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Zhao Q, Liu J, Chen L, Gao Z, Lin M, Wang Y, Xiao Z, Chen Y, Huang X. Phytomedicine Fructus Aurantii-derived two absorbed compounds unlock antidepressant and prokinetic multi-functions via modulating 5-HT 3/GHSR. J Ethnopharmacol 2024; 323:117703. [PMID: 38185260 DOI: 10.1016/j.jep.2024.117703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 12/02/2023] [Accepted: 01/02/2024] [Indexed: 01/09/2024]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Fructus Aurantii (FA), a well-known phytomedicine, has been employed to evoke antidepressant and prokinetic multi-functions. Therein, systematically identifying bioactive components and the referred mechanism is essential for FA. AIM OF THE STUDY This study was planned to answer "2 W" (What and Why), such as which components and pathways contribute to FA's multi-functions. We aimed to identify bioactive compounds as the key for opening the lock of FA's multi-functions, and the molecule mechanisms are their naturally matched lock cylinder. MATERIALS AND METHODS The phytochemical content of FA extract was determined, and the compounds were identified in rats pretreated with FA using liquid chromatography with mass spectrometry (LC-MS). The contribution strategy was used to assess bioactive compounds' efficacy (doses = their content in FA) in model rats with the mechanism. The changes in functional brain regions were determined via 7.0 T functional magnetic resonance imaging-blood oxygen level-dependent (fMRI-BOLD). RESULT Eight phytochemicals' content was detected, and merely six components were identified in rats in vivo. Meranzin hydrate + hesperidin (MH), as the primary contributor of FA, exerted antidepressant and prokinetic effects (improvement of indexes for immobility time, gastric emptying, intestinal transit, CRH, ghrelin, ACTH, DA, NA, 5-HT, CORT, and 5-HT3) by regulating 5-HT3/Growth hormone secretagogue receptor (GHSR) pathway. These results were validated by 5-HT2A, 5-HT3, and GHSR receptor antagonists combined with molecule docking. MH restored the excessive BOLD activation of the left accumbens nucleus, left corpus callosum and hypothalamus preoptic region. CONCLUSION Absorbed MH accounts for FA's anti-depressant and prokinetic efficacy in acutely-stressed rats, primarily via 5-HT3/GHSR shared regulation.
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Affiliation(s)
- Qiulong Zhao
- Institute of TCM-Related Comorbid Depression, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Jin Liu
- Medical College, Xiamen University, School of Medicine, Xiamen, 361102, China
| | - Li Chen
- Institute of TCM-Related Comorbid Depression, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Zhao Gao
- Institute of TCM-Related Comorbid Depression, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Muhai Lin
- Medical College, Xiamen University, School of Medicine, Xiamen, 361102, China
| | - Yun Wang
- Medical College, Xiamen University, School of Medicine, Xiamen, 361102, China
| | - Zhe Xiao
- Medical College, Xiamen University, School of Medicine, Xiamen, 361102, China
| | - Yi Chen
- Medical College, Xiamen University, School of Medicine, Xiamen, 361102, China
| | - Xi Huang
- Institute of TCM-Related Comorbid Depression, Nanjing University of Chinese Medicine, Nanjing 210023, China; Medical College, Xiamen University, School of Medicine, Xiamen, 361102, China.
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Cerri DH, Albaugh DL, Walton LR, Katz B, Wang TW, Chao THH, Zhang W, Nonneman RJ, Jiang J, Lee SH, Etkin A, Hall CN, Stuber GD, Shih YYI. Distinct neurochemical influences on fMRI response polarity in the striatum. Nat Commun 2024; 15:1916. [PMID: 38429266 PMCID: PMC10907631 DOI: 10.1038/s41467-024-46088-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 02/13/2024] [Indexed: 03/03/2024] Open
Abstract
The striatum, known as the input nucleus of the basal ganglia, is extensively studied for its diverse behavioral roles. However, the relationship between its neuronal and vascular activity, vital for interpreting functional magnetic resonance imaging (fMRI) signals, has not received comprehensive examination within the striatum. Here, we demonstrate that optogenetic stimulation of dorsal striatal neurons or their afferents from various cortical and subcortical regions induces negative striatal fMRI responses in rats, manifesting as vasoconstriction. These responses occur even with heightened striatal neuronal activity, confirmed by electrophysiology and fiber-photometry. In parallel, midbrain dopaminergic neuron optogenetic modulation, coupled with electrochemical measurements, establishes a link between striatal vasodilation and dopamine release. Intriguingly, in vivo intra-striatal pharmacological manipulations during optogenetic stimulation highlight a critical role of opioidergic signaling in generating striatal vasoconstriction. This observation is substantiated by detecting striatal vasoconstriction in brain slices after synthetic opioid application. In humans, manipulations aimed at increasing striatal neuronal activity likewise elicit negative striatal fMRI responses. Our results emphasize the necessity of considering vasoactive neurotransmission alongside neuronal activity when interpreting fMRI signal.
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Affiliation(s)
- Domenic H Cerri
- Center for Animal MRI, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Neurology, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Daniel L Albaugh
- Center for Animal MRI, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Neurology, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Lindsay R Walton
- Center for Animal MRI, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Neurology, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Brittany Katz
- Center for Animal MRI, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Neurology, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tzu-Wen Wang
- Center for Animal MRI, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Neurology, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tzu-Hao Harry Chao
- Center for Animal MRI, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Neurology, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Weiting Zhang
- Center for Animal MRI, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Neurology, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Randal J Nonneman
- Center for Animal MRI, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Neurology, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jing Jiang
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Pediatrics, University of Iowa Carver College of Medicine, Iowa City, IA, USA
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, IA, USA
- Iowa Neuroscience Institute, University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | - Sung-Ho Lee
- Center for Animal MRI, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Neurology, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Amit Etkin
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Alto Neuroscience, Los Altos, CA, USA
| | - Catherine N Hall
- Sussex Neuroscience, University of Sussex, Falmer, United Kingdom
- School of Psychology, University of Sussex, Falmer, United Kingdom
| | - Garret D Stuber
- Center for Neurobiology of Addiction, Pain, and Emotion, University of Washington, Seattle, WA, USA
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA
- Department of Pharmacology, University of Washington, Seattle, WA, USA
| | - Yen-Yu Ian Shih
- Center for Animal MRI, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Biomedical Research Imaging Center, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Neurology, the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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Dresbach S, Huber R, Gulban OF, Pizzuti A, Trampel R, Ivanov D, Weiskopf N, Goebel R. Characterisation of laminar and vascular spatiotemporal dynamics of CBV and BOLD signals using VASO and ME-GRE at 7T in humans. bioRxiv 2024:2024.01.25.576050. [PMID: 38410457 PMCID: PMC10896347 DOI: 10.1101/2024.01.25.576050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Interpretation of cortical laminar functional magnetic resonance imaging (fMRI) activity requires detailed knowledge of the spatiotemporal haemodynamic response across vascular compartments due to the well-known vascular biases (e.g. the draining veins). Further complications arise from the spatiotemporal hemodynamic response that differs depending on the duration of stimulation. This information is crucial for future studies using depth-dependent cerebral blood volume (CBV) measurements, which promise higher specificity for the cortical microvasculature than the blood oxygenation level dependent (BOLD) contrast. To date, direct information about CBV dynamics with respect to stimulus duration, cortical depth and vasculature is missing in humans. Therefore, we characterized the cortical depth-dependent CBV-haemodynamic responses across a wide set of stimulus durations with 0.9 mm isotropic spatial and 0.785 seconds effective temporal resolution in humans using slice-selective slab-inversion vascular space occupancy (SS-SI VASO). Additionally, we investigated signal contributions from macrovascular compartments using fine-scale vascular information from multi-echo gradient-echo (ME-GRE) data at 0.35 mm isotropic resolution. In total, this resulted in >7.5h of scanning per participant (n=5). We have three major findings: (I) While we could demonstrate that 1 second stimulation is viable using VASO, more than 12 seconds stimulation provides better CBV responses in terms of specificity to microvasculature, but durations beyond 24 seconds of stimulation may be wasteful for certain applications. (II) We observe that CBV responses show dilation patterns across the cortex. (III) While we found increasingly strong BOLD signal responses in vessel-dominated voxels with longer stimulation durations, we found increasingly strong CBV signal responses in vessel-dominated voxels only until 4 second stimulation durations. After 4 seconds, only the signal from non-vessel dominated voxels kept increasing. This might explain why CBV responses are more specific to the underlying neuronal activity for long stimulus durations.
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Affiliation(s)
- Sebastian Dresbach
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Renzo Huber
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- National Institutes of Health, Bethesda, MD, USA
| | - Omer Faruk Gulban
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Brain innovation, Maastricht, the Netherlands
| | - Alessandra Pizzuti
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Brain innovation, Maastricht, the Netherlands
| | - Robert Trampel
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Dimo Ivanov
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth System Sciences, Leipzig University, Linnéstraße 5, 04103 Leipzig, Germany
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3AR, UK
| | - Rainer Goebel
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Brain innovation, Maastricht, the Netherlands
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Hike D, Liu X, Xie Z, Zhang B, Choi S, Zhou XA, Liu A, Murstein A, Jiang Y, Devor A, Yu X. High-resolution awake mouse fMRI at 14 Tesla. bioRxiv 2023:2023.12.08.570803. [PMID: 38106227 PMCID: PMC10723470 DOI: 10.1101/2023.12.08.570803] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
High-resolution awake mouse fMRI remains challenging despite extensive efforts to address motion-induced artifacts and stress. This study introduces an implantable radiofrequency (RF) surface coil design that minimizes image distortion caused by the air/tissue interface of mouse brains while simultaneously serving as a headpost for fixation during scanning. Using a 14T scanner, high-resolution fMRI enabled brain-wide functional mapping of visual and vibrissa stimulation at 100×100×200μm resolution with a 2s per frame sampling rate. Besides activated ascending visual and vibrissa pathways, robust BOLD responses were detected in the anterior cingulate cortex upon visual stimulation and spread through the ventral retrosplenial area (VRA) with vibrissa air-puff stimulation, demonstrating higher-order sensory processing in association cortices of awake mice. In particular, the rapid hemodynamic responses in VRA upon vibrissa stimulation showed a strong correlation with the hippocampus, thalamus, and prefrontal cortical areas. Cross-correlation analysis with designated VRA responses revealed early positive BOLD signals at the contralateral barrel cortex (BC) occurring 2 seconds prior to the air-puff in awake mice with repetitive stimulation, which was not detectable with the randomized stimulation paradigm. This early BC activation indicated learned anticipation through the vibrissa system and association cortices in awake mice under continuous training of repetitive air-puff stimulation. This work establishes a high-resolution awake mouse fMRI platform, enabling brain-wide functional mapping of sensory signal processing in higher association cortical areas.
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Affiliation(s)
- David Hike
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, Massachusetts, USA 02129
| | - Xiaochen Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, Massachusetts, USA 02129
| | - Zeping Xie
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, Massachusetts, USA 02129
| | - Bei Zhang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, Massachusetts, USA 02129
| | - Sangcheon Choi
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, Massachusetts, USA 02129
| | - Xiaoqing Alice Zhou
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, Massachusetts, USA 02129
| | - Andy Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, Massachusetts, USA 02129
- Graduate program in Neuroscience, Boston University, Commonwealth Ave, Boston, Massachusetts, USA, 02215
| | - Alyssa Murstein
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, Massachusetts, USA 02129
- Graduate program in Neuroscience, Boston University, Commonwealth Ave, Boston, Massachusetts, USA, 02215
| | - Yuanyuan Jiang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, Massachusetts, USA 02129
| | - Anna Devor
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, Massachusetts, USA 02129
- Department of Biomedical Engineering, Boston University, 610 Commonwealth Avenue, Boston, Massachusetts, USA, 02215
| | - Xin Yu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, Massachusetts, USA 02129
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Kalva SK, Deán-Ben XL, Reiss M, Razansky D. Spiral volumetric optoacoustic tomography for imaging whole-body biodynamics in small animals. Nat Protoc 2023; 18:2124-2142. [PMID: 37208409 DOI: 10.1038/s41596-023-00834-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Accepted: 03/20/2023] [Indexed: 05/21/2023]
Abstract
Fast tracking of biological dynamics across multiple murine organs using the currently commercially available whole-body preclinical imaging systems is hindered by their limited contrast, sensitivity and spatial or temporal resolution. Spiral volumetric optoacoustic tomography (SVOT) provides optical contrast, with an unprecedented level of spatial and temporal resolution, by rapidly scanning a mouse using spherical arrays, thus overcoming the current limitations in whole-body imaging. The method enables the visualization of deep-seated structures in living mammalian tissues in the near-infrared spectral window, while further providing unrivalled image quality and rich spectroscopic optical contrast. Here, we describe the detailed procedures for SVOT imaging of mice and provide specific details on how to implement a SVOT system, including component selection, system arrangement and alignment, as well as the image processing methods. The step-by-step guide for the rapid panoramic (360°) head-to-tail whole-body imaging of a mouse includes the rapid visualization of contrast agent perfusion and biodistribution. The isotropic spatial resolution possible with SVOT can reach 90 µm in 3D, while alternative steps enable whole-body scans in less than 2 s, unattainable with other preclinical imaging modalities. The method further allows the real-time (100 frames per second) imaging of biodynamics at the whole-organ level. The multiscale imaging capacity provided by SVOT can be used for visualizing rapid biodynamics, monitoring responses to treatments and stimuli, tracking perfusion, and quantifying total body accumulation and clearance dynamics of molecular agents and drugs. Depending on the imaging procedure, the protocol requires 1-2 h to complete by users trained in animal handling and biomedical imaging.
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Affiliation(s)
- Sandeep Kumar Kalva
- Institute of Pharmacology and Toxicology and Institute for Biomedical Engineering, Faculty of Medicine, University of Zurich, Zurich, Switzerland
- Institute for Biomedical Engineering, Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland
| | - Xosé Luís Deán-Ben
- Institute of Pharmacology and Toxicology and Institute for Biomedical Engineering, Faculty of Medicine, University of Zurich, Zurich, Switzerland
- Institute for Biomedical Engineering, Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland
| | - Michael Reiss
- Institute of Pharmacology and Toxicology and Institute for Biomedical Engineering, Faculty of Medicine, University of Zurich, Zurich, Switzerland
- Institute for Biomedical Engineering, Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland
| | - Daniel Razansky
- Institute of Pharmacology and Toxicology and Institute for Biomedical Engineering, Faculty of Medicine, University of Zurich, Zurich, Switzerland.
- Institute for Biomedical Engineering, Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland.
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Teal LB, Ingram SM, Bubser M, McClure E, Jones CK. The Evolving Role of Animal Models in the Discovery and Development of Novel Treatments for Psychiatric Disorders. Adv Neurobiol 2023; 30:37-99. [PMID: 36928846 DOI: 10.1007/978-3-031-21054-9_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
Historically, animal models have been routinely used in the characterization of novel chemical entities (NCEs) for various psychiatric disorders. Animal models have been essential in the in vivo validation of novel drug targets, establishment of lead compound pharmacokinetic to pharmacodynamic relationships, optimization of lead compounds through preclinical candidate selection, and development of translational measures of target occupancy and functional target engagement. Yet, with decades of multiple NCE failures in Phase II and III efficacy trials for different psychiatric disorders, the utility and value of animal models in the drug discovery process have come under intense scrutiny along with the widespread withdrawal of the pharmaceutical industry from psychiatric drug discovery. More recently, the development and utilization of animal models for the discovery of psychiatric NCEs has undergone a dynamic evolution with the application of the Research Domain Criteria (RDoC) framework for better design of preclinical to clinical translational studies combined with innovative genetic, neural circuitry-based, and automated testing technologies. In this chapter, the authors will discuss this evolving role of animal models for improving the different stages of the discovery and development in the identification of next generation treatments for psychiatric disorders.
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Affiliation(s)
- Laura B Teal
- Department of Pharmacology, Vanderbilt University, Nashville, TN, USA
- Warren Center for Neuroscience Drug Discovery, Vanderbilt University, Nashville, TN, USA
| | - Shalonda M Ingram
- Department of Pharmacology, Vanderbilt University, Nashville, TN, USA
- Warren Center for Neuroscience Drug Discovery, Vanderbilt University, Nashville, TN, USA
| | - Michael Bubser
- Department of Pharmacology, Vanderbilt University, Nashville, TN, USA
- Warren Center for Neuroscience Drug Discovery, Vanderbilt University, Nashville, TN, USA
| | - Elliott McClure
- College of Pharmacy and Health Sciences, Lipscomb University, Nashville, TN, USA
| | - Carrie K Jones
- Department of Pharmacology, Vanderbilt University, Nashville, TN, USA.
- Warren Center for Neuroscience Drug Discovery, Vanderbilt University, Nashville, TN, USA.
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Lee JY, You T, Woo CW, Kim SG. Optogenetic fMRI for Brain-Wide Circuit Analysis of Sensory Processing. Int J Mol Sci 2022; 23:ijms232012268. [PMID: 36293125 PMCID: PMC9602603 DOI: 10.3390/ijms232012268] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 10/12/2022] [Accepted: 10/12/2022] [Indexed: 11/20/2022] Open
Abstract
Sensory processing is a complex neurological process that receives, integrates, and responds to information from one's own body and environment, which is closely related to survival as well as neurological disorders. Brain-wide networks of sensory processing are difficult to investigate due to their dynamic regulation by multiple brain circuits. Optogenetics, a neuromodulation technique that uses light-sensitive proteins, can be combined with functional magnetic resonance imaging (ofMRI) to measure whole-brain activity. Since ofMRI has increasingly been used for investigating brain circuits underlying sensory processing for over a decade, we systematically reviewed recent ofMRI studies of sensory circuits and discussed the challenges of optogenetic fMRI in rodents.
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Affiliation(s)
- Jeong-Yun Lee
- Center for Neuroscience Imaging Research (CNIR), Institute for Basic Science (IBS), Suwon 16419, Korea
| | - Taeyi You
- Center for Neuroscience Imaging Research (CNIR), Institute for Basic Science (IBS), Suwon 16419, Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon 16419, Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon 16419, Korea
| | - Choong-Wan Woo
- Center for Neuroscience Imaging Research (CNIR), Institute for Basic Science (IBS), Suwon 16419, Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon 16419, Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon 16419, Korea
| | - Seong-Gi Kim
- Center for Neuroscience Imaging Research (CNIR), Institute for Basic Science (IBS), Suwon 16419, Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon 16419, Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon 16419, Korea
- Correspondence: ; Tel.: +82-31-299-4350; Fax: +82-31-299-4506
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Just N, Chevillard PM, Migaud M. Imaging and spectroscopic methods to investigate adult neurogenesis in vivo: New models and new avenues. Front Neurosci 2022; 16:933947. [PMID: 35992937 PMCID: PMC9389108 DOI: 10.3389/fnins.2022.933947] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 07/11/2022] [Indexed: 11/25/2022] Open
Abstract
Adult neurogenesis (AN) can be defined as the birth and development of new neurons in adulthood. Until the 1990s, AN was deemed not to happen after birth. Gradually, several groups demonstrated that specific zones of the brain of various species had a neurogenic potential. AN could be the key to treating a large range of neurodegenerative, neuropsychiatric, and metabolic diseases, with a better understanding of the mechanisms allowing for regeneration of new neurons. Despite this promising prospect, the existence of AN has not been validated in vivo in humans and therefore remains controversial. Moreover, the weight of AN-induced plasticity against other mechanisms of brain plasticity is not known, adding to the controversy. In this review, we would like to show that recent technical advances in brain MR imaging methods combined with improved models can resolve the debate.
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Affiliation(s)
- Nathalie Just
- Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager og Hvidovre, Hvidovre, Denmark
- Physiologie de la Reproduction et des Comportements, Centre INRAE Val de Loire, CNRS, IFCE, INRAE, and Université de Tours, Nouzilly, France
- *Correspondence: Nathalie Just
| | - Pierre-Marie Chevillard
- Physiologie de la Reproduction et des Comportements, Centre INRAE Val de Loire, CNRS, IFCE, INRAE, and Université de Tours, Nouzilly, France
| | - Martine Migaud
- Physiologie de la Reproduction et des Comportements, Centre INRAE Val de Loire, CNRS, IFCE, INRAE, and Université de Tours, Nouzilly, France
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Zhang WT, Chao THH, Yang Y, Wang TW, Lee SH, Oyarzabal EA, Zhou J, Nonneman R, Pegard NC, Zhu H, Cui G, Shih YYI. Spectral fiber photometry derives hemoglobin concentration changes for accurate measurement of fluorescent sensor activity. Cell Rep Methods 2022; 2:100243. [PMID: 35880016 PMCID: PMC9308135 DOI: 10.1016/j.crmeth.2022.100243] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 04/08/2022] [Accepted: 06/08/2022] [Indexed: 12/22/2022]
Abstract
Fiber photometry is an emerging technique for recording fluorescent sensor activity in the brain. However, significant hemoglobin absorption artifacts in fiber photometry data may be misinterpreted as sensor activity changes. Because hemoglobin exists widely in the brain, and its concentration varies temporally, such artifacts could impede the accuracy of photometry recordings. Here we present use of spectral photometry and computational methods to quantify photon absorption effects by using activity-independent fluorescence signals, which can be used to derive oxy- and deoxy-hemoglobin concentration changes. Although these changes are often temporally delayed compared with the fast-responding fluorescence spikes, we found that erroneous interpretation may occur when examining pharmacology-induced sustained changes and that sometimes hemoglobin absorption could flip the GCaMP signal polarity. We provide hemoglobin-based correction methods to restore fluorescence signals and compare our results with other commonly used approaches. We also demonstrated the utility of spectral fiber photometry for delineating regional differences in hemodynamic response functions.
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Affiliation(s)
- Wei-Ting Zhang
- Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Tzu-Hao Harry Chao
- Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yue Yang
- Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Tzu-Wen Wang
- Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Sung-Ho Lee
- Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Esteban A. Oyarzabal
- Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jingheng Zhou
- In Vivo Neurobiology Group, Neurobiology Laboratory, National Institute of Environmental Health Sciences, National Institutes of Health, Durham, NC 27709, USA
| | - Randy Nonneman
- Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Nicolas C. Pegard
- Department of Applied Physical Sciences, UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hongtu Zhu
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Guohong Cui
- In Vivo Neurobiology Group, Neurobiology Laboratory, National Institute of Environmental Health Sciences, National Institutes of Health, Durham, NC 27709, USA
| | - Yen-Yu Ian Shih
- Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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10
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Kosten L, Emmi SA, Missault S, Keliris GA. Combining magnetic resonance imaging with readout and/or perturbation of neural activity in animal models: Advantages and pitfalls. Front Neurosci 2022; 16:938665. [PMID: 35911983 PMCID: PMC9334914 DOI: 10.3389/fnins.2022.938665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 06/28/2022] [Indexed: 11/13/2022] Open
Abstract
One of the main challenges in brain research is to link all aspects of brain function: on a cellular, systemic, and functional level. Multimodal neuroimaging methodology provides a continuously evolving platform. Being able to combine calcium imaging, optogenetics, electrophysiology, chemogenetics, and functional magnetic resonance imaging (fMRI) as part of the numerous efforts on brain functional mapping, we have a unique opportunity to better understand brain function. This review will focus on the developments in application of these tools within fMRI studies and highlight the challenges and choices neurosciences face when designing multimodal experiments.
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Affiliation(s)
- Lauren Kosten
- Bio-Imaging Lab, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Serena Alexa Emmi
- Bio-Imaging Lab, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Stephan Missault
- Bio-Imaging Lab, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Georgios A. Keliris
- Bio-Imaging Lab, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Foundation for Research & Technology – Hellas, Heraklion, Greece
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11
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Ioanas HI, Schlegel F, Skachokova Z, Schroeter A, Husak T, Rudin M. Hybrid fiber optic-fMRI for multimodal cell-specific recording and manipulation of neural activity in rodents. Neurophotonics 2022; 9:032206. [PMID: 35355657 PMCID: PMC8936941 DOI: 10.1117/1.nph.9.3.032206] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Accepted: 01/19/2022] [Indexed: 05/08/2023]
Abstract
Significance: Multiscale imaging holds particular relevance to neuroscience, where it helps integrate the cellular and molecular biological scale, which is most accessible to interventions, with holistic organ-level evaluations, most relevant with respect to function. Being inextricably interdisciplinary, multiscale imaging benefits substantially from incremental technology adoption, and a detailed overview of the state-of-the-art is vital to an informed application of imaging methods. Aim: In this article, we lay out the background and methodological aspects of multimodal approaches combining functional magnetic resonance imaging (fMRI) with simultaneous optical measurement or stimulation. Approach: We focus on optical techniques as these allow, in conjunction with genetically encoded proteins (e.g. calcium indicators or optical signal transducers), unprecedented read-out and control specificity for individual cell-types during fMRI experiments, while leveraging non-interfering modalities. Results: A variety of different solutions for optical/fMRI methods has been reported ranging from bulk fluorescence recordings via fiber photometry to high resolution microscopy. In particular, the plethora of optogenetic tools has enabled the transformation of stimulus-evoked fMRI into a cell biological interrogation method. We discuss the capabilities and limitations of these genetically encoded molecular tools in the study of brain phenomena of great methodological and neuropsychiatric interest-such as neurovascular coupling (NVC) and neuronal network mapping. We provide a methodological description of this interdisciplinary field of study, and focus in particular on the limitations of the widely used blood oxygen level dependent (BOLD) signal and how multimodal readouts can shed light on the contributions arising from neurons, astrocytes, or the vasculature. Conclusion: We conclude that information from multiple signaling pathways must be incorporated in future forward models of the BOLD response to prevent erroneous conclusions when using fMRI as a surrogate measure for neural activity. Further, we highlight the potential of direct neuronal stimulation via genetically defined brain networks towards advancing neurophysiological understanding and better estimating effective connectivity.
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Affiliation(s)
- Horea-Ioan Ioanas
- University of Zurich Institute for Biomedical Engineering, ETH, Zürich, Switzerland
- Massachusetts Institute of Technology, Department of Biological Engineering, Cambridge, Massachusetts, United States
- Dartmouth College, Center for Open Neuroscience, Hanover, New Hampshire, United States
- Address all correspondence to Markus Rudin, ; Horea-Ioan Ioanas,
| | - Felix Schlegel
- University of Zurich Institute for Biomedical Engineering, ETH, Zürich, Switzerland
| | - Zhiva Skachokova
- University of Zurich Institute for Biomedical Engineering, ETH, Zürich, Switzerland
| | - Aileen Schroeter
- University of Zurich Institute for Biomedical Engineering, ETH, Zürich, Switzerland
- University of Zurich, USZ Innovation Hub, Zurich, Switzerland
| | - Tetiana Husak
- Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, Cambridge, Massachusetts, United States
| | - Markus Rudin
- University of Zurich Institute for Biomedical Engineering, ETH, Zürich, Switzerland
- The LOOP Zurich, Zurich, Switzerland
- Address all correspondence to Markus Rudin, ; Horea-Ioan Ioanas,
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12
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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13
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Cleppien D, Aedo-Jury F, Stroh A. Beyond correlation: functional OPTO-MAgnetic Integration Concept (OPTOMAIC) to reveal the brain-wide signature of local neuronal signals-of-interest. Neurophotonics 2022; 9:032213. [PMID: 35813935 PMCID: PMC9259002 DOI: 10.1117/1.nph.9.3.032213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 06/01/2022] [Indexed: 06/15/2023]
Abstract
Significance: Due to the vascular origin of the fMRI signal, the spatiotemporally precise interpretation of the blood oxygen level-dependent (BOLD) response as brain-wide correlate of neuronal activity is limited. Optical fiber-based neuronal calcium recordings provide a specific and temporally highly resolved signal yet lacking brain-wide coverage. The cross-modal integration of both modalities holds the potential for unique synergies. Aim: The OPTO-MAgnetic Integration Concept (OPTOMAIC) extracts the very fraction of the BOLD response that reacts to optically recorded neuronal signals-of-interest. Approach and Results: First, OPTOMAIC identifies the trials containing neuronal signal-of-interest (SoI) in the optical recordings. The long duration of the BOLD response is considered by calculating and thresholding neuronal interevent intervals. The resulting optical regression vector is probed for a positive BOLD response with single-event and single-voxel resolution, generating a BOLD response matrix containing only those events and voxels with both a neuronal SoI and a positive fMRI signal increase. Last, the onset of the BOLD response is being quantified, representing the section of the BOLD response most reliably reporting at least components of the neuronal signal. Conclusions: The seven OPTOMAIC steps result in a brain-wide BOLD signature reflecting the underlying neuronal SoI with utmost cross-modal integration depth and taking full advantage of the specific strengths of each method.
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Affiliation(s)
- Dirk Cleppien
- Leibniz Institute for Resilience Research, Mainz, Germany
| | | | - Albrecht Stroh
- Leibniz Institute for Resilience Research, Mainz, Germany
- Institute of Pathophysiology, University Medical Center of the Johannes Gutenberg University, Mainz, Germany
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14
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Chao THH, Zhang WT, Hsu LM, Cerri DH, Wang TW, Shih YYI. Computing hemodynamic response functions from concurrent spectral fiber-photometry and fMRI data. Neurophotonics 2022; 9:032205. [PMID: 35005057 PMCID: PMC8734587 DOI: 10.1117/1.nph.9.3.032205] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 11/15/2021] [Indexed: 05/31/2023]
Abstract
Significance: Although emerging evidence suggests that the hemodynamic response function (HRF) can vary by brain region and species, a single, canonical, human-based HRF is widely used in animal studies. Therefore, the development of flexible, accessible, brain-region specific HRF calculation approaches is paramount as hemodynamic animal studies become increasingly popular. Aim: To establish an fMRI-compatible, spectral, fiber-photometry platform for HRF calculation and validation in any rat brain region. Approach: We used our platform to simultaneously measure (a) neuronal activity via genetically encoded calcium indicators (GCaMP6f), (b) local cerebral blood volume (CBV) from intravenous Rhodamine B dye, and (c) whole brain CBV via fMRI with the Feraheme contrast agent. Empirical HRFs were calculated with GCaMP6f and Rhodamine B recordings from rat brain regions during resting-state and task-based paradigms. Results: We calculated empirical HRFs for the rat primary somatosensory, anterior cingulate, prelimbic, retrosplenial, and anterior insular cortical areas. Each HRF was faster and narrower than the canonical HRF and no significant difference was observed between these cortical regions. When used in general linear model analyses of corresponding fMRI data, the empirical HRFs showed better detection performance than the canonical HRF. Conclusions: Our findings demonstrate the viability and utility of fiber-photometry-based HRF calculations. This platform is readily scalable to multiple simultaneous recording sites, and adaptable to study transfer functions between stimulation events, neuronal activity, neurotransmitter release, and hemodynamic responses.
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Affiliation(s)
- Tzu-Hao H. Chao
- University of North Carolina at Chapel Hill, Center for Animal MRI, Chapel Hill. North Carolina, United States
- University of North Carolina at Chapel Hill, Biomedical Research Imaging Center, Chapel Hill. North Carolina, United States
- University of North Carolina at Chapel Hill, Department of Neurology, Chapel Hill. North Carolina, United States
| | - Wei-Ting Zhang
- University of North Carolina at Chapel Hill, Center for Animal MRI, Chapel Hill. North Carolina, United States
- University of North Carolina at Chapel Hill, Biomedical Research Imaging Center, Chapel Hill. North Carolina, United States
- University of North Carolina at Chapel Hill, Department of Neurology, Chapel Hill. North Carolina, United States
| | - Li-Ming Hsu
- University of North Carolina at Chapel Hill, Center for Animal MRI, Chapel Hill. North Carolina, United States
- University of North Carolina at Chapel Hill, Biomedical Research Imaging Center, Chapel Hill. North Carolina, United States
- University of North Carolina at Chapel Hill, Department of Neurology, Chapel Hill. North Carolina, United States
| | - Domenic H. Cerri
- University of North Carolina at Chapel Hill, Center for Animal MRI, Chapel Hill. North Carolina, United States
- University of North Carolina at Chapel Hill, Biomedical Research Imaging Center, Chapel Hill. North Carolina, United States
- University of North Carolina at Chapel Hill, Department of Neurology, Chapel Hill. North Carolina, United States
| | - Tzu-Wen Wang
- University of North Carolina at Chapel Hill, Center for Animal MRI, Chapel Hill. North Carolina, United States
- University of North Carolina at Chapel Hill, Biomedical Research Imaging Center, Chapel Hill. North Carolina, United States
| | - Yen-Yu I. Shih
- University of North Carolina at Chapel Hill, Center for Animal MRI, Chapel Hill. North Carolina, United States
- University of North Carolina at Chapel Hill, Biomedical Research Imaging Center, Chapel Hill. North Carolina, United States
- University of North Carolina at Chapel Hill, Department of Neurology, Chapel Hill. North Carolina, United States
- University of North Carolina at Chapel Hill, Department of Biomedical Engineering, Chapel Hill. North Carolina, United States
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15
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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16
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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] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 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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17
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Ni R. Magnetic Resonance Imaging in Tauopathy Animal Models. Front Aging Neurosci 2022; 13:791679. [PMID: 35145392 PMCID: PMC8821905 DOI: 10.3389/fnagi.2021.791679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 12/27/2021] [Indexed: 11/13/2022] Open
Abstract
The microtubule-associated protein tau plays an important role in tauopathic diseases such as Alzheimer’s disease and primary tauopathies such as progressive supranuclear palsy and corticobasal degeneration. Tauopathy animal models, such as transgenic, knock-in mouse and rat models, recapitulating tauopathy have facilitated the understanding of disease mechanisms. Aberrant accumulation of hyperphosphorylated tau contributes to synaptic deficits, neuroinflammation, and neurodegeneration, leading to cognitive impairment in animal models. Recent advances in molecular imaging using positron emission tomography (PET) and magnetic resonance imaging (MRI) have provided valuable insights into the time course of disease pathophysiology in tauopathy animal models. High-field MRI has been applied for in vivo imaging in animal models of tauopathy, including diffusion tensor imaging for white matter integrity, arterial spin labeling for cerebral blood flow, resting-state functional MRI for functional connectivity, volumetric MRI for neurodegeneration, and MR spectroscopy. In addition, MR contrast agents for non-invasive imaging of tau have been developed recently. Many preclinical MRI indicators offer excellent translational value and provide a blueprint for clinical MRI in the brains of patients with tauopathies. In this review, we summarized the recent advances in using MRI to visualize the pathophysiology of tauopathy in small animals. We discussed the outstanding challenges in brain imaging using MRI in small animals and propose a future outlook for visualizing tau-related alterations in the brains of animal models.
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Affiliation(s)
- Ruiqing Ni
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
- *Correspondence: Ruiqing Ni,
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18
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Abdelfattah AS, Ahuja S, Akkin T, Allu SR, Brake J, Boas DA, Buckley EM, Campbell RE, Chen AI, Cheng X, Čižmár T, Costantini I, De Vittorio M, Devor A, Doran PR, El Khatib M, Emiliani V, Fomin-Thunemann N, Fainman Y, Fernandez-Alfonso T, Ferri CGL, Gilad A, Han X, Harris A, Hillman EMC, Hochgeschwender U, Holt MG, Ji N, Kılıç K, Lake EMR, Li L, Li T, Mächler P, Miller EW, Mesquita RC, Nadella KMNS, Nägerl UV, Nasu Y, Nimmerjahn A, Ondráčková P, Pavone FS, Perez Campos C, Peterka DS, Pisano F, Pisanello F, Puppo F, Sabatini BL, Sadegh S, Sakadzic S, Shoham S, Shroff SN, Silver RA, Sims RR, Smith SL, Srinivasan VJ, Thunemann M, Tian L, Tian L, Troxler T, Valera A, Vaziri A, Vinogradov SA, Vitale F, Wang LV, Uhlířová H, Xu C, Yang C, Yang MH, Yellen G, Yizhar O, Zhao Y. Neurophotonic tools for microscopic measurements and manipulation: status report. Neurophotonics 2022; 9:013001. [PMID: 35493335 PMCID: PMC9047450 DOI: 10.1117/1.nph.9.s1.013001] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Neurophotonics was launched in 2014 coinciding with the launch of the BRAIN Initiative focused on development of technologies for advancement of neuroscience. For the last seven years, Neurophotonics' agenda has been well aligned with this focus on neurotechnologies featuring new optical methods and tools applicable to brain studies. While the BRAIN Initiative 2.0 is pivoting towards applications of these novel tools in the quest to understand the brain, this status report reviews an extensive and diverse toolkit of novel methods to explore brain function that have emerged from the BRAIN Initiative and related large-scale efforts for measurement and manipulation of brain structure and function. Here, we focus on neurophotonic tools mostly applicable to animal studies. A companion report, scheduled to appear later this year, will cover diffuse optical imaging methods applicable to noninvasive human studies. For each domain, we outline the current state-of-the-art of the respective technologies, identify the areas where innovation is needed, and provide an outlook for the future directions.
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Affiliation(s)
- Ahmed S. Abdelfattah
- Brown University, Department of Neuroscience, Providence, Rhode Island, United States
| | - Sapna Ahuja
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | - Taner Akkin
- University of Minnesota, Department of Biomedical Engineering, Minneapolis, Minnesota, United States
| | - Srinivasa Rao Allu
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | - Joshua Brake
- Harvey Mudd College, Department of Engineering, Claremont, California, United States
| | - David A. Boas
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Erin M. Buckley
- Georgia Institute of Technology and Emory University, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
- Emory University, Department of Pediatrics, Atlanta, Georgia, United States
| | - Robert E. Campbell
- University of Tokyo, Department of Chemistry, Tokyo, Japan
- University of Alberta, Department of Chemistry, Edmonton, Alberta, Canada
| | - Anderson I. Chen
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Xiaojun Cheng
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Tomáš Čižmár
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Irene Costantini
- University of Florence, European Laboratory for Non-Linear Spectroscopy, Department of Biology, Florence, Italy
- National Institute of Optics, National Research Council, Rome, Italy
| | - Massimo De Vittorio
- Istituto Italiano di Tecnologia, Center for Biomolecular Nanotechnologies, Arnesano, Italy
| | - Anna Devor
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
| | - Patrick R. Doran
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Mirna El Khatib
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | | | - Natalie Fomin-Thunemann
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Yeshaiahu Fainman
- University of California San Diego, Department of Electrical and Computer Engineering, La Jolla, California, United States
| | - Tomas Fernandez-Alfonso
- University College London, Department of Neuroscience, Physiology and Pharmacology, London, United Kingdom
| | - Christopher G. L. Ferri
- University of California San Diego, Departments of Neurosciences, La Jolla, California, United States
| | - Ariel Gilad
- The Hebrew University of Jerusalem, Institute for Medical Research Israel–Canada, Department of Medical Neurobiology, Faculty of Medicine, Jerusalem, Israel
| | - Xue Han
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Andrew Harris
- Weizmann Institute of Science, Department of Brain Sciences, Rehovot, Israel
| | | | - Ute Hochgeschwender
- Central Michigan University, Department of Neuroscience, Mount Pleasant, Michigan, United States
| | - Matthew G. Holt
- University of Porto, Instituto de Investigação e Inovação em Saúde (i3S), Porto, Portugal
| | - Na Ji
- University of California Berkeley, Department of Physics, Berkeley, California, United States
| | - Kıvılcım Kılıç
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Evelyn M. R. Lake
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, Connecticut, United States
| | - Lei Li
- California Institute of Technology, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, Pasadena, California, United States
| | - Tianqi Li
- University of Minnesota, Department of Biomedical Engineering, Minneapolis, Minnesota, United States
| | - Philipp Mächler
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Evan W. Miller
- University of California Berkeley, Departments of Chemistry and Molecular & Cell Biology and Helen Wills Neuroscience Institute, Berkeley, California, United States
| | | | | | - U. Valentin Nägerl
- Interdisciplinary Institute for Neuroscience University of Bordeaux & CNRS, Bordeaux, France
| | - Yusuke Nasu
- University of Tokyo, Department of Chemistry, Tokyo, Japan
| | - Axel Nimmerjahn
- Salk Institute for Biological Studies, Waitt Advanced Biophotonics Center, La Jolla, California, United States
| | - Petra Ondráčková
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Francesco S. Pavone
- National Institute of Optics, National Research Council, Rome, Italy
- University of Florence, European Laboratory for Non-Linear Spectroscopy, Department of Physics, Florence, Italy
| | - Citlali Perez Campos
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, United States
| | - Darcy S. Peterka
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, United States
| | - Filippo Pisano
- Istituto Italiano di Tecnologia, Center for Biomolecular Nanotechnologies, Arnesano, Italy
| | - Ferruccio Pisanello
- Istituto Italiano di Tecnologia, Center for Biomolecular Nanotechnologies, Arnesano, Italy
| | - Francesca Puppo
- University of California San Diego, Departments of Neurosciences, La Jolla, California, United States
| | - Bernardo L. Sabatini
- Harvard Medical School, Howard Hughes Medical Institute, Department of Neurobiology, Boston, Massachusetts, United States
| | - Sanaz Sadegh
- University of California San Diego, Departments of Neurosciences, La Jolla, California, United States
| | - Sava Sakadzic
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
| | - Shy Shoham
- New York University Grossman School of Medicine, Tech4Health and Neuroscience Institutes, New York, New York, United States
| | - Sanaya N. Shroff
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - R. Angus Silver
- University College London, Department of Neuroscience, Physiology and Pharmacology, London, United Kingdom
| | - Ruth R. Sims
- Sorbonne University, INSERM, CNRS, Institut de la Vision, Paris, France
| | - Spencer L. Smith
- University of California Santa Barbara, Department of Electrical and Computer Engineering, Santa Barbara, California, United States
| | - Vivek J. Srinivasan
- New York University Langone Health, Departments of Ophthalmology and Radiology, New York, New York, United States
| | - Martin Thunemann
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Lei Tian
- Boston University, Departments of Electrical Engineering and Biomedical Engineering, Boston, Massachusetts, United States
| | - Lin Tian
- University of California Davis, Department of Biochemistry and Molecular Medicine, Davis, California, United States
| | - Thomas Troxler
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | - Antoine Valera
- University College London, Department of Neuroscience, Physiology and Pharmacology, London, United Kingdom
| | - Alipasha Vaziri
- Rockefeller University, Laboratory of Neurotechnology and Biophysics, New York, New York, United States
- The Rockefeller University, The Kavli Neural Systems Institute, New York, New York, United States
| | - Sergei A. Vinogradov
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | - Flavia Vitale
- Center for Neuroengineering and Therapeutics, Departments of Neurology, Bioengineering, Physical Medicine and Rehabilitation, Philadelphia, Pennsylvania, United States
| | - Lihong V. Wang
- California Institute of Technology, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, Pasadena, California, United States
| | - Hana Uhlířová
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Chris Xu
- Cornell University, School of Applied and Engineering Physics, Ithaca, New York, United States
| | - Changhuei Yang
- California Institute of Technology, Departments of Electrical Engineering, Bioengineering and Medical Engineering, Pasadena, California, United States
| | - Mu-Han Yang
- University of California San Diego, Department of Electrical and Computer Engineering, La Jolla, California, United States
| | - Gary Yellen
- Harvard Medical School, Department of Neurobiology, Boston, Massachusetts, United States
| | - Ofer Yizhar
- Weizmann Institute of Science, Department of Brain Sciences, Rehovot, Israel
| | - Yongxin Zhao
- Carnegie Mellon University, Department of Biological Sciences, Pittsburgh, Pennsylvania, United States
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19
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Chen Y, Wang Q, Choi S, Zeng H, Takahashi K, Qian C, Yu X. Focal fMRI signal enhancement with implantable inductively coupled detectors. Neuroimage 2021; 247:118793. [PMID: 34896291 PMCID: PMC8842502 DOI: 10.1016/j.neuroimage.2021.118793] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Revised: 12/02/2021] [Accepted: 12/07/2021] [Indexed: 12/16/2022] Open
Abstract
Despite extensive efforts to increase the signal-to-noise ratio (SNR) of fMRI images for brain-wide mapping, technical advances of focal brain signal enhancement are lacking, in particular, for animal brain imaging. Emerging studies have combined fMRI with fiber optic-based optogenetics to decipher circuit-specific neuromodulation from meso to macroscales. High-resolution fMRI is needed to integrate hemodynamic responses into cross-scale functional dynamics, but the SNR remains a limiting factor given the complex implantation setup of animal brains. Here, we developed a multimodal fMRI imaging platform with an implanted inductive coil detector. This detector boosts the tSNR of MRI images, showing a 2–3-fold sensitivity gain over conventional coil configuration. In contrast to the cryoprobe or array coils with limited spaces for implanted brain interface, this setup offers a unique advantage to study brain circuit connectivity with optogenetic stimulation and can be further extended to other multimodal fMRI mapping schemes.
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Affiliation(s)
- Yi Chen
- Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany; Max Planck Institute for the Science of Light, 91058, Erlangen, Germany
| | - Qi Wang
- Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany; Graduate Training Centre of Neuroscience, University of Tuebingen, 72076 Tuebingen, Germany
| | - Sangcheon Choi
- Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany; Graduate Training Centre of Neuroscience, University of Tuebingen, 72076 Tuebingen, Germany
| | - Hang Zeng
- Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany; Graduate Training Centre of Neuroscience, University of Tuebingen, 72076 Tuebingen, Germany
| | - Kengo Takahashi
- Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany; Graduate Training Centre of Neuroscience, University of Tuebingen, 72076 Tuebingen, Germany
| | - Chunqi Qian
- Department of Radiology, Michigan State University, East Lansing, MI 48824, USA.
| | - Xin Yu
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA.
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20
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Walton LR, Verber M, Lee SH, Chao THH, Wightman RM, Shih YYI. Simultaneous fMRI and fast-scan cyclic voltammetry bridges evoked oxygen and neurotransmitter dynamics across spatiotemporal scales. Neuroimage 2021; 244:118634. [PMID: 34624504 PMCID: PMC8667333 DOI: 10.1016/j.neuroimage.2021.118634] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 09/10/2021] [Accepted: 10/04/2021] [Indexed: 12/24/2022] Open
Abstract
The vascular contributions of neurotransmitters to the hemodynamic response are gaining more attention in neuroimaging studies, as many neurotransmitters are vasomodulatory. To date, well-established electrochemical techniques that detect neurotransmission in high magnetic field environments are limited. Here, we propose an experimental setting enabling simultaneous fast-scan cyclic voltammetry (FSCV) and blood oxygenation level-dependent functional magnetic imaging (BOLD fMRI) to measure both local tissue oxygen and dopamine responses, and global BOLD changes, respectively. By using MR-compatible materials and the proposed data acquisition schemes, FSCV detected physiological analyte concentrations with high temporal resolution and spatial specificity inside of a 9.4 T MRI bore. We found that tissue oxygen and BOLD correlate strongly, and brain regions that encode dopamine amplitude differences can be identified via modeling simultaneously acquired dopamine FSCV and BOLD fMRI time-courses. This technique provides complementary neurochemical and hemodynamic information and expands the scope of studying the influence of local neurotransmitter release over the entire brain.
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Affiliation(s)
- Lindsay R Walton
- Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America; Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America; Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America.
| | - Matthew Verber
- Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America; Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America; Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Sung-Ho Lee
- Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America; Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America; Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Tzu-Hao Harry Chao
- Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America; Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America; Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - R Mark Wightman
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Yen-Yu Ian Shih
- Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America; Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America; Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America.
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21
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Pais-Roldán P, Mateo C, Pan WJ, Acland B, Kleinfeld D, Snyder LH, Yu X, Keilholz S. Contribution of animal models toward understanding resting state functional connectivity. Neuroimage 2021; 245:118630. [PMID: 34644593 DOI: 10.1016/j.neuroimage.2021.118630] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 08/06/2021] [Accepted: 09/29/2021] [Indexed: 12/27/2022] Open
Abstract
Functional connectivity, which reflects the spatial and temporal organization of intrinsic activity throughout the brain, is one of the most studied measures in human neuroimaging research. The noninvasive acquisition of resting state functional magnetic resonance imaging (rs-fMRI) allows the characterization of features designated as functional networks, functional connectivity gradients, and time-varying activity patterns that provide insight into the intrinsic functional organization of the brain and potential alterations related to brain dysfunction. Functional connectivity, hence, captures dimensions of the brain's activity that have enormous potential for both clinical and preclinical research. However, the mechanisms underlying functional connectivity have yet to be fully characterized, hindering interpretation of rs-fMRI studies. As in other branches of neuroscience, the identification of the neurophysiological processes that contribute to functional connectivity largely depends on research conducted on laboratory animals, which provide a platform where specific, multi-dimensional investigations that involve invasive measurements can be carried out. These highly controlled experiments facilitate the interpretation of the temporal correlations observed across the brain. Indeed, information obtained from animal experimentation to date is the basis for our current understanding of the underlying basis for functional brain connectivity. This review presents a compendium of some of the most critical advances in the field based on the efforts made by the animal neuroimaging community.
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22
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Chen Q, Jin T, Qi W, Xi L. Dual-model wearable photoacoustic microscopy and electroencephalograph: study of neurovascular coupling in anesthetized and freely moving rats. Biomed Opt Express 2021; 12:6614-6628. [PMID: 34745760 PMCID: PMC8547996 DOI: 10.1364/boe.438596] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 09/11/2021] [Accepted: 09/14/2021] [Indexed: 05/29/2023]
Abstract
Observing microscale neurovascular dynamics under different physiological conditions is of great importance to understanding brain functions and disorders. Here, we report a dual-model wearable device and an auxiliary data processing algorithm to derive neurovascular dynamics. The device integrates high-resolution photoacoustic microscopy and electroencephalography (EEG), which allows observing capillary-level hemodynamics and neural activities in anesthesia and freely moving rats. By using the developed algorithm, multiple photoacoustic/EEG parameters extracted and correlated enables investigation of the interplay between neural and vascular activities. We employed this platform to study the neurovascular coupling during different types of seizures in rats under various physiological conditions. We observed cerebral vascular vasodilation/constriction corresponding well to the seizure on/off in rats under regular anesthesia conditions, showing a strong neurovascular coupling coefficient. In rats under weak anesthesia and freely moving conditions, more intense cerebral hemodynamics and neural activities occurred with a weaker neurovascular coupling coefficient. The comprehensively quantitative analyses suggest that anesthesia has a dominant impact on the seizure onset and affect the neurovascular coupling correlation in the current drug-induced localized seizure model. Our study reveals that the designed platform has the potential to support studies on brain functions and disorders in diseased rodent models in various physiological states.
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Affiliation(s)
- Qian Chen
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Tian Jin
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, China
| | - Weizhi Qi
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, China
| | - Lei Xi
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, China
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23
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Kumamoto T, Tsurugizawa T. Potential of Multiscale Astrocyte Imaging for Revealing Mechanisms Underlying Neurodevelopmental Disorders. Int J Mol Sci 2021; 22:10312. [PMID: 34638653 DOI: 10.3390/ijms221910312] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 09/21/2021] [Accepted: 09/22/2021] [Indexed: 01/18/2023] Open
Abstract
Astrocytes provide trophic and metabolic support to neurons and modulate circuit formation during development. In addition, astrocytes help maintain neuronal homeostasis through neurovascular coupling, blood-brain barrier maintenance, clearance of metabolites and nonfunctional proteins via the glymphatic system, extracellular potassium buffering, and regulation of synaptic activity. Thus, astrocyte dysfunction may contribute to a myriad of neurological disorders. Indeed, astrocyte dysfunction during development has been implicated in Rett disease, Alexander's disease, epilepsy, and autism, among other disorders. Numerous disease model mice have been established to investigate these diseases, but important preclinical findings on etiology and pathophysiology have not translated into clinical interventions. A multidisciplinary approach is required to elucidate the mechanism of these diseases because astrocyte dysfunction can result in altered neuronal connectivity, morphology, and activity. Recent progress in neuroimaging techniques has enabled noninvasive investigations of brain structure and function at multiple spatiotemporal scales, and these technologies are expected to facilitate the translation of preclinical findings to clinical studies and ultimately to clinical trials. Here, we review recent progress on astrocyte contributions to neurodevelopmental and neuropsychiatric disorders revealed using novel imaging techniques, from microscopy scale to mesoscopic scale.
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24
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Chen X, Jiang Y, Choi S, Pohmann R, Scheffler K, Kleinfeld D, Yu X. Assessment of single-vessel cerebral blood velocity by phase contrast fMRI. PLoS Biol 2021; 19:e3000923. [PMID: 34499636 PMCID: PMC8454982 DOI: 10.1371/journal.pbio.3000923] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 09/21/2021] [Accepted: 07/28/2021] [Indexed: 12/30/2022] Open
Abstract
Current approaches to high-field functional MRI (fMRI) provide 2 means to map hemodynamics at the level of single vessels in the brain. One is through changes in deoxyhemoglobin in venules, i.e., blood oxygenation level-dependent (BOLD) fMRI, while the second is through changes in arteriole diameter, i.e., cerebral blood volume (CBV) fMRI. Here, we introduce cerebral blood flow-related velocity-based fMRI, denoted CBFv-fMRI, which uses high-resolution phase contrast (PC) MRI to form velocity measurements of flow. We use CBFv-fMRI in measure changes in blood velocity in single penetrating microvessels across rat parietal cortex. In contrast to the venule-dominated BOLD and arteriole-dominated CBV fMRI signals, CBFv-fMRI is comparable from both arterioles and venules. A single fMRI platform is used to map changes in blood pO2 (BOLD), volume (CBV), and velocity (CBFv). This combined high-resolution single-vessel fMRI mapping scheme enables vessel-specific hemodynamic mapping in animal models of normal and diseased states and further has translational potential to map vascular dementia in diseased or injured human brains with ultra-high-field fMRI.
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Affiliation(s)
- Xuming Chen
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Department of Neurology, Wuhan University, Renmin Hospital, Wuhan, China
| | - Yuanyuan Jiang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, United States of America
| | - Sangcheon Choi
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tübingen, Tübingen, Germany
| | - Rolf Pohmann
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Klaus Scheffler
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Department for Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
| | - David Kleinfeld
- Department of Physics, University of California at San Diego, La Jolla, California, United States of America
- Section of Neurobiology, University of California at San Diego, La Jolla, California, United States of America
| | - Xin Yu
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, United States of America
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25
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Edelman BJ, Ielacqua GD, Chan RW, Asaad M, Choy M, Lee JH. High-sensitivity detection of optogenetically-induced neural activity with functional ultrasound imaging. Neuroimage 2021; 242:118434. [PMID: 34333106 DOI: 10.1016/j.neuroimage.2021.118434] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Revised: 07/20/2021] [Accepted: 07/26/2021] [Indexed: 12/11/2022] Open
Abstract
Whole-brain imaging approaches and optogenetic manipulations are powerful tools to map brain-wide neural circuits in vivo. To date, functional magnetic resonance imaging (fMRI) provides the most comprehensive evaluation of such large-scale circuitry. However, functional ultrasound imaging (fUSI) has recently emerged as a complementary imaging modality that can extend such measurements towards the context of diverse behavioral states and tasks. Nevertheless, in order to properly interpret the fUSI signal during these complicated scenarios, it must first be carefully validated against well-established technologies, such as fMRI, in highly controlled experimental settings. Here, to address this need, we compared subsequent fMRI and fUSI recordings in response to direct neuronal activation via optogenetics in the same animals under an identical anesthetic protocol. Specifically, we applied various intensities of light stimulation to the primary motor cortex (M1) of mice and compared the spatiotemporal dynamics of the elicited fMRI and fUSI signals. Overall, our general linear model analysis (t-scores) and time series analysis (z-scores) revealed that fUSI was more sensitive than fMRI for detecting optogenetically-induced neuronal activation. Local field potential recordings in the bilateral M1 and striatum also better co-localized with fUSI activation patterns than those of fMRI. Finally, the fUSI response contained distinct arterial and venous components that provide vascular readouts of neuronal activity with vessel-type specificity.
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26
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Provansal M, Labernède G, Joffrois C, Rizkallah A, Goulet R, Valet M, Deschamps W, Ferrari U, Chaffiol A, Dalkara D, Sahel JA, Tanter M, Picaud S, Gauvain G, Arcizet F. Functional ultrasound imaging of the spreading activity following optogenetic stimulation of the rat visual cortex. Sci Rep 2021; 11:12603. [PMID: 34131223 DOI: 10.1038/s41598-021-91972-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 05/31/2021] [Indexed: 02/05/2023] Open
Abstract
Optogenetics has revolutionized neurosciences by allowing fine control of neuronal activity. An important aspect for this control is assessing the activation and/or adjusting the stimulation, which requires imaging the entire volume of optogenetically-induced neuronal activity. An ideal technique for this aim is fUS imaging, which allows one to generate brain-wide activation maps with submesoscopic spatial resolution. However, optical stimulation of the brain with blue light might lead to non-specific activations at high irradiances. fUS imaging of optogenetic activations can be obtained at these wavelengths using lower light power (< 2mW) but it limits the depth of directly activatable neurons from the cortical surface. Our main goal was to report that we can detect specific optogenetic activations in V1 even in deep layers following stimulation at the cortical surface. Here, we show the possibility to detect deep optogenetic activations in anesthetized rats expressing the red-shifted opsin ChrimsonR in V1 using fUS imaging. We demonstrate the optogenetic specificity of these activations and their neuronal origin with electrophysiological recordings. Finally, we show that the optogenetic response initiated in V1 spreads to downstream (LGN) and upstream (V2) visual areas.
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27
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Robin J, Rau R, Lafci B, Schroeter A, Reiss M, Deán-Ben XL, Goksel O, Razansky D. Hemodynamic response to sensory stimulation in mice: Comparison between functional ultrasound and optoacoustic imaging. Neuroimage 2021; 237:118111. [PMID: 33940140 DOI: 10.1016/j.neuroimage.2021.118111] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 02/18/2021] [Accepted: 04/19/2021] [Indexed: 11/23/2022] Open
Abstract
Intense efforts are underway to develop functional imaging modalities for capturing brain activity at the whole organ scale with high spatial and temporal resolution. Functional optoacoustic (fOA) imaging is emerging as a new tool to monitor multiple hemodynamic parameters across the mouse brain, but its sound validation against other neuroimaging modalities is often lacking. Here we investigate mouse brain responses to peripheral sensory stimulation using both fOA and functional ultrasound (fUS) imaging. The two modalities operate under similar spatio-temporal resolution regime, with a potential to provide synergistic and complementary hemodynamic readouts. Specific contralateral activation was observed with sub-millimeter spatial resolution with both methods. Sensitivity to hemodynamic activity was found to be on comparable levels, with the strongest responses obtained in the oxygenated hemoglobin channel of fOA. While the techniques attained highly correlated hemodynamic responses, the differential fOA readings of oxygenated and deoxygenated haemoglobin provided complementary information to the blood flow contrast of fUS. The multi-modal approach may thus emerge as a powerful tool providing new insights into brain function, complementing our current knowledge generated with well-established neuroimaging methods.
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28
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Klink PC, Aubry JF, Ferrera VP, Fox AS, Froudist-Walsh S, Jarraya B, Konofagou EE, Krauzlis RJ, Messinger A, Mitchell AS, Ortiz-Rios M, Oya H, Roberts AC, Roe AW, Rushworth MFS, Sallet J, Schmid MC, Schroeder CE, Tasserie J, Tsao DY, Uhrig L, Vanduffel W, Wilke M, Kagan I, Petkov CI. Combining brain perturbation and neuroimaging in non-human primates. Neuroimage 2021; 235:118017. [PMID: 33794355 DOI: 10.1016/j.neuroimage.2021.118017] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 03/07/2021] [Accepted: 03/22/2021] [Indexed: 12/11/2022] Open
Abstract
Brain perturbation studies allow detailed causal inferences of behavioral and neural processes. Because the combination of brain perturbation methods and neural measurement techniques is inherently challenging, research in humans has predominantly focused on non-invasive, indirect brain perturbations, or neurological lesion studies. Non-human primates have been indispensable as a neurobiological system that is highly similar to humans while simultaneously being more experimentally tractable, allowing visualization of the functional and structural impact of systematic brain perturbation. This review considers the state of the art in non-human primate brain perturbation with a focus on approaches that can be combined with neuroimaging. We consider both non-reversible (lesions) and reversible or temporary perturbations such as electrical, pharmacological, optical, optogenetic, chemogenetic, pathway-selective, and ultrasound based interference methods. Method-specific considerations from the research and development community are offered to facilitate research in this field and support further innovations. We conclude by identifying novel avenues for further research and innovation and by highlighting the clinical translational potential of the methods.
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Affiliation(s)
- P Christiaan Klink
- Department of Vision & Cognition, Netherlands Institute for Neuroscience, Meibergdreef 47, 1105 BA Amsterdam, the Netherlands.
| | - Jean-François Aubry
- Physics for Medicine Paris, Inserm U1273, CNRS UMR 8063, ESPCI Paris, PSL University, Paris, France
| | - Vincent P Ferrera
- Department of Neuroscience & Department of Psychiatry, Columbia University Medical Center, New York, NY, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Andrew S Fox
- Department of Psychology & California National Primate Research Center, University of California, Davis, CA, USA
| | | | - Béchir Jarraya
- NeuroSpin, Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA), Institut National de la Santé et de la Recherche Médicale (INSERM), Cognitive Neuroimaging Unit, Université Paris-Saclay, France; Foch Hospital, UVSQ, Suresnes, France
| | - Elisa E Konofagou
- Ultrasound and Elasticity Imaging Laboratory, Department of Biomedical Engineering, Columbia University, New York, NY, USA; Department of Radiology, Columbia University, New York, NY, USA
| | - Richard J Krauzlis
- Laboratory of Sensorimotor Research, National Eye Institute, Bethesda, MD, USA
| | - Adam Messinger
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA
| | - Anna S Mitchell
- Department of Experimental Psychology, Oxford University, Oxford, United Kingdom
| | - Michael Ortiz-Rios
- Newcastle University Medical School, Newcastle upon Tyne NE1 7RU, United Kingdom; German Primate Center, Leibniz Institute for Primate Research, Kellnerweg 4, 37077 Göttingen, Germany
| | - Hiroyuki Oya
- Iowa Neuroscience Institute, Carver College of Medicine, University of Iowa, Iowa City, IA, USA; Department of Neurosurgery, University of Iowa, Iowa city, IA, USA
| | - Angela C Roberts
- Department of Physiology, Development and Neuroscience, Cambridge University, Cambridge, United Kingdom
| | - Anna Wang Roe
- Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou 310029, China
| | | | - Jérôme Sallet
- Department of Experimental Psychology, Oxford University, Oxford, United Kingdom; Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute, U1208 Bron, France; Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
| | - Michael Christoph Schmid
- Newcastle University Medical School, Newcastle upon Tyne NE1 7RU, United Kingdom; Faculty of Science and Medicine, University of Fribourg, Chemin du Musée 5, CH-1700 Fribourg, Switzerland
| | - Charles E Schroeder
- Nathan Kline Institute, Orangeburg, NY, USA; Columbia University, New York, NY, USA
| | - Jordy Tasserie
- NeuroSpin, Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA), Institut National de la Santé et de la Recherche Médicale (INSERM), Cognitive Neuroimaging Unit, Université Paris-Saclay, France
| | - Doris Y Tsao
- Division of Biology and Biological Engineering, Tianqiao and Chrissy Chen Institute for Neuroscience; Howard Hughes Medical Institute; Computation and Neural Systems, Caltech, Pasadena, CA, USA
| | - Lynn Uhrig
- NeuroSpin, Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA), Institut National de la Santé et de la Recherche Médicale (INSERM), Cognitive Neuroimaging Unit, Université Paris-Saclay, France
| | - Wim Vanduffel
- Laboratory for Neuro- and Psychophysiology, Neurosciences Department, KU Leuven Medical School, Leuven, Belgium; Leuven Brain Institute, KU Leuven, Leuven Belgium; Harvard Medical School, Boston, MA, USA; Massachusetts General Hospital, Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Melanie Wilke
- German Primate Center, Leibniz Institute for Primate Research, Kellnerweg 4, 37077 Göttingen, Germany; Department of Cognitive Neurology, University Medicine Göttingen, Göttingen, Germany
| | - Igor Kagan
- German Primate Center, Leibniz Institute for Primate Research, Kellnerweg 4, 37077 Göttingen, Germany.
| | - Christopher I Petkov
- Newcastle University Medical School, Newcastle upon Tyne NE1 7RU, United Kingdom.
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Kılıç K, Desjardins M, Tang J, Thunemann M, Sunil S, Erdener ŞE, Postnov DD, Boas DA, Devor A. Chronic Cranial Windows for Long Term Multimodal Neurovascular Imaging in Mice. Front Physiol 2021; 11:612678. [PMID: 33551837 PMCID: PMC7862556 DOI: 10.3389/fphys.2020.612678] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 12/18/2020] [Indexed: 01/18/2023] Open
Abstract
Chronic cranial windows allow for longitudinal brain imaging experiments in awake, behaving mice. Different imaging technologies have their unique advantages and combining multiple imaging modalities offers measurements of a wide spectrum of neuronal, glial, vascular, and metabolic parameters needed for comprehensive investigation of physiological and pathophysiological mechanisms. Here, we detail a suite of surgical techniques for installation of different cranial windows targeted for specific imaging technologies and their combination. Following these techniques and practices will yield higher experimental success and reproducibility of results.
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Affiliation(s)
- Kıvılcım Kılıç
- Biomedical Engineering, Boston University, Boston, MA, United States
| | - Michèle Desjardins
- Centre de recherche du CHU de Québec, Université Laval, Quebec City, QC, Canada
| | - Jianbo Tang
- Biomedical Engineering, Boston University, Boston, MA, United States.,Department of Biomedical Engineering, SUSTech, Shenzhen, China
| | - Martin Thunemann
- Biomedical Engineering, Boston University, Boston, MA, United States
| | - Smrithi Sunil
- Biomedical Engineering, Boston University, Boston, MA, United States
| | - Şefik Evren Erdener
- Biomedical Engineering, Boston University, Boston, MA, United States.,Institute of Neurological Sciences and Psychiatry, Hacettepe Üniversitesi, Ankara, Turkey
| | - Dmitry D Postnov
- Biomedical Engineering, Boston University, Boston, MA, United States.,Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - David A Boas
- Biomedical Engineering, Boston University, Boston, MA, United States
| | - Anna Devor
- Biomedical Engineering, Boston University, Boston, MA, United States
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30
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Abstract
Neuroscience has seen substantial development in non-invasive methods available for investigating the living human brain. However, these tools are limited to coarse macroscopic measures of neural activity that aggregate the diverse responses of thousands of cells. To access neural activity at the cellular and circuit level, researchers instead rely on invasive recordings in animals. Recent advances in invasive methods now permit large-scale recording and circuit-level manipulations with exquisite spatio-temporal precision. Yet, there has been limited progress in relating these microcircuit measures to complex cognition and behaviour observed in humans. Contemporary neuroscience thus faces an explanatory gap between macroscopic descriptions of the human brain and microscopic descriptions in animal models. To close the explanatory gap, we propose adopting a cross-species approach. Despite dramatic differences in the size of mammalian brains, this approach is broadly justified by preserved homology. Here, we outline a three-armed approach for effective cross-species investigation that highlights the need to translate different measures of neural activity into a common space. We discuss how a cross-species approach has the potential to transform basic neuroscience while also benefiting neuropsychiatric drug development where clinical translation has, to date, seen minimal success. This article is part of the theme issue 'Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity'.
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Affiliation(s)
- Helen C. Barron
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford OX1 3TH, UK
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Rogier B. Mars
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford OX3 9DU, UK
- Donders Institute for Brain, Cognition and Behavior, Radboud University, 6525 AJ Nijmegen, The Netherlands
| | - David Dupret
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford OX1 3TH, UK
| | - Jason P. Lerch
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford OX3 9DU, UK
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, CanadaM5G 1L7
| | - Cassandra Sampaio-Baptista
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford OX3 9DU, UK
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow G12 8QB, UK
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31
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Bergel A, Tiran E, Deffieux T, Demené C, Tanter M, Cohen I. Adaptive modulation of brain hemodynamics across stereotyped running episodes. Nat Commun 2020; 11:6193. [PMID: 33273463 PMCID: PMC7713412 DOI: 10.1038/s41467-020-19948-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 11/06/2020] [Indexed: 12/14/2022] Open
Abstract
During locomotion, theta and gamma rhythms are essential to ensure timely communication between brain structures. However, their metabolic cost and contribution to neuroimaging signals remain elusive. To finely characterize neurovascular interactions during locomotion, we simultaneously recorded mesoscale brain hemodynamics using functional ultrasound (fUS) and local field potentials (LFP) in numerous brain structures of freely-running overtrained rats. Locomotion events were reliably followed by a surge in blood flow in a sequence involving the retrosplenial cortex, dorsal thalamus, dentate gyrus and CA regions successively, with delays ranging from 0.8 to 1.6 seconds after peak speed. Conversely, primary motor cortex was suppressed and subsequently recruited during reward uptake. Surprisingly, brain hemodynamics were strongly modulated across trials within the same recording session; cortical blood flow sharply decreased after 10-20 runs, while hippocampal responses strongly and linearly increased, particularly in the CA regions. This effect occurred while running speed and theta activity remained constant and was accompanied by an increase in the power of hippocampal, but not cortical, high-frequency oscillations (100-150 Hz). Our findings reveal distinct vascular subnetworks modulated across fast and slow timescales and suggest strong hemodynamic adaptation, despite the repetition of a stereotyped behavior.
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Affiliation(s)
- Antoine Bergel
- Sorbonne Université, CNRS, INSERM, Institut de Biologie Paris Seine-Neuroscience, 75005, Paris, France.
- Physique pour la Médecine Paris, INSERM U1273, ESPCI Paris, CNRS FRE 2031, PSL Université Recherche, Paris, France.
| | - Elodie Tiran
- Physique pour la Médecine Paris, INSERM U1273, ESPCI Paris, CNRS FRE 2031, PSL Université Recherche, Paris, France
| | - Thomas Deffieux
- Physique pour la Médecine Paris, INSERM U1273, ESPCI Paris, CNRS FRE 2031, PSL Université Recherche, Paris, France
| | - Charlie Demené
- Physique pour la Médecine Paris, INSERM U1273, ESPCI Paris, CNRS FRE 2031, PSL Université Recherche, Paris, France
| | - Mickaël Tanter
- Physique pour la Médecine Paris, INSERM U1273, ESPCI Paris, CNRS FRE 2031, PSL Université Recherche, Paris, France.
| | - Ivan Cohen
- Sorbonne Université, CNRS, INSERM, Institut de Biologie Paris Seine-Neuroscience, 75005, Paris, France.
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32
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Sobczak F, He Y, Sejnowski TJ, Yu X. Predicting the fMRI Signal Fluctuation with Recurrent Neural Networks Trained on Vascular Network Dynamics. Cereb Cortex 2020; 31:826-844. [PMID: 32940658 PMCID: PMC7906791 DOI: 10.1093/cercor/bhaa260] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 07/19/2020] [Accepted: 08/12/2020] [Indexed: 02/06/2023] Open
Abstract
Resting-state functional MRI (rs-fMRI) studies have revealed specific low-frequency hemodynamic signal fluctuations (<0.1 Hz) in the brain, which could be related to neuronal oscillations through the neurovascular coupling mechanism. Given the vascular origin of the fMRI signal, it remains challenging to separate the neural correlates of global rs-fMRI signal fluctuations from other confounding sources. However, the slow-oscillation detected from individual vessels by single-vessel fMRI presents strong correlation to neural oscillations. Here, we use recurrent neural networks (RNNs) to predict the future temporal evolution of the rs-fMRI slow oscillation from both rodent and human brains. The RNNs trained with vessel-specific rs-fMRI signals encode the unique brain oscillatory dynamic feature, presenting more effective prediction than the conventional autoregressive model. This RNN-based predictive modeling of rs-fMRI datasets from the Human Connectome Project (HCP) reveals brain state-specific characteristics, demonstrating an inverse relationship between the global rs-fMRI signal fluctuation with the internal default-mode network (DMN) correlation. The RNN prediction method presents a unique data-driven encoding scheme to specify potential brain state differences based on the global fMRI signal fluctuation, but not solely dependent on the global variance.
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Affiliation(s)
- Filip Sobczak
- Translational Neuroimaging and Neural Control Group, High Field Magnetic Resonance Department, Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany.,Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tuebingen, 72074 Tuebingen, Germany
| | - Yi He
- Translational Neuroimaging and Neural Control Group, High Field Magnetic Resonance Department, Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany.,Danish Research Centre for Magnetic Resonance, 2650, Hvidovre, Denmark
| | - Terrence J Sejnowski
- Howard Hughes Medical Institute, Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA.,Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Xin Yu
- Translational Neuroimaging and Neural Control Group, High Field Magnetic Resonance Department, Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
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