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Chen Y, Fernandez Z, Scheel N, Gifani M, Zhu DC, Counts SE, Dorrance AM, Razansky D, Yu X, Qian W, Qian C. Novel inductively coupled ear-bars (ICEs) to enhance restored fMRI signal from susceptibility compensation in rats. Cereb Cortex 2024; 34:bhad479. [PMID: 38100332 PMCID: PMC10793587 DOI: 10.1093/cercor/bhad479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 11/20/2023] [Accepted: 11/21/2023] [Indexed: 12/17/2023] Open
Abstract
Functional magnetic resonance imaging faces inherent challenges when applied to deep-brain areas in rodents, e.g. entorhinal cortex, due to the signal loss near the ear cavities induced by susceptibility artifacts and reduced sensitivity induced by the long distance from the surface array coil. Given the pivotal roles of deep brain regions in various diseases, optimized imaging techniques are needed. To mitigate susceptibility-induced signal losses, we introduced baby cream into the middle ear. To enhance the detection sensitivity of deep brain regions, we implemented inductively coupled ear-bars, resulting in approximately a 2-fold increase in sensitivity in entorhinal cortex. Notably, the inductively coupled ear-bar can be seamlessly integrated as an add-on device, without necessitating modifications to the scanner interface. To underscore the versatility of inductively coupled ear-bars, we conducted echo-planner imaging-based task functional magnetic resonance imaging in rats modeling Alzheimer's disease. As a proof of concept, we also demonstrated resting-state-functional magnetic resonance imaging connectivity maps originating from the left entorhinal cortex-a central hub for memory and navigation networks-to amygdala hippocampal area, Insular Cortex, Prelimbic Systems, Cingulate Cortex, Secondary Visual Cortex, and Motor Cortex. This work demonstrates an optimized procedure for acquiring large-scale networks emanating from a previously challenging seed region by conventional magnetic resonance imaging detectors, thereby facilitating improved observation of functional magnetic resonance imaging outcomes.
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Affiliation(s)
- Yi Chen
- Department of High-field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tuebingen 72076, Germany
- Department of Radiology and Cognitive Imaging Research Center, Michigan State University, East Lansing, MI 48824, United States
| | - Zachary Fernandez
- Department of Radiology and Cognitive Imaging Research Center, Michigan State University, East Lansing, MI 48824, United States
- Neuroscience Program, Michigan State University, East Lansing, MI 48824, United States
| | - Norman Scheel
- Department of Radiology and Cognitive Imaging Research Center, Michigan State University, East Lansing, MI 48824, United States
| | - Mahsa Gifani
- Department of Translational Neuroscience, Michigan State University, Grand Rapids, MI 49503, United States
| | - David C Zhu
- Department of Radiology and Cognitive Imaging Research Center, Michigan State University, East Lansing, MI 48824, United States
- Neuroscience Program, Michigan State University, East Lansing, MI 48824, United States
| | - Scott E Counts
- Neuroscience Program, Michigan State University, East Lansing, MI 48824, United States
- Department of Translational Neuroscience, Michigan State University, Grand Rapids, MI 49503, United States
- Department of Family Medicine, Michigan State University, Grand Rapids, MI 49503, United States
- Department of Hauenstein Neurosciences Center, Mercy Health Saint Mary’s Hospital, Grand Rapids, MI 49508, United States
- Michigan Alzheimer’s Disease Research Center, Ann Arbor, MI 48105, United States
| | - Anne M Dorrance
- Neuroscience Program, Michigan State University, East Lansing, MI 48824, United States
- Department of Pharmacology and Toxicology, Michigan State University, East Lansing, MI 48824, United States
| | - Daniel Razansky
- Institute of Pharmacology and Toxicology and Institute for Biomedical Engineering, Faculty of Medicine, University of Zurich, Zurich 8006, Switzerland
- Department of Information Technology and Electrical Engineering, ETH Zurich, Institute for Biomedical Engineering, , Zurich 8092, Switzerland
- Zurich Neuroscience Center, Zurich 8057, Switzerland
| | - Xin Yu
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02114, United States
| | - Wei Qian
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, United States
| | - Chunqi Qian
- Department of Radiology and Cognitive Imaging Research Center, Michigan State University, East Lansing, MI 48824, United States
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, United States
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Lambers H, Wachsmuth L, Lippe C, Faber C. The impact of vasomotion on analysis of rodent fMRI data. Front Neurosci 2023; 17:1064000. [PMID: 36908777 PMCID: PMC9998505 DOI: 10.3389/fnins.2023.1064000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 02/06/2023] [Indexed: 03/14/2023] Open
Abstract
Introduction Small animal fMRI is an essential part of translational research in the cognitive neurosciences. Due to small dimensions and animal physiology preclinical fMRI is prone to artifacts that may lead to misinterpretation of the data. To reach unbiased translational conclusions, it is, therefore, crucial to identify potential sources of experimental noise and to develop correction methods for contributions that cannot be avoided such as physiological noise. Aim of this study was to assess origin and prevalence of hemodynamic oscillations (HDO) in preclinical fMRI in rat, as well as their impact on data analysis. Methods Following the development of algorithms for HDO detection and suppression, HDO prevalence in fMRI measurements was investigated for different anesthetic regimens, comprising isoflurane and medetomidine, and for both gradient echo and spin echo fMRI sequences. In addition to assessing the effect of vasodilation on HDO, it was studied if HDO have a direct neuronal correlate using local field potential (LFP) recordings. Finally, the impact of HDO on analysis of fMRI data was assessed, studying both the impact on calculation of activation maps as well as the impact on brain network analysis. Overall, 303 fMRI measurements and 32 LFP recordings were performed in 71 rats. Results In total, 62% of the fMRI measurements showed HDO with a frequency of (0.20 ± 0.02) Hz. This frequent occurrence indicated that HDO cannot be generally neglected in fMRI experiments. Using the developed algorithms, HDO were detected with a specificity of 95%, and removed efficiently from the signal time courses. HDO occurred brain-wide under vasoconstrictive conditions in both small and large blood vessels. Vasodilation immediately interrupted HDO, which, however, returned within 1 h under vasoconstrictive conditions. No direct neuronal correlate of HDO was observed in LFP recordings. HDO significantly impacted analysis of fMRI data, leading to altered cluster sizes and F-values for activated voxels, as well as altered brain networks, when comparing data with and without HDO. Discussion We therefore conclude that HDO are caused by vasomotion under certain anesthetic conditions and should be corrected during fMRI data analysis to avoid bias.
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Affiliation(s)
| | - Lydia Wachsmuth
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Chris Lippe
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Cornelius Faber
- Clinic of Radiology, University of Münster, Münster, Germany
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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] [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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Fang H, Yu X. Special Section Guest Editorial: Hybrid Photonic/X Neurointerfaces. NEUROPHOTONICS 2022; 9:032201. [PMID: 36196247 PMCID: PMC9524387 DOI: 10.1117/1.nph.9.3.032201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The article introduces the Special Section on Hybrid Photonic/X Neurointerfaces for Neurophotonics Volume 9 Issue 3.
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Affiliation(s)
- Hui Fang
- Dartmouth College, Hanover, New Hampshire, United States
| | - Xin Yu
- MGH/HST Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
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