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Boisserand LSB, Geraldo LH, Bouchart J, El Kamouh MR, Lee S, Sanganahalli BG, Spajer M, Zhang S, Lee S, Parent M, Xue Y, Skarica M, Yin X, Guegan J, Boyé K, Saceanu Leser F, Jacob L, Poulet M, Li M, Liu X, Velazquez SE, Singhabahu R, Robinson ME, Askenase MH, Osherov A, Sestan N, Zhou J, Alitalo K, Song E, Eichmann A, Sansing LH, Benveniste H, Hyder F, Thomas JL. VEGF-C prophylaxis favors lymphatic drainage and modulates neuroinflammation in a stroke model. J Exp Med 2024; 221:e20221983. [PMID: 38442272 PMCID: PMC10913814 DOI: 10.1084/jem.20221983] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 11/13/2023] [Accepted: 01/25/2024] [Indexed: 03/07/2024] Open
Abstract
Meningeal lymphatic vessels (MLVs) promote tissue clearance and immune surveillance in the central nervous system (CNS). Vascular endothelial growth factor-C (VEGF-C) regulates MLV development and maintenance and has therapeutic potential for treating neurological disorders. Herein, we investigated the effects of VEGF-C overexpression on brain fluid drainage and ischemic stroke outcomes in mice. Intracerebrospinal administration of an adeno-associated virus expressing mouse full-length VEGF-C (AAV-mVEGF-C) increased CSF drainage to the deep cervical lymph nodes (dCLNs) by enhancing lymphatic growth and upregulated neuroprotective signaling pathways identified by single nuclei RNA sequencing of brain cells. In a mouse model of ischemic stroke, AAV-mVEGF-C pretreatment reduced stroke injury and ameliorated motor performances in the subacute stage, associated with mitigated microglia-mediated inflammation and increased BDNF signaling in brain cells. Neuroprotective effects of VEGF-C were lost upon cauterization of the dCLN afferent lymphatics and not mimicked by acute post-stroke VEGF-C injection. We conclude that VEGF-C prophylaxis promotes multiple vascular, immune, and neural responses that culminate in a protection against neurological damage in acute ischemic stroke.
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Affiliation(s)
| | - Luiz Henrique Geraldo
- Cardiovascular Research Center, Yale University School of Medicine, New Haven, CT, USA
| | - Jean Bouchart
- Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
| | - Marie-Renee El Kamouh
- Paris Brain Institute, Université Pierre et Marie Curie Paris 06 UMRS1127, Sorbonne Université, Paris, France
| | - Seyoung Lee
- Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
| | | | - Myriam Spajer
- Paris Brain Institute, Université Pierre et Marie Curie Paris 06 UMRS1127, Sorbonne Université, Paris, France
| | - Shenqi Zhang
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Department of Neurosurgery, Yale University School of Medicine, New Haven, CT, USA
| | - Sungwoon Lee
- Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
| | - Maxime Parent
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Yuechuan Xue
- Department of Anesthesiology, Yale School of Medicine, New Haven, CT, USA
| | - Mario Skarica
- Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA
- Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Xiangyun Yin
- Department of Ophthalmology and Visual Science, Yale University School of Medicine, New Haven, CT, USA
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | - Justine Guegan
- Paris Brain Institute, Université Pierre et Marie Curie Paris 06 UMRS1127, Sorbonne Université, Paris, France
| | - Kevin Boyé
- Paris Cardiovascular Research Center, INSERM U970, Paris, France
| | - Felipe Saceanu Leser
- Paris Cardiovascular Research Center, INSERM U970, Paris, France
- Glial Cell Biology Laboratory, Biomedical Sciences Institute, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Laurent Jacob
- Paris Cardiovascular Research Center, INSERM U970, Paris, France
| | - Mathilde Poulet
- Paris Cardiovascular Research Center, INSERM U970, Paris, France
| | - Mingfeng Li
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA
- Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Xiodan Liu
- Department of Anesthesiology, Yale School of Medicine, New Haven, CT, USA
| | - Sofia E. Velazquez
- Cardiovascular Research Center, Yale University School of Medicine, New Haven, CT, USA
| | - Ruchith Singhabahu
- Paris Brain Institute, Université Pierre et Marie Curie Paris 06 UMRS1127, Sorbonne Université, Paris, France
| | - Mark E. Robinson
- Center of Molecular and Cellular Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
| | | | - Artem Osherov
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | - Nenad Sestan
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA
- Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT, USA
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Comparative Medicine, Yale School of Medicine, New Haven, CT, USA
- Yale Child Study Center, Yale School of Medicine, New Haven, CT, USA
- Program in Cellular Neuroscience, Neurodegeneration and Repair, Yale School of Medicine, New Haven, CT, USA
| | - Jiangbing Zhou
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Department of Neurosurgery, Yale University School of Medicine, New Haven, CT, USA
| | - Kari Alitalo
- Faculty of Medicine, Wihuri Research Institute and Translational Cancer Biology Program, University of Helsinki, Helsinki, Finland
| | - Eric Song
- Department of Ophthalmology and Visual Science, Yale University School of Medicine, New Haven, CT, USA
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | - Anne Eichmann
- Cardiovascular Research Center, Yale University School of Medicine, New Haven, CT, USA
- Paris Cardiovascular Research Center, INSERM U970, Paris, France
- Department of Cellular and Molecular Physiology, Yale School of Medicine, New Haven, CT, USA
| | - Lauren H. Sansing
- Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
- Department of Ophthalmology and Visual Science, Yale University School of Medicine, New Haven, CT, USA
| | - Helene Benveniste
- Department of Anesthesiology, Yale School of Medicine, New Haven, CT, USA
| | - Fahmeed Hyder
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Jean-Leon Thomas
- Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
- Paris Brain Institute, Université Pierre et Marie Curie Paris 06 UMRS1127, Sorbonne Université, Paris, France
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2
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Ahmed S, Polis B, Jamwal S, Sanganahalli BG, Kaswan ZM, Islam R, Kim D, Bowers C, Giuliano L, Biederer T, Hyder F, Kaffman A. Transient Impairment in Microglial Function Causes Sex-Specific Deficits in Synaptic and Hippocampal Function in Mice Exposed to Early Adversity. bioRxiv 2024:2024.02.14.580284. [PMID: 38405887 PMCID: PMC10888912 DOI: 10.1101/2024.02.14.580284] [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] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Abnormal development and function of the hippocampus are two of the most consistent findings in humans and rodents exposed to early life adversity, with males often being more affected than females. Using the limited bedding (LB) paradigm as a rodent model of early life adversity, we found that male adolescent mice that had been exposed to LB exhibit significant deficits in contextual fear conditioning and synaptic connectivity in the hippocampus, which are not observed in females. This is linked to altered developmental refinement of connectivity, with LB severely impairing microglial-mediated synaptic pruning in the hippocampus of male and female pups on postnatal day 17 (P17), but not in adolescent P33 mice when levels of synaptic engulfment by microglia are substantially lower. Since the hippocampus undergoes intense synaptic pruning during the second and third weeks of life, we investigated whether microglia are required for the synaptic and behavioral aberrations observed in adolescent LB mice. Indeed, transient ablation of microglia from P13-21, in normally developing mice caused sex-specific behavioral and synaptic abnormalities similar to those observed in adolescent LB mice. Furthermore, chemogenetic activation of microglia during the same period reversed the microglial-mediated phagocytic deficits at P17 and restored normal contextual fear conditioning and synaptic connectivity in adolescent LB male mice. Our data support an additional contribution of astrocytes in the sex-specific effects of LB, with increased expression of the membrane receptor MEGF10 and enhanced synaptic engulfment in hippocampal astrocytes of 17-day-old LB females, but not in LB male littermates. This finding suggests a potential compensatory mechanism that may explain the relative resilience of LB females. Collectively, these studies highlight a novel role for glial cells in mediating sex-specific hippocampal deficits in a mouse model of early-life adversity.
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Affiliation(s)
- Sahabuddin Ahmed
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, Suite 901, New Haven CT, 06511, USA
| | - Baruh Polis
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, Suite 901, New Haven CT, 06511, USA
| | - Sumit Jamwal
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, Suite 901, New Haven CT, 06511, USA
| | - Basavaraju G. Sanganahalli
- 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
| | - Zoe MacDowell Kaswan
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, Suite 901, New Haven CT, 06511, USA
| | - Rafiad Islam
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, Suite 901, New Haven CT, 06511, USA
| | - Dana Kim
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, Suite 901, New Haven CT, 06511, USA
| | - Christian Bowers
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, Suite 901, New Haven CT, 06511, USA
| | - Lauryn Giuliano
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, Suite 901, New Haven CT, 06511, USA
| | - Thomas Biederer
- Department of Neurology, Yale School of Medicine, 100 College Street, New Haven, CT 06510, 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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3
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Mihailovic JM, Sanganahalli BG, Hyder F, Chitturi J, Elkabes S, Heary RF, Kannurpatti SS. Cross-hemicord spinal fiber reorganization associates with cortical sensory and motor network expansion in the rat model of hemicontusion cervical spinal cord injury. Neurosci Lett 2024; 820:137607. [PMID: 38141752 PMCID: PMC10797561 DOI: 10.1016/j.neulet.2023.137607] [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: 09/13/2023] [Revised: 12/01/2023] [Accepted: 12/17/2023] [Indexed: 12/25/2023]
Abstract
Magnetic resonance imaging plays an important role in characterizing microstructural changes and reorganization after traumatic injuries to the nervous system. In this study, we tested the feasibility of ex-vivo spinal cord diffusion tensor imaging (DTI) in combination with in vivo brain functional MRI to characterize spinal reorganization and its supraspinal association after a hemicontusion cervical spinal cord injury (SCI). DTI parameters (fractional anisotropy [FA], mean diffusion [MD]) and fiber orientation changes related to reorganization in the contused cervical spinal cord were compared to sham specimens. Altered fiber density and fiber directions occurred across the ipsilateral and contralateral hemicords but with only ipsilateral FA and MD changes. The hemicontusion SCI resulted in ipsilateral fiber breaks, voids and vivid fiber reorientations along the injury epicenter. Fiber directional changes below the injury level were primarily inter-hemispheric, indicating prominent below-level cross-hemispheric reorganization. In vivo resting state functional connectivity of the brain from the respective rats before obtaining the spinal cord samples indicated spatial expansion and increased connectivity strength across both the sensory and motor networks after SCI. The consistency of the neuroplastic changes along the neuraxis (both brain and spinal cord) at the single-subject level, indicates that distinctive reorganizational relationships exist between the spinal cord and the brain post-SCI.
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Affiliation(s)
- Jelena M Mihailovic
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 300 Cedar St, New Haven, CT 06520, United States.
| | - Basavaraju G Sanganahalli
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 300 Cedar St, New Haven, CT 06520, United States.
| | - Fahmeed Hyder
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 300 Cedar St, New Haven, CT 06520, United States.
| | - Jyothsna Chitturi
- Department of Radiology, Rutgers Biomedical and Health Sciences-New Jersey Medical School, 30 Bergen Street, Newark, NJ 07103, United States
| | - Stella Elkabes
- Department of Neurosurgery, Rutgers Biomedical and Health Sciences-New Jersey Medical School. 205 South Orange Avenue, Newark, NJ 07103, United States.
| | - Robert F Heary
- Division of Neurosurgery, Hackensack Meridian School of Medicine, Mountainside Medical Center, Montclair, NJ, United States.
| | - Sridhar S Kannurpatti
- Department of Radiology, Rutgers Biomedical and Health Sciences-New Jersey Medical School, 30 Bergen Street, Newark, NJ 07103, United States.
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4
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Arul MR, Alahmadi I, Turro DG, Ruikar A, Abdulmalik S, Williams JT, Sanganahalli BG, Liang BT, Verma R, Kumbar SG. Fluorescent liposomal nanocarriers for targeted drug delivery in ischemic stroke therapy. Biomater Sci 2023; 11:7856-7866. [PMID: 37902365 PMCID: PMC10697427 DOI: 10.1039/d3bm00951c] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 10/18/2023] [Indexed: 10/31/2023]
Abstract
Ischemic stroke causes acute CNS injury and long-term disability, with limited treatment options such as surgical clot removal or clot-busting drugs. Neuroprotective therapies are needed to protect vulnerable brain regions. The purinergic receptor P2X4 is activated during stroke and exacerbates post-stroke damage. The chemical compound 5-(3-Bromophenyl)-1,3-dihydro-2H-Benzofuro[3,2-e]-1,4-diazepin-2-one (5BDBD) inhibits P2X4 and has shown neuroprotective effects in rodents. However, it is difficult to formulate for systemic delivery to the CNS. The current manuscript reports for the first time, the synthesis and characterization of 5BDBD PEGylated liposomal formulations and evaluates their feasibility to treat stroke in a preclinical mice model. A PEGylated liposomal formulation of 5BDBD was synthesized and characterized, with encapsulation efficacy of >80%, and release over 48 hours. In vitro and in vivo experiments with Nile red encapsulation showed cytocompatibility and CNS infiltration of nanocarriers. Administered 4 or 28 hours after stroke onset, the nanoformulation provided significant neuroprotection, reducing infarct volume by ∼50% compared to controls. It outperformed orally-administered 5BDBD with a lower dose and shorter treatment duration, suggesting precise delivery by nanoformulation improves outcomes. The fluorescent nanoformulations may serve as a platform for delivering and tracking therapeutic agents for stroke treatment.
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Affiliation(s)
- Michael R Arul
- Department of Orthopaedic Surgery, University of Connecticut Health, Farmington, CT, USA.
| | - Ibtihal Alahmadi
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, USA
| | | | - Aditya Ruikar
- Department of Orthopaedic Surgery, University of Connecticut Health, Farmington, CT, USA.
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, USA
| | - Sama Abdulmalik
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, USA
| | | | - Basavaraju G Sanganahalli
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Bruce T Liang
- Calhuan Cardiology Centre, UConn Health, Farmington, CT, USA
| | - Rajkumar Verma
- Department of Neurosciences, UConn Health, Farmington, CT, USA.
| | - Sangamesh G Kumbar
- Department of Orthopaedic Surgery, University of Connecticut Health, Farmington, CT, USA.
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, USA
- Department of Materials Science and Engineering, University of Connecticut, Storrs, CT, USA
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5
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James S, Sanggaard S, Akif A, Mishra SK, Sanganahalli BG, Blumenfeld H, Verhagen JV, Hyder F, Herman P. Spatiotemporal features of neurovascular (un)coupling with stimulus-induced activity and hypercapnia challenge in cerebral cortex and olfactory bulb. J Cereb Blood Flow Metab 2023; 43:1891-1904. [PMID: 37340791 PMCID: PMC10676132 DOI: 10.1177/0271678x231183887] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 05/12/2023] [Accepted: 05/15/2023] [Indexed: 06/22/2023]
Abstract
Carbon dioxide (CO2) is traditionally considered as metabolic waste, yet its regulation is critical for brain function. It is well accepted that hypercapnia initiates vasodilation, but its effect on neuronal activity is less clear. Distinguishing how stimulus- and CO2-induced vasodilatory responses are (dis)associated with neuronal activity has profound clinical and experimental relevance. We used an optical method in mice to simultaneously image fluorescent calcium (Ca2+) transients from neurons and reflectometric hemodynamic signals during brief sensory stimuli (i.e., hindpaw, odor) and CO2 exposure (i.e., 5%). Stimuli-induced neuronal and hemodynamic responses swiftly increased within locally activated regions exhibiting robust neurovascular coupling. However, hypercapnia produced slower global vasodilation which was temporally uncoupled to neuronal deactivation. With trends consistent across cerebral cortex and olfactory bulb as well as data from GCaMP6f/jRGECO1a mice (i.e., green/red Ca2+ fluorescence), these results unequivocally reveal that stimuli and CO2 generate comparable vasodilatory responses but contrasting neuronal responses. In summary, observations of stimuli-induced regional neurovascular coupling and CO2-induced global neurovascular uncoupling call for careful appraisal when using CO2 in gas mixtures to affect vascular tone and/or neuronal excitability, because CO2 is both a potent vasomodulator and a neuromodulator.
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Affiliation(s)
- Shaun James
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Simon Sanggaard
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Adil Akif
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Sandeep K Mishra
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | | | - Hal Blumenfeld
- Department of Neurology, Yale University, New Haven, CT, USA
- Department of Neuroscience, Yale University, New Haven, CT, USA
| | - Justus V Verhagen
- Department of Neuroscience, Yale University, New Haven, CT, USA
- John B. Pierce Laboratory, New Haven, CT, USA
| | - Fahmeed Hyder
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Peter Herman
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
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6
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Lyden PD, Diniz MA, Bosetti F, Lamb J, Nagarkatti KA, Rogatko A, Kim S, Cabeen RP, Koenig JI, Akhter K, Arbab AS, Avery BD, Beatty HE, Bibic A, Cao S, Simoes Braga Boisserand L, Chamorro A, Chauhan A, Diaz-Perez S, Dhandapani K, Dhanesha N, Goh A, Herman AL, Hyder F, Imai T, Johnson CW, Khan MB, Kamat P, Karuppagounder SS, Kumskova M, Mihailovic JM, Mandeville JB, Morais A, Patel RB, Sanganahalli BG, Smith C, Shi Y, Sutariya B, Thedens D, Qin T, Velazquez SE, Aronowski J, Ayata C, Chauhan AK, Leira EC, Hess DC, Koehler RC, McCullough LD, Sansing LH. A multi-laboratory preclinical trial in rodents to assess treatment candidates for acute ischemic stroke. Sci Transl Med 2023; 15:eadg8656. [PMID: 37729432 DOI: 10.1126/scitranslmed.adg8656] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 08/31/2023] [Indexed: 09/22/2023]
Abstract
Human diseases may be modeled in animals to allow preclinical assessment of putative new clinical interventions. Recent, highly publicized failures of large clinical trials called into question the rigor, design, and value of preclinical assessment. We established the Stroke Preclinical Assessment Network (SPAN) to design and implement a randomized, controlled, blinded, multi-laboratory trial for the rigorous assessment of candidate stroke treatments combined with intravascular thrombectomy. Efficacy and futility boundaries in a multi-arm multi-stage statistical design aimed to exclude from further study highly effective or futile interventions after each of four sequential stages. Six independent research laboratories performed a standard focal cerebral ischemic insult in five animal models that included equal numbers of males and females: young mice, young rats, aging mice, mice with diet-induced obesity, and spontaneously hypertensive rats. The laboratories adhered to a common protocol and efficiently enrolled 2615 animals with full data completion and comprehensive animal tracking. SPAN successfully implemented treatment masking, randomization, prerandomization inclusion and exclusion criteria, and blinded assessment of outcomes. The SPAN design and infrastructure provide an effective approach that could be used in similar preclinical, multi-laboratory studies in other disease areas and should help improve reproducibility in translational science.
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Affiliation(s)
- Patrick D Lyden
- Department of Physiology and Neuroscience, Zilkha Neurogenetic Institute, Keck School of Medicine of USC, Los Angeles, CA 90033, USA
- Department of Neurology, Keck School of Medicine of USC, Los Angeles, CA 90033, USA
| | - Márcio A Diniz
- Biostatistics and Bioinformatics Research Center, Samuel Oschin Comprehensive Cancer Center, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Francesca Bosetti
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Jessica Lamb
- Department of Physiology and Neuroscience, Zilkha Neurogenetic Institute, Keck School of Medicine of USC, Los Angeles, CA 90033, USA
| | - Karisma A Nagarkatti
- Department of Physiology and Neuroscience, Zilkha Neurogenetic Institute, Keck School of Medicine of USC, Los Angeles, CA 90033, USA
| | - André Rogatko
- Biostatistics and Bioinformatics Research Center, Samuel Oschin Comprehensive Cancer Center, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Sungjin Kim
- Biostatistics and Bioinformatics Research Center, Samuel Oschin Comprehensive Cancer Center, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Ryan P Cabeen
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Imaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, CA 90033, USA
| | - James I Koenig
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Kazi Akhter
- Department of Radiology, Johns Hopkins University, Baltimore, MD 21218-2625, USA
| | - Ali S Arbab
- Biochemistry and Molecular Biology, Medical College of Georgia, Augusta University, Augusta, GA 30912-0004, USA
| | - Brooklyn D Avery
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University, Baltimore, MD 21218-2625, USA
| | - Hannah E Beatty
- Department of Neurology, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Adnan Bibic
- Department of Radiology, Johns Hopkins University, Baltimore, MD 21218-2625, USA
| | - Suyi Cao
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University, Baltimore, MD 21218-2625, USA
| | | | - Angel Chamorro
- Department of Neurology, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
- Department of Neurology, Hospital Clinic, University of Barcelona, Barcelona 08036, Spain
| | - Anjali Chauhan
- Department of Neurology, McGovern Medical School, University of Texas HSC, Houston, TX 77030, USA
| | - Sebastian Diaz-Perez
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Krishnan Dhandapani
- Department Neurosurgery, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA
| | - Nirav Dhanesha
- Department of Internal Medicine, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
| | - Andrew Goh
- Department of Neurology, McGovern Medical School, University of Texas HSC, Houston, TX 77030, USA
| | - Alison L Herman
- Department of Neurology, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Fahmeed Hyder
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06520, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Takahiko Imai
- Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Conor W Johnson
- Department of Neurology, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Mohammad B Khan
- Department of Neurology, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA
| | - Pradip Kamat
- Department of Neurology, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA
| | | | - Mariia Kumskova
- Department of Internal Medicine, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
| | - Jelena M Mihailovic
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06520, USA
| | - Joseph B Mandeville
- Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Andreia Morais
- Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Rakesh B Patel
- Department of Internal Medicine, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
| | | | - Cameron Smith
- Department of Neurology, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA
| | - Yanrong Shi
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University, Baltimore, MD 21218-2625, USA
| | - Brijesh Sutariya
- Department of Internal Medicine, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
| | - Daniel Thedens
- Department of Radiology, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
| | - Tao Qin
- Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Sofia E Velazquez
- Department of Neurology, Yale University School of Medicine, New Haven, CT 06520, USA
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Jaroslaw Aronowski
- Department of Neurology, McGovern Medical School, University of Texas HSC, Houston, TX 77030, USA
| | - Cenk Ayata
- Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Anil K Chauhan
- Department of Internal Medicine, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
| | - Enrique C Leira
- Department of Neurology, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
- Department of Neurosurgery, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
- Department of Epidemiology, College of Public Health, University of Iowa, Iowa City, IA 52242, USA
| | - David C Hess
- Department of Neurology, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA
| | - Raymond C Koehler
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University, Baltimore, MD 21218-2625, USA
| | - Louise D McCullough
- Department of Neurology, McGovern Medical School, University of Texas HSC, Houston, TX 77030, USA
| | - Lauren H Sansing
- Department of Neurology, Yale University School of Medicine, New Haven, CT 06520, USA
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT 06520, USA
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Simoes Braga Boisserand L, Bouchart J, Geraldo LH, Lee S, Sanganahalli BG, Parent M, Zhang S, Xue Y, Skarica M, Guegan J, Li M, Liu X, Poulet M, Askanase M, Osherov A, Spajer M, Kamouh MRE, Eichmann A, Alitalo K, Zhou J, Sestan N, Sansing LH, Benveniste H, Hyder F, Thomas JL. VEGF-C promotes brain-derived fluid drainage, confers neuroprotection, and improves stroke outcomes. bioRxiv 2023:2023.05.30.542708. [PMID: 37398128 PMCID: PMC10312491 DOI: 10.1101/2023.05.30.542708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Meningeal lymphatic vessels promote tissue clearance and immune surveillance in the central nervous system (CNS). Vascular endothelium growth factor-C (VEGF-C) is essential for meningeal lymphatic development and maintenance and has therapeutic potential for treating neurological disorders, including ischemic stroke. We have investigated the effects of VEGF-C overexpression on brain fluid drainage, single cell transcriptome in the brain, and stroke outcomes in adult mice. Intra-cerebrospinal fluid administration of an adeno-associated virus expressing VEGF-C (AAV-VEGF-C) increases the CNS lymphatic network. Post-contrast T1 mapping of the head and neck showed that deep cervical lymph node size and drainage of CNS-derived fluids were increased. Single nuclei RNA sequencing revealed a neuro-supportive role of VEGF-C via upregulation of calcium and brain-derived neurotrophic factor (BDNF) signaling pathways in brain cells. In a mouse model of ischemic stroke, AAV-VEGF-C pretreatment reduced stroke injury and ameliorated motor performances in the subacute stage. AAV-VEGF-C thus promotes CNS-derived fluid and solute drainage, confers neuroprotection, and reduces ischemic stroke damage. Short abstract Intrathecal delivery of VEGF-C increases the lymphatic drainage of brain-derived fluids confers neuroprotection, and improves neurological outcomes after ischemic stroke.
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8
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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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9
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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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10
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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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11
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McCafferty C, Gruenbaum BF, Tung R, Li JJ, Zheng X, Salvino P, Vincent P, Kratochvil Z, Ryu JH, Khalaf A, Swift K, Akbari R, Islam W, Antwi P, Johnson EA, Vitkovskiy P, Sampognaro J, Freedman IG, Kundishora A, Depaulis A, David F, Crunelli V, Sanganahalli BG, Herman P, Hyder F, Blumenfeld H. Decreased but diverse activity of cortical and thalamic neurons in consciousness-impairing rodent absence seizures. Nat Commun 2023; 14:117. [PMID: 36627270 PMCID: PMC9832004 DOI: 10.1038/s41467-022-35535-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [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: 06/28/2022] [Accepted: 12/08/2022] [Indexed: 01/12/2023] Open
Abstract
Absence seizures are brief episodes of impaired consciousness, behavioral arrest, and unresponsiveness, with yet-unknown neuronal mechanisms. Here we report that an awake female rat model recapitulates the behavioral, electroencephalographic, and cortical functional magnetic resonance imaging characteristics of human absence seizures. Neuronally, seizures feature overall decreased but rhythmic firing of neurons in cortex and thalamus. Individual cortical and thalamic neurons express one of four distinct patterns of seizure-associated activity, one of which causes a transient initial peak in overall firing at seizure onset, and another which drives sustained decreases in overall firing. 40-60 s before seizure onset there begins a decline in low frequency electroencephalographic activity, neuronal firing, and behavior, but an increase in higher frequency electroencephalography and rhythmicity of neuronal firing. Our findings demonstrate that prolonged brain state changes precede consciousness-impairing seizures, and that during seizures distinct functional groups of cortical and thalamic neurons produce an overall transient firing increase followed by a sustained firing decrease, and increased rhythmicity.
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Affiliation(s)
- Cian McCafferty
- Department of Neurology, Yale School of Medicine, New Haven, CT, 06520, USA
- Department of Anatomy & Neuroscience, University College Cork, Cork, Ireland
| | | | - Renee Tung
- Department of Neurology, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Jing-Jing Li
- Department of Neurology, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Xinyuan Zheng
- Department of Neurology, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Peter Salvino
- Department of Neurology, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Peter Vincent
- Department of Neurology, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Zachary Kratochvil
- Department of Neurology, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Jun Hwan Ryu
- Department of Neurology, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Aya Khalaf
- Department of Neurology, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Kohl Swift
- Department of Neurology, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Rashid Akbari
- Department of Neurology, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Wasif Islam
- Department of Neurology, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Prince Antwi
- Department of Neurology, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Emily A Johnson
- Department of Neurology, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Petr Vitkovskiy
- Department of Neurology, Yale School of Medicine, New Haven, CT, 06520, USA
| | - James Sampognaro
- Department of Neurology, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Isaac G Freedman
- Department of Neurology, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Adam Kundishora
- Department of Neurology, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Antoine Depaulis
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000, Grenoble, France
| | - François David
- Neuroscience Division, School of Bioscience, Cardiff University, Cardiff, UK
| | - Vincenzo Crunelli
- Neuroscience Division, School of Bioscience, Cardiff University, Cardiff, UK
| | - Basavaraju G Sanganahalli
- Magnetic Resonance Research Center, Yale University, New Haven, CT, 06520, USA
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, 06520, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
| | - Peter Herman
- Magnetic Resonance Research Center, Yale University, New Haven, CT, 06520, USA
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, 06520, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
| | - Fahmeed Hyder
- Magnetic Resonance Research Center, Yale University, New Haven, CT, 06520, USA
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, 06520, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
| | - Hal Blumenfeld
- Department of Neurology, Yale School of Medicine, New Haven, CT, 06520, USA.
- Magnetic Resonance Research Center, Yale University, New Haven, CT, 06520, USA.
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, 06520, USA.
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, 06520, USA.
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12
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Sanganahalli BG, Pavuluri S, Chitturi J, Herman P, Elkabes S, Heary R, Hyder F, Kannurpatti SS. Lateralized Supraspinal Functional Connectivity Correlate with Pain and Motor Dysfunction in Rat Hemicontusion Cervical Spinal Cord Injury. Neurotrauma Rep 2022; 3:421-432. [PMID: 36337081 PMCID: PMC9622206 DOI: 10.1089/neur.2022.0040] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Afferent nociceptive activity in the reorganizing spinal cord after SCI influences supraspinal regions to establish pain. Clinical evidence of poor motor functional recovery in SCI patients with pain, led us to hypothesize that sensory-motor integration transforms into sensory-motor interference to manifest pain. This was tested by investigating supraspinal changes in a rat model of hemicontusion cervical SCI. Animals displayed ipsilateral forelimb motor dysfunction and pain, which persisted at 6 weeks after SCI. Using resting state fMRI at 8 weeks after SCI, RSFC across 14 ROIs involved in nociception, indicated lateral differences with a relatively weaker right-right connectivity (deafferented-contralateral) compared to left-left (unaffected-ipsilateral). However, the sensory (S1) and motor (M1/M2) networks showed greater RSFC using right hemisphere ROI seeds when compared to left. Voxel seeds from the somatosensory forelimb (S1FL) and M1/M2 representations reproduced the SCI-induced sensory and motor RSFC enhancements observed using the ROI seeds. Larger local connectivity occurred in the right sensory and motor networks amidst a decreasing overall local connectivity. This maladaptive reorganization of the right (deafferented) hemisphere localized the sensory component of pain emerging from the ipsilateral forepaw. A significant expansion of the sensory and motor network s overlap occurred globally after SCI when compared to sham, supporting the hypothesis that sensory and motor interference manifests pain. Voxel-seed based analysis revealed greater sensory and motor network overlap in the left hemisphere when compared to the right. This left predominance of the overlap suggested relatively larger pain processing in the unaffected hemisphere, when compared to the deafferented side.
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Affiliation(s)
- Basavaraju G. Sanganahalli
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Swathi Pavuluri
- Department of Radiology, Rutgers Biomedical and Health Sciences–New Jersey Medical School, Newark, New Jersey, USA
| | - Jyothsna Chitturi
- Department of Radiology, Rutgers Biomedical and Health Sciences–New Jersey Medical School, Newark, New Jersey, USA
| | - Peter Herman
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Stella Elkabes
- Department of Neurosurgery, Rutgers Biomedical and Health Sciences–New Jersey Medical School, Newark, New Jersey, USA
| | - Robert Heary
- Hackensack Meridian School of Medicine, Mountainside Medical Center, Montclair, New Jersey, USA
| | - Fahmeed Hyder
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Sridhar S. Kannurpatti
- Department of Radiology, Rutgers Biomedical and Health Sciences–New Jersey Medical School, Newark, New Jersey, USA.,Address correspondence to: Sridhar S. Kannurpatti, PhD, Department of Radiology, RUTGERS–New Jersey Medical School, MSB, F-506, 185 South Orange Avenue, Newark, NJ 07103, USA.
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13
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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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14
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Lyden PD, Bosetti F, Diniz MA, Rogatko A, Koenig JI, Lamb J, Nagarkatti KA, Cabeen RP, Hess DC, Kamat P, Khan MB, Wood K, Dhandapani K, Arbab AS, Leira EC, Chauhan AK, Dhanesha N, Patel RB, Kumskova M, Thedens D, Morais A, Imai T, Qin T, Ayata C, Boisserand LSB, Herman AL, Beatty HE, Velazquez SE, Diaz-Perez S, Sanganahalli BG, Mihailovic JM, Hyder F, Sansing LH, Koehler RC, Lannon S, Shi Y, Karuppagounder SS, Bibic A, Akhter K, Aronowski J, McCullough LD, Chauhan A, Goh A. The Stroke Preclinical Assessment Network: Rationale, Design, Feasibility, and Stage 1 Results. Stroke 2022; 53:1802-1812. [PMID: 35354299 PMCID: PMC9038686 DOI: 10.1161/strokeaha.121.038047] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [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: 11/23/2021] [Accepted: 01/26/2022] [Indexed: 12/12/2022]
Abstract
Cerebral ischemia and reperfusion initiate cellular events in brain that lead to neurological disability. Investigating these cellular events provides ample targets for developing new treatments. Despite considerable work, no such therapy has translated into successful stroke treatment. Among other issues-such as incomplete mechanistic knowledge and faulty clinical trial design-a key contributor to prior translational failures may be insufficient scientific rigor during preclinical assessment: nonblinded outcome assessment; missing randomization; inappropriate sample sizes; and preclinical assessments in young male animals that ignore relevant biological variables, such as age, sex, and relevant comorbid diseases. Promising results are rarely replicated in multiple laboratories. We sought to address some of these issues with rigorous assessment of candidate treatments across 6 independent research laboratories. The Stroke Preclinical Assessment Network (SPAN) implements state-of-the-art experimental design to test the hypothesis that rigorous preclinical assessment can successfully reduce or eliminate common sources of bias in choosing treatments for evaluation in clinical studies. SPAN is a randomized, placebo-controlled, blinded, multilaboratory trial using a multi-arm multi-stage protocol to select one or more putative stroke treatments with an implied high likelihood of success in human clinical stroke trials. The first stage of SPAN implemented procedural standardization and experimental rigor. All participating research laboratories performed middle cerebral artery occlusion surgery adhering to a common protocol and rapidly enrolled 913 mice in the first of 4 planned stages with excellent protocol adherence, remarkable data completion and low rates of subject loss. SPAN stage 1 successfully implemented treatment masking, randomization, prerandomization inclusion/exclusion criteria, and blinded assessment to exclude bias. Our data suggest that a large, multilaboratory, preclinical assessment effort to reduce known sources of bias is feasible and practical. Subsequent SPAN stages will evaluate candidate treatments for potential success in future stroke clinical trials using aged animals and animals with comorbid conditions.
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Affiliation(s)
- Patrick D. Lyden
- Department of Physiology and Neuroscience, Zilkha Neurogenetic Institute, Keck School of Medicine at USC; Los Angeles, CA USA
- Department of Neurology, Keck School of Medicine at USC; Los Angeles, CA USA
| | - Francesca Bosetti
- National Institute of Neurological Disorders and Stroke, National Institutes of Health; Bethesda, MD USA
| | - Márcio A. Diniz
- Biostatistics and Bioinformatics Research Center, Samuel Oschin Comprehensive Cancer Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - André Rogatko
- Biostatistics and Bioinformatics Research Center, Samuel Oschin Comprehensive Cancer Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - James I. Koenig
- National Institute of Neurological Disorders and Stroke, National Institutes of Health; Bethesda, MD USA
| | - Jessica Lamb
- Department of Physiology and Neuroscience, Zilkha Neurogenetic Institute, Keck School of Medicine at USC; Los Angeles, CA USA
| | - Karisma A. Nagarkatti
- Department of Physiology and Neuroscience, Zilkha Neurogenetic Institute, Keck School of Medicine at USC; Los Angeles, CA USA
| | - Ryan P. Cabeen
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Imaging and Informatics Institute, Keck School of Medicine of USC; Los Angeles, CA USA
| | - David C. Hess
- Department of Neurology, Medical College of Georgia, Augusta University, Augusta, GA, USA
| | - Pradip Kamat
- Department of Neurology, Medical College of Georgia, Augusta University, Augusta, GA, USA
| | - Mohammad B. Khan
- Department of Neurology, Medical College of Georgia, Augusta University, Augusta, GA, USA
| | - Kristofer Wood
- Department of Neurology, Medical College of Georgia, Augusta University, Augusta, GA, USA
| | - Krishnan Dhandapani
- Department of Neurosurgery, Medical College of Georgia, Augusta University, Augusta, GA, USA
| | - Ali S. Arbab
- Department of Biochemistry and Molecular Biology, Medical College of Georgia, Augusta University, Augusta, GA, USA
| | - Enrique C. Leira
- Department of Neurology, Carver College of Medicine, College of Public Health, University of Iowa
- Department of Neurosurgery, Carver College of Medicine, College of Public Health, University of Iowa
- Department of Epidemiology, Carver College of Medicine, College of Public Health, University of Iowa
| | - Anil K. Chauhan
- Department of Internal Medicine, Carver College of Medicine, College of Public Health, University of Iowa
| | - Nirav Dhanesha
- Department of Internal Medicine, Carver College of Medicine, College of Public Health, University of Iowa
| | - Rakesh B. Patel
- Department of Internal Medicine, Carver College of Medicine, College of Public Health, University of Iowa
| | - Mariia Kumskova
- Department of Internal Medicine, Carver College of Medicine, College of Public Health, University of Iowa
| | - Daniel Thedens
- Department of Radiology, Carver College of Medicine, College of Public Health, University of Iowa
| | - Andreia Morais
- Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | - Takahiko Imai
- Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | - Tao Qin
- Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | - Cenk Ayata
- Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
| | | | - Alison L. Herman
- Department of Neurology, Yale University School of Medicine, New Haven, CT USA
| | - Hannah E. Beatty
- Department of Neurology, Yale University School of Medicine, New Haven, CT USA
| | - Sofia E. Velazquez
- Department of Neurology, Yale University School of Medicine, New Haven, CT USA
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT USA
| | - Sebastian Diaz-Perez
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT USA
| | | | - Jelena M. Mihailovic
- Departments of Radiology and Biomedical Imaging, Yale University, New Haven, CT USA
| | - Fahmeed Hyder
- Departments of Radiology and Biomedical Imaging, Yale University, New Haven, CT USA
- Departments of Biomedical Engineering, Yale University, New Haven, CT USA
| | - Lauren H. Sansing
- Department of Neurology, Yale University School of Medicine, New Haven, CT USA
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT USA
| | - Raymond C. Koehler
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University; Baltimore, MD USA
| | - Steven Lannon
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University; Baltimore, MD USA
| | - Yanrong Shi
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University; Baltimore, MD USA
| | | | - Adnan Bibic
- Department of Radiology, Johns Hopkins University; Baltimore, MD USA
| | - Kazi Akhter
- Department of Radiology, Johns Hopkins University; Baltimore, MD USA
| | - Jaroslaw Aronowski
- Department of Neurology, McGovern Medical School, University of Texas HSC, Houston, TX, USA
| | - Louise D. McCullough
- Department of Neurology, McGovern Medical School, University of Texas HSC, Houston, TX, USA
| | - Anjali Chauhan
- Department of Neurology, McGovern Medical School, University of Texas HSC, Houston, TX, USA
| | - Andrew Goh
- Department of Neurology, McGovern Medical School, University of Texas HSC, Houston, TX, USA
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15
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Sanganahalli BG, Chitturi J, Herman P, Elkabes S, Heary R, Hyder F, Kannurpatti S. Supraspinal sensorimotor and pain-related reorganization after a hemicontusion rat cervical spinal cord injury. J Neurotrauma 2021; 38:3393-3405. [PMID: 34714150 DOI: 10.1089/neu.2021.0190] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [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: 11/13/2022] Open
Abstract
Since the presence of pain impedes motor recovery in individuals with spinal cord injury (SCI), it is necessary to understand their supraspinal substrates in translational animal models. Using functional magnetic resonance imaging (fMRI) in a rat model of hemicontusion cervical SCI, supraspinal changes were mapped and correlated with sensorimotor behavioral outcomes. Female adult rats underwent sham or SCI using a 2.5 mm impactor and 150 kDyne force. SCI permanently impaired motor activity in only the ipsilesional forelimb along with thermal hyperalgesia at 5 and 6 wks. Spinal MRI at 8 wks after SCI showed ipsilateral T1 and T2 lesions with no discernable lesions across shams. fMRI mapping during electrical forepaw stimulation indicated SCI-induced sensorimotor reorganization with an expansion of the contralesional forelimb representation. Resting state fMRI based functional connectivity density (FCD), a marker of regional neuronal hubs increased or decreased across brain regions involved in nociception. FCD increases after SCI were in the primary and secondary somatosensory cortices (S1 and S2), anterior cingulate cortex (ACC), insula and the prefrontal cortex (PFC) and decreases were across the hippocampus, thalamus, hypothalamus and amygdala in SCI. Resting state functional connectivity (RSFC) assessments from the FCD altered regions of interest indicated cortico-cortical RSFC increases and cortico-insular, cortico-thalamic and cortico-hypothalamic RSFC decreases after SCI. Hippocampus, amygdala and thalamus showed decreased RSFC with most cortical regions and between themselves except the hippocampus-amygdala network, which showed increased RSFC after SCI. While select nociceptive region's intrinsic activity associated strongly with evoked pain behaviors after SCI (eg., PFC, ACC, hippocampus, thalamus, hypothalamus, M1 and S1BF) other nociceptive regions had weaker associations (eg., amygdala, insula, auditory cortex, S1FL, S1HL, S2 and M2), but differed significantly in their intrinsic activities between sham and SCI. The weaker associated nociceptive regions may possibly encode both the evoked and affective components of pain.
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Affiliation(s)
- Basavaraju G Sanganahalli
- Yale University School of Medicine, 12228, Diagnostic Radiology, New Haven, Connecticut, United States;
| | - Jyothsna Chitturi
- Rutgers Biomedical and Health Sciences, 5751, Radiology, Newark, New Jersey, United States;
| | - Peter Herman
- Yale University School of Medicine, 12228, Magnetic Resonance Research Center, Department of Diagnostic Radiology and Biomedical Engineering, Section of Bioimaging Science, New Haven, Connecticut, United States;
| | - Stella Elkabes
- Rutgers Biomedical and Health Sciences, 5751, Neurosurgery, Newark, New Jersey, United States;
| | - Robert Heary
- Hackensack Meridian School of Medicine, 576909, Nutley, New Jersey, United States;
| | | | - Sridhar Kannurpatti
- Rutgers Biomedical and Health Sciences, 5751, Radiology, Newark, New Jersey, United States;
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16
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Farina MG, Sandhu MRS, Parent M, Sanganahalli BG, Derbin M, Dhaher R, Wang H, Zaveri HP, Zhou Y, Danbolt NC, Hyder F, Eid T. Small loci of astroglial glutamine synthetase deficiency in the postnatal brain cause epileptic seizures and impaired functional connectivity. Epilepsia 2021; 62:2858-2870. [PMID: 34536233 DOI: 10.1111/epi.17072] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 09/03/2021] [Accepted: 09/03/2021] [Indexed: 12/15/2022]
Abstract
OBJECTIVE The astroglial enzyme glutamine synthetase (GS) is deficient in small loci in the brain in adult patients with different types of focal epilepsy; however, the role of this deficiency in the pathogenesis of epilepsy has been difficult to assess due to a lack of sufficiently sensitive and specific animal models. The aim of this study was to develop an in vivo approach for precise and specific deletions of the GS gene in the postnatal brain. METHODS We stereotaxically injected various adeno-associated virus (AAV)-Cre recombinase constructs into the hippocampal formation and neocortex in 22-70-week-old GSflox/flox mice to knock out the GS gene in a specific and focal manner. The mice were subjected to seizure threshold determination, continuous video-electroencephalographic recordings, advanced in vivo neuroimaging, and immunocytochemistry for GS. RESULTS The construct AAV8-glial fibrillary acidic protein-green fluorescent protein-Cre eliminated GS in >99% of astrocytes in the injection center with a gradual return to full GS expression toward the periphery. Such focal GS deletion reduced seizure threshold, caused spontaneous recurrent seizures, and diminished functional connectivity. SIGNIFICANCE These results suggest that small loci of GS deficiency in the postnatal brain are sufficient to cause epilepsy and impaired functional connectivity. Additionally, given the high specificity and precise spatial resolution of our GS knockdown approach, we anticipate that this model will be extremely useful for rigorous in vivo and ex vivo studies of astroglial GS function at the brain-region and single-cell levels.
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Affiliation(s)
- Maxwell G Farina
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Mani Ratnesh S Sandhu
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Maxime Parent
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Basavaraju G Sanganahalli
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Matthew Derbin
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Roni Dhaher
- Department of Neurosurgery, Yale School of Medicine, New Haven, Connecticut, USA
| | - Helen Wang
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Hitten P Zaveri
- Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Yun Zhou
- Institute for Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Niels C Danbolt
- Institute for Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Fahmeed Hyder
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Tore Eid
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, Connecticut, USA
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17
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Chitturi J, Sanganahalli BG, Herman P, Hyder F, Ni L, Elkabes S, Heary R, Kannurpatti SS. Association Between Magnetic Resonance Imaging-Based Spinal Morphometry and Sensorimotor Behavior in a Hemicontusion Model of Incomplete Cervical Spinal Cord Injury in Rats. Brain Connect 2020; 10:479-489. [PMID: 32981350 DOI: 10.1089/brain.2020.0812] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [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: 12/25/2022] Open
Abstract
Aim: Structural connectivity in the reorganizing spinal cord after injury dictates functional connectivity and hence the neurological outcome. As magnetic resonance imaging (MRI)-based structural parameters are mostly accessible across spinal cord injury (SCI) patients, we studied MRI-based spinal morphological changes and their relationship to neurological outcome in the rat model of cervical SCI. Introduction: Functional connectivity assessments on patients with SCI rely heavily on MRI-based approaches to investigate the complete neural axis (both spinal cord and brain). Hence, underlying MRI-based structural and morphometric changes in the reorganizing spinal cord and their relationship to neurological outcomes is crucial for meaningful interpretation of functional connectivity changes across the neural axis. Methods: Young adult rats, aged 1.5 months, underwent a precise mechanical impact hemicontusion incomplete cervical SCI at the C4/C5 level, after which sensorimotor behavioral assessments were tracked during the reorganization period of 1-6 weeks, followed by MRI of the cervical spinal cord at 8 weeks after SCI. Results: A significant ipsilesional forelimb motor debilitation was observed from 1 to 6 weeks after injury. Heat sensitivity testing (Hargreaves) showed ipsilesional forelimb hypersensitivity at 5 and 6 weeks after SCI. MRI of the cervical spine showed ipsilateral T1- and T2-weighted lesions across all SCI rats compared with no significant lesions in sham rats. Morphometric assessments of the lesional and nonlesional changes showed the diverse nature of their interindividual variability in the SCI receiving rats. While the various T1 and T2 MRI lesional volumes associated weakly or moderately with neurological outcome, the nonlesional spinal morphometric changes associated much more strongly. The results have important implications for interpreting functional MRI-based functional connectivity after SCI by providing vital underlying structural changes and their relative neurological impact. Impact statement Functional connectivity assessments on patients with SCI relies heavily upon MRI based approaches. Hence, underlying MRI based structural and morphometric changes in the reorganizing spinal cord and its relationship to neurological outcomes is vital for meaningful interpretation of functional connectivity changes across the complete neural axis (both spinal cord and the brain).
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Affiliation(s)
- Jyothsna Chitturi
- Department of Radiology, New Jersey Medical School, Rutgers Biomedical and Health Sciences, Newark, New Jersey, USA
| | - Basavaraju G Sanganahalli
- Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, USA.,Magnetic Resonance Research Center (MRRC), Yale University, New Haven, Connecticut, USA.,Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University, New Haven, Connecticut, USA
| | - Peter Herman
- Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, USA.,Magnetic Resonance Research Center (MRRC), Yale University, New Haven, Connecticut, USA.,Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University, New Haven, Connecticut, USA
| | - Fahmeed Hyder
- Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, USA.,Magnetic Resonance Research Center (MRRC), Yale University, New Haven, Connecticut, USA.,Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University, New Haven, Connecticut, USA.,Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA
| | - Li Ni
- Department of Neurosurgery, New Jersey Medical School, Rutgers Biomedical and Health Sciences, Newark, New Jersey, USA
| | - Stella Elkabes
- Department of Neurosurgery, New Jersey Medical School, Rutgers Biomedical and Health Sciences, Newark, New Jersey, USA
| | - Robert Heary
- Hackensack University School of Medicine, Nutley, New Jersey, USA
| | - Sridhar S Kannurpatti
- Department of Radiology, New Jersey Medical School, Rutgers Biomedical and Health Sciences, Newark, New Jersey, USA
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18
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Lin AL, Parikh I, Yanckello LM, White RS, Hartz AMS, Taylor CE, McCulloch SD, Thalman SW, Xia M, McCarty K, Ubele M, Head E, Hyder F, Sanganahalli BG. APOE genotype-dependent pharmacogenetic responses to rapamycin for preventing Alzheimer's disease. Neurobiol Dis 2020; 139:104834. [PMID: 32173556 PMCID: PMC7486698 DOI: 10.1016/j.nbd.2020.104834] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [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: 12/01/2019] [Revised: 02/21/2020] [Accepted: 03/11/2020] [Indexed: 01/07/2023] Open
Abstract
The ε4 allele of Apolipoprotein (APOE4) is the strongest genetic risk factor for Alzheimer's disease (AD), the most common form of dementia. Cognitively normal APOE4 carriers have developed amyloid beta (Aβ) plaques and cerebrovascular, metabolic and structural deficits decades before showing the cognitive impairment. Interventions that can inhibit Aβ retention and restore the brain functions to normal would be critical to prevent AD for the asymptomatic APOE4 carriers. A major goal of the study was to identify the potential usefulness of rapamycin (Rapa), a pharmacological intervention for extending longevity, for preventing AD in the mice that express human APOE4 gene and overexpress Aβ (the E4FAD mice). Another goal of the study was to identify the potential pharmacogenetic differences in response to rapamycin between the E4FAD and E3FAD mice, the mice with human APOE ε3 allele. We used multi-modal MRI to measure in vivo cerebral blood flow (CBF), neurotransmitter levels, white matter integrity, water content, cerebrovascular reactivity (CVR) and somatosensory response; used behavioral assessments to determine cognitive function; used biochemistry assays to determine Aβ retention and blood-brain barrier (BBB) functions; and used metabolomics to identify brain metabolic changes. We found that in the E4FAD mice, rapamycin normalized bodyweight, restored CBF (especially in female), BBB activity for Aβ transport, neurotransmitter levels, neuronal integrity and free fatty acid level, and reduced Aβ retention, which were not observe in the E3FAD-Rapa mice. In contrast, E3FAD-Rapa mice had lower CVR responses, lower anxiety and reduced glycolysis in the brain, which were not seen in the E4FAD-Rapa mice. Further, rapamycin appeared to normalize lipid-associated metabolism in the E4FAD mice, while slowed overall glucose-associated metabolism in the E3FAD mice. Finally, rapamycin enhanced overall water content, water diffusion in white matter, and spatial memory in both E3FAD and E4FAD mice, but did not impact the somatosensory responses under hindpaw stimulation. Our findings indicated that rapamycin was able to restore brain functions and reduce AD risk for young, asymptomatic E4FAD mice, and there were pharmacogenetic differences between the E3FAD and E4FAD mice. As the multi-modal MRI methods used in the study are readily to be used in humans and rapamycin is FDA-approved, our results may pave a way for future clinical testing of the pharmacogenetic responses in humans with different APOE alleles, and potentially using rapamycin to prevent AD for asymptomatic APOE4 carriers.
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Affiliation(s)
- Ai-Ling Lin
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, United States of America; Department of Pharmacology and Nutritional Science, University of Kentucky, Lexington, KY, United States of America; Department of Neuroscience, University of Kentucky, Lexington, KY, United States of America; F. Joseph Halcomb III, Department of Biomedical Engineering, University of Kentucky, Lexington, KY, United States of America.
| | - Ishita Parikh
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, United States of America; Oklahoma Medical Research Foundation, Oklahoma City, OK, United States of America
| | - Lucille M Yanckello
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, United States of America; Department of Pharmacology and Nutritional Science, University of Kentucky, Lexington, KY, United States of America
| | - Renee S White
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, United States of America; Georgetown College, Georgetown, KY, United States of America
| | - Anika M S Hartz
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, United States of America; Department of Pharmacology and Nutritional Science, University of Kentucky, Lexington, KY, United States of America
| | - Chase E Taylor
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, United States of America
| | | | - Scott W Thalman
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, United States of America; F. Joseph Halcomb III, Department of Biomedical Engineering, University of Kentucky, Lexington, KY, United States of America
| | - Mengfan Xia
- Department of Pharmacology and Nutritional Science, University of Kentucky, Lexington, KY, United States of America
| | - Katie McCarty
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, United States of America
| | - Margo Ubele
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, United States of America
| | - Elizabeth Head
- Department of Pathology & Laboratory Medicine, University of California, Irvine, CA, United States of America; University of California Irvine Institute for Memory Impairments and Neurological Disorders, Irvine, CA, United States of America
| | - Fahmeed Hyder
- Magnetic Resonance Research Center, Yale University, New Haven, CT, United States of America; Quantitative Neuroscience with Magnetic Resonance Core Center, Yale University, New Haven, CT, United States of America; Department of Diagnostic Radiology, Yale University, New Haven, CT, United States of America; Department of Biomedical Engineering, Yale University, New Haven, CT, United States of America
| | - Basavaraju G Sanganahalli
- Magnetic Resonance Research Center, Yale University, New Haven, CT, United States of America; Quantitative Neuroscience with Magnetic Resonance Core Center, Yale University, New Haven, CT, United States of America; Department of Diagnostic Radiology, Yale University, New Haven, CT, United States of America; Department of Biomedical Engineering, Yale University, New Haven, CT, United States of America
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Parent M, Chitturi J, Santhakumar V, Hyder F, Sanganahalli BG, Kannurpatti SS. Kaempferol Treatment after Traumatic Brain Injury during Early Development Mitigates Brain Parenchymal Microstructure and Neural Functional Connectivity Deterioration at Adolescence. J Neurotrauma 2020; 37:966-974. [PMID: 31830867 PMCID: PMC7175625 DOI: 10.1089/neu.2019.6486] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [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] [Indexed: 02/06/2023] Open
Abstract
Targeting mitochondrial ion homeostasis using Kaempferol, a mitochondrial Ca2+ uniporter channel activator, improves energy metabolism and behavior soon after a traumatic brain injury (TBI) in developing rats. Because of broad TBI pathophysiology and brain mitochondrial heterogeneity, Kaempferol-mediated early-stage behavioral and brain metabolic benefits may accrue from diverse sources within the brain. We hypothesized that Kaempferol influences TBI outcome by differentially impacting the neural, vascular, and synaptic/axonal compartments. After TBI at early development (P31), functional magnetic resonance imaging and diffusion tensor imaging (DTI) were applied to determine imaging outcomes at adolescence (2 months post-injury). Vehicle and Kaempferol treatments were made at 1, 24, and 48 h post-TBI, and their effects were assessed at adolescence. A significant increase in neural connectivity was observed after Kaempferol treatment as assessed by the spatial extent and strength of the somatosensory cortical and hippocampal resting-state functional connectivity (RSFC) networks. However, no significant RSFC changes were observed in the thalamus. DTI measures of fractional anisotropy (FA) and apparent diffusion coefficient, representing synaptic/axonal and microstructural integrity, showed significant improvements after Kaempferol treatment, with highest changes in the frontal and parietal cortices and hippocampus. Kaempferol treatment also increased corpus callosal FA, indicating measurable improvement in the interhemispheric structural connectivity. TBI prognosis was significantly altered at adolescence by early Kaempferol treatment, with improved neural connectivity, neurovascular coupling, and parenchymal microstructure in select brain regions. However, Kaempferol failed to improve vasomotive function across the whole brain, as measured by cerebrovascular reactivity. The differential effects of Kaempferol treatment on various brain functional compartments support diverse cellular-level mitochondrial functional outcomes in vivo.
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Affiliation(s)
- Maxime Parent
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut
| | - Jyothsna Chitturi
- Department of Radiology, Rutgers Biomedical and Health Sciences–New Jersey Medical School, Newark, New Jersey
| | - Vijayalakshmi Santhakumar
- Department of Pharmacology, Physiology and Neuroscience, Rutgers Biomedical and Health Sciences-New Jersey Medical School, Medical Science Building, Newark, New Jersey
- Department of Molecular, Cell and Systems Neuroscience, University of California at Riverside, Riverside, California
| | - Fahmeed Hyder
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut
| | - Basavaraju G. Sanganahalli
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut
| | - Sridhar S. Kannurpatti
- Department of Radiology, Rutgers Biomedical and Health Sciences–New Jersey Medical School, Newark, New Jersey
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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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21
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Parent M, Li Y, Santhakumar V, Hyder F, Sanganahalli BG, Kannurpatti SS. Alterations of Parenchymal Microstructure, Neuronal Connectivity, and Cerebrovascular Resistance at Adolescence after Mild-to-Moderate Traumatic Brain Injury in Early Development. J Neurotrauma 2019; 36:601-608. [PMID: 29855211 PMCID: PMC6354598 DOI: 10.1089/neu.2018.5741] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [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] [Indexed: 12/15/2022] Open
Abstract
Traumatic brain injury (TBI) is a leading cause of morbidity in children. To investigate outcome of early developmental TBI during adolescence, a rat model of fluid percussion injury was developed, where previous work reported deficits in sensorimotor behavior and cortical blood flow at adolescence.1 Based on the nonlocalized outcome, we hypothesized that multiple neurophysiological components of brain function, namely neuronal connectivity, synapse/axonal microstructural integrity, and neurovascular function, are altered and magnetic resonance imaging (MRI) methods could be used to determine regional alterations. Adolescent outcomes of developmental TBI were studied 2 months after injury, using functional MRI (fMRI) and diffusion tensor imaging (DTI). fMRI-based resting-state functional connectivity (RSFC), representing neural connectivity, was significantly altered between sham and TBI. RSFC strength decreased in the cortex, hippocampus, and thalamus, accompanied by decrease in spatial extent of their corresponding RSFC networks and interhemispheric asymmetry. Cerebrovascular reactivity to arterial CO2 changes diminished after TBI across both hemispheres, with a more pronounced decrease in the ipsilateral hippocampus, thalamus, and motor cortex. DTI measures of fractional anisotropy and apparent diffusion coefficient, reporting on axonal and microstructural integrity of the brain, indicated similar interhemispheric asymmetry, with highest change in the ipsilateral hippocampus and regions adjoining the ipsilateral thalamus, hypothalamus, and amygdala. TBI-induced corpus callosal microstructural alterations indicated measurable changes in interhemispheric structural connectivity. Hippocampus, thalamus, and select cortical regions were most consistently affected in multiple imaging markers. The multi-modal MRI results demonstrate cortical and subcortical alterations in neural connectivity, cerebrovascular resistance, and parenchymal microstructure in the adolescent brain, indicating the highly diffuse and persistent nature of the lateral fluid percussion TBI early in development.
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Affiliation(s)
- Maxime Parent
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut
| | - Ying Li
- Department of Pharmacology, Physiology and Neuroscience, Rutgers Biomedical and Health Sciences–New Jersey Medical School, Newark, New Jersey
| | - Vijayalakshmi Santhakumar
- Department of Pharmacology, Physiology and Neuroscience, Rutgers Biomedical and Health Sciences–New Jersey Medical School, Newark, New Jersey
- Department of Molecular, Cell and Systems Neuroscience, University of California at Riverside, Riverside, California
| | - Fahmeed Hyder
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut
| | - Basavaraju G. Sanganahalli
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut
| | - Sridhar S. Kannurpatti
- Department of Radiology, Rutgers Biomedical and Health Sciences–New Jersey Medical School, Newark, New Jersey
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22
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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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Sanganahalli BG, Herman P, Rothman DL, Blumenfeld H, Hyder F. Metabolic demands of neural-hemodynamic associated and disassociated areas in brain. J Cereb Blood Flow Metab 2016; 36:1695-1707. [PMID: 27562867 PMCID: PMC5076793 DOI: 10.1177/0271678x16664531] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Revised: 07/04/2016] [Accepted: 07/19/2016] [Indexed: 12/31/2022]
Abstract
Interpretation of regional blood oxygenation level-dependent (BOLD) responses in functional magnetic resonance imaging (fMRI) is contingent on whether local field potential (LFP) and multi-unit activity (MUA) is either dissociated or associated. To examine whether neural-hemodynamic associated and dissociated areas have different metabolic demands, we recorded sensory-evoked responses of BOLD signal, blood flow (CBF), and blood volume (CBV), which with calibrated fMRI provided oxidative metabolism (CMRO2) from rat's ventral posterolateral thalamic nucleus (VPL) and somatosensory forelimb cortex (S1FL) and compared these neuroimaging signals to neurophysiological recordings. MUA faithfully recorded evoked latency differences between VPL and S1FL because evoked MUA in these regions were similar in magnitude. Since evoked LFP was significantly attenuated in VPL, we extracted the time courses of the weaker thalamic LFP to compare with the stronger cortical LFP using wavelet transform. BOLD and CBV responses were greater in S1FL than in VPL, similar to LFP regional differences. CBF and CMRO2 responses were both comparably larger in S1FL and VPL. Despite different levels of CBF-CMRO2 and LFP-MUA couplings in VPL and S1FL, the CMRO2 was well matched with MUA in both regions. These results suggest that neural-hemodynamic associated and dissociated areas in VPL and S1FL can have similar metabolic demands.
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Affiliation(s)
- Basavaraju G Sanganahalli
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, USA Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University, New Haven, USA Department of Radiology and Biomedical Imaging, Yale University, New Haven, USA
| | - Peter Herman
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, USA Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University, New Haven, USA Department of Radiology and Biomedical Imaging, Yale University, New Haven, USA
| | - Douglas L Rothman
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, USA Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University, New Haven, USA Department of Radiology and Biomedical Imaging, Yale University, New Haven, USA Department of Biomedical Engineering, Yale University, New Haven, USA
| | - Hal Blumenfeld
- Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University, New Haven, USA Department of Neurology, Yale University, New Haven, USA Department of Neurobiology, Yale University, New Haven, USA
| | - Fahmeed Hyder
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, USA Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University, New Haven, USA Department of Radiology and Biomedical Imaging, Yale University, New Haven, USA Department of Biomedical Engineering, Yale University, New Haven, USA
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Shu CY, Sanganahalli BG, Coman D, Herman P, Hyder F. New horizons in neurometabolic and neurovascular coupling from calibrated fMRI. Prog Brain Res 2016; 225:99-122. [PMID: 27130413 DOI: 10.1016/bs.pbr.2016.02.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Neurovascular coupling relates changes in neuronal activity to constriction/dilation of microvessels. However neurometabolic coupling, which is less well known, relates alterations in neuronal activity with metabolic demands. The link between the blood oxygenation level dependent (BOLD) signal and neural activity opened doors for functional MRI (fMRI) to be a powerful neuroimaging tool in the neurosciences. But due to the complex makeup of BOLD contrast, researchers began to investigate the relationship between BOLD signal and blood flow and/or volume changes during functional brain activation, which together provided the tools to measure oxygen consumption on the basis of the biophysical model of BOLD. This field is called calibrated fMRI, thereby allowed probing of both neurometabolic and neurovascular couplings for a variety of health conditions in animals and humans. Calibrated fMRI may provide brain disorder biomarkers that could be used for monitoring effective therapies.
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Affiliation(s)
- C Y Shu
- Yale University, New Haven, CT, United States
| | - B G Sanganahalli
- Yale University, New Haven, CT, United States; Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, United States
| | - D Coman
- Yale University, New Haven, CT, United States; Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, United States
| | - P Herman
- Yale University, New Haven, CT, United States; Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, United States
| | - F Hyder
- Yale University, New Haven, CT, United States; Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, United States.
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Pirazzoli V, Ayeni D, Meador CB, Sanganahalli BG, Hyder F, de Stanchina E, Goldberg SB, Pao W, Politi K. Afatinib plus Cetuximab Delays Resistance Compared to Single-Agent Erlotinib or Afatinib in Mouse Models of TKI-Naïve EGFR L858R-Induced Lung Adenocarcinoma. Clin Cancer Res 2016; 22:426-35. [PMID: 26341921 PMCID: PMC4715986 DOI: 10.1158/1078-0432.ccr-15-0620] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [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: 03/13/2015] [Accepted: 08/18/2015] [Indexed: 01/14/2023]
Abstract
PURPOSE The EGFR tyrosine kinase inhibitors (TKIs), erlotinib and afatinib, have transformed the treatment of advanced EGFR-mutant lung adenocarcinoma. However, almost all patients who respond develop acquired resistance on average approximately 1 year after starting therapy. Resistance is commonly due to a secondary mutation in EGFR (EGFR(T790M)). We previously found that the combination of the EGFR TKI afatinib and the EGFR antibody cetuximab could overcome EGFR(T790M)-mediated resistance in preclinical models. This combination has shown a 29% response rate in a clinical trial in patients with acquired resistance to first-generation TKIs. An outstanding question is whether this regimen is beneficial when used as first-line therapy. EXPERIMENTAL DESIGN Using mouse models of EGFR-mutant lung cancer, we tested whether the combination of afatinib plus cetuximab delivered upfront to mice with TKI-naïve EGFR(L858R)-induced lung adenocarcinomas delayed tumor relapse and drug-resistance compared with single-agent TKIs. RESULTS Afatinib plus cetuximab markedly delayed the time to relapse and incidence of drug-resistant tumors, which occurred in only 63.6% of the mice, in contrast to erlotinib or afatinib treatment where 100% of mice developed resistance. Mechanisms of tumor escape observed in afatinib plus cetuximab resistant tumors include the EGFR(T790M) mutation and Kras mutations. Experiments in cell lines and xenografts confirmed that the afatinib plus cetuximab combination does not suppress the emergence of EGFR(T790M). CONCLUSIONS These results highlight the potential of afatinib plus cetuximab as an effective treatment strategy for patients with TKI-naïve EGFR-mutant lung cancer and indicate that clinical trial development in this area is warranted.
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Affiliation(s)
- Valentina Pirazzoli
- Yale Cancer Center, Yale University School of Medicine, New Haven, Connecticut
| | - Deborah Ayeni
- Experimental Pathology Graduate Program, Graduate School of Arts and Sciences, Yale University, New Haven, Connecticut. Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
| | - Catherine B Meador
- Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Basavaraju G Sanganahalli
- Department of Diagnostic Radiology and Magnetic Resonance Research Center, Yale University School of Medicine, New Haven, Connecticut
| | - Fahmeed Hyder
- Department of Diagnostic Radiology and Magnetic Resonance Research Center, Yale University School of Medicine, New Haven, Connecticut. Department of Biomedical Engineering, Yale University School of Engineering and Applied Science, New Haven, Connecticut
| | - Elisa de Stanchina
- Antitumor Assessment Core, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Sarah B Goldberg
- Department of Medicine, Section of Medical Oncology, Yale University School of Medicine, New Haven, Connecticut
| | - William Pao
- Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, Tennessee. Vanderbilt-Ingram Cancer Center, Nashville, Tennessee. Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Katerina Politi
- Yale Cancer Center, Yale University School of Medicine, New Haven, Connecticut. Department of Pathology, Yale University School of Medicine, New Haven, Connecticut. Department of Medicine, Section of Medical Oncology, Yale University School of Medicine, New Haven, Connecticut.
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Sanganahalli BG, Rebello MR, Herman P, Papademetris X, Shepherd GM, Verhagen JV, Hyder F. Comparison of glomerular activity patterns by fMRI and wide-field calcium imaging: Implications for principles underlying odor mapping. Neuroimage 2015; 126:208-18. [PMID: 26631819 DOI: 10.1016/j.neuroimage.2015.11.048] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2015] [Revised: 11/18/2015] [Accepted: 11/20/2015] [Indexed: 10/22/2022] Open
Abstract
Functional imaging signals arise from distinct metabolic and hemodynamic events at the neuropil, but how these processes are influenced by pre- and post-synaptic activities need to be understood for quantitative interpretation of stimulus-evoked mapping data. The olfactory bulb (OB) glomeruli, spherical neuropil regions with well-defined neuronal circuitry, can provide insights into this issue. Optical calcium-sensitive fluorescent dye imaging (OICa(2+)) reflects dynamics of pre-synaptic input to glomeruli, whereas high-resolution functional magnetic resonance imaging (fMRI) using deoxyhemoglobin contrast reveals neuropil function within the glomerular layer where both pre- and post-synaptic activities contribute. We imaged odor-specific activity patterns of the dorsal OB in the same anesthetized rats with fMRI and OICa(2+) and then co-registered the respective maps to compare patterns in the same space. Maps by each modality were very reproducible as trial-to-trial patterns for a given odor, overlapping by ~80%. Maps evoked by ethyl butyrate and methyl valerate for a given modality overlapped by ~80%, suggesting activation of similar dorsal glomerular networks by these odors. Comparison of maps generated by both methods for a given odor showed ~70% overlap, indicating similar odor-specific maps by each method. These results suggest that odor-specific glomerular patterns by high-resolution fMRI primarily tracks pre-synaptic input to the OB. Thus combining OICa(2+) and fMRI lays the framework for studies of OB processing over a range of spatiotemporal scales, where OICa(2+) can feature the fast dynamics of dorsal glomerular clusters and fMRI can map the entire glomerular sheet in the 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.
| | - Michelle R Rebello
- Department of Neurobiology, 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; 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
| | - Xenophon Papademetris
- 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
| | | | - Justus V Verhagen
- Department of Neurobiology, 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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Shu CY, Sanganahalli BG, Coman D, Herman P, Rothman DL, Hyder F. Quantitative β mapping for calibrated fMRI. Neuroimage 2015; 126:219-28. [PMID: 26619788 DOI: 10.1016/j.neuroimage.2015.11.042] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [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: 07/23/2015] [Revised: 11/12/2015] [Accepted: 11/16/2015] [Indexed: 11/27/2022] Open
Abstract
The metabolic and hemodynamic dependencies of the blood oxygenation level-dependent (BOLD) signal form the basis for calibrated fMRI, where the focus is on oxidative energy demanded by neural activity. An important part of calibrated fMRI is the power-law relationship between the BOLD signal and the deoxyhemoglobin concentration, which in turn is related to the ratio between oxidative demand (CMRO2) and blood flow (CBF). The power-law dependence between BOLD signal and deoxyhemoglobin concentration is signified by a scaling exponent β. Until recently most studies assumed a β value of 1.5, which is based on numerical simulations of the extravascular BOLD component. Since the basal value of CMRO2 and CBF can vary from subject-to-subject and/or region-to-region, a method to independently measure β in vivo should improve the accuracy of calibrated fMRI results. We describe a new method for β mapping through characterizing R2' - the most sensitive relaxation component of BOLD signal (i.e., the reversible magnetic susceptibility component that is predominantly of extravascular origin at high magnetic field) - as a function of intravascular magnetic susceptibility induced by an FDA-approved superparamagnetic contrast agent. In α-chloralose anesthetized rat brain, at 9.4 T, we measured β values of ~0.8 uniformly across large neocortical swathes, with lower magnitude and more heterogeneity in subcortical areas. Comparison of β maps in rats anesthetized with medetomidine and α-chloralose revealed that β is independent of neural activity levels at these resting states. We anticipate that this method for β mapping can help facilitate calibrated fMRI for clinical studies.
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Affiliation(s)
- Christina Y Shu
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
| | - Basavaraju G Sanganahalli
- Department of Radiology and Biomedical Imaging and Magnetic Resonance Research Center, Yale University, New Haven, CT, USA
| | - Daniel Coman
- Department of Radiology and Biomedical Imaging and Magnetic Resonance Research Center, Yale University, New Haven, CT, USA
| | - Peter Herman
- Department of Radiology and Biomedical Imaging and Magnetic Resonance Research Center, Yale University, New Haven, CT, USA
| | - Douglas L Rothman
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging and Magnetic Resonance Research Center, Yale University, New Haven, CT, USA
| | - Fahmeed Hyder
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging and Magnetic Resonance Research Center, Yale University, New Haven, CT, USA.
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Kannurpatti SS, Sanganahalli BG, Herman P, Hyder F. Role of mitochondrial calcium uptake homeostasis in resting state fMRI brain networks. NMR Biomed 2015; 28:1579-1588. [PMID: 26439799 PMCID: PMC4621005 DOI: 10.1002/nbm.3421] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [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] [Received: 05/28/2015] [Revised: 08/27/2015] [Accepted: 09/01/2015] [Indexed: 06/05/2023]
Abstract
Mitochondrial Ca(2+) uptake influences both brain energy metabolism and neural signaling. Given that brain mitochondrial organelles are distributed in relation to vascular density, which varies considerably across brain regions, we hypothesized different physiological impacts of mitochondrial Ca(2+) uptake across brain regions. We tested the hypothesis by monitoring brain "intrinsic activity" derived from the resting state functional MRI (fMRI) blood oxygen level dependent (BOLD) fluctuations in different functional networks spanning the somatosensory cortex, caudate putamen, hippocampus and thalamus, in normal and perturbed mitochondrial Ca(2+) uptake states. In anesthetized rats at 11.7 T, mitochondrial Ca(2+) uptake was inhibited or enhanced respectively by treatments with Ru360 or kaempferol. Surprisingly, mitochondrial Ca(2+) uptake inhibition by Ru360 and enhancement by kaempferol led to similar dose-dependent decreases in brain-wide intrinsic activities in both the frequency domain (spectral amplitude) and temporal domain (resting state functional connectivity; RSFC). The fact that there were similar dose-dependent decreases in the frequency and temporal domains of the resting state fMRI-BOLD fluctuations during mitochondrial Ca(2+) uptake inhibition or enhancement indicated that mitochondrial Ca(2+) uptake and its homeostasis may strongly influence the brain's functional organization at rest. Interestingly, the resting state fMRI-derived intrinsic activities in the caudate putamen and thalamic regions saturated much faster with increasing dosage of either drug treatment than the drug-induced trends observed in cortical and hippocampal regions. Regional differences in how the spectral amplitude and RSFC changed with treatment indicate distinct mitochondrion-mediated spontaneous neuronal activity coupling within the various RSFC networks determined by resting state fMRI.
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Affiliation(s)
| | - Basavaraju G. Sanganahalli
- Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, CT 06520-808
- Magnetic Resonance Research Center (MRRC), Yale University School of Medicine, New Haven, CT 06520-808
- Core Center for Quantitative Neuroscience with Magnetic Resonance (QNMR), Yale University School of Medicine, New Haven, CT 06520-808
| | - Peter Herman
- Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, CT 06520-808
- Magnetic Resonance Research Center (MRRC), Yale University School of Medicine, New Haven, CT 06520-808
- Core Center for Quantitative Neuroscience with Magnetic Resonance (QNMR), Yale University School of Medicine, New Haven, CT 06520-808
| | - Fahmeed Hyder
- Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, CT 06520-808
- Department of Biomedical Engineering, Yale University School of Medicine, New Haven, CT 06520-808
- Magnetic Resonance Research Center (MRRC), Yale University School of Medicine, New Haven, CT 06520-808
- Core Center for Quantitative Neuroscience with Magnetic Resonance (QNMR), Yale University School of Medicine, New Haven, CT 06520-808
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Shu CY, Herman P, Coman D, Sanganahalli BG, Wang H, Juchem C, Rothman DL, de Graaf RA, Hyder F. Brain region and activity-dependent properties of M for calibrated fMRI. Neuroimage 2015; 125:848-856. [PMID: 26529646 DOI: 10.1016/j.neuroimage.2015.10.083] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [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: 08/24/2015] [Revised: 10/28/2015] [Accepted: 10/30/2015] [Indexed: 11/28/2022] Open
Abstract
Calibrated fMRI extracts changes in oxidative energy demanded by neural activity based on hemodynamic and metabolic dependencies of the blood oxygenation level-dependent (BOLD) response. This procedure requires the parameter M, which is determined from the dynamic range of the BOLD signal between deoxyhemoglobin (paramagnetic) and oxyhemoglobin (diamagnetic). Since it is unclear if the range of M-values in human calibrated fMRI is due to regional/state differences, we conducted a 9.4T study to measure M-values across brain regions in deep (α-chloralose) and light (medetomidine) anesthetized rats, as verified by electrophysiology. Because BOLD signal is captured differentially by gradient-echo (R2*) and spin-echo (R2) relaxation rates, we measured M-values by the product of the fMRI echo time and R2' (i.e., the reversible magnetic susceptibility component), which is given by the absolute difference between R2* and R2. While R2' mapping was shown to be dependent on the k-space sampling method used, at nominal spatial resolutions achieved at high magnetic field of 9.4T the M-values were quite homogenous across cortical gray matter. However cortical M-values varied in relation to neural activity between brain states. The findings from this study could improve precision of future calibrated fMRI studies by focusing on the global uniformity of M-values in gray matter across different resting activity levels.
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Affiliation(s)
- Christina Y Shu
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
| | - Peter Herman
- Department of Radiology and Biomedical Imaging and Magnetic Resonance Research Center, Yale University, New Haven, CT, USA
| | - Daniel Coman
- Department of Radiology and Biomedical Imaging and Magnetic Resonance Research Center, Yale University, New Haven, CT, USA
| | - Basavaraju G Sanganahalli
- Department of Radiology and Biomedical Imaging and Magnetic Resonance Research Center, Yale University, New Haven, CT, USA
| | - Helen Wang
- Department of Radiology and Biomedical Imaging and Magnetic Resonance Research Center, Yale University, New Haven, CT, USA
| | - Christoph Juchem
- Department of Radiology and Biomedical Imaging and Magnetic Resonance Research Center, Yale University, New Haven, CT, USA; Department of Neurology, Yale University, New Haven, CT, USA
| | - Douglas L Rothman
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging and Magnetic Resonance Research Center, Yale University, New Haven, CT, USA
| | - Robin A de Graaf
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging and Magnetic Resonance Research Center, Yale University, New Haven, CT, USA
| | - Fahmeed Hyder
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging and Magnetic Resonance Research Center, Yale University, New Haven, CT, USA.
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Coman D, Sanganahalli BG, Jiang L, Hyder F, Behar KL. Distribution of temperature changes and neurovascular coupling in rat brain following 3,4-methylenedioxymethamphetamine (MDMA, "ecstasy") exposure. NMR Biomed 2015; 28:1257-66. [PMID: 26286889 PMCID: PMC4573923 DOI: 10.1002/nbm.3375] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [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] [Received: 09/24/2014] [Revised: 06/22/2015] [Accepted: 07/19/2015] [Indexed: 05/05/2023]
Abstract
(+/-)3,4-methylenedioxymethamphetamine (MDMA, "ecstasy") is an abused psychostimulant that produces strong monoaminergic stimulation and whole-body hyperthermia. MDMA-induced thermogenesis involves activation of uncoupling proteins (UCPs), primarily a type specific to skeletal muscle (UCP-3) and absent from the brain, although other UCP types are expressed in the brain (e.g. thalamus) and might contribute to thermogenesis. Since neuroimaging of brain temperature could provide insights into MDMA action, we measured spatial distributions of systemically administered MDMA-induced temperature changes and dynamics in rat cortex and subcortex using a novel magnetic resonance method, Biosensor Imaging of Redundant Deviation in Shifts (BIRDS), with an exogenous temperature-sensitive probe (thulium ion and macrocyclic chelate 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetramethyl-1,4,7,10-tetraacetate (DOTMA(4-))). The MDMA-induced temperature rise was greater in the cortex than in the subcortex (1.6 ± 0.4 °C versus 1.3 ± 0.4 °C) and occurred more rapidly (2.0 ± 0.2 °C/h versus 1.5 ± 0.2 °C/h). MDMA-induced temperature changes and dynamics in the cortex and body were correlated, although the body temperature exceeded the cortex temperature before and after MDMA. Temperature, neuronal activity, and blood flow (CBF) were measured simultaneously in the cortex and subcortex (i.e. thalamus) to investigate possible differences of MDMA-induced warming across brain regions. MDMA-induced warming correlated with increases in neuronal activity and blood flow in the cortex, suggesting that the normal neurovascular response to increased neural activity was maintained. In contrast to the cortex, a biphasic relationship was seen in the subcortex (i.e. thalamus), with a decline in CBF as temperature and neural activity rose, transitioning to a rise in CBF for temperature above 37 °C, suggesting that MDMA affected CBF and neurovascular coupling differently in subcortical regions. Considering that MDMA effects on CBF and heat dissipation (as well as potential heat generation) may vary regionally, neuroprotection may require different cooling strategies.
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Affiliation(s)
- Daniel Coman
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT 06520, USA
- Core Center for Quantitative Neuroscience with Magnetic Resonance (QNMR), Yale University, New Haven, CT 06520, USA
- Department of Diagnostic Radiology, Yale University, New Haven, CT 06520, USA
| | - Basavaraju G. Sanganahalli
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT 06520, USA
- Core Center for Quantitative Neuroscience with Magnetic Resonance (QNMR), Yale University, New Haven, CT 06520, USA
- Department of Diagnostic Radiology, Yale University, New Haven, CT 06520, USA
| | - Lihong Jiang
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT 06520, USA
- Department of Diagnostic Radiology, Yale University, New Haven, CT 06520, USA
| | - Fahmeed Hyder
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT 06520, USA
- Core Center for Quantitative Neuroscience with Magnetic Resonance (QNMR), Yale University, New Haven, CT 06520, USA
- Department of Diagnostic Radiology, Yale University, New Haven, CT 06520, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Kevin L. Behar
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT 06520, USA
- Department of Psychiatry, Yale University, New Haven, CT 06520, USA
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Youngblood MW, Chen WC, Mishra AM, Enamandram S, Sanganahalli BG, Motelow JE, Bai HX, Frohlich F, Gribizis A, Lighten A, Hyder F, Blumenfeld H. Rhythmic 3-4Hz discharge is insufficient to produce cortical BOLD fMRI decreases in generalized seizures. Neuroimage 2015; 109:368-77. [PMID: 25562830 DOI: 10.1016/j.neuroimage.2014.12.066] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2014] [Revised: 12/01/2014] [Accepted: 12/25/2014] [Indexed: 01/13/2023] Open
Abstract
Absence seizures are transient episodes of impaired consciousness accompanied by 3-4 Hz spike-wave discharge on electroencephalography (EEG). Human functional magnetic resonance imaging (fMRI) studies have demonstrated widespread cortical decreases in the blood oxygen-level dependent (BOLD) signal that may play an important role in the pathophysiology of these seizures. Animal models could provide an opportunity to investigate the fundamental mechanisms of these changes, however they have so far failed to consistently replicate the cortical fMRI decreases observed in human patients. This may be due to important differences between human seizures and animal models, including a lack of cortical development in rodents or differences in the frequencies of rodent (7-8 Hz) and human (3-4 Hz) spike-wave discharges. To examine the possible contributions of these differences, we developed a ferret model that exhibits 3-4 Hz spike-wave seizures in the presence of a sulcated cortex. Measurements of BOLD fMRI and simultaneous EEG demonstrated cortical fMRI increases during and following spike-wave seizures in ferrets. However unlike human patients, significant fMRI decreases were not observed. The lack of fMRI decreases was consistent across seizures of different durations, discharge frequencies, and anesthetic regimes, and using fMRI analysis models similar to human patients. In contrast, generalized tonic-clonic seizures under the same conditions elicited sustained postictal fMRI decreases, verifying that the lack of fMRI decreases with spike-wave was not due to technical factors. These findings demonstrate that 3-4 Hz spike-wave discharge in a sulcated animal model does not necessarily produce fMRI decreases, leaving the mechanism for this phenomenon open for further investigation.
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Affiliation(s)
- Mark W Youngblood
- Department of Neurology, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520, USA
| | - William C Chen
- Department of Neurology, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520, USA
| | - Asht M Mishra
- Department of Neurology, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520, USA; Core Center for Quantitative Neuroscience with Magnetic Resonance (QNMR), New Haven, CT 06520, USA
| | - Sheila Enamandram
- Department of Neurology, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520, USA
| | - Basavaraju G Sanganahalli
- Core Center for Quantitative Neuroscience with Magnetic Resonance (QNMR), New Haven, CT 06520, USA; Department of Diagnostic Radiology, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520, USA
| | - Joshua E Motelow
- Department of Neurology, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520, USA
| | - Harrison X Bai
- Department of Neurology, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520, USA
| | - Flavio Frohlich
- Department of Neurobiology, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520, USA
| | - Alexandra Gribizis
- Department of Neurology, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520, USA
| | - Alexis Lighten
- Department of Neurology, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520, USA
| | - Fahmeed Hyder
- Core Center for Quantitative Neuroscience with Magnetic Resonance (QNMR), New Haven, CT 06520, USA; Department of Diagnostic Radiology, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520, USA; Department of Biomedical Engineering, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520, USA
| | - Hal Blumenfeld
- Department of Neurology, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520, USA; Core Center for Quantitative Neuroscience with Magnetic Resonance (QNMR), New Haven, CT 06520, USA; Department of Neurobiology, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520, USA; Department of Neurosurgery, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520, USA.
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Juchem C, Herman P, Sanganahalli BG, Brown PB, McIntyre S, Nixon TW, Green D, Hyder F, de Graaf RA. DYNAmic Multi-coIl TEchnique (DYNAMITE) shimming of the rat brain at 11.7 T. NMR Biomed 2014; 27:897-906. [PMID: 24839167 PMCID: PMC4120278 DOI: 10.1002/nbm.3133] [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] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2014] [Revised: 03/31/2014] [Accepted: 04/07/2014] [Indexed: 05/22/2023]
Abstract
The in vivo rat model is a workhorse in neuroscience research, preclinical studies and drug development. A repertoire of MR tools has been developed for its investigation; however, high levels of B0 magnetic field homogeneity are required for meaningful results. The homogenization of magnetic fields in the rat brain, i.e. shimming, is a difficult task because of a multitude of complex, susceptibility-induced field distortions. Conventional shimming with spherical harmonic (SH) functions is capable of compensating for shallow field distortions in limited areas, e.g. in the cortex, but performs poorly in difficult-to-shim subcortical structures or for the entire brain. Based on the recently introduced multi-coil approach for magnetic field modeling, the DYNAmic Multi-coIl TEchnique (DYNAMITE) is introduced for magnetic field shimming of the in vivo rat brain and its benefits for gradient-echo echo-planar imaging (EPI) are demonstrated. An integrated multi-coil/radiofrequency (MC/RF) system comprising 48 individual localized DC coils for B0 shimming and a surface transceive RF coil has been developed that allows MR investigations of the anesthetized rat brain in vivo. DYNAMITE shimming with this MC/RF set-up is shown to reduce the B0 standard deviation to a third of that achieved with current shim technology employing static first- through third-order SH shapes. The EPI signal over the rat brain increased by 31%, and a 24% gain in usable EPI voxels could be realized. DYNAMITE shimming is expected to critically benefit a wide range of preclinical and neuroscientific MR research. Improved magnetic field homogeneity, together with the achievable large brain coverage of this method, will be crucial when signal pathways, cortical circuitry or the brain's default network are studied. Together with the efficiency gains of MC-based shimming compared with SH approaches demonstrated recently, DYNAMITE shimming has the potential to replace conventional SH shim systems in small-bore animal scanners.
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Affiliation(s)
- Christoph Juchem
- Yale University School of Medicine, Department of Diagnostic Radiology, MR Research Center (MRRC), 300 Cedar Street, New Haven, CT 06520, USA
| | - Peter Herman
- Yale University School of Medicine, Department of Diagnostic Radiology, MR Research Center (MRRC), 300 Cedar Street, New Haven, CT 06520, USA
| | - Basavaraju G. Sanganahalli
- Yale University School of Medicine, Department of Diagnostic Radiology, MR Research Center (MRRC), 300 Cedar Street, New Haven, CT 06520, USA
| | - Peter B. Brown
- Yale University School of Medicine, Department of Diagnostic Radiology, MR Research Center (MRRC), 300 Cedar Street, New Haven, CT 06520, USA
| | - Scott McIntyre
- Yale University School of Medicine, Department of Diagnostic Radiology, MR Research Center (MRRC), 300 Cedar Street, New Haven, CT 06520, USA
| | - Terence W. Nixon
- Yale University School of Medicine, Department of Diagnostic Radiology, MR Research Center (MRRC), 300 Cedar Street, New Haven, CT 06520, USA
| | - Dan Green
- Agilent Technologies, Research Products Division, Yarnton, OX5 1QU, United Kingdom
| | - Fahmeed Hyder
- Yale University School of Medicine, Department of Diagnostic Radiology, MR Research Center (MRRC), 300 Cedar Street, New Haven, CT 06520, USA
| | - Robin A. de Graaf
- Yale University School of Medicine, Department of Diagnostic Radiology, MR Research Center (MRRC), 300 Cedar Street, New Haven, CT 06520, USA
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Mishra AM, Bai X, Sanganahalli BG, Waxman SG, Shatillo O, Grohn O, Hyder F, Pitkänen A, Blumenfeld H. Decreased resting functional connectivity after traumatic brain injury in the rat. PLoS One 2014; 9:e95280. [PMID: 24748279 PMCID: PMC3991600 DOI: 10.1371/journal.pone.0095280] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [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: 10/09/2013] [Accepted: 03/25/2014] [Indexed: 01/19/2023] Open
Abstract
Traumatic brain injury (TBI) contributes to about 10% of acquired epilepsy. Even though the mechanisms of post-traumatic epileptogenesis are poorly known, a disruption of neuronal networks predisposing to altered neuronal synchrony remains a viable candidate mechanism. We tested a hypothesis that resting state BOLD-fMRI functional connectivity can reveal network abnormalities in brain regions that are connected to the lesioned cortex, and that these changes associate with functional impairment, particularly epileptogenesis. TBI was induced using lateral fluid-percussion injury in seven adult male Sprague-Dawley rats followed by functional imaging at 9.4T 4 months later. As controls we used six sham-operated animals that underwent all surgical operations but were not injured. Electroencephalogram (EEG)-functional magnetic resonance imaging (fMRI) was performed to measure resting functional connectivity. A week after functional imaging, rats were implanted with bipolar skull electrodes. After recovery, rats underwent pentyleneterazol (PTZ) seizure-susceptibility test under EEG. For image analysis, four pairs of regions of interests were analyzed in each hemisphere: ipsilateral and contralateral frontal and parietal cortex, hippocampus, and thalamus. High-pass and low-pass filters were applied to functional imaging data. Group statistics comparing injured and sham-operated rats and correlations over time between each region were calculated. In the end, rats were perfused for histology. None of the rats had epileptiform discharges during functional imaging. PTZ-test, however revealed increased seizure susceptibility in injured rats as compared to controls. Group statistics revealed decreased connectivity between the ipsilateral and contralateral parietal cortex and between the parietal cortex and hippocampus on the side of injury as compared to sham-operated animals. Injured animals also had abnormal negative connectivity between the ipsilateral and contralateral parietal cortex and other regions. Our data provide the first evidence on abnormal functional connectivity after experimental TBI assessed with resting state BOLD-fMRI.
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Affiliation(s)
- Asht Mangal Mishra
- Department of Neurobiology, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Core Center for Quantitative Neuroscience with Magnetic Resonance, Yale University, New Haven, Connecticut, United States of America
| | - Xiaoxiao Bai
- Department of Neurobiology, Yale University School of Medicine, New Haven, Connecticut, United States of America
| | - Basavaraju G. Sanganahalli
- Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Core Center for Quantitative Neuroscience with Magnetic Resonance, Yale University, New Haven, Connecticut, United States of America
| | - Stephen G. Waxman
- Department of Neurobiology, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Center for Neuroscience and Regeneration Research, West Haven, Connecticut, United States of America
| | - Olena Shatillo
- Department of Neurobiology, A. I. Virtanen Institute of Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Olli Grohn
- Biomedical NMR research group, Biomedical Imaging Unit, University of Eastern Finland, Kuopio, Finland
| | - Fahmeed Hyder
- Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Department of Biomedical Engineering, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Core Center for Quantitative Neuroscience with Magnetic Resonance, Yale University, New Haven, Connecticut, United States of America
| | - Asla Pitkänen
- Department of Neurobiology, A. I. Virtanen Institute of Molecular Sciences, University of Eastern Finland, Kuopio, Finland
- Department of Neurology, Kuopio University Hospital, Kuopio, Finland
| | - Hal Blumenfeld
- Department of Neurobiology, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Department of Neurosurgery, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Core Center for Quantitative Neuroscience with Magnetic Resonance, Yale University, New Haven, Connecticut, United States of America
- * E-mail:
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Coman D, Sanganahalli BG, Cheng D, McCarthy T, Rothman DL, Hyder F. Mapping phosphorylation rate of fluoro-deoxy-glucose in rat brain by (19)F chemical shift imaging. Magn Reson Imaging 2013; 32:305-13. [PMID: 24581725 DOI: 10.1016/j.mri.2013.10.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [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: 01/10/2013] [Revised: 07/05/2013] [Accepted: 10/03/2013] [Indexed: 10/25/2022]
Abstract
(19)F magnetic resonance spectroscopy (MRS) studies of 2-fluoro-2-deoxy-d-glucose (FDG) and 2-fluoro-2-deoxy-d-glucose-6-phosphate (FDG-6P) can be used for directly assessing total glucose metabolism in vivo. To date, (19)F MRS measurements of FDG phosphorylation in the brain have either been achieved ex vivo from extracted tissue or in vivo by unusually long acquisition times. Electrophysiological and functional magnetic resonance imaging (fMRI) measurements indicate that FDG doses up to 500 mg/kg can be tolerated with minimal side effects on cerebral physiology and evoked fMRI-BOLD responses to forepaw stimulation. In halothane-anesthetized rats, we report localized in vivo detection and separation of FDG and FDG-6P MRS signals with (19)F 2D chemical shift imaging (CSI) at 11.7 T. A metabolic model based on reversible transport between plasma and brain tissue, which included a non-saturable plasma to tissue component, was used to calculate spatial distribution of FDG and FDG-6P concentrations in rat brain. In addition, spatial distribution of rate constants and metabolic fluxes of FDG to FDG-6P conversion were estimated. Mapping the rate of FDG to FDG-6P conversion by (19)F CSI provides an MR methodology that could impact other in vivo applications such as characterization of tumor pathophysiology.
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Affiliation(s)
- Daniel Coman
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT 06520, USA; Core Center for Quantitative Neuroscience with Magnetic Resonance (QNMR), Yale University, New Haven, CT 06520, USA; Department of Diagnostic Radiology, Yale University, New Haven, CT 06520, USA.
| | - Basavaraju G Sanganahalli
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT 06520, USA; Core Center for Quantitative Neuroscience with Magnetic Resonance (QNMR), Yale University, New Haven, CT 06520, USA; Department of Diagnostic Radiology, Yale University, New Haven, CT 06520, USA
| | - David Cheng
- Department of Diagnostic Radiology, Yale University, New Haven, CT 06520, USA
| | | | - Douglas L Rothman
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT 06520, USA; Core Center for Quantitative Neuroscience with Magnetic Resonance (QNMR), Yale University, New Haven, CT 06520, USA; Department of Diagnostic Radiology, Yale University, New Haven, CT 06520, USA; Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Fahmeed Hyder
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT 06520, USA; Core Center for Quantitative Neuroscience with Magnetic Resonance (QNMR), Yale University, New Haven, CT 06520, USA; Department of Diagnostic Radiology, Yale University, New Haven, CT 06520, USA; Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA.
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Mishra AM, Bai X, Motelow JE, DeSalvo MN, Danielson N, Sanganahalli BG, Hyder F, Blumenfeld H. Increased resting functional connectivity in spike-wave epilepsy in WAG/Rij rats. Epilepsia 2013; 54:1214-22. [PMID: 23815571 PMCID: PMC3703864 DOI: 10.1111/epi.12227] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [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] [Accepted: 04/23/2013] [Indexed: 12/17/2022]
Abstract
PURPOSE Functional magnetic resonance imaging (fMRI)-based resting functional connectivity is well suited for measuring slow correlated activity throughout brain networks. Epilepsy involves chronic changes in normal brain networks, and recent work demonstrated enhanced resting fMRI connectivity between the hemispheres in childhood absence epilepsy. An animal model of this phenomenon would be valuable for investigating fundamental mechanisms and testing therapeutic interventions. METHODS We used fMRI-based resting functional connectivity for studying brain networks involved in absence epilepsy. Wistar Albino Glaxo rats from Rijswijk (WAG/Rij) exhibit spontaneous episodes of staring and unresponsiveness accompanied by spike-wave discharges (SWDs) resembling human absence seizures in behavior and electroencephalography (EEG). Simultaneous EEG-fMRI data in epileptic WAG/Rij rats in comparison to nonepileptic Wistar controls were acquired at 9.4 T. Regions showing cortical fMRI increases during SWDs were used to define reference regions for connectivity analysis to investigate whether chronic seizure activity is associated with changes in network resting functional connectivity. KEY FINDINGS We observed high degrees of cortical-cortical correlations in all WAG/Rij rats at rest (when no SWDs were present), but not in nonepileptic controls. Strongest connectivity was seen between regions most intensely involved in seizures, mainly in the bilateral somatosensory and adjacent cortices. Group statistics revealed that resting interhemispheric cortical-cortical correlations were significantly higher in WAG/Rij rats compared to nonepileptic controls. SIGNIFICANCE These findings suggest that activity-dependent plasticity may lead to long-term changes in epileptic networks even at rest. The results show a marked difference between the epileptic and nonepileptic animals in cortical-cortical connectivity, indicating that this may be a useful interictal biomarker associated with the epileptic state.
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Affiliation(s)
- Asht M. Mishra
- Department of Neurology, Yale University School of Medicine, 333 Cedar Street, New Haven, Connecticut 06520, USA
- Department of Core Center for Quantitative Neuroscience with Magnetic Resonance (QNMR), Yale University School of Medicine, 333 Cedar Street, New Haven, Connecticut 06520, USA
| | - Xiaoxiao Bai
- Department of Neurology, Yale University School of Medicine, 333 Cedar Street, New Haven, Connecticut 06520, USA
| | - Joshua E. Motelow
- Department of Neurology, Yale University School of Medicine, 333 Cedar Street, New Haven, Connecticut 06520, USA
| | - Matthew N. DeSalvo
- Department of Neurology, Yale University School of Medicine, 333 Cedar Street, New Haven, Connecticut 06520, USA
| | - Nathan Danielson
- Department of Neurology, Yale University School of Medicine, 333 Cedar Street, New Haven, Connecticut 06520, USA
| | - Basavaraju G. Sanganahalli
- Department of Diagnostic Radiology, Yale University School of Medicine, 333 Cedar Street, New Haven, Connecticut 06520, USA
- Department of Core Center for Quantitative Neuroscience with Magnetic Resonance (QNMR), Yale University School of Medicine, 333 Cedar Street, New Haven, Connecticut 06520, USA
| | - Fahmeed Hyder
- Department of Diagnostic Radiology, Yale University School of Medicine, 333 Cedar Street, New Haven, Connecticut 06520, USA
- Department of Core Center for Quantitative Neuroscience with Magnetic Resonance (QNMR), Yale University School of Medicine, 333 Cedar Street, New Haven, Connecticut 06520, USA
| | - Hal Blumenfeld
- Department of Neurology, Yale University School of Medicine, 333 Cedar Street, New Haven, Connecticut 06520, USA
- Department of Core Center for Quantitative Neuroscience with Magnetic Resonance (QNMR), Yale University School of Medicine, 333 Cedar Street, New Haven, Connecticut 06520, USA
- Department of Neurobiology, Yale University School of Medicine, 333 Cedar Street, New Haven, Connecticut 06520, USA
- Department of Neurosurgery, Yale University School of Medicine, 333 Cedar Street, New Haven, Connecticut 06520, USA
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Sanganahalli BG, Herman P, Behar KL, Blumenfeld H, Rothman DL, Hyder F. Functional MRI and neural responses in a rat model of Alzheimer's disease. Neuroimage 2013; 79:404-11. [PMID: 23648961 DOI: 10.1016/j.neuroimage.2013.04.099] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2013] [Revised: 04/17/2013] [Accepted: 04/20/2013] [Indexed: 12/28/2022] Open
Abstract
Based on the hypothesis that brain plaques and tangles can affect cortical function in Alzheimer's disease (AD), we investigated functional responses in an AD rat model (called the Samaritan Alzheimer's rat achieved by ventricular infusion of amyloid peptide) and age-matched healthy control. High-field functional magnetic resonance imaging (fMRI) and extracellular neural activity measurements were applied to characterize sensory-evoked responses. Electrical stimulation of the forepaw led to BOLD and neural responses in the contralateral somatosensory cortex and thalamus. In AD brain we noted much smaller BOLD activation patterns in the somatosensory cortex (i.e., about 50% less activated voxels compared to normal brain). While magnitudes of BOLD and neural responses in the cerebral cortex were markedly attenuated in AD rats compared to normal rats (by about 50%), the dynamic coupling between the BOLD and neural responses in the cerebral cortex, as assessed by transfer function analysis, remained unaltered between the groups. However thalamic BOLD and neural responses were unaltered in AD brain compared to controls. Thus cortical responses in the AD model were indeed diminished compared to controls, but the thalamic responses in the AD and control rats were quite similar. Therefore these results suggest that Alzheimer's disease may affect cortical function more than subcortical function, which may have implications for interpreting altered human brain functional responses in fMRI studies of Alzheimer's disease.
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Sanganahalli BG, Herman P, Hyder F, Kannurpatti SS. Mitochondrial functional state impacts spontaneous neocortical activity and resting state FMRI. PLoS One 2013; 8:e63317. [PMID: 23650561 PMCID: PMC3641133 DOI: 10.1371/journal.pone.0063317] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [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: 01/07/2013] [Accepted: 04/01/2013] [Indexed: 11/19/2022] Open
Abstract
Mitochondrial Ca2+ uptake, central to neural metabolism and function, is diminished in aging whereas enhanced after acute/sub-acute traumatic brain injury. To develop relevant translational models for these neuropathologies, we determined the impact of perturbed mitochondrial Ca2+ uptake capacities on intrinsic brain activity using clinically relevant markers. From a multi-compartment estimate of probable baseline Ca2+ ranges in the brain, we hypothesized that reduced or enhanced mitochondrial Ca2+ uptake capacity would decrease or increase spontaneous neuronal activity respectively. As resting state fMRI-BOLD fluctuations and stimulus-evoked BOLD responses have similar physiological origins [1] and stimulus-evoked neuronal and hemodynamic responses are modulated by mitochondrial Ca2+ uptake capacity [2], [3] respectively, we tested our hypothesis by measuring hemodynamic fluctuations and spontaneous neuronal activities during normal and altered mitochondrial functional states. Mitochondrial Ca2+ uptake capacity was perturbed by pharmacologically inhibiting or enhancing the mitochondrial Ca2+ uniporter (mCU) activity. Neuronal electrical activity and cerebral blood flow (CBF) fluctuations were measured simultaneously and integrated with fMRI-BOLD fluctuations at 11.7T. mCU inhibition reduced spontaneous neuronal activity and the resting state functional connectivity (RSFC), whereas mCU enhancement increased spontaneous neuronal activity but reduced RSFC. We conclude that increased or decreased mitochondrial Ca2+ uptake capacities lead to diminished resting state modes of brain functional connectivity.
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Affiliation(s)
- Basavaraju G. Sanganahalli
- Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Department of Magnetic Resonance Research Center, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Core Center for Quantitative Neuroscience with Magnetic Resonance, Yale University School of Medicine, New Haven, Connecticut, United States of America
| | - Peter Herman
- Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Department of Magnetic Resonance Research Center, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Core Center for Quantitative Neuroscience with Magnetic Resonance, Yale University School of Medicine, New Haven, Connecticut, United States of America
| | - Fahmeed Hyder
- Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Department of Biomedical Engineering, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Department of Magnetic Resonance Research Center, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Core Center for Quantitative Neuroscience with Magnetic Resonance, Yale University School of Medicine, New Haven, Connecticut, United States of America
| | - Sridhar S. Kannurpatti
- Department of Radiology, UMDNJ-New Jersey Medical School, Newark, New Jersey, United States of America
- * E-mail:
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Herzog RI, Jiang L, Herman P, Zhao C, Sanganahalli BG, Mason GF, Hyder F, Rothman DL, Sherwin RS, Behar KL. Lactate preserves neuronal metabolism and function following antecedent recurrent hypoglycemia. J Clin Invest 2013; 123:1988-98. [PMID: 23543056 DOI: 10.1172/jci65105] [Citation(s) in RCA: 75] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2012] [Accepted: 01/31/2013] [Indexed: 12/30/2022] Open
Abstract
Hypoglycemia occurs frequently during intensive insulin therapy in patients with both type 1 and type 2 diabetes and remains the single most important obstacle in achieving tight glycemic control. Using a rodent model of hypoglycemia, we demonstrated that exposure to antecedent recurrent hypoglycemia leads to adaptations of brain metabolism so that modest increments in circulating lactate allow the brain to function normally under acute hypoglycemic conditions. We characterized 3 major factors underlying this effect. First, we measured enhanced transport of lactate both into as well as out of the brain that resulted in only a small increase of its contribution to total brain oxidative capacity, suggesting that it was not the major fuel. Second, we observed a doubling of the glucose contribution to brain metabolism under hypoglycemic conditions that restored metabolic activity to levels otherwise only observed at euglycemia. Third, we determined that elevated lactate is critical for maintaining glucose metabolism under hypoglycemia, which preserves neuronal function. These unexpected findings suggest that while lactate uptake was enhanced, it is insufficient to support metabolism as an alternate substrate to replace glucose. Lactate is, however, able to modulate metabolic and neuronal activity, serving as a "metabolic regulator" instead.
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Affiliation(s)
- Raimund I Herzog
- Department of Internal Medicine, Section of Endocrinology, Yale School of Medicine, New Haven, Connecticut 06520-8040, USA.
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Hyder F, Herman P, Sanganahalli BG, Coman D, Blumenfeld H, Rothman DL. Role of ongoing, intrinsic activity of neuronal populations for quantitative neuroimaging of functional magnetic resonance imaging-based networks. Brain Connect 2013; 1:185-93. [PMID: 22433047 DOI: 10.1089/brain.2011.0032] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
A primary objective in neuroscience is to determine how neuronal populations process information within networks. In humans and animal models, functional magnetic resonance imaging (fMRI) is gaining increasing popularity for network mapping. Although neuroimaging with fMRI-conducted with or without tasks-is actively discovering new brain networks, current fMRI data analysis schemes disregard the importance of the total neuronal activity in a region. In task fMRI experiments, the baseline is differenced away to disclose areas of small evoked changes in the blood oxygenation level-dependent (BOLD) signal. In resting-state fMRI experiments, the spotlight is on regions revealed by correlations of tiny fluctuations in the baseline (or spontaneous) BOLD signal. Interpretation of fMRI-based networks is obscured further, because the BOLD signal indirectly reflects neuronal activity, and difference/correlation maps are thresholded. Since the small changes of BOLD signal typically observed in cognitive fMRI experiments represent a minimal fraction of the total energy/activity in a given area, the relevance of fMRI-based networks is uncertain, because the majority of neuronal energy/activity is ignored. Thus, another alternative for quantitative neuroimaging of fMRI-based networks is a perspective in which the activity of a neuronal population is accounted for by the demanded oxidative energy (CMR(O2)). In this article, we argue that network mapping can be improved by including neuronal energy/activity of both the information about baseline and small differences/fluctuations of BOLD signal. Thus, total energy/activity information can be obtained through use of calibrated fMRI to quantify differences of ΔCMR(O2) and through resting-state positron emission tomography/magnetic resonance spectroscopy measurements for average CMR(O2).
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Affiliation(s)
- Fahmeed Hyder
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, Connecticut, USA.
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Eke A, Herman P, Sanganahalli BG, Hyder F, Mukli P, Nagy Z. Pitfalls in Fractal Time Series Analysis: fMRI BOLD as an Exemplary Case. Front Physiol 2012; 3:417. [PMID: 23227008 PMCID: PMC3513686 DOI: 10.3389/fphys.2012.00417] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [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: 07/15/2012] [Accepted: 10/12/2012] [Indexed: 12/12/2022] Open
Abstract
This article will be positioned on our previous work demonstrating the importance of adhering to a carefully selected set of criteria when choosing the suitable method from those available ensuring its adequate performance when applied to real temporal signals, such as fMRI BOLD, to evaluate one important facet of their behavior, fractality. Earlier, we have reviewed on a range of monofractal tools and evaluated their performance. Given the advance in the fractal field, in this article we will discuss the most widely used implementations of multifractal analyses, too. Our recommended flowchart for the fractal characterization of spontaneous, low frequency fluctuations in fMRI BOLD will be used as the framework for this article to make certain that it will provide a hands-on experience for the reader in handling the perplexed issues of fractal analysis. The reason why this particular signal modality and its fractal analysis has been chosen was due to its high impact on today’s neuroscience given it had powerfully emerged as a new way of interpreting the complex functioning of the brain (see “intrinsic activity”). The reader will first be presented with the basic concepts of mono and multifractal time series analyses, followed by some of the most relevant implementations, characterization by numerical approaches. The notion of the dichotomy of fractional Gaussian noise and fractional Brownian motion signal classes and their impact on fractal time series analyses will be thoroughly discussed as the central theme of our application strategy. Sources of pitfalls and way how to avoid them will be identified followed by a demonstration on fractal studies of fMRI BOLD taken from the literature and that of our own in an attempt to consolidate the best practice in fractal analysis of empirical fMRI BOLD signals mapped throughout the brain as an exemplary case of potentially wide interest.
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Affiliation(s)
- Andras Eke
- Institute of Human Physiology and Clinical Experimental Research, Semmelweis University Budapest, Hungary ; Diagnostic Radiology, Yale University New Haven, CT, USA
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Bailey CJ, Sanganahalli BG, Herman P, Blumenfeld H, Gjedde A, Hyder F. Analysis of time and space invariance of BOLD responses in the rat visual system. ACTA ACUST UNITED AC 2012; 23:210-22. [PMID: 22298731 DOI: 10.1093/cercor/bhs008] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Neuroimaging studies of functional magnetic resonance imaging (fMRI) and electrophysiology provide the linkage between neural activity and the blood oxygenation level-dependent (BOLD) response. Here, BOLD responses to light flashes were imaged at 11.7T and compared with neural recordings from superior colliculus (SC) and primary visual cortex (V1) in rat brain--regions with different basal blood flow and energy demand. Our goal was to assess neurovascular coupling in V1 and SC as reflected by temporal/spatial variances of impulse response functions (IRFs) and assess, if any, implications for general linear modeling (GLM) of BOLD responses. Light flashes induced high magnitude neural/BOLD responses reproducibly from both regions. However, neural/BOLD responses from SC and V1 were markedly different. SC signals followed the boxcar shape of the stimulation paradigm at all flash rates, whereas V1 signals were characterized by onset/offset transients that exhibited different flash rate dependencies. We find that IRF(SC) is generally time-invariant across wider flash rate range compared with IRF(V1), whereas IRF(SC) and IRF(V1) are both space invariant. These results illustrate the importance of measured neural signals for interpretation of fMRI by showing that GLM of BOLD responses may lead to misinterpretation of neural activity in some cases.
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Herman P, Sanganahalli BG, Hyder F, Eke A. Fractal analysis of spontaneous fluctuations of the BOLD signal in rat brain. Neuroimage 2011; 58:1060-9. [PMID: 21777682 DOI: 10.1016/j.neuroimage.2011.06.082] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2011] [Revised: 06/14/2011] [Accepted: 06/26/2011] [Indexed: 12/01/2022] Open
Abstract
Analysis of task-evoked fMRI data ignores low frequency fluctuations (LFF) of the resting-state the BOLD signal, yet LFF of the spontaneous BOLD signal is crucial for analysis of resting-state connectivity maps. We characterized the LFF of resting-state BOLD signal at 11.7T in α-chloralose and domitor anesthetized rat brain and modeled the spontaneous signal as a scale-free (i.e., fractal) distribution of amplitude power (|A|²) across a frequency range (f) compatible with an |A(f)|² ∝ 1/f(β) model where β is the scaling exponent (or spectral index). We compared β values from somatosensory forelimb area (S1FL), cingulate cortex (CG), and caudate putamen (CPu). With α-chloralose, S1FL and CG β values dropped from ~0.7 at in vivo to ~0.1 at post mortem (p<0.0002), whereas CPu β values dropped from ~0.3 at in vivo to ~0.1 at post mortem (p<0.002). With domitor, cortical (S1FL, CG) β values were slightly higher than with α-chloralose, while subcortical (CPu) β values were similar with α-chloralose. Although cortical and subcortical β values with both anesthetics were significantly different in vivo (p<0.002), at post mortem β values in these regions were not significantly different and approached zero (i.e., range of -0.1 to 0.2). Since a water phantom devoid of susceptibility gradients had a β value of zero (i.e., random), we conclude that deoxyhemoglobin present in voxels post-sacrifice still impacts tissue water diffusion. These results suggest that in the anesthetized rat brain the LFF of BOLD signal at 11.7T follow a general 1/f(β) model of fractality where β is a variable responding to physiology. We describe typical experimental pitfalls which may elude detection of fractality in the resting-state BOLD signal.
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Affiliation(s)
- Peter Herman
- Magnetic Resonance Research Center, Yale University, New Haven, Connecticut, USA
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Hyder F, Sanganahalli BG, Herman P, Coman D, Maandag NJG, Behar KL, Blumenfeld H, Rothman DL. Neurovascular and Neurometabolic Couplings in Dynamic Calibrated fMRI: Transient Oxidative Neuroenergetics for Block-Design and Event-Related Paradigms. Front Neuroenergetics 2010; 2. [PMID: 20838476 PMCID: PMC2936934 DOI: 10.3389/fnene.2010.00018] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2010] [Accepted: 07/02/2010] [Indexed: 11/13/2022]
Abstract
Functional magnetic resonance imaging (fMRI) with blood-oxygenation level dependent (BOLD) contrast is an important tool for mapping brain activity. Interest in quantitative fMRI has renewed awareness in importance of oxidative neuroenergetics, as reflected by cerebral metabolic rate of oxygen consumption(CMRO2), for supporting brain function. Relationships between BOLD signal and the underlying neurophysiological parameters have been elucidated to allow determination of dynamic changes inCMRO2 by "calibrated fMRI," which require multi-modal measurements of BOLD signal along with cerebral blood flow (CBF) and volume (CBV). But how doCMRO2 changes, steady-state or transient, derived from calibrated fMRI compare with neural activity recordings of local field potential (LFP) and/or multi-unit activity (MUA)? Here we discuss recent findings primarily from animal studies which allow high magnetic fields studies for superior BOLD sensitivity as well as multi-modal CBV and CBF measurements in conjunction with LFP and MUA recordings from activated sites. A key observation is that while relationships between neural activity and sensory stimulus features range from linear to non-linear, associations between hyperemic components (BOLD, CBF, CBV) and neural activity (LFP, MUA) are almost always linear. More importantly, the results demonstrate good agreement between the changes inCMRO2 and independent measures of LFP or MUA. The tight neurovascular and neurometabolic couplings, observed from steady-state conditions to events separated by <200 ms, suggest rapid oxygen equilibration between blood and tissue pools and thus calibrated fMRI at high magnetic fields can provide high spatiotemporal mapping ofCMRO2 changes.
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Affiliation(s)
- Fahmeed Hyder
- Magnetic Resonance Research Center, School of Medicine, Yale University New Haven, CT, USA
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Abstract
Because of the importance of oxidative energetics for cerebral function, extraction of oxygen consumption (CMR(O2)) from blood oxygenation level-dependent (BOLD) signal using multi-modal measurements of blood flow (CBF) and volume (CBV) has become an accepted functional magnetic resonance imaging (fMRI) technique. This approach, termed calibrated fMRI, is based on a biophysical model which describes tissue oxygen extraction at steady-state. A problem encountered for calculating dynamic CMR(O2) relates to concerns whether the conventional BOLD model can be applied transiently. In particular, it is unclear whether calculation of CMR(O2) differs between short and long stimuli. Linearity was experimentally demonstrated between BOLD-related components and neural activity, thereby making it possible to use calibrated fMRI in a dynamic manner. We used multi-modal fMRI and electrophysiology, in alpha-chloralose anesthetized rats during forepaw stimulation to show that respective transfer functions (of BOLD, CBV, CBF) generated by deconvolution with neural activity are time invariant, for events in the millisecond to minute range. These results allowed extraction of a significant component of the BOLD signal that can be ascribed to CMR(O2) transients. We discuss the importance of minimizing residual signal, represented by the difference between modeled and raw signals, in convolution analysis of multi-modal signals.
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Affiliation(s)
- Peter Herman
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, Connecticut 06520, USA.
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Abstract
Functional magnetic resonance imaging (fMRI) has become a popular functional imaging tool for human studies. Future diagnostic use of fMRI depends, however, on a suitable neurophysiologic interpretation of the blood oxygenation level dependent (BOLD) signal change. This particular goal is best achieved in animal models primarily due to the invasive nature of other methods used and/or pharmacological agents applied to probe different nuances of neuronal (and glial) activity coupled to the BOLD signal change. In the last decade, we have directed our efforts towards the development of stimulation protocols for a variety of modalities in rodents with fMRI. Cortical perception of the natural world relies on the formation of multi-dimensional representation of stimuli impinging on the different sensory systems, leading to the hypothesis that a sensory stimulus may have very different neurophysiologic outcome(s) when paired with a near simultaneous event in another modality. Before approaching this level of complexity, reliable measures must be obtained of the relatively small changes in the BOLD signal and other neurophysiologic markers (electrical activity, blood flow) induced by different peripheral stimuli. Here we describe different tactile (i.e., forepaw, whisker) and non-tactile (i.e., olfactory, visual) sensory paradigms applied to the anesthetized rat. The main focus is on development and validation of methods for reproducible stimulation of each sensory modality applied independently or in conjunction with one another, both inside and outside the magnet. We discuss similarities and/or differences across the sensory systems as well as advantages they may have for studying essential neuroscientific questions. We envisage that the different sensory paradigms described here may be applied directly to studies of multi-sensory interactions in anesthetized rats, en route to a rudimentary understanding of the awake functioning brain where various sensory cues presumably interrelate.
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Affiliation(s)
- Basavaraju G. Sanganahalli
- Department of Diagnostic Radiology Yale University, New Haven, Connecticut, USA,Department of Quantitative Neuroscience with Magnetic Resonance (QNMR), Yale University, New Haven, Connecticut, USA,Department of Magnetic Resonance Research Center, Yale University, New Haven, Connecticut, USA
| | - Christopher J. Bailey
- Department of Diagnostic Radiology Yale University, New Haven, Connecticut, USA,Department of Magnetic Resonance Research Center, Yale University, New Haven, Connecticut, USA,Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark
| | - Peter Herman
- Department of Diagnostic Radiology Yale University, New Haven, Connecticut, USA,Department of Quantitative Neuroscience with Magnetic Resonance (QNMR), Yale University, New Haven, Connecticut, USA,Department of Magnetic Resonance Research Center, Yale University, New Haven, Connecticut, USA,Institute of Human Physiology and Clinical Experimental Research, Semmelweis University, Budapest, Hungary
| | - Fahmeed Hyder
- Department of Diagnostic Radiology Yale University, New Haven, Connecticut, USA,Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA,Department of Quantitative Neuroscience with Magnetic Resonance (QNMR), Yale University, New Haven, Connecticut, USA,Department of Magnetic Resonance Research Center, Yale University, New Haven, Connecticut, USA
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Abstract
We measured frequency-dependent functional MRI (fMRI) activations (at 11.7 T) in the somatosensory cortex with whisker and forepaw stimuli in the same alpha-chloralose anesthetized rats. Whisker and forepaw stimuli were attained by computer-controlled pulses of air puffs and electrical currents, respectively. Air puffs deflected (+/-2 mm) the chosen whisker(s) in the right snout in the rostral to caudal direction, and electrical currents (2 mA amplitude, 0.3 ms duration) stimulated the left forepaw with subcutaneous copper electrodes placed between the second and fourth digits. In the same subject, unimodal stimulation of whisker and forepaw gave rise to significant blood oxygen level-dependent (BOLD) signal increases in corresponding contralateral somatosensory areas of whisker barrel field (S1BF) and forelimb (S1FL), respectively, with no significant spatial overlap between these regions. The BOLD responses in S1(BF) and S1(FL) regions were found to be differentially variable with frequency of each stimulus type. In the S1BF, a linear increase in the BOLD response was observed with whisker stimulation frequency of up to approximately 12 Hz, beyond which the response seemed to saturate (and/or slightly attenuate) up to the maximum frequency studied (i.e. 30 Hz). In the S1FL, the magnitude of the BOLD response was largest at forepaw stimulation frequency between 1.5 and 3 Hz, beyond which the response diminished with little or no activity at frequencies higher than 20 Hz. The volume of tissue activated by each stimulus type followed a similar pattern to that of the stimulation frequency dependence. These results of bimodal whisker and forepaw stimuli in the same subject may provide a framework to study interactions of different tactile modules, with both fMRI and neurophysiology (i.e. inside and outside the magnet).
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Affiliation(s)
- Basavaraju G. Sanganahalli
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT 06520, USA
- Quantitative Neuroscience with Magnetic Resonance (QNMR), Yale University, New Haven, CT 06520, USA
- Department of Diagnostic Radiology, Yale University, New Haven, CT 06520, USA
| | - Peter Herman
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT 06520, USA
- Quantitative Neuroscience with Magnetic Resonance (QNMR), Yale University, New Haven, CT 06520, USA
- Department of Diagnostic Radiology, Yale University, New Haven, CT 06520, USA
- Institute of Human Physiology and Clinical Experimental Research, Semmelweis University, Budapest, Hungary
| | - Fahmeed Hyder
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT 06520, USA
- Quantitative Neuroscience with Magnetic Resonance (QNMR), Yale University, New Haven, CT 06520, USA
- Department of Diagnostic Radiology, Yale University, New Haven, CT 06520, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
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47
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Schridde U, Khubchandani M, Motelow JE, Sanganahalli BG, Hyder F, Blumenfeld H. Negative BOLD with large increases in neuronal activity. ACTA ACUST UNITED AC 2007; 18:1814-27. [PMID: 18063563 DOI: 10.1093/cercor/bhm208] [Citation(s) in RCA: 179] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) is widely used in neuroscience to study brain activity. However, BOLD fMRI does not measure neuronal activity directly but depends on cerebral blood flow (CBF), cerebral blood volume (CBV), and cerebral metabolic rate of oxygen (CMRO(2)) consumption. Using fMRI, CBV, CBF, neuronal recordings, and CMRO(2) modeling, we investigated how the signals are related during seizures in rats. We found that increases in hemodynamic, neuronal, and metabolic activity were associated with positive BOLD signals in the cortex, but with negative BOLD signals in hippocampus. Our data show that negative BOLD signals do not necessarily imply decreased neuronal activity or CBF, but can result from increased neuronal activity, depending on the interplay between hemodynamics and metabolism. Caution should be used in interpreting fMRI signals because the relationship between neuronal activity and BOLD signals may depend on brain region and state and can be different during normal and pathological conditions.
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Affiliation(s)
- Ulrich Schridde
- Department of Neurology, Yale University, New Haven, CT 06510, USA
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48
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Sanganahalli BG, Joshi PG, Joshi NB. NMDA and non-NMDA receptors stimulation causes differential oxidative stress in rat cortical slices. Neurochem Int 2006; 49:475-80. [PMID: 16860439 DOI: 10.1016/j.neuint.2006.03.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2006] [Accepted: 03/02/2006] [Indexed: 10/24/2022]
Abstract
Glutamate receptor activated neuronal cell death is attributed to a massive influx of Ca(2+) and subsequent formation of reactive oxygen species (ROS) but the relative contribution of NMDA and non-NMDA sub-types of glutamate receptors in excitotoxicity is not known. In the present study, we have examined the role of NMDA and non-NMDA receptors in glutamate-induced neuronal injury in cortical slices from young (20+/-2 day) and adult (80+/-5 day) rats. Treatment of slices with glutamate receptor agonists NMDA, AMPA and KA elicited the formation of reactive oxygen species (ROS) and neuronal cell death. In young slices, NMDA receptor stimulation caused a higher ROS formation and neurotoxicity, but KA was more effective in producing ROS and cell death in adult slices. AMPA exhibited an intermediate effect on ROS formation and toxicity in both the age groups. A significant protection in glutamate mediated ROS formation and neurotoxicity was observed in presence of NMDA or/and non-NMDA receptors antagonists APV and NBQX, respectively. This further confirms the involvement of both NMDA and non-NMDA receptors in glutamate mediated neurotoxicity. In adult slices, we did not find positive correlation between ligand induced neurotoxicity and mitochondrial depolarization. Though, NMDA and KA stimulation produced differential effect on ROS formation and neurotoxicity in young and adult slices, the mitochondrial depolarization was higher and comparable on NMDA stimulation in both the age groups as compared to KA, suggesting that the mitochondrial depolarization may not be a good indicator for neurotoxicity. Our results demonstrate that both NMDA and non-NMDA sub-types of glutamate receptors are involved in glutamate mediated neurotoxicity but their relative contribution is highly dependent on the age of the animal.
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Affiliation(s)
- Basavaraju G Sanganahalli
- Department of Biophysics, National Institute of Mental Health and Neuro Sciences, Bangalore 560029, India
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49
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Sanganahalli BG, Joshi PG, Joshi NB. Xanthine oxidase, nitric oxide synthase and phospholipase A2 produce reactive oxygen species via mitochondria. Brain Res 2005; 1037:200-3. [PMID: 15777770 DOI: 10.1016/j.brainres.2005.01.013] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2004] [Revised: 12/19/2004] [Accepted: 01/01/2005] [Indexed: 10/25/2022]
Abstract
The formation of reactive oxygen species (ROS) has been suggested to be associated with excitotoxicity but the involvement of cytoplasmic enzymes in ROS formation is not clearly known. In the present study, we examined the role of xanthine oxidase (XO), nitric oxide synthase (NOS) and phospholipase A(2) (PLA(2)) in glutamate-induced oxidative stress in rat cortical slices. Glutamate-induced ROS formation and mitochondrial depolarization were measured in rat cortical slices in presence of allopurinol, L-NAME and 4-bromophenacylbromide, the specific inhibitors of XO, NOS and PLA(2), respectively. Upon stimulation of slices with glutamate, a significant increase in ROS formation and mitochondrial depolarization was observed. However, pretreatment of slices with allopurinol, L-NAME and 4-bromophenacylbromide inhibited the glutamate-induced ROS formation and mitochondrial depolarization. The glutamate-induced ROS formation was dependent on the concentration of these inhibitors and also on the duration of the treatment. Allopurinol was found to be less effective as compared to L-NAME and 4-bromophenacylbromide. The combined treatment of slices with these enzyme inhibitors showed further inhibition in ROS formation and mitochondrial depolarization. The inhibition in ROS formation as well as mitochondrial depolarization by allopurinol, L-NAME and 4-bromophenacylbromide clearly suggests that the activation of XO, NOS and PLA(2) by calcium during glutamate receptor stimulation may release some chemicals which depolarize mitochondria resulting in ROS formation.
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Affiliation(s)
- Basavaraju G Sanganahalli
- Department of Biophysics, National Institute of Mental Health and Neuro Sciences, Bangalore-560029, India
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50
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Abstract
Excitatory amino acid glutamate is involved in neurotransmission in the nervous system but it becomes a potent neurotoxin under variety of conditions. However, the molecular mechanism of excitotoxicity is not known completely. We have studied the influence of glutamate on intracellular calcium and mitochondrial functions in cortical slices from young and adult rats. The slices from both the age groups exhibited comparable intracellular calcium changes upon glutamate stimulation. Glutamate treatment caused a decrease in adenosine 5'-diphosphate/adenosine 5'-triphosphate (ADP/ATP) and an increase in nicotinamide adenine dinucleotide/nicotinamide adenine dinucleotide reduced form (NAD/NADH) ratio in both the age groups but the magnitude and the nature of temporal change was different. Glutamate-induced decrease in ATP/ADP and increase in NAD/NADH ratio was significantly higher in slices from the adult as compared to the young rats. The slices from young rats elicited slightly higher mitochondrial depolarization than adult rats. However, the formation of reactive oxygen species (ROS) and lactate dehydrogenase (LDH) release were significantly higher in adult rats as compared to young rats. Glutamate-induced mitochondrial depolarization, ROS formation and LDH release were highly dependent on the presence of Ca(2+) in the extracellular medium. The treatment of slices with mitochondrial inhibitors rotenone and oligomycin inhibited ROS formation and LDH release substantially. Our results suggest that the glutamate-induced increase in intracellular calcium is not the only factor responsible for neuronal cell death but the mitochondrial functions could be crucial in excitotoxicity.
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Affiliation(s)
- S S Kannurpatti
- Department of Biophysics, National Institute of Mental Health and Neuro Sciences, Bangalore 560 029, India
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