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Li YT, Lee HJ, Lin FH. Functional magnetic resonance imaging signal has sub-second temporal accuracy. J Cereb Blood Flow Metab 2024; 44:1643-1654. [PMID: 39234985 PMCID: PMC11418691 DOI: 10.1177/0271678x241241136] [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] [Received: 10/03/2023] [Revised: 02/21/2024] [Accepted: 02/24/2024] [Indexed: 09/06/2024]
Abstract
Neuronal activation sequence information is essential for understanding brain functions. Extracting such timing information from blood-oxygenation-level-dependent functional magnetic resonance imaging (fMRI) signals is confounded by local cerebral vascular reactivity (CVR), which varies across brain locations. Thus, detecting neuronal synchrony as well as inferring inter-regional causal modulation using fMRI signals can be biased. Here we used fast fMRI measurements sampled at 10 Hz to measure the fMRI latency difference between visual and sensorimotor areas when participants engaged in a visuomotor task. The regional fMRI timing was calibrated by subtracting the CVR latency measured by a breath-holding task. After CVR calibration, the fMRI signal at the lateral geniculate nucleus (LGN) preceded that at the visual cortex by 496 ms, followed by the fMRI signal at the sensorimotor cortex with a latency of 464 ms. Sequential LGN, visual, and sensorimotor cortex activations were found in each participant after the CVR calibration. These inter-regional fMRI timing differences across and within participants were more closely related to the reaction time after the CVR calibration. Our results suggested the feasibility of mapping brain activity using fMRI with accuracy in hundreds of milliseconds.
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Affiliation(s)
- Yi-Tien Li
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan
- Neuroscience Research Center, Taipei Medical University, Taipei, Taiwan
- Ph.D. Program in Medical Neuroscience, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Hsin-Ju Lee
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Fa-Hsuan Lin
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
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2
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Amor Z, Ciuciu P, G R C, Daval-Frérot G, Mauconduit F, Thirion B, Vignaud A. Non-Cartesian 3D-SPARKLING vs Cartesian 3D-EPI encoding schemes for functional Magnetic Resonance Imaging at 7 Tesla. PLoS One 2024; 19:e0299925. [PMID: 38739571 PMCID: PMC11090341 DOI: 10.1371/journal.pone.0299925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 02/16/2024] [Indexed: 05/16/2024] Open
Abstract
The quest for higher spatial and/or temporal resolution in functional MRI (fMRI) while preserving a sufficient temporal signal-to-noise ratio (tSNR) has generated a tremendous amount of methodological contributions in the last decade ranging from Cartesian vs. non-Cartesian readouts, 2D vs. 3D acquisition strategies, parallel imaging and/or compressed sensing (CS) accelerations and simultaneous multi-slice acquisitions to cite a few. In this paper, we investigate the use of a finely tuned version of 3D-SPARKLING. This is a non-Cartesian CS-based acquisition technique for high spatial resolution whole-brain fMRI. We compare it to state-of-the-art Cartesian 3D-EPI during both a retinotopic mapping paradigm and resting-state acquisitions at 1mm3 (isotropic spatial resolution). This study involves six healthy volunteers and both acquisition sequences were run on each individual in a randomly-balanced order across subjects. The performances of both acquisition techniques are compared to each other in regards to tSNR, sensitivity to the BOLD effect and spatial specificity. Our findings reveal that 3D-SPARKLING has a higher tSNR than 3D-EPI, an improved sensitivity to detect the BOLD contrast in the gray matter, and an improved spatial specificity. Compared to 3D-EPI, 3D-SPARKLING yields, on average, 7% more activated voxels in the gray matter relative to the total number of activated voxels.
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Affiliation(s)
- Zaineb Amor
- CEA, Joliot, NeuroSpin, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Philippe Ciuciu
- CEA, Joliot, NeuroSpin, Université Paris-Saclay, Gif-sur-Yvette, France
- Inria, MIND team, Université Paris-Saclay, Palaiseau, France
| | - Chaithya G R
- CEA, Joliot, NeuroSpin, Université Paris-Saclay, Gif-sur-Yvette, France
- Inria, MIND team, Université Paris-Saclay, Palaiseau, France
| | - Guillaume Daval-Frérot
- CEA, Joliot, NeuroSpin, Université Paris-Saclay, Gif-sur-Yvette, France
- Inria, MIND team, Université Paris-Saclay, Palaiseau, France
- Siemens Heathineers, Courbevoie, France
| | - Franck Mauconduit
- CEA, Joliot, NeuroSpin, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Bertrand Thirion
- CEA, Joliot, NeuroSpin, Université Paris-Saclay, Gif-sur-Yvette, France
- Inria, MIND team, Université Paris-Saclay, Palaiseau, France
| | - Alexandre Vignaud
- CEA, Joliot, NeuroSpin, Université Paris-Saclay, Gif-sur-Yvette, France
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3
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Báez-Yáñez MG, Siero JCW, Petridou N. A mechanistic computational framework to investigate the hemodynamic fingerprint of the blood oxygenation level-dependent signal. NMR IN BIOMEDICINE 2023; 36:e5026. [PMID: 37643645 DOI: 10.1002/nbm.5026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 07/18/2023] [Accepted: 07/28/2023] [Indexed: 08/31/2023]
Abstract
Blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) is one of the most used imaging techniques to map brain activity or to obtain clinical information about human cortical vasculature, in both healthy and disease conditions. Nevertheless, BOLD fMRI is an indirect measurement of brain functioning triggered by neurovascular coupling. The origin of the BOLD signal is quite complex, and the signal formation thus depends, among other factors, on the topology of the cortical vasculature and the associated hemodynamic changes. To understand the hemodynamic evolution of the BOLD signal response in humans, it is beneficial to have a computational framework available that virtually resembles the human cortical vasculature, and simulates hemodynamic changes and corresponding MRI signal changes via interactions of intrinsic biophysical and magnetic properties of the tissues. To this end, we have developed a mechanistic computational framework that simulates the hemodynamic fingerprint of the BOLD signal based on a statistically defined, three-dimensional, vascular model that approaches the human cortical vascular architecture. The microvasculature is approximated through a Voronoi tessellation method and the macrovasculature is adapted from two-photon microscopy mice data. Using this computational framework, we simulated hemodynamic changes-cerebral blood flow, cerebral blood volume, and blood oxygen saturation-induced by virtual arterial dilation. Then we computed local magnetic field disturbances generated by the vascular topology and the corresponding blood oxygen saturation changes. This mechanistic computational framework also considers the intrinsic biophysical and magnetic properties of nearby tissue, such as water diffusion and relaxation properties, resulting in a dynamic BOLD signal response. The proposed mechanistic computational framework provides an integrated biophysical model that can offer better insights regarding the spatial and temporal properties of the BOLD signal changes.
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Affiliation(s)
- Mario Gilberto Báez-Yáñez
- Department of Radiology, Centre for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jeroen C W Siero
- Department of Radiology, Centre for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
- Spinoza Centre for Neuroimaging Amsterdam, Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
| | - Natalia Petridou
- Department of Radiology, Centre for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
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4
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Bailes SM, Gomez DEP, Setzer B, Lewis LD. Resting-state fMRI signals contain spectral signatures of local hemodynamic response timing. eLife 2023; 12:e86453. [PMID: 37565644 PMCID: PMC10506795 DOI: 10.7554/elife.86453] [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: 01/27/2023] [Accepted: 08/10/2023] [Indexed: 08/12/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) has proven to be a powerful tool for noninvasively measuring human brain activity; yet, thus far, fMRI has been relatively limited in its temporal resolution. A key challenge is understanding the relationship between neural activity and the blood-oxygenation-level-dependent (BOLD) signal obtained from fMRI, generally modeled by the hemodynamic response function (HRF). The timing of the HRF varies across the brain and individuals, confounding our ability to make inferences about the timing of the underlying neural processes. Here, we show that resting-state fMRI signals contain information about HRF temporal dynamics that can be leveraged to understand and characterize variations in HRF timing across both cortical and subcortical regions. We found that the frequency spectrum of resting-state fMRI signals significantly differs between voxels with fast versus slow HRFs in human visual cortex. These spectral differences extended to subcortex as well, revealing significantly faster hemodynamic timing in the lateral geniculate nucleus of the thalamus. Ultimately, our results demonstrate that the temporal properties of the HRF impact the spectral content of resting-state fMRI signals and enable voxel-wise characterization of relative hemodynamic response timing. Furthermore, our results show that caution should be used in studies of resting-state fMRI spectral properties, because differences in fMRI frequency content can arise from purely vascular origins. This finding provides new insight into the temporal properties of fMRI signals across voxels, which is crucial for accurate fMRI analyses, and enhances the ability of fast fMRI to identify and track fast neural dynamics.
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Affiliation(s)
- Sydney M Bailes
- Department of Biomedical Engineering, Boston UniversityBostonUnited States
| | - Daniel EP Gomez
- Department of Biomedical Engineering, Boston UniversityBostonUnited States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General HospitalCharlestownUnited States
- Department of Radiology, Harvard Medical SchoolBostonUnited States
| | - Beverly Setzer
- Department of Biomedical Engineering, Boston UniversityBostonUnited States
- Graduate Program for Neuroscience, Boston UniversityBostonUnited States
| | - Laura D Lewis
- Department of Biomedical Engineering, Boston UniversityBostonUnited States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General HospitalCharlestownUnited States
- Institute for Medical Engineering and Science, Massachusetts Institute of TechnologyCambridgeUnited States
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of TechnologyCambridgeUnited States
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5
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Bailes SM, Gomez DEP, Setzer B, Lewis LD. Resting-state fMRI signals contain spectral signatures of local hemodynamic response timing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.25.525528. [PMID: 36747821 PMCID: PMC9900794 DOI: 10.1101/2023.01.25.525528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Functional magnetic resonance imaging (fMRI) has proven to be a powerful tool for noninvasively measuring human brain activity; yet, thus far, fMRI has been relatively limited in its temporal resolution. A key challenge is understanding the relationship between neural activity and the blood-oxygenation-level-dependent (BOLD) signal obtained from fMRI, generally modeled by the hemodynamic response function (HRF). The timing of the HRF varies across the brain and individuals, confounding our ability to make inferences about the timing of the underlying neural processes. Here we show that resting-state fMRI signals contain information about HRF temporal dynamics that can be leveraged to understand and characterize variations in HRF timing across both cortical and subcortical regions. We found that the frequency spectrum of resting-state fMRI signals significantly differs between voxels with fast versus slow HRFs in human visual cortex. These spectral differences extended to subcortex as well, revealing significantly faster hemodynamic timing in the lateral geniculate nucleus of the thalamus. Ultimately, our results demonstrate that the temporal properties of the HRF impact the spectral content of resting-state fMRI signals and enable voxel-wise characterization of relative hemodynamic response timing. Furthermore, our results show that caution should be used in studies of resting-state fMRI spectral properties, as differences can arise from purely vascular origins. This finding provides new insight into the temporal properties of fMRI signals across voxels, which is crucial for accurate fMRI analyses, and enhances the ability of fast fMRI to identify and track fast neural dynamics.
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Affiliation(s)
| | - Daniel E. P. Gomez
- Department of Biomedical Engineering, Boston, MA, 02215, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
| | - Beverly Setzer
- Department of Biomedical Engineering, Boston, MA, 02215, USA
- Graduate Program for Neuroscience, Boston University, Boston, MA, 02215, USA
| | - Laura D. Lewis
- Department of Biomedical Engineering, Boston, MA, 02215, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
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6
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Setzer B, Fultz NE, Gomez DEP, Williams SD, Bonmassar G, Polimeni JR, Lewis LD. A temporal sequence of thalamic activity unfolds at transitions in behavioral arousal state. Nat Commun 2022; 13:5442. [PMID: 36114170 PMCID: PMC9481532 DOI: 10.1038/s41467-022-33010-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 08/29/2022] [Indexed: 11/30/2022] Open
Abstract
Awakening from sleep reflects a profound transformation in neural activity and behavior. The thalamus is a key controller of arousal state, but whether its diverse nuclei exhibit coordinated or distinct activity at transitions in behavioral arousal state is unknown. Using fast fMRI at ultra-high field (7 Tesla), we measured sub-second activity across thalamocortical networks and within nine thalamic nuclei to delineate these dynamics during spontaneous transitions in behavioral arousal state. We discovered a stereotyped sequence of activity across thalamic nuclei and cingulate cortex that preceded behavioral arousal after a period of inactivity, followed by widespread deactivation. These thalamic dynamics were linked to whether participants subsequently fell back into unresponsiveness, with unified thalamic activation reflecting maintenance of behavior. These results provide an outline of the complex interactions across thalamocortical circuits that orchestrate behavioral arousal state transitions, and additionally, demonstrate that fast fMRI can resolve sub-second subcortical dynamics in the human brain.
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Affiliation(s)
- Beverly Setzer
- Graduate Program for Neuroscience, Boston University, Boston, MA, 02215, USA
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA
| | - Nina E Fultz
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
| | - Daniel E P Gomez
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | | | - Giorgio Bonmassar
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
- Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Laura D Lewis
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA.
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA.
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7
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Gnanateja GN, Devaraju DS, Heyne M, Quique YM, Sitek KR, Tardif MC, Tessmer R, Dial HR. On the Role of Neural Oscillations Across Timescales in Speech and Music Processing. Front Comput Neurosci 2022; 16:872093. [PMID: 35814348 PMCID: PMC9260496 DOI: 10.3389/fncom.2022.872093] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 05/24/2022] [Indexed: 11/25/2022] Open
Abstract
This mini review is aimed at a clinician-scientist seeking to understand the role of oscillations in neural processing and their functional relevance in speech and music perception. We present an overview of neural oscillations, methods used to study them, and their functional relevance with respect to music processing, aging, hearing loss, and disorders affecting speech and language. We first review the oscillatory frequency bands and their associations with speech and music processing. Next we describe commonly used metrics for quantifying neural oscillations, briefly touching upon the still-debated mechanisms underpinning oscillatory alignment. Following this, we highlight key findings from research on neural oscillations in speech and music perception, as well as contributions of this work to our understanding of disordered perception in clinical populations. Finally, we conclude with a look toward the future of oscillatory research in speech and music perception, including promising methods and potential avenues for future work. We note that the intention of this mini review is not to systematically review all literature on cortical tracking of speech and music. Rather, we seek to provide the clinician-scientist with foundational information that can be used to evaluate and design research studies targeting the functional role of oscillations in speech and music processing in typical and clinical populations.
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Affiliation(s)
- G. Nike Gnanateja
- Department of Communication Science and Disorders, University of Pittsburgh, Pittsburgh, PA, United States
| | - Dhatri S. Devaraju
- Department of Communication Science and Disorders, University of Pittsburgh, Pittsburgh, PA, United States
| | - Matthias Heyne
- Department of Communication Science and Disorders, University of Pittsburgh, Pittsburgh, PA, United States
| | - Yina M. Quique
- Center for Education in Health Sciences, Northwestern University, Chicago, IL, United States
| | - Kevin R. Sitek
- Department of Communication Science and Disorders, University of Pittsburgh, Pittsburgh, PA, United States
| | - Monique C. Tardif
- Department of Communication Science and Disorders, University of Pittsburgh, Pittsburgh, PA, United States
| | - Rachel Tessmer
- Department of Speech, Language, and Hearing Sciences, The University of Texas at Austin, Austin, TX, United States
| | - Heather R. Dial
- Department of Speech, Language, and Hearing Sciences, The University of Texas at Austin, Austin, TX, United States
- Department of Communication Sciences and Disorders, University of Houston, Houston, TX, United States
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8
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Polimeni JR, Lewis LD. Imaging faster neural dynamics with fast fMRI: A need for updated models of the hemodynamic response. Prog Neurobiol 2021; 207:102174. [PMID: 34525404 PMCID: PMC8688322 DOI: 10.1016/j.pneurobio.2021.102174] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Revised: 07/30/2021] [Accepted: 09/08/2021] [Indexed: 12/20/2022]
Abstract
Fast fMRI enables the detection of neural dynamics over timescales of hundreds of milliseconds, suggesting it may provide a new avenue for studying subsecond neural processes in the human brain. The magnitudes of these fast fMRI dynamics are far greater than predicted by canonical models of the hemodynamic response. Several studies have established nonlinear properties of the hemodynamic response that have significant implications for fast fMRI. We first review nonlinear properties of the hemodynamic response function that may underlie fast fMRI signals. We then illustrate the breakdown of canonical hemodynamic response models in the context of fast neural dynamics. We will then argue that the canonical hemodynamic response function is not likely to reflect the BOLD response to neuronal activity driven by sparse or naturalistic stimuli or perhaps to spontaneous neuronal fluctuations in the resting state. These properties suggest that fast fMRI is capable of tracking surprisingly fast neuronal dynamics, and we discuss the neuroscientific questions that could be addressed using this approach.
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Affiliation(s)
- Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA; Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Laura D Lewis
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Biomedical Engineering, Boston University, Boston, MA, USA.
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9
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Vizioli L, Yacoub E, Lewis LD. How pushing the spatiotemporal resolution of fMRI can advance neuroscience. Prog Neurobiol 2021; 207:102184. [PMID: 34767874 DOI: 10.1016/j.pneurobio.2021.102184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Luca Vizioli
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN 55455, United States; Department of Neurosurgery, University of Minnesota, Minneapolis, MN 55455, United States.
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN 55455, United States
| | - Laura D Lewis
- Department of Biomedical Engineering, Boston University, Boston, MA United States; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA United States
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10
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Dowdle LT, Ghose G, Chen CCC, Ugurbil K, Yacoub E, Vizioli L. Statistical power or more precise insights into neuro-temporal dynamics? Assessing the benefits of rapid temporal sampling in fMRI. Prog Neurobiol 2021; 207:102171. [PMID: 34492308 DOI: 10.1016/j.pneurobio.2021.102171] [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: 03/01/2021] [Revised: 08/09/2021] [Accepted: 09/02/2021] [Indexed: 01/25/2023]
Abstract
Functional magnetic resonance imaging (fMRI), a non-invasive and widely used human neuroimaging method, is most known for its spatial precision. However, there is a growing interest in its temporal sensitivity. This is despite the temporal blurring of neuronal events by the blood oxygen level dependent (BOLD) signal, the peak of which lags neuronal firing by 4-6 seconds. Given this, the goal of this review is to answer a seemingly simple question - "What are the benefits of increased temporal sampling for fMRI?". To answer this, we have combined fMRI data collected at multiple temporal scales, from 323 to 1000 milliseconds, with a review of both historical and contemporary temporal literature. After a brief discussion of technological developments that have rekindled interest in temporal research, we next consider the potential statistical and methodological benefits. Most importantly, we explore how fast fMRI can uncover previously unobserved neuro-temporal dynamics - effects that are entirely missed when sampling at conventional 1 to 2 second rates. With the intrinsic link between space and time in fMRI, this temporal renaissance also delivers improvements in spatial precision. Far from producing only statistical gains, the array of benefits suggest that the continued temporal work is worth the effort.
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Affiliation(s)
- Logan T Dowdle
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN, 55455, United States; Department of Neurosurgery, University of Minnesota, 500 SE Harvard St, Minneapolis, MN, 55455, United States; Department of Neuroscience, University of Minnesota, 321 Church St SE, Minneapolis, MN, 55455, United States.
| | - Geoffrey Ghose
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN, 55455, United States; Department of Neuroscience, University of Minnesota, 321 Church St SE, Minneapolis, MN, 55455, United States
| | - Clark C C Chen
- Department of Neurosurgery, University of Minnesota, 500 SE Harvard St, Minneapolis, MN, 55455, United States
| | - Kamil Ugurbil
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN, 55455, United States
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN, 55455, United States
| | - Luca Vizioli
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN, 55455, United States; Department of Neurosurgery, University of Minnesota, 500 SE Harvard St, Minneapolis, MN, 55455, United States.
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11
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Dynamics of fMRI patterns reflect sub-second activation sequences and reveal replay in human visual cortex. Nat Commun 2021; 12:1795. [PMID: 33741933 PMCID: PMC7979874 DOI: 10.1038/s41467-021-21970-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Accepted: 02/16/2021] [Indexed: 01/31/2023] Open
Abstract
Neural computations are often fast and anatomically localized. Yet, investigating such computations in humans is challenging because non-invasive methods have either high temporal or spatial resolution, but not both. Of particular relevance, fast neural replay is known to occur throughout the brain in a coordinated fashion about which little is known. We develop a multivariate analysis method for functional magnetic resonance imaging that makes it possible to study sequentially activated neural patterns separated by less than 100 ms with precise spatial resolution. Human participants viewed five images individually and sequentially with speeds up to 32 ms between items. Probabilistic pattern classifiers were trained on activation patterns in visual and ventrotemporal cortex during individual image trials. Applied to sequence trials, probabilistic classifier time courses allow the detection of neural representations and their order. Order detection remains possible at speeds up to 32 ms between items (plus 100 ms per item). The frequency spectrum of the sequentiality metric distinguishes between sub- versus supra-second sequences. Importantly, applied to resting-state data our method reveals fast replay of task-related stimuli in visual cortex. This indicates that non-hippocampal replay occurs even after tasks without memory requirements and shows that our method can be used to detect such spontaneously occurring replay.
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12
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Hennig J, Kiviniemi V, Riemenschneider B, Barghoorn A, Akin B, Wang F, LeVan P. 15 Years MR-encephalography. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2020; 34:85-108. [PMID: 33079327 PMCID: PMC7910380 DOI: 10.1007/s10334-020-00891-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 09/02/2020] [Accepted: 09/29/2020] [Indexed: 02/07/2023]
Abstract
Objective This review article gives an account of the development of the MR-encephalography (MREG) method, which started as a mere ‘Gedankenexperiment’ in 2005 and gradually developed into a method for ultrafast measurement of physiological activities in the brain. After going through different approaches covering k-space with radial, rosette, and concentric shell trajectories we have settled on a stack-of-spiral trajectory, which allows full brain coverage with (nominal) 3 mm isotropic resolution in 100 ms. The very high acceleration factor is facilitated by the near-isotropic k-space coverage, which allows high acceleration in all three spatial dimensions. Methods The methodological section covers the basic sequence design as well as recent advances in image reconstruction including the targeted reconstruction, which allows real-time feedback applications, and—most recently—the time-domain principal component reconstruction (tPCR), which applies a principal component analysis of the acquired time domain data as a sparsifying transformation to improve reconstruction speed as well as quality. Applications Although the BOLD-response is rather slow, the high speed acquisition of MREG allows separation of BOLD-effects from cardiac and breathing related pulsatility. The increased sensitivity enables direct detection of the dynamic variability of resting state networks as well as localization of single interictal events in epilepsy patients. A separate and highly intriguing application is aimed at the investigation of the glymphatic system by assessment of the spatiotemporal patterns of cardiac and breathing related pulsatility. Discussion MREG has been developed to push the speed limits of fMRI. Compared to multiband-EPI this allows considerably faster acquisition at the cost of reduced image quality and spatial resolution.
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Affiliation(s)
- Juergen Hennig
- Department of Radiology, Medical Physics, Faculty of Medicine, Medical Center University of Freiburg, University of Freiburg, Freiburg, Germany. .,Center for Basics in NeuroModulation (NeuroModulBasics), Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | - Vesa Kiviniemi
- Oulu Functional NeuroImaging Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - Bruno Riemenschneider
- Department of Radiology, Center for Biomedical Imaging, New York University Grossman School of Medicine, New York, NY, USA
| | - Antonia Barghoorn
- Department of Radiology, Medical Physics, Faculty of Medicine, Medical Center University of Freiburg, University of Freiburg, Freiburg, Germany.,Center for Basics in NeuroModulation (NeuroModulBasics), Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Burak Akin
- Department of Radiology, Medical Physics, Faculty of Medicine, Medical Center University of Freiburg, University of Freiburg, Freiburg, Germany.,Center for Basics in NeuroModulation (NeuroModulBasics), Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Fei Wang
- Department of Radiology, Medical Physics, Faculty of Medicine, Medical Center University of Freiburg, University of Freiburg, Freiburg, Germany.,Center for Basics in NeuroModulation (NeuroModulBasics), Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Pierre LeVan
- Departments of Radiology and Paediatrics, Hotchkiss Brain Institute and Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
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13
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Järvelä M, Raatikainen V, Kotila A, Kananen J, Korhonen V, Uddin LQ, Ansakorpi H, Kiviniemi V. Lag Analysis of Fast fMRI Reveals Delayed Information Flow Between the Default Mode and Other Networks in Narcolepsy. Cereb Cortex Commun 2020; 1:tgaa073. [PMID: 34296133 PMCID: PMC8153076 DOI: 10.1093/texcom/tgaa073] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 09/29/2020] [Accepted: 09/29/2020] [Indexed: 11/12/2022] Open
Abstract
Narcolepsy is a chronic neurological disease characterized by dysfunction of the hypocretin system in brain causing disruption in the wake-promoting system. In addition to sleep attacks and cataplexy, patients with narcolepsy commonly report cognitive symptoms while objective deficits in sustained attention and executive function have been observed. Prior resting-state functional magnetic resonance imaging (fMRI) studies in narcolepsy have reported decreased inter/intranetwork connectivity regarding the default mode network (DMN). Recently developed fast fMRI data acquisition allows more precise detection of brain signal propagation with a novel dynamic lag analysis. In this study, we used fast fMRI data to analyze dynamics of inter resting-state network (RSN) information signaling between narcolepsy type 1 patients (NT1, n = 23) and age- and sex-matched healthy controls (HC, n = 23). We investigated dynamic connectivity properties between positive and negative peaks and, furthermore, their anticorrelative (pos-neg) counterparts. The lag distributions were significantly (P < 0.005, familywise error rate corrected) altered in 24 RSN pairs in NT1. The DMN was involved in 83% of the altered RSN pairs. We conclude that narcolepsy type 1 is characterized with delayed and monotonic inter-RSN information flow especially involving anticorrelations, which are known to be characteristic behavior of the DMN regarding neurocognition.
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Affiliation(s)
- M Järvelä
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, 90220 Oulu, Finland
| | - V Raatikainen
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, 90220 Oulu, Finland
| | - A Kotila
- Research Unit of Logopedics, University of Oulu, 90014 Oulu, Finland
| | - J Kananen
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, 90220 Oulu, Finland
| | - V Korhonen
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, 90220 Oulu, Finland
| | - L Q Uddin
- Department of Psychology, University of Miami, Coral Gables, 33124 FL, USA
| | - H Ansakorpi
- Research Unit of Clinical Neuroscience, Neurology, University of Oulu, 90014 Oulu, Finland
| | - V Kiviniemi
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, 90220 Oulu, Finland
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14
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He Y, Wang M, Yu X. High spatiotemporal vessel-specific hemodynamic mapping with multi-echo single-vessel fMRI. J Cereb Blood Flow Metab 2020; 40:2098-2114. [PMID: 31696765 PMCID: PMC7786852 DOI: 10.1177/0271678x19886240] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
High-resolution fMRI enables noninvasive mapping of the hemodynamic responses from individual penetrating vessels in animal brains. Here, a 2D multi-echo single-vessel fMRI (MESV-fMRI) method has been developed to map the fMRI signal from arterioles and venules with a 100 ms sampling rate at multiple echo times (TE, 3-30 ms) and short acquisition windows (<1 ms). The T2*-weighted signal shows the increased extravascular effect on venule voxels as a function of TE. In contrast, the arteriole voxels show an increased fMRI signal with earlier onset than venules voxels at the short TE (3 ms) with increased blood inflow and volume effects. MESV-fMRI enables vessel-specific T2* mapping and presents T2*-based fMRI time courses with higher contrast-to-noise ratios (CNRs) than the T2*-weighted fMRI signal at a given TE. The vessel-specific T2* mapping also allows semi-quantitative estimation of the oxygen saturation levels (Y) and their changes (ΔY) at a given blood volume fraction upon neuronal activation. The MESV-fMRI method enables vessel-specific T2* measurements with high spatiotemporal resolution for better modeling of the fMRI signal based on the hemodynamic parameters.
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Affiliation(s)
- Yi He
- Translational Neuroimaging and Neural Control Group, High Field Magnetic Resonance Department, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany.,Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tuebingen, Tuebingen, Germany.,Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Maosen Wang
- Translational Neuroimaging and Neural Control Group, High Field Magnetic Resonance Department, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany.,Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tuebingen, Tuebingen, Germany
| | - Xin Yu
- Translational Neuroimaging and Neural Control Group, High Field Magnetic Resonance Department, 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
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15
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Dynamic mode decomposition of resting-state and task fMRI. Neuroimage 2019; 194:42-54. [DOI: 10.1016/j.neuroimage.2019.03.019] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Accepted: 03/08/2019] [Indexed: 12/19/2022] Open
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16
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Sabatinelli D, Frank DW. Assessing the Primacy of Human Amygdala-Inferotemporal Emotional Scene Discrimination with Rapid Whole-Brain fMRI. Neuroscience 2019; 406:212-224. [DOI: 10.1016/j.neuroscience.2019.03.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 02/28/2019] [Accepted: 03/01/2019] [Indexed: 01/09/2023]
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17
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Fast imaging for mapping dynamic networks. Neuroimage 2018; 180:547-558. [DOI: 10.1016/j.neuroimage.2017.08.029] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2017] [Revised: 07/21/2017] [Accepted: 08/09/2017] [Indexed: 01/22/2023] Open
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18
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Keinänen T, Rytky S, Korhonen V, Huotari N, Nikkinen J, Tervonen O, Palva JM, Kiviniemi V. Fluctuations of the EEG-fMRI correlation reflect intrinsic strength of functional connectivity in default mode network. J Neurosci Res 2018; 96:1689-1698. [PMID: 29761531 DOI: 10.1002/jnr.24257] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 04/20/2018] [Accepted: 04/23/2018] [Indexed: 01/14/2023]
Abstract
Both functional magnetic resonance imaging (fMRI) and electrophysiological recordings have revealed that resting-state functional connectivity is temporally variable in human brain. Combined full-band electroencephalography-fMRI (fbEEG-fMRI) studies have shown that infraslow (<.1 Hz) fluctuations in EEG scalp potential are correlated with the blood-oxygen-level-dependent (BOLD) fMRI signals and that also this correlation appears variable over time. Here, we used simultaneous fbEEG-fMRI to test the hypothesis that correlation dynamics between BOLD and fbEEG signals could be explained by fluctuations in the activation properties of resting-state networks (RSNs) such as the extent or strength of their activation. We used ultrafast magnetic resonance encephalography (MREG) fMRI to enable temporally accurate and statistically robust short-time-window comparisons of infra-slow fbEEG and BOLD signals. We found that the temporal fluctuations in the fbEEG-BOLD correlation were dependent on RSN connectivity strength, but not on the mean signal level or magnitude of RSN activation or motion during scanning. Moreover, the EEG-fMRI correlations were strongest when the intrinsic RSN connectivity was strong and close to the pial surface. Conversely, weak fbEEG-BOLD correlations were attributable to periods of less coherent or spatially more scattered intrinsic RSN connectivity, or RSN activation in deeper cerebral structures. The results thus show that the on-average low correlations between infra-slow EEG and BOLD signals are, in fact, governed by the momentary coherence and depth of the underlying RSN activation, and may reach systematically high values with appropriate source activities. These findings further consolidate the notion of slow scalp potentials being directly coupled to hemodynamic fluctuations.
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Affiliation(s)
- Tuija Keinänen
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland.,Department of Clinical Neurophysiology, Oulu University Hospital, Oulu, Finland
| | - Seppo Rytky
- Department of Clinical Neurophysiology, Oulu University Hospital, Oulu, Finland
| | - Vesa Korhonen
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - Niko Huotari
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - Juha Nikkinen
- Department of Oncology and Radiotherapy, Oulu University Hospital, Oulu, Finland
| | - Osmo Tervonen
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - J Matias Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Vesa Kiviniemi
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
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19
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Simultaneous multi-slice inverse imaging of the human brain. Sci Rep 2017; 7:17019. [PMID: 29208906 PMCID: PMC5717110 DOI: 10.1038/s41598-017-16976-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2017] [Accepted: 11/20/2017] [Indexed: 11/26/2022] Open
Abstract
Ultrafast functional magnetic resonance imaging (fMRI) can measure blood oxygen level dependent (BOLD) signals with high sensitivity and specificity. Here we propose a novel method: simultaneous multi-slice inverse imaging (SMS-InI) — a combination of simultaneous multi-slice excitation, simultaneous echo refocusing (SER), blipped controlled aliasing in parallel imaging echo-planar imaging (EPI), and regularized image reconstruction. Using a 32-channel head coil array on a 3 T scanner, SMS-InI achieves nominal isotropic 5-mm spatial resolution and 10 Hz sampling rate at the whole-brain level. Compared with traditional inverse imaging, we found that SMS-InI has higher spatial resolution with lower signal leakage and higher time-domain signal-to-noise ratio with the optimized regularization parameter in the reconstruction. SMS-InI achieved higher effective resolution and higher detection power in detecting visual cortex activity than InI. SMS-InI also detected subcortical fMRI signals with the similar sensitivity and localization accuracy like EPI. The spatiotemporal resolution of SMS-InI was used to reveal that presenting visual stimuli with 0.2 s latency between left and right visual hemifield led to 0.2 s relative hemodynamic response latency between the left and right visual cortices. Together, these results indicate that SMS-InI is a useful tool in measuring cortical and subcortical hemodynamic responses with high spatiotemporal resolution.
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20
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Lin FH, Polimeni JR, Lin JFL, Tsai KWK, Chu YH, Wu PY, Li YT, Hsu YC, Tsai SY, Kuo WJ. Relative latency and temporal variability of hemodynamic responses at the human primary visual cortex. Neuroimage 2017; 164:194-201. [PMID: 28119135 DOI: 10.1016/j.neuroimage.2017.01.041] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2016] [Revised: 12/29/2016] [Accepted: 01/17/2017] [Indexed: 01/07/2023] Open
Abstract
The blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) signal is a robust surrogate for local neuronal activity. However, it has been shown to vary substantially across subjects, brain regions, and repetitive measurements. This variability represents a limit to the precision of the BOLD response and the ability to reliably discriminate brain hemodynamic responses elicited by external stimuli or behavior that are nearby in time. While the temporal variability of the BOLD signal at human visual cortex has been found in the range of a few hundreds of milliseconds, the spatial distributions of the average and standard deviation of this temporal variability have not been quantitatively characterized. Here we use fMRI measurements with a high sampling rate (10Hz) to map the latency, intra- and inter-subject variability of the evoked BOLD signal in human primary (V1) visual cortices using an event-related fMRI paradigm. The latency relative to the average BOLD signal evoked by 30 stimuli was estimated to be 0.03±0.20s. Within V1, the absolute value of the relative BOLD latency was found correlated to intra- and inter-subject temporal variability. After comparing these measures to retinotopic maps, we found that locations with V1 areas sensitive to smaller eccentricity have later responses and smaller inter-subject variabilities. These correlations were found from data with either short inter-stimulus interval (ISI; average 4s) or long ISI (average 30s). Maps of the relative latency as well as inter-/intra-subject variability were found visually asymmetric between hemispheres. Our results suggest that the latency and variability of regional BOLD signal measured with high spatiotemporal resolution may be used to detect regional differences in hemodynamics to inform fMRI studies. However, the physiological origins of timing index distributions and their hemispheric asymmetry remain to be investigated.
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Affiliation(s)
- Fa-Hsuan Lin
- Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan; Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA 02129, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jo-Fu Lotus Lin
- Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan
| | - Kevin W-K Tsai
- Aim for the Top University Project Office, National Taiwan Normal University, Taipei, Taiwan
| | - Ying-Hua Chu
- Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan
| | - Pu-Yeh Wu
- Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan
| | - Yi-Tien Li
- Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan
| | - Yi-Cheng Hsu
- Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan
| | - Shang-Yueh Tsai
- Institute of Applied Physics, National Chengchi University, Taipei, Taiwan; Research Center for Mind Brain and Learning, National Chengchi University, Taipei, Taiwan
| | - Wen-Jui Kuo
- Institute of Neuroscience, National Yang-Ming University, 155 Sec. 2, Li-Nung Street, Taipei 112, Taiwan.
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21
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Raatikainen V, Huotari N, Korhonen V, Rasila A, Kananen J, Raitamaa L, Keinänen T, Kantola J, Tervonen O, Kiviniemi V. Combined spatiotemporal ICA (stICA) for continuous and dynamic lag structure analysis of MREG data. Neuroimage 2017; 148:352-363. [PMID: 28088482 DOI: 10.1016/j.neuroimage.2017.01.024] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2016] [Revised: 01/10/2017] [Accepted: 01/11/2017] [Indexed: 01/30/2023] Open
Abstract
This study investigated lag structure in the resting-state fMRI by applying a novel independent component (ICA) method to magnetic resonance encephalography (MREG) data. Briefly, the spatial ICA (sICA) was used for defining the frontal and back nodes of the default mode network (DMN), and the temporal ICA (tICA), which is enabled by the high temporal resolution of MREG (TR=100ms), was used to separate both neuronal and physiological components of these two spatial map regions. Subsequently, lag structure was investigated between the frontal (DMNvmpf) and posterior (DMNpcc) DMN nodes using both conventional method with all-time points and a sliding-window approach. A rigorous noise exclusion criterion was applied for tICs to remove physiological pulsations, motion and system artefacts. All the de-noised tICs were used to calculate the null-distributions both for expected lag variability over time and over subjects. Lag analysis was done for the three highest correlating denoised tICA pairs. Mean time lag of 0.6s (± 0.5 std) and mean absolute correlation of 0.69 (± 0.08) between the highest correlating tICA pairs of DMN nodes was observed throughout the whole analyzed period. In dynamic 2min window analysis, there was large variability over subjects as ranging between 1-10sec. Directionality varied between these highly correlating sources an average 28.8% of the possible number of direction changes. The null models show highly consistent correlation and lag structure between DMN nodes both in continuous and dynamic analysis. The mean time lag of a null-model over time between all denoised DMN nodes was 0.0s and, thus the probability of having either DMNpcc or DMNvmpf as a preceding component is near equal. All the lag values of highest correlating tICA pairs over subjects lie within the standard deviation range of a null-model in whole time window analysis, supporting the earlier findings that there is a consistent temporal lag structure across groups of individuals. However, in dynamic analysis, there are lag values exceeding the threshold of significance of a null-model meaning that there might be biologically meaningful variation in this measure. Taken together the variability in lag and the presence of high activity peaks during strong connectivity indicate that individual avalanches may play an important role in defining dynamic independence in resting state connectivity within networks.
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Affiliation(s)
- Ville Raatikainen
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland; Research unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland.
| | - Niko Huotari
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland; Research unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Vesa Korhonen
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland; Research unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Aleksi Rasila
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
| | - Janne Kananen
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland; Research unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Lauri Raitamaa
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland; Research unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Tuija Keinänen
- Research unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland; Department of Clinical Neurophysiology, Oulu University Hospital, Oulu, Finland
| | - Jussi Kantola
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
| | - Osmo Tervonen
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland; Research unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Vesa Kiviniemi
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland; Research unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland
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22
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Fraga de Abreu VH, Peck KK, Petrovich-Brennan NM, Woo KM, Holodny AI. Brain Tumors: The Influence of Tumor Type and Routine MR Imaging Characteristics at BOLD Functional MR Imaging in the Primary Motor Gyrus. Radiology 2016; 281:876-883. [PMID: 27383533 DOI: 10.1148/radiol.2016151951] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Purpose To evaluate the effects of histologic features and anatomic magnetic resonance (MR) imaging characteristics of brain tumors on the functional MR imaging signal in the primary motor cortex (PMC), as false-negative blood oxygen level-dependent (BOLD) functional MR imaging activation can limit the accurate localization of eloquent cortices. Materials and Methods Institutional review board approval was obtained, and informed consent was waived for this HIPAA-compliant retrospective study. It comprised 63 patients referred between 2006 and 2014 for preoperative functional MR imaging localization of the Rolandic cortex. The patients had glioblastoma multiforme (GBM) (n = 20), metastasis (n = 21), or meningioma (n = 22). The volumes of functional MR imaging activation were measured during performance of a bilateral hand motor task. Ratios of functional MR imaging activation were normalized to PMC volume. Statistical analysis was performed for the following: (a) differences between hemispheres within each histologic tumor type (paired Wilcoxon test), (b) differences across tumor types (Kruskal-Wallis and Fisher tests), (c) pairwise tests between tumor types (Mann-Whitney U test), (d) relationships between fast fluid-attenuated inversion recovery (FLAIR) data and enhancement volume with activation (Spearman rank correlation coefficient), and (e) differences in activation volumes by tumor location (Mann-Whitney U test). Results A significant interhemispheric difference was found between the activation volumes in GBMs (mean, 511.43 voxels ± 307.73 [standard deviation] and 330.78 voxels ± 278.95; P < .01) but not in metastases (504.68 voxels ± 220.98 and 460.22 voxels ± 276.83; P = .15) or meningiomas (424.07 voxels ± 247.58 and 415.18 voxels ± 222.36; P = .85). GBMs showed significantly lower activation ratios (median, 0.49; range, 0.04-1.15) than metastases (median, 0.79; range, 0.28-1.66; P = .043) and meningiomas (median, 0.91; range, 0.52-2.05; P < .01). There was a moderate correlation with the volumes of FLAIR abnormality in metastases (ρ = -0.50) and meningiomas (ρ = -0.55). Enhancement volume (ρ = -0.11) and tumor distance from the PMC (median, 0.73 and range, 0.04-2.05 for near and median, 0.82 and range, 0.39-1.66 for far; P = .14) did not influence activation. Conclusion BOLD functional MR imaging activation in the ipsilateral PMC is influenced by tumor type and is significantly reduced in GBMs. FLAIR abnormality correlates moderately with the activation ratios in metastases and meningiomas. © RSNA, 2016 Online supplemental material is available for this article.
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Affiliation(s)
- Vitor Hugo Fraga de Abreu
- From the Functional MRI Laboratory, Department of Radiology (V.H.F.d.A., K.K.P., N.M.P., A.I.H.), the Department of Medical Physics (K.K.P.), the Department of Epidemiology-Biostatistics (K.M.W.), and the Brain Tumor Center (A.I.H.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10065; and the Department of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (V.H.F.d.A.)
| | - Kyung K Peck
- From the Functional MRI Laboratory, Department of Radiology (V.H.F.d.A., K.K.P., N.M.P., A.I.H.), the Department of Medical Physics (K.K.P.), the Department of Epidemiology-Biostatistics (K.M.W.), and the Brain Tumor Center (A.I.H.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10065; and the Department of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (V.H.F.d.A.)
| | - Nicole M Petrovich-Brennan
- From the Functional MRI Laboratory, Department of Radiology (V.H.F.d.A., K.K.P., N.M.P., A.I.H.), the Department of Medical Physics (K.K.P.), the Department of Epidemiology-Biostatistics (K.M.W.), and the Brain Tumor Center (A.I.H.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10065; and the Department of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (V.H.F.d.A.)
| | - Kaitlin M Woo
- From the Functional MRI Laboratory, Department of Radiology (V.H.F.d.A., K.K.P., N.M.P., A.I.H.), the Department of Medical Physics (K.K.P.), the Department of Epidemiology-Biostatistics (K.M.W.), and the Brain Tumor Center (A.I.H.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10065; and the Department of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (V.H.F.d.A.)
| | - Andrei I Holodny
- From the Functional MRI Laboratory, Department of Radiology (V.H.F.d.A., K.K.P., N.M.P., A.I.H.), the Department of Medical Physics (K.K.P.), the Department of Epidemiology-Biostatistics (K.M.W.), and the Brain Tumor Center (A.I.H.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10065; and the Department of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (V.H.F.d.A.)
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23
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Lin FH, Chu YH, Hsu YC, Lin JFL, Tsai KWK, Tsai SY, Kuo WJ. Significant feed-forward connectivity revealed by high frequency components of BOLD fMRI signals. Neuroimage 2015. [DOI: 10.1016/j.neuroimage.2015.07.036] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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24
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Sabatinelli D, Frank DW, Wanger TJ, Dhamala M, Adhikari BM, Li X. The timing and directional connectivity of human frontoparietal and ventral visual attention networks in emotional scene perception. Neuroscience 2014; 277:229-38. [PMID: 25018086 DOI: 10.1016/j.neuroscience.2014.07.005] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2014] [Revised: 07/01/2014] [Accepted: 07/02/2014] [Indexed: 12/12/2022]
Abstract
Electrocortical and hemodynamic measures reliably identify enhanced activity in the ventral and dorsal visual cortices during the perception of emotionally arousing versus neutral images, an effect that may reflect directive feedback from the subcortical amygdala. However, other brain regions strongly modulate visual attention, such as frontal eye fields (FEF) and intraparietal sulcus (IPS). Here we employ rapid sampling of BOLD signal (4 Hz) in the amygdala, fusiform gyrus (FG), FEF and IPS in 42 human participants as they viewed a series of emotional and neutral natural scene photographs balanced for luminosity and complexity, to test whether emotional discrimination is evident in dorsal structures prior to such discrimination in the amygdala and FG. Granger causality analyses were used to assess directional connectivity within dorsal and ventral networks. Results demonstrate emotionally-enhanced peak BOLD signal in the amygdala, FG, FEF, and IPS, with the onset of BOLD signal discrimination occurring between 2 and 3s after stimulus onset in ventral structures, and between 4 and 5s in FEF and IPS. Granger causality estimates yield stronger directional connectivity from IPS to FEF than the reverse in this emotional picture paradigm. Consistent with a reentrant perspective of emotional scene perception, greater directional connectivity was found from the amygdala to FG compared to the reverse. These data support a perspective in which the registration of emotional scene content is orchestrated by the amygdala and rostral inferotemporal visual cortex.
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Affiliation(s)
- D Sabatinelli
- Department of Psychology & Neuroscience, BioImaging Research Center, University of Georgia, Athens, GA 30602, United States.
| | - D W Frank
- Department of Psychology & Neuroscience, BioImaging Research Center, University of Georgia, Athens, GA 30602, United States
| | - T J Wanger
- Department of Psychology & Neuroscience, BioImaging Research Center, University of Georgia, Athens, GA 30602, United States
| | - M Dhamala
- Department of Physics & Astronomy, Neuroscience Institute, Center for Behavioral Neuroscience, Georgia State University, Atlanta, GA 30302, United States
| | - B M Adhikari
- Department of Physics & Astronomy, Neuroscience Institute, Center for Behavioral Neuroscience, Georgia State University, Atlanta, GA 30302, United States
| | - X Li
- Department of Computer Science, BioImaging Research Center, University of Georgia, Athens, GA 30602, United States
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25
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Increasing fMRI sampling rate improves Granger causality estimates. PLoS One 2014; 9:e100319. [PMID: 24968356 PMCID: PMC4072680 DOI: 10.1371/journal.pone.0100319] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2013] [Accepted: 05/26/2014] [Indexed: 11/19/2022] Open
Abstract
Estimation of causal interactions between brain areas is necessary for elucidating large-scale functional brain networks underlying behavior and cognition. Granger causality analysis of time series data can quantitatively estimate directional information flow between brain regions. Here, we show that such estimates are significantly improved when the temporal sampling rate of functional magnetic resonance imaging (fMRI) is increased 20-fold. Specifically, healthy volunteers performed a simple visuomotor task during blood oxygenation level dependent (BOLD) contrast based whole-head inverse imaging (InI). Granger causality analysis based on raw InI BOLD data sampled at 100-ms resolution detected the expected causal relations, whereas when the data were downsampled to the temporal resolution of 2 s typically used in echo-planar fMRI, the causality could not be detected. An additional control analysis, in which we SINC interpolated additional data points to the downsampled time series at 0.1-s intervals, confirmed that the improvements achieved with the real InI data were not explainable by the increased time-series length alone. We therefore conclude that the high-temporal resolution of InI improves the Granger causality connectivity analysis of the human brain.
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Schepers IM, Yoshor D, Beauchamp MS. Electrocorticography Reveals Enhanced Visual Cortex Responses to Visual Speech. Cereb Cortex 2014; 25:4103-10. [PMID: 24904069 DOI: 10.1093/cercor/bhu127] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Human speech contains both auditory and visual components, processed by their respective sensory cortices. We test a simple model in which task-relevant speech information is enhanced during cortical processing. Visual speech is most important when the auditory component is uninformative. Therefore, the model predicts that visual cortex responses should be enhanced to visual-only (V) speech compared with audiovisual (AV) speech. We recorded neuronal activity as patients perceived auditory-only (A), V, and AV speech. Visual cortex showed strong increases in high-gamma band power and strong decreases in alpha-band power to V and AV speech. Consistent with the model prediction, gamma-band increases and alpha-band decreases were stronger for V speech. The model predicts that the uninformative nature of the auditory component (not simply its absence) is the critical factor, a prediction we tested in a second experiment in which visual speech was paired with auditory white noise. As predicted, visual speech with auditory noise showed enhanced visual cortex responses relative to AV speech. An examination of the anatomical locus of the effects showed that all visual areas, including primary visual cortex, showed enhanced responses. Visual cortex responses to speech are enhanced under circumstances when visual information is most important for comprehension.
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Affiliation(s)
- Inga M Schepers
- Department of Neurobiology and Anatomy, University of Texas Medical School at Houston, Houston, TX, USA Current Address: Department of Psychology, Oldenburg University, Oldenburg, Germany
| | - Daniel Yoshor
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Michael S Beauchamp
- Department of Neurobiology and Anatomy, University of Texas Medical School at Houston, Houston, TX, USA
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Yu X, Qian C, Chen DY, Dodd S, Koretsky AP. Deciphering laminar-specific neural inputs with line-scanning fMRI. Nat Methods 2014; 11:55-8. [PMID: 24240320 PMCID: PMC4276040 DOI: 10.1038/nmeth.2730] [Citation(s) in RCA: 121] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2013] [Accepted: 10/23/2013] [Indexed: 11/08/2022]
Abstract
Using a line-scanning method during functional magnetic resonance imaging (fMRI), we obtained high temporal (50-ms) and spatial (50-μm) resolution information along the cortical thickness and showed that the laminar position of fMRI onset coincides with distinct neural inputs in rat somatosensory and motor cortices. This laminar-specific fMRI onset allowed us to identify the neural inputs underlying ipsilateral fMRI activation in the barrel cortex due to peripheral denervation-induced plasticity.
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Affiliation(s)
- Xin Yu
- Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Chunqi Qian
- Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Der-yow Chen
- Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Stephen Dodd
- Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Alan P. Koretsky
- Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
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28
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Chang WT, Setsompop K, Ahveninen J, Belliveau JW, Witzel T, Lin FH. Improving the spatial resolution of magnetic resonance inverse imaging via the blipped-CAIPI acquisition scheme. Neuroimage 2013; 91:401-11. [PMID: 24374076 DOI: 10.1016/j.neuroimage.2013.12.037] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2013] [Revised: 12/11/2013] [Accepted: 12/19/2013] [Indexed: 11/26/2022] Open
Abstract
Using simultaneous acquisition from multiple channels of a radio-frequency (RF) coil array, magnetic resonance inverse imaging (InI) achieves functional MRI acquisitions at a rate of 100ms per whole-brain volume. InI accelerates the scan by leaving out partition encoding steps and reconstructs images by solving under-determined inverse problems using RF coil sensitivity information. Hence, the correlated spatial information available in the coil array causes spatial blurring in the InI reconstruction. Here, we propose a method that employs gradient blips in the partition encoding direction during the acquisition to provide extra spatial encoding in order to better differentiate signals from different partitions. According to our simulations, this blipped-InI (bInI) method can increase the average spatial resolution by 15.1% (1.3mm) across the whole brain and from 32.6% (4.2mm) in subcortical regions, as compared to the InI method. In a visual fMRI experiment, we demonstrate that, compared to InI, the spatial distribution of bInI BOLD response is more consistent with that of a conventional echo-planar imaging (EPI) at the level of individual subjects. With the improved spatial resolution, especially in subcortical regions, bInI can be a useful fMRI tool for obtaining high spatiotemporal information for clinical and cognitive neuroscience studies.
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Affiliation(s)
- Wei-Tang Chang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, USA; Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, USA
| | - Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, USA
| | - John W Belliveau
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA
| | - Thomas Witzel
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, USA
| | - Fa-Hsuan Lin
- Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan.
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Altmann CF, Gaese BH. Representation of frequency-modulated sounds in the human brain. Hear Res 2013; 307:74-85. [PMID: 23933098 DOI: 10.1016/j.heares.2013.07.018] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2013] [Revised: 07/26/2013] [Accepted: 07/27/2013] [Indexed: 10/26/2022]
Abstract
Frequency-modulation is a ubiquitous sound feature present in communicative sounds of various animal species and humans. Functional imaging of the human auditory system has seen remarkable advances in the last two decades and studies pertaining to frequency-modulation have centered around two major questions: a) are there dedicated feature-detectors encoding frequency-modulation in the brain and b) is there concurrent representation with amplitude-modulation, another temporal sound feature? In this review, we first describe how these two questions are motivated by psychophysical studies and neurophysiology in animal models. We then review how human non-invasive neuroimaging studies have furthered our understanding of the representation of frequency-modulated sounds in the brain. Finally, we conclude with some suggestions on how human neuroimaging could be used in future studies to address currently still open questions on this fundamental sound feature. This article is part of a Special Issue entitled Human Auditory Neuroimaging.
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Affiliation(s)
- Christian F Altmann
- Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan; Career-Path Promotion Unit for Young Life Scientists, Kyoto University, Kyoto 606-8501, Japan.
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