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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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2
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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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Heard M, Li X, Lee YS. Hybrid auditory fMRI: In pursuit of increasing data acquisition while decreasing the impact of scanner noise. J Neurosci Methods 2021; 358:109198. [PMID: 33901568 DOI: 10.1016/j.jneumeth.2021.109198] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 03/28/2021] [Accepted: 04/16/2021] [Indexed: 11/17/2022]
Abstract
BACKGROUND Two challenges in auditory fMRI include the loud scanner noise during sound presentation and slow data acquisition. Here, we introduce a new auditory imaging protocol, termed "hybrid", that alleviates these obstacles. NEW METHOD We designed a within-subject experiment (N = 14) wherein language-driven activity was measured by hybrid, interleaved silent (ISSS), and continuous multiband acquisition. To determine the advantage of noise attenuation during sound presentation, hybrid was compared to multiband. To identify the benefits of increased temporal resolution, hybrid was compared to ISSS. Data were evaluated by whole-brain univariate general linear modeling (GLM) and multivariate pattern analysis (MVPA). RESULTS Comparison with existing methods: CONCLUSIONS: Our data revealed that hybrid imaging restored neural activity in the canonical language network that was absent due to the loud noise or slow sampling in the conventional imaging protocols. With its noise-attenuated sound presentation windows and increased acquisition speed, the hybrid protocol is well-suited for auditory fMRI research tracking neural activity pertaining to fast, time-varying acoustic events.
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Affiliation(s)
- Matthew Heard
- School of Behavioral and Brain Sciences, University of Texas at Dallas, United States
| | - Xiangrui Li
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, United States
| | - Yune S Lee
- School of Behavioral and Brain Sciences, University of Texas at Dallas, United States; Center for BrainHealth, University of Texas at Dallas, United States.
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4
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Seidel P, Levine SM, Tahedl M, Schwarzbach JV. Temporal Signal-to-Noise Changes in Combined Multislice- and In-Plane-Accelerated Echo-Planar Imaging with a 20- and 64-Channel Coil. Sci Rep 2020; 10:5536. [PMID: 32218476 PMCID: PMC7099092 DOI: 10.1038/s41598-020-62590-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 03/17/2020] [Indexed: 11/08/2022] Open
Abstract
Echo-planar imaging (EPI) is the most common method of functional MRI for acquiring the blood oxygenation level-dependent (BOLD) contrast, allowing the acquisition of an entire brain volume within seconds. However, because imaging protocols are limited by hardware (e.g., fast gradient switching), researchers must compromise between spatial resolution, temporal resolution, or whole-brain coverage. Earlier attempts to circumvent this problem included developing protocols in which slices of a volume were acquired faster (i.e., in-plane acceleration (S)) or simultaneously (i.e., multislice acceleration (M)). However, applying acceleration methods can lead to a reduction in the temporal signal-to-noise ratio (tSNR): a critical measure of signal stability over time. Using a 20- and 64-channel receiver coil, we show that enabling S-acceleration consistently yielded a substantial decrease in tSNR, regardless of the receiver coil, whereas M-acceleration yielded less pronounced tSNR decrease. Moreover, tSNR losses tended to occur in temporal, insular, and medial brain regions and were more noticeable with the 20-channel coil, while with the 64-channel coil, the tSNR in lateral frontoparietal regions remained relatively stable up to six-fold M-acceleration producing comparable tSNR to that of no acceleration. Such methodological explorations can guide researchers and clinicians in optimizing imaging protocols depending on the brain regions under investigation.
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Affiliation(s)
- Philipp Seidel
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
- Faculty of Psychology and Education Sciences, University of Coimbra, Coimbra, Portugal
| | - Seth M Levine
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Marlene Tahedl
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Jens V Schwarzbach
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany.
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Bhandari R, Kirilina E, Caan M, Suttrup J, De Sanctis T, De Angelis L, Keysers C, Gazzola V. Does higher sampling rate (multiband + SENSE) improve group statistics - An example from social neuroscience block design at 3T. Neuroimage 2020; 213:116731. [PMID: 32173409 PMCID: PMC7181191 DOI: 10.1016/j.neuroimage.2020.116731] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 02/27/2020] [Accepted: 03/09/2020] [Indexed: 02/06/2023] Open
Abstract
Multiband (MB) or Simultaneous multi-slice (SMS) acquisition schemes allow the acquisition of MRI signals from more than one spatial coordinate at a time. Commercial availability has brought this technique within the reach of many neuroscientists and psychologists. Most early evaluation of the performance of MB acquisition employed resting state fMRI or the most basic tasks. In this study, we tested whether the advantages of using MB acquisition schemes generalize to group analyses using a cognitive task more representative of typical cognitive neuroscience applications. Twenty-three subjects were scanned on a Philips 3 T scanner using five sequences, up to eight-fold acceleration with MB-factors 1 to 4, SENSE factors up to 2 and corresponding TRs of 2.45s down to 0.63s, while they viewed (i) movie blocks showing complex actions with hand object interactions and (ii) control movie blocks without hand object interaction. Data were processed using a widely used analysis pipeline implemented in SPM12 including the unified segmentation and canonical HRF modelling. Using random effects group-level, voxel-wise analysis we found that all sequences were able to detect the basic action observation network known to be recruited by our task. The highest t-values were found for sequences with MB4 acceleration. For the MB1 sequence, a 50% bigger voxel volume was needed to reach comparable t-statistics. The group-level t-values for resting state networks (RSNs) were also highest for MB4 sequences. Here the MB1 sequence with larger voxel size did not perform comparable to the MB4 sequence. Altogether, we can thus recommend the use of MB4 (and SENSE 1.5 or 2) on a Philips scanner when aiming to perform group-level analyses using cognitive block design fMRI tasks and voxel sizes in the range of cortical thickness (e.g. 2.7 mm isotropic). While results will not be dramatically changed by the use of multiband, our results suggest that MB will bring a moderate but significant benefit.
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Affiliation(s)
- Ritu Bhandari
- Netherlands Institute for Neuroscience, KNAW, Amsterdam, the Netherlands.
| | - Evgeniya Kirilina
- Center for Cognitive Neuroscience, Free University, Berlin, Germany; Max Plank Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Matthan Caan
- Spinoza Centre for Neuroimaging, Amsterdam, the Netherlands; Amsterdam UMC, University of Amsterdam, Biomedical Engineering & Physics, Amsterdam, the Netherlands
| | - Judith Suttrup
- Netherlands Institute for Neuroscience, KNAW, Amsterdam, the Netherlands
| | - Teresa De Sanctis
- Netherlands Institute for Neuroscience, KNAW, Amsterdam, the Netherlands
| | - Lorenzo De Angelis
- Netherlands Institute for Neuroscience, KNAW, Amsterdam, the Netherlands
| | - Christian Keysers
- Netherlands Institute for Neuroscience, KNAW, Amsterdam, the Netherlands; Department of Psychology, University of Amsterdam, the Netherlands
| | - Valeria Gazzola
- Netherlands Institute for Neuroscience, KNAW, Amsterdam, the Netherlands; Department of Psychology, University of Amsterdam, the Netherlands.
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6
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Aquino KM, Sokoliuk R, Pakenham DO, Sanchez-Panchuelo RM, Hanslmayr S, Mayhew SD, Mullinger KJ, Francis ST. Addressing challenges of high spatial resolution UHF fMRI for group analysis of higher-order cognitive tasks: An inter-sensory task directing attention between visual and somatosensory domains. Hum Brain Mapp 2018; 40:1298-1316. [PMID: 30430706 PMCID: PMC6865556 DOI: 10.1002/hbm.24450] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Revised: 10/05/2018] [Accepted: 10/12/2018] [Indexed: 01/20/2023] Open
Abstract
Functional MRI at ultra‐high field (UHF, ≥7 T) provides significant increases in BOLD contrast‐to‐noise ratio (CNR) compared with conventional field strength (3 T), and has been exploited for reduced field‐of‐view, high spatial resolution mapping of primary sensory areas. Applying these high spatial resolution methods to investigate whole brain functional responses to higher‐order cognitive tasks leads to a number of challenges, in particular how to perform robust group‐level statistical analyses. This study addresses these challenges using an inter‐sensory cognitive task which modulates top‐down attention at graded levels between the visual and somatosensory domains. At the individual level, highly focal functional activation to the task and task difficulty (modulated by attention levels) were detectable due to the high CNR at UHF. However, to assess group level effects, both anatomical and functional variability must be considered during analysis. We demonstrate the importance of surface over volume normalisation and the requirement of no spatial smoothing when assessing highly focal activity. Using novel group analysis on anatomically parcellated brain regions, we show that in higher cognitive areas (parietal and dorsal‐lateral‐prefrontal cortex) fMRI responses to graded attention levels were modulated quadratically, whilst in visual cortex and VIP, responses were modulated linearly. These group fMRI responses were not seen clearly using conventional second‐level GLM analyses, illustrating the limitations of a conventional approach when investigating such focal responses in higher cognitive regions which are more anatomically variable. The approaches demonstrated here complement other advanced analysis methods such as multivariate pattern analysis, allowing UHF to be fully exploited in cognitive neuroscience.
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Affiliation(s)
- Kevin M Aquino
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom.,Brain and Mental Health Laboratory, Monash University, Clayton, Australia.,School of Physics, University of Sydney, Sydney, Australia
| | - Rodika Sokoliuk
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Daisie O Pakenham
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Rosa Maria Sanchez-Panchuelo
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Simon Hanslmayr
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Stephen D Mayhew
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Karen J Mullinger
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom.,Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Susan T Francis
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
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Temporal multivariate pattern analysis (tMVPA): A single trial approach exploring the temporal dynamics of the BOLD signal. J Neurosci Methods 2018; 308:74-87. [PMID: 29969602 PMCID: PMC6447290 DOI: 10.1016/j.jneumeth.2018.06.029] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Revised: 06/29/2018] [Accepted: 06/29/2018] [Indexed: 01/09/2023]
Abstract
Background: fMRI provides spatial resolution that is unmatched by non-invasive neuroimaging techniques. Its temporal dynamics however are typically neglected due to the sluggishness of the hemodynamic signal. New Methods: We present temporal multivariate pattern analysis (tMVPA), a method for investigating the temporal evolution of neural representations in fMRI data, computed on single-trial BOLD time-courses, leveraging both spatial and temporal components of the fMRI signal. We implemented an expanding sliding window approach that allows identifying the time-window of an effect. Results: We demonstrate that tMVPA can successfully detect condition-specific multivariate modulations over time, in the absence of mean BOLD amplitude differences. Using Monte-Carlo simulations and synthetic data, we quantified family-wise error rate (FWER) and statistical power. Both at the group and single-subject levels, FWER was either at or significantly below 5%. We reached the desired power with 18 subjects and 12 trials for the group level, and with 14 trials in the single-subject scenario. Comparison with existing methods: We compare the tMVPA statistical evaluation to that of a linear support vector machine (SVM). SVM outperformed tMVPA with large N and trial numbers. Conversely, tMVPA, leveraging on single trials analyses, outperformed SVM in low N and trials and in a single-subject scenario. Conclusion: Recent evidence suggesting that the BOLD signal carries finer-grained temporal information than previously thought, advocates the need for analytical tools, such as tMVPA, tailored to investigate BOLD temporal dynamics. The comparable performance between tMVPA and SVM, a powerful and reliable tool for fMRI, supports the validity of our technique.
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8
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Janssen N, Hernández-Cabrera JA, Foronda LE. Improving the signal detection accuracy of functional Magnetic Resonance Imaging. Neuroimage 2018; 176:92-109. [PMID: 29655939 DOI: 10.1016/j.neuroimage.2018.01.076] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2017] [Revised: 11/27/2017] [Accepted: 01/29/2018] [Indexed: 10/17/2022] Open
Abstract
A major drawback of functional Magnetic Resonance Imaging (fMRI) concerns the lack of detection accuracy of the measured signal. Although this limitation stems in part from the neuro-vascular nature of the fMRI signal, it also reflects particular methodological decisions in the fMRI data analysis pathway. Here we show that the signal detection accuracy of fMRI is affected by the specific way in which whole-brain volumes are created from individually acquired brain slices, and by the method of statistically extracting signals from the sampled data. To address these limitations, we propose a new framework for fMRI data analysis. The new framework creates whole-brain volumes from individual brain slices that are all acquired at the same point in time relative to a presented stimulus. These whole-brain volumes contain minimal temporal distortions, and are available at a high temporal resolution. In addition, statistical signal extraction occurred on the basis of a non-standard time point-by-time point approach. We evaluated the detection accuracy of the extracted signal in the standard and new framework with simulated and real-world fMRI data. The new slice-based data-analytic framework yields greatly improved signal detection accuracy of fMRI signals.
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Affiliation(s)
- Niels Janssen
- Psychology Department, Universidad de la Laguna, Tenerife, Spain; Institute of Biomedical Technologies, Universidad de la Laguna, Tenerife, Spain; Institute of Neurosciences, Universidad de La Laguna, Tenerife, Spain.
| | - Juan A Hernández-Cabrera
- Psychology Department, Universidad de la Laguna, Tenerife, Spain; Basque Center on Cognition, Brain and Language, San Sebastián, Spain
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Vu AT, Beckett A, Setsompop K, Feinberg DA. Evaluation of SLIce Dithered Enhanced Resolution Simultaneous MultiSlice (SLIDER-SMS) for human fMRI. Neuroimage 2018; 164:164-171. [PMID: 28185951 PMCID: PMC5547021 DOI: 10.1016/j.neuroimage.2017.02.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2016] [Revised: 01/19/2017] [Accepted: 02/01/2017] [Indexed: 11/22/2022] Open
Abstract
High isotropic resolution fMRI is challenging primarily due to long repetition times (TR) and insufficient SNR, especially at lower field strengths. Recently, Simultaneous Multi-Slice (SMS) imaging with blipped-CAIPI has substantially reduced scan time and improved SNR efficiency of fMRI. Similarly, super-resolution techniques utilizing sub- voxel spatial shifts in the slice direction have increased both resolution and SNR efficiency. Here we demonstrate the synergistic combination of SLIce Dithered Enhanced Resolution (SLIDER) and SMS for high-resolution, high-SNR whole brain fMRI in comparison to standard resolution fMRI data as well as high-resolution data. With SLIDER-SMS, high spatial frequency information is recovered (unaliased) even in absence of super-resolution deblurring algorithms. Additionally we find that BOLD CNR (as measured by t-value in a visual checkerboard paradigm) is improved by as much as 100% relative to traditionally acquired high- resolution data. Using this gain in CNR, we are able to obtain unprecedented nominally isotropic resolutions at 3T (0.66 mm) and 7T (0.45 mm).
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Affiliation(s)
- An T Vu
- San Francisco VA Health Care System, Center for Imaging of Neurodegenerative Disease, San Francisco, CA, United States; University of California, Berkeley, Berkeley, CA, United States; Advanced MRI Technologies, Sebastopol, CA, United States.
| | - Alex Beckett
- University of California, Berkeley, Berkeley, CA, United States; Advanced MRI Technologies, Sebastopol, CA, United States
| | - Kawin Setsompop
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital Charlestown, MA, United States
| | - David A Feinberg
- University of California, Berkeley, Berkeley, CA, United States; Advanced MRI Technologies, Sebastopol, CA, United States
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10
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De Martino F, Yacoub E, Kemper V, Moerel M, Uludağ K, De Weerd P, Ugurbil K, Goebel R, Formisano E. The impact of ultra-high field MRI on cognitive and computational neuroimaging. Neuroimage 2017; 168:366-382. [PMID: 28396293 DOI: 10.1016/j.neuroimage.2017.03.060] [Citation(s) in RCA: 73] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2016] [Revised: 03/20/2017] [Accepted: 03/29/2017] [Indexed: 01/14/2023] Open
Abstract
The ability to measure functional brain responses non-invasively with ultra high field MRI (7 T and above) represents a unique opportunity in advancing our understanding of the human brain. Compared to lower fields (3 T and below), ultra high field MRI has an increased sensitivity, which can be used to acquire functional images with greater spatial resolution, and greater specificity of the blood oxygen level dependent (BOLD) signal to the underlying neuronal responses. Together, increased resolution and specificity enable investigating brain functions at a submillimeter scale, which so far could only be done with invasive techniques. At this mesoscopic spatial scale, perception, cognition and behavior can be probed at the level of fundamental units of neural computations, such as cortical columns, cortical layers, and subcortical nuclei. This represents a unique and distinctive advantage that differentiates ultra high from lower field imaging and that can foster a tighter link between fMRI and computational modeling of neural networks. So far, functional brain mapping at submillimeter scale has focused on the processing of sensory information and on well-known systems for which extensive information is available from invasive recordings in animals. It remains an open challenge to extend this methodology to uniquely human functions and, more generally, to systems for which animal models may be problematic. To succeed, the possibility to acquire high-resolution functional data with large spatial coverage, the availability of computational models of neural processing as well as accurate biophysical modeling of neurovascular coupling at mesoscopic scale all appear necessary.
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Affiliation(s)
- Federico De Martino
- Department of Cognitive Neurosciences, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55, 6229 ER Maastricht, The Netherlands; Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, 2021 sixth street SE, 55455 Minneapolis, MN, USA.
| | - Essa Yacoub
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, 2021 sixth street SE, 55455 Minneapolis, MN, USA
| | - Valentin Kemper
- Department of Cognitive Neurosciences, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55, 6229 ER Maastricht, The Netherlands
| | - Michelle Moerel
- Department of Cognitive Neurosciences, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55, 6229 ER Maastricht, The Netherlands; Maastricht Center for System Biology, Maastricht University, Universiteitssingel 60, 6229 ER Maastricht, The Netherlands
| | - Kâmil Uludağ
- Department of Cognitive Neurosciences, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55, 6229 ER Maastricht, The Netherlands
| | - Peter De Weerd
- Department of Cognitive Neurosciences, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55, 6229 ER Maastricht, The Netherlands
| | - Kamil Ugurbil
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, 2021 sixth street SE, 55455 Minneapolis, MN, USA
| | - Rainer Goebel
- Department of Cognitive Neurosciences, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55, 6229 ER Maastricht, The Netherlands
| | - Elia Formisano
- Department of Cognitive Neurosciences, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55, 6229 ER Maastricht, The Netherlands; Maastricht Center for System Biology, Maastricht University, Universiteitssingel 60, 6229 ER Maastricht, The Netherlands
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11
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Rogers TT, Wolmetz M. Conceptual knowledge representation: A cross-section of current research. Cogn Neuropsychol 2016; 33:121-9. [PMID: 27454108 DOI: 10.1080/02643294.2016.1188066] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
How is conceptual knowledge encoded in the brain? This special issue of Cognitive Neuropsychology takes stock of current efforts to answer this question through a variety of methods and perspectives. Across this work, three questions recur, each fundamental to knowledge representation in the mind and brain. First, what are the elements of conceptual representation? Second, to what extent are conceptual representations embodied in sensory and motor systems? Third, how are conceptual representations shaped by context, especially linguistic context? In this introductory article we provide relevant background on these themes and introduce how they are addressed by our contributing authors.
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Affiliation(s)
- Timothy T Rogers
- a Department of Psychology , University of Wisconsin , Madison , WI , USA
| | - Michael Wolmetz
- b Intelligent Systems Center , Johns Hopkins University Applied Physics Laboratory , Laurel , MD , USA
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