1
|
Lin Y, Ding Y, Chang S, Ge X, Sui X, Jiang Y. RS 2-Net: An end-to-end deep learning framework for rodent skull stripping in multi-center brain MRI. Neuroimage 2024; 298:120769. [PMID: 39122056 DOI: 10.1016/j.neuroimage.2024.120769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 08/01/2024] [Accepted: 08/02/2024] [Indexed: 08/12/2024] Open
Abstract
Skull stripping is a crucial preprocessing step in magnetic resonance imaging (MRI), where experts manually create brain masks. This labor-intensive process heavily relies on the annotator's expertise, as automation faces challenges such as low tissue contrast, significant variations in image resolution, and blurred boundaries between the brain and surrounding tissues, particularly in rodents. In this study, we have developed a lightweight framework based on Swin-UNETR to automate the skull stripping process in MRI scans of mice and rats. The primary objective of this framework is to eliminate the need for preprocessing, reduce the workload, and provide an out-of-the-box solution capable of adapting to various MRI image resolutions. By employing a lightweight neural network, we aim to lower the performance requirements of the framework. To validate the effectiveness of our approach, we trained and evaluated the network using publicly available multi-center data, encompassing 1,037 rodents and 1,142 images from 89 centers, resulting in a preliminary mean Dice coefficient of 0.9914. The framework, data, and pre-trained models can be found on the following link: https://github.com/VitoLin21/Rodent-Skull-Stripping.
Collapse
Affiliation(s)
| | | | | | - Xinting Ge
- Shandong Normal University, Jinan, China
| | | | | |
Collapse
|
2
|
Desrosiers-Grégoire G, Devenyi GA, Grandjean J, Chakravarty MM. A standardized image processing and data quality platform for rodent fMRI. Nat Commun 2024; 15:6708. [PMID: 39112455 PMCID: PMC11306392 DOI: 10.1038/s41467-024-50826-8] [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: 10/13/2023] [Accepted: 07/22/2024] [Indexed: 08/10/2024] Open
Abstract
Functional magnetic resonance imaging in rodents holds great potential for advancing our understanding of brain networks. Unlike the human community, there remains no standardized resource in rodents for image processing, analysis and quality control, posing significant reproducibility limitations. Our software platform, Rodent Automated Bold Improvement of EPI Sequences, is a pipeline designed to address these limitations for preprocessing, quality control, and confound correction, along with best practices for reproducibility and transparency. We demonstrate the robustness of the preprocessing workflow by validating performance across multiple acquisition sites and both mouse and rat data. Building upon a thorough investigation into data quality metrics across acquisition sites, we introduce guidelines for the quality control of network analysis and offer recommendations for addressing issues. Taken together, this software platform will allow the emerging community to adopt reproducible practices and foster progress in translational neuroscience.
Collapse
Affiliation(s)
- Gabriel Desrosiers-Grégoire
- Computational Brain Anatomy Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, QC, Canada.
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada.
| | - Gabriel A Devenyi
- Computational Brain Anatomy Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Joanes Grandjean
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - M Mallar Chakravarty
- Computational Brain Anatomy Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, QC, Canada.
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada.
- Department of Psychiatry, McGill University, Montreal, QC, Canada.
- Department of Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada.
| |
Collapse
|
3
|
Nghiem TAE, Lee B, Chao THH, Branigan NK, Mistry PK, Shih YYI, Menon V. Space wandering in the rodent default mode network. Proc Natl Acad Sci U S A 2024; 121:e2315167121. [PMID: 38557177 PMCID: PMC11009630 DOI: 10.1073/pnas.2315167121] [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: 09/02/2023] [Accepted: 01/17/2024] [Indexed: 04/04/2024] Open
Abstract
The default mode network (DMN) is a large-scale brain network known to be suppressed during a wide range of cognitive tasks. However, our comprehension of its role in naturalistic and unconstrained behaviors has remained elusive because most research on the DMN has been conducted within the restrictive confines of MRI scanners. Here, we use multisite GCaMP (a genetically encoded calcium indicator) fiber photometry with simultaneous videography to probe DMN function in awake, freely exploring rats. We examined neural dynamics in three core DMN nodes-the retrosplenial cortex, cingulate cortex, and prelimbic cortex-as well as the anterior insula node of the salience network, and their association with the rats' spatial exploration behaviors. We found that DMN nodes displayed a hierarchical functional organization during spatial exploration, characterized by stronger coupling with each other than with the anterior insula. Crucially, these DMN nodes encoded the kinematics of spatial exploration, including linear and angular velocity. Additionally, we identified latent brain states that encoded distinct patterns of time-varying exploration behaviors and found that higher linear velocity was associated with enhanced DMN activity, heightened synchronization among DMN nodes, and increased anticorrelation between the DMN and anterior insula. Our findings highlight the involvement of the DMN in collectively and dynamically encoding spatial exploration in a real-world setting. Our findings challenge the notion that the DMN is primarily a "task-negative" network disengaged from the external world. By illuminating the DMN's role in naturalistic behaviors, our study underscores the importance of investigating brain network function in ecologically valid contexts.
Collapse
Affiliation(s)
| | - Byeongwook Lee
- Department of Psychiatry & Behavioral Sciences, Stanford University, Palo Alto, CA94304
| | - Tzu-Hao Harry Chao
- Center for Animal MRI, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
| | - Nicholas K. Branigan
- Department of Psychiatry & Behavioral Sciences, Stanford University, Palo Alto, CA94304
| | - Percy K. Mistry
- Department of Psychiatry & Behavioral Sciences, Stanford University, Palo Alto, CA94304
| | - Yen-Yu Ian Shih
- Center for Animal MRI, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC27514
| | - Vinod Menon
- Department of Psychiatry & Behavioral Sciences, Stanford University, Palo Alto, CA94304
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, CA94304
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA94305
| |
Collapse
|
4
|
Wachsmuth L, Hebbelmann L, Prade J, Kohnert LC, Lambers H, Lüttjohann A, Budde T, Hess A, Faber C. Epilepsy-related functional brain network alterations are already present at an early age in the GAERS rat model of genetic absence epilepsy. Front Neurol 2024; 15:1355862. [PMID: 38529038 PMCID: PMC10961455 DOI: 10.3389/fneur.2024.1355862] [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: 12/14/2023] [Accepted: 02/16/2024] [Indexed: 03/27/2024] Open
Abstract
Introduction Genetic Absence Epilepsy Rats from Strasbourg (GAERS) represent a model of genetic generalized epilepsy. The present longitudinal study in GAERS and age-matched non-epileptic controls (NEC) aimed to characterize the epileptic brain network using two functional measures, resting state-functional magnetic resonance imaging (rs-fMRI) and manganese-enhanced MRI (MEMRI) combined with morphometry, and to investigate potential brain network alterations, following long-term seizure activity. Methods Repeated rs-fMRI measurements at 9.4 T between 3 and 8 months of age were combined with MEMRI at the final time point of the study. We used graph theory analysis to infer community structure and global and local network parameters from rs-fMRI data and compared them to brain region-wise manganese accumulation patterns and deformation-based morphometry (DBM). Results Functional connectivity (FC) was generally higher in GAERS when compared to NEC. Global network parameters and community structure were similar in NEC and GAERS, suggesting efficiently functioning networks in both strains. No progressive FC changes were observed in epileptic animals. Network-based statistics (NBS) revealed stronger FC within the cortical community, including regions of association and sensorimotor cortex, and with basal ganglia and limbic regions in GAERS, irrespective of age. Higher manganese accumulation in GAERS than in NEC was observed at 8 months of age, consistent with higher overall rs-FC, particularly in sensorimotor cortex and association cortex regions. Functional measures showed less similarity in subcortical regions. Whole brain volumes of 8 months-old GAERS were higher when compared to age-matched NEC, and DBM revealed increased volumes of several association and sensorimotor cortex regions and of the thalamus. Discussion rs-fMRI, MEMRI, and volumetric data collectively suggest the significance of cortical networks in GAERS, which correlates with an increased fronto-central connectivity in childhood absence epilepsy (CAE). Our findings also verify involvement of basal ganglia and limbic regions. Epilepsy-related network alterations are already present in juvenile animals. Consequently, this early condition seems to play a greater role in dynamic brain function than chronic absence seizures.
Collapse
Affiliation(s)
- Lydia Wachsmuth
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Leo Hebbelmann
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Jutta Prade
- Department of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Laura C. Kohnert
- Department of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | | | | | - Thomas Budde
- Institute of Physiology I, University of Münster, Münster, Germany
| | - Andreas Hess
- Department of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
- Department of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
- FAU NeW – Research Center for New Bioactive Compounds, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Cornelius Faber
- Clinic of Radiology, University of Münster, Münster, Germany
| |
Collapse
|
5
|
Grandjean J, Lake EMR, Pagani M, Mandino F. What N Is N-ough for MRI-Based Animal Neuroimaging? eNeuro 2024; 11:ENEURO.0531-23.2024. [PMID: 38499355 PMCID: PMC10950324 DOI: 10.1523/eneuro.0531-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 02/08/2024] [Accepted: 02/13/2024] [Indexed: 03/20/2024] Open
Abstract
Fueled by the recent and controversial brain-wide association studies in humans, the animal neuroimaging community has also begun questioning whether using larger sample sizes is necessary for ethical and effective scientific progress. In this opinion piece, we illustrate two opposing views on sample size extremes in MRI-based animal neuroimaging.
Collapse
Affiliation(s)
- Joanes Grandjean
- Donders Institute for Brain, Cognition, and Behaviour, Nijmegen 6500HB, The Netherlands
- Department for Medical Imaging, Radboud University Medical Center, Nijmegen 6500HB, The Netherlands
| | - Evelyn M R Lake
- Departments of Radiology and Biomedical Imaging, New Haven, Connecticut 06519
- Biomedical Engineering, Yale School of Medicine, New Haven, Connecticut 06519
| | - Marco Pagani
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto 38068, Italy
- IMT School for Advanced Studies, Lucca 55100, Italy
| | - Francesca Mandino
- Departments of Radiology and Biomedical Imaging, New Haven, Connecticut 06519
| |
Collapse
|
6
|
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] [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.
Collapse
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.
| |
Collapse
|
7
|
Sourty M, Nasseef MT, Champagnol-Di Liberti C, Mondino M, Noblet V, Parise EM, Markovic T, Browne CJ, Darcq E, Nestler EJ, Kieffer BL. Manipulating ΔFOSB in D1-Type Medium Spiny Neurons of the Nucleus Accumbens Reshapes Whole-Brain Functional Connectivity. Biol Psychiatry 2024; 95:266-274. [PMID: 37517704 PMCID: PMC10834364 DOI: 10.1016/j.biopsych.2023.07.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 06/22/2023] [Accepted: 07/24/2023] [Indexed: 08/01/2023]
Abstract
BACKGROUND The transcription factor ΔFOSB, acting in the nucleus accumbens, has been shown to control transcriptional and behavioral responses to opioids and other drugs of abuse. However, circuit-level consequences of ΔFOSB induction on the rest of the brain, which are required for its regulation of complex behavior, remain unknown. METHODS We used an epigenetic approach in mice to suppress or activate the endogenous Fosb gene and thereby decrease or increase, respectively, levels of ΔFOSB selectively in D1-type medium spiny neurons of the nucleus accumbens and tested whether these modifications affect the organization of functional connectivity (FC) in the brain. We acquired functional magnetic resonance imaging data at rest and in response to a morphine challenge and analyzed both stationary and dynamic FC patterns. RESULTS The 2 manipulations modified brainwide communication markedly and differently. ΔFOSB down- and upregulation had overlapping effects on prefrontal- and retrosplenial cortex-centered networks, but also generated specific FC signatures for epithalamus (habenula) and dopaminergic/serotonergic centers, respectively. Analysis of dynamic FC patterns showed that increasing ΔFOSB essentially altered responsivity to morphine and uncovered striking modifications of the roles of the epithalamus and amygdala in brain communication, particularly upon ΔFOSB downregulation. CONCLUSIONS These novel findings illustrate how it is possible to link activity of a transcription factor within a single cell type of an identified brain region to consequent changes in circuit function brainwide by use of functional magnetic resonance imaging, and they pave the way for fundamental advances in bridging the gap between transcriptional and brain connectivity mechanisms underlying opioid addiction.
Collapse
Affiliation(s)
- Marion Sourty
- Institut National de la Santé et de la Recherche Médicale U1114, University of Strasbourg, Strasbourg, France; iCube, University of Strasbourg, Centre National de la Recherche Scientifique, Strasbourg, France
| | - Md Taufiq Nasseef
- Douglas Research Center, Department of Psychiatry, McGill University, Montréal, Quebec, Canada; Department of Mathematics, College of Science and Humanity Studies, Prince Sattam Bin Abdulaziz University, Riyadh, Saudi Arabia
| | | | - Mary Mondino
- iCube, University of Strasbourg, Centre National de la Recherche Scientifique, Strasbourg, France
| | - Vincent Noblet
- iCube, University of Strasbourg, Centre National de la Recherche Scientifique, Strasbourg, France
| | - Eric M Parise
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Tamara Markovic
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Caleb J Browne
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Emmanuel Darcq
- Institut National de la Santé et de la Recherche Médicale U1114, University of Strasbourg, Strasbourg, France; Douglas Research Center, Department of Psychiatry, McGill University, Montréal, Quebec, Canada
| | - Eric J Nestler
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York.
| | - Brigitte L Kieffer
- Institut National de la Santé et de la Recherche Médicale U1114, University of Strasbourg, Strasbourg, France; Douglas Research Center, Department of Psychiatry, McGill University, Montréal, Quebec, Canada.
| |
Collapse
|
8
|
Mandino F, Vujic S, Grandjean J, Lake EMR. Where do we stand on fMRI in awake mice? Cereb Cortex 2024; 34:bhad478. [PMID: 38100331 PMCID: PMC10793583 DOI: 10.1093/cercor/bhad478] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 11/17/2023] [Accepted: 11/18/2023] [Indexed: 12/17/2023] Open
Abstract
Imaging awake animals is quickly gaining traction in neuroscience as it offers a means to eliminate the confounding effects of anesthesia, difficulties of inter-species translation (when humans are typically imaged while awake), and the inability to investigate the full range of brain and behavioral states in unconscious animals. In this systematic review, we focus on the development of awake mouse blood oxygen level dependent functional magnetic resonance imaging (fMRI). Mice are widely used in research due to their fast-breeding cycle, genetic malleability, and low cost. Functional MRI yields whole-brain coverage and can be performed on both humans and animal models making it an ideal modality for comparing study findings across species. We provide an analysis of 30 articles (years 2011-2022) identified through a systematic literature search. Our conclusions include that head-posts are favorable, acclimation training for 10-14 d is likely ample under certain conditions, stress has been poorly characterized, and more standardization is needed to accelerate progress. For context, an overview of awake rat fMRI studies is also included. We make recommendations that will benefit a wide range of neuroscience applications.
Collapse
Affiliation(s)
- Francesca Mandino
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, United States
| | - Stella Vujic
- Department of Computer Science, Yale University, New Haven, CT 06520, United States
| | - Joanes Grandjean
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
- Department for Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Evelyn M R Lake
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, United States
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, United States
| |
Collapse
|
9
|
Mandino F, Shen X, Desrosiers-Gregoire G, O'Connor D, Mukherjee B, Owens A, Qu A, Onofrey J, Papademetris X, Chakravarty MM, Strittmatter SM, Lake EM. Aging-Dependent Loss of Connectivity in Alzheimer's Model Mice with Rescue by mGluR5 Modulator. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.15.571715. [PMID: 38260465 PMCID: PMC10802481 DOI: 10.1101/2023.12.15.571715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Amyloid accumulation in Alzheimer's disease (AD) is associated with synaptic damage and altered connectivity in brain networks. While measures of amyloid accumulation and biochemical changes in mouse models have utility for translational studies of certain therapeutics, preclinical analysis of altered brain connectivity using clinically relevant fMRI measures has not been well developed for agents intended to improve neural networks. Here, we conduct a longitudinal study in a double knock-in mouse model for AD ( App NL-G-F /hMapt ), monitoring brain connectivity by means of resting-state fMRI. While the 4-month-old AD mice are indistinguishable from wild-type controls (WT), decreased connectivity in the default-mode network is significant for the AD mice relative to WT mice by 6 months of age and is pronounced by 9 months of age. In a second cohort of 20-month-old mice with persistent functional connectivity deficits for AD relative to WT, we assess the impact of two-months of oral treatment with a silent allosteric modulator of mGluR5 (BMS-984923) known to rescue synaptic density. Functional connectivity deficits in the aged AD mice are reversed by the mGluR5-directed treatment. The longitudinal application of fMRI has enabled us to define the preclinical time trajectory of AD-related changes in functional connectivity, and to demonstrate a translatable metric for monitoring disease emergence, progression, and response to synapse-rescuing treatment.
Collapse
|
10
|
Pagani M, Gutierrez-Barragan D, de Guzman AE, Xu T, Gozzi A. Mapping and comparing fMRI connectivity networks across species. Commun Biol 2023; 6:1238. [PMID: 38062107 PMCID: PMC10703935 DOI: 10.1038/s42003-023-05629-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 11/22/2023] [Indexed: 12/18/2023] Open
Abstract
Technical advances in neuroimaging, notably in fMRI, have allowed distributed patterns of functional connectivity to be mapped in the human brain with increasing spatiotemporal resolution. Recent years have seen a growing interest in extending this approach to rodents and non-human primates to understand the mechanism of fMRI connectivity and complement human investigations of the functional connectome. Here, we discuss current challenges and opportunities of fMRI connectivity mapping across species. We underscore the critical importance of physiologically decoding neuroimaging measures of brain (dys)connectivity via multiscale mechanistic investigations in animals. We next highlight a set of general principles governing the organization of mammalian connectivity networks across species. These include the presence of evolutionarily conserved network systems, a dominant cortical axis of functional connectivity, and a common repertoire of topographically conserved fMRI spatiotemporal modes. We finally describe emerging approaches allowing comparisons and extrapolations of fMRI connectivity findings across species. As neuroscientists gain access to increasingly sophisticated perturbational, computational and recording tools, cross-species fMRI offers novel opportunities to investigate the large-scale organization of the mammalian brain in health and disease.
Collapse
Affiliation(s)
- Marco Pagani
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
- Autism Center, Child Mind Institute, New York, NY, USA
- IMT School for Advanced Studies, Lucca, Italy
| | - Daniel Gutierrez-Barragan
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - A Elizabeth de Guzman
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Ting Xu
- Center for the Integrative Developmental Neuroscience, Child Mind Institute, New York, NY, USA
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy.
| |
Collapse
|
11
|
Jiang S, Yang C, Wang R, Bao X. Resting-state functional connectivity in a non-human primate model of cortical ischemic stroke in area F1. Magn Reson Imaging 2023; 104:121-128. [PMID: 37844784 DOI: 10.1016/j.mri.2023.10.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 09/07/2023] [Accepted: 10/12/2023] [Indexed: 10/18/2023]
Abstract
BACKGROUND The application of functional MRI to non-human primates after stroke has not yet been undertaken. This is the first study to explore the functional connectivity changes in non-human primate models during acute stages after stroke onset. METHODS Nineteen healthy male cynomolgus monkeys (4-5 years) were used in this study. The photothrombosis model was employed to induce focal ischemic stroke in F1 area in the monkey's left hemisphere. T1-weighted structural images and resting-state functional magnetic resonance imaging (rs-fMRI) of all subjects were obtained using a 3.0 Tesla MRI system on the third day following stroke. Based on the D99 atlas, the structural and functional changes of bilateral F1 areas in monkeys were analyzed using region of interest (ROI)-based functional connectivity (FC). The bilateral F1 areas were selected as the seed regions due to their crucial role in motor control and their potential to unveil the comprehensive functional reorganization of the motor system at a whole-brain level following stroke. RESULTS Ischemic lesions were observed after the stroke, with larger lesion volumes associated with poorer neurological dysfunction. Compared with baseline condition, left area F1 demonstrated decreased FC with the left cerebellum, left ventral pons and left 5_(PEa). When the ROI was located in the right area F1, ischemic monkeys showed decreased FC in left ventral pons, left cerebellum, left primary visual cortex and left 5_(PEa), accompanied by increased FC in the right orbitofrontal cortex. Importantly, the degree of altered FC between left area F1 and left cerebellum was associated with upper limb tone. CONCLUSIONS These results provide valuable insights into the early-stage functional connectivity changes in the F1 areas of monkeys under ischemic conditions, highlighting the potential involvement of specific brain regions in the pathophysiology of ischemic injury.
Collapse
Affiliation(s)
- Shenzhong Jiang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chengxian Yang
- Department of Orthopaedics, Peking University First Hospital, Beijing, China
| | - Renzhi Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Xinjie Bao
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| |
Collapse
|
12
|
Nghiem TAE, Lee B, Chao THH, Branigan NK, Mistry PK, Shih YYI, Menon V. Space wandering in the rodent default mode network. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.31.555793. [PMID: 37693501 PMCID: PMC10491169 DOI: 10.1101/2023.08.31.555793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
The default mode network (DMN) is a large-scale brain network known to be suppressed during a wide range of cognitive tasks. However, our comprehension of its role in naturalistic and unconstrained behaviors has remained elusive because most research on the DMN has been conducted within the restrictive confines of MRI scanners. Here we use multisite GCaMP fiber photometry with simultaneous videography to probe DMN function in awake, freely exploring rats. We examined neural dynamics in three core DMN nodes- the retrosplenial cortex, cingulate cortex, and prelimbic cortex- as well as the anterior insula node of the salience network, and their association with the rats' spatial exploration behaviors. We found that DMN nodes displayed a hierarchical functional organization during spatial exploration, characterized by stronger coupling with each other than with the anterior insula. Crucially, these DMN nodes encoded the kinematics of spatial exploration, including linear and angular velocity. Additionally, we identified latent brain states that encoded distinct patterns of time-varying exploration behaviors and discovered that higher linear velocity was associated with enhanced DMN activity, heightened synchronization among DMN nodes, and increased anticorrelation between the DMN and anterior insula. Our findings highlight the involvement of the DMN in collectively and dynamically encoding spatial exploration in a real-world setting. Our findings challenge the notion that the DMN is primarily a "task-negative" network disengaged from the external world. By illuminating the DMN's role in naturalistic behaviors, our study underscores the importance of investigating brain network function in ecologically valid contexts.
Collapse
Affiliation(s)
| | - Byeongwook Lee
- Department of Psychiatry & Behavioral Sciences, Stanford University
| | - Tzu-Hao Harry Chao
- Center for Animal MRI, University of North Carolina at Chapel Hill
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill
- Department of Neurology, University of North Carolina at Chapel Hill
| | | | - Percy K. Mistry
- Department of Psychiatry & Behavioral Sciences, Stanford University
| | - Yen-Yu Ian Shih
- Center for Animal MRI, University of North Carolina at Chapel Hill
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill
- Department of Neurology, University of North Carolina at Chapel Hill
- Department of Biomedical Engineering, University of North Carolina at Chapel Hill
| | - Vinod Menon
- Department of Psychiatry & Behavioral Sciences, Stanford University
- Department of Neurology & Neurological Sciences, Stanford University
- Wu Tsai Neurosciences Institute, Stanford University
| |
Collapse
|
13
|
Mahani FSN, Kalantari A, Fink GR, Hoehn M, Aswendt M. A systematic review of the relationship between magnetic resonance imaging based resting-state and structural networks in the rodent brain. Front Neurosci 2023; 17:1194630. [PMID: 37554291 PMCID: PMC10405456 DOI: 10.3389/fnins.2023.1194630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 07/05/2023] [Indexed: 08/10/2023] Open
Abstract
Recent developments in rodent brain imaging have enabled translational characterization of functional and structural connectivity at the whole brain level in vivo. Nevertheless, fundamental questions about the link between structural and functional networks remain unsolved. In this review, we systematically searched for experimental studies in rodents investigating both structural and functional network measures, including studies correlating functional connectivity using resting-state functional MRI with diffusion tensor imaging or viral tracing data. We aimed to answer whether functional networks reflect the architecture of the structural connectome, how this reciprocal relationship changes throughout a disease, how structural and functional changes relate to each other, and whether changes follow the same timeline. We present the knowledge derived exclusively from studies that included in vivo imaging of functional and structural networks. The limited number of available reports makes it difficult to draw general conclusions besides finding a spatial and temporal decoupling between structural and functional networks during brain disease. Data suggest that when overcoming the currently limited evidence through future studies with combined imaging in various disease models, it will be possible to explore the interaction between both network systems as a disease or recovery biomarker.
Collapse
Affiliation(s)
- Fatemeh S. N. Mahani
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Aref Kalantari
- Department of Neurology, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Gereon R. Fink
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Mathias Hoehn
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany
| | - Markus Aswendt
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| |
Collapse
|
14
|
Obrecht M, Zurbruegg S, Accart N, Lambert C, Doelemeyer A, Ledermann B, Beckmann N. Magnetic resonance imaging and ultrasound elastography in the context of preclinical pharmacological research: significance for the 3R principles. Front Pharmacol 2023; 14:1177421. [PMID: 37448960 PMCID: PMC10337591 DOI: 10.3389/fphar.2023.1177421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 06/16/2023] [Indexed: 07/18/2023] Open
Abstract
The 3Rs principles-reduction, refinement, replacement-are at the core of preclinical research within drug discovery, which still relies to a great extent on the availability of models of disease in animals. Minimizing their distress, reducing their number as well as searching for means to replace them in experimental studies are constant objectives in this area. Due to its non-invasive character in vivo imaging supports these efforts by enabling repeated longitudinal assessments in each animal which serves as its own control, thereby enabling to reduce considerably the animal utilization in the experiments. The repetitive monitoring of pathology progression and the effects of therapy becomes feasible by assessment of quantitative biomarkers. Moreover, imaging has translational prospects by facilitating the comparison of studies performed in small rodents and humans. Also, learnings from the clinic may be potentially back-translated to preclinical settings and therefore contribute to refining animal investigations. By concentrating on activities around the application of magnetic resonance imaging (MRI) and ultrasound elastography to small rodent models of disease, we aim to illustrate how in vivo imaging contributes primarily to reduction and refinement in the context of pharmacological research.
Collapse
Affiliation(s)
- Michael Obrecht
- Diseases of Aging and Regenerative Medicines, Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - Stefan Zurbruegg
- Neurosciences Department, Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - Nathalie Accart
- Diseases of Aging and Regenerative Medicines, Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - Christian Lambert
- Diseases of Aging and Regenerative Medicines, Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - Arno Doelemeyer
- Diseases of Aging and Regenerative Medicines, Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - Birgit Ledermann
- 3Rs Leader, Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - Nicolau Beckmann
- Diseases of Aging and Regenerative Medicines, Novartis Institutes for BioMedical Research, Basel, Switzerland
| |
Collapse
|
15
|
Kalantari A, Szczepanik M, Heunis S, Mönch C, Hanke M, Wachtler T, Aswendt M. How to establish and maintain a multimodal animal research dataset using DataLad. Sci Data 2023; 10:357. [PMID: 37277500 DOI: 10.1038/s41597-023-02242-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 05/15/2023] [Indexed: 06/07/2023] Open
Abstract
Sharing of data, processing tools, and workflows require open data hosting services and management tools. Despite FAIR guidelines and the increasing demand from funding agencies and publishers, only a few animal studies share all experimental data and processing tools. We present a step-by-step protocol to perform version control and remote collaboration for large multimodal datasets. A data management plan was introduced to ensure data security in addition to a homogeneous file and folder structure. Changes to the data were automatically tracked using DataLad and all data was shared on the research data platform GIN. This simple and cost-effective workflow facilitates the adoption of FAIR data logistics and processing workflows by making the raw and processed data available and providing the technical infrastructure to independently reproduce the data processing steps. It enables the community to collect heterogeneously acquired and stored datasets not limited to a specific category of data and serves as a technical infrastructure blueprint with rich potential to improve data handling at other sites and extend to other research areas.
Collapse
Affiliation(s)
- Aref Kalantari
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Cologne, Germany
| | - Michał Szczepanik
- Psychoinformatics Lab, Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Stephan Heunis
- Psychoinformatics Lab, Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Christian Mönch
- Psychoinformatics Lab, Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Michael Hanke
- Psychoinformatics Lab, Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Thomas Wachtler
- Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, München, Germany
| | - Markus Aswendt
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Cologne, Germany.
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Jülich, Jülich, Germany.
| |
Collapse
|
16
|
Tavares AAS, Mezzanotte L, McDougald W, Bernsen MR, Vanhove C, Aswendt M, Ielacqua GD, Gremse F, Moran CM, Warnock G, Kuntner C, Huisman MC. Community Survey Results Show that Standardisation of Preclinical Imaging Techniques Remains a Challenge. Mol Imaging Biol 2023; 25:560-568. [PMID: 36482032 PMCID: PMC10172263 DOI: 10.1007/s11307-022-01790-6] [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: 10/06/2022] [Revised: 11/15/2022] [Accepted: 11/16/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE To support acquisition of accurate, reproducible and high-quality preclinical imaging data, various standardisation resources have been developed over the years. However, it is unclear the impact of those efforts in current preclinical imaging practices. To better understand the status quo in the field of preclinical imaging standardisation, the STANDARD group of the European Society of Molecular Imaging (ESMI) put together a community survey and a forum for discussion at the European Molecular Imaging Meeting (EMIM) 2022. This paper reports on the results from the STANDARD survey and the forum discussions that took place at EMIM2022. PROCEDURES The survey was delivered to the community by the ESMI office and was promoted through the Society channels, email lists and webpages. The survey contained seven sections organised as generic questions and imaging modality-specific questions. The generic questions focused on issues regarding data acquisition, data processing, data storage, publishing and community awareness of international guidelines for animal research. Specific questions on practices in optical imaging, PET, CT, SPECT, MRI and ultrasound were further included. RESULTS Data from the STANDARD survey showed that 47% of survey participants do not have or do not know if they have QC/QA guidelines at their institutes. Additionally, a large variability exists in the ways data are acquired, processed and reported regarding general aspects as well as modality-specific aspects. Moreover, there is limited awareness of the existence of international guidelines on preclinical (imaging) research practices. CONCLUSIONS Standardisation of preclinical imaging techniques remains a challenge and hinders the transformative potential of preclinical imaging to augment biomedical research pipelines by serving as an easy vehicle for translation of research findings to the clinic. Data collected in this project show that there is a need to promote and disseminate already available tools to standardise preclinical imaging practices.
Collapse
Affiliation(s)
- Adriana A S Tavares
- BHF-University Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK.
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK.
| | - Laura Mezzanotte
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Wendy McDougald
- BHF-University Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
- Siemens, Molecular Imaging, Hoffman Estates, IL, USA
| | - Monique R Bernsen
- AMIE Core Facility, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Christian Vanhove
- Faculty of Engineering and Architecture, Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Markus Aswendt
- Faculty of Medicine, Dept. of Neurology, University of Cologne, and University Hospital Cologne, Cologne, Germany
| | - Giovanna D Ielacqua
- Max-Delbrück Center for Molecular Medicine, in the Helmholtz Association, Berlin, Germany
| | - Felix Gremse
- Gremse-IT GmbH, Aachen, Germany
- Experimental Molecular Imaging, RWTH Aachen University Clinic, Aachen, Germany
| | - Carmel M Moran
- BHF-University Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | | | - Claudia Kuntner
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Marc C Huisman
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Amsterdam, The Netherlands
| |
Collapse
|
17
|
Garin CM, Dhenain M. Mean amplitude of low frequency fluctuations measured by fMRI at 11.7 T in the aging brain of mouse lemur primate. Sci Rep 2023; 13:7970. [PMID: 37198192 DOI: 10.1038/s41598-023-33482-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 04/13/2023] [Indexed: 05/19/2023] Open
Abstract
Non-human primates are a critical species for the identification of key biological mechanisms in normal and pathological aging. One of these primates, the mouse lemur, has been widely studied as a model of cerebral aging or Alzheimer's disease. The amplitude of low-frequency fluctuations of blood oxygenation level-dependent (BOLD) can be measured with functional MRI. Within specific frequency bands (e.g. the 0.01-0.1 Hz), these amplitudes were proposed to indirectly reflect neuronal activity as well as glucose metabolism. Here, we first created whole brain maps of the mean amplitude of low frequency fluctuations (mALFF) in young mouse lemurs (mean ± SD: 2.1 ± 0.8 years). Then, we extracted mALFF in old lemurs (mean ± SD: 8.8 ± 1.1 years) to identify age-related changes. A high level of mALFF was detected in the temporal cortex (Brodmann area 20), somatosensory areas (Brodmann area 5), insula (Brodmann areas 13-6) and the parietal cortex (Brodmann area 7) of healthy young mouse lemurs. Aging was associated with alterations of mALFF in somatosensory areas (Brodmann area 5) and the parietal cortex (Brodmann area 7).
Collapse
Affiliation(s)
- Clément M Garin
- UMR 9199, Neurodegenerative Diseases Laboratory, Centre National de la Recherche Scientifique (CNRS), Université Paris-Sud, Université Paris-Saclay, 18 Route du Panorama, 92265, Fontenay-aux-Roses, France
- Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), Direction de la Recherche Fondamentale (DRF), Institut François Jacob, MIRCen, 18 Route du Panorama, 92265, Fontenay-aux-Roses Cedex, France
| | - Marc Dhenain
- UMR 9199, Neurodegenerative Diseases Laboratory, Centre National de la Recherche Scientifique (CNRS), Université Paris-Sud, Université Paris-Saclay, 18 Route du Panorama, 92265, Fontenay-aux-Roses, France.
- Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), Direction de la Recherche Fondamentale (DRF), Institut François Jacob, MIRCen, 18 Route du Panorama, 92265, Fontenay-aux-Roses Cedex, France.
| |
Collapse
|
18
|
Grandjean J, Desrosiers-Gregoire G, Anckaerts C, Angeles-Valdez D, Ayad F, Barrière DA, Blockx I, Bortel A, Broadwater M, Cardoso BM, Célestine M, Chavez-Negrete JE, Choi S, Christiaen E, Clavijo P, Colon-Perez L, Cramer S, Daniele T, Dempsey E, Diao Y, Doelemeyer A, Dopfel D, Dvořáková L, Falfán-Melgoza C, Fernandes FF, Fowler CF, Fuentes-Ibañez A, Garin CM, Gelderman E, Golden CEM, Guo CCG, Henckens MJAG, Hennessy LA, Herman P, Hofwijks N, Horien C, Ionescu TM, Jones J, Kaesser J, Kim E, Lambers H, Lazari A, Lee SH, Lillywhite A, Liu Y, Liu YY, López-Castro A, López-Gil X, Ma Z, MacNicol E, Madularu D, Mandino F, Marciano S, McAuslan MJ, McCunn P, McIntosh A, Meng X, Meyer-Baese L, Missault S, Moro F, Naessens DMP, Nava-Gomez LJ, Nonaka H, Ortiz JJ, Paasonen J, Peeters LM, Pereira M, Perez PD, Pompilus M, Prior M, Rakhmatullin R, Reimann HM, Reinwald J, Del Rio RT, Rivera-Olvera A, Ruiz-Pérez D, Russo G, Rutten TJ, Ryoke R, Sack M, Salvan P, Sanganahalli BG, Schroeter A, Seewoo BJ, Selingue E, Seuwen A, Shi B, Sirmpilatze N, Smith JAB, Smith C, Sobczak F, Stenroos PJ, Straathof M, Strobelt S, Sumiyoshi A, Takahashi K, Torres-García ME, Tudela R, van den Berg M, van der Marel K, van Hout ATB, Vertullo R, Vidal B, Vrooman RM, Wang VX, Wank I, Watson DJG, Yin T, Zhang Y, Zurbruegg S, Achard S, Alcauter S, Auer DP, Barbier EL, Baudewig J, Beckmann CF, Beckmann N, Becq GJPC, Blezer ELA, Bolbos R, Boretius S, Bouvard S, Budinger E, Buxbaum JD, Cash D, Chapman V, Chuang KH, Ciobanu L, Coolen BF, Dalley JW, Dhenain M, Dijkhuizen RM, Esteban O, Faber C, Febo M, Feindel KW, Forloni G, Fouquet J, Garza-Villarreal EA, Gass N, Glennon JC, Gozzi A, Gröhn O, Harkin A, Heerschap A, Helluy X, Herfert K, Heuser A, Homberg JR, Houwing DJ, Hyder F, Ielacqua GD, Jelescu IO, Johansen-Berg H, Kaneko G, Kawashima R, Keilholz SD, Keliris GA, Kelly C, Kerskens C, Khokhar JY, Kind PC, Langlois JB, Lerch JP, López-Hidalgo MA, Manahan-Vaughan D, Marchand F, Mars RB, Marsella G, Micotti E, Muñoz-Moreno E, Near J, Niendorf T, Otte WM, Pais-Roldán P, Pan WJ, Prado-Alcalá RA, Quirarte GL, Rodger J, Rosenow T, Sampaio-Baptista C, Sartorius A, Sawiak SJ, Scheenen TWJ, Shemesh N, Shih YYI, Shmuel A, Soria G, Stoop R, Thompson GJ, Till SM, Todd N, Van Der Linden A, van der Toorn A, van Tilborg GAF, Vanhove C, Veltien A, Verhoye M, Wachsmuth L, Weber-Fahr W, Wenk P, Yu X, Zerbi V, Zhang N, Zhang BB, Zimmer L, Devenyi GA, Chakravarty MM, Hess A. A consensus protocol for functional connectivity analysis in the rat brain. Nat Neurosci 2023; 26:673-681. [PMID: 36973511 PMCID: PMC10493189 DOI: 10.1038/s41593-023-01286-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 02/15/2023] [Indexed: 03/29/2023]
Abstract
Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows for comparisons with data modalities collected under invasive or terminal procedures. Currently, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. Here we introduce StandardRat, a consensus rat functional magnetic resonance imaging acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired from rats across 46 centers. We developed a reproducible pipeline for analyzing rat data acquired with diverse protocols and determined experimental and processing parameters associated with the robust detection of functional connectivity across centers. We show that the standardized protocol enhances biologically plausible functional connectivity patterns relative to previous acquisitions. The protocol and processing pipeline described here is openly shared with the neuroimaging community to promote interoperability and cooperation toward tackling the most important challenges in neuroscience.
Collapse
Affiliation(s)
- Joanes Grandjean
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands.
- Department for Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - Gabriel Desrosiers-Gregoire
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, QC, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
| | - Cynthia Anckaerts
- Bio-imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Diego Angeles-Valdez
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, Mexico
| | - Fadi Ayad
- Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - David A Barrière
- UMR INRAE/CNRS 7247 Physiologie des Comportements et de la Reproduction, Physiologie de la reproduction et des comportements, Centre de recherche INRAE de Nouzilly, Tours, France
| | - Ines Blockx
- Bio-imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Aleksandra Bortel
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Margaret Broadwater
- Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Beatriz M Cardoso
- Preclinical MRI, Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Marina Célestine
- Laboratoire des Maladies Neurodégénératives, Molecular Imaging Research Center (MIRCen), Université Paris-Saclay, Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA), CNRS, Fontenay-aux-Roses, France
| | - Jorge E Chavez-Negrete
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, México
| | - Sangcheon Choi
- Translational Neuroimaging and Neural Control Group, High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
- Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tuebingen, Tuebingen, Germany
| | - Emma Christiaen
- Institute Biomedical Technology (IBiTech), Electronics and Information Systems (ELIS), Ghent University, Gent, Belgium
| | - Perrin Clavijo
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology, Atlanta, GA, USA
| | - Luis Colon-Perez
- Department of Pharmacology & Neuroscience, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Samuel Cramer
- Translational Neuroimaging and Systems Neuroscience Lab, Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Tolomeo Daniele
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | - Elaine Dempsey
- Neuropsychopharmacology Research Group, School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Dublin, Ireland
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Yujian Diao
- CIBM Center for Biomedical Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Laboratory for Functional and Metabolic Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Arno Doelemeyer
- Musculoskeletal Diseases Department, Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - David Dopfel
- Translational Neuroimaging and Systems Neuroscience Lab, Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Lenka Dvořáková
- Biomedical Imaging Unit, A.I.V. Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Claudia Falfán-Melgoza
- Translational Imaging, Department of Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, Mannheim, Germany
| | - Francisca F Fernandes
- Preclinical MRI, Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Caitlin F Fowler
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, QC, Canada
- Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Antonio Fuentes-Ibañez
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, México
| | - Clément M Garin
- Laboratoire des Maladies Neurodégénératives, Molecular Imaging Research Center (MIRCen), Université Paris-Saclay, Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA), CNRS, Fontenay-aux-Roses, France
| | - Eveline Gelderman
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Carla E M Golden
- Seaver Autism Center for Research & Treatment, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Chao C G Guo
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Marloes J A G Henckens
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
- Department of Neuroscience and Pharmacology, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Lauren A Hennessy
- Experimental and Regenerative Neurosciences, School of Biological Sciences, University of Western Australia, Crawley, WA, Australia
- Brain Plasticity Group, Perron Institute for Neurological and Translational Science, Nedlands, WA, Australia
| | - Peter Herman
- Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
- Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University School of Medicine, New Haven, CT, USA
| | - Nita Hofwijks
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Corey Horien
- Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Tudor M Ionescu
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University of Tuebingen, Tuebingen, Germany
| | - Jolyon Jones
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Johannes Kaesser
- Institute of Experimental and Clinical Pharmacology and Toxicology, FAU Erlangen-Nürnberg, Erlangen, Germany
| | - Eugene Kim
- Biomarker Research And Imaging in Neuroscience (BRAIN) Centre, Department of Neuroimaging King's College London, London, UK
| | - Henriette Lambers
- Experimental Magnetic Resonance Group, Clinic of Radiology, University of Münster, Münster, Germany
| | - Alberto Lazari
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
| | - Sung-Ho Lee
- Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Amanda Lillywhite
- School of Life Sciences, University of Nottingham, Nottingham, UK
- Pain Centre Versus Arthritis, University of Nottingham, Nottingham, UK
| | - Yikang Liu
- Translational Neuroimaging and Systems Neuroscience Lab, Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Yanyan Y Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Alejandra López-Castro
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, Mexico
| | - Xavier López-Gil
- Magnetic Imaging Resonance Core Facility, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain
| | - Zilu Ma
- Translational Neuroimaging and Systems Neuroscience Lab, Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Eilidh MacNicol
- Biomarker Research And Imaging in Neuroscience (BRAIN) Centre, Department of Neuroimaging King's College London, London, UK
| | - Dan Madularu
- Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
- Center for Translational Neuroimaging, Northeastern University, Boston, MA, USA
| | - Francesca Mandino
- Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Sabina Marciano
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University of Tuebingen, Tuebingen, Germany
| | - Matthew J McAuslan
- Neuropsychopharmacology Research Group, School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Dublin, Ireland
| | - Patrick McCunn
- Khokhar Lab, Department of Anatomy and Cell Biology, Western University, London, ON, Canada
| | - Alison McIntosh
- Neuropsychopharmacology Research Group, School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Dublin, Ireland
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Xianzong Meng
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Lisa Meyer-Baese
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology, Atlanta, GA, USA
| | - Stephan Missault
- Bio-imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Federico Moro
- Laboratory of Acute Brain Injury and Therapeutic Strategies, Department of NeuroscienceIstituto di Ricerche Farmacologiche Mario Negri, IRCCS, Milan, Italy
| | - Daphne M P Naessens
- Biomedical Engineering and Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Laura J Nava-Gomez
- Facultad de Medicina, Universidad Autónoma de Querétaro, Querétaro, México
- Escuela Nacional de Estudios Superiores, Juriquilla, Universidad Nacional Autónoma de México, Querétaro, México
| | - Hiroi Nonaka
- Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Juan J Ortiz
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, México
| | - Jaakko Paasonen
- Biomedical Imaging Unit, A.I.V. Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Lore M Peeters
- Bio-imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Mickaël Pereira
- Lyon Neuroscience Research Center, Université Claude Bernard Lyon 1, INSERM, CNRS, Lyon, France
| | - Pablo D Perez
- Translational Neuroimaging and Systems Neuroscience Lab, Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Marjory Pompilus
- Febo Laboratory, Department of Psychiatry, University of Florida, Gainesville, FL, USA
| | - Malcolm Prior
- School of Medicine, University of Nottingham, Nottingham, UK
| | | | - Henning M Reimann
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max-Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Jonathan Reinwald
- Translational Imaging, Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Rodrigo Triana Del Rio
- Psychiatric neurosciences, Center for Psychiatric Neuroscience, Lausanne University and University Hospital Center, Unicentre, Lausanne, Switzerland
| | - Alejandro Rivera-Olvera
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | | | - Gabriele Russo
- Department of Neurophysiology, Medical Faculty, Ruhr University Bochum, Bochum, Germany
| | - Tobias J Rutten
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Rie Ryoke
- Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Markus Sack
- Translational Imaging, Department of Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, Mannheim, Germany
| | - Piergiorgio Salvan
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
| | - Basavaraju G Sanganahalli
- Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
- Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University School of Medicine, New Haven, CT, USA
| | - Aileen Schroeter
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Bhedita J Seewoo
- Experimental and Regenerative Neurosciences, School of Biological Sciences, University of Western Australia, Crawley, WA, Australia
- Brain Plasticity Group, Perron Institute for Neurological and Translational Science, Nedlands, WA, Australia
- Centre for Microscopy, Characterisation & Analysis, Research Infrastructure Centres, University of Western Australia, Nedlands, WA, Australia
| | | | - Aline Seuwen
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Bowen Shi
- iHuman Institute, ShanghaiTech University, Shanghai, China
| | - Nikoloz Sirmpilatze
- Functional Imaging Laboratory, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany
- Faculty of Biology and Psychology, Georg-August University of Göttingen, Göttingen, Germany
- DFG Research Center for Nanoscale Microscopy and Molecular Physiology of the Brain (CNMPB), Göttingen, Germany
| | - Joanna A B Smith
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, UK
- Patrick Wild Centre, University of Edinburgh, Edinburgh, UK
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Corrie Smith
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology, Atlanta, GA, USA
| | - Filip Sobczak
- Translational Neuroimaging and Neural Control Group, High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
- Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tuebingen, Tuebingen, Germany
| | - Petteri J Stenroos
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, Grenoble, France
| | - Milou Straathof
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht & Utrecht University, Utrecht, The Netherlands
| | - Sandra Strobelt
- Institute of Experimental and Clinical Pharmacology and Toxicology, FAU Erlangen-Nürnberg, Erlangen, Germany
| | - Akira Sumiyoshi
- Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
- National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Kengo Takahashi
- Translational Neuroimaging and Neural Control Group, High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
- Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tuebingen, Tuebingen, Germany
| | - Maria E Torres-García
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, México
| | - Raul Tudela
- Group of Biomedical Imaging, Consorcio Centro de Investigación Biomédica en Red (CIBER) de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), University of Barcelona, Barcelona, Spain
| | - Monica van den Berg
- Bio-imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Kajo van der Marel
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht & Utrecht University, Utrecht, The Netherlands
| | - Aran T B van Hout
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Roberta Vertullo
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Benjamin Vidal
- Lyon Neuroscience Research Center, Université Claude Bernard Lyon 1, INSERM, CNRS, Lyon, France
| | - Roël M Vrooman
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Victora X Wang
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Isabel Wank
- Institute of Experimental and Clinical Pharmacology and Toxicology, FAU Erlangen-Nürnberg, Erlangen, Germany
| | - David J G Watson
- School of Life Sciences, University of Nottingham, Nottingham, UK
| | - Ting Yin
- Animal Imaging and Technology Section, Center for Biomedical Imaging, École polytechnique fédérale de Lausanne, Lausanne, Switzerland
| | - Yongzhi Zhang
- Focused Ultrasound Laboratory, Department of Radiology Brigham and Women's Hospital, Boston, MA, USA
| | - Stefan Zurbruegg
- Neurosciences Department, Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - Sophie Achard
- Inria, University Grenoble Alpes, CNRS, Grenoble, France
| | - Sarael Alcauter
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, México
| | - Dorothee P Auer
- School of Medicine, University of Nottingham, Nottingham, UK
- NIHR Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Emmanuel L Barbier
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, Grenoble, France
| | - Jürgen Baudewig
- Functional Imaging Laboratory, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany
| | - Christian F Beckmann
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
| | - Nicolau Beckmann
- Musculoskeletal Diseases Department, Novartis Institutes for BioMedical Research, Basel, Switzerland
| | | | - Erwin L A Blezer
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht & Utrecht University, Utrecht, The Netherlands
| | | | - Susann Boretius
- Functional Imaging Laboratory, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany
- Faculty of Biology and Psychology, Georg-August University of Göttingen, Göttingen, Germany
- DFG Research Center for Nanoscale Microscopy and Molecular Physiology of the Brain (CNMPB), Göttingen, Germany
| | - Sandrine Bouvard
- Lyon Neuroscience Research Center, Université Claude Bernard Lyon 1, INSERM, CNRS, Lyon, France
| | - Eike Budinger
- Combinatorial NeuroImaging Core Facility, Leibniz Institute for Neurobiology, Magdeburg, Germany
- Center for Behavioral Brain Sciences, Magdeburg, Germany
| | - Joseph D Buxbaum
- Seaver Autism Center for Research & Treatment, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Diana Cash
- Biomarker Research And Imaging in Neuroscience (BRAIN) Centre, Department of Neuroimaging King's College London, London, UK
| | - Victoria Chapman
- School of Life Sciences, University of Nottingham, Nottingham, UK
- Pain Centre Versus Arthritis, University of Nottingham, Nottingham, UK
- NIHR Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Kai-Hsiang Chuang
- Queensland Brain Institute and Centre for Advanced Imaging, University of Queensland, St. Lucia, QLD, Australia
| | | | - Bram F Coolen
- Biomedical Engineering and Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Jeffrey W Dalley
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Marc Dhenain
- Laboratoire des Maladies Neurodégénératives, Molecular Imaging Research Center (MIRCen), Université Paris-Saclay, Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA), CNRS, Fontenay-aux-Roses, France
| | - Rick M Dijkhuizen
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht & Utrecht University, Utrecht, The Netherlands
| | - Oscar Esteban
- Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Cornelius Faber
- Experimental Magnetic Resonance Group, Clinic of Radiology, University of Münster, Münster, Germany
| | - Marcelo Febo
- Febo Laboratory, Department of Psychiatry, University of Florida, Gainesville, FL, USA
| | - Kirk W Feindel
- Centre for Microscopy, Characterisation & Analysis, Research Infrastructure Centres, University of Western Australia, Nedlands, WA, Australia
| | - Gianluigi Forloni
- Biology of Neurodogenerative Disorders, Department of Neuroscience Istituto di Ricerche Farmacologiche Mario Negri, IRCCS, Milan, Italy
| | - Jérémie Fouquet
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, QC, Canada
| | - Eduardo A Garza-Villarreal
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, Mexico
| | - Natalia Gass
- Translational Imaging, Department of Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, Mannheim, Germany
| | - Jeffrey C Glennon
- Conway Institute of Biomedical and Biomolecular Sciences, School of Medicine, University College Dublin, Dublin, Ireland
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Olli Gröhn
- Biomedical Imaging Unit, A.I.V. Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Andrew Harkin
- Neuropsychopharmacology Research Group, School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Dublin, Ireland
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Arend Heerschap
- Department for Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Xavier Helluy
- Department of Neurophysiology, Medical Faculty, Ruhr University Bochum, Bochum, Germany
- Department of Biopsychology, Institute of Cognitive Neuroscience, Ruhr University Bochum, Bochum, Germany
| | - Kristina Herfert
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University of Tuebingen, Tuebingen, Germany
| | - Arnd Heuser
- Max-Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Judith R Homberg
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Danielle J Houwing
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
| | - Fahmeed Hyder
- Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
- Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University School of Medicine, New Haven, CT, USA
| | | | - Ileana O Jelescu
- CIBM Center for Biomedical Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Heidi Johansen-Berg
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
| | - Gen Kaneko
- School of Arts & Sciences, University of Houston-Victoria, Victoria, TX, USA
| | - Ryuta Kawashima
- Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Shella D Keilholz
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology, Atlanta, GA, USA
| | - Georgios A Keliris
- Bio-imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Clare Kelly
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
- School of Psychology, Trinity College Dublin, Dublin, Ireland
- Department of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Christian Kerskens
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
- Trinity Centre for Biomedical Engineering, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Jibran Y Khokhar
- Khokhar Lab, Department of Anatomy and Cell Biology, Western University, London, ON, Canada
| | - Peter C Kind
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, UK
- Patrick Wild Centre, University of Edinburgh, Edinburgh, UK
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK
- Centre for Brain Development and Repair, Institute for Stem Cell Biology and Regenerative Medicine, Bangalore, India
| | | | - Jason P Lerch
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
- Department of Medical Biophysics, University of Toronto, Toronto, QC, Canada
| | - Monica A López-Hidalgo
- Escuela Nacional de Estudios Superiores, Juriquilla, Universidad Nacional Autónoma de México, Querétaro, México
| | | | - Fabien Marchand
- Université Clermont Auvergne, Inserm U1107 Neuro-Dol, Pharmacologie Fondamentale et Clinique de la Douleur, Clermont-Ferrand, France
| | - Rogier B Mars
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
| | - Gerardo Marsella
- Animal Care Unit, Istituto di Ricerche Farmacologiche Mario Negri, IRCCS, Milan, Italy
| | - Edoardo Micotti
- Biology of Neurodogenerative Disorders, Department of Neuroscience Istituto di Ricerche Farmacologiche Mario Negri, IRCCS, Milan, Italy
| | - Emma Muñoz-Moreno
- Magnetic Imaging Resonance Core Facility, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain
| | - Jamie Near
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, QC, Canada
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, QC, Canada
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max-Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- Experimental and Clinical Research Center, A Joint Cooperation Between the Charité Medical Faculty and the Max-Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Willem M Otte
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht & Utrecht University, Utrecht, The Netherlands
- Department of Pediatric Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht & Utrecht University, Utrecht, The Netherlands
| | - Patricia Pais-Roldán
- Translational Neuroimaging and Neural Control Group, High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
- Medical Imaging Physics (INM-4), Institute of Neuroscience and Medicine, Forschungszentrum Juelich, Juelich, Germany
| | - Wen-Ju Pan
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology, Atlanta, GA, USA
| | - Roberto A Prado-Alcalá
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, México
| | - Gina L Quirarte
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, México
| | - Jennifer Rodger
- Experimental and Regenerative Neurosciences, School of Biological Sciences, University of Western Australia, Crawley, WA, Australia
- Brain Plasticity Group, Perron Institute for Neurological and Translational Science, Nedlands, WA, Australia
| | - Tim Rosenow
- Centre for Microscopy, Characterisation and Analysis, University of Western Australia, Crawley, WA, Australia
| | - Cassandra Sampaio-Baptista
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
- School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
| | - Alexander Sartorius
- Translational Imaging, Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Stephen J Sawiak
- Translational Neuroimaging Laboratory, Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Tom W J Scheenen
- Department for Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
- Erwin L. Hahn Institute for MR Imaging, University of Duisburg-Essen, Essen, Germany
| | - Noam Shemesh
- Preclinical MRI, Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Yen-Yu Ian Shih
- Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Amir Shmuel
- Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- Department of Physiology, McGill University, Montreal, QC, Canada
| | - Guadalupe Soria
- Laboratory of Surgical Neuroanatomy, Institute of Neuroscience, University of Barcelona, Barcelona, Spain
| | - Ron Stoop
- Psychiatric neurosciences, Center for Psychiatric Neuroscience, Lausanne University and University Hospital Center, Unicentre, Lausanne, Switzerland
| | | | - Sally M Till
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, UK
- Patrick Wild Centre, University of Edinburgh, Edinburgh, UK
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Nick Todd
- Focused Ultrasound Laboratory, Department of Radiology Brigham and Women's Hospital, Boston, MA, USA
| | - Annemie Van Der Linden
- Bio-imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Annette van der Toorn
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht & Utrecht University, Utrecht, The Netherlands
| | - Geralda A F van Tilborg
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht & Utrecht University, Utrecht, The Netherlands
| | - Christian Vanhove
- Institute Biomedical Technology (IBiTech), Electronics and Information Systems (ELIS), Ghent University, Gent, Belgium
| | - Andor Veltien
- Department for Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Marleen Verhoye
- Bio-imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Lydia Wachsmuth
- Experimental Magnetic Resonance Group, Clinic of Radiology, University of Münster, Münster, Germany
| | - Wolfgang Weber-Fahr
- Translational Imaging, Department of Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, Mannheim, Germany
| | - Patricia Wenk
- Combinatorial NeuroImaging Core Facility, Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Xin Yu
- Translational Neuroimaging and Neural Control Group, High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Valerio Zerbi
- Neuro-X Institute, School of Engineering (STI), EPFL, Lausanne, Switzerland
- Centre for Biomedical Imaging (CIBM), Lausanne, Switzerland
| | - Nanyin Zhang
- Translational Neuroimaging and Systems Neuroscience Lab, Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Baogui B Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Luc Zimmer
- Lyon Neuroscience Research Center, Université Claude Bernard Lyon 1, INSERM, CNRS, Lyon, France
- CERMEP - Imagerie du vivant, Lyon, France
- Hospices Civils de Lyon, Lyon, France
| | - Gabriel A Devenyi
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, QC, Canada
- Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Andreas Hess
- Institute of Experimental and Clinical Pharmacology and Toxicology, FAU Erlangen-Nürnberg, Erlangen, Germany
| |
Collapse
|
19
|
Gozzi A, Zerbi V. Modeling Brain Dysconnectivity in Rodents. Biol Psychiatry 2023; 93:419-429. [PMID: 36517282 DOI: 10.1016/j.biopsych.2022.09.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 08/19/2022] [Accepted: 09/10/2022] [Indexed: 02/04/2023]
Abstract
Altered or atypical functional connectivity as measured with functional magnetic resonance imaging (fMRI) is a hallmark feature of brain connectopathy in psychiatric, developmental, and neurological disorders. However, the biological underpinnings and etiopathological significance of this phenomenon remain unclear. The recent development of MRI-based techniques for mapping brain function in rodents provides a powerful platform to uncover the determinants of functional (dys)connectivity, whether they are genetic mutations, environmental risk factors, or specific cellular and circuit dysfunctions. Here, we summarize the recent contribution of rodent fMRI toward a deeper understanding of network dysconnectivity in developmental and psychiatric disorders. We highlight substantial correspondences in the spatiotemporal organization of rodent and human fMRI networks, supporting the translational relevance of this approach. We then show how this research platform might help us comprehend the importance of connectional heterogeneity in complex brain disorders and causally relate multiscale pathogenic contributors to functional dysconnectivity patterns. Finally, we explore how perturbational techniques can be used to dissect the fundamental aspects of fMRI coupling and reveal the causal contribution of neuromodulatory systems to macroscale network activity, as well as its altered dynamics in brain diseases. These examples outline how rodent functional imaging is poised to advance our understanding of the bases and determinants of human functional dysconnectivity.
Collapse
Affiliation(s)
- Alessandro Gozzi
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems, Rovereto, Italy.
| | - Valerio Zerbi
- Neuro-X Institute, School of Engineering, École polytechnique fédérale de Lausanne, Lausanne, Switzerland; CIBM Center for Biomedical Imaging, Lausanne, Switzerland.
| |
Collapse
|
20
|
Menon V, Cerri D, Lee B, Yuan R, Lee SH, Shih YYI. Optogenetic stimulation of anterior insular cortex neurons in male rats reveals causal mechanisms underlying suppression of the default mode network by the salience network. Nat Commun 2023; 14:866. [PMID: 36797303 PMCID: PMC9935890 DOI: 10.1038/s41467-023-36616-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 02/09/2023] [Indexed: 02/18/2023] Open
Abstract
The salience network (SN) and default mode network (DMN) play a crucial role in cognitive function. The SN, anchored in the anterior insular cortex (AI), has been hypothesized to modulate DMN activity during stimulus-driven cognition. However, the causal neural mechanisms underlying changes in DMN activity and its functional connectivity with the SN are poorly understood. Here we combine feedforward optogenetic stimulation with fMRI and computational modeling to dissect the causal role of AI neurons in dynamic functional interactions between SN and DMN nodes in the male rat brain. Optogenetic stimulation of Chronos-expressing AI neurons suppressed DMN activity, and decreased AI-DMN and intra-DMN functional connectivity. Our findings demonstrate that feedforward optogenetic stimulation of AI neurons induces dynamic suppression and decoupling of the DMN and elucidates previously unknown features of rodent brain network organization. Our study advances foundational knowledge of causal mechanisms underlying dynamic cross-network interactions and brain network switching.
Collapse
Grants
- R01 MH121069 NIMH NIH HHS
- P50 HD103573 NICHD NIH HHS
- T32 AA007573 NIAAA NIH HHS
- R01 NS091236 NINDS NIH HHS
- R01 MH126518 NIMH NIH HHS
- S10 MH124745 NIMH NIH HHS
- U01 AA020023 NIAAA NIH HHS
- R01 MH111429 NIMH NIH HHS
- S10 OD026796 NIH HHS
- R01 NS086085 NINDS NIH HHS
- R01 EB022907 NIBIB NIH HHS
- P60 AA011605 NIAAA NIH HHS
- RF1 NS086085 NINDS NIH HHS
- RF1 MH117053 NIMH NIH HHS
- This work was supported in part by the National Institute of Mental Health (R01MH121069 to V.M., and R01MH126518, RF1MH117053, R01MH111429, S10MH124745 to Y.-Y.I.S.), National Institute on Alcohol Abuse and Alcoholism (P60AA011605 and U01AA020023 to Y.-Y.I.S., T32AA007573 to D.C.), National Institute of Neurological Disorders and Stroke (R01NS086085 to V.M., R01NS091236 to Y.-Y.I.S.), National Institute of Child Health and Human Development (P50HD103573 to Y.-Y.I.S.), National Institute of Biomedical Imaging and Bioengineering (R01EB022907 to V.M.), and National Institute of Health Office of the Director (S10OD026796 to Y.-Y.I.S.).
Collapse
Affiliation(s)
- Vinod Menon
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, 94305, USA.
- Department of Neurology & Neurological Sciences, Stanford University School of Medicine, Stanford, CA, 94305, USA.
- Wu Tsai Neuroscience Institute, Stanford University School of Medicine, Stanford, CA, 94305, USA.
| | - Domenic Cerri
- 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
| | - Byeongwook Lee
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Rui Yuan
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, 94305, 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
| | - 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.
| |
Collapse
|
21
|
An optimized bioluminescent substrate for non-invasive imaging in the brain. Nat Chem Biol 2023; 19:731-739. [PMID: 36759751 DOI: 10.1038/s41589-023-01265-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 01/13/2023] [Indexed: 02/11/2023]
Abstract
Bioluminescence imaging (BLI) allows non-invasive visualization of cells and biochemical events in vivo and thus has become an indispensable technique in biomedical research. However, BLI in the central nervous system remains challenging because luciferases show relatively poor performance in the brain with existing substrates. Here, we report the discovery of a NanoLuc substrate with improved brain performance, cephalofurimazine (CFz). CFz paired with Antares luciferase produces greater than 20-fold more signal from the brain than the standard combination of D-luciferin with firefly luciferase. At standard doses, Antares-CFz matches AkaLuc-AkaLumine/TokeOni in brightness, while occasional higher dosing of CFz can be performed to obtain threefold more signal. CFz should allow the growing number of NanoLuc-based indicators to be applied to the brain with high sensitivity. Using CFz, we achieve video-rate non-invasive imaging of Antares in brains of freely moving mice and demonstrate non-invasive calcium imaging of sensory-evoked activity in genetically defined neurons.
Collapse
|
22
|
Morrissey ZD, Gao J, Zhan L, Li W, Fortel I, Saido T, Saito T, Bakker A, Mackin S, Ajilore O, Lazarov O, Leow AD. Hippocampal functional connectivity across age in an App knock-in mouse model of Alzheimer's disease. Front Aging Neurosci 2023; 14:1085989. [PMID: 36711209 PMCID: PMC9878347 DOI: 10.3389/fnagi.2022.1085989] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 12/28/2022] [Indexed: 01/15/2023] Open
Abstract
Introduction Alzheimer's disease (AD) is a progressive neurodegenerative disease. The early processes of AD, however, are not fully understood and likely begin years before symptoms manifest. Importantly, disruption of the default mode network, including the hippocampus, has been implicated in AD. Methods To examine the role of functional network connectivity changes in the early stages of AD, we performed resting-state functional magnetic resonance imaging (rs-fMRI) using a mouse model harboring three familial AD mutations (App NL-G-F/NL-G-F knock-in, APPKI) in female mice in early, middle, and late age groups. The interhemispheric and intrahemispheric functional connectivity (FC) of the hippocampus was modeled across age. Results We observed higher interhemispheric functional connectivity (FC) in the hippocampus across age. This was reduced, however, in APPKI mice in later age. Further, we observed loss of hemispheric asymmetry in FC in APPKI mice. Discussion Together, this suggests that there are early changes in hippocampal FC prior to heavy onset of amyloid β plaques, and which may be clinically relevant as an early biomarker of AD.
Collapse
Affiliation(s)
- Zachery D. Morrissey
- Graduate Program in Neuroscience, University of Illinois at Chicago, Chicago, IL, United States
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
- Department of Anatomy & Cell Biology, University of Illinois at Chicago, Chicago, IL, United States
| | - Jin Gao
- Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL, United States
- Preclinical Imaging Core, University of Illinois at Chicago, Chicago, IL, United States
| | - Liang Zhan
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Weiguo Li
- Preclinical Imaging Core, University of Illinois at Chicago, Chicago, IL, United States
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States
- Department of Radiology, Northwestern University, Chicago, IL, United States
| | - Igor Fortel
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States
| | - Takaomi Saido
- Laboratory for Proteolytic Neuroscience, RIKEN Center for Brain Science, Wako, Japan
| | - Takashi Saito
- Department of Neurocognitive Science, Institute of Brain Science, Nagoya City University, Nagoya, Japan
| | - Arnold Bakker
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, United States
- Department of Neurology, Johns Hopkins University, Baltimore, MD, United States
| | - Scott Mackin
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
| | - Orly Lazarov
- Department of Anatomy & Cell Biology, University of Illinois at Chicago, Chicago, IL, United States
| | - Alex D. Leow
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, United States
| |
Collapse
|
23
|
Cramer SW, Haley SP, Popa LS, Carter RE, Scott E, Flaherty EB, Dominguez J, Aronson JD, Sabal L, Surinach D, Chen CC, Kodandaramaiah SB, Ebner TJ. Wide-field calcium imaging reveals widespread changes in cortical functional connectivity following mild traumatic brain injury in the mouse. Neurobiol Dis 2023; 176:105943. [PMID: 36476979 PMCID: PMC9972226 DOI: 10.1016/j.nbd.2022.105943] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 11/29/2022] [Accepted: 12/02/2022] [Indexed: 12/12/2022] Open
Abstract
>2.5 million individuals in the United States suffer mild traumatic brain injuries (mTBI) annually. Mild TBI is characterized by a brief period of altered consciousness, without objective findings of anatomic injury on clinical imaging or physical deficit on examination. Nevertheless, a subset of mTBI patients experience persistent subjective symptoms and repeated mTBI can lead to quantifiable neurological deficits, suggesting that each mTBI alters neurophysiology in a deleterious manner not detected using current clinical methods. To better understand these effects, we performed mesoscopic Ca2+ imaging in mice to evaluate how mTBI alters patterns of neuronal interactions across the dorsal cerebral cortex. Spatial Independent Component Analysis (sICA) and Localized semi-Nonnegative Matrix Factorization (LocaNMF) were used to quantify changes in cerebral functional connectivity (FC). Repetitive, mild, controlled cortical impacts induce temporary neuroinflammatory responses, characterized by increased density of microglia exhibiting de-ramified morphology. These temporary neuro-inflammatory changes were not associated with compromised cognitive performance in the Barnes maze or motor function as assessed by rotarod. However, long-term alterations in functional connectivity (FC) were observed. Widespread, bilateral changes in FC occurred immediately following impact and persisted for up to 7 weeks, the duration of the experiment. Network alterations include decreases in global efficiency, clustering coefficient, and nodal strength, thereby disrupting functional interactions and information flow throughout the dorsal cerebral cortex. A subnetwork analysis shows the largest disruptions in FC were concentrated near the impact site. Therefore, mTBI induces a transient neuroinflammation, without alterations in cognitive or motor behavior, and a reorganized cortical network evidenced by the widespread, chronic alterations in cortical FC.
Collapse
Affiliation(s)
- Samuel W Cramer
- Department of Neurosurgery, University of Minnesota, Minneapolis, MN 55455, USA
| | - Samuel P Haley
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - Laurentiu S Popa
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - Russell E Carter
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - Earl Scott
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - Evelyn B Flaherty
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - Judith Dominguez
- Department of Mechanical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Justin D Aronson
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - Luke Sabal
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - Daniel Surinach
- Department of Mechanical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Clark C Chen
- Department of Neurosurgery, University of Minnesota, Minneapolis, MN 55455, USA
| | | | - Timothy J Ebner
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA.
| |
Collapse
|
24
|
Padawer-Curry JA, Bowen RM, Jarang A, Wang X, Lee JM, Bauer AQ. Wide-Field Optical Imaging in Mouse Models of Ischemic Stroke. Methods Mol Biol 2023; 2616:113-151. [PMID: 36715932 DOI: 10.1007/978-1-0716-2926-0_11] [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] [Indexed: 01/31/2023]
Abstract
Functional neuroimaging is a powerful tool for evaluating how local and global brain circuits evolve after focal ischemia and how these changes relate to functional recovery. For example, acutely after stroke, changes in functional brain organization relate to initial deficit and are predictive of recovery potential. During recovery, the reemergence and restoration of connections lost due to stroke correlate with recovery of function. Thus, information gleaned from functional neuroimaging can be used as a proxy for behavior and inform on the efficacy of interventional strategies designed to affect plasticity mechanisms after injury. And because these findings are consistently observed across species, bridge measurements can be made in animal models to enrich findings in human stroke populations. In mice, genetic engineering techniques have provided several new opportunities for extending optical neuroimaging methods to more direct measures of neuronal activity. These developments are especially useful in the context of stroke where neurovascular coupling can be altered, potentially limiting imaging measures based on hemodynamic activity alone. This chapter is designed to give an overview of functional wide-field optical imaging (WFOI) for applications in rodent models of stroke, primarily in the mouse. The goal is to provide a protocol for laboratories that want to incorporate an affordable functional neuroimaging assay into their current research thrusts, but perhaps lack the background knowledge or equipment for developing a new arm of research in their lab. Within, we offer a comprehensive guide developing and applying WFOI technology with the hope of facilitating accessibility of neuroimaging technology to other researchers in the stroke field.
Collapse
Affiliation(s)
- Jonah A Padawer-Curry
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
- Imaging Science PhD Program, Washington University in St. Louis, St. Louis, MO, USA
| | - Ryan M Bowen
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Anmol Jarang
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Xiaodan Wang
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Jin-Moo Lee
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Adam Q Bauer
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA.
- Imaging Science PhD Program, Washington University in St. Louis, St. Louis, MO, USA.
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA.
| |
Collapse
|
25
|
Miranda L, Bordes J, Gasperoni S, Lopez JP. Increasing resolution in stress neurobiology: from single cells to complex group behaviors. Stress 2023; 26:2186141. [PMID: 36855966 DOI: 10.1080/10253890.2023.2186141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/02/2023] Open
Abstract
Stress can have severe psychological and physiological consequences. Thus, inappropriate regulation of the stress response is linked to the etiology of mood and anxiety disorders. The generation and implementation of preclinical animal models represent valuable tools to explore and characterize the mechanisms underlying the pathophysiology of stress-related psychiatric disorders and the development of novel pharmacological strategies. In this commentary, we discuss the strengths and limitations of state-of-the-art molecular and computational advances employed in stress neurobiology research, with a focus on the ever-increasing spatiotemporal resolution in cell biology and behavioral science. Finally, we share our perspective on future directions in the fields of preclinical and human stress research.
Collapse
Affiliation(s)
- Lucas Miranda
- Department of Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | - Joeri Bordes
- Research Group Neurobiology of Stress Resilience, Max Planck Institute of Psychiatry, Munich, Germany
| | - Serena Gasperoni
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Juan Pablo Lopez
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| |
Collapse
|
26
|
de Bartolomeis A, De Simone G, Ciccarelli M, Castiello A, Mazza B, Vellucci L, Barone A. Antipsychotics-Induced Changes in Synaptic Architecture and Functional Connectivity: Translational Implications for Treatment Response and Resistance. Biomedicines 2022; 10:biomedicines10123183. [PMID: 36551939 PMCID: PMC9776416 DOI: 10.3390/biomedicines10123183] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 12/02/2022] [Accepted: 12/04/2022] [Indexed: 12/14/2022] Open
Abstract
Schizophrenia is a severe mental illness characterized by alterations in processes that regulate both synaptic plasticity and functional connectivity between brain regions. Antipsychotics are the cornerstone of schizophrenia pharmacological treatment and, beyond occupying dopamine D2 receptors, can affect multiple molecular targets, pre- and postsynaptic sites, as well as intracellular effectors. Multiple lines of evidence point to the involvement of antipsychotics in sculpting synaptic architecture and remodeling the neuronal functional unit. Furthermore, there is an increasing awareness that antipsychotics with different receptor profiles could yield different interregional patterns of co-activation. In the present systematic review, we explored the fundamental changes that occur under antipsychotics' administration, the molecular underpinning, and the consequences in both acute and chronic paradigms. In addition, we investigated the relationship between synaptic plasticity and functional connectivity and systematized evidence on different topographical patterns of activation induced by typical and atypical antipsychotics.
Collapse
|
27
|
Ciric R, Thompson WH, Lorenz R, Goncalves M, MacNicol EE, Markiewicz CJ, Halchenko YO, Ghosh SS, Gorgolewski KJ, Poldrack RA, Esteban O. TemplateFlow: FAIR-sharing of multi-scale, multi-species brain models. Nat Methods 2022; 19:1568-1571. [PMID: 36456786 PMCID: PMC9718663 DOI: 10.1038/s41592-022-01681-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 10/14/2022] [Indexed: 12/03/2022]
Abstract
Reference anatomies of the brain ('templates') and corresponding atlases are the foundation for reporting standardized neuroimaging results. Currently, there is no registry of templates and atlases; therefore, the redistribution of these resources occurs either bundled within existing software or in ad hoc ways such as downloads from institutional sites and general-purpose data repositories. We introduce TemplateFlow as a publicly available framework for human and non-human brain models. The framework combines an open database with software for access, management, and vetting, allowing scientists to share their resources under FAIR-findable, accessible, interoperable, and reusable-principles. TemplateFlow enables multifaceted insights into brains across species, and supports multiverse analyses testing whether results generalize across standard references, scales, and in the long term, species.
Collapse
Affiliation(s)
- Rastko Ciric
- Department of Psychology, Stanford University, Stanford, CA, USA.
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
| | - William H Thompson
- Department of Psychology, Stanford University, Stanford, CA, USA
- Department of Applied Information Technology, University of Gothenburg, Gothenburg, Sweden
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Romy Lorenz
- Department of Psychology, Stanford University, Stanford, CA, USA
- MRC CBU, University of Cambridge, Cambridge, UK
- Department of Neurophysics, MPI, Leipzig, Germany
| | | | - Eilidh E MacNicol
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | | | - Yaroslav O Halchenko
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Satrajit S Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Otolaryngology, Harvard Medical School, Boston, MA, USA
| | | | | | - Oscar Esteban
- Department of Psychology, Stanford University, Stanford, CA, USA.
- Department of Radiology, University Hospital of Lausanne and University of Lausanne, Lausanne, Switzerland.
| |
Collapse
|
28
|
Tsurugizawa T, Kumamoto T, Yoshioka Y. Utilization of potato starch suspension for MR-microimaging in ex vivo mouse embryos. iScience 2022; 25:105694. [PMID: 36567713 PMCID: PMC9768372 DOI: 10.1016/j.isci.2022.105694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 07/31/2022] [Accepted: 11/24/2022] [Indexed: 12/05/2022] Open
Abstract
Magnetic resonance (MR) microimaging of the mouse embryo is a promising tool to noninvasively investigate the microstructure of the brain of a developing mouse. The proton-free fluid is used for the liquid surrounding the specimen in MR microimaging, but the potential issue of image quality remains due to the air bubbles on the specimen and the retained water proton in the curvature of the embryo. Furthermore, the specimen may move during the scanning, resulting in motion artifact. Here, we developed the new concept of the ex vivo microimaging protocol with the robust method using the potato starch-containing biological polymers. Potato starch suspension with PBS significantly reduced T1 and T2 signal intensity of the suspension and strongly suppressed the motion of the embryo. Furthermore, potato starch-PBS suspension is stable for long-time scanning at room temperature. These results indicate the utility of potato starch suspension for MR microimaging in mouse embryos.
Collapse
Affiliation(s)
- Tomokazu Tsurugizawa
- Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Higashi, Tsukuba 305-8568, Japan,Faculty of Engineering, Information and Systems, University of Tsukuba, Tsukuba 305-8573, Japan,Jikei University School of Medicine, 3-25-8 Nishishinbashi, Tokyo 105-8461, Japan,Graduate School of Frontier Biosciences, Osaka University, Suita 565-0871, Japan,Center for Information and Neural Networks (CiNet), Osaka University and National Institute of Information and Communications Technology (NICT), Suita 565-0871, Japan,Corresponding author
| | - Takuma Kumamoto
- Developmental Neuroscience Project, Department of Brain & Neurosciences, Tokyo Metropolitan Institute of Medical Science, Tokyo 156-8506, Japan
| | - Yoshichika Yoshioka
- Graduate School of Frontier Biosciences, Osaka University, Suita 565-0871, Japan,Center for Information and Neural Networks (CiNet), Osaka University and National Institute of Information and Communications Technology (NICT), Suita 565-0871, Japan,Corresponding author
| |
Collapse
|
29
|
Chen Z, Gezginer I, Augath MA, Ren W, Liu YH, Ni R, Deán-Ben XL, Razansky D. Hybrid magnetic resonance and optoacoustic tomography (MROT) for preclinical neuroimaging. LIGHT, SCIENCE & APPLICATIONS 2022; 11:332. [PMID: 36418860 PMCID: PMC9684112 DOI: 10.1038/s41377-022-01026-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 10/13/2022] [Accepted: 10/24/2022] [Indexed: 05/17/2023]
Abstract
Multi-modal imaging is essential for advancing our understanding of brain function and unraveling pathophysiological processes underlying neurological and psychiatric disorders. Magnetic resonance (MR) and optoacoustic (OA) imaging have been shown to provide highly complementary contrasts and capabilities for preclinical neuroimaging. True integration between these modalities can thus offer unprecedented capabilities for studying the rodent brain in action. We report on a hybrid magnetic resonance and optoacoustic tomography (MROT) system for concurrent noninvasive structural and functional imaging of the mouse brain. Volumetric OA tomography was designed as an insert into a high-field MR scanner by integrating a customized MR-compatible spherical transducer array, an illumination module, and a dedicated radiofrequency coil. A tailored data processing pipeline has been developed to mitigate signal crosstalk and accurately register image volumes acquired with T1-weighted, angiography, and blood oxygenation level-dependent (BOLD) sequences onto the corresponding vascular and oxygenation data recorded with the OA modality. We demonstrate the concurrent acquisition of dual-mode anatomical and angiographic brain images with the scanner, as well as real-time functional readings of multiple hemodynamic parameters from animals subjected to oxygenation stress. Our approach combines the functional and molecular imaging advantages of OA with the superb soft-tissue contrast of MR, further providing an excellent platform for cross-validation of functional readings by the two modalities.
Collapse
Affiliation(s)
- Zhenyue Chen
- Institute for Biomedical Engineering and Institute of Pharmacology and Toxicology, Faculty of Medicine, University of Zurich, Zurich, Switzerland
- Institute for Biomedical Engineering, Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland
| | - Irmak Gezginer
- Institute for Biomedical Engineering and Institute of Pharmacology and Toxicology, Faculty of Medicine, University of Zurich, Zurich, Switzerland
- Institute for Biomedical Engineering, Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland
| | - Mark-Aurel Augath
- Institute for Biomedical Engineering and Institute of Pharmacology and Toxicology, Faculty of Medicine, University of Zurich, Zurich, Switzerland
- Institute for Biomedical Engineering, Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland
| | - Wuwei Ren
- Institute for Biomedical Engineering and Institute of Pharmacology and Toxicology, Faculty of Medicine, University of Zurich, Zurich, Switzerland
- Institute for Biomedical Engineering, Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland
| | - Yu-Hang Liu
- Institute for Biomedical Engineering and Institute of Pharmacology and Toxicology, Faculty of Medicine, University of Zurich, Zurich, Switzerland
- Institute for Biomedical Engineering, Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland
| | - Ruiqing Ni
- Institute for Biomedical Engineering and Institute of Pharmacology and Toxicology, Faculty of Medicine, University of Zurich, Zurich, Switzerland
- Institute for Biomedical Engineering, Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland
- Zurich Neuroscience Center (ZNZ), Zurich, Switzerland
| | - Xosé Luís Deán-Ben
- Institute for Biomedical Engineering and Institute of Pharmacology and Toxicology, 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 for Biomedical Engineering and Institute of Pharmacology and Toxicology, Faculty of Medicine, University of Zurich, Zurich, Switzerland.
- Institute for Biomedical Engineering, Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland.
- Zurich Neuroscience Center (ZNZ), Zurich, Switzerland.
| |
Collapse
|
30
|
Zhang X. Effects of Anesthesia on Cerebral Blood Flow and Functional Connectivity of Nonhuman Primates. Vet Sci 2022; 9:vetsci9100516. [PMID: 36288129 PMCID: PMC9609818 DOI: 10.3390/vetsci9100516] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 09/14/2022] [Accepted: 09/19/2022] [Indexed: 02/07/2023] Open
Abstract
Nonhuman primates (NHPs) are the closest living relatives of humans and play a critical and unique role in neuroscience research and pharmaceutical development. General anesthesia is usually required in neuroimaging studies of NHPs to keep the animal from stress and motion. However, the adverse effects of anesthesia on cerebral physiology and neural activity are pronounced and can compromise the data collection and interpretation. Functional connectivity is frequently examined using resting-state functional MRI (rsfMRI) to assess the functional abnormality in the animal brain under anesthesia. The fMRI signal can be dramatically suppressed by most anesthetics in a dose-dependent manner. In addition, rsfMRI studies may be further compromised by inter-subject variations when the sample size is small (as seen in most neuroscience studies of NHPs). Therefore, proper use of anesthesia is strongly demanded to ensure steady and consistent physiology maintained during rsfMRI data collection of each subject. The aim of this review is to summarize typical anesthesia used in rsfMRI scans of NHPs and the effects of anesthetics on cerebral physiology and functional connectivity. Moreover, the protocols with optimal rsfMRI data acquisition and anesthesia procedures for functional connectivity study of macaque monkeys are introduced.
Collapse
Affiliation(s)
- Xiaodong Zhang
- EPC Imaging Center and Division of Neuropharmacology and Neurologic Diseases, Emory National Primate Research Center, Emory University, 954 Gatewood RD, Atlanta, GA 30329, USA
| |
Collapse
|
31
|
Schwalm M, Tabuena DR, Easton C, Richner TJ, Mourad P, Watari H, Moody WJ, Stroh A. Functional States Shape the Spatiotemporal Representation of Local and Cortex-wide Neural Activity in Mouse Sensory Cortex. J Neurophysiol 2022; 128:763-777. [PMID: 35975935 DOI: 10.1152/jn.00424.2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
The spatiotemporal representation of neural activity during rest and upon sensory stimulation in cortical areas is highly dynamic, and may be predominantly governed by cortical state. On the mesoscale level, intrinsic neuronal activity ranges from a persistent state, generally associated with a sustained depolarization of neurons, to a bimodal, slow-wave like state with bursts of neuronal activation, alternating with silent periods. These different activity states are prevalent under certain types of sedatives, or are associated with specific behavioral or vigilance conditions. Neurophysiological experiments assessing circuit activity, usually assume a constant underlying state, yet reports of variability of neuronal responses under seemingly constant conditions are common in the field. Even when a certain type of neural activity or cortical state can stably be maintained over time, the associated response properties are highly relevant for explaining experimental outcomes. Here we describe the spatiotemporal characteristics of ongoing activity and sensory evoked responses under two predominant functional states in the sensory cortices of mice: persistent activity (PA) and slow wave activity (SWA). Using electrophysiological recordings, and local and wide-field calcium recordings, we examine whether spontaneous and sensory evoked neuronal activity propagate throughout the cortex in a state dependent manner. We find that PA and SWA differ in their spatiotemporal characteristics which determine the cortical network's response to a sensory stimulus. During PA state, sensory stimulation elicits gamma-based short-latency responses which precisely follow each stimulation pulse and are prone to adaptation upon higher stimulation frequencies. Sensory responses during SWA are more variable, dependent on refractory periods following spontaneous slow waves. While spontaneous slow waves propagated in anterior-posterior direction in a majority of observations, the direction of propagation of stimulus-elicited wave depends on the sensory modality. These findings suggest that cortical state explains variance and should be considered when investigating multi-scale correlates of functional neurocircuit activity.
Collapse
Affiliation(s)
- Miriam Schwalm
- Institute of Pathophysiology, University Medical Center Mainz, Mainz, Germany.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Dennis R Tabuena
- Department of Biology, University of Washington, Seattle, WA, United States
| | - Curtis Easton
- Department of Biology, University of Washington, Seattle, WA, United States
| | - Thomas J Richner
- Department of Biology, University of Washington, Seattle, WA, United States
| | - Pierre Mourad
- Department of Biology, University of Washington, Seattle, WA, United States
| | - Hirofumi Watari
- Institute of Pathophysiology, University Medical Center Mainz, Mainz, Germany.,Department of Biology, University of Washington, Seattle, WA, United States
| | - William J Moody
- Department of Biology, University of Washington, Seattle, WA, United States
| | - Albrecht Stroh
- Institute of Pathophysiology, University Medical Center Mainz, Mainz, Germany.,Leibniz Institute for Resilience Research, Mainz, Germany
| |
Collapse
|
32
|
Fadel LC, Patel IV, Romero J, Tan IC, Kesler SR, Rao V, Subasinghe SAAS, Ray RS, Yustein JT, Allen MJ, Gibson BW, Verlinden JJ, Fayn S, Ruggiero N, Ortiz C, Hipskind E, Feng A, Iheanacho C, Wang A, Pautler RG. A Mouse Holder for Awake Functional Imaging in Unanesthetized Mice: Applications in 31P Spectroscopy, Manganese-Enhanced Magnetic Resonance Imaging Studies, and Resting-State Functional Magnetic Resonance Imaging. BIOSENSORS 2022; 12:616. [PMID: 36005011 PMCID: PMC9406174 DOI: 10.3390/bios12080616] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 08/01/2022] [Accepted: 08/02/2022] [Indexed: 05/28/2023]
Abstract
Anesthesia is often used in preclinical imaging studies that incorporate mouse or rat models. However, multiple reports indicate that anesthesia has significant physiological impacts. Thus, there has been great interest in performing imaging studies in awake, unanesthetized animals to obtain accurate results without the confounding physiological effects of anesthesia. Here, we describe a newly designed mouse holder that is interfaceable with existing MRI systems and enables awake in vivo mouse imaging. This holder significantly reduces head movement of the awake animal compared to previously designed holders and allows for the acquisition of improved anatomical images. In addition to applications in anatomical T2-weighted magnetic resonance imaging (MRI), we also describe applications in acquiring 31P spectra, manganese-enhanced magnetic resonance imaging (MEMRI) transport rates and resting-state functional magnetic resonance imaging (rs-fMRI) in awake animals and describe a successful conditioning paradigm for awake imaging. These data demonstrate significant differences in 31P spectra, MEMRI transport rates, and rs-fMRI connectivity between anesthetized and awake animals, emphasizing the importance of performing functional studies in unanesthetized animals. Furthermore, these studies demonstrate that the mouse holder presented here is easy to construct and use, compatible with standard Bruker systems for mouse imaging, and provides rigorous results in awake mice.
Collapse
Affiliation(s)
- Lindsay C. Fadel
- Department Integrative Physiology, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Ivany V. Patel
- Department Integrative Physiology, Baylor College of Medicine, Houston, TX 77030, USA
- School of Humanities, Rice University, Houston, TX 77005, USA
| | - Jonathan Romero
- Department Integrative Physiology, Baylor College of Medicine, Houston, TX 77030, USA
- Small Animal Imaging Facility, Texas Children’s Hospital, Houston, TX 77030, USA
| | - I-Chih Tan
- Bioengineering Core, Advanced Technology Core, Baylor College of Medicine, Houston, TX 77030, USA
| | - Shelli R. Kesler
- School of Nursing, University of Texas at Austin, Austin, TX 78712, USA
| | - Vikram Rao
- School of Nursing, University of Texas at Austin, Austin, TX 78712, USA
| | | | - Russell S. Ray
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jason T. Yustein
- Cancer and Cell Biology Program, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Pediatrics, Texas Children’s Cancer and Hematology Centers and The Faris D. Virani Ewing, Houston, TX 77030, USA
- Sarcoma Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Matthew J. Allen
- Department of Chemistry, Wayne State University, Detroit, MI 48202, USA
| | - Brian W. Gibson
- Department Integrative Physiology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Justin J. Verlinden
- Department Integrative Physiology, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Neuroscience, Augustana College, Rock Island, IL 61201, USA
| | - Stanley Fayn
- Department Integrative Physiology, Baylor College of Medicine, Houston, TX 77030, USA
- School of Molecular and Cellular Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Nicole Ruggiero
- Department Integrative Physiology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Caitlyn Ortiz
- Department Integrative Physiology, Baylor College of Medicine, Houston, TX 77030, USA
- Small Animal Imaging Facility, Texas Children’s Hospital, Houston, TX 77030, USA
| | - Elizabeth Hipskind
- Department Integrative Physiology, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Aaron Feng
- Department Integrative Physiology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Chijindu Iheanacho
- Department Integrative Physiology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Alex Wang
- Department Integrative Physiology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Robia G. Pautler
- Department Integrative Physiology, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
- Small Animal Imaging Facility, Texas Children’s Hospital, Houston, TX 77030, USA
- Department of Radiology, Baylor College of Medicine, Houston, TX 77030, USA
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX 77030, USA
| |
Collapse
|
33
|
Wlodkowic D, Bownik A, Leitner C, Stengel D, Braunbeck T. Beyond the behavioural phenotype: Uncovering mechanistic foundations in aquatic eco-neurotoxicology. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 829:154584. [PMID: 35306067 DOI: 10.1016/j.scitotenv.2022.154584] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 03/09/2022] [Accepted: 03/11/2022] [Indexed: 06/14/2023]
Abstract
During the last decade, there has been an increase in awareness of how anthropogenic pollution can alter behavioural traits of diverse aquatic organisms. Apart from understanding profound ecological implications, alterations in neuro-behavioural indices have emerged as sensitive and physiologically integrative endpoints in chemical risk assessment. Accordingly, behavioural ecotoxicology and broader eco-neurotoxicology are becoming increasingly popular fields of research that span a plethora of fundamental laboratory experimentations as well as applied field-based studies. Despite mounting interest in aquatic behavioural ecotoxicology studies, there is, however, a considerable paucity in deciphering the mechanistic foundations underlying behavioural alterations upon exposure to pollutants. The behavioural phenotype is indeed the highest-level integrative neurobiological phenomenon, but at its core lie myriads of intertwined biochemical, cellular, and physiological processes. Therefore, the mechanisms that underlie changes in behavioural phenotypes can stem among others from dysregulation of neurotransmitter pathways, electrical signalling, and cell death of discrete cell populations in the central and peripheral nervous systems. They can, however, also be a result of toxicity to sensory organs and even metabolic dysfunctions. In this critical review, we outline why behavioural phenotyping should be the starting point that leads to actual discovery of fundamental mechanisms underlying actions of neurotoxic and neuromodulating contaminants. We highlight potential applications of the currently existing and emerging neurobiology and neurophysiology analytical strategies that should be embraced and more broadly adopted in behavioural ecotoxicology. Such strategies can provide new mechanistic discoveries instead of only observing the end sum phenotypic effects.
Collapse
Affiliation(s)
- Donald Wlodkowic
- The Neurotox Laboratory, School of Science, RMIT University, Melbourne, Australia.
| | - Adam Bownik
- Department of Hydrobiology and Protection of Ecosystems, Faculty of Environmental Biology, University of Life Sciences, Lublin, Poland
| | - Carola Leitner
- Aquatic Ecology and Toxicology, Centre for Organismal Studies, University of Heidelberg, Im Neuenheimer Feld 504, D-69120 Heidelberg, Germany
| | - Daniel Stengel
- Aquatic Ecology and Toxicology, Centre for Organismal Studies, University of Heidelberg, Im Neuenheimer Feld 504, D-69120 Heidelberg, Germany
| | - Thomas Braunbeck
- Aquatic Ecology and Toxicology, Centre for Organismal Studies, University of Heidelberg, Im Neuenheimer Feld 504, D-69120 Heidelberg, Germany
| |
Collapse
|
34
|
Dyer L, Parker A, Paphiti K, Sanderson J. Lightsheet Microscopy. Curr Protoc 2022; 2:e448. [PMID: 35838628 DOI: 10.1002/cpz1.448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In this paper, we review lightsheet (selective plane illumination) microscopy for mouse developmental biologists. There are different means of forming the illumination sheet, and we discuss these. We explain how we introduced the lightsheet microscope economically into our core facility and present our results on fixed and living samples. We also describe methods of clearing fixed samples for three-dimensional imaging and discuss the various means of preparing samples with particular reference to mouse cilia, adipose spheroids, and cochleae. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC.
Collapse
Affiliation(s)
- Laura Dyer
- MRC Harwell Institute, Mammalian Genetics Unit, Harwell Campus, Oxfordshire, UK
| | - Andrew Parker
- MRC Harwell Institute, Mammalian Genetics Unit, Harwell Campus, Oxfordshire, UK
| | - Keanu Paphiti
- MRC Harwell Institute, Mammalian Genetics Unit, Harwell Campus, Oxfordshire, UK
| | - Jeremy Sanderson
- MRC Harwell Institute, Mammalian Genetics Unit, Harwell Campus, Oxfordshire, UK
| |
Collapse
|
35
|
Guilbert J, Légaré A, De Koninck P, Desrosiers P, Desjardins M. Toward an integrative neurovascular framework for studying brain networks. NEUROPHOTONICS 2022; 9:032211. [PMID: 35434179 PMCID: PMC8989057 DOI: 10.1117/1.nph.9.3.032211] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 03/11/2022] [Indexed: 05/28/2023]
Abstract
Brain functional connectivity based on the measure of blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signals has become one of the most widely used measurements in human neuroimaging. However, the nature of the functional networks revealed by BOLD fMRI can be ambiguous, as highlighted by a recent series of experiments that have suggested that typical resting-state networks can be replicated from purely vascular or physiologically driven BOLD signals. After going through a brief review of the key concepts of brain network analysis, we explore how the vascular and neuronal systems interact to give rise to the brain functional networks measured with BOLD fMRI. This leads us to emphasize a view of the vascular network not only as a confounding element in fMRI but also as a functionally relevant system that is entangled with the neuronal network. To study the vascular and neuronal underpinnings of BOLD functional connectivity, we consider a combination of methodological avenues based on multiscale and multimodal optical imaging in mice, used in combination with computational models that allow the integration of vascular information to explain functional connectivity.
Collapse
Affiliation(s)
- Jérémie Guilbert
- Université Laval, Department of Physics, Physical Engineering, and Optics, Québec, Canada
- Université Laval, Centre de recherche du CHU de Québec, Québec, Canada
| | - Antoine Légaré
- Université Laval, Department of Physics, Physical Engineering, and Optics, Québec, Canada
- Centre de recherche CERVO, Québec, Canada
- Université Laval, Department of Biochemistry, Microbiology, and Bioinformatics, Québec, Canada
| | - Paul De Koninck
- Centre de recherche CERVO, Québec, Canada
- Université Laval, Department of Biochemistry, Microbiology, and Bioinformatics, Québec, Canada
| | - Patrick Desrosiers
- Université Laval, Department of Physics, Physical Engineering, and Optics, Québec, Canada
- Centre de recherche CERVO, Québec, Canada
| | - Michèle Desjardins
- Université Laval, Department of Physics, Physical Engineering, and Optics, Québec, Canada
- Université Laval, Centre de recherche du CHU de Québec, Québec, Canada
| |
Collapse
|
36
|
Beloate LN, Zhang N. Connecting the dots between cell populations, whole-brain activity, and behavior. NEUROPHOTONICS 2022; 9:032208. [PMID: 35350137 PMCID: PMC8957372 DOI: 10.1117/1.nph.9.3.032208] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Accepted: 02/25/2022] [Indexed: 06/14/2023]
Abstract
Simultaneously manipulating and monitoring both microscopic and macroscopic brain activity in vivo and identifying the linkage to behavior are powerful tools in neuroscience research. These capabilities have been realized with the recent technical advances of optogenetics and its combination with fMRI, here termed "opto-fMRI." Opto-fMRI allows for targeted brain region-, cell-type-, or projection-specific manipulation and targeted Ca 2 + activity measurement to be linked with global brain signaling and behavior. We cover the history, technical advances, applications, and important considerations of opto-fMRI in anesthetized and awake rodents and the future directions of the combined techniques in neuroscience and neuroimaging.
Collapse
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
| |
Collapse
|
37
|
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] [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.
Collapse
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
| |
Collapse
|
38
|
Functional ultrasound imaging of recent and remote memory recall in the associative fear neural network in mice. Behav Brain Res 2022; 428:113862. [DOI: 10.1016/j.bbr.2022.113862] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 03/20/2022] [Accepted: 03/25/2022] [Indexed: 11/21/2022]
|
39
|
Sirmpilatze N, Mylius J, Ortiz-Rios M, Baudewig J, Paasonen J, Golkowski D, Ranft A, Ilg R, Gröhn O, Boretius S. Spatial signatures of anesthesia-induced burst-suppression differ between primates and rodents. eLife 2022; 11:74813. [PMID: 35607889 PMCID: PMC9129882 DOI: 10.7554/elife.74813] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 05/01/2022] [Indexed: 01/19/2023] Open
Abstract
During deep anesthesia, the electroencephalographic (EEG) signal of the brain alternates between bursts of activity and periods of relative silence (suppressions). The origin of burst-suppression and its distribution across the brain remain matters of debate. In this work, we used functional magnetic resonance imaging (fMRI) to map the brain areas involved in anesthesia-induced burst-suppression across four mammalian species: humans, long-tailed macaques, common marmosets, and rats. At first, we determined the fMRI signatures of burst-suppression in human EEG-fMRI data. Applying this method to animal fMRI datasets, we found distinct burst-suppression signatures in all species. The burst-suppression maps revealed a marked inter-species difference: in rats, the entire neocortex engaged in burst-suppression, while in primates most sensory areas were excluded—predominantly the primary visual cortex. We anticipate that the identified species-specific fMRI signatures and whole-brain maps will guide future targeted studies investigating the cellular and molecular mechanisms of burst-suppression in unconscious states. The development of anesthesia was a significant advance in medicine. It allows individuals to undergo surgery without feeling pain or remembering the experience. But scientists still do not know how anesthesia works. During anesthesia, scientists have measured brain activity using electroencephalograms (EEG) and found that the brain appears to turn on and off. Comatose patients also have similar switches between bursts of electrical activity and periods of silence. This burst-suppression pattern may be related to unconsciousness. But scientists still have many questions about how anesthesia causes burst-suppression. One challenge is that while an EEG can tell scientists when the brain turns on and off, it does not show exactly where this occurs. Another imaging method called functional Magnetic Resonance Imaging (fMRI) may fill this gap by allowing scientists to map where the brain activity occurs. Sirmpilatze et al. have created detailed maps of burst-suppression in humans, primates, and rats under anesthesia by analyzing brain scans using fMRI. In rats, the entire outer layer or cortex of the brain underwent a synchronized pattern of burst-suppression. In humans and primates, areas of the brain like those responsible for eyesight did not follow the rest of the cortex in switching on and off. The experiments reveal crucial differences in how rats and humans and other primates respond to anesthesia. The fMRI mapping technique Sirmpilatze et al. created may help scientists learn more about these differences and why some parts of human brains do not undergo burst-suppression. This may help scientists learn more about unconsciousness and help improve anesthesia or the care of comatose patients.
Collapse
Affiliation(s)
- Nikoloz Sirmpilatze
- Functional Imaging Laboratory, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany.,Georg-August University of Göttingen, Göttingen, Germany.,International Max Planck Research School for Neurosciences, Göttingen, Germany
| | - Judith Mylius
- Functional Imaging Laboratory, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany
| | - Michael Ortiz-Rios
- Functional Imaging Laboratory, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany
| | - Jürgen Baudewig
- Functional Imaging Laboratory, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany
| | - Jaakko Paasonen
- A.I.V. Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Daniel Golkowski
- Department of Neurology, Klinikum Rechts der Isar der Technischen Universität München, Munich, Germany.,Department of Neurology, Heidelberg University Hospital, Heidelberg, Germany
| | - Andreas Ranft
- Department of Anesthesiology and Intensive Care Medicine, Klinikum Rechts der Isar der Technischen Universität München, Munich, Germany
| | - Rüdiger Ilg
- Department of Neurology, Klinikum Rechts der Isar der Technischen Universität München, Munich, Germany.,Department of Neurology, Asklepios Stadtklinik Bad Tölz, Bad Tölz, Germany
| | - Olli Gröhn
- A.I.V. Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Susann Boretius
- Functional Imaging Laboratory, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany.,Georg-August University of Göttingen, Göttingen, Germany.,International Max Planck Research School for Neurosciences, Göttingen, Germany.,Leibniz Science Campus Primate Cognition, Göttingen, Germany
| |
Collapse
|
40
|
Xu M, Bo B, Pei M, Chen Y, Shu CY, Qin Q, Hirschler L, Warnking JM, Barbier EL, Wei Z, Lu H, Herman P, Hyder F, Liu ZJ, Liang Z, Thompson GJ. High-resolution relaxometry-based calibrated fMRI in murine brain: Metabolic differences between awake and anesthetized states. J Cereb Blood Flow Metab 2022; 42:811-825. [PMID: 34910894 PMCID: PMC9014688 DOI: 10.1177/0271678x211062279] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Functional magnetic resonance imaging (fMRI) techniques using the blood-oxygen level-dependent (BOLD) signal have shown great potential as clinical biomarkers of disease. Thus, using these techniques in preclinical rodent models is an urgent need. Calibrated fMRI is a promising technique that can provide high-resolution mapping of cerebral oxygen metabolism (CMRO2). However, calibrated fMRI is difficult to use in rodent models for several reasons: rodents are anesthetized, stimulation-induced changes are small, and gas challenges induce noisy CMRO2 predictions. We used, in mice, a relaxometry-based calibrated fMRI method which uses cerebral blood flow (CBF) and the BOLD-sensitive magnetic relaxation component, R2', the same parameter derived in the deoxyhemoglobin-dilution model of calibrated fMRI. This method does not use any gas challenges, which we tested on mice in both awake and anesthetized states. As anesthesia induces a whole-brain change, our protocol allowed us to overcome the former limitations of rodent studies using calibrated fMRI. We revealed 1.5-2 times higher CMRO2, dependent upon brain region, in the awake state versus the anesthetized state. Our results agree with alternative measurements of whole-brain CMRO2 in the same mice and previous human anesthesia studies. The use of calibrated fMRI in rodents has much potential for preclinical fMRI.
Collapse
Affiliation(s)
- Mengyang Xu
- iHuman Institute, ShanghaiTech University, Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai, China.,Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Binshi Bo
- CAS Center for Excellence in Brain Sciences and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China
| | - Mengchao Pei
- CAS Center for Excellence in Brain Sciences and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China
| | - Yuyan Chen
- CAS Center for Excellence in Brain Sciences and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China
| | - Christina Y Shu
- Biomedical Engineering, Yale University, New Haven, CT, USA.,Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, USA
| | - Qikai Qin
- iHuman Institute, ShanghaiTech University, Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai, China.,Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Lydiane Hirschler
- Grenoble Institut des Neurosciences, Inserm, Univ. Grenoble Alpes, Grenoble, France.,C.J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Jan M Warnking
- Grenoble Institut des Neurosciences, Inserm, Univ. Grenoble Alpes, Grenoble, France
| | - Emmanuel L Barbier
- Grenoble Institut des Neurosciences, Inserm, Univ. Grenoble Alpes, Grenoble, France
| | - Zhiliang Wei
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
| | - Hanzhang Lu
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
| | - Peter Herman
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, USA.,Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University, New Haven, CT, USA.,Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Fahmeed Hyder
- Biomedical Engineering, Yale University, New Haven, CT, USA.,Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, USA.,Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University, New Haven, CT, USA.,Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Zhi-Jie Liu
- iHuman Institute, ShanghaiTech University, Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai, China.,Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Zhifeng Liang
- CAS Center for Excellence in Brain Sciences and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China
| | | |
Collapse
|
41
|
An evolutionary gap in primate default mode network organization. Cell Rep 2022; 39:110669. [PMID: 35417698 PMCID: PMC9088817 DOI: 10.1016/j.celrep.2022.110669] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 09/21/2021] [Accepted: 03/21/2022] [Indexed: 12/03/2022] Open
Abstract
The human default mode network (DMN) is engaged at rest and in cognitive states such as self-directed thoughts. Interconnected homologous cortical areas in primates constitute a network considered as the equivalent. Here, based on a cross-species comparison of the DMN between humans and non-hominoid primates (macaques, marmosets, and mouse lemurs), we report major dissimilarities in connectivity profiles. Most importantly, the medial prefrontal cortex (mPFC) of non-hominoid primates is poorly engaged with the posterior cingulate cortex (PCC), though strong correlated activity between the human PCC and the mPFC is a key feature of the human DMN. Instead, a fronto-temporal resting-state network involving the mPFC was detected consistently across non-hominoid primate species. These common functional features shared between non-hominoid primates but not with humans suggest a substantial gap in the organization of the primate’s DMN and its associated cognitive functions. By comparing resting-state networks in humans, macaques, marmosets, and mouse lemurs, Garin et al. identify two networks in non-hominoid primates that include homolog areas of the human default mode network. The mPFC and PCC are tightly connected in the human DMN but poorly connected to each other across non-hominoid primates.
Collapse
|
42
|
Brier LM, Zhang X, Bice AR, Gaines SH, Landsness EC, Lee JM, Anastasio MA, Culver JP. A Multivariate Functional Connectivity Approach to Mapping Brain Networks and Imputing Neural Activity in Mice. Cereb Cortex 2022; 32:1593-1607. [PMID: 34541601 PMCID: PMC9016290 DOI: 10.1093/cercor/bhab282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 07/12/2021] [Accepted: 07/14/2021] [Indexed: 11/13/2022] Open
Abstract
Temporal correlation analysis of spontaneous brain activity (e.g., Pearson "functional connectivity," FC) has provided insights into the functional organization of the human brain. However, bivariate analysis techniques such as this are often susceptible to confounding physiological processes (e.g., sleep, Mayer-waves, breathing, motion), which makes it difficult to accurately map connectivity in health and disease as these physiological processes affect FC. In contrast, a multivariate approach to imputing individual neural networks from spontaneous neuroimaging data could be influential to our conceptual understanding of FC and provide performance advantages. Therefore, we analyzed neural calcium imaging data from Thy1-GCaMP6f mice while either awake, asleep, anesthetized, during low and high bouts of motion, or before and after photothrombotic stroke. A linear support vector regression approach was used to determine the optimal weights for integrating the signals from the remaining pixels to accurately predict neural activity in a region of interest (ROI). The resultant weight maps for each ROI were interpreted as multivariate functional connectivity (MFC), resembled anatomical connectivity, and demonstrated a sparser set of strong focused positive connections than traditional FC. While global variations in data have large effects on standard correlation FC analysis, the MFC mapping methods were mostly impervious. Lastly, MFC analysis provided a more powerful connectivity deficit detection following stroke compared to traditional FC.
Collapse
Affiliation(s)
- Lindsey M Brier
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Xiaohui Zhang
- Department of Bioengineering, University of Illinois, Urbana-Champaign, IL 61801, USA
| | - Annie R Bice
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Seana H Gaines
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Eric C Landsness
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Jin-Moo Lee
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Mark A Anastasio
- Department of Bioengineering, University of Illinois, Urbana-Champaign, IL 61801, USA
| | - Joseph P Culver
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Biomedical Engineering, Washington University School of Engineering, St. Louis, MO 63105, USA
- Department of Electrical and Systems Engineering, Washington University School of Engineering, St. Louis, MO 63112, USA
- Department of Physics, Washington University School of Arts and Science, St. Louis, MO 63130, USA
| |
Collapse
|
43
|
Scharwächter L, Schmitt FJ, Pallast N, Fink GR, Aswendt M. Network analysis of neuroimaging in mice. Neuroimage 2022; 253:119110. [PMID: 35311664 DOI: 10.1016/j.neuroimage.2022.119110] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 03/01/2022] [Accepted: 03/15/2022] [Indexed: 10/18/2022] Open
Abstract
Graph theory allows assessing changes of neuronal connectivity and interactions of brain regions in response to local lesions, e.g., after stroke, and global perturbations, e.g., due to psychiatric dysfunctions or neurodegenerative disorders. Consequently, network analysis based on constructing graphs from structural and functional MRI connectivity matrices is increasingly used in clinical studies. In contrast, in mouse neuroimaging, the focus is mainly on basic connectivity parameters, i.e., the correlation coefficient or fiber counts, whereas more advanced network analyses remain rarely used. This review summarizes graph theoretical measures and their interpretation to describe networks derived from recent in vivo mouse brain studies. To facilitate the entry into the topic, we explain the related mathematical definitions, provide a dedicated software toolkit, and discuss practical considerations for the application to rs-fMRI and DTI. This way, we aim to foster cross-species comparisons and the application of standardized measures to classify and interpret network changes in translational brain disease studies.
Collapse
Affiliation(s)
- Leon Scharwächter
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany
| | - Felix J Schmitt
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany; University of Cologne, Institute of Zoology, Dept. of Computational Systems Neuroscience, Cologne, Germany
| | - Niklas Pallast
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany
| | - Gereon R Fink
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Germany
| | - Markus Aswendt
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Germany.
| |
Collapse
|
44
|
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: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [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.
Collapse
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
| |
Collapse
|
45
|
Hsu LM, Wang S, Walton L, Wang TWW, Lee SH, Shih YYI. 3D U-Net Improves Automatic Brain Extraction for Isotropic Rat Brain Magnetic Resonance Imaging Data. Front Neurosci 2021; 15:801008. [PMID: 34975392 PMCID: PMC8716693 DOI: 10.3389/fnins.2021.801008] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Accepted: 11/15/2021] [Indexed: 12/24/2022] Open
Abstract
Brain extraction is a critical pre-processing step in brain magnetic resonance imaging (MRI) analytical pipelines. In rodents, this is often achieved by manually editing brain masks slice-by-slice, a time-consuming task where workloads increase with higher spatial resolution datasets. We recently demonstrated successful automatic brain extraction via a deep-learning-based framework, U-Net, using 2D convolutions. However, such an approach cannot make use of the rich 3D spatial-context information from volumetric MRI data. In this study, we advanced our previously proposed U-Net architecture by replacing all 2D operations with their 3D counterparts and created a 3D U-Net framework. We trained and validated our model using a recently released CAMRI rat brain database acquired at isotropic spatial resolution, including T2-weighted turbo-spin-echo structural MRI and T2*-weighted echo-planar-imaging functional MRI. The performance of our 3D U-Net model was compared with existing rodent brain extraction tools, including Rapid Automatic Tissue Segmentation, Pulse-Coupled Neural Network, SHape descriptor selected External Regions after Morphologically filtering, and our previously proposed 2D U-Net model. 3D U-Net demonstrated superior performance in Dice, Jaccard, center-of-mass distance, Hausdorff distance, and sensitivity. Additionally, we demonstrated the reliability of 3D U-Net under various noise levels, evaluated the optimal training sample sizes, and disseminated all source codes publicly, with a hope that this approach will benefit rodent MRI research community. Significant Methodological Contribution: We proposed a deep-learning-based framework to automatically identify the rodent brain boundaries in MRI. With a fully 3D convolutional network model, 3D U-Net, our proposed method demonstrated improved performance compared to current automatic brain extraction methods, as shown in several qualitative metrics (Dice, Jaccard, PPV, SEN, and Hausdorff). We trust that this tool will avoid human bias and streamline pre-processing steps during 3D high resolution rodent brain MRI data analysis. The software developed herein has been disseminated freely to the community.
Collapse
Affiliation(s)
- Li-Ming Hsu
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States,Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States,*Correspondence: Li-Ming Hsu,
| | - Shuai Wang
- School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, China
| | - Lindsay Walton
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States,Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Tzu-Wen Winnie Wang
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States,Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Sung-Ho Lee
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States,Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Yen-Yu Ian Shih
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States,Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States,Yen-Yu Ian Shih,
| |
Collapse
|
46
|
Reactive Astrocytes Prevent Maladaptive Plasticity after Ischemic Stroke. Prog Neurobiol 2021; 209:102199. [PMID: 34921928 DOI: 10.1016/j.pneurobio.2021.102199] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 10/14/2021] [Accepted: 12/13/2021] [Indexed: 12/24/2022]
Abstract
Restoration of functional connectivity is a major contributor to functional recovery after stroke. We investigated the role of reactive astrocytes in functional connectivity and recovery after photothrombotic stroke in mice with attenuated reactive gliosis (GFAP-/-Vim-/-). Infarct volume and longitudinal functional connectivity changes were determined by in vivo T2-weighted magnetic resonance imaging (MRI) and resting-state functional MRI. Sensorimotor function was assessed with behavioral tests, and glial and neural plasticity responses were quantified in the peri-infarct region. Four weeks after stroke, GFAP-/-Vim-/- mice showed impaired recovery of sensorimotor function and aberrant restoration of global neuronal connectivity. These mice also exhibited maladaptive plasticity responses, shown by higher number of lost and newly formed functional connections between primary and secondary targets of cortical stroke regions and increased peri-infarct expression of the axonal plasticity marker Gap43. We conclude that reactive astrocytes modulate recovery-promoting plasticity responses after ischemic stroke.
Collapse
|
47
|
Functional ultrasound imaging: A useful tool for functional connectomics? Neuroimage 2021; 245:118722. [PMID: 34800662 DOI: 10.1016/j.neuroimage.2021.118722] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 09/15/2021] [Accepted: 11/10/2021] [Indexed: 12/28/2022] Open
Abstract
Functional ultrasound (fUS) is a hemodynamic-based functional neuroimaging technique, primarily used in animal models, that combines a high spatiotemporal resolution, a large field of view, and compatibility with behavior. These assets make fUS especially suited to interrogating brain activity at the systems level. In this review, we describe the technical capabilities offered by fUS and discuss how this technique can contribute to the field of functional connectomics. First, fUS can be used to study intrinsic functional connectivity, namely patterns of correlated activity between brain regions. In this area, fUS has made the most impact by following connectivity changes in disease models, across behavioral states, or dynamically. Second, fUS can also be used to map brain-wide pathways associated with an external event. For example, fUS has helped obtain finer descriptions of several sensory systems, and uncover new pathways implicated in specific behaviors. Additionally, combining fUS with direct circuit manipulations such as optogenetics is an attractive way to map the brain-wide connections of defined neuronal populations. Finally, technological improvements and the application of new analytical tools promise to boost fUS capabilities. As brain coverage and the range of behavioral contexts that can be addressed with fUS keep on increasing, we believe that fUS-guided connectomics will only expand in the future. In this regard, we consider the incorporation of fUS into multimodal studies combining diverse techniques and behavioral tasks to be the most promising research avenue.
Collapse
|
48
|
Beckmann KM, Wang-Leandro A, Richter H, Bektas RN, Steffen F, Dennler M, Carrera I, Haller S. Increased resting state connectivity in the anterior default mode network of idiopathic epileptic dogs. Sci Rep 2021; 11:23854. [PMID: 34903807 PMCID: PMC8668945 DOI: 10.1038/s41598-021-03349-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 11/30/2021] [Indexed: 12/11/2022] Open
Abstract
Epilepsy is one of the most common chronic, neurological diseases in humans and dogs and considered to be a network disease. In human epilepsy altered functional connectivity in different large-scale networks have been identified with functional resting state magnetic resonance imaging. Since large-scale resting state networks have been consistently identified in anesthetised dogs’ application of this technique became promising in canine epilepsy research. The aim of the present study was to investigate differences in large-scale resting state networks in epileptic dogs compared to healthy controls. Our hypothesis was, that large-scale networks differ between epileptic dogs and healthy control dogs. A group of 17 dogs (Border Collies and Greater Swiss Mountain Dogs) with idiopathic epilepsy was compared to 20 healthy control dogs under a standardized sevoflurane anaesthesia protocol. Group level independent component analysis with dimensionality of 20 components, dual regression and two-sample t test were performed and revealed significantly increased functional connectivity in the anterior default mode network of idiopathic epileptic dogs compared to healthy control dogs (p = 0.00060). This group level differences between epileptic dogs and healthy control dogs identified using a rather simple data driven approach could serve as a starting point for more advanced resting state network analysis in epileptic dogs.
Collapse
Affiliation(s)
- Katrin M Beckmann
- Section of Neurology, Department of Small Animals, Vetsuisse Faculty Zurich, University of Zurich, Zurich, Switzerland.
| | - Adriano Wang-Leandro
- Clinic for Diagnostic Imaging, Department of Diagnostics and Clinical Services, Vetsuisse-Faculty Zurich, University of Zurich, Zurich, Switzerland
| | - Henning Richter
- Clinic for Diagnostic Imaging, Department of Diagnostics and Clinical Services, Vetsuisse-Faculty Zurich, University of Zurich, Zurich, Switzerland.,Clinic for Neuroradiology, University Hospital Bonn, Bonn, Germany
| | - Rima N Bektas
- Section of Anaesthesiology, Department of Diagnostics and Clinical Services, Vetsuisse Faculty, University of Zurich, Zurich, Switzerland
| | - Frank Steffen
- Section of Neurology, Department of Small Animals, Vetsuisse Faculty Zurich, University of Zurich, Zurich, Switzerland
| | - Matthias Dennler
- Clinic for Diagnostic Imaging, Department of Diagnostics and Clinical Services, Vetsuisse-Faculty Zurich, University of Zurich, Zurich, Switzerland
| | - Ines Carrera
- Willows Veterinary Centre and Referral Service, Highlands Road, Shirley, UK
| | - Sven Haller
- Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden.,Faculty of Medicine, University of Geneva, Geneva, Switzerland
| |
Collapse
|
49
|
Lee SH, Broadwater MA, Ban W, Wang TWW, Kim HJ, Dumas JS, Vetreno RP, Herman MA, Morrow AL, Besheer J, Kash TL, Boettiger CA, Robinson DL, Crews FT, Shih YYI. An isotropic EPI database and analytical pipelines for rat brain resting-state fMRI. Neuroimage 2021; 243:118541. [PMID: 34478824 PMCID: PMC8561231 DOI: 10.1016/j.neuroimage.2021.118541] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 08/08/2021] [Accepted: 08/30/2021] [Indexed: 12/24/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (fMRI) has drastically expanded the scope of brain research by advancing our knowledge about the topologies, dynamics, and interspecies translatability of functional brain networks. Several databases have been developed and shared in accordance with recent key initiatives in the rodent fMRI community to enhance the transparency, reproducibility, and interpretability of data acquired at various sites. Despite these pioneering efforts, one notable challenge preventing efficient standardization in the field is the customary choice of anisotropic echo planar imaging (EPI) schemes with limited spatial coverage. Imaging with anisotropic resolution and/or reduced brain coverage has significant shortcomings including reduced registration accuracy and increased deviation in brain feature detection. Here we proposed a high-spatial-resolution (0.4 mm), isotropic, whole-brain EPI protocol for the rat brain using a horizontal slicing scheme that can maintain a functionally relevant repetition time (TR), avoid high gradient duty cycles, and offer unequivocal whole-brain coverage. Using this protocol, we acquired resting-state EPI fMRI data from 87 healthy rats under the widely used dexmedetomidine sedation supplemented with low-dose isoflurane on a 9.4 T MRI system. We developed an EPI template that closely approximates the Paxinos and Watson's rat brain coordinate system and demonstrated its ability to improve the accuracy of group-level approaches and streamline fMRI data pre-processing. Using this database, we employed a multi-scale dictionary-learning approach to identify reliable spatiotemporal features representing rat brain intrinsic activity. Subsequently, we performed k-means clustering on those features to obtain spatially discrete, functional regions of interest (ROIs). Using Euclidean-based hierarchical clustering and modularity-based partitioning, we identified the topological organizations of the rat brain. Additionally, the identified group-level FC network appeared robust across strains and sexes. The "triple-network" commonly adapted in human fMRI were resembled in the rat brain. Through this work, we disseminate raw and pre-processed isotropic EPI data, a rat brain EPI template, as well as identified functional ROIs and networks in standardized rat brain coordinates. We also make our analytical pipelines and scripts publicly available, with the hope of facilitating rat brain resting-state fMRI study standardization.
Collapse
Affiliation(s)
- Sung-Ho Lee
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA,Department of Neurology, University of North Carolina, Chapel Hill, NC, USA,Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Corresponding authors at: Center for Animal MRI, 125 Mason Farm Road, CB# 7513, University of North Carolina, Chapel Hill, NC 27599, USA. (S.-H. Lee), (Y.-Y.I. Shih)
| | - Margaret A. Broadwater
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA,Department of Neurology, University of North Carolina, Chapel Hill, NC, USA,Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA
| | - Woomi Ban
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Tzu-Wen Winnie Wang
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Hyeon-Joong Kim
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA,Department of Neurology, University of North Carolina, Chapel Hill, NC, USA
| | - Jaiden Seongmi Dumas
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA,Department of Neurology, University of North Carolina, Chapel Hill, NC, USA,Department of Quantitative Biology, University of North Carolina, Chapel Hill, NC, USA
| | - Ryan P. Vetreno
- Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Department of Pharmacology, University of North Carolina, Chapel Hill, NC, USA
| | - Melissa A. Herman
- Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Department of Pharmacology, University of North Carolina, Chapel Hill, NC, USA
| | - A. Leslie Morrow
- Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA,Department of Pharmacology, University of North Carolina, Chapel Hill, NC, USA
| | - Joyce Besheer
- Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Thomas L. Kash
- Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Department of Pharmacology, University of North Carolina, Chapel Hill, NC, USA
| | - Charlotte A. Boettiger
- Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA,Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC, USA
| | - Donita L. Robinson
- Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Fulton T. Crews
- Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA,Department of Pharmacology, University of North Carolina, Chapel Hill, NC, USA
| | - Yen-Yu Ian Shih
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA,Department of Neurology, University of North Carolina, Chapel Hill, NC, USA,Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Corresponding authors at: Center for Animal MRI, 125 Mason Farm Road, CB# 7513, University of North Carolina, Chapel Hill, NC 27599, USA. (S.-H. Lee), (Y.-Y.I. Shih)
| |
Collapse
|
50
|
Chakravarty MM, Guma E. Small animal imaging presents an opportunity for improving translational research in biological psychiatry. J Psychiatry Neurosci 2021; 46:E579-E582. [PMID: 34670841 PMCID: PMC8532952 DOI: 10.1503/jpn.210172] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Affiliation(s)
| | - Elisa Guma
- From the Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Research Institute, Montreal, Que., Canada (Chakravarty, Guma); the Department of Psychiatry, McGill University, Montreal, Que., Canada (Chakravarty); the Department of Biological and Biomedical Engineering, McGill University, Montreal, Que., Canada (Chakravarty); and the Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Intramural Program, USA (Guma)
| |
Collapse
|