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Hike D, Liu X, Xie Z, Zhang B, Choi S, Zhou XA, Liu A, Murstein A, Jiang Y, Devor A, Yu X. High-resolution awake mouse fMRI at 14 Tesla. bioRxiv 2023:2023.12.08.570803. [PMID: 38106227 PMCID: PMC10723470 DOI: 10.1101/2023.12.08.570803] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
High-resolution awake mouse fMRI remains challenging despite extensive efforts to address motion-induced artifacts and stress. This study introduces an implantable radiofrequency (RF) surface coil design that minimizes image distortion caused by the air/tissue interface of mouse brains while simultaneously serving as a headpost for fixation during scanning. Using a 14T scanner, high-resolution fMRI enabled brain-wide functional mapping of visual and vibrissa stimulation at 100×100×200μm resolution with a 2s per frame sampling rate. Besides activated ascending visual and vibrissa pathways, robust BOLD responses were detected in the anterior cingulate cortex upon visual stimulation and spread through the ventral retrosplenial area (VRA) with vibrissa air-puff stimulation, demonstrating higher-order sensory processing in association cortices of awake mice. In particular, the rapid hemodynamic responses in VRA upon vibrissa stimulation showed a strong correlation with the hippocampus, thalamus, and prefrontal cortical areas. Cross-correlation analysis with designated VRA responses revealed early positive BOLD signals at the contralateral barrel cortex (BC) occurring 2 seconds prior to the air-puff in awake mice with repetitive stimulation, which was not detectable with the randomized stimulation paradigm. This early BC activation indicated learned anticipation through the vibrissa system and association cortices in awake mice under continuous training of repetitive air-puff stimulation. This work establishes a high-resolution awake mouse fMRI platform, enabling brain-wide functional mapping of sensory signal processing in higher association cortical areas.
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Affiliation(s)
- David Hike
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, Massachusetts, USA 02129
| | - Xiaochen Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, Massachusetts, USA 02129
| | - Zeping Xie
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, Massachusetts, USA 02129
| | - Bei Zhang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, Massachusetts, USA 02129
| | - Sangcheon Choi
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, Massachusetts, USA 02129
| | - Xiaoqing Alice Zhou
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, Massachusetts, USA 02129
| | - Andy Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, Massachusetts, USA 02129
- Graduate program in Neuroscience, Boston University, Commonwealth Ave, Boston, Massachusetts, USA, 02215
| | - Alyssa Murstein
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, Massachusetts, USA 02129
- Graduate program in Neuroscience, Boston University, Commonwealth Ave, Boston, Massachusetts, USA, 02215
| | - Yuanyuan Jiang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, Massachusetts, USA 02129
| | - Anna Devor
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, Massachusetts, USA 02129
- Department of Biomedical Engineering, Boston University, 610 Commonwealth Avenue, Boston, Massachusetts, USA, 02215
| | - Xin Yu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, Massachusetts, USA 02129
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Achard S, Coeurjolly JF, de Micheaux PL, Lbath H, Richiardi J. Inter-regional correlation estimators for functional magnetic resonance imaging. Neuroimage 2023; 282:120388. [PMID: 37805021 DOI: 10.1016/j.neuroimage.2023.120388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 09/05/2023] [Accepted: 09/22/2023] [Indexed: 10/09/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) functional connectivity between brain regions is often computed using parcellations defined by functional or structural atlases. Typically, some kind of voxel averaging is performed to obtain a single temporal correlation estimate per region pair. However, several estimators can be defined for this task, with various assumptions and degrees of robustness to local noise, global noise, and region size. In this paper, we systematically present and study the properties of 9 different functional connectivity estimators taking into account the spatial structure of fMRI data, based on a simple fMRI data spatial model. These include 3 existing estimators and 6 novel estimators. We demonstrate the empirical properties of the estimators using synthetic, animal, and human data, in terms of graph structure, repeatability and reproducibility, discriminability, dependence on region size, as well as local and global noise robustness. We prove analytically the link between regional intra-correlation and inter-region correlation, and show that the choice of estimator has a strong influence on inter-correlation values. Some estimators, including the commonly used correlation of averages (ca), are positively biased, and have more dependence to region size and intra-correlation than robust alternatives, resulting in spatially-dependent bias. We define the new local correlation of averages estimator with better theoretical guarantees, lower bias, significantly lower dependence on region size (Spearman correlation 0.40 vs 0.55, paired t-test T=27.2, p=1.1e-47), at negligible cost to discriminative power, compared to the ca estimator. The difference in connectivity pattern between the estimators is not distributed uniformly throughout the brain, but rather shows a clear ventral-dorsal gradient, suggesting that region size and intra-correlation plays an important role in shaping functional networks defined using the ca estimator, and leading to non-trivial differences in their connectivity structure. We provide an open source R package and equivalent Python implementation to facilitate the use of the new estimators, together with preprocessed rat time-series.
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Affiliation(s)
- Sophie Achard
- Univ. Grenoble Alpes, CNRS, Inria, Grenoble-INP, LJK, 38000 Grenoble, France.
| | | | - Pierre Lafaye de Micheaux
- AMIS, Université Paul Valéry Montpellier 3, France; PreMeDICaL - Precision Medicine by Data Integration and Causal Learning, Inria Sophia Antipolis, France; Desbrest Institute of Epidemiology and Public Health, Univ Montpellier, INSERM, Montpellier, France
| | - Hanâ Lbath
- Univ. Grenoble Alpes, CNRS, Inria, Grenoble-INP, LJK, 38000 Grenoble, France
| | - Jonas Richiardi
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland
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Roger E, Torlay L, Banjac S, Mosca C, Minotti L, Kahane P, Baciu M. Prediction of the clinical and naming status after anterior temporal lobe resection in patients with epilepsy. Epilepsy Behav 2021; 124:108357. [PMID: 34717247 DOI: 10.1016/j.yebeh.2021.108357] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 09/15/2021] [Accepted: 09/25/2021] [Indexed: 01/20/2023]
Abstract
By assessing the cognitive capital, neuropsychological evaluation (NPE) plays a vital role in the perioperative workup of patients with refractory focal epilepsy. In this retrospective study, we used cutting-edge statistical approaches to examine a group of 47 patients with refractory temporal lobe epilepsy (TLE), who underwent standard anterior temporal lobectomy (ATL). Our objective was to determine whether NPE may represent a robust predictor of the postoperative status, two years after surgery. Specifically, based on pre- and postsurgical neuropsychological data, we estimated the sensitivity of cognitive indicators to predict and to disentangle phenotypes associated with more or less favorable outcomes. Engel (ENG) scores were used to assess clinical outcome, and picture naming (NAM) performance to estimate naming status. Two methods were applied: (a) machine learning (ML) to explore cognitive sensitivity to postoperative outcomes; and (b) graph theory (GT) to assess network properties reflecting favorable vs. less favorable phenotypes after surgery. Specific neuropsychological indices assessing language, memory, and executive functions can globally predict outcomes. Interestingly, preoperative cognitive networks associated with poor postsurgical outcome already exhibit an atypical, highly modular and less densely interconnected configuration. We provide statistical and clinical tools to anticipate the condition after surgery and achieve a more personalized clinical management. Our results also shed light on possible mechanisms put in place for cognitive adaptation after acute injury of central nervous system in relation with surgery.
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Affiliation(s)
- Elise Roger
- Univ. Grenoble Alpes, CNRS LPNC UMR 5105, 38000 Grenoble, France.
| | - Laurent Torlay
- Univ. Grenoble Alpes, CNRS LPNC UMR 5105, 38000 Grenoble, France
| | - Sonja Banjac
- Univ. Grenoble Alpes, CNRS LPNC UMR 5105, 38000 Grenoble, France
| | - Chrystèle Mosca
- Univ. Grenoble Alpes, Grenoble Institute of Neuroscience, Synchronisation et modulation des réseaux neuronaux dans l'épilepsie' & Neurology Department, 38000 Grenoble, France
| | - Lorella Minotti
- Univ. Grenoble Alpes, Grenoble Institute of Neuroscience, Synchronisation et modulation des réseaux neuronaux dans l'épilepsie' & Neurology Department, 38000 Grenoble, France
| | - Philippe Kahane
- Univ. Grenoble Alpes, Grenoble Institute of Neuroscience, Synchronisation et modulation des réseaux neuronaux dans l'épilepsie' & Neurology Department, 38000 Grenoble, France
| | - Monica Baciu
- Univ. Grenoble Alpes, CNRS LPNC UMR 5105, 38000 Grenoble, France
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Pais-Roldán P, Mateo C, Pan WJ, Acland B, Kleinfeld D, Snyder LH, Yu X, Keilholz S. Contribution of animal models toward understanding resting state functional connectivity. Neuroimage 2021; 245:118630. [PMID: 34644593 DOI: 10.1016/j.neuroimage.2021.118630] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 08/06/2021] [Accepted: 09/29/2021] [Indexed: 12/27/2022] Open
Abstract
Functional connectivity, which reflects the spatial and temporal organization of intrinsic activity throughout the brain, is one of the most studied measures in human neuroimaging research. The noninvasive acquisition of resting state functional magnetic resonance imaging (rs-fMRI) allows the characterization of features designated as functional networks, functional connectivity gradients, and time-varying activity patterns that provide insight into the intrinsic functional organization of the brain and potential alterations related to brain dysfunction. Functional connectivity, hence, captures dimensions of the brain's activity that have enormous potential for both clinical and preclinical research. However, the mechanisms underlying functional connectivity have yet to be fully characterized, hindering interpretation of rs-fMRI studies. As in other branches of neuroscience, the identification of the neurophysiological processes that contribute to functional connectivity largely depends on research conducted on laboratory animals, which provide a platform where specific, multi-dimensional investigations that involve invasive measurements can be carried out. These highly controlled experiments facilitate the interpretation of the temporal correlations observed across the brain. Indeed, information obtained from animal experimentation to date is the basis for our current understanding of the underlying basis for functional brain connectivity. This review presents a compendium of some of the most critical advances in the field based on the efforts made by the animal neuroimaging community.
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Verma N, Mudge JD, Kasole M, Chen RC, Blanz SL, Trevathan JK, Lovett EG, Williams JC, Ludwig KA. Auricular Vagus Neuromodulation-A Systematic Review on Quality of Evidence and Clinical Effects. Front Neurosci 2021; 15:664740. [PMID: 33994937 PMCID: PMC8120162 DOI: 10.3389/fnins.2021.664740] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 03/25/2021] [Indexed: 12/13/2022] Open
Abstract
Background: The auricular branch of the vagus nerve runs superficially, which makes it a favorable target for non-invasive stimulation techniques to modulate vagal activity. For this reason, there have been many early-stage clinical trials on a diverse range of conditions. These trials often report conflicting results for the same indication. Methods: Using the Cochrane Risk of Bias tool we conducted a systematic review of auricular vagus nerve stimulation (aVNS) randomized controlled trials (RCTs) to identify the factors that led to these conflicting results. The majority of aVNS studies were assessed as having "some" or "high" risk of bias, which makes it difficult to interpret their results in a broader context. Results: There is evidence of a modest decrease in heart rate during higher stimulation dosages, sometimes at above the level of sensory discomfort. Findings on heart rate variability conflict between studies and are hindered by trial design, including inappropriate washout periods, and multiple methods used to quantify heart rate variability. There is early-stage evidence to suggest aVNS may reduce circulating levels and endotoxin-induced levels of inflammatory markers. Studies on epilepsy reached primary endpoints similar to previous RCTs testing implantable vagus nerve stimulation therapy. Preliminary evidence shows that aVNS ameliorated pathological pain but not evoked pain. Discussion: Based on results of the Cochrane analysis we list common improvements for the reporting of results, which can be implemented immediately to improve the quality of evidence. In the long term, existing data from aVNS studies and salient lessons from drug development highlight the need for direct measures of local neural target engagement. Direct measures of neural activity around the electrode will provide data for the optimization of electrode design, placement, and stimulation waveform parameters to improve on-target engagement and minimize off-target activation. Furthermore, direct measures of target engagement, along with consistent evaluation of blinding success, must be used to improve the design of controls-a major source of concern identified in the Cochrane analysis. The need for direct measures of neural target engagement and consistent evaluation of blinding success is applicable to the development of other paresthesia-inducing neuromodulation therapies and their control designs.
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Affiliation(s)
- Nishant Verma
- Department of Biomedical Engineering, University of Wisconsin – Madison, Madison, WI, United States
- Wisconsin Institute for Translational Neuroengineering (WITNe) – Madison, Madison, WI, United States
| | - Jonah D. Mudge
- Department of Biomedical Engineering, University of Wisconsin – Madison, Madison, WI, United States
- Wisconsin Institute for Translational Neuroengineering (WITNe) – Madison, Madison, WI, United States
| | - Maïsha Kasole
- Department of Biomedical Engineering, University of Wisconsin – Madison, Madison, WI, United States
- Wisconsin Institute for Translational Neuroengineering (WITNe) – Madison, Madison, WI, United States
| | - Rex C. Chen
- Department of Biomedical Engineering, University of Wisconsin – Madison, Madison, WI, United States
- Wisconsin Institute for Translational Neuroengineering (WITNe) – Madison, Madison, WI, United States
| | - Stephan L. Blanz
- Department of Biomedical Engineering, University of Wisconsin – Madison, Madison, WI, United States
- Wisconsin Institute for Translational Neuroengineering (WITNe) – Madison, Madison, WI, United States
| | - James K. Trevathan
- Department of Biomedical Engineering, University of Wisconsin – Madison, Madison, WI, United States
- Wisconsin Institute for Translational Neuroengineering (WITNe) – Madison, Madison, WI, United States
| | | | - Justin C. Williams
- Department of Biomedical Engineering, University of Wisconsin – Madison, Madison, WI, United States
- Wisconsin Institute for Translational Neuroengineering (WITNe) – Madison, Madison, WI, United States
- Department of Neurosurgery, University of Wisconsin – Madison, Madison, WI, United States
| | - Kip A. Ludwig
- Department of Biomedical Engineering, University of Wisconsin – Madison, Madison, WI, United States
- Wisconsin Institute for Translational Neuroengineering (WITNe) – Madison, Madison, WI, United States
- Department of Neurosurgery, University of Wisconsin – Madison, Madison, WI, United States
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