berg H, Kaneko G, Kawashima R, Keilholz SD, Keliris GA, Kelly C, Kerskens C, Khokhar JY, Kind PC, Langlois JB, Lerch JP, López-Hidalgo MA, Manahan-Vaughan D, Marchand F, Mars RB, Marsella G, Micotti E, Muñoz-Moreno E, Near J, Niendorf T, Otte WM, Pais-Roldán P, Pan WJ, Prado-Alcalá RA, Quirarte GL, Rodger J, Rosenow T, Sampaio-Baptista C, Sartorius A, Sawiak SJ, Scheenen TWJ, Shemesh N, Shih YYI, Shmuel A, Soria G, Stoop R, Thompson GJ, Till SM, Todd N, Van Der Linden A, van der Toorn A, van Tilborg GAF, Vanhove C, Veltien A, Verhoye M, Wachsmuth L, Weber-Fahr W, Wenk P, Yu X, Zerbi V, Zhang N, Zhang BB, Zimmer L, Devenyi GA, Chakravarty MM, Hess A. Author Correction: A consensus protocol for functional connectivity analysis in the rat brain. Nat Neurosci 2023:10.1038/s41593-023-01328-1. [PMID: 37072562 DOI: 10.1038/s41593-023-01328-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Affiliation(s)
- Joanes Grandjean
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands.
- Department for Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - Gabriel Desrosiers-Gregoire
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, QC, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
| | - Cynthia Anckaerts
- Bio-imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Diego Angeles-Valdez
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, Mexico
| | - Fadi Ayad
- Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - David A Barrière
- UMR INRAE/CNRS 7247 Physiologie des Comportements et de la Reproduction, Physiologie de la reproduction et des comportements, Centre de recherche INRAE de Nouzilly, Tours, France
| | - Ines Blockx
- Bio-imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Aleksandra Bortel
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Margaret Broadwater
- Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Beatriz M Cardoso
- Preclinical MRI, Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Marina Célestine
- Laboratoire des Maladies Neurodégénératives, Molecular Imaging Research Center (MIRCen), Université Paris-Saclay, Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA), CNRS, Fontenay-aux-Roses, France
| | - Jorge E Chavez-Negrete
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, México
| | - Sangcheon Choi
- Translational Neuroimaging and Neural Control Group, High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
- Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tuebingen, Tuebingen, Germany
| | - Emma Christiaen
- Institute Biomedical Technology (IBiTech), Electronics and Information Systems (ELIS), Ghent University, Gent, Belgium
| | - Perrin Clavijo
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology, Atlanta, GA, USA
| | - Luis Colon-Perez
- Department of Pharmacology & Neuroscience, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Samuel Cramer
- Translational Neuroimaging and Systems Neuroscience Lab, Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Tolomeo Daniele
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | - Elaine Dempsey
- Neuropsychopharmacology Research Group, School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Dublin, Ireland
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Yujian Diao
- CIBM Center for Biomedical Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Laboratory for Functional and Metabolic Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Arno Doelemeyer
- Musculoskeletal Diseases Department, Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - David Dopfel
- Translational Neuroimaging and Systems Neuroscience Lab, Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Lenka Dvořáková
- Biomedical Imaging Unit, A.I.V. Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Claudia Falfán-Melgoza
- Translational Imaging, Department of Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, Mannheim, Germany
| | - Francisca F Fernandes
- Preclinical MRI, Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Caitlin F Fowler
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, QC, Canada
- Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Antonio Fuentes-Ibañez
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, México
| | - Clément M Garin
- Laboratoire des Maladies Neurodégénératives, Molecular Imaging Research Center (MIRCen), Université Paris-Saclay, Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA), CNRS, Fontenay-aux-Roses, France
| | - Eveline Gelderman
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Carla E M Golden
- Seaver Autism Center for Research & Treatment, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Chao C G Guo
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Marloes J A G Henckens
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
- Department of Neuroscience and Pharmacology, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Lauren A Hennessy
- Experimental and Regenerative Neurosciences, School of Biological Sciences, University of Western Australia, Crawley, WA, Australia
- Brain Plasticity Group, Perron Institute for Neurological and Translational Science, Nedlands, WA, Australia
| | - Peter Herman
- Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
- Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University School of Medicine, New Haven, CT, USA
| | - Nita Hofwijks
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Corey Horien
- Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Tudor M Ionescu
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University of Tuebingen, Tuebingen, Germany
| | - Jolyon Jones
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Johannes Kaesser
- Institute of Experimental and Clinical Pharmacology and Toxicology, FAU Erlangen-Nürnberg, Erlangen, Germany
| | - Eugene Kim
- Biomarker Research And Imaging in Neuroscience (BRAIN) Centre, Department of Neuroimaging King's College London, London, UK
| | - Henriette Lambers
- Experimental Magnetic Resonance Group, Clinic of Radiology, University of Münster, Münster, Germany
| | - Alberto Lazari
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
| | - Sung-Ho Lee
- Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Amanda Lillywhite
- School of Life Sciences, University of Nottingham, Nottingham, UK
- Pain Centre Versus Arthritis, University of Nottingham, Nottingham, UK
| | - Yikang Liu
- Translational Neuroimaging and Systems Neuroscience Lab, Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Yanyan Y Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Alejandra López-Castro
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, Mexico
| | - Xavier López-Gil
- Magnetic Imaging Resonance Core Facility, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain
| | - Zilu Ma
- Translational Neuroimaging and Systems Neuroscience Lab, Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Eilidh MacNicol
- Biomarker Research And Imaging in Neuroscience (BRAIN) Centre, Department of Neuroimaging King's College London, London, UK
| | - Dan Madularu
- Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
- Center for Translational Neuroimaging, Northeastern University, Boston, MA, USA
| | - Francesca Mandino
- Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Sabina Marciano
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University of Tuebingen, Tuebingen, Germany
| | - Matthew J McAuslan
- Neuropsychopharmacology Research Group, School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Dublin, Ireland
| | - Patrick McCunn
- Khokhar Lab, Department of Anatomy and Cell Biology, Western University, London, ON, Canada
| | - Alison McIntosh
- Neuropsychopharmacology Research Group, School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Dublin, Ireland
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Xianzong Meng
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Lisa Meyer-Baese
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology, Atlanta, GA, USA
| | - Stephan Missault
- Bio-imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Federico Moro
- Laboratory of Acute Brain Injury and Therapeutic Strategies, Department of NeuroscienceIstituto di Ricerche Farmacologiche Mario Negri, IRCCS, Milan, Italy
| | - Daphne M P Naessens
- Biomedical Engineering and Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Laura J Nava-Gomez
- Facultad de Medicina, Universidad Autónoma de Querétaro, Querétaro, México
- Escuela Nacional de Estudios Superiores, Juriquilla, Universidad Nacional Autónoma de México, Querétaro, México
| | - Hiroi Nonaka
- Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Juan J Ortiz
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, México
| | - Jaakko Paasonen
- Biomedical Imaging Unit, A.I.V. Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Lore M Peeters
- Bio-imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Mickaël Pereira
- Lyon Neuroscience Research Center, Université Claude Bernard Lyon 1, INSERM, CNRS, Lyon, France
| | - Pablo D Perez
- Translational Neuroimaging and Systems Neuroscience Lab, Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Marjory Pompilus
- Febo Laboratory, Department of Psychiatry, University of Florida, Gainesville, FL, USA
| | - Malcolm Prior
- School of Medicine, University of Nottingham, Nottingham, UK
| | | | - Henning M Reimann
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max-Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Jonathan Reinwald
- Translational Imaging, Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Rodrigo Triana Del Rio
- Psychiatric neurosciences, Center for Psychiatric Neuroscience, Lausanne University and University Hospital Center, Unicentre, Lausanne, Switzerland
| | - Alejandro Rivera-Olvera
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | | | - Gabriele Russo
- Department of Neurophysiology, Medical Faculty, Ruhr University Bochum, Bochum, Germany
| | - Tobias J Rutten
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Rie Ryoke
- Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Markus Sack
- Translational Imaging, Department of Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, Mannheim, Germany
| | - Piergiorgio Salvan
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
| | - Basavaraju G Sanganahalli
- Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
- Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University School of Medicine, New Haven, CT, USA
| | - Aileen Schroeter
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Bhedita J Seewoo
- Experimental and Regenerative Neurosciences, School of Biological Sciences, University of Western Australia, Crawley, WA, Australia
- Brain Plasticity Group, Perron Institute for Neurological and Translational Science, Nedlands, WA, Australia
- Centre for Microscopy, Characterisation & Analysis, Research Infrastructure Centres, University of Western Australia, Nedlands, WA, Australia
| | | | - Aline Seuwen
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Bowen Shi
- iHuman Institute, ShanghaiTech University, Shanghai, China
| | - Nikoloz Sirmpilatze
- Functional Imaging Laboratory, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany
- Faculty of Biology and Psychology, Georg-August University of Göttingen, Göttingen, Germany
- DFG Research Center for Nanoscale Microscopy and Molecular Physiology of the Brain (CNMPB), Göttingen, Germany
| | - Joanna A B Smith
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, UK
- Patrick Wild Centre, University of Edinburgh, Edinburgh, UK
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Corrie Smith
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology, Atlanta, GA, USA
| | - Filip Sobczak
- Translational Neuroimaging and Neural Control Group, High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
- Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tuebingen, Tuebingen, Germany
| | - Petteri J Stenroos
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, Grenoble, France
| | - Milou Straathof
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht & Utrecht University, Utrecht, The Netherlands
| | - Sandra Strobelt
- Institute of Experimental and Clinical Pharmacology and Toxicology, FAU Erlangen-Nürnberg, Erlangen, Germany
| | - Akira Sumiyoshi
- Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
- National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Kengo Takahashi
- Translational Neuroimaging and Neural Control Group, High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
- Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tuebingen, Tuebingen, Germany
| | - Maria E Torres-García
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, México
| | - Raul Tudela
- Group of Biomedical Imaging, Consorcio Centro de Investigación Biomédica en Red (CIBER) de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), University of Barcelona, Barcelona, Spain
| | - Monica van den Berg
- Bio-imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Kajo van der Marel
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht & Utrecht University, Utrecht, The Netherlands
| | - Aran T B van Hout
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Roberta Vertullo
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Benjamin Vidal
- Lyon Neuroscience Research Center, Université Claude Bernard Lyon 1, INSERM, CNRS, Lyon, France
| | - Roël M Vrooman
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Victora X Wang
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Isabel Wank
- Institute of Experimental and Clinical Pharmacology and Toxicology, FAU Erlangen-Nürnberg, Erlangen, Germany
| | - David J G Watson
- School of Life Sciences, University of Nottingham, Nottingham, UK
| | - Ting Yin
- Animal Imaging and Technology Section, Center for Biomedical Imaging, École polytechnique fédérale de Lausanne, Lausanne, Switzerland
| | - Yongzhi Zhang
- Focused Ultrasound Laboratory, Department of Radiology Brigham and Women's Hospital, Boston, MA, USA
| | - Stefan Zurbruegg
- Neurosciences Department, Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - Sophie Achard
- Inria, University Grenoble Alpes, CNRS, Grenoble, France
| | - Sarael Alcauter
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, México
| | - Dorothee P Auer
- School of Medicine, University of Nottingham, Nottingham, UK
- NIHR Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Emmanuel L Barbier
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, Grenoble, France
| | - Jürgen Baudewig
- Functional Imaging Laboratory, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany
| | - Christian F Beckmann
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
| | - Nicolau Beckmann
- Musculoskeletal Diseases Department, Novartis Institutes for BioMedical Research, Basel, Switzerland
| | | | - Erwin L A Blezer
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht & Utrecht University, Utrecht, The Netherlands
| | | | - Susann Boretius
- Functional Imaging Laboratory, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany
- Faculty of Biology and Psychology, Georg-August University of Göttingen, Göttingen, Germany
- DFG Research Center for Nanoscale Microscopy and Molecular Physiology of the Brain (CNMPB), Göttingen, Germany
| | - Sandrine Bouvard
- Lyon Neuroscience Research Center, Université Claude Bernard Lyon 1, INSERM, CNRS, Lyon, France
| | - Eike Budinger
- Combinatorial NeuroImaging Core Facility, Leibniz Institute for Neurobiology, Magdeburg, Germany
- Center for Behavioral Brain Sciences, Magdeburg, Germany
| | - Joseph D Buxbaum
- Seaver Autism Center for Research & Treatment, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Diana Cash
- Biomarker Research And Imaging in Neuroscience (BRAIN) Centre, Department of Neuroimaging King's College London, London, UK
| | - Victoria Chapman
- School of Life Sciences, University of Nottingham, Nottingham, UK
- Pain Centre Versus Arthritis, University of Nottingham, Nottingham, UK
- NIHR Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Kai-Hsiang Chuang
- Queensland Brain Institute and Centre for Advanced Imaging, University of Queensland, St. Lucia, QLD, Australia
| | | | - Bram F Coolen
- Biomedical Engineering and Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Jeffrey W Dalley
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Marc Dhenain
- Laboratoire des Maladies Neurodégénératives, Molecular Imaging Research Center (MIRCen), Université Paris-Saclay, Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA), CNRS, Fontenay-aux-Roses, France
| | - Rick M Dijkhuizen
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht & Utrecht University, Utrecht, The Netherlands
| | - Oscar Esteban
- Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Cornelius Faber
- Experimental Magnetic Resonance Group, Clinic of Radiology, University of Münster, Münster, Germany
| | - Marcelo Febo
- Febo Laboratory, Department of Psychiatry, University of Florida, Gainesville, FL, USA
| | - Kirk W Feindel
- Centre for Microscopy, Characterisation & Analysis, Research Infrastructure Centres, University of Western Australia, Nedlands, WA, Australia
| | - Gianluigi Forloni
- Biology of Neurodogenerative Disorders, Department of Neuroscience Istituto di Ricerche Farmacologiche Mario Negri, IRCCS, Milan, Italy
| | - Jérémie Fouquet
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, QC, Canada
| | - Eduardo A Garza-Villarreal
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, Mexico
| | - Natalia Gass
- Translational Imaging, Department of Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, Mannheim, Germany
| | - Jeffrey C Glennon
- Conway Institute of Biomedical and Biomolecular Sciences, School of Medicine, University College Dublin, Dublin, Ireland
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Olli Gröhn
- Biomedical Imaging Unit, A.I.V. Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Andrew Harkin
- Neuropsychopharmacology Research Group, School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Dublin, Ireland
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Arend Heerschap
- Department for Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Xavier Helluy
- Department of Neurophysiology, Medical Faculty, Ruhr University Bochum, Bochum, Germany
- Department of Biopsychology, Institute of Cognitive Neuroscience, Ruhr University Bochum, Bochum, Germany
| | - Kristina Herfert
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University of Tuebingen, Tuebingen, Germany
| | - Arnd Heuser
- Max-Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Judith R Homberg
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Danielle J Houwing
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Fahmeed Hyder
- Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
- Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University School of Medicine, New Haven, CT, USA
| | | | - Ileana O Jelescu
- CIBM Center for Biomedical Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Heidi Johansen-Berg
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
| | - Gen Kaneko
- School of Arts & Sciences, University of Houston-Victoria, Victoria, TX, USA
| | - Ryuta Kawashima
- Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Shella D Keilholz
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology, Atlanta, GA, USA
| | - Georgios A Keliris
- Bio-imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Clare Kelly
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
- School of Psychology, Trinity College Dublin, Dublin, Ireland
- Department of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Christian Kerskens
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
- Trinity Centre for Biomedical Engineering, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Jibran Y Khokhar
- Khokhar Lab, Department of Anatomy and Cell Biology, Western University, London, ON, Canada
| | - Peter C Kind
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, UK
- Patrick Wild Centre, University of Edinburgh, Edinburgh, UK
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK
- Centre for Brain Development and Repair, Institute for Stem Cell Biology and Regenerative Medicine, Bangalore, India
| | | | - Jason P Lerch
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
- Department of Medical Biophysics, University of Toronto, Toronto, QC, Canada
| | - Monica A López-Hidalgo
- Escuela Nacional de Estudios Superiores, Juriquilla, Universidad Nacional Autónoma de México, Querétaro, México
| | | | - Fabien Marchand
- Université Clermont Auvergne, Inserm U1107 Neuro-Dol, Pharmacologie Fondamentale et Clinique de la Douleur, Clermont-Ferrand, France
| | - Rogier B Mars
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
| | - Gerardo Marsella
- Animal Care Unit, Istituto di Ricerche Farmacologiche Mario Negri, IRCCS, Milan, Italy
| | - Edoardo Micotti
- Biology of Neurodogenerative Disorders, Department of Neuroscience Istituto di Ricerche Farmacologiche Mario Negri, IRCCS, Milan, Italy
| | - Emma Muñoz-Moreno
- Magnetic Imaging Resonance Core Facility, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain
| | - Jamie Near
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, QC, Canada
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, QC, Canada
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max-Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- Experimental and Clinical Research Center, A Joint Cooperation Between the Charité Medical Faculty and the Max-Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Willem M Otte
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht & Utrecht University, Utrecht, The Netherlands
- Department of Pediatric Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht & Utrecht University, Utrecht, The Netherlands
| | - Patricia Pais-Roldán
- Translational Neuroimaging and Neural Control Group, High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
- Medical Imaging Physics (INM-4), Institute of Neuroscience and Medicine, Forschungszentrum Juelich, Juelich, Germany
| | - Wen-Ju Pan
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology, Atlanta, GA, USA
| | - Roberto A Prado-Alcalá
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, México
| | - Gina L Quirarte
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, México
| | - Jennifer Rodger
- Experimental and Regenerative Neurosciences, School of Biological Sciences, University of Western Australia, Crawley, WA, Australia
- Brain Plasticity Group, Perron Institute for Neurological and Translational Science, Nedlands, WA, Australia
| | - Tim Rosenow
- Centre for Microscopy, Characterisation and Analysis, University of Western Australia, Crawley, WA, Australia
| | - Cassandra Sampaio-Baptista
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
- School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
| | - Alexander Sartorius
- Translational Imaging, Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Stephen J Sawiak
- Translational Neuroimaging Laboratory, Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Tom W J Scheenen
- Department for Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
- Erwin L. Hahn Institute for MR Imaging, University of Duisburg-Essen, Essen, Germany
| | - Noam Shemesh
- Preclinical MRI, Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Yen-Yu Ian Shih
- Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Amir Shmuel
- Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- Department of Physiology, McGill University, Montreal, QC, Canada
| | - Guadalupe Soria
- Laboratory of Surgical Neuroanatomy, Institute of Neuroscience, University of Barcelona, Barcelona, Spain
| | - Ron Stoop
- Psychiatric neurosciences, Center for Psychiatric Neuroscience, Lausanne University and University Hospital Center, Unicentre, Lausanne, Switzerland
| | | | - Sally M Till
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, UK
- Patrick Wild Centre, University of Edinburgh, Edinburgh, UK
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Nick Todd
- Focused Ultrasound Laboratory, Department of Radiology Brigham and Women's Hospital, Boston, MA, USA
| | - Annemie Van Der Linden
- Bio-imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Annette van der Toorn
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht & Utrecht University, Utrecht, The Netherlands
| | - Geralda A F van Tilborg
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht & Utrecht University, Utrecht, The Netherlands
| | - Christian Vanhove
- Institute Biomedical Technology (IBiTech), Electronics and Information Systems (ELIS), Ghent University, Gent, Belgium
| | - Andor Veltien
- Department for Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Marleen Verhoye
- Bio-imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Lydia Wachsmuth
- Experimental Magnetic Resonance Group, Clinic of Radiology, University of Münster, Münster, Germany
| | - Wolfgang Weber-Fahr
- Translational Imaging, Department of Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, Mannheim, Germany
| | - Patricia Wenk
- Combinatorial NeuroImaging Core Facility, Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Xin Yu
- Translational Neuroimaging and Neural Control Group, High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Valerio Zerbi
- Neuro-X Institute, School of Engineering (STI), EPFL, Lausanne, Switzerland
- Centre for Biomedical Imaging (CIBM), Lausanne, Switzerland
| | - Nanyin Zhang
- Translational Neuroimaging and Systems Neuroscience Lab, Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Baogui B Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Luc Zimmer
- Lyon Neuroscience Research Center, Université Claude Bernard Lyon 1, INSERM, CNRS, Lyon, France
- CERMEP - Imagerie du vivant, Lyon, France
- Hospices Civils de Lyon, Lyon, France
| | - Gabriel A Devenyi
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, QC, Canada
- Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Andreas Hess
- Institute of Experimental and Clinical Pharmacology and Toxicology, FAU Erlangen-Nürnberg, Erlangen, Germany
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Grandjean J, Desrosiers-Gregoire G, Anckaerts C, Angeles-Valdez D, Ayad F, Barrière DA, Blockx I, Bortel A, Broadwater M, Cardoso BM, Célestine M, Chavez-Negrete JE, Choi S, Christiaen E, Clavijo P, Colon-Perez L, Cramer S, Daniele T, Dempsey E, Diao Y, Doelemeyer A, Dopfel D, Dvořáková L, Falfán-Melgoza C, Fernandes FF, Fowler CF, Fuentes-Ibañez A, Garin CM, Gelderman E, Golden CEM, Guo CCG, Henckens MJAG, Hennessy LA, Herman P, Hofwijks N, Horien C, Ionescu TM, Jones J, Kaesser J, Kim E, Lambers H, Lazari A, Lee SH, Lillywhite A, Liu Y, Liu YY, López-Castro A, López-Gil X, Ma Z, MacNicol E, Madularu D, Mandino F, Marciano S, McAuslan MJ, McCunn P, McIntosh A, Meng X, Meyer-Baese L, Missault S, Moro F, Naessens DMP, Nava-Gomez LJ, Nonaka H, Ortiz JJ, Paasonen J, Peeters LM, Pereira M, Perez PD, Pompilus M, Prior M, Rakhmatullin R, Reimann HM, Reinwald J, Del Rio RT, Rivera-Olvera A, Ruiz-Pérez D, Russo G, Rutten TJ, Ryoke R, Sack M, Salvan P, Sanganahalli BG, Schroeter A, Seewoo BJ, Selingue E, Seuwen A, Shi B, Sirmpilatze N, Smith JAB, Smith C, Sobczak F, Stenroos PJ, Straathof M, Strobelt S, Sumiyoshi A, Takahashi K, Torres-García ME, Tudela R, van den Berg M, van der Marel K, van Hout ATB, Vertullo R, Vidal B, Vrooman RM, Wang VX, Wank I, Watson DJG, Yin T, Zhang Y, Zurbruegg S, Achard S, Alcauter S, Auer DP, Barbier EL, Baudewig J, Beckmann CF, Beckmann N, Becq GJPC, Blezer ELA, Bolbos R, Boretius S, Bouvard S, Budinger E, Buxbaum JD, Cash D, Chapman V, Chuang KH, Ciobanu L, Coolen BF, Dalley JW, Dhenain M, Dijkhuizen RM, Esteban O, Faber C, Febo M, Feindel KW, Forloni G, Fouquet J, Garza-Villarreal EA, Gass N, Glennon JC, Gozzi A, Gröhn O, Harkin A, Heerschap A, Helluy X, Herfert K, Heuser A, Homberg JR, Houwing DJ, Hyder F, Ielacqua GD, Jelescu IO, Johansen-Berg H, Kaneko G, Kawashima R, Keilholz SD, Keliris GA, Kelly C, Kerskens C, Khokhar JY, Kind PC, Langlois JB, Lerch JP, López-Hidalgo MA, Manahan-Vaughan D, Marchand F, Mars RB, Marsella G, Micotti E, Muñoz-Moreno E, Near J, Niendorf T, Otte WM, Pais-Roldán P, Pan WJ, Prado-Alcalá RA, Quirarte GL, Rodger J, Rosenow T, Sampaio-Baptista C, Sartorius A, Sawiak SJ, Scheenen TWJ, Shemesh N, Shih YYI, Shmuel A, Soria G, Stoop R, Thompson GJ, Till SM, Todd N, Van Der Linden A, van der Toorn A, van Tilborg GAF, Vanhove C, Veltien A, Verhoye M, Wachsmuth L, Weber-Fahr W, Wenk P, Yu X, Zerbi V, Zhang N, Zhang BB, Zimmer L, Devenyi GA, Chakravarty MM, Hess A. A consensus protocol for functional connectivity analysis in the rat brain. Nat Neurosci 2023; 26:673-681. [PMID: 36973511 PMCID: PMC10493189 DOI: 10.1038/s41593-023-01286-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 02/15/2023] [Indexed: 03/29/2023]
Abstract
Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows for comparisons with data modalities collected under invasive or terminal procedures. Currently, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. Here we introduce StandardRat, a consensus rat functional magnetic resonance imaging acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired from rats across 46 centers. We developed a reproducible pipeline for analyzing rat data acquired with diverse protocols and determined experimental and processing parameters associated with the robust detection of functional connectivity across centers. We show that the standardized protocol enhances biologically plausible functional connectivity patterns relative to previous acquisitions. The protocol and processing pipeline described here is openly shared with the neuroimaging community to promote interoperability and cooperation toward tackling the most important challenges in neuroscience.
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Affiliation(s)
- Joanes Grandjean
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands.
- Department for Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - Gabriel Desrosiers-Gregoire
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, QC, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
| | - Cynthia Anckaerts
- Bio-imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Diego Angeles-Valdez
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, Mexico
| | - Fadi Ayad
- Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - David A Barrière
- UMR INRAE/CNRS 7247 Physiologie des Comportements et de la Reproduction, Physiologie de la reproduction et des comportements, Centre de recherche INRAE de Nouzilly, Tours, France
| | - Ines Blockx
- Bio-imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Aleksandra Bortel
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Margaret Broadwater
- Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Beatriz M Cardoso
- Preclinical MRI, Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Marina Célestine
- Laboratoire des Maladies Neurodégénératives, Molecular Imaging Research Center (MIRCen), Université Paris-Saclay, Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA), CNRS, Fontenay-aux-Roses, France
| | - Jorge E Chavez-Negrete
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, México
| | - Sangcheon Choi
- Translational Neuroimaging and Neural Control Group, High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
- Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tuebingen, Tuebingen, Germany
| | - Emma Christiaen
- Institute Biomedical Technology (IBiTech), Electronics and Information Systems (ELIS), Ghent University, Gent, Belgium
| | - Perrin Clavijo
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology, Atlanta, GA, USA
| | - Luis Colon-Perez
- Department of Pharmacology & Neuroscience, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Samuel Cramer
- Translational Neuroimaging and Systems Neuroscience Lab, Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Tolomeo Daniele
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | - Elaine Dempsey
- Neuropsychopharmacology Research Group, School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Dublin, Ireland
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Yujian Diao
- CIBM Center for Biomedical Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Laboratory for Functional and Metabolic Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Arno Doelemeyer
- Musculoskeletal Diseases Department, Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - David Dopfel
- Translational Neuroimaging and Systems Neuroscience Lab, Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Lenka Dvořáková
- Biomedical Imaging Unit, A.I.V. Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Claudia Falfán-Melgoza
- Translational Imaging, Department of Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, Mannheim, Germany
| | - Francisca F Fernandes
- Preclinical MRI, Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Caitlin F Fowler
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, QC, Canada
- Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Antonio Fuentes-Ibañez
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, México
| | - Clément M Garin
- Laboratoire des Maladies Neurodégénératives, Molecular Imaging Research Center (MIRCen), Université Paris-Saclay, Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA), CNRS, Fontenay-aux-Roses, France
| | - Eveline Gelderman
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Carla E M Golden
- Seaver Autism Center for Research & Treatment, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Chao C G Guo
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Marloes J A G Henckens
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
- Department of Neuroscience and Pharmacology, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Lauren A Hennessy
- Experimental and Regenerative Neurosciences, School of Biological Sciences, University of Western Australia, Crawley, WA, Australia
- Brain Plasticity Group, Perron Institute for Neurological and Translational Science, Nedlands, WA, Australia
| | - Peter Herman
- Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
- Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University School of Medicine, New Haven, CT, USA
| | - Nita Hofwijks
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Corey Horien
- Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Tudor M Ionescu
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University of Tuebingen, Tuebingen, Germany
| | - Jolyon Jones
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Johannes Kaesser
- Institute of Experimental and Clinical Pharmacology and Toxicology, FAU Erlangen-Nürnberg, Erlangen, Germany
| | - Eugene Kim
- Biomarker Research And Imaging in Neuroscience (BRAIN) Centre, Department of Neuroimaging King's College London, London, UK
| | - Henriette Lambers
- Experimental Magnetic Resonance Group, Clinic of Radiology, University of Münster, Münster, Germany
| | - Alberto Lazari
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
| | - Sung-Ho Lee
- Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Amanda Lillywhite
- School of Life Sciences, University of Nottingham, Nottingham, UK
- Pain Centre Versus Arthritis, University of Nottingham, Nottingham, UK
| | - Yikang Liu
- Translational Neuroimaging and Systems Neuroscience Lab, Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Yanyan Y Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Alejandra López-Castro
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, Mexico
| | - Xavier López-Gil
- Magnetic Imaging Resonance Core Facility, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain
| | - Zilu Ma
- Translational Neuroimaging and Systems Neuroscience Lab, Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Eilidh MacNicol
- Biomarker Research And Imaging in Neuroscience (BRAIN) Centre, Department of Neuroimaging King's College London, London, UK
| | - Dan Madularu
- Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
- Center for Translational Neuroimaging, Northeastern University, Boston, MA, USA
| | - Francesca Mandino
- Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Sabina Marciano
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University of Tuebingen, Tuebingen, Germany
| | - Matthew J McAuslan
- Neuropsychopharmacology Research Group, School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Dublin, Ireland
| | - Patrick McCunn
- Khokhar Lab, Department of Anatomy and Cell Biology, Western University, London, ON, Canada
| | - Alison McIntosh
- Neuropsychopharmacology Research Group, School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Dublin, Ireland
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Xianzong Meng
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Lisa Meyer-Baese
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology, Atlanta, GA, USA
| | - Stephan Missault
- Bio-imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Federico Moro
- Laboratory of Acute Brain Injury and Therapeutic Strategies, Department of NeuroscienceIstituto di Ricerche Farmacologiche Mario Negri, IRCCS, Milan, Italy
| | - Daphne M P Naessens
- Biomedical Engineering and Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Laura J Nava-Gomez
- Facultad de Medicina, Universidad Autónoma de Querétaro, Querétaro, México
- Escuela Nacional de Estudios Superiores, Juriquilla, Universidad Nacional Autónoma de México, Querétaro, México
| | - Hiroi Nonaka
- Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Juan J Ortiz
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, México
| | - Jaakko Paasonen
- Biomedical Imaging Unit, A.I.V. Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Lore M Peeters
- Bio-imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Mickaël Pereira
- Lyon Neuroscience Research Center, Université Claude Bernard Lyon 1, INSERM, CNRS, Lyon, France
| | - Pablo D Perez
- Translational Neuroimaging and Systems Neuroscience Lab, Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Marjory Pompilus
- Febo Laboratory, Department of Psychiatry, University of Florida, Gainesville, FL, USA
| | - Malcolm Prior
- School of Medicine, University of Nottingham, Nottingham, UK
| | | | - Henning M Reimann
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max-Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Jonathan Reinwald
- Translational Imaging, Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Rodrigo Triana Del Rio
- Psychiatric neurosciences, Center for Psychiatric Neuroscience, Lausanne University and University Hospital Center, Unicentre, Lausanne, Switzerland
| | - Alejandro Rivera-Olvera
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | | | - Gabriele Russo
- Department of Neurophysiology, Medical Faculty, Ruhr University Bochum, Bochum, Germany
| | - Tobias J Rutten
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Rie Ryoke
- Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Markus Sack
- Translational Imaging, Department of Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, Mannheim, Germany
| | - Piergiorgio Salvan
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
| | - Basavaraju G Sanganahalli
- Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
- Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University School of Medicine, New Haven, CT, USA
| | - Aileen Schroeter
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Bhedita J Seewoo
- Experimental and Regenerative Neurosciences, School of Biological Sciences, University of Western Australia, Crawley, WA, Australia
- Brain Plasticity Group, Perron Institute for Neurological and Translational Science, Nedlands, WA, Australia
- Centre for Microscopy, Characterisation & Analysis, Research Infrastructure Centres, University of Western Australia, Nedlands, WA, Australia
| | | | - Aline Seuwen
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Bowen Shi
- iHuman Institute, ShanghaiTech University, Shanghai, China
| | - Nikoloz Sirmpilatze
- Functional Imaging Laboratory, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany
- Faculty of Biology and Psychology, Georg-August University of Göttingen, Göttingen, Germany
- DFG Research Center for Nanoscale Microscopy and Molecular Physiology of the Brain (CNMPB), Göttingen, Germany
| | - Joanna A B Smith
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, UK
- Patrick Wild Centre, University of Edinburgh, Edinburgh, UK
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Corrie Smith
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology, Atlanta, GA, USA
| | - Filip Sobczak
- Translational Neuroimaging and Neural Control Group, High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
- Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tuebingen, Tuebingen, Germany
| | - Petteri J Stenroos
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, Grenoble, France
| | - Milou Straathof
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht & Utrecht University, Utrecht, The Netherlands
| | - Sandra Strobelt
- Institute of Experimental and Clinical Pharmacology and Toxicology, FAU Erlangen-Nürnberg, Erlangen, Germany
| | - Akira Sumiyoshi
- Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
- National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Kengo Takahashi
- Translational Neuroimaging and Neural Control Group, High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
- Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tuebingen, Tuebingen, Germany
| | - Maria E Torres-García
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, México
| | - Raul Tudela
- Group of Biomedical Imaging, Consorcio Centro de Investigación Biomédica en Red (CIBER) de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), University of Barcelona, Barcelona, Spain
| | - Monica van den Berg
- Bio-imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Kajo van der Marel
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht & Utrecht University, Utrecht, The Netherlands
| | - Aran T B van Hout
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Roberta Vertullo
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Benjamin Vidal
- Lyon Neuroscience Research Center, Université Claude Bernard Lyon 1, INSERM, CNRS, Lyon, France
| | - Roël M Vrooman
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Victora X Wang
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Isabel Wank
- Institute of Experimental and Clinical Pharmacology and Toxicology, FAU Erlangen-Nürnberg, Erlangen, Germany
| | - David J G Watson
- School of Life Sciences, University of Nottingham, Nottingham, UK
| | - Ting Yin
- Animal Imaging and Technology Section, Center for Biomedical Imaging, École polytechnique fédérale de Lausanne, Lausanne, Switzerland
| | - Yongzhi Zhang
- Focused Ultrasound Laboratory, Department of Radiology Brigham and Women's Hospital, Boston, MA, USA
| | - Stefan Zurbruegg
- Neurosciences Department, Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - Sophie Achard
- Inria, University Grenoble Alpes, CNRS, Grenoble, France
| | - Sarael Alcauter
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, México
| | - Dorothee P Auer
- School of Medicine, University of Nottingham, Nottingham, UK
- NIHR Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Emmanuel L Barbier
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, Grenoble, France
| | - Jürgen Baudewig
- Functional Imaging Laboratory, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany
| | - Christian F Beckmann
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
| | - Nicolau Beckmann
- Musculoskeletal Diseases Department, Novartis Institutes for BioMedical Research, Basel, Switzerland
| | | | - Erwin L A Blezer
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht & Utrecht University, Utrecht, The Netherlands
| | | | - Susann Boretius
- Functional Imaging Laboratory, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany
- Faculty of Biology and Psychology, Georg-August University of Göttingen, Göttingen, Germany
- DFG Research Center for Nanoscale Microscopy and Molecular Physiology of the Brain (CNMPB), Göttingen, Germany
| | - Sandrine Bouvard
- Lyon Neuroscience Research Center, Université Claude Bernard Lyon 1, INSERM, CNRS, Lyon, France
| | - Eike Budinger
- Combinatorial NeuroImaging Core Facility, Leibniz Institute for Neurobiology, Magdeburg, Germany
- Center for Behavioral Brain Sciences, Magdeburg, Germany
| | - Joseph D Buxbaum
- Seaver Autism Center for Research & Treatment, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Diana Cash
- Biomarker Research And Imaging in Neuroscience (BRAIN) Centre, Department of Neuroimaging King's College London, London, UK
| | - Victoria Chapman
- School of Life Sciences, University of Nottingham, Nottingham, UK
- Pain Centre Versus Arthritis, University of Nottingham, Nottingham, UK
- NIHR Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Kai-Hsiang Chuang
- Queensland Brain Institute and Centre for Advanced Imaging, University of Queensland, St. Lucia, QLD, Australia
| | | | - Bram F Coolen
- Biomedical Engineering and Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Jeffrey W Dalley
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Marc Dhenain
- Laboratoire des Maladies Neurodégénératives, Molecular Imaging Research Center (MIRCen), Université Paris-Saclay, Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA), CNRS, Fontenay-aux-Roses, France
| | - Rick M Dijkhuizen
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht & Utrecht University, Utrecht, The Netherlands
| | - Oscar Esteban
- Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Cornelius Faber
- Experimental Magnetic Resonance Group, Clinic of Radiology, University of Münster, Münster, Germany
| | - Marcelo Febo
- Febo Laboratory, Department of Psychiatry, University of Florida, Gainesville, FL, USA
| | - Kirk W Feindel
- Centre for Microscopy, Characterisation & Analysis, Research Infrastructure Centres, University of Western Australia, Nedlands, WA, Australia
| | - Gianluigi Forloni
- Biology of Neurodogenerative Disorders, Department of Neuroscience Istituto di Ricerche Farmacologiche Mario Negri, IRCCS, Milan, Italy
| | - Jérémie Fouquet
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, QC, Canada
| | - Eduardo A Garza-Villarreal
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, Mexico
| | - Natalia Gass
- Translational Imaging, Department of Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, Mannheim, Germany
| | - Jeffrey C Glennon
- Conway Institute of Biomedical and Biomolecular Sciences, School of Medicine, University College Dublin, Dublin, Ireland
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Olli Gröhn
- Biomedical Imaging Unit, A.I.V. Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Andrew Harkin
- Neuropsychopharmacology Research Group, School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Dublin, Ireland
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Arend Heerschap
- Department for Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Xavier Helluy
- Department of Neurophysiology, Medical Faculty, Ruhr University Bochum, Bochum, Germany
- Department of Biopsychology, Institute of Cognitive Neuroscience, Ruhr University Bochum, Bochum, Germany
| | - Kristina Herfert
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University of Tuebingen, Tuebingen, Germany
| | - Arnd Heuser
- Max-Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Judith R Homberg
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Danielle J Houwing
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Fahmeed Hyder
- Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
- Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University School of Medicine, New Haven, CT, USA
| | | | - Ileana O Jelescu
- CIBM Center for Biomedical Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Heidi Johansen-Berg
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
| | - Gen Kaneko
- School of Arts & Sciences, University of Houston-Victoria, Victoria, TX, USA
| | - Ryuta Kawashima
- Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Shella D Keilholz
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology, Atlanta, GA, USA
| | - Georgios A Keliris
- Bio-imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Clare Kelly
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
- School of Psychology, Trinity College Dublin, Dublin, Ireland
- Department of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Christian Kerskens
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
- Trinity Centre for Biomedical Engineering, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Jibran Y Khokhar
- Khokhar Lab, Department of Anatomy and Cell Biology, Western University, London, ON, Canada
| | - Peter C Kind
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, UK
- Patrick Wild Centre, University of Edinburgh, Edinburgh, UK
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK
- Centre for Brain Development and Repair, Institute for Stem Cell Biology and Regenerative Medicine, Bangalore, India
| | | | - Jason P Lerch
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
- Department of Medical Biophysics, University of Toronto, Toronto, QC, Canada
| | - Monica A López-Hidalgo
- Escuela Nacional de Estudios Superiores, Juriquilla, Universidad Nacional Autónoma de México, Querétaro, México
| | | | - Fabien Marchand
- Université Clermont Auvergne, Inserm U1107 Neuro-Dol, Pharmacologie Fondamentale et Clinique de la Douleur, Clermont-Ferrand, France
| | - Rogier B Mars
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
| | - Gerardo Marsella
- Animal Care Unit, Istituto di Ricerche Farmacologiche Mario Negri, IRCCS, Milan, Italy
| | - Edoardo Micotti
- Biology of Neurodogenerative Disorders, Department of Neuroscience Istituto di Ricerche Farmacologiche Mario Negri, IRCCS, Milan, Italy
| | - Emma Muñoz-Moreno
- Magnetic Imaging Resonance Core Facility, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain
| | - Jamie Near
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, QC, Canada
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, QC, Canada
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max-Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- Experimental and Clinical Research Center, A Joint Cooperation Between the Charité Medical Faculty and the Max-Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Willem M Otte
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht & Utrecht University, Utrecht, The Netherlands
- Department of Pediatric Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht & Utrecht University, Utrecht, The Netherlands
| | - Patricia Pais-Roldán
- Translational Neuroimaging and Neural Control Group, High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
- Medical Imaging Physics (INM-4), Institute of Neuroscience and Medicine, Forschungszentrum Juelich, Juelich, Germany
| | - Wen-Ju Pan
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology, Atlanta, GA, USA
| | - Roberto A Prado-Alcalá
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, México
| | - Gina L Quirarte
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, México
| | - Jennifer Rodger
- Experimental and Regenerative Neurosciences, School of Biological Sciences, University of Western Australia, Crawley, WA, Australia
- Brain Plasticity Group, Perron Institute for Neurological and Translational Science, Nedlands, WA, Australia
| | - Tim Rosenow
- Centre for Microscopy, Characterisation and Analysis, University of Western Australia, Crawley, WA, Australia
| | - Cassandra Sampaio-Baptista
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
- School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
| | - Alexander Sartorius
- Translational Imaging, Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Stephen J Sawiak
- Translational Neuroimaging Laboratory, Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Tom W J Scheenen
- Department for Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
- Erwin L. Hahn Institute for MR Imaging, University of Duisburg-Essen, Essen, Germany
| | - Noam Shemesh
- Preclinical MRI, Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Yen-Yu Ian Shih
- Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Amir Shmuel
- Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- Department of Physiology, McGill University, Montreal, QC, Canada
| | - Guadalupe Soria
- Laboratory of Surgical Neuroanatomy, Institute of Neuroscience, University of Barcelona, Barcelona, Spain
| | - Ron Stoop
- Psychiatric neurosciences, Center for Psychiatric Neuroscience, Lausanne University and University Hospital Center, Unicentre, Lausanne, Switzerland
| | | | - Sally M Till
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, UK
- Patrick Wild Centre, University of Edinburgh, Edinburgh, UK
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Nick Todd
- Focused Ultrasound Laboratory, Department of Radiology Brigham and Women's Hospital, Boston, MA, USA
| | - Annemie Van Der Linden
- Bio-imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Annette van der Toorn
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht & Utrecht University, Utrecht, The Netherlands
| | - Geralda A F van Tilborg
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht & Utrecht University, Utrecht, The Netherlands
| | - Christian Vanhove
- Institute Biomedical Technology (IBiTech), Electronics and Information Systems (ELIS), Ghent University, Gent, Belgium
| | - Andor Veltien
- Department for Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Marleen Verhoye
- Bio-imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Lydia Wachsmuth
- Experimental Magnetic Resonance Group, Clinic of Radiology, University of Münster, Münster, Germany
| | - Wolfgang Weber-Fahr
- Translational Imaging, Department of Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, Mannheim, Germany
| | - Patricia Wenk
- Combinatorial NeuroImaging Core Facility, Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Xin Yu
- Translational Neuroimaging and Neural Control Group, High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Valerio Zerbi
- Neuro-X Institute, School of Engineering (STI), EPFL, Lausanne, Switzerland
- Centre for Biomedical Imaging (CIBM), Lausanne, Switzerland
| | - Nanyin Zhang
- Translational Neuroimaging and Systems Neuroscience Lab, Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Baogui B Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Luc Zimmer
- Lyon Neuroscience Research Center, Université Claude Bernard Lyon 1, INSERM, CNRS, Lyon, France
- CERMEP - Imagerie du vivant, Lyon, France
- Hospices Civils de Lyon, Lyon, France
| | - Gabriel A Devenyi
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, QC, Canada
- Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Andreas Hess
- Institute of Experimental and Clinical Pharmacology and Toxicology, FAU Erlangen-Nürnberg, Erlangen, Germany
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7
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Yu Y, Qiu Y, Li G, Zhang K, Bo B, Pei M, Ye J, Thompson GJ, Cang J, Fang F, Feng Y, Duan X, Tong C, Liang Z. Sleep fMRI with simultaneous electrophysiology at 9.4 T in male mice. Nat Commun 2023; 14:1651. [PMID: 36964161 PMCID: PMC10039056 DOI: 10.1038/s41467-023-37352-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 03/14/2023] [Indexed: 03/26/2023] Open
Abstract
Sleep is ubiquitous and essential, but its mechanisms remain unclear. Studies in animals and humans have provided insights of sleep at vastly different spatiotemporal scales. However, challenges remain to integrate local and global information of sleep. Therefore, we developed sleep fMRI based on simultaneous electrophysiology at 9.4 T in male mice. Optimized un-anesthetized mouse fMRI setup allowed manifestation of NREM and REM sleep, and a large sleep fMRI dataset was collected and openly accessible. State dependent global patterns were revealed, and state transitions were found to be global, asymmetrical and sequential, which can be predicted up to 17.8 s using LSTM models. Importantly, sleep fMRI with hippocampal recording revealed potentiated sharp-wave ripple triggered global patterns during NREM than awake state, potentially attributable to co-occurrence of spindle events. To conclude, we established mouse sleep fMRI with simultaneous electrophysiology, and demonstrated its capability by revealing global dynamics of state transitions and neural events.
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Affiliation(s)
- Yalin Yu
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yue Qiu
- Department of Anesthesia, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Gen Li
- Department of Biomedical Engineering, College of Future Technology, Academy for Advanced Interdisciplinary Studies, National Biomedical Imaging Centre, Peking University, Beijing, China
| | - Kaiwei Zhang
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Binshi Bo
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Mengchao Pei
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Jingjing Ye
- iHuman Institute, ShanghaiTech University, Shanghai, China
| | | | - Jing Cang
- Department of Anesthesia, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Fang Fang
- Department of Anesthesia, Zhongshan Hospital, Fudan University, Shanghai, China
- The Central Hospital of Xuhui District, Shanghai, China
| | - Yanqiu Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Xiaojie Duan
- Department of Biomedical Engineering, College of Future Technology, Academy for Advanced Interdisciplinary Studies, National Biomedical Imaging Centre, Peking University, Beijing, China.
| | - Chuanjun Tong
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
| | - Zhifeng Liang
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
- Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, China.
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8
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Xin Y, Liu S, Liu Y, Qian Z, Liu H, Zhang B, Guo T, Thompson GJ, Stevens RC, Sharpless KB, Dong J, Shui W. Affinity selection of double-click triazole libraries for rapid discovery of allosteric modulators for GLP-1 receptor. Proc Natl Acad Sci U S A 2023; 120:e2220767120. [PMID: 36893261 PMCID: PMC10243133 DOI: 10.1073/pnas.2220767120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 02/02/2023] [Indexed: 03/11/2023] Open
Abstract
The recently developed double-click reaction sequence [G. Meng et al., Nature 574, 86-89 (2019)] is expected to vastly expand the number and diversity of synthetically accessible 1,2,3-triazole derivatives. However, it remains elusive how to rapidly navigate the extensive chemical space created by double-click chemistry for bioactive compound discovery. In this study, we selected a particularly challenging drug target, the glucagon-like-peptide-1 receptor (GLP-1R), to benchmark our new platform for the design, synthesis, and screening of double-click triazole libraries. First, we achieved a streamlined synthesis of customized triazole libraries on an unprecedented scale (composed of 38,400 new compounds). By interfacing affinity-selection mass spectrometry and functional assays, we identified a series of positive allosteric modulators (PAMs) with unreported scaffolds that can selectively and robustly enhance the signaling activity of the endogenous GLP-1(9-36) peptide. Intriguingly, we further revealed an unexpected binding mode of new PAMs which likely act as a molecular glue between the receptor and the peptide agonist. We anticipate the merger of double-click library synthesis with the hybrid screening platform allows for efficient and economic discovery of drug candidates or chemical probes for various therapeutic targets.
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Affiliation(s)
- Ye Xin
- iHuman Institute, ShanghaiTech University, Shanghai201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai201210, China
- University of Chinese Academy of Sciences, Beijing100049, China
| | - Shuo Liu
- School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai200240, China
| | - Yan Liu
- iHuman Institute, ShanghaiTech University, Shanghai201210, China
| | - Zhen Qian
- iHuman Institute, ShanghaiTech University, Shanghai201210, China
| | - Hongyue Liu
- iHuman Institute, ShanghaiTech University, Shanghai201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai201210, China
- University of Chinese Academy of Sciences, Beijing100049, China
| | - Bingjie Zhang
- iHuman Institute, ShanghaiTech University, Shanghai201210, China
| | - Taijie Guo
- School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai200240, China
| | | | - Raymond C. Stevens
- iHuman Institute, ShanghaiTech University, Shanghai201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai201210, China
| | - K. Barry Sharpless
- Department of Chemistry, The Scripps Research Institute, La Jolla, CA92037
| | - Jiajia Dong
- School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai200240, China
- Institute of Translational Medicine, Zhangjiang Institute for Advanced Study, National Facility for Translational Medicine (Shanghai), Shanghai Jiao Tong University, Shanghai200240, China
- Shanghai Artificial Intelligence Laboratory, Shanghai200232, China
| | - Wenqing Shui
- iHuman Institute, ShanghaiTech University, Shanghai201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai201210, China
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9
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de Arce KP, Ribic A, Chowdhury D, Watters K, Thompson GJ, Sanganahalli BG, Lippard ETC, Rohlmann A, Strittmatter SM, Missler M, Hyder F, Biederer T. Concerted roles of LRRTM1 and SynCAM 1 in organizing prefrontal cortex synapses and cognitive functions. Nat Commun 2023; 14:459. [PMID: 36709330 PMCID: PMC9884278 DOI: 10.1038/s41467-023-36042-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 01/13/2023] [Indexed: 01/29/2023] Open
Abstract
Multiple trans-synaptic complexes organize synapse development, yet their roles in the mature brain and cooperation remain unclear. We analyzed the postsynaptic adhesion protein LRRTM1 in the prefrontal cortex (PFC), a region relevant to cognition and disorders. LRRTM1 knockout (KO) mice had fewer synapses, and we asked whether other synapse organizers counteract further loss. This determined that the immunoglobulin family member SynCAM 1 controls synapse number in PFC and was upregulated upon LRRTM1 loss. Combined LRRTM1 and SynCAM 1 deletion substantially lowered dendritic spine number in PFC, but not hippocampus, more than the sum of single KO impairments. Their cooperation extended presynaptically, and puncta of Neurexins, LRRTM1 partners, were less abundant in double KO (DKO) PFC. Electrophysiology and fMRI demonstrated aberrant neuronal activity in DKO mice. Further, DKO mice were impaired in social interactions and cognitive tasks. Our results reveal concerted roles of LRRTM1 and SynCAM 1 across synaptic, network, and behavioral domains.
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Affiliation(s)
- Karen Perez de Arce
- Department of Neuroscience, Tufts University School of Medicine, Boston, MA, USA
- Neuroscience Department, Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - Adema Ribic
- Department of Neuroscience, Tufts University School of Medicine, Boston, MA, USA
- Department of Psychology, University of Virginia, Charlottesville, VA, USA
| | | | - Katherine Watters
- Department of Neuroscience, Tufts University School of Medicine, Boston, MA, USA
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Garth J Thompson
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- iHuman Institute, ShanghaiTech University, Shanghai, China
| | | | - Elizabeth T C Lippard
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry, University of Texas, Austin, TX, USA
| | - Astrid Rohlmann
- Institute of Anatomy and Molecular Neurobiology, Westfälische Wilhelms-University, Münster, Germany
| | - Stephen M Strittmatter
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Markus Missler
- Institute of Anatomy and Molecular Neurobiology, Westfälische Wilhelms-University, Münster, Germany
| | - Fahmeed Hyder
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Thomas Biederer
- Department of Neuroscience, Tufts University School of Medicine, Boston, MA, USA.
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA.
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10
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Li A, Zhang S, Loconte V, Liu Y, Ekman A, Thompson GJ, Sali A, Stevens RC, White K, Singla J, Sun L. An intensity-based post-processing tool for 3D instance segmentation of organelles in soft X-ray tomograms. PLoS One 2022; 17:e0269887. [PMID: 36048824 PMCID: PMC9436087 DOI: 10.1371/journal.pone.0269887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 05/29/2022] [Indexed: 11/29/2022] Open
Abstract
Investigating the 3D structures and rearrangements of organelles within a single cell is critical for better characterizing cellular function. Imaging approaches such as soft X-ray tomography have been widely applied to reveal a complex subcellular organization involving multiple inter-organelle interactions. However, 3D segmentation of organelle instances has been challenging despite its importance in organelle characterization. Here we propose an intensity-based post-processing tool to identify and separate organelle instances. Our tool separates sphere-like (insulin vesicle) and columnar-shaped organelle instances (mitochondrion) based on the intensity of raw tomograms, semantic segmentation masks, and organelle morphology. We validate our tool using synthetic tomograms of organelles and experimental tomograms of pancreatic β-cells to separate insulin vesicle and mitochondria instances. As compared to the commonly used connected regions labeling, watershed, and watershed + Gaussian filter methods, our tool results in improved accuracy in identifying organelles in the synthetic tomograms and an improved description of organelle structures in β-cell tomograms. In addition, under different experimental treatment conditions, significant changes in volumes and intensities of both insulin vesicle and mitochondrion are observed in our instance results, revealing their potential roles in maintaining normal β-cell function. Our tool is expected to be applicable for improving the instance segmentation of other images obtained from different cell types using multiple imaging modalities.
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Affiliation(s)
- Angdi Li
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Shuning Zhang
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Valentina Loconte
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Yan Liu
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Axel Ekman
- Department of Anatomy, University of California San Francisco, San Francisco, CA, United States of America
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
| | | | - Andrej Sali
- California Institute for Quantitative Biosciences, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, United States of America
| | - Raymond C. Stevens
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- Department of Biological Sciences, Bridge Institute, University of Southern California, Los Angeles, CA, United States of America
- Department of Chemistry, Bridge Institute, University of Southern California, Los Angeles, CA, United States of America
| | - Kate White
- Department of Chemistry, Bridge Institute, University of Southern California, Los Angeles, CA, United States of America
- * E-mail: (KW); (JS); (LS)
| | - Jitin Singla
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
- * E-mail: (KW); (JS); (LS)
| | - Liping Sun
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- * E-mail: (KW); (JS); (LS)
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11
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Sanganahalli BG, Thompson GJ, Parent M, Verhagen JV, Blumenfeld H, Herman P, Hyder F. Thalamic activations in rat brain by fMRI during tactile (forepaw, whisker) and non-tactile (visual, olfactory) sensory stimulations. PLoS One 2022; 17:e0267916. [PMID: 35522646 PMCID: PMC9075615 DOI: 10.1371/journal.pone.0267916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 04/18/2022] [Indexed: 11/18/2022] Open
Abstract
The thalamus is a crucial subcortical hub that impacts cortical activity. Tracing experiments in animals and post-mortem humans suggest rich morphological specificity of the thalamus. Very few studies reported rodent thalamic activations by functional MRI (fMRI) as compared to cortical activations for different sensory stimuli. Here, we show different portions of the rat thalamus in response to tactile (forepaw, whisker) and non-tactile (visual, olfactory) sensory stimuli with high field fMRI (11.7T) using a custom-build quadrature surface coil to capture high sensitivity signals from superficial and deep brain regions simultaneously. Results demonstrate reproducible thalamic activations during both tactile and non-tactile stimuli. Forepaw and whisker stimuli activated broader regions within the thalamus: ventral posterior lateral (VPL), ventral posterior medial (VPM), lateral posterior mediorostral (LPMR) and posterior medial (POm) thalamic nuclei. Visual stimuli activated dorsal lateral geniculate nucleus (DLG) of the thalamus but also parts of the superior/inferior colliculus, whereas olfactory stimuli activated specifically the mediodorsal nucleus of the thalamus (MDT). BOLD activations in LGN and MDT were much stronger than in VPL, VPM, LPMR and POm. These fMRI-based thalamic activations suggest that forepaw and whisker (i.e., tactile) stimuli engage VPL, VPM, LPMR and POm whereas visual and olfactory (i.e., non-tactile) stimuli, respectively, recruit DLG and MDT exclusively.
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Affiliation(s)
- Basavaraju G. Sanganahalli
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, Connecticut, United States of America,Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, United States of America,* E-mail: (BGS); (FH)
| | - Garth J. Thompson
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, Connecticut, United States of America,Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, United States of America,iHuman Institute, ShanghaiTech University, Shanghai, China
| | - Maxime Parent
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, Connecticut, United States of America,Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, United States of America
| | - Justus V. Verhagen
- The John B. Pierce Laboratory, New Haven, Connecticut, United States of America,Department of Neuroscience, Yale University, New Haven, Connecticut, United States of America
| | - Hal Blumenfeld
- Department of Neuroscience, Yale University, New Haven, Connecticut, United States of America,Department of Neurology, Yale University, New Haven, Connecticut, United States of America,Department of Neurosurgery, Yale University, New Haven, Connecticut, United States of America
| | - Peter Herman
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, Connecticut, United States of America,Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, United States of America
| | - Fahmeed Hyder
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, Connecticut, United States of America,Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, United States of America,Department of Biomedical Engineering, Yale University, New Haven, Connecticut, United States of America,* E-mail: (BGS); (FH)
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12
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Xu M, Bo B, Pei M, Chen Y, Shu CY, Qin Q, Hirschler L, Warnking JM, Barbier EL, Wei Z, Lu H, Herman P, Hyder F, Liu ZJ, Liang Z, Thompson GJ. High-resolution relaxometry-based calibrated fMRI in murine brain: Metabolic differences between awake and anesthetized states. J Cereb Blood Flow Metab 2022; 42:811-825. [PMID: 34910894 PMCID: PMC9014688 DOI: 10.1177/0271678x211062279] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Functional magnetic resonance imaging (fMRI) techniques using the blood-oxygen level-dependent (BOLD) signal have shown great potential as clinical biomarkers of disease. Thus, using these techniques in preclinical rodent models is an urgent need. Calibrated fMRI is a promising technique that can provide high-resolution mapping of cerebral oxygen metabolism (CMRO2). However, calibrated fMRI is difficult to use in rodent models for several reasons: rodents are anesthetized, stimulation-induced changes are small, and gas challenges induce noisy CMRO2 predictions. We used, in mice, a relaxometry-based calibrated fMRI method which uses cerebral blood flow (CBF) and the BOLD-sensitive magnetic relaxation component, R2', the same parameter derived in the deoxyhemoglobin-dilution model of calibrated fMRI. This method does not use any gas challenges, which we tested on mice in both awake and anesthetized states. As anesthesia induces a whole-brain change, our protocol allowed us to overcome the former limitations of rodent studies using calibrated fMRI. We revealed 1.5-2 times higher CMRO2, dependent upon brain region, in the awake state versus the anesthetized state. Our results agree with alternative measurements of whole-brain CMRO2 in the same mice and previous human anesthesia studies. The use of calibrated fMRI in rodents has much potential for preclinical fMRI.
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Affiliation(s)
- Mengyang Xu
- iHuman Institute, ShanghaiTech University, Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai, China.,Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Binshi Bo
- CAS Center for Excellence in Brain Sciences and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China
| | - Mengchao Pei
- CAS Center for Excellence in Brain Sciences and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China
| | - Yuyan Chen
- CAS Center for Excellence in Brain Sciences and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China
| | - Christina Y Shu
- Biomedical Engineering, Yale University, New Haven, CT, USA.,Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, USA
| | - Qikai Qin
- iHuman Institute, ShanghaiTech University, Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai, China.,Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Lydiane Hirschler
- Grenoble Institut des Neurosciences, Inserm, Univ. Grenoble Alpes, Grenoble, France.,C.J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Jan M Warnking
- Grenoble Institut des Neurosciences, Inserm, Univ. Grenoble Alpes, Grenoble, France
| | - Emmanuel L Barbier
- Grenoble Institut des Neurosciences, Inserm, Univ. Grenoble Alpes, Grenoble, France
| | - Zhiliang Wei
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
| | - Hanzhang Lu
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
| | - Peter Herman
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, USA.,Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University, New Haven, CT, USA.,Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Fahmeed Hyder
- Biomedical Engineering, Yale University, New Haven, CT, USA.,Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, USA.,Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University, New Haven, CT, USA.,Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Zhi-Jie Liu
- iHuman Institute, ShanghaiTech University, Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai, China.,Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Zhifeng Liang
- CAS Center for Excellence in Brain Sciences and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China
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13
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Zhang M, Qin Q, Zhang S, Liu W, Meng H, Xu M, Huang X, Lin X, Lin M, Herman P, Hyder F, Stevens RC, Wang Z, Li B, Thompson GJ. Aerobic glycolysis imaging of epileptic foci during the inter-ictal period. EBioMedicine 2022; 79:104004. [PMID: 35436726 PMCID: PMC9035653 DOI: 10.1016/j.ebiom.2022.104004] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 03/29/2022] [Accepted: 03/29/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND In drug-resistant epilepsy, surgical resection of the epileptic focus can end seizures. However, success is dependent on the ability to identify foci locations and, unfortunately, current methods like electrophysiology and positron emission tomography can give contradictory results. During seizures, glucose is metabolized at epileptic foci through aerobic glycolysis, which can be imaged through the oxygen-glucose index (OGI) biomarker. However, inter-ictal (between seizures) OGI changes have not been studied, which has limited its application. METHODS 18 healthy controls and 24 inter-ictal, temporal lobe epilepsy patients underwent simultaneous positron emission tomography (PET) and magnetic resonance imaging (MRI) scans. We used [18F]fluorodeoxyglucose-PET (FDG-PET) to detect cerebral glucose metabolism, and calibrated functional MRI to acquire relative oxygen consumption. With these data, we calculated relative OGI maps. FINDINGS While bilaterally symmetrical in healthy controls, we observed, in patients during the inter-ictal period, higher OGI ipsilateral to the epileptic focus than contralateral. While traditional FDG-PET results and temporal lobe OGI results usually both agreed with invasive electrophysiology, in cases where FDG-PET disagreed with electrophysiology, temporal lobe OGI agreed with electrophysiology, and vice-versa. INTERPRETATION As either our novel epilepsy biomarker or traditional approaches located foci in every case, our work provides promising insights into metabolic changes in epilepsy. Our method allows single-session OGI measurement which can be useful in other diseases. FUNDING This work was supported by ShanghaiTech University, the Shanghai Municipal Government, the National Natural Science Foundation of China Grant (No. 81950410637) and Shanghai Municipal Key Clinical Specialty (No. shslczdzk03403). F. H. and P. H. were supported by USA National Institute of Health grants (R01 NS-100106, R01 MH-067528).Z. W. was supported by the Key-Area Research and Development Program of Guangdong Province (2019B030335001), National Natural Science Foundation of China (No. 82151303), and National Key R&D Program of China (No. 2021ZD0204002).
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Affiliation(s)
- Miao Zhang
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Qikai Qin
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shuning Zhang
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Liu
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Hongping Meng
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Mengyang Xu
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China; University of Chinese Academy of Sciences, Beijing 100049, China; Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xinyun Huang
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xiaozhu Lin
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Mu Lin
- MR Collaboration, Siemens Healthineers Ltd., Shanghai 201318, China
| | - Peter Herman
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven 06520, USA; Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University, New Haven 06520, USA; Radiology and Biomedical Imaging, Yale University, New Haven 06520, USA
| | - Fahmeed Hyder
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven 06520, USA; Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University, New Haven 06520, USA; Radiology and Biomedical Imaging, Yale University, New Haven 06520, USA; Biomedical Engineering, Yale University, New Haven 06520, USA
| | - Raymond C Stevens
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Zheng Wang
- School of Psychological and Cognitive Sciences; Beijing Key Laboratory of Behavior and Mental Health; IDG/McGovern Institute for Brain Research; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Biao Li
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Ruijin Center, Shanghai 200025, China.
| | - Garth J Thompson
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China.
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14
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Cai A, Zheng N, Thompson GJ, Wu Y, Nie B, Lin K, Su P, Wu J, Manyande A, Zhu L, Wang J, Xu F. Longitudinal neural connection detection using a ferritin-encoding adeno-associated virus vector and in vivo MRI method. Hum Brain Mapp 2021; 42:5010-5022. [PMID: 34288264 PMCID: PMC8449107 DOI: 10.1002/hbm.25596] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 05/20/2021] [Accepted: 07/06/2021] [Indexed: 01/17/2023] Open
Abstract
The investigation of neural circuits is important for interpreting both healthy brain function and psychiatric disorders. Currently, the architecture of neural circuits is always investigated with fluorescent protein encoding neurotropic virus and ex vivo fluorescent imaging technology. However, it is difficult to obtain a whole‐brain neural circuit connection in living animals, due to the limited fluorescent imaging depth. Herein, the noninvasive, whole‐brain imaging technique of MRI and the hypotoxicity virus vector AAV (adeno‐associated virus) were combined to investigate the whole‐brain neural circuits in vivo. AAV2‐retro are an artificially‐evolved virus vector that permits access to the terminal of neurons and retrograde transport to their cell bodies. By expressing the ferritin protein which could accumulate iron ions and influence the MRI contrast, the neurotropic virus can cause MRI signal changes in the infected regions. For mice injected with the ferritin‐encoding virus vector (rAAV2‐retro‐CAG‐Ferritin) in the caudate putamen (CPu), several regions showed significant changes in MRI contrasts, such as PFC (prefrontal cortex), HIP (hippocampus), Ins (insular cortex) and BLA (basolateral amygdala). The expression of ferritin in those regions was also verified with ex vivo fluorescence imaging. In addition, we demonstrated that changes in T2 relaxation time could be used to identify the spread area of the virus in the brain over time. Thus, the neural connections could be longitudinally detected with the in vivo MRI method. This novel technique could be utilized to observe the viral infection process and detect the neural circuits in a living animal.
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Affiliation(s)
- Aoling Cai
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.,Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Wuhan, China
| | - Ning Zheng
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Wuhan, China
| | | | - Yang Wu
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Wuhan, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Binbin Nie
- Key Laboratory of Nuclear Radiation and Nuclear Energy Technology, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
| | - Kunzhang Lin
- Shenzhen Key Lab of Neuropsychiatric Modulation, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Key Laboratory of Brain Connectome and Manipulation, the Brain Cognition and Brain Disease Institute (BCBDI), Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
| | - Peng Su
- Shenzhen Key Lab of Neuropsychiatric Modulation, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Key Laboratory of Brain Connectome and Manipulation, the Brain Cognition and Brain Disease Institute (BCBDI), Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
| | - Jinfeng Wu
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Wuhan, China
| | - Anne Manyande
- School of Human and Social Sciences, University of West London, London, UK
| | - LingQiang Zhu
- Department of Pathophysiology, Key Lab of Neurological Disorder of Education Ministry, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jie Wang
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Wuhan, China.,University of Chinese Academy of Sciences, Beijing, China.,Hebei Provincial Key Laboratory of Basic Medicine for Diabetes, 2nd Hospital of Shijiazhuang, Shijiazhuang, Hebei, China
| | - Fuqiang Xu
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.,Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Wuhan, China.,University of Chinese Academy of Sciences, Beijing, China.,Shenzhen Key Lab of Neuropsychiatric Modulation, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Key Laboratory of Brain Connectome and Manipulation, the Brain Cognition and Brain Disease Institute (BCBDI), Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China.,Center for Excellence in Brain Science and Intelligent Technology, Chinese Academy of Sciences, Shanghai, China
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15
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Affiliation(s)
- J Jean Chen
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Peter Herman
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States
| | - Shella Keilholz
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
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16
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Li H, Yang J, Tian C, Diao M, Wang Q, Zhao S, Li S, Tan F, Hua T, Qin Y, Lin CP, Deska-Gauthier D, Thompson GJ, Zhang Y, Shui W, Liu ZJ, Wang T, Zhong G. Organized cannabinoid receptor distribution in neurons revealed by super-resolution fluorescence imaging. Nat Commun 2020; 11:5699. [PMID: 33177502 PMCID: PMC7659323 DOI: 10.1038/s41467-020-19510-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 10/15/2020] [Indexed: 11/22/2022] Open
Abstract
G-protein-coupled receptors (GPCRs) play important roles in cellular functions. However, their intracellular organization is largely unknown. Through investigation of the cannabinoid receptor 1 (CB1), we discovered periodically repeating clusters of CB1 hotspots within the axons of neurons. We observed these CB1 hotspots interact with the membrane-associated periodic skeleton (MPS) forming a complex crucial in the regulation of CB1 signaling. Furthermore, we found that CB1 hotspot periodicity increased upon CB1 agonist application, and these activated CB1 displayed less dynamic movement compared to non-activated CB1. Our results suggest that CB1 forms periodic hotspots organized by the MPS as a mechanism to increase signaling efficacy upon activation. Despite the importance of G-protein-coupled receptors in many cellular functions, their intracellular organisation is largely unknown. The authors identified periodically repeating clusters of cannabinoid receptor 1 hotspots within neuronal axons that are dynamically regulated by CB1 agonists.
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Affiliation(s)
- Hui Li
- iHuman Institute, ShanghaiTech University, 201210, Shanghai, China
| | - Jie Yang
- iHuman Institute, ShanghaiTech University, 201210, Shanghai, China.,University of the Chinese Academy of Sciences, 100049, Beijing, China.,School of Life Science and Technology, ShanghaiTech University, 201210, Shanghai, China
| | - Cuiping Tian
- iHuman Institute, ShanghaiTech University, 201210, Shanghai, China
| | - Min Diao
- iHuman Institute, ShanghaiTech University, 201210, Shanghai, China
| | - Quan Wang
- iHuman Institute, ShanghaiTech University, 201210, Shanghai, China.,University of the Chinese Academy of Sciences, 100049, Beijing, China.,School of Life Science and Technology, ShanghaiTech University, 201210, Shanghai, China
| | - Simeng Zhao
- iHuman Institute, ShanghaiTech University, 201210, Shanghai, China
| | - Shanshan Li
- iHuman Institute, ShanghaiTech University, 201210, Shanghai, China
| | - Fangzhi Tan
- iHuman Institute, ShanghaiTech University, 201210, Shanghai, China
| | - Tian Hua
- iHuman Institute, ShanghaiTech University, 201210, Shanghai, China
| | - Ya Qin
- School of Life Science and Technology, ShanghaiTech University, 201210, Shanghai, China
| | - Chao-Po Lin
- School of Life Science and Technology, ShanghaiTech University, 201210, Shanghai, China
| | - Dylan Deska-Gauthier
- Department of Medical Neuroscience, Dalhousie University, Halifax, NS, B3H 4R2, Canada
| | - Garth J Thompson
- iHuman Institute, ShanghaiTech University, 201210, Shanghai, China
| | - Ying Zhang
- Department of Medical Neuroscience, Dalhousie University, Halifax, NS, B3H 4R2, Canada
| | - Wenqing Shui
- iHuman Institute, ShanghaiTech University, 201210, Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, 201210, Shanghai, China
| | - Zhi-Jie Liu
- iHuman Institute, ShanghaiTech University, 201210, Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, 201210, Shanghai, China
| | - Tong Wang
- School of Life Science and Technology, ShanghaiTech University, 201210, Shanghai, China
| | - Guisheng Zhong
- iHuman Institute, ShanghaiTech University, 201210, Shanghai, China. .,School of Life Science and Technology, ShanghaiTech University, 201210, Shanghai, China.
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17
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Sanganahalli BG, Baker KL, Thompson GJ, Herman P, Shepherd GM, Verhagen JV, Hyder F. Orthonasal versus retronasal glomerular activity in rat olfactory bulb by fMRI. Neuroimage 2020; 212:116664. [PMID: 32087375 DOI: 10.1016/j.neuroimage.2020.116664] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 01/22/2020] [Accepted: 02/16/2020] [Indexed: 02/05/2023] Open
Abstract
Odorants can reach olfactory receptor neurons (ORNs) by two routes: orthonasally, when volatiles enter the nasal cavity during inhalation/sniffing, and retronasally, when food volatiles released in the mouth pass into the nasal cavity during exhalation/eating. Previous work in humans has shown that both delivery routes of the same odorant can evoke distinct perceptions and patterns of neural responses in the brain. Each delivery route is known to influence specific responses across the dorsal region of the glomerular sheet in the olfactory bulb (OB), but spatial distributions across the entire glomerular sheet throughout the whole OB remain largely unexplored. We used functional MRI (fMRI) to measure and compare activations across the entire glomerular sheet in rat OB resulting from both orthonasal and retronasal stimulations of the same odors. We observed reproducible fMRI activation maps of the whole OB during both orthonasal and retronasal stimuli. However, retronasal stimuli required double the orthonasal odor concentration for similar response amplitudes. Regardless, both the magnitude and spatial extent of activity were larger during orthonasal versus retronasal stimuli for the same odor. Orthonasal and retronasal response patterns show overlap as well as some route-specific dominance. Orthonasal maps were dominant in dorsal-medial regions, whereas retronasal maps were dominant in caudal and lateral regions. These different whole OB encodings likely underlie differences in odor perception between these biologically important routes for odorants among mammals. These results establish the relationships between orthonasal and retronasal odor representations in the rat OB.
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Affiliation(s)
- Basavaraju G Sanganahalli
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, USA; Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA.
| | - Keeley L Baker
- Department of Neuroscience, Yale University, New Haven, CT, USA; The John B. Pierce Laboratory, New Haven, CT, USA
| | - Garth J Thompson
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, USA; iHuman Institute, ShanghaiTech University, Shanghai, 201210, China
| | - Peter Herman
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, USA; Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | | | - Justus V Verhagen
- Department of Neuroscience, Yale University, New Haven, CT, USA; The John B. Pierce Laboratory, New Haven, CT, USA
| | - Fahmeed Hyder
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, USA; Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA; Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
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18
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Fu AZ, Hall JP, Bailey A, Kemp J, Thompson GJ, Lee C. P14.30 Treatment outcomes of newly-diagnosed glioblastoma multiforme (GBM) by O6-methylguanine DNA methyltransferase promoter (MGMT) status: a multi-country study. Neuro Oncol 2019. [DOI: 10.1093/neuonc/noz126.265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
INTRODUCTION
This study evaluated the relationship of MGMT status with first-line (1L) treatment outcomes of patients with newly-diagnosed GBM in France, Germany, Italy, Spain, the UK (EU5), and Canada.
MATERIALS AND METHODS
Medical oncologists and neuro-oncologists across EU5 and Canada completed a point in time, cross-sectional survey for the next 8 GBM patients seen between May and July 2016 within EU5 and Canada. All results apart from time to progression (TTP) were presented for patients receiving 1L active drug treatment. TTP was calculated from initiation of 1L treatment to initiation of second-line treatment. Results presented are statistically significant (p<0.05) unless otherwise specified. Bases vary depending on data availability.
RESULTS
A total of 241 physicians reported on 1747 patients with GBM. 875 were receiving 1L active drug treatment at time of survey. Mean age was 59.7 years (median=61) and 34.6% were women. Mean life expectancy was 14.9 months (median=12) at diagnosis and mean TTP was 8.5 months (median=7.3). Surgery was performed in 62% of patients (n=546) prior to 1L drug treatment; 38% of patients (n=329) had no surgery. Patients with surgery had a higher life expectancy at diagnosis vs patients with no surgery prior to 1L (mean=16.4 vs 12.2 months; median=15.0 vs 12.0). Patients who received corticosteroids (n=524) vs no corticosteroids during radiotherapy (n=64) had a shorter life expectancy at diagnosis (mean=15.0 vs 16.8 months, p=0.07; median=12.5 vs 13.9) and were more likely to have 8 or more inpatient days due to GBM (21% vs 8%, p=0.07) in the last 3 months prior to the survey. 62% of patients (n=541) had an MGMT-status recorded (tested: methylated or unmethylated), and 38% (n=334) were untested/ awaiting results (untested) at 1L. MGMT-tested patients had better life expectancy at diagnosis (mean=16.1 vs 12.9 months; median=15.0 vs 12.0) and longer TTP (mean=8.9 vs 7.8 months; median=7.8 vs 6.4) than untested patients. Among MGMT-tested patients, 58% were methylated and 42% were unmethylated. Methylated patients had similar life expectancy at diagnosis (mean=15.9 vs 16.3 months, p=0.85; median=15.0 vs 15.0) and TTP (mean=9.0 vs 8.8 months, p=0.42; median=8.0 vs 7.5) as unmethylated patients.
CONCLUSIONS
This analysis provides valuable insights into the 1L treatment outcomes of GBM patients in EU5 and Canada. Patients who did not undergo surgery had worse treatment outcomes. Steroid use appears to be associated with worse outcomes and higher healthcare resource utilization. Patient treatment outcomes varied depending on whether they are MGMT tested.
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Affiliation(s)
- A Z Fu
- Bristol-Myers Squibb, Lawrenceville, NJ, United States
- Department of Oncology, Georgetown University Medical Center, Washington, DC, United States
| | - J P Hall
- Adelphi Real World, Manchester, United Kingdom
| | - A Bailey
- Adelphi Real World, Manchester, United Kingdom
| | - J Kemp
- Adelphi Real World, Manchester, United Kingdom
| | | | - C Lee
- Bristol-Myers Squibb, Lawrenceville, NJ, United States
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19
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Fu AZ, Thompson GJ, Hall JP, Bailey A, Kemp J, Lee C. P14.36 Treatment patterns by O6-methylguanine DNA methyltransferase promoter (MGMT) status in newly-diagnosed glioblastoma multiforme (GBM): a multi-country chart review study. Neuro Oncol 2019. [DOI: 10.1093/neuonc/noz126.271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
INTRODUCTION
This study explored the relationship between MGMT testing and treatment patterns of patients with newly-diagnosed GBM from France, Germany, Italy, Spain, the UK (EU5), and Canada.
MATERIALS AND METHODS
Medical oncologists and neuro-oncologists across EU5 and Canada completed a point in time, cross-sectional survey for the next eight GBM patients seen between May and July 2016 (GBM Disease-specific ProgrammeTM). Statistically significant differences (p<0.05) between groups are presented.
RESULTS
A total of 241 physicians reported on 1,747 GBM patients. 1L patients had mean age 59.7 years (SD=12.3) and 36% were female. Of 1,113 (64%) patients who had an MGMT test performed with results recorded (tested), 58% (n=651) were methylated and 42% (n=462) were unmethylated. The remaining 634 patients (36%) were MGMT untested or awaiting MGMT results at time of survey (untested). Overall, 63% of patients received surgery prior to their 1L drug therapy, 78% received radiotherapy (RT; mean 4.3 sessions) in conjunction with 1L drug therapy, 90% received corticosteroids during RT, and 89% received temozolomide (TMZ). Patients who received corticosteroids during RT received similar drug treatments to those that did not, but were less likely to receive surgery prior to 1L treatment (65% vs 83%). MGMT-tested patients were more likely to receive surgery (66% vs 57%) and RT (81% vs 71%) than untested patients. Tested patients were also more likely to receive TMZ (92% vs 83%), and less likely to receive procarbazine+/lomustine+/vincristine (PCV; 3% vs 7%) or other chemotherapies (5% vs 11%). For 1L patients that experienced side effects, the most common effects included fatigue (74%), nausea (60%), and appetite loss (59%). Untested patients were more likely to stop their 1L drug treatment due to progression/recurrence of GBM (44% vs 36%). Patients who received surgery prior to 1L treatment were more likely to receive TMZ than those who did not (93% vs 82%). Among MGMT tested patients at 1L, methylated patients were more likely to receive RT (84% vs 78%) and TMZ (95% vs 89%) than unmethylated patients, and less likely to receive PCV (2% vs 5%) or other chemotherapy (4% vs 7%). Methylated patients with reported treatment-related side effects were less likely to experience dehydration (0% vs 10%), loss of strength/unusual weakness (5% vs 25%), memory problems (16% vs 35%), and nausea (51% vs 75%).
CONCLUSIONS
More than one-third of GBM patients in EU5 and Canada are not tested for MGMT-methylation. Untested patients are less likely to receive standard treatments than tested patients. Generally, TMZ is used in most patients regardless of MGMT testing and status. MGMT-methylated patients are more likely to receive standard treatments and experience fewer side effects than MGMT-unmethylated patients.
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Affiliation(s)
- A Z Fu
- Bristol-Myers Squibb, Lawrenceville, NJ, United States
- Department of Oncology, Georgetown University Medical Center, Washington, DC, United States
| | | | - J P Hall
- Adelphi Real World, Manchester, United Kingdom
| | - A Bailey
- Adelphi Real World, Manchester, United Kingdom
| | - J Kemp
- Adelphi Real World, Manchester, United Kingdom
| | - C Lee
- Bristol-Myers Squibb, Lawrenceville, NJ, United States
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20
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Galang KC, Croft JR, Thompson GJ, Percival-Smith A. Analysis of the Drosophila melanogaster anti-ovarian response to honey bee queen mandibular pheromone. Insect Mol Biol 2019; 28:99-111. [PMID: 30159981 DOI: 10.1111/imb.12531] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Queen mandibular pheromone (QMP) is a potent reproductive signal to which honey bee workers respond by suppressing their ovaries and adopting alloparental roles within the colony. This anti-ovarian effect of QMP on workers can, surprisingly, be induced in other insects, including fruit flies, in which females exposed to synthetic QMP develop smaller ovaries with fewer eggs. In this study, we use the Drosophila melanogaster model to identify the components of synthetic QMP required for the anti-ovarian effect. We found that virgin females respond strongly to 9-oxo-2-decenoic acid and 10-hydroxy-2-decenoic acid (10HDA), suggesting that the decenoic acid components of QMP are essential for the anti-ovarian response. Further, a nuclear factor of activated T-cells reporter system revealed neurones expressing the olfactory receptors Or-56a, Or-49b and Or-98a are activated by QMP in the antenna. In addition, we used olfactory receptor GAL4 drivers and a neuronal activator (a neuronal activating bacterial sodium channel) to test whether the candidate neurones are potential labelled lines for a decenoic acid response. We identified Or-49b as a potential candidate receiver of the 10HDA signal. Finally, the anti-ovarian response to synthetic QMP is not mediated by decreasing the titre of the reproductive hormones ecdysone and juvenile hormone.
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Affiliation(s)
- K C Galang
- Department of Biology, The University of Western Ontario, London, ON, Canada
| | - J R Croft
- Department of Biology, The University of Western Ontario, London, ON, Canada
| | - G J Thompson
- Department of Biology, The University of Western Ontario, London, ON, Canada
| | - A Percival-Smith
- Department of Biology, The University of Western Ontario, London, ON, Canada
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21
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Billings JCW, Thompson GJ, Pan WJ, Magnuson ME, Medda A, Keilholz S. Disentangling Multispectral Functional Connectivity With Wavelets. Front Neurosci 2018; 12:812. [PMID: 30459548 PMCID: PMC6232345 DOI: 10.3389/fnins.2018.00812] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 10/18/2018] [Indexed: 02/01/2023] Open
Abstract
The field of brain connectomics develops our understanding of the brain's intrinsic organization by characterizing trends in spontaneous brain activity. Linear correlations in spontaneous blood-oxygen level dependent functional magnetic resonance imaging (BOLD-fMRI) fluctuations are often used as measures of functional connectivity (FC), that is, as a quantity describing how similarly two brain regions behave over time. Given the natural spectral scaling of BOLD-fMRI signals, it may be useful to represent BOLD-fMRI as multiple processes occurring over multiple scales. The wavelet domain presents a transform space well suited to the examination of multiscale systems as the wavelet basis set is constructed from a self-similar rescaling of a time and frequency delimited kernel. In the present study, we utilize wavelet transforms to examine fluctuations in whole-brain BOLD-fMRI connectivity as a function of wavelet spectral scale in a sample (N = 31) of resting healthy human volunteers. Information theoretic criteria measure relatedness between spectrally-delimited FC graphs. Voxelwise comparisons of between-spectra graph structures illustrate the development of preferential functional networks across spectral bands.
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Affiliation(s)
- Jacob C W Billings
- Graduate Division of Biological and Biomedical Sciences - Program in Neuroscience, Emory University, Atlanta, GA, United States.,Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Garth J Thompson
- Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.,iHuman Institute, ShanghaiTech University, Pudong, China
| | - Wen-Ju Pan
- Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Matthew E Magnuson
- Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Alessio Medda
- Aerospace Transportation and Advanced Systems, Georgia Tech Research Institute, Atlanta, GA, United States
| | - Shella Keilholz
- Graduate Division of Biological and Biomedical Sciences - Program in Neuroscience, Emory University, Atlanta, GA, United States.,Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
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Vos de Wael R, Hyder F, Thompson GJ. Effects of Tissue-Specific Functional Magnetic Resonance Imaging Signal Regression on Resting-State Functional Connectivity. Brain Connect 2018; 7:482-490. [PMID: 28825320 DOI: 10.1089/brain.2016.0465] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Neuroimaging studies typically consider white matter as unchanging in different neural and metabolic states. However, a recent study demonstrated that white matter signal regression (WMSR) produced a similar loss of neurometabolic information to global (whole-brain) signal regression (GSR) in resting-state functional magnetic resonance imaging (R-fMRI) data. This was unexpected as the loss of information would normally be attributed to neural activity within gray matter correlating with the global R-fMRI signal. Indeed, WMSR has been suggested as an alternative to avoid such pitfalls in GSR. To address these concerns about tissue-specific regression in R-fMRI data analysis, we performed GSR, WMSR, and gray matter signal regression (GMSR) on R-fMRI data from the 1000 Functional Connectomes Project. We describe several regional and motion-related differences between different types of regressions. However, the overall effects of concern, particularly network-specific alteration of correlation coefficients, are present for all regressions. This suggests that tissue-specific regression is not an adequate strategy to counter pitfalls of GSR. Conversely, if GSR is desired, but the studied disease state excludes either gray matter or white matter from analysis (e.g., due to tissue atrophy), our results indicate that WMSR or GMSR may reproduce the gross effects of GSR.
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Affiliation(s)
- Reinder Vos de Wael
- 1 McConnell Brain Imaging Centre, McGill University , Montreal, Canada .,2 Neuroimaging Center, University of Groningen , Groningen, The Netherlands .,3 Magnetic Resonance Research Center (MRRC), Yale University , New Haven, Connecticut
| | - Fahmeed Hyder
- 3 Magnetic Resonance Research Center (MRRC), Yale University , New Haven, Connecticut.,4 Department of Radiology and Biomedical Imaging, Yale University , New Haven, Connecticut.,5 Department of Biomedical Engineering, Yale University , New Haven, Connecticut.,6 Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University , New Haven, Connecticut
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Grooms JK, Thompson GJ, Pan WJ, Billings J, Schumacher EH, Epstein CM, Keilholz SD. Infraslow Electroencephalographic and Dynamic Resting State Network Activity. Brain Connect 2018; 7:265-280. [PMID: 28462586 DOI: 10.1089/brain.2017.0492] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
A number of studies have linked the blood oxygenation level dependent (BOLD) signal to electroencephalographic (EEG) signals in traditional frequency bands (δ, θ, α, β, and γ), but the relationship between BOLD and its direct frequency correlates in the infraslow band (<1 Hz) has been little studied. Previously, work in rodents showed that infraslow local field potentials play a role in functional connectivity, particularly in the dynamic organization of large-scale networks. To examine the relationship between infraslow activity and network dynamics in humans, direct current (DC) EEG and resting state magnetic resonance imaging data were acquired simultaneously. The DC EEG signals were correlated with the BOLD signal in patterns that resembled resting state networks. Subsequent dynamic analysis showed that the correlation between DC EEG and the BOLD signal varied substantially over time, even within individual subjects. The variation in DC EEG appears to reflect the time-varying contribution of different resting state networks. Furthermore, some of the patterns of DC EEG and BOLD correlation are consistent with previous work demonstrating quasiperiodic spatiotemporal patterns of large-scale network activity in resting state. These findings demonstrate that infraslow electrical activity is linked to BOLD fluctuations in humans and that it may provide a basis for large-scale organization comparable to that observed in animal studies.
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Affiliation(s)
- Joshua K Grooms
- 1 Department of Biomedical Engineering, Georgia Institute of Technology and Emory University , Atlanta, Georgia
| | - Garth J Thompson
- 2 Magnetic Resonance Research Center (MRRC) and Radiology and Biomedical Imaging, Yale University , New Haven, Connecticut
| | - Wen-Ju Pan
- 1 Department of Biomedical Engineering, Georgia Institute of Technology and Emory University , Atlanta, Georgia
| | - Jacob Billings
- 3 Department of Neuroscience, Emory University , Atlanta, Georgia
| | - Eric H Schumacher
- 4 Department of Psychology, Georgia Institute of Technology , Atlanta, Georgia
| | - Charles M Epstein
- 5 Department of Neurology, Emory University School of Medicine , Atlanta, Georgia
| | - Shella D Keilholz
- 1 Department of Biomedical Engineering, Georgia Institute of Technology and Emory University , Atlanta, Georgia .,3 Department of Neuroscience, Emory University , Atlanta, Georgia
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24
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Johnson FK, Delpech JC, Thompson GJ, Wei L, Hao J, Herman P, Hyder F, Kaffman A. Amygdala hyper-connectivity in a mouse model of unpredictable early life stress. Transl Psychiatry 2018; 8:49. [PMID: 29463821 PMCID: PMC5820270 DOI: 10.1038/s41398-018-0092-z] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2017] [Revised: 12/06/2017] [Accepted: 12/13/2017] [Indexed: 01/09/2023] Open
Abstract
Childhood maltreatment is associated with a wide range of psychopathologies including anxiety that emerge in childhood and in many cases persist in adulthood. Increased amygdala activation in response to threat and abnormal amygdala connectivity with frontolimbic brain regions, such as the hippocampus and the prefrontal cortex, are some of the most consistent findings seen in individuals exposed to childhood maltreatment. The underlying mechanisms responsible for these changes are difficult to study in humans but can be elucidated using animal models of early-life stress. Such studies are especially powerful in the mouse where precise control of the genetic background and the stress paradigm can be coupled with resting-state fMRI (rsfMRI) to map abnormal connectivity in circuits that regulate anxiety. To address this issue we first compared the effects of two models of early-life stress, limited bedding (LB) and unpredictable postnatal stress (UPS), on anxiety-like behavior in juvenile and adult mice. We found that UPS, but not LB, causes a robust increase in anxiety in juvenile and adult male mice. Next, we used rsfMRI to compare frontolimbic connectivity in control and UPS adult male mice. We found increased amygdala-prefrontal cortex and amygdala-hippocampus connectivity in UPS. The strength of the amygdala-hippocampal and amygdala-prefrontal cortex connectivity was highly correlated with anxiety-like behavior in the open-field test and elevated plus maze. These findings are the first to link hyperconnectivity in frontolimbic circuits and increased anxiety in a mouse model of early-life stress, allowing for more mechanistic understanding of parallel findings in humans.
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Affiliation(s)
- Frances K Johnson
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, Suite 901, New Haven, CT, 06511, USA
| | - Jean-Christophe Delpech
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, Suite 901, New Haven, CT, 06511, USA
- Department of Newborn Medicine, Boston Children's Hospital, Harvard medical school, Boston, MA, 02115, USA
| | - Garth J Thompson
- Department of Radiology & Biomedical Imaging and Magnetic Resonance Research Center, Yale University, New Haven, CT, 06520, USA
- iHuman Institute, ShanghaiTech University, 393 Middle Huaxia Road, Ren Building, Room B204, Zhangjiang, Pudong, Shanghai, 201210, China
| | - Lan Wei
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, Suite 901, New Haven, CT, 06511, USA
| | - Jin Hao
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, Suite 901, New Haven, CT, 06511, USA
| | - Peter Herman
- Department of Radiology & Biomedical Imaging and Magnetic Resonance Research Center, Yale University, New Haven, CT, 06520, USA
| | - Fahmeed Hyder
- Department of Radiology & Biomedical Imaging and Magnetic Resonance Research Center, Yale University, New Haven, CT, 06520, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06519, USA
| | - Arie Kaffman
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, Suite 901, New Haven, CT, 06511, USA.
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Thompson GJ, Sanganahalli BG, Baker KL, Herman P, Shepherd GM, Verhagen JV, Hyder F. Spontaneous activity forms a foundation for odor-evoked activation maps in the rat olfactory bulb. Neuroimage 2018; 172:586-596. [PMID: 29374582 DOI: 10.1016/j.neuroimage.2018.01.051] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Revised: 01/16/2018] [Accepted: 01/20/2018] [Indexed: 12/12/2022] Open
Abstract
Fluctuations in spontaneous activity have been observed by many neuroimaging techniques, but because these resting-state changes are not evoked by stimuli, it is difficult to determine how they relate to task-evoked activations. We conducted multi-modal neuroimaging scans of the rat olfactory bulb, both with and without odor, to examine interaction between spontaneous and evoked activities. Independent component analysis of spontaneous fluctuations revealed resting-state networks, and odor-evoked changes revealed activation maps. We constructed simulated activation maps using resting-state networks that were highly correlated to evoked activation maps. Simulated activation maps derived by intrinsic optical signal (IOS), which covers the dorsal portion of the glomerular sheet, significantly differentiated one odor's evoked activation map from the other two. To test the hypothesis that spontaneous activity of the entire glomerular sheet is relevant for representing odor-evoked activations, we used functional magnetic resonance imaging (fMRI) to map the entire glomerular sheet. In contrast to the IOS results, the fMRI-derived simulated activation maps significantly differentiated all three odors' evoked activation maps. Importantly, no evoked activation maps could be significantly differentiated using simulated activation maps produced using phase-randomized resting-state networks. Given that some highly organized resting-state networks did not correlate with any odors' evoked activation maps, we posit that these resting-state networks may characterize evoked activation maps associated with odors not studied. These results emphasize that fluctuations in spontaneous activity form a foundation for active processing, signifying the relevance of resting-state mapping to functional neuroimaging.
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Affiliation(s)
- Garth J Thompson
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Basavaraju G Sanganahalli
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA; Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University, New Haven, CT, USA
| | - Keeley L Baker
- Department of Neuroscience, Yale University, New Haven, CT, USA; The John B. Pierce Laboratory, New Haven, CT USA
| | - Peter Herman
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA; Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University, New Haven, CT, USA
| | | | - Justus V Verhagen
- Department of Neuroscience, Yale University, New Haven, CT, USA; The John B. Pierce Laboratory, New Haven, CT USA
| | - Fahmeed Hyder
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA; Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University, New Haven, CT, USA; Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
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Abstract
Resting state fMRI (rsfMRI) as a technique showed much initial promise for use in psychiatric and neurological diseases where diagnosis and treatment were difficult. To realize this promise, many groups have moved towards examining "dynamic rsfMRI," which relies on the assumption that rsfMRI measurements on short time scales remain relevant to the underlying neural and metabolic activity. Many dynamic rsfMRI studies have demonstrated differences between clinical or behavioral groups beyond what static rsfMRI measured, suggesting a neurometabolic basis. Correlative studies combining dynamic rsfMRI and other physiological measurements have supported this. However, they also indicate multiple mechanisms and, if using correlation alone, it is difficult to separate cause and effect. Hypothesis-driven studies are needed, a few of which have begun to illuminate the underlying neurometabolic mechanisms that shape observed differences in dynamic rsfMRI. While the number of potential noise sources, potential actual neurometabolic sources, and methodological considerations can seem overwhelming, dynamic rsfMRI provides a rich opportunity in systems neuroscience. Even an incrementally better understanding of the neurometabolic basis of dynamic rsfMRI would expand rsfMRI's research and clinical utility, and the studies described herein take the first steps on that path forward.
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Affiliation(s)
- Garth J Thompson
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China.
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Thompson GJ, Riedl V, Grimmer T, Drzezga A, Herman P, Hyder F. The Whole-Brain "Global" Signal from Resting State fMRI as a Potential Biomarker of Quantitative State Changes in Glucose Metabolism. Brain Connect 2016; 6:435-47. [PMID: 27029438 DOI: 10.1089/brain.2015.0394] [Citation(s) in RCA: 61] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
The evolution of functional magnetic resonance imaging to resting state (R-fMRI) allows measurement of changes in brain networks attributed to state changes, such as in neuropsychiatric diseases versus healthy controls. Since these networks are observed by comparing normalized R-fMRI signals, it is difficult to determine the metabolic basis of such group differences. To investigate the metabolic basis of R-fMRI network differences within a normal range, eyes open versus eyes closed in healthy human subjects was used. R-fMRI was recorded simultaneously with fluoro-deoxyglucose positron emission tomography (FDG-PET). Higher baseline FDG was observed in the eyes open state. Variance-based metrics calculated from R-fMRI did not match the baseline shift in FDG. Functional connectivity density (FCD)-based metrics showed a shift similar to the baseline shift of FDG, however, this was lost if R-fMRI "nuisance signals" were regressed before FCD calculation. Average correlation with the mean R-fMRI signal across the whole brain, generally regarded as a "nuisance signal," also showed a shift similar to the baseline of FDG. Thus, despite lacking a baseline itself, changes in whole-brain correlation may reflect changes in baseline brain metabolism. Conversely, variance-based metrics may remain similar between states due to inherent region-to-region differences overwhelming the differences between normal physiological states. As most previous studies have excluded the spatial means of R-fMRI metrics from their analysis, this work presents the first evidence of a potential R-fMRI biomarker for baseline shifts in quantifiable metabolism between brain states.
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Affiliation(s)
- Garth J Thompson
- 1 Magnetic Resonance Research Center (MRRC), Yale University , New Haven, Connecticut.,2 Department of Radiology and Biomedical Imaging, Yale University , New Haven, Connecticut
| | - Valentin Riedl
- 3 Department of Neuroradiology, Technische Universität München , München, Germany .,4 Department of Nuclear Medicine, Technische Universität München , München, Germany .,5 Neuroimaging Center, Technische Universität München , München, Germany
| | - Timo Grimmer
- 5 Neuroimaging Center, Technische Universität München , München, Germany .,6 Department of Psychiatry, Technische Universität München , München, Germany
| | | | - Peter Herman
- 1 Magnetic Resonance Research Center (MRRC), Yale University , New Haven, Connecticut.,2 Department of Radiology and Biomedical Imaging, Yale University , New Haven, Connecticut.,8 Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University , New Haven, Connecticut
| | - Fahmeed Hyder
- 1 Magnetic Resonance Research Center (MRRC), Yale University , New Haven, Connecticut.,2 Department of Radiology and Biomedical Imaging, Yale University , New Haven, Connecticut.,8 Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University , New Haven, Connecticut.,9 Department of Biomedical Engineering, Yale University , New Haven, Connecticut
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Thompson GJ, Pan WJ, Billings JCW, Grooms JK, Shakil S, Jaeger D, Keilholz SD. Phase-amplitude coupling and infraslow (<1 Hz) frequencies in the rat brain: relationship to resting state fMRI. Front Integr Neurosci 2014; 8:41. [PMID: 24904325 PMCID: PMC4034045 DOI: 10.3389/fnint.2014.00041] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2014] [Accepted: 04/25/2014] [Indexed: 11/17/2022] Open
Abstract
Resting state functional magnetic resonance imaging (fMRI) can identify network alterations that occur in complex psychiatric diseases and behaviors, but its interpretation is difficult because the neural basis of the infraslow BOLD fluctuations is poorly understood. Previous results link dynamic activity during the resting state to both infraslow frequencies in local field potentials (LFP) (<1 Hz) and band-limited power in higher frequency LFP (>1 Hz). To investigate the relationship between these frequencies, LFPs were recorded from rats under two anesthetics: isoflurane and dexmedetomidine. Signal phases were calculated from low-frequency LFP and compared to signal amplitudes from high-frequency LFP to determine if modulation existed between the two frequency bands (phase-amplitude coupling). Isoflurane showed significant, consistent phase-amplitude coupling at nearly all pairs of frequencies, likely due to the burst-suppression pattern of activity that it induces. However, no consistent phase-amplitude coupling was observed in rats that were anesthetized with dexmedetomidine. fMRI-LFP correlations under isoflurane using high frequency LFP were reduced when the low frequency LFP's influence was accounted for, but not vice-versa, or in any condition under dexmedetomidine. The lack of consistent phase-amplitude coupling under dexmedetomidine and lack of shared variance between high frequency and low frequency LFP as it relates to fMRI suggests that high and low frequency neural electrical signals may contribute differently, possibly even independently, to resting state fMRI. This finding suggests that researchers take care in interpreting the neural basis of resting state fMRI, as multiple dynamic factors in the underlying electrophysiology could be driving any particular observation.
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Affiliation(s)
- Garth J Thompson
- Magnetic Resonance Imaging of Neural Dynamics Lab, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University Atlanta, GA, USA
| | - Wen-Ju Pan
- Magnetic Resonance Imaging of Neural Dynamics Lab, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University Atlanta, GA, USA
| | - Jacob C W Billings
- Magnetic Resonance Imaging of Neural Dynamics Lab, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University Atlanta, GA, USA
| | - Joshua K Grooms
- Magnetic Resonance Imaging of Neural Dynamics Lab, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University Atlanta, GA, USA
| | - Sadia Shakil
- Department of Electrical and Computer Engineering, Georgia Institute of Technology Atlanta, GA, USA
| | - Dieter Jaeger
- Computational Neuroscience Lab, Department of Biology, Emory University Atlanta, GA, USA
| | - Shella D Keilholz
- Magnetic Resonance Imaging of Neural Dynamics Lab, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University Atlanta, GA, USA
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29
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Magnuson ME, Thompson GJ, Pan WJ, Keilholz SD. Effects of severing the corpus callosum on electrical and BOLD functional connectivity and spontaneous dynamic activity in the rat brain. Brain Connect 2014; 4:15-29. [PMID: 24117343 DOI: 10.1089/brain.2013.0167] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
Functional networks, defined by synchronous spontaneous blood oxygenation level-dependent (BOLD) oscillations between spatially distinct brain regions, appear to be essential to brain function and have been implicated in disease states, cognitive capacity, and sensing and motor processes. While the topographical extent and behavioral function of these networks has been extensively investigated, the neural functions that create and maintain these synchronizations remain mysterious. In this work callosotomized rodents are examined, providing a unique platform for evaluating the influence of structural connectivity via the corpus callosum on bilateral resting state functional connectivity. Two experimental groups were assessed, a full callosotomy group, in which the corpus callosum was completely sectioned, and a sham callosotomy group, in which the gray matter was sectioned but the corpus callosum remained intact. Results indicated a significant reduction in interhemispheric connectivity in the full callosotomy group as compared with the sham group in primary somatosensory cortex and caudate-putamen regions. Similarly, electrophysiology revealed significantly reduced bilateral correlation in band limited power. Bilateral gamma Band-limited power connectivity was most strongly affected by the full callosotomy procedure. This work represents a robust finding indicating the corpus callosum's influence on maintaining integrity in bilateral functional networks; further, functional magnetic resonance imaging (fMRI) and electrophysiological connectivity share a similar decrease in connectivity as a result of the callosotomy, suggesting that fMRI-measured functional connectivity reflects underlying changes in large-scale coordinated electrical activity. Finally, spatiotemporal dynamic patterns were evaluated in both groups; the full callosotomy rodents displayed a striking loss of bilaterally synchronous propagating waves of cortical activity.
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Affiliation(s)
- Matthew E Magnuson
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University , Atlanta, Georgia
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30
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Abstract
Functional connectivity mapping with resting-state magnetic resonance imaging (MRI) has become an immensely powerful technique that provides insight into both normal cognitive function and disruptions linked to neurological disorders. Traditionally, connectivity is mapped using data from an entire scan (minutes), but it is well known that cognitive processes occur on much shorter time scales (seconds). Recent studies have demonstrated that the correlation between the blood oxygenation level-dependent (BOLD) MRI signal from different areas varies over time, motivating a further exploration of these fluctuations in apparent connectivity. However, it has also been shown that similar changes in correlation can arise when the timing relationships between voxels are randomized (Handwerker et al., 2012 ). In this work, we show that functional connectivity in the anesthetized rat exhibits dynamic properties that are similar to those previously observed in awake humans (Chang and Glover, 2010 ) and anesthetized monkeys (Hutchison et al., 2012 ). Sliding window correlation between BOLD time courses obtained from bilateral cortical and subcortical regions of interest results in periods of variable positive and negative correlation for most pairs of areas except homologous areas in opposite hemispheres, which exhibit a primarily positive correlation. A comparison with sliding window correlation of randomly matched time courses suggests that with the exception of homologous areas and sensorimotor connections, the dynamics cannot be distinguished from random fluctuations in correlation over time, supporting the idea that some of these dynamic patterns may be due to inherent properties of the signal rather than variations in neural coherence. Within the pairs of areas where the dynamics are most different from those of randomly matched time courses, ten common patterns of connectivity are identified, and their occurrence as a function of time is plotted for all animals. The observation of time-varying correlation in the rodent model will facilitate the future multimodal experiments needed to determine whether the changes in apparent connectivity are linked to underlying neural variability.
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Affiliation(s)
- Shella D Keilholz
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia 30322, USA.
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Abstract
A defining characteristic of eusocial animals is their division of labour into reproductive and nonreproductive specialists. Here, we used a microarray study to identify genes associated with functional sterility in the worker honey bee Apis mellifera. We contrasted gene expression in workers from a functionally sterile wild-type strain with that in a mutant (anarchist) strain selected for high rates of ovary activation. We identified a small set of genes from the brain (n = 7) and from the abdomen (n = 5) that are correlated in their expression with early stages of ovary activation. Sterile wild-type workers up-regulated two unknown genes and a homologue of Drosophila CG6004. By contrast, reproductive anarchist workers up-regulated genes for the yolk protein vitellogenin, venom peptides and a member of the AdoHycase superfamily, among others. The differentially expressed genes identified are likely to be involved in early differentiation into sterile and reproductive worker phenotypes and may therefore form part of the gene networks associated with the regulation of honey bee worker sterility. Our study may have lacked sufficient power to detect all but a minority of biologically relevant changes taking place; however, the differential expression of vitellogenin and a putative AdoHycase suggests that our screen has captured core reproductive genes and that ovary activation may involve an epigenetic mechanism.
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Affiliation(s)
- G J Thompson
- Behaviour and Genetics of Social Insects Laboratory, School of Biological Sciences, University of Sydney, NSW, Australia.
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Evans JD, Aronstein K, Chen YP, Hetru C, Imler JL, Jiang H, Kanost M, Thompson GJ, Zou Z, Hultmark D. Immune pathways and defence mechanisms in honey bees Apis mellifera. Insect Mol Biol 2006; 15:645-56. [PMID: 17069638 PMCID: PMC1847501 DOI: 10.1111/j.1365-2583.2006.00682.x] [Citation(s) in RCA: 621] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Social insects are able to mount both group-level and individual defences against pathogens. Here we focus on individual defences, by presenting a genome-wide analysis of immunity in a social insect, the honey bee Apis mellifera. We present honey bee models for each of four signalling pathways associated with immunity, identifying plausible orthologues for nearly all predicted pathway members. When compared to the sequenced Drosophila and Anopheles genomes, honey bees possess roughly one-third as many genes in 17 gene families implicated in insect immunity. We suggest that an implied reduction in immune flexibility in bees reflects either the strength of social barriers to disease, or a tendency for bees to be attacked by a limited set of highly coevolved pathogens.
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Affiliation(s)
- J D Evans
- USDA-ARS Bee Research Laboratory, Beltsville, MD, USA.
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Thompson GJ, Kucharski R, Maleszka R, Oldroyd BP. Towards a molecular definition of worker sterility: differential gene expression and reproductive plasticity in honey bees. Insect Mol Biol 2006; 15:637-44. [PMID: 17069629 PMCID: PMC1847478 DOI: 10.1111/j.1365-2583.2006.00678.x] [Citation(s) in RCA: 31] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
We show that differences in the reproductive development of honey bee workers are associated with locus-specific changes to abundance of messenger RNA. Using a cross-fostering field experiment to control for differences related to age and environment, we compared the gene expression profiles of functionally sterile workers (wild-type) and those from a mutant strain in which workers are reproductively active (anarchist). Among the set of three genes that are significantly differentially expressed are two major royal jelly proteins that are up-regulated in wild-type heads. This discovery is consistent with sterile workers synthesizing royal jelly as food for developing brood. Likewise, the relative underexpression of these two royal jellies in anarchist workers is consistent with these workers' characteristic avoidance of alloparental behaviour, in favour of selfish egg-laying. Overall, there is a trend for the most differentially expressed genes to be up-regulated in wild-type workers. This pattern suggests that functional sterility in honey bee workers may generally involve the expression of a suite of genes that effectively 'switch' ovaries off, and that selfish reproduction in honey bee workers, though rare, is the default developmental pathway that results when ovary activation is not suppressed.
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Affiliation(s)
- G J Thompson
- School of Biological Sciences, University of Sydney, Sydney NSW, Australia.
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Abstract
Resolving the phylogenetic history of a 'true' worker caste in termites is essential to our understanding of termite eusocial evolution. Whether this caste is ancient and monophyletic or derived and polyphyletic will have a tremendous impact on our interpretation of termite eusocial history and remains an outstanding question in termite biology. Recent work has begun to re-examine this question in light of new phylogenetic information, but new questions have now arisen about how best to model character state changes in termite caste systems. In the present paper, we compare the models of Grandcolas and D'Haese [J. Evol. Biol. 15 (2002) 885] and Thompson et al. [J. Evol Biol. 13 (2000) 8691 and attempt to make explicit how these proposals differ with respect to the number of, and homology between, character states. We highlight the support each model has for the two principal, but competing, evolutionary hypotheses outlined above.
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Affiliation(s)
- G J Thompson
- Department of Biological Sciences, Simon Fraser University, Burnaby, BC, Canada.
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Thompson GJ, Langlais C, Cain K, Conley EC, Cohen GM. Elevated extracellular [K+] inhibits death-receptor- and chemical-mediated apoptosis prior to caspase activation and cytochrome c release. Biochem J 2001; 357:137-45. [PMID: 11415444 PMCID: PMC1221936 DOI: 10.1042/0264-6021:3570137] [Citation(s) in RCA: 41] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Efflux of intracellular K(+) and cell shrinkage are features of apoptosis in many experimental systems, and a regulatory role has been proposed for cytoplasmic [K(+)] in initiating apoptosis. We have investigated this in both death-receptor-mediated and chemical-induced apoptosis. Using Jurkat T cells pre-loaded with the K(+) ion surrogate (86)Rb(+), we have demonstrated an efflux of intracellular K(+) during apoptosis that was concomitant with, but did not precede, other apoptotic changes, including phosphatidylserine externalization, mitochondrial depolarization and cell shrinkage. To further clarify the role of K(+) ions in apoptosis, cytoprotection by elevated extracellular [K(+)] was studied. Induction of apoptosis by diverse death-receptor and chemical stimuli in two cell lines was inhibited prior to phosphatidylserine externalization, mitochondrial depolarization, cytochrome c release and caspase activation. Using a cell-free system, we have demonstrated a novel mechanism by which increasing [K(+)] inhibited caspase activation. In control dATP-activated lysates, Apaf-1 oligomerized to a biologically active caspase processing approximately 700 kDa complex and an inactive approximately 1.4 MDa complex. Increasing [K(+)] inhibited caspase activation by preventing formation of the approximately 700 kDa complex, but not of the inactive complex. Thus intracellular and extracellular [K(+)] markedly affect caspase activation and the initiation of apoptosis induced by both death-receptor ligation and chemical stress.
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Affiliation(s)
- G J Thompson
- MRC Toxicology Unit, Hodgkin Building, University of Leicester, P.O. Box 138, Lancaster Road, Leicester LE1 9HN, UK
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Thompson GJ, Miller LR, Lenz M, Crozier RH. Phylogenetic analysis and trait evolution in Australian lineages of drywood termites (Isoptera, Kalotermitidae). Mol Phylogenet Evol 2000; 17:419-29. [PMID: 11133196 DOI: 10.1006/mpev.2000.0852] [Citation(s) in RCA: 48] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
A phylogenetic analysis of Australian drywood termites (Isoptera, Kalotermitidae) based on partial sequence from the cytochrome oxidase II (COII) and cytochrome b genes is presented. In addition to providing new information on the evolutionary relationships among 25 species from seven genera, we evaluate the relative likelihoods of alternative topological hypotheses, including those derived from morphology-based classifications. We also test the applicability of a molecular clock for estimating the age of the Kalotermitidae and infer the evolution of species-specific variation for habitat type and soldier caste phragmosis by mapping this information onto the independently derived phylogeny. Maximum-likelihood analysis of both nucleotide and protein sequences from a multigene data set jointly support a single topology, which is shown to be the best estimate of the true phylogeny among the alternatives tested. Our results support the monophyly of all genera but question the discrimination between Procryptotermes and Cryptotermes. A basal dichotomy among generic groups suggests two principle lines of divergence within the family. Intergeneric relationships show mixed congruence to previous proposals, resulting in one morphology-based classification being rejected. A molecular clock hypothesis is not supported due to significant among-lineage rate heterogeneity in the COII gene. Patterns revealed through trait mapping suggest that the most recently diverged taxa tend to occupy the driest habitats and that these same taxa reflect a defensive transition away from large mandibulate soldiers toward small phragmotic soldiers. The association between habitat and defensibility supports the hypothesis that these two characters have been tightly linked throughout the social diversification of termites.
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Affiliation(s)
- G J Thompson
- Department of Genetics and Evolution, La Trobe University, Bundoora, Victoria, 3083, Australia.
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Affiliation(s)
- G J Thompson
- School of Tropical Biology, James Cook University, Townsville, Queensland 4811, Australia.
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Simonian PT, Thompson GJ, Emley W, Harrington RM, Benirschke SK, Swiontkowski MF. Angulated screw placement in the lateral condylar buttress plate for supracondylar femoral fractures. Injury 1998; 29:101-4. [PMID: 10721403 DOI: 10.1016/s0020-1383(97)00140-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Certain supracondylar femoral fractures are not amenable to internal fixation with fixed angle devices. In these instances, the condylar buttress plate is the recommended alternative; however, this is a less rigid device. Because of the decreased rigidity and strength of this device, there is a tendency toward varus angulation and malunion. In six fresh-frozen human knee specimens, segmental osteotomies were created to mimic supracondylar femoral fractures. The medial cortex was completely removed to make the fracture unstable to varus deformity. The fracture was fixed with a lateral condylar buttress plate using 4.5 mm screws. Each specimen was tested once with all the screws installed perpendicular to the plate, and again with the middle screw, just proximal to the fracture, angled 45 degrees diagonally across the fracture into the subchondral bone of the medial femoral condyle. For the construct with all screws placed perpendicular to the buttress plate, the initial stiffness was 410 N/mm, and after 1000 cycles it was 230 N/mm. With a screw placed diagonally across the fracture site, stiffness increased to 833 N/mm on the first cycle, and 796 N/mm after 1000 cycles. In all specimens with the screws placed perpendicular to the plate, the distal fragment had a permanent varus deformity after 1000 cycles, under no load, of 0.91 mm. For the diagonal screw condition, the average magnitude for all six specimens was 0.42 mm. This simple means of screw angulation in the plate strengthened the overall construct to resist the tendency toward varus deformity. The attractive features include the ease of application, and the use of an existing construct.
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Affiliation(s)
- P T Simonian
- Department of Orthopaedics, Harborview Medical Center, University of Washington, Seattle, USA
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Abstract
Metal ions released from the implant surface are suspected of playing some contributing role in loosening of hip and knee prostheses. previous work in this laboratory demonstrated that sublethal doses of the ionic constituents of Ti-6Al-4V alloy suppressed expression of the osteoblastic phenotype and deposition of a mineralized matrix. The purpose of this work was to further explore this suppression as a function of the normal time-course of phenotype expression. Bone marrow stromal cells were harvested from juvenile rats and exposed to time-staggered doses of a solution of ions representing Ti-6Al-4V alloy. Cells were cultured for four weeks and assayed for total protein, alkaline phosphatase, intra-and extracellular osteocalcin, and calcium. Ti-6Al-4V solutions were found to produce little difference from control solutions for total protein or alkaline phosphatase levels, but strongly inhibited osteocalcin synthesis. Calcium levels were reduced when ions were added before a critical point of osteoblastic differentiation (between 2 and 3 weeks after seeding). These results indicate that ions associated with Ti-6Al-4V alloy inhibited the normal differentiation of bone marrow stromal cells to mature osteoblasts in vitro, suggesting that ions released from implants in vivo may contribute to implant failure by impairing normal bone deposition.
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Affiliation(s)
- G J Thompson
- Center for Biomedical Engineering, University of Kentucky, Lexington 40506-0070, USA
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Thompson GJ, Puleo DA. Effects of sublethal metal ion concentrations on osteogenic cells derived from bone marrow stromal cells. J Appl Biomater 1995; 6:249-58. [PMID: 8589510 DOI: 10.1002/jab.770060406] [Citation(s) in RCA: 71] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Ions released from implant surfaces are suspected of playing some role in osteolysis surrounding metal prostheses. To understand how ions may affect osteogenesis, previous work exposed osteogenic cells to metal ions to study acute cytotoxic responses. The purpose of this study was to assess the long-term effects of sublethal ion concentrations on osteogenic cell proliferation and function. Bone marrow stromal cells were harvested from juvenile rats and exposed to solutions of ions associated with Co-Cr-Mo and Ti-6Al-4V implants. Cells were cultured for up to 4 weeks and assayed for total protein, alkaline phosphatase, osteocalcin, and calcium. Other than V+5, none of the ions affected cell proliferation, indicating that the chosen concentrations were sublethal as desired. V+5 elicited delayed gross toxicity not previously observed during acute experiments. At the chosen concentrations, Co+2, Cr+6, Mo+6, and Co-Cr-Mo alloy elicited little effect on cell proliferation and moderate effects on matrix mineralization. Cultures exposed to Ti+4, Al+3, and Ti-6Al-4V alloy also showed no decrease in cell number, but did show near total suppression of osteocalcin secretion and matrix mineralization. These results suggest that ions released from Ti alloy implants may interfere with osteoblastic cell differentiation, contributing to periprosthetic osteolysis by impairing normal osteogenesis.
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Affiliation(s)
- G J Thompson
- Center for Biomedical Engineering, University of Kentucky, Lexington 40506-0070, USA
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Vohra RK, Thompson GJ, Sharma H, Carr HM, Walker MG. Fibronectin coating of expanded polytetrafluoroethylene (ePTFE) grafts and its role in endothelial seeding. Artif Organs 1990; 14:41-5. [PMID: 2302075 DOI: 10.1111/j.1525-1594.1990.tb01590.x] [Citation(s) in RCA: 32] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Although fibronectin's role as a matrix to improve endothelial seeding has been demonstrated by other workers, the optimum concentration for use has never been described. Attachment of fibronectin to expanded polytetrafluoroethylene (ePTFE) was measured, using 125I-radiolabeled protein, at different concentrations and for different time periods. The absolute amount of fibronectin bound to the graft increased with the concentrations used in coating (p less than 0.001) and also with time (p less than 0.01); e.g., at 50 micrograms/ml, 90 min of incubation produced a molecular attachment of 4.0 x 10(11)/cm2 of graft. However, its percentage attachment decreased with a rise in concentration (p less than 0.001). After an initial loss of 22% in 30 min, the fibronectin-graft bond was found to be stable when exposed to a shear stress produced by flow at 200 ml/min. No significant difference in the cell adherence could be found in grafts coated with fibronectin concentrations of 50, 150, and 250 micrograms/ml, although it was significantly less at 10 and 25 micrograms/ml (p less than 0.05).
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Affiliation(s)
- R K Vohra
- Department of Vascular Surgery, Manchester Royal Infirmary, England
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Rowland GF, Engelbrecht DJ, Pool EJ, Schmollgruber EC, Thompson GJ, van der Merwe KJ. The use of peroxidase anti-peroxidase (PAP) complexes in the detection of plant viruses by ELISA. J Virol Methods 1989; 25:259-69. [PMID: 2685004 DOI: 10.1016/0166-0934(89)90053-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
An ELISA test system for detection of plant viruses in field samples is described, based on the unlabelled antibody method using a peroxidase-antiperoxidase (PAP) complex. Novel features of the system include the use of acid-treated naked bacteria as combined carrier-adjuvants for the production of rabbit antiviral antibodies, and the use of acid-treated chicken antibodies (IgY) for antigen trapping in the ELISA. Systems for detection of potato virus Y (PVY), potato leafroll virus (PLRV), grapevine fanleaf virus (GFV) and grapevine virus A (GVA) were developed and compared with conventional direct double antibody sandwich (DAS) systems in tests with both purified virus and field samples. The PAP systems offer improved sensitivity, no background problems in the outer rows of the microtitre plates and are much easier to visualize with the naked eye if no plate reader is available.
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Affiliation(s)
- G F Rowland
- Bioclones (Pty) Ltd, c/o Department of Biochemistry, University of Stellenbosch, Republic of South Africa
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Thompson GJ. Transurethral Prostatic Resection: A General Estimate and a Special Study of Twenty Physician-Patients. Cal West Med 1934; 40:1-6. [PMID: 18742732 PMCID: PMC1658930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
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