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Mandino F, Vujic S, Grandjean J, Lake EMR. Where do we stand on fMRI in awake mice? Cereb Cortex 2024; 34:bhad478. [PMID: 38100331 PMCID: PMC10793583 DOI: 10.1093/cercor/bhad478] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 11/17/2023] [Accepted: 11/18/2023] [Indexed: 12/17/2023] Open
Abstract
Imaging awake animals is quickly gaining traction in neuroscience as it offers a means to eliminate the confounding effects of anesthesia, difficulties of inter-species translation (when humans are typically imaged while awake), and the inability to investigate the full range of brain and behavioral states in unconscious animals. In this systematic review, we focus on the development of awake mouse blood oxygen level dependent functional magnetic resonance imaging (fMRI). Mice are widely used in research due to their fast-breeding cycle, genetic malleability, and low cost. Functional MRI yields whole-brain coverage and can be performed on both humans and animal models making it an ideal modality for comparing study findings across species. We provide an analysis of 30 articles (years 2011-2022) identified through a systematic literature search. Our conclusions include that head-posts are favorable, acclimation training for 10-14 d is likely ample under certain conditions, stress has been poorly characterized, and more standardization is needed to accelerate progress. For context, an overview of awake rat fMRI studies is also included. We make recommendations that will benefit a wide range of neuroscience applications.
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Affiliation(s)
- Francesca Mandino
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, United States
| | - Stella Vujic
- Department of Computer Science, Yale University, New Haven, CT 06520, United States
| | - 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
| | - Evelyn M R Lake
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, United States
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, United States
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2
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Pagani M, Gutierrez-Barragan D, de Guzman AE, Xu T, Gozzi A. Mapping and comparing fMRI connectivity networks across species. Commun Biol 2023; 6:1238. [PMID: 38062107 PMCID: PMC10703935 DOI: 10.1038/s42003-023-05629-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 11/22/2023] [Indexed: 12/18/2023] Open
Abstract
Technical advances in neuroimaging, notably in fMRI, have allowed distributed patterns of functional connectivity to be mapped in the human brain with increasing spatiotemporal resolution. Recent years have seen a growing interest in extending this approach to rodents and non-human primates to understand the mechanism of fMRI connectivity and complement human investigations of the functional connectome. Here, we discuss current challenges and opportunities of fMRI connectivity mapping across species. We underscore the critical importance of physiologically decoding neuroimaging measures of brain (dys)connectivity via multiscale mechanistic investigations in animals. We next highlight a set of general principles governing the organization of mammalian connectivity networks across species. These include the presence of evolutionarily conserved network systems, a dominant cortical axis of functional connectivity, and a common repertoire of topographically conserved fMRI spatiotemporal modes. We finally describe emerging approaches allowing comparisons and extrapolations of fMRI connectivity findings across species. As neuroscientists gain access to increasingly sophisticated perturbational, computational and recording tools, cross-species fMRI offers novel opportunities to investigate the large-scale organization of the mammalian brain in health and disease.
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Affiliation(s)
- Marco Pagani
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
- Autism Center, Child Mind Institute, New York, NY, USA
- IMT School for Advanced Studies, Lucca, Italy
| | - Daniel Gutierrez-Barragan
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - A Elizabeth de Guzman
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Ting Xu
- Center for the Integrative Developmental Neuroscience, Child Mind Institute, New York, NY, USA
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy.
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3
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Li Q, Zhang N. Sex differences in resting-state functional networks in awake rats. Brain Struct Funct 2023; 228:1411-1423. [PMID: 37261489 DOI: 10.1007/s00429-023-02657-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 05/18/2023] [Indexed: 06/02/2023]
Abstract
Sex-related differences can be found in many brain disorders and psychophysiological traits, highlighting the importance to systematically understand the sex differences in brain function in humans and animal models. Despite emerging effort to address sex differences in behaviors and disease models in rodents, how brain-wide functional connectivity (FC) patterns differ between male and female rats remains largely unknown. Here, we used resting-state functional magnetic resonance imaging (rsfMRI) to investigate regional and systems-level differences between female and male rats. Our data show that female rats display stronger hypothalamus connectivity, whereas male rats exhibit more prominent striatum-related connectivity. At the global scale, female rats demonstrate stronger segregation within the cortical and subcortical systems, while male rats display more prominent cortico-subcortical interactions, particularly between the cortex and striatum. Taken together, these data provide a comprehensive framework of sex differences in resting-state connectivity patterns in the awake rat brain, and offer a reference for studies aiming to reveal sex-related FC differences in different animal models of brain disorders.
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Affiliation(s)
- Qiong Li
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, State College, USA
| | - Nanyin Zhang
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, State College, USA.
- Center for Neurotechnology in Mental Health Research, The Pennsylvania State University, University Park, State College, 16802, USA.
- Center for Neural Engineering, The Pennsylvania State University, University Park, State College, 16802, USA.
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4
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Li Q, Zhang N. Sex differences in resting-state functional networks in awake rats. RESEARCH SQUARE 2023:rs.3.rs-2684325. [PMID: 36993730 PMCID: PMC10055639 DOI: 10.21203/rs.3.rs-2684325/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/13/2023]
Abstract
Sex-related differences can be found in many brain disorders and psychophysiological traits, highlighting the importance to systematically understand the sex differences in brain function in humans and animal models. Despite emerging effort to address sex differences in behaviors and disease models in rodents, how brain-wide functional connectivity (FC) patterns differ between male and female rats remains largely unknown. Here we used resting-state functional magnetic resonance imaging (rsfMRI) to investigate regional and systems-level differences between female and male rats. Our data show that female rats display stronger hypothalamus connectivity, whereas male rats exhibit more prominent striatum-related connectivity. At the global scale, female rats demonstrate stronger segregation within the cortical and subcortical systems, while male rats display more prominent cortico-subcortical interactions, particularly between the cortex and striatum. Taken together, these data provide a comprehensive framework of sex differences in resting-state connectivity patterns in the awake rat brain, and offer a reference for studies aiming to reveal sex-related FC differences in different animal models of brain disorders.
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Affiliation(s)
- Qiong Li
- The Pennsylvania State University
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5
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Han X, Cramer SR, Zhang N. Deriving causal relationships in resting-state functional connectivity using SSFO-based optogenetic fMRI. J Neural Eng 2022; 19:10.1088/1741-2552/ac9d66. [PMID: 36301683 PMCID: PMC9681600 DOI: 10.1088/1741-2552/ac9d66] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 10/25/2022] [Indexed: 01/07/2023]
Abstract
Objective.The brain network has been extensively studied as a collection of brain regions that are functionally inter-connected. However, the study of the causal relationship in brain-wide functional connectivity, which is critical to the brain function, remains challenging. We aim to examine the feasibility of using (SSFO)-based optogenetic functional magnetic resonance imaging to infer the causal relationship (i.e. directional information) in the brain network.Approach.We combined SSFO-based optogenetics with fMRI in a resting-state rodent model to study how a local increase of excitability affects brain-wide neural activity and resting-state functional connectivity (RSFC). We incorporated Pearson's correlation and partial correlation analyses in a graphic model to derive the directional information in connections exhibiting RSFC modulations.Main results. When the dentate gyrus (DG) was sensitized by SSFO activation, we found significantly changed activity and connectivity in several brain regions associated with the DG, particularly in the medial prefrontal cortex Our causal inference result shows an 84%-100% accuracy rate compared to the directional information based on anatomical tracing data.Significance.This study establishes a system to investigate the relationship between local region activity and RSFC modulation, and provides a way to analyze the underlying causal relationship between brain regions.
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Affiliation(s)
- Xu Han
- Graduate Program in Molecular, Cellular, and Integrative Biosciences, The Pennsylvania State University, University Park, USA
| | - Samuel R. Cramer
- The Neuroscience Graduate Program, The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, USA
| | - Nanyin Zhang
- Graduate Program in Molecular, Cellular, and Integrative Biosciences, The Pennsylvania State University, University Park, USA
- The Neuroscience Graduate Program, The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, USA
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, USA
- Center for Neurotechnology in Mental Health Research, The Pennsylvania State University, University Park, USA 16802
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6
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Li Q, Zhao W, Liu S, Zhao Y, Pan W, Wang X, Liu Z, Xu Y. Partial resistance to citalopram in a Wistar-Kyoto rat model of depression: An evaluation using resting-state functional MRI and graph analysis. J Psychiatr Res 2022; 151:242-251. [PMID: 35500452 DOI: 10.1016/j.jpsychires.2022.04.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 03/20/2022] [Accepted: 04/18/2022] [Indexed: 10/18/2022]
Abstract
Wistar-Kyoto (WKY) rats as an endogenous depression model partially lack a response to classic selective serotonin reuptake inhibitors (SSRIs). Thus, this strain has the potential to be established as a model of treatment-resistant depression (TRD). However, the SSRI resistance in WKY rats is still not fully understood. In this study, WKY and control rats were subjected to a series of tests, namely, a forced swim test (FST), a sucrose preference test (SPT), and an open field test (OFT), and were scanned in a 7.0-T MRI scanner before and after three-week citalopram or saline administration. Behavioral results demonstrated that WKY rats had increased immobility in the FST and decreased sucrose preference in the SPT and central time spent in the OFT. However, citalopram did not improve immobility in the FST. The amplitude of low-frequency fluctuation (ALFF) analysis showed regional changes in the striatum and hippocampus of WKY rats. However, citalopram partially reversed the ALFF value in the dorsal part of the two regions. Functional connectivity (FC) analysis showed that FC strengths were decreased in WKY rats compared with controls. Nevertheless, citalopram partially increased FC strengths in WKY rats. Based on FC, global graph analysis demonstrated decreased network efficiency in WKY + saline group compared with control + saline group, but citalopram showed weak network efficiency improvement. In conclusion, resting-state fMRI results implied widely affected brain function at both regional and global levels in WKY rats. Citalopram had only partial effects on these functional changes, indicating a potential treatment resistance mechanism.
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Affiliation(s)
- Qi Li
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China; Shanxi Provincial Key Laboratory of Brain Science and Neuropsychiatric Diseases, First Hospital of Shanxi Medical University, Taiyuan, China; Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, 20147, USA
| | - Wentao Zhao
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China; Shanxi Provincial Key Laboratory of Brain Science and Neuropsychiatric Diseases, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Sha Liu
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China; Shanxi Provincial Key Laboratory of Brain Science and Neuropsychiatric Diseases, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Yu Zhao
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China; Shanxi Provincial Key Laboratory of Brain Science and Neuropsychiatric Diseases, First Hospital of Shanxi Medical University, Taiyuan, China; National Key Disciplines, Key Laboratory for Cellular Physiology of Ministry of Education, Department of Neurobiology, Shanxi Medical University, Taiyuan, China
| | - Weixing Pan
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, 20147, USA
| | - Xiao Wang
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China; Shanxi Provincial Key Laboratory of Brain Science and Neuropsychiatric Diseases, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Zhifen Liu
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China; Shanxi Provincial Key Laboratory of Brain Science and Neuropsychiatric Diseases, First Hospital of Shanxi Medical University, Taiyuan, China.
| | - Yong Xu
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China; Shanxi Provincial Key Laboratory of Brain Science and Neuropsychiatric Diseases, First Hospital of Shanxi Medical University, Taiyuan, China; Department of Mental Health, Shanxi Medical University, Taiyuan, China; National Key Disciplines, Key Laboratory for Cellular Physiology of Ministry of Education, Department of Neurobiology, Shanxi Medical University, Taiyuan, China.
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7
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Beloate LN, Zhang N. Connecting the dots between cell populations, whole-brain activity, and behavior. NEUROPHOTONICS 2022; 9:032208. [PMID: 35350137 PMCID: PMC8957372 DOI: 10.1117/1.nph.9.3.032208] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Accepted: 02/25/2022] [Indexed: 06/14/2023]
Abstract
Simultaneously manipulating and monitoring both microscopic and macroscopic brain activity in vivo and identifying the linkage to behavior are powerful tools in neuroscience research. These capabilities have been realized with the recent technical advances of optogenetics and its combination with fMRI, here termed "opto-fMRI." Opto-fMRI allows for targeted brain region-, cell-type-, or projection-specific manipulation and targeted Ca 2 + activity measurement to be linked with global brain signaling and behavior. We cover the history, technical advances, applications, and important considerations of opto-fMRI in anesthetized and awake rodents and the future directions of the combined techniques in neuroscience and neuroimaging.
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Affiliation(s)
- Lauren N. Beloate
- Pennsylvania State University, Department of Biomedical Engineering, Pennsylvania, United States
| | - Nanyin Zhang
- Pennsylvania State University, Department of Biomedical Engineering, Pennsylvania, United States
- Pennsylvania State University, Huck Institutes of the Life Sciences, Pennsylvania, United States
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8
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Dvořáková L, Stenroos P, Paasonen E, Salo RA, Paasonen J, Gröhn O. Light sedation with short habituation time for large-scale functional magnetic resonance imaging studies in rats. NMR IN BIOMEDICINE 2022; 35:e4679. [PMID: 34961988 PMCID: PMC9285600 DOI: 10.1002/nbm.4679] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 12/14/2021] [Accepted: 12/15/2021] [Indexed: 06/14/2023]
Abstract
Traditionally, preclinical resting state functional magnetic resonance imaging (fMRI) studies have been performed in anesthetized animals. Nevertheless, as anesthesia affects the functional connectivity (FC) in the brain, there has been a growing interest in imaging in the awake state. Obviously, awake imaging requires resource- and time-consuming habituation prior to data acquisition to reduce the stress and motion of the animals. Light sedation has been a less widely exploited alternative for awake imaging, requiring shorter habituation times, while still reducing the effect of anesthesia. Here, we imaged 102 rats under light sedation and 10 awake animals to conduct an FC analysis. We established an automated data-processing pipeline suitable for both groups. Additionally, the same pipeline was used on data obtained from an openly available awake rat database (289 measurements in 90 rats). The FC pattern in the light sedation measurements closely resembled the corresponding patterns in both onsite and offsite awake datasets. However, fewer datasets had to be excluded due to movement in rats with light sedation. The temporal analysis of FC in the lightly sedated group indicated a lingering effect of anesthesia that stabilized after the first 5 min. In summary, our results indicate that the light sedation protocol is a valid alternative for large-scale studies where awake protocols may become prohibitively resource-demanding, as it provides similar results to awake imaging, preserves more scans, and requires shorter habituation times. The large amount of fMRI data obtained in this work are openly available for further analyses.
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Affiliation(s)
- Lenka Dvořáková
- A. I. V. Institute for Molecular SciencesUniversity of Eastern FinlandKuopioFinland
| | - Petteri Stenroos
- A. I. V. Institute for Molecular SciencesUniversity of Eastern FinlandKuopioFinland
- Grenoble Institut des NeurosciencesUniversité Grenoble AlpesGrenobleFrance
| | - Ekaterina Paasonen
- A. I. V. Institute for Molecular SciencesUniversity of Eastern FinlandKuopioFinland
| | - Raimo A. Salo
- A. I. V. Institute for Molecular SciencesUniversity of Eastern FinlandKuopioFinland
| | - Jaakko Paasonen
- A. I. V. Institute for Molecular SciencesUniversity of Eastern FinlandKuopioFinland
| | - Olli Gröhn
- A. I. V. Institute for Molecular SciencesUniversity of Eastern FinlandKuopioFinland
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9
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Subcortical control of the default mode network: Role of the basal forebrain and implications for neuropsychiatric disorders. Brain Res Bull 2022; 185:129-139. [PMID: 35562013 PMCID: PMC9290753 DOI: 10.1016/j.brainresbull.2022.05.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 04/29/2022] [Accepted: 05/04/2022] [Indexed: 01/03/2023]
Abstract
The precise interplay between large-scale functional neural systems throughout the brain is essential for performance of cognitive processes. In this review we focus on the default mode network (DMN), one such functional network that is active during periods of quiet wakefulness and believed to be involved in introspection and planning. Abnormalities in DMN functional connectivity and activation appear across many neuropsychiatric disorders, including schizophrenia. Recent evidence suggests subcortical regions including the basal forebrain are functionally and structurally important for regulation of DMN activity. Within the basal forebrain, subregions like the ventral pallidum may influence DMN activity and the nucleus basalis of Meynert can inhibit switching between brain networks. Interactions between DMN and other functional networks including the medial frontoparietal network (default), lateral frontoparietal network (control), midcingulo-insular network (salience), and dorsal frontoparietal network (attention) are also discussed in the context of neuropsychiatric disorders. Several subtypes of basal forebrain neurons have been identified including basal forebrain parvalbumin-containing or somatostatin-containing neurons which can regulate cortical gamma band oscillations and DMN-like behaviors, and basal forebrain cholinergic neurons which might gate access to sensory information during reinforcement learning. In this review, we explore this evidence, discuss the clinical implications on neuropsychiatric disorders, and compare neuroanatomy in the human vs rodent DMN. Finally, we address technological advancements which could help provide a more complete understanding of modulation of DMN function and describe newly identified BF therapeutic targets that could potentially help restore DMN-associated functional deficits in patients with a variety of neuropsychiatric disorders.
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10
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Xu N, LaGrow TJ, Anumba N, Lee A, Zhang X, Yousefi B, Bassil Y, Clavijo GP, Khalilzad Sharghi V, Maltbie E, Meyer-Baese L, Nezafati M, Pan WJ, Keilholz S. Functional Connectivity of the Brain Across Rodents and Humans. Front Neurosci 2022; 16:816331. [PMID: 35350561 PMCID: PMC8957796 DOI: 10.3389/fnins.2022.816331] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 02/14/2022] [Indexed: 12/15/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI), which measures the spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal, is increasingly utilized for the investigation of the brain's physiological and pathological functional activity. Rodents, as a typical animal model in neuroscience, play an important role in the studies that examine the neuronal processes that underpin the spontaneous fluctuations in the BOLD signal and the functional connectivity that results. Translating this knowledge from rodents to humans requires a basic knowledge of the similarities and differences across species in terms of both the BOLD signal fluctuations and the resulting functional connectivity. This review begins by examining similarities and differences in anatomical features, acquisition parameters, and preprocessing techniques, as factors that contribute to functional connectivity. Homologous functional networks are compared across species, and aspects of the BOLD fluctuations such as the topography of the global signal and the relationship between structural and functional connectivity are examined. Time-varying features of functional connectivity, obtained by sliding windowed approaches, quasi-periodic patterns, and coactivation patterns, are compared across species. Applications demonstrating the use of rs-fMRI as a translational tool for cross-species analysis are discussed, with an emphasis on neurological and psychiatric disorders. Finally, open questions are presented to encapsulate the future direction of the field.
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Affiliation(s)
- Nan Xu
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Theodore J. LaGrow
- Electrical and Computer Engineering, Georgia Tech, Atlanta, GA, United States
| | - Nmachi Anumba
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Azalea Lee
- Neuroscience Graduate Program, Emory University, Atlanta, GA, United States
- Emory University School of Medicine, Atlanta, GA, United States
| | - Xiaodi Zhang
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Behnaz Yousefi
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Yasmine Bassil
- Neuroscience Graduate Program, Emory University, Atlanta, GA, United States
| | - Gloria P. Clavijo
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | | | - Eric Maltbie
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Lisa Meyer-Baese
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Maysam Nezafati
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Wen-Ju Pan
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Shella Keilholz
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
- Neuroscience Graduate Program, Emory University, Atlanta, GA, United States
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11
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Schaeffer DJ, Klassen LM, Hori Y, Tian X, Szczupak D, Yen CCC, Cléry JC, Gilbert KM, Gati JS, Menon RS, Liu C, Everling S, Silva AC. An open access resource for functional brain connectivity from fully awake marmosets. Neuroimage 2022; 252:119030. [PMID: 35217206 PMCID: PMC9048130 DOI: 10.1016/j.neuroimage.2022.119030] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 01/19/2022] [Accepted: 02/21/2022] [Indexed: 12/27/2022] Open
Abstract
The common marmoset (Callithrix jacchus) is quickly gaining traction as a premier neuroscientific model. However, considerable progress is still needed in understanding the functional and structural organization of the marmoset brain to rival that documented in longstanding preclinical model species, like mice, rats, and Old World primates. To accelerate such progress, we present the Marmoset Functional Brain Connectivity Resource (marmosetbrainconnectome.org), currently consisting of over 70 h of resting-state fMRI (RS-fMRI) data acquired at 500 µm isotropic resolution from 31 fully awake marmosets in a common stereotactic space. Three-dimensional functional connectivity (FC) maps for every cortical and subcortical gray matter voxel are stored online. Users can instantaneously view, manipulate, and download any whole-brain functional connectivity (FC) topology (at the subject- or group-level) along with the raw datasets and preprocessing code. Importantly, researchers can use this resource to test hypotheses about FC directly - with no additional analyses required - yielding whole-brain correlations for any gray matter voxel on demand. We demonstrate the resource's utility for presurgical planning and comparison with tracer-based neuronal connectivity as proof of concept. Complementing existing structural connectivity resources for the marmoset brain, the Marmoset Functional Brain Connectivity Resource affords users the distinct advantage of exploring the connectivity of any voxel in the marmoset brain, not limited to injection sites nor constrained by regional atlases. With the entire raw database (RS-fMRI and structural images) and preprocessing code openly available for download and use, we expect this resource to be broadly valuable to test novel hypotheses about the functional organization of the marmoset brain.
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Affiliation(s)
- David J Schaeffer
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA 15261, United States.
| | - L Martyn Klassen
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, ON, Canada
| | - Yuki Hori
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, ON, Canada
| | - Xiaoguang Tian
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA 15261, United States
| | - Diego Szczupak
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA 15261, United States
| | - Cecil Chern-Chyi Yen
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Justine C Cléry
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, ON, Canada
| | - Kyle M Gilbert
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, ON, Canada
| | - Joseph S Gati
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, ON, Canada
| | - Ravi S Menon
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, ON, Canada; Department of Physiology and Pharmacology, University of Western Ontario, London, Ontario, Canada
| | - CiRong Liu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Stefan Everling
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, ON, Canada; Department of Physiology and Pharmacology, University of Western Ontario, London, Ontario, Canada
| | - Afonso C Silva
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA 15261, United States
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12
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Odenkirk M, Horman BM, Dodds JN, Patisaul HB, Baker ES. Combining Micropunch Histology and Multidimensional Lipidomic Measurements for In-Depth Tissue Mapping. ACS MEASUREMENT SCIENCE AU 2022; 2:67-75. [PMID: 35647605 PMCID: PMC9139744 DOI: 10.1021/acsmeasuresciau.1c00035] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
While decades of technical and analytical advancements have been utilized to discover novel lipid species, increase speciation, and evaluate localized lipid dysregulation at subtissue, cellular, and subcellular levels, many challenges still exist. One limitation is that the acquisition of both in-depth spatial information and comprehensive lipid speciation is extremely difficult, especially when biological material is limited or lipids are at low abundance. In neuroscience, for example, it is often desired to focus on only one brain region or subregion, which can be well under a square millimeter for rodents. Herein, we evaluate a micropunch histology method where cortical brain tissue at 2.0, 1.25, 1.0, 0.75, 0.5, and 0.25 mm diameter sizes and 1 mm depth was collected and analyzed with multidimensional liquid chromatography, ion mobility spectrometry, collision induced dissociation, and mass spectrometry (LC-IMS-CID-MS) measurements. Lipid extraction was optimized for the small sample sizes, and assessment of lipidome coverage for the 2.0 to 0.25 mm diameter sizes showed a decline from 304 to 198 lipid identifications as validated by all 4 analysis dimensions (~35% loss in coverage for ~88% less tissue). While losses were observed, the ~200 lipids and estimated 4630 neurons contained within the 0.25 punch still provided in-depth characterization of the small tissue region. Furthermore, while localization routinely achieved by mass spectrometry imaging (MSI) and single cell analyses is greater, this diameter is sufficiently small to isolate subcortical, hypothalamic, and other brain regions in adult rats, thereby increasing the coverage of lipids within relevant spatial windows without sacrificing speciation. Therefore, micropunch histology enables in-depth, region-specific lipid evaluations, an approach that will prove beneficial to a variety of lipidomic applications.
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Affiliation(s)
- Melanie
T. Odenkirk
- Department
of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Brian M. Horman
- Department
of Biological Sciences, North Carolina State
University, Raleigh, North Carolina 27695, United States
| | - James N. Dodds
- Department
of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Heather B. Patisaul
- Department
of Biological Sciences, North Carolina State
University, Raleigh, North Carolina 27695, United States
- Center
for Human Health and the Environment, North
Carolina State University, Raleigh, North Carolina 27695, United States
| | - Erin S. Baker
- Department
of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
- Comparative
Medicine Institute, North Carolina State
University, Raleigh, North Carolina 27695, United States
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13
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Ma Z, Zhang Q, Tu W, Zhang N. Gaining insight into the neural basis of resting-state fMRI signal. Neuroimage 2022; 250:118960. [PMID: 35121182 PMCID: PMC8935501 DOI: 10.1016/j.neuroimage.2022.118960] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 01/30/2022] [Accepted: 01/31/2022] [Indexed: 01/01/2023] Open
Abstract
The blood oxygenation level-dependent (BOLD)-based resting-state functional magnetic resonance imaging (rsfMRI) has been widely used as a non-invasive tool to map brain-wide connectivity architecture. However, the neural basis underpinning the resting-state BOLD signal remains elusive. In this study, we combined simultaneous calcium-based fiber photometry with rsfMRI in awake animals to examine the relationship of the BOLD signal and spiking activity at the resting state. We observed robust couplings between calcium and BOLD signals in the dorsal hippocampus as well as other distributed areas in the default mode network (DMN), suggesting that the calcium measurement can reliably predict the rsfMRI signal. In addition, using the calcium signal recorded as the ground truth, we assessed the impacts of different rsfMRI data preprocessing pipelines on functional connectivity mapping. Overall, our results provide important evidence suggesting that spiking activity measured by the calcium signal plays a key role in the neural mechanism of resting-state BOLD signal.
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Affiliation(s)
- Zilu Ma
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, USA
| | - Qingqing Zhang
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, USA
| | - Wenyu Tu
- The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, USA
| | - Nanyin Zhang
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, USA; The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, USA.
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14
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Paasonen J, Stenroos P, Laakso H, Pirttimäki T, Paasonen E, Salo RA, Tanila H, Idiyatullin D, Garwood M, Michaeli S, Mangia S, Gröhn O. Whole-brain studies of spontaneous behavior in head-fixed rats enabled by zero echo time MB-SWIFT fMRI. Neuroimage 2022; 250:118924. [PMID: 35065267 PMCID: PMC9464759 DOI: 10.1016/j.neuroimage.2022.118924] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 12/22/2021] [Accepted: 01/18/2022] [Indexed: 11/21/2022] Open
Abstract
Understanding the link between the brain activity and behavior is a key challenge in modern neuroscience. Behavioral neuroscience, however, lacks tools to record whole-brain activity in complex behavioral settings. Here we demonstrate that a novel Multi-Band SWeep Imaging with Fourier Transformation (MB-SWIFT) functional magnetic resonance imaging (fMRI) approach enables whole-brain studies in spontaneously behaving head-fixed rats. First, we show anatomically relevant functional parcellation. Second, we show sensory, motor, exploration, and stress-related brain activity in relevant networks during corresponding spontaneous behavior. Third, we show odor-induced activation of olfactory system with high correlation between the fMRI and behavioral responses. We conclude that the applied methodology enables novel behavioral study designs in rodents focusing on tasks, cognition, emotions, physical exercise, and social interaction. Importantly, novel zero echo time and large bandwidth approaches, such as MB-SWIFT, can be applied for human behavioral studies, allowing more freedom as body movement is dramatically less restricting factor.
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Affiliation(s)
- Jaakko Paasonen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Petteri Stenroos
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland; Institute of Neuroscience, Grenoble, France
| | - Hanne Laakso
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Tiina Pirttimäki
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Ekaterina Paasonen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Raimo A Salo
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Heikki Tanila
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Djaudat Idiyatullin
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, USA
| | - Michael Garwood
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, USA
| | - Shalom Michaeli
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, USA
| | - Silvia Mangia
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, USA
| | - Olli Gröhn
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland.
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15
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Hsu LM, Wang S, Walton L, Wang TWW, Lee SH, Shih YYI. 3D U-Net Improves Automatic Brain Extraction for Isotropic Rat Brain Magnetic Resonance Imaging Data. Front Neurosci 2021; 15:801008. [PMID: 34975392 PMCID: PMC8716693 DOI: 10.3389/fnins.2021.801008] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Accepted: 11/15/2021] [Indexed: 12/24/2022] Open
Abstract
Brain extraction is a critical pre-processing step in brain magnetic resonance imaging (MRI) analytical pipelines. In rodents, this is often achieved by manually editing brain masks slice-by-slice, a time-consuming task where workloads increase with higher spatial resolution datasets. We recently demonstrated successful automatic brain extraction via a deep-learning-based framework, U-Net, using 2D convolutions. However, such an approach cannot make use of the rich 3D spatial-context information from volumetric MRI data. In this study, we advanced our previously proposed U-Net architecture by replacing all 2D operations with their 3D counterparts and created a 3D U-Net framework. We trained and validated our model using a recently released CAMRI rat brain database acquired at isotropic spatial resolution, including T2-weighted turbo-spin-echo structural MRI and T2*-weighted echo-planar-imaging functional MRI. The performance of our 3D U-Net model was compared with existing rodent brain extraction tools, including Rapid Automatic Tissue Segmentation, Pulse-Coupled Neural Network, SHape descriptor selected External Regions after Morphologically filtering, and our previously proposed 2D U-Net model. 3D U-Net demonstrated superior performance in Dice, Jaccard, center-of-mass distance, Hausdorff distance, and sensitivity. Additionally, we demonstrated the reliability of 3D U-Net under various noise levels, evaluated the optimal training sample sizes, and disseminated all source codes publicly, with a hope that this approach will benefit rodent MRI research community. Significant Methodological Contribution: We proposed a deep-learning-based framework to automatically identify the rodent brain boundaries in MRI. With a fully 3D convolutional network model, 3D U-Net, our proposed method demonstrated improved performance compared to current automatic brain extraction methods, as shown in several qualitative metrics (Dice, Jaccard, PPV, SEN, and Hausdorff). We trust that this tool will avoid human bias and streamline pre-processing steps during 3D high resolution rodent brain MRI data analysis. The software developed herein has been disseminated freely to the community.
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Affiliation(s)
- Li-Ming Hsu
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States,Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States,*Correspondence: Li-Ming Hsu,
| | - Shuai Wang
- School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, China
| | - Lindsay Walton
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States,Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Tzu-Wen Winnie Wang
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States,Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Sung-Ho Lee
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States,Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Yen-Yu Ian Shih
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States,Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States,Yen-Yu Ian Shih,
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16
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Lee SH, Broadwater MA, Ban W, Wang TWW, Kim HJ, Dumas JS, Vetreno RP, Herman MA, Morrow AL, Besheer J, Kash TL, Boettiger CA, Robinson DL, Crews FT, Shih YYI. An isotropic EPI database and analytical pipelines for rat brain resting-state fMRI. Neuroimage 2021; 243:118541. [PMID: 34478824 PMCID: PMC8561231 DOI: 10.1016/j.neuroimage.2021.118541] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 08/08/2021] [Accepted: 08/30/2021] [Indexed: 12/24/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (fMRI) has drastically expanded the scope of brain research by advancing our knowledge about the topologies, dynamics, and interspecies translatability of functional brain networks. Several databases have been developed and shared in accordance with recent key initiatives in the rodent fMRI community to enhance the transparency, reproducibility, and interpretability of data acquired at various sites. Despite these pioneering efforts, one notable challenge preventing efficient standardization in the field is the customary choice of anisotropic echo planar imaging (EPI) schemes with limited spatial coverage. Imaging with anisotropic resolution and/or reduced brain coverage has significant shortcomings including reduced registration accuracy and increased deviation in brain feature detection. Here we proposed a high-spatial-resolution (0.4 mm), isotropic, whole-brain EPI protocol for the rat brain using a horizontal slicing scheme that can maintain a functionally relevant repetition time (TR), avoid high gradient duty cycles, and offer unequivocal whole-brain coverage. Using this protocol, we acquired resting-state EPI fMRI data from 87 healthy rats under the widely used dexmedetomidine sedation supplemented with low-dose isoflurane on a 9.4 T MRI system. We developed an EPI template that closely approximates the Paxinos and Watson's rat brain coordinate system and demonstrated its ability to improve the accuracy of group-level approaches and streamline fMRI data pre-processing. Using this database, we employed a multi-scale dictionary-learning approach to identify reliable spatiotemporal features representing rat brain intrinsic activity. Subsequently, we performed k-means clustering on those features to obtain spatially discrete, functional regions of interest (ROIs). Using Euclidean-based hierarchical clustering and modularity-based partitioning, we identified the topological organizations of the rat brain. Additionally, the identified group-level FC network appeared robust across strains and sexes. The "triple-network" commonly adapted in human fMRI were resembled in the rat brain. Through this work, we disseminate raw and pre-processed isotropic EPI data, a rat brain EPI template, as well as identified functional ROIs and networks in standardized rat brain coordinates. We also make our analytical pipelines and scripts publicly available, with the hope of facilitating rat brain resting-state fMRI study standardization.
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Affiliation(s)
- Sung-Ho Lee
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA,Department of Neurology, University of North Carolina, Chapel Hill, NC, USA,Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Corresponding authors at: Center for Animal MRI, 125 Mason Farm Road, CB# 7513, University of North Carolina, Chapel Hill, NC 27599, USA. (S.-H. Lee), (Y.-Y.I. Shih)
| | - Margaret A. Broadwater
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA,Department of Neurology, University of North Carolina, Chapel Hill, NC, USA,Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA
| | - Woomi Ban
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Tzu-Wen Winnie Wang
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Hyeon-Joong Kim
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA,Department of Neurology, University of North Carolina, Chapel Hill, NC, USA
| | - Jaiden Seongmi Dumas
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA,Department of Neurology, University of North Carolina, Chapel Hill, NC, USA,Department of Quantitative Biology, University of North Carolina, Chapel Hill, NC, USA
| | - Ryan P. Vetreno
- Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Department of Pharmacology, University of North Carolina, Chapel Hill, NC, USA
| | - Melissa A. Herman
- Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Department of Pharmacology, University of North Carolina, Chapel Hill, NC, USA
| | - A. Leslie Morrow
- Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA,Department of Pharmacology, University of North Carolina, Chapel Hill, NC, USA
| | - Joyce Besheer
- Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Thomas L. Kash
- Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Department of Pharmacology, University of North Carolina, Chapel Hill, NC, USA
| | - Charlotte A. Boettiger
- Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA,Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC, USA
| | - Donita L. Robinson
- Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Fulton T. Crews
- Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA,Department of Pharmacology, University of North Carolina, Chapel Hill, NC, USA
| | - Yen-Yu Ian Shih
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA,Department of Neurology, University of North Carolina, Chapel Hill, NC, USA,Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Corresponding authors at: Center for Animal MRI, 125 Mason Farm Road, CB# 7513, University of North Carolina, Chapel Hill, NC 27599, USA. (S.-H. Lee), (Y.-Y.I. Shih)
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17
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Chang HH, Yeh SJ, Chiang MC, Hsieh ST. Automatic brain extraction and hemisphere segmentation in rat brain MR images after stroke using deformable models. Med Phys 2021; 48:6036-6050. [PMID: 34388268 DOI: 10.1002/mp.15157] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 07/26/2021] [Accepted: 07/27/2021] [Indexed: 12/25/2022] Open
Abstract
PURPOSE Experimental ischemic stroke models play an essential role in understanding the mechanisms of cerebral ischemia and evaluating the development of pathological extent. An important precursor to the investigation of ischemic strokes associated with rodents is the brain extraction and hemisphere segmentation in rat brain diffusion-weighted imaging (DWI) and T2-weighted MRI (T2WI) images. Accurate and reliable image segmentation tools for extracting the rat brain and hemispheres in the MR images are critical in subsequent processes, such as lesion identification and injury analysis. This study is an attempt to investigate rat brain extraction and hemisphere segmentation algorithms that are practicable in both DWI and T2WI images. METHODS To automatically perform brain extraction, the proposed framework is based on an efficient geometric deformable model. By introducing an additional image force in response to the rat brain characteristics into the skull stripping model, we establish a unique rat brain extraction scheme in DWI and T2WI images. For the subsequent hemisphere segmentation, we develop an efficient brain feature detection algorithm to approximately separate the rat brain. A refinement process is enforced by constructing a gradient vector flow in the proximity of the midsurface, where a parametric active contour is attracted to achieve hemisphere segmentation. RESULTS Extensive experiments with 55 DWI and T2WI subjects were executed in comparison with the state-of-the-art methods. Experimental results indicated that our rat brain extraction and hemisphere segmentation schemes outperformed the competitive methods and exhibited high performance both qualitatively and quantitatively. For rat brain extraction, the average Dice scores were 97.13% and 97.42% in DWI and T2WI image volumes, respectively. Rat hemisphere segmentation results based on the Hausdorff distance metric revealed average values of 0.17 and 0.15 mm for DWI and T2WI subjects, respectively. CONCLUSIONS We believe that the established frameworks are advantageous to facilitate preclinical stroke investigation and relevant neuroscience research that requires accurate brain extraction and hemisphere segmentation using rat DWI and T2WI images.
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Affiliation(s)
- Herng-Hua Chang
- Computational Biomedical Engineering Laboratory (CBEL), Department of Engineering Science and Ocean Engineering, National Taiwan University, Taipei, Taiwan
| | - Shin-Joe Yeh
- Graduate Institute of Anatomy and Cell Biology, College of Medicine, National Taiwan University, Taipei, Taiwan.,Department of Neurology and Stroke Center, National Taiwan University Hospital, Taipei, Taiwan
| | - Ming-Chang Chiang
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Sung-Tsang Hsieh
- Graduate Institute of Anatomy and Cell Biology, College of Medicine, National Taiwan University, Taipei, Taiwan.,Department of Neurology and Stroke Center, National Taiwan University Hospital, Taipei, Taiwan.,Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan.,Graduate Institute of Brain and Mind Sciences, College of Medicine, National Taiwan University, Taipei, Taiwan.,Center of Precision Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
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18
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Tu W, Ma Z, Zhang N. Brain network reorganization after targeted attack at a hub region. Neuroimage 2021; 237:118219. [PMID: 34052466 PMCID: PMC8289586 DOI: 10.1016/j.neuroimage.2021.118219] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 04/14/2021] [Accepted: 05/26/2021] [Indexed: 01/01/2023] Open
Abstract
The architecture of brain networks has been extensively studied in multiple species. However, exactly how the brain network reconfigures when a local region, particularly a hub region, stops functioning remains elusive. By combining chemogenetics and resting-state functional magnetic resonance imaging (rsfMRI) in an awake rodent model, we investigated the causal impact of acutely inactivating a hub region (i.e. the dorsal anterior cingulate cortex) on brain network properties. We found that suppressing neural activity in a hub could have a ripple effect that went beyond the hub-related connections and propagated to other neural connections across multiple brain systems. In addition, hub dysfunction affected the topological architecture of the whole-brain network in terms of the network resilience and segregation. Selectively inhibiting excitatory neurons in the hub further changed network integration. None of these changes were observed in sham rats or when a non-hub region (i.e. the primary visual cortex) was perturbed. This study has established a system that allows for mechanistically dissecting the relationship between local regions and brain network properties. Our data provide direct evidence supporting the hypothesis that acute dysfunction of a brain hub can cause large-scale network changes. These results also provide a comprehensive framework documenting the differential impact of hub versus non-hub nodes on network dynamics.
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Affiliation(s)
- Wenyu Tu
- Neuroscience Program, The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
| | - Zilu Ma
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Nanyin Zhang
- Neuroscience Program, The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA; Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802, USA.
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19
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Tu W, Ma Z, Ma Y, Dopfel D, Zhang N. Suppressing Anterior Cingulate Cortex Modulates Default Mode Network and Behavior in Awake Rats. Cereb Cortex 2021; 31:312-323. [PMID: 32820327 PMCID: PMC7727348 DOI: 10.1093/cercor/bhaa227] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 06/30/2020] [Accepted: 07/26/2020] [Indexed: 12/28/2022] Open
Abstract
The default mode network (DMN) is a principal brain network in the mammalian brain. Although the DMN in humans has been extensively studied with respect to network structure, function, and clinical implications, our knowledge of DMN in animals remains limited. In particular, the functional role of DMN nodes, and how DMN organization relates to DMN-relevant behavior are still elusive. Here we investigated the causal relationship of inactivating a pivotal node of DMN (i.e., dorsal anterior cingulate cortex [dACC]) on DMN function, network organization, and behavior by combining chemogenetics, resting-state functional magnetic resonance imaging (rsfMRI) and behavioral tests in awake rodents. We found that suppressing dACC activity profoundly changed the activity and connectivity of DMN, and these changes were associated with altered DMN-related behavior in animals. The chemo-rsfMRI-behavior approach opens an avenue to mechanistically dissecting the relationships between a specific node, brain network function, and behavior. Our data suggest that, like in humans, DMN in rodents is a functional network with coordinated activity that mediates behavior.
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Affiliation(s)
- Wenyu Tu
- Neuroscience Program, The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
| | - Zilu Ma
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Yuncong Ma
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - David Dopfel
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Nanyin Zhang
- Neuroscience Program, The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
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20
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Li Y, Lee J, Long X, Qiao Y, Ma T, He Q, Cao P, Zhang X, Zheng H. A Magnetic Resonance-Guided Focused Ultrasound Neuromodulation System With a Whole Brain Coil Array for Nonhuman Primates at 3 T. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:4401-4412. [PMID: 32833632 DOI: 10.1109/tmi.2020.3019087] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The phased-array radio frequency (RF) coil plays a vital role in magnetic resonance-guided focused ultrasound (MRgFUS) neuromodulation studies, where accurate brain functional stimulations and neural circuit observations are required. Although various designs of phased-array coils have been reported, few are suitable for ultrasound stimulations. In this study, an MRgFUS neuromodulation system comprised of a whole brain coverage non-human primate (NHP) RF coil and an MRI-compatible ultrasound device was developed. When compared to a single loop coil, the NHP coil provided up to a 50% increase in the signal-to-noise ratio (SNR) in the brain and acquired better anatomical image-quality. The NHP coil also demonstrated the ability to achieve higher spatial resolution and reduce distortion in echo-planer imaging (EPI). Ultrasound beam characteristics and transcranial magnetic resonance acoustic radiation force (MR-ARF) were measured for simulated positions, and calculated B0 maps were employed to establish MRI-compatibility. The differences between focused off and on ultrasound techniques were measured using SNR, g-factors, and temporal SNR (tSNR) analyses and all deviations were under 2.3%. The EPI images quality and stable tSNR demonstrated the suitability of the MRgFUS neuromodulation system to conduct functional MRI studies. Last, the time course of the blood oxygen level dependent (BOLD) signal of posterior cingulate cortex in a focused ultrasound neuromodulation study was detected and repeated with MR thermometry.
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21
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Increased wiring cost during development is driven by long-range cortical, but not subcortical connections. Neuroimage 2020; 225:117463. [PMID: 33075559 PMCID: PMC7812615 DOI: 10.1016/j.neuroimage.2020.117463] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 10/05/2020] [Accepted: 10/09/2020] [Indexed: 12/31/2022] Open
Abstract
The brain undergoes a protracted, metabolically expensive maturation process from childhood to adulthood. Therefore, it is crucial to understand how network cost is distributed among different brain systems as the brain matures. To address this issue, here we examined developmental changes in wiring cost and brain network topology using resting-state functional magnetic resonance imaging (rsfMRI) data longitudinally collected in awake rats from the juvenile age to adulthood. We found that the wiring cost increased in the vast majority of cortical connections but decreased in most subcortico-subcortical connections. Importantly, the developmental increase in wiring cost was dominantly driven by long-range cortical, but not subcortical connections, which was consistent with more pronounced increase in network integration in the cortical network. These results collectively indicate that there is a non-uniform distribution of network cost as the brain matures, and network resource is dominantly consumed for the development of the cortex, but not subcortex from the juvenile age to adulthood.
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22
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Hsu LM, Wang S, Ranadive P, Ban W, Chao THH, Song S, Cerri DH, Walton LR, Broadwater MA, Lee SH, Shen D, Shih YYI. Automatic Skull Stripping of Rat and Mouse Brain MRI Data Using U-Net. Front Neurosci 2020; 14:568614. [PMID: 33117118 PMCID: PMC7575753 DOI: 10.3389/fnins.2020.568614] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 08/13/2020] [Indexed: 11/13/2022] Open
Abstract
Accurate removal of magnetic resonance imaging (MRI) signal outside the brain, a.k.a., skull stripping, is a key step in the brain image pre-processing pipelines. In rodents, this is mostly achieved by manually editing a brain mask, which is time-consuming and operator dependent. Automating this step is particularly challenging in rodents as compared to humans, because of differences in brain/scalp tissue geometry, image resolution with respect to brain-scalp distance, and tissue contrast around the skull. In this study, we proposed a deep-learning-based framework, U-Net, to automatically identify the rodent brain boundaries in MR images. The U-Net method is robust against inter-subject variability and eliminates operator dependence. To benchmark the efficiency of this method, we trained and validated our model using both in-house collected and publicly available datasets. In comparison to current state-of-the-art methods, our approach achieved superior averaged Dice similarity coefficient to ground truth T2-weighted rapid acquisition with relaxation enhancement and T2∗-weighted echo planar imaging data in both rats and mice (all p < 0.05), demonstrating robust performance of our approach across various MRI protocols.
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Affiliation(s)
- Li-Ming Hsu
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Neurology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Shuai Wang
- Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Paridhi Ranadive
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Woomi Ban
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Tzu-Hao Harry Chao
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Neurology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Sheng Song
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Neurology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Domenic Hayden Cerri
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Neurology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Lindsay R Walton
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Neurology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Margaret A Broadwater
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Neurology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Sung-Ho Lee
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Neurology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Dinggang Shen
- Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
| | - Yen-Yu Ian Shih
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Neurology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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23
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Becq GJPC, Habet T, Collomb N, Faucher M, Delon-Martin C, Coizet V, Achard S, Barbier EL. Functional connectivity is preserved but reorganized across several anesthetic regimes. Neuroimage 2020; 219:116945. [DOI: 10.1016/j.neuroimage.2020.116945] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 04/21/2020] [Accepted: 05/11/2020] [Indexed: 12/12/2022] Open
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24
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Becq GJPC, Barbier EL, Achard S. Brain networks of rats under anesthesia using resting-state fMRI: comparison with dead rats, random noise and generative models of networks. J Neural Eng 2020; 17:045012. [PMID: 32580176 DOI: 10.1088/1741-2552/ab9fec] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
OBJECTIVE Connectivity networks are crucial to understand the brain resting-state activity using functional magnetic resonance imaging (rs-fMRI). Alterations of these brain networks may highlight important findings concerning the resilience of the brain to different disorders. The focus of this paper is to evaluate the robustness of brain network estimations, discriminate them under anesthesia and compare them to generative models. APPROACH The extraction of brain functional connectivity (FC) networks is difficult and biased due to the properties of the data: low signal to noise ratio, high dimension low sample size. We propose to use wavelet correlations to assess FC between brain areas under anesthesia using four anesthetics (isoflurane, etomidate, medetomidine, urethane). The networks are then deduced from the functional connectivity matrices by applying statistical thresholds computed using the number of samples at a given scale of wavelet decomposition. Graph measures are extracted and extensive comparisons with generative models of structured networks are conducted. MAIN RESULTS The sample size and filtering are critical to obtain significant correlations values and thereby detect connections between regions. This is necessary to construct networks different from random ones as shown using rs-fMRI brain networks of dead rats. Brain networks under anesthesia on rats have topological features that are mixing small-world, scale-free and random networks. Betweenness centrality indicates that hubs are present in brain networks obtained from anesthetized rats but locations of these hubs are altered by anesthesia. SIGNIFICANCE Understanding the effects of anesthesia on brain areas is of particular importance in the context of animal research since animal models are commonly used to explore functions, evaluate lesions or illnesses, and test new drugs. More generally, results indicate that the use of correlations in the context of fMRI signals is robust but must be treated with caution. Solutions are proposed in order to control spurious correlations by setting them to zero.
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Affiliation(s)
- G J-P C Becq
- University Grenoble Alpes, CNRS, Grenoble INP, Gipsa-lab, 38000, Grenoble, France
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25
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Liu Y, Perez PD, Ma Z, Ma Z, Dopfel D, Cramer S, Tu W, Zhang N. An open database of resting-state fMRI in awake rats. Neuroimage 2020; 220:117094. [PMID: 32610063 PMCID: PMC7605641 DOI: 10.1016/j.neuroimage.2020.117094] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Revised: 06/10/2020] [Accepted: 06/18/2020] [Indexed: 12/15/2022] Open
Abstract
Rodent models are essential to translational research in health and disease. Investigation in rodent brain function and organization at the systems level using resting-state functional magnetic resonance imaging (rsfMRI) has become increasingly popular. Due to this rapid progress, publicly shared rodent rsfMRI databases can be of particular interest and importance to the scientific community, as inspired by human neuroscience and psychiatric research that are substantially facilitated by open human neuroimaging datasets. However, such databases in rats are still rare. In this paper, we share an open rsfMRI database acquired in 90 rats with a well-established awake imaging paradigm that avoids anesthesia interference. Both raw and preprocessed data are made publicly available. Procedures in data preprocessing to remove artefacts induced by the scanner, head motion and non-neural physiological noise are described in details. We also showcase inter-regional functional connectivity and functional networks obtained from the database.
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Affiliation(s)
- Yikang Liu
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Pablo D Perez
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Zilu Ma
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Zhiwei Ma
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - David Dopfel
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Samuel Cramer
- Neuroscience Program, The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Wenyu Tu
- Neuroscience Program, The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Nanyin Zhang
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; Neuroscience Program, The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, 16802, USA.
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26
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Peeters LM, Hinz R, Detrez JR, Missault S, De Vos WH, Verhoye M, Van der Linden A, Keliris GA. Chemogenetic silencing of neurons in the mouse anterior cingulate area modulates neuronal activity and functional connectivity. Neuroimage 2020; 220:117088. [PMID: 32592851 DOI: 10.1016/j.neuroimage.2020.117088] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Revised: 06/14/2020] [Accepted: 06/22/2020] [Indexed: 01/05/2023] Open
Abstract
The anterior cingulate area (ACC) is an integral part of the prefrontal cortex in mice and supports cognitive functions, including attentional processes, motion planning and execution as well as remote memory, fear and pain. Previous anatomical and functional imaging studies demonstrated that the ACC is interconnected with numerous brain regions, such as motor and sensory cortices, amygdala and limbic areas, suggesting it serves as a hub in functional networks. However, the exact role of the ACC in regulating functional network activity and connectivity remains to be elucidated. Recently developed neuromodulatory techniques, such as Designer Receptors Exclusively Activated by Designer Drugs (DREADDs) allow for precise control of neuronal activity. In this study, we used an inhibitory kappa-opioid receptor DREADD (KORD) to temporally inhibit neuronal firing in the right ACC of mice and assessed functional network activity and connectivity using non-invasive functional magnetic resonance imaging (MRI). We demonstrated that KORD-induced inhibition of the right ACC induced blood oxygenation-level dependent (BOLD) signal decreases and increases in connected brain regions of both hemispheres. More specifically, altered neuronal activity could be observed in functional brain networks including connections with sensory cortex, thalamus, basolateral amygdala and ventral pallidum, areas involved in attention processes, working memory, fear behavior and reward respectively. Furthermore, these modulations in neuronal activity were associated with decreased intra- and interhemispheric functional connectivity. Our results consolidate the hub role of the mouse ACC in functional networks and further demonstrate that the combination of the DREADD technology and non-invasive functional imaging methods is a valuable tool for unraveling mechanisms of network function and dysfunction by reversible inactivation of selected targets.
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Affiliation(s)
- Lore M Peeters
- Bio-Imaging Lab, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Belgium
| | - Rukun Hinz
- Bio-Imaging Lab, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Belgium
| | - Jan R Detrez
- Laboratory for Cell Biology and Histology, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Belgium
| | - Stephan Missault
- Bio-Imaging Lab, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Belgium
| | - Winnok H De Vos
- Laboratory for Cell Biology and Histology, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Belgium
| | - Marleen Verhoye
- Bio-Imaging Lab, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Belgium
| | | | - Georgios A Keliris
- Bio-Imaging Lab, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Belgium.
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27
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Abstract
Although often used as a nuisance in resting-state functional magnetic resonance imaging (rsfMRI), the global brain signal in humans and anesthetized animals has important neural basis. However, our knowledge of the global signal in awake rodents is sparse. To bridge this gap, we systematically analyzed rsfMRI data acquired with a conventional single-echo (SE) echo planar imaging (EPI) sequence in awake rats. The spatial pattern of rsfMRI frames during peaks of the global signal exhibited prominent co-activations in the thalamo-cortical and hippocampo-cortical networks, as well as in the basal forebrain, hinting that these neural networks might contribute to the global brain signal in awake rodents. To validate this concept, we acquired rsfMRI data using a multi-echo (ME) EPI sequence and removed non-neural components in the rsfMRI signal. Consistent co-activation patterns were obtained in extensively de-noised ME-rsfMRI data, corroborating the finding from SE-rsfMRI data. Furthermore, during rsfMRI experiments, we simultaneously recorded neural spiking activities in the hippocampus using GCaMP-based fiber photometry. The hippocampal calcium activity exhibited significant correspondence with the global rsfMRI signal. These data collectively suggest that the global rsfMRI signal contains significant neural components that involve coordinated activities in the thalamo-cortical and hippocampo-cortical networks. These results provide important insight into the neural substrate of the global brain signal in awake rodents.
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28
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Paasonen J, Laakso H, Pirttimäki T, Stenroos P, Salo RA, Zhurakovskaya E, Lehto LJ, Tanila H, Garwood M, Michaeli S, Idiyatullin D, Mangia S, Gröhn O. Multi-band SWIFT enables quiet and artefact-free EEG-fMRI and awake fMRI studies in rat. Neuroimage 2019; 206:116338. [PMID: 31730923 PMCID: PMC7008094 DOI: 10.1016/j.neuroimage.2019.116338] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 10/18/2019] [Accepted: 11/04/2019] [Indexed: 12/11/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) studies in animal models provide invaluable information regarding normal and abnormal brain function, especially when combined with complementary stimulation and recording techniques. The echo planar imaging (EPI) pulse sequence is the most common choice for fMRI investigations, but it has several shortcomings. EPI is one of the loudest sequences and very prone to movement and susceptibility-induced artefacts, making it suboptimal for awake imaging. Additionally, the fast gradient-switching of EPI induces disrupting currents in simultaneous electrophysiological recordings. Therefore, we investigated whether the unique features of Multi-Band SWeep Imaging with Fourier Transformation (MB-SWIFT) overcome these issues at a high 9.4 T magnetic field, making it a potential alternative to EPI. MB-SWIFT had 32-dB and 20-dB lower peak and average sound pressure levels, respectively, than EPI with typical fMRI parameters. Body movements had little to no effect on MB-SWIFT images or functional connectivity analyses, whereas they severely affected EPI data. The minimal gradient steps of MB-SWIFT induced significantly lower currents in simultaneous electrophysiological recordings than EPI, and there were no electrode-induced distortions in MB-SWIFT images. An independent component analysis of the awake rat functional connectivity data obtained with MB-SWIFT resulted in near whole-brain level functional parcellation, and simultaneous electrophysiological and fMRI measurements in isoflurane-anesthetized rats indicated that MB-SWIFT signal is tightly linked to neuronal resting-state activity. Therefore, we conclude that the MB-SWIFT sequence is a robust preclinical brain mapping tool that can overcome many of the drawbacks of conventional EPI fMRI at high magnetic fields.
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Affiliation(s)
- Jaakko Paasonen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Hanne Laakso
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland; Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Tiina Pirttimäki
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland; Department of Psychology, University of Jyväskyla, Jyväskyla, Finland
| | - Petteri Stenroos
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Raimo A Salo
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Ekaterina Zhurakovskaya
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland; Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Lauri J Lehto
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland; Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Heikki Tanila
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Michael Garwood
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Shalom Michaeli
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Djaudat Idiyatullin
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Silvia Mangia
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Olli Gröhn
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland.
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29
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Liu Y, Zhang N. Propagations of spontaneous brain activity in awake rats. Neuroimage 2019; 202:116176. [PMID: 31513942 DOI: 10.1016/j.neuroimage.2019.116176] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2019] [Revised: 08/08/2019] [Accepted: 09/05/2019] [Indexed: 01/06/2023] Open
Abstract
Slow propagations of spontaneous brain activity have been reported in multiple species. However, systematical investigation of the organization of such brain activity is still lacking. In this study, we analyzed propagations of spontaneous brain activity using a reference library of characteristic resting-state functional connectivity (RSFC) patterns in awake rodents. We found that transitions through multiple distinct RSFC patterns were reproducible not only in transition sequences but also in transition time delays. In addition, the organization of these transitions and their spatiotemporal dynamic patterns were revealed using a graphical model. We further identified prominent brain regions involved in these transitions. These results provide a comprehensive framework of brainwide propagations of spontaneous activity in awake rats. This study also offers a new tool to study the spatiotemporal dynamics of activity in the resting brain.
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Affiliation(s)
- Yikang Liu
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Nanyin Zhang
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, 16802, USA.
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30
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Dopfel D, Perez PD, Verbitsky A, Bravo-Rivera H, Ma Y, Quirk GJ, Zhang N. Individual variability in behavior and functional networks predicts vulnerability using an animal model of PTSD. Nat Commun 2019; 10:2372. [PMID: 31147546 PMCID: PMC6543038 DOI: 10.1038/s41467-019-09926-z] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Accepted: 04/08/2019] [Indexed: 12/31/2022] Open
Abstract
Only a minority of individuals experiencing trauma subsequently develop post-traumatic stress disorder (PTSD). However, whether differences in vulnerability to PTSD result from a predisposition or trauma exposure remains unclear. A major challenge in differentiating these possibilities is that clinical studies focus on individuals already exposed to trauma without pre-trauma conditions. Here, using the predator scent model of PTSD in rats and a longitudinal design, we measure pre-trauma brain-wide neural circuit functional connectivity, behavioral and corticosterone responses to trauma exposure, and post-trauma anxiety. Freezing during predator scent exposure correlates with functional connectivity in a set of neural circuits, indicating pre-existing circuit function can predispose animals to differential fearful responses to threats. Counterintuitively, rats with lower freezing show more avoidance of the predator scent, a prolonged corticosterone response, and higher anxiety long after exposure. This study provides a framework of pre-existing circuit function that determines threat responses, which might directly relate to PTSD-like behaviors.
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Affiliation(s)
- David Dopfel
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, 16802, USA
| | - Pablo D Perez
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, 16802, USA
| | - Alexander Verbitsky
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, 16802, USA
| | - Hector Bravo-Rivera
- Department of Anatomy & Neurobiology, University of Puerto Rico School of Medicine, San Juan, 00936, Puerto Rico
| | - Yuncong Ma
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, 16802, USA
| | - Gregory J Quirk
- Department of Anatomy & Neurobiology, University of Puerto Rico School of Medicine, San Juan, 00936, Puerto Rico
- Department of Psychiatry, University of Puerto Rico School of Medicine, San Juan, 00936, Puerto Rico
| | - Nanyin Zhang
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, 16802, USA.
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31
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Liang X, Hsu LM, Lu H, Sumiyoshi A, He Y, Yang Y. The Rich-Club Organization in Rat Functional Brain Network to Balance Between Communication Cost and Efficiency. Cereb Cortex 2019; 28:924-935. [PMID: 28108494 DOI: 10.1093/cercor/bhw416] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2016] [Accepted: 12/23/2016] [Indexed: 01/22/2023] Open
Abstract
Network analyses of structural connectivity in the brain have highlighted a set of highly connected hubs that are densely interconnected, forming a "rich-club" substrate in diverse species. Here, we demonstrate the existence of rich-club organization in functional brain networks of rats. Densely interconnected rich-club regions are found to be distributed in multiple brain modules, with the majority located within the putative default mode network. Rich-club members exhibit high wiring cost (as measured by connection distance) and high metabolic running cost (as surrogated by cerebral blood flow), which may have evolved to achieve high network communications to support efficient brain functions. Furthermore, by adopting a forepaw electrical stimulation paradigm, we find that the rich-club organization of the rat functional network remains almost the same as in the resting state, whereas path motif analysis reveals significant differences, suggesting the rat brain reorganizes its topological routes by increasing locally oriented shortcuts but reducing rich-club member-involved paths to conserve metabolic running cost during unimodal stimulation. Together, our results suggest that the neuronal system is organized and dynamically operated in an economic way to balance between cost minimization and topological/functional efficiency.
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Affiliation(s)
- Xia Liang
- Neuroimaging Research Branch, National Institute on Drug Abuse, Baltimore, MD 21224, USA.,Research Center of Basic Space Science, Harbin Institute of Technology, Harbin 150001, China
| | - Li-Ming Hsu
- Neuroimaging Research Branch, National Institute on Drug Abuse, Baltimore, MD 21224, USA.,Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei 11221, Taiwan
| | - Hanbing Lu
- Neuroimaging Research Branch, National Institute on Drug Abuse, Baltimore, MD 21224, USA
| | - Akira Sumiyoshi
- Neuroimaging Research Branch, National Institute on Drug Abuse, Baltimore, MD 21224, USA.,Institute of Development, Aging and Cancer, Tohoku University, Sendai 9808575, Japan
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, Baltimore, MD 21224, USA
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32
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Evaluation of nuisance removal for functional MRI of rodent brain. Neuroimage 2019; 188:694-709. [DOI: 10.1016/j.neuroimage.2018.12.048] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 12/12/2018] [Accepted: 12/22/2018] [Indexed: 12/17/2022] Open
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33
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Dopfel D, Zhang N. Mapping stress networks using functional magnetic resonance imaging in awake animals. Neurobiol Stress 2018; 9:251-263. [PMID: 30450389 PMCID: PMC6234259 DOI: 10.1016/j.ynstr.2018.06.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2018] [Revised: 05/27/2018] [Accepted: 06/26/2018] [Indexed: 12/15/2022] Open
Abstract
The neurobiology of stress is studied through behavioral neuroscience, endocrinology, neuronal morphology and neurophysiology. There is a shift in focus toward progressive changes throughout stress paradigms and individual susceptibility to stress that requires methods that allow for longitudinal study design and study of individual differences in stress response. Functional magnetic resonance imaging (fMRI), with the advantages of noninvasiveness and a large field of view, can be used for functionally mapping brain-wide regions and circuits critical to the stress response, making it suitable for longitudinal studies and understanding individual variability of short-term and long-term consequences of stress exposure. In addition, fMRI can be applied to both animals and humans, which is highly valuable in translating findings across species and examining whether the physiology and neural circuits involved in the stress response are conserved in mammals. However, compared to human fMRI studies, there are a number of factors that are essential for the success of fMRI studies in animals. This review discussed the use of fMRI in animal studies of stress. It reviewed advantages, challenges and technical considerations of the animal fMRI methodology as well as recent literature of stress studies using fMRI in animals. It also highlighted the development of combining fMRI with other methods and the future potential of fMRI in animal studies of stress. We conclude that animal fMRI studies, with their flexibility, low cost and short time frame compared to human studies, are crucial to advancing our understanding of the neurobiology of stress.
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Affiliation(s)
- David Dopfel
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, 16802, USA
| | - Nanyin Zhang
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, 16802, USA
- The Huck Institutes of Life Sciences, Pennsylvania State University, University Park, PA, 16802, USA
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34
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Ji M, Xia J, Tang X, Yang J. Altered functional connectivity within the default mode network in two animal models with opposing episodic memories. PLoS One 2018; 13:e0202661. [PMID: 30226886 PMCID: PMC6143184 DOI: 10.1371/journal.pone.0202661] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Accepted: 08/07/2018] [Indexed: 12/31/2022] Open
Abstract
Memory enhancement and memory decline are two opposing cognitive performances commonly observed in clinical practice, yet the neural mechanisms underlying these two different phenomena remain poorly understood. Accumulating evidence has demonstrated that the default-mode network (DMN) is implicated in diverse cognitive, social, and affective processes. In the present study, we used the retrosplenial cortex as a seed region to study the functional connectivity within the DMN in two animal models with opposing episodic memories, of which memory enhancement was induced by footshocks to mimic posttraumatic stress disorder (PTSD) and memory decline was induced by lipopolysaccharide (LPS) challenge to mimic sepsis-associated encephalopathy (SAE). Our results showed that LPS challenge and footshocks induced opposing episodic memories. With regard to the imaging data, there were significant differences in the functional connectivity between the retrosplenial cortex and the medial prefrontal cortex (mPFC), insular lobe, left piriform cortex, left sensory cortex, and right visual cortex among the three groups. Post-hoc comparisons showed the LPS group had a significantly increased functional connectivity between the retrosplenial cortex and mPFC as compared with the control group. Compared with the LPS group, the PTSD group displayed significantly decreased functional connectivity between the retrosplenial cortex and the right visual cortex, retrosplenial cortex, insular lobe, left piriform cortex, and left sensory cortex. In summary, our study suggests that there is a significant difference in the functional connectivity within the DMN between SAE and PTSD rats.
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Affiliation(s)
- Muhuo Ji
- Department of Anesthesiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China
| | - Jiangyan Xia
- Department of Anesthesiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China
| | - Xiaohui Tang
- Department of Anesthesiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China
| | - Jianjun Yang
- Department of Anesthesiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China
- * E-mail:
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Arefin TM, Mechling AE, Meirsman AC, Bienert T, Hübner NS, Lee HL, Ben Hamida S, Ehrlich A, Roquet D, Hennig J, von Elverfeldt D, Kieffer BL, Harsan LA. Remodeling of Sensorimotor Brain Connectivity in Gpr88-Deficient Mice. Brain Connect 2018; 7:526-540. [PMID: 28882062 DOI: 10.1089/brain.2017.0486] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Recent studies have demonstrated that orchestrated gene activity and expression support synchronous activity of brain networks. However, there is a paucity of information on the consequences of single gene function on overall brain functional organization and connectivity and how this translates at the behavioral level. In this study, we combined mouse mutagenesis with functional and structural magnetic resonance imaging (MRI) to determine whether targeted inactivation of a single gene would modify whole-brain connectivity in live animals. The targeted gene encodes GPR88 (G protein-coupled receptor 88), an orphan G protein-coupled receptor enriched in the striatum and previously linked to behavioral traits relevant to neuropsychiatric disorders. Connectivity analysis of Gpr88-deficient mice revealed extensive remodeling of intracortical and cortico-subcortical networks. Most prominent modifications were observed at the level of retrosplenial cortex connectivity, central to the default mode network (DMN) whose alteration is considered a hallmark of many psychiatric conditions. Next, somatosensory and motor cortical networks were most affected. These modifications directly relate to sensorimotor gating deficiency reported in mutant animals and also likely underlie their hyperactivity phenotype. Finally, we identified alterations within hippocampal and dorsal striatum functional connectivity, most relevant to a specific learning deficit that we previously reported in Gpr88-/- animals. In addition, amygdala connectivity with cortex and striatum was weakened, perhaps underlying the risk-taking behavior of these animals. This is the first evidence demonstrating that GPR88 activity shapes the mouse brain functional and structural connectome. The concordance between connectivity alterations and behavior deficits observed in Gpr88-deficient mice suggests a role for GPR88 in brain communication.
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Affiliation(s)
- Tanzil Mahmud Arefin
- 1 Department of Radiology, Medical Physics, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg , Freiburg, Germany .,2 Faculty of Biology, University of Freiburg , Freiburg, Germany .,3 Bernstein Center Freiburg, University of Freiburg , Freiburg, Germany .,4 Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine , New York, New York
| | - Anna E Mechling
- 1 Department of Radiology, Medical Physics, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg , Freiburg, Germany .,2 Faculty of Biology, University of Freiburg , Freiburg, Germany
| | - Aura Carole Meirsman
- 5 Département de Médecine Translationnelle et Neurogénétique, Institut de Génétique et de Biologie Moléculaire et Cellulaire, INSERM U-964, CNRS UMR-7104, Université de Strasbourg , Illkirch-Graffenstaden, France .,6 Neuroscience Paris Seine, Institut de Biologie Paris Seine , CNRS UMR 8246/INSERM U1130/Université Pierre et Marie Currie, Paris, France
| | - Thomas Bienert
- 1 Department of Radiology, Medical Physics, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg , Freiburg, Germany
| | - Neele Saskia Hübner
- 1 Department of Radiology, Medical Physics, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg , Freiburg, Germany .,2 Faculty of Biology, University of Freiburg , Freiburg, Germany
| | - Hsu-Lei Lee
- 1 Department of Radiology, Medical Physics, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg , Freiburg, Germany
| | - Sami Ben Hamida
- 5 Département de Médecine Translationnelle et Neurogénétique, Institut de Génétique et de Biologie Moléculaire et Cellulaire, INSERM U-964, CNRS UMR-7104, Université de Strasbourg , Illkirch-Graffenstaden, France .,7 Douglas Mental Health Institute, Department of Psychiatry, McGill University , Montreal, Quebec, Canada
| | - Aliza Ehrlich
- 5 Département de Médecine Translationnelle et Neurogénétique, Institut de Génétique et de Biologie Moléculaire et Cellulaire, INSERM U-964, CNRS UMR-7104, Université de Strasbourg , Illkirch-Graffenstaden, France .,7 Douglas Mental Health Institute, Department of Psychiatry, McGill University , Montreal, Quebec, Canada
| | - Dan Roquet
- 8 Engineering Science, Computer Science and Imaging Laboratory (ICube), Integrative Multimodal Imaging in Healthcare, University of Strasbourg-CNRS , Strasbourg, France
| | - Jürgen Hennig
- 1 Department of Radiology, Medical Physics, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg , Freiburg, Germany
| | - Dominik von Elverfeldt
- 1 Department of Radiology, Medical Physics, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg , Freiburg, Germany
| | - Brigitte Lina Kieffer
- 5 Département de Médecine Translationnelle et Neurogénétique, Institut de Génétique et de Biologie Moléculaire et Cellulaire, INSERM U-964, CNRS UMR-7104, Université de Strasbourg , Illkirch-Graffenstaden, France .,7 Douglas Mental Health Institute, Department of Psychiatry, McGill University , Montreal, Quebec, Canada
| | - Laura-Adela Harsan
- 1 Department of Radiology, Medical Physics, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg , Freiburg, Germany .,8 Engineering Science, Computer Science and Imaging Laboratory (ICube), Integrative Multimodal Imaging in Healthcare, University of Strasbourg-CNRS , Strasbourg, France .,9 Department of Biophysics and Nuclear Medicine, Faculty of Medicine, University Hospital Strasbourg , Strasbourg, France
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36
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Ma Z, Ma Y, Zhang N. Development of brain-wide connectivity architecture in awake rats. Neuroimage 2018; 176:380-389. [PMID: 29738909 DOI: 10.1016/j.neuroimage.2018.05.009] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Accepted: 05/02/2018] [Indexed: 12/13/2022] Open
Abstract
Childhood and adolescence are both critical developmental periods, evidenced by complex neurophysiological changes the brain undergoes and high occurrence rates of neuropsychiatric disorders during these periods. Despite substantial progress in elucidating the developmental trajectories of individual neural circuits, our knowledge of developmental changes of whole-brain connectivity architecture in animals is sparse. To fill this gap, here we longitudinally acquired rsfMRI data in awake rats during five developmental stages from juvenile to adulthood. We found that the maturation timelines of brain circuits were heterogeneous and system specific. Functional connectivity (FC) tended to decrease in subcortical circuits, but increase in cortical circuits during development. In addition, the developing brain exhibited hemispheric functional specialization, evidenced by reduced inter-hemispheric FC between homotopic regions, and lower similarity of region-to-region FC patterns between the two hemispheres. Finally, we showed that whole-brain network development was characterized by reduced clustering (i.e. local communication) but increased integration (distant communication). Taken together, the present study has systematically characterized the development of brain-wide connectivity architecture from juvenile to adulthood in awake rats. It also serves as a critical reference point for understanding circuit- and network-level changes in animal models of brain development-related disorders. Furthermore, FC data during brain development in awake rodents contain high translational value and can shed light onto comparative neuroanatomy.
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Affiliation(s)
- Zilu Ma
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Yuncong Ma
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Nanyin Zhang
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802, USA.
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Colon-Perez LM, Pino JA, Saha K, Pompilus M, Kaplitz S, Choudhury N, Jagnarine DA, Geste JR, Levin BA, Wilks I, Setlow B, Bruijnzeel AW, Khoshbouei H, Torres GE, Febo M. Functional connectivity, behavioral and dopaminergic alterations 24 hours following acute exposure to synthetic bath salt drug methylenedioxypyrovalerone. Neuropharmacology 2018; 137:178-193. [PMID: 29729891 DOI: 10.1016/j.neuropharm.2018.04.031] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Revised: 04/26/2018] [Accepted: 04/29/2018] [Indexed: 12/22/2022]
Abstract
Among cathinone drugs known as bath salts, methylenedioxypyrovalerone (MDPV) exerts its potent actions via the dopamine (DA) system, and at intoxicating doses may produce adverse behavioral effects. Previous work by our group suggests that prolonged alterations in correlated neural activity between cortical and striatal areas could underlie, at least in part, the adverse reactions to this bath salt drug. In the present study, we assessed the effect of acute MDPV administration on brain functional connectivity at 1 and 24 h in rats. Using graph theory metrics to assess in vivo brain functional network organization we observed that 24 h after MDPV administration there was an increased clustering coefficient, rich club index, and average path length. Increases in these metrics suggests that MDPV produces a prolonged pattern of correlated activity characterized by greater interactions between subsets of high degree nodes but a reduced interaction with regions outside this core subset. Further analysis revealed that the core set of nodes include prefrontal cortical, amygdala, hypothalamic, somatosensory and striatal areas. At the molecular level, MDPV downregulated the dopamine transporter (DAT) in striatum and produced a shift in its subcellular distribution, an effect likely to involve rapid internalization at the membrane. These new findings suggest that potent binding of MDPV to DAT may trigger internalization and a prolonged alteration in homeostatic regulation of DA and functional brain network reorganization. We propose that the observed MDPV-induced network reorganization and DAergic changes may contribute to previously reported adverse behavioral responses to MDPV.
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Affiliation(s)
- Luis M Colon-Perez
- Department of Psychiatry, College of Medicine, University of Florida, Gainesville, FL 32610, USA; Center for Addiction Research and Education (CARE), College of Medicine, University of Florida, Gainesville, FL 32610, USA; Advanced Magnetic Resonance Imaging and Spectroscopy (AMRIS) Facility, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Jose A Pino
- Department of Pharmacology and Therapeutics, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Kaustuv Saha
- Department of Neuroscience, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Marjory Pompilus
- Department of Psychiatry, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Sherman Kaplitz
- Department of Psychiatry, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Nafisa Choudhury
- Department of Psychiatry, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Darin A Jagnarine
- Department of Psychiatry, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Jean R Geste
- Department of Psychiatry, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Brandon A Levin
- Department of Psychiatry, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Isaac Wilks
- Department of Psychiatry, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Barry Setlow
- Department of Psychiatry, College of Medicine, University of Florida, Gainesville, FL 32610, USA; Department of Neuroscience, College of Medicine, University of Florida, Gainesville, FL 32610, USA; The McKnight Brain Institute, College of Medicine, University of Florida, Gainesville, FL 32610, USA; Center for Addiction Research and Education (CARE), College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Adriaan W Bruijnzeel
- Department of Psychiatry, College of Medicine, University of Florida, Gainesville, FL 32610, USA; Department of Neuroscience, College of Medicine, University of Florida, Gainesville, FL 32610, USA; The McKnight Brain Institute, College of Medicine, University of Florida, Gainesville, FL 32610, USA; Center for Addiction Research and Education (CARE), College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Habibeh Khoshbouei
- Department of Neuroscience, College of Medicine, University of Florida, Gainesville, FL 32610, USA; The McKnight Brain Institute, College of Medicine, University of Florida, Gainesville, FL 32610, USA; Center for Addiction Research and Education (CARE), College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Gonzalo E Torres
- Department of Pharmacology and Therapeutics, College of Medicine, University of Florida, Gainesville, FL 32610, USA; Center for Addiction Research and Education (CARE), College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Marcelo Febo
- Department of Psychiatry, College of Medicine, University of Florida, Gainesville, FL 32610, USA; Department of Neuroscience, College of Medicine, University of Florida, Gainesville, FL 32610, USA; The McKnight Brain Institute, College of Medicine, University of Florida, Gainesville, FL 32610, USA; Center for Addiction Research and Education (CARE), College of Medicine, University of Florida, Gainesville, FL 32610, USA; Advanced Magnetic Resonance Imaging and Spectroscopy (AMRIS) Facility, College of Medicine, University of Florida, Gainesville, FL 32610, USA.
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38
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Abstract
Spontaneous brain activity, typically investigated using resting-state fMRI (rsfMRI), provides a measure of inter-areal resting-state functional connectivity (RSFC). Although it has been established that RSFC is non-stationary, previous dynamic rsfMRI studies mainly focused on revealing the spatial characteristics of dynamic RSFC patterns, but the temporal relationship between these RSFC patterns remains elusive. Here we investigated the temporal organization of characteristic RSFC patterns in awake rats and humans. We found that transitions between RSFC patterns were not random but followed specific sequential orders. The organization of RSFC pattern transitions was further analyzed using graph theory, and pivotal RSFC patterns in transitions were identified. This study has demonstrated that spontaneous brain activity is not only nonrandom spatially, but also nonrandom temporally, and this feature is well conserved between rodents and humans. These results offer new insights into understanding the spatiotemporal dynamics of spontaneous activity in the mammalian brain.
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Affiliation(s)
- Zhiwei Ma
- Department of Biomedical Engineering, The Huck Institutes of Life Sciences, The Pennsylvania State University, State College, United States
| | - Nanyin Zhang
- Department of Biomedical Engineering, The Huck Institutes of Life Sciences, The Pennsylvania State University, State College, United States
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39
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Carmichael O, Schwarz AJ, Chatham CH, Scott D, Turner JA, Upadhyay J, Coimbra A, Goodman JA, Baumgartner R, English BA, Apolzan JW, Shankapal P, Hawkins KR. The role of fMRI in drug development. Drug Discov Today 2018; 23:333-348. [PMID: 29154758 PMCID: PMC5931333 DOI: 10.1016/j.drudis.2017.11.012] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2017] [Revised: 10/19/2017] [Accepted: 11/13/2017] [Indexed: 12/17/2022]
Abstract
Functional magnetic resonance imaging (fMRI) has been known for over a decade to have the potential to greatly enhance the process of developing novel therapeutic drugs for prevalent health conditions. However, the use of fMRI in drug development continues to be relatively limited because of a variety of technical, biological, and strategic barriers that continue to limit progress. Here, we briefly review the roles that fMRI can have in the drug development process and the requirements it must meet to be useful in this setting. We then provide an update on our current understanding of the strengths and limitations of fMRI as a tool for drug developers and recommend activities to enhance its utility.
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Affiliation(s)
- Owen Carmichael
- Pennington Biomedical Research Center, Baton Rouge, LA, USA.
| | | | - Christopher H Chatham
- Translational Medicine Neuroscience and Biomarkers, Roche Innovation Center, Basel, Switzerland
| | | | - Jessica A Turner
- Psychology Department & Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | | | | | | | - Richard Baumgartner
- Biostatistics and Research Decision Sciences (BARDS), Merck & Co., Inc., Kenilworth, NJ, USA
| | | | - John W Apolzan
- Pennington Biomedical Research Center, Baton Rouge, LA, USA
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40
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Cao J, Lu KH, Powley TL, Liu Z. Vagal nerve stimulation triggers widespread responses and alters large-scale functional connectivity in the rat brain. PLoS One 2017; 12:e0189518. [PMID: 29240833 PMCID: PMC5730194 DOI: 10.1371/journal.pone.0189518] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2017] [Accepted: 11/28/2017] [Indexed: 11/18/2022] Open
Abstract
Vagus nerve stimulation (VNS) is a therapy for epilepsy and depression. However, its efficacy varies and its mechanism remains unclear. Prior studies have used functional magnetic resonance imaging (fMRI) to map brain activations with VNS in human brains, but have reported inconsistent findings. The source of inconsistency is likely attributable to the complex temporal characteristics of VNS-evoked fMRI responses that cannot be fully explained by simplified response models in the conventional model-based analysis for activation mapping. To address this issue, we acquired 7-Tesla blood oxygenation level dependent fMRI data from anesthetized Sprague-Dawley rats receiving electrical stimulation at the left cervical vagus nerve. Using spatially independent component analysis, we identified 20 functional brain networks and detected the network-wise activations with VNS in a data-driven manner. Our results showed that VNS activated 15 out of 20 brain networks, and the activated regions covered >76% of the brain volume. The time course of the evoked response was complex and distinct across regions and networks. In addition, VNS altered the strengths and patterns of correlations among brain networks relative to those in the resting state. The most notable changes in network-network interactions were related to the limbic system. Together, such profound and widespread effects of VNS may underlie its unique potential for a wide range of therapeutics to relieve central or peripheral conditions.
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Affiliation(s)
- Jiayue Cao
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, United States
- Purdue Institute of Integrative Neuroscience, Purdue University, West Lafayette, Indiana, United States
| | - Kun-Han Lu
- Purdue Institute of Integrative Neuroscience, Purdue University, West Lafayette, Indiana, United States
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, United States
| | - Terry L Powley
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, United States
- Purdue Institute of Integrative Neuroscience, Purdue University, West Lafayette, Indiana, United States
- Department of Psychological Science, Purdue University, West Lafayette, Indiana, United States
| | - Zhongming Liu
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, United States
- Purdue Institute of Integrative Neuroscience, Purdue University, West Lafayette, Indiana, United States
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, United States
- * E-mail:
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41
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Schwalm M, Schmid F, Wachsmuth L, Backhaus H, Kronfeld A, Aedo Jury F, Prouvot PH, Fois C, Albers F, van Alst T, Faber C, Stroh A. Cortex-wide BOLD fMRI activity reflects locally-recorded slow oscillation-associated calcium waves. eLife 2017; 6:27602. [PMID: 28914607 PMCID: PMC5658067 DOI: 10.7554/elife.27602] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Accepted: 09/14/2017] [Indexed: 01/08/2023] Open
Abstract
Spontaneous slow oscillation-associated slow wave activity represents an internally generated state which is characterized by alternations of network quiescence and stereotypical episodes of neuronal activity - slow wave events. However, it remains unclear which macroscopic signal is related to these active periods of the slow wave rhythm. We used optic fiber-based calcium recordings of local neural populations in cortex and thalamus to detect neurophysiologically defined slow calcium waves in isoflurane anesthetized rats. The individual slow wave events were used for an event-related analysis of simultaneously acquired whole-brain BOLD fMRI. We identified BOLD responses directly related to onsets of slow calcium waves, revealing a cortex-wide BOLD correlate: the entire cortex was engaged in this specific type of slow wave activity. These findings demonstrate a direct relation of defined neurophysiological events to a specific BOLD activity pattern and were confirmed for ongoing slow wave activity by independent component and seed-based analyses. When a person is in a deep non-dreaming sleep, neurons in their brain alternate slowly between periods of silence and periods of activity. This gives rise to low-frequency brain rhythms called slow waves, which are thought to help stabilize memories. Slow wave activity can be detected on multiple scales, from the pattern of electrical impulses sent by an individual neuron to the collective activity of the brain’s entire outer layer, the cortex. But does slow wave activity in an individual group of neurons in the cortex affect the activity of the rest of the brain? To find out, Schwalm, Schmid, Wachsmuth et al. took advantage of the fact that slow waves also occur under general anesthesia, and placed anesthetized rats inside miniature whole-brain scanners. A small region of cortex in each rat had been injected with a dye that fluoresces whenever the neurons in that region are active. An optical fiber was lowered into the rat’s brain to transmit the fluorescence signals to a computer. Monitoring these signals while the animals lay inside the scanner revealed that slow-wave activity in any one group of cortical neurons was accompanied by slow-wave activity across the cortex as a whole. This relationship was seen only for slow waves, and not for other brain rhythms. Slow waves seem to occur in all species of animal with a backbone, and in both healthy and diseased brains. While it is not possible to inject fluorescent dyes into the human brain, it is possible to monitor neuronal activity using electrodes. Comparing local electrode recordings with measures of whole-brain activity from scanners could thus allow similar experiments to be performed in people. There is growing evidence – from animal models and from studies of patients – that slow waves may be altered in Alzheimer’s disease. Further work is required to determine whether detecting these changes could help diagnose disease at earlier stages, and whether reversing them may have therapeutic potential.
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Affiliation(s)
- Miriam Schwalm
- Focus Program Translational Neurosciences, Institute for Microscopic Anatomy and Neurobiology, Johannes Gutenberg-University Mainz, Mainz, Germany.,GRADE Brain, Goethe Graduate Academy, Goethe University Frankfurt am Main, Frankfurt, Germany
| | - Florian Schmid
- Department of Clinical Radiology, University Hospital Münster, Münster, Germany
| | - Lydia Wachsmuth
- Department of Clinical Radiology, University Hospital Münster, Münster, Germany
| | - Hendrik Backhaus
- Focus Program Translational Neurosciences, Institute for Microscopic Anatomy and Neurobiology, Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Andrea Kronfeld
- Focus Program Translational Neurosciences, Institute for Microscopic Anatomy and Neurobiology, Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Felipe Aedo Jury
- Focus Program Translational Neurosciences, Institute for Microscopic Anatomy and Neurobiology, Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Pierre-Hugues Prouvot
- Focus Program Translational Neurosciences, Institute for Microscopic Anatomy and Neurobiology, Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Consuelo Fois
- Focus Program Translational Neurosciences, Institute for Microscopic Anatomy and Neurobiology, Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Franziska Albers
- Department of Clinical Radiology, University Hospital Münster, Münster, Germany
| | - Timo van Alst
- Department of Clinical Radiology, University Hospital Münster, Münster, Germany
| | - Cornelius Faber
- Department of Clinical Radiology, University Hospital Münster, Münster, Germany
| | - Albrecht Stroh
- Focus Program Translational Neurosciences, Institute for Microscopic Anatomy and Neurobiology, Johannes Gutenberg-University Mainz, Mainz, Germany
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42
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Ma Y, Hamilton C, Zhang N. Dynamic Connectivity Patterns in Conscious and Unconscious Brain. Brain Connect 2016; 7:1-12. [PMID: 27846731 DOI: 10.1089/brain.2016.0464] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Brain functional connectivity undergoes dynamic changes from the awake to unconscious states. However, how the dynamics of functional connectivity patterns are linked to consciousness at the behavioral level remains elusive. In this study, we acquired resting-state functional magnetic resonance imaging data during wakefulness and graded levels of consciousness in rats. Data were analyzed using a dynamic approach combining the sliding window method and k-means clustering. Our results demonstrate that whole-brain networks contained several quasi-stable patterns that dynamically recurred from the awake state into anesthetized states. Remarkably, two brain connectivity states with distinct spatial similarity to the structure of anatomical connectivity were strongly biased toward high and low consciousness levels, respectively. These results provide compelling neuroimaging evidence linking the dynamics of whole-brain functional connectivity patterns and states of consciousness at the behavioral level.
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Affiliation(s)
- Yuncong Ma
- 1 Department of Biomedical Engineering, Pennsylvania State University, University Park , Pennsylvania
| | - Christina Hamilton
- 2 The Huck Institutes of Life Sciences, Pennsylvania State University, University Park , Pennsylvania
| | - Nanyin Zhang
- 1 Department of Biomedical Engineering, Pennsylvania State University, University Park , Pennsylvania.,2 The Huck Institutes of Life Sciences, Pennsylvania State University, University Park , Pennsylvania
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43
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Gao YR, Ma Y, Zhang Q, Winder AT, Liang Z, Antinori L, Drew PJ, Zhang N. Time to wake up: Studying neurovascular coupling and brain-wide circuit function in the un-anesthetized animal. Neuroimage 2016; 153:382-398. [PMID: 27908788 PMCID: PMC5526447 DOI: 10.1016/j.neuroimage.2016.11.069] [Citation(s) in RCA: 124] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Revised: 11/18/2016] [Accepted: 11/27/2016] [Indexed: 01/08/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) has allowed the noninvasive study of task-based and resting-state brain dynamics in humans by inferring neural activity from blood-oxygenation-level dependent (BOLD) signal changes. An accurate interpretation of the hemodynamic changes that underlie fMRI signals depends on the understanding of the quantitative relationship between changes in neural activity and changes in cerebral blood flow, oxygenation and volume. While there has been extensive study of neurovascular coupling in anesthetized animal models, anesthesia causes large disruptions of brain metabolism, neural responsiveness and cardiovascular function. Here, we review work showing that neurovascular coupling and brain circuit function in the awake animal are profoundly different from those in the anesthetized state. We argue that the time is right to study neurovascular coupling and brain circuit function in the awake animal to bridge the physiological mechanisms that underlie animal and human neuroimaging signals, and to interpret them in light of underlying neural mechanisms. Lastly, we discuss recent experimental innovations that have enabled the study of neurovascular coupling and brain-wide circuit function in un-anesthetized and behaving animal models.
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Affiliation(s)
- Yu-Rong Gao
- Neuroscience Graduate Program, Pennsylvania State University, University Park, PA 16802, Unidted States; Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA 16802, Unidted States
| | - Yuncong Ma
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA 16802, Unidted States
| | - Qingguang Zhang
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA 16802, Unidted States
| | - Aaron T Winder
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA 16802, Unidted States
| | - Zhifeng Liang
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA 16802, Unidted States
| | - Lilith Antinori
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA 16802, Unidted States
| | - Patrick J Drew
- Neuroscience Graduate Program, Pennsylvania State University, University Park, PA 16802, Unidted States; Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA 16802, Unidted States; Department of Neurosurgery, Pennsylvania State University, University Park, PA 16802, Unidted States; Department of Biomedical Engineering, Pennsylvania State University, University Park, PA 16802, Unidted States.
| | - Nanyin Zhang
- Neuroscience Graduate Program, Pennsylvania State University, University Park, PA 16802, Unidted States; Department of Biomedical Engineering, Pennsylvania State University, University Park, PA 16802, Unidted States.
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