1
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Mazor M, Mukamel R. A Randomization-Based, Model-Free Approach to Functional Neuroimaging: A Proof of Concept. ENTROPY (BASEL, SWITZERLAND) 2024; 26:751. [PMID: 39330084 PMCID: PMC11431619 DOI: 10.3390/e26090751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2024] [Revised: 08/30/2024] [Accepted: 08/31/2024] [Indexed: 09/28/2024]
Abstract
Functional neuroimaging analysis takes noisy multidimensional measurements as input and produces statistical inferences regarding the functional properties of brain regions as output. Such inferences are most commonly model-based, in that they assume a model of how neural activity translates to the measured signal (blood oxygenation level-dependent signal in the case of functional MRI). The use of models increases statistical sensitivity and makes it possible to ask fine-grained theoretical questions. However, this comes at the cost of making theoretical assumptions about the underlying data-generating process. An advantage of model-free approaches is that they can be used in cases where model assumptions are known not to hold. To this end, we introduce a randomization-based, model-free approach to functional neuroimaging. TWISTER randomization makes it possible to infer functional selectivity from correlations between experimental runs. We provide a proof of concept in the form of a visuomotor mapping experiment and discuss the possible strengths and limitations of this new approach in light of our empirical results.
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Affiliation(s)
- Matan Mazor
- All Souls College, University of Oxford, Oxford OX1 4AL, UK
- School of Psychological Sciences, University of Oxford, Oxford OX1 2JD, UK
| | - Roy Mukamel
- Sagol School of Neuroscience, Tel-Aviv University, Tel Aviv 69978, Israel
- School of Psychological Sciences, Tel-Aviv University, Tel Aviv 69978, Israel
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2
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Kim JH, De Asis-Cruz J, Krishnamurthy D, Limperopoulos C. Toward a more informative representation of the fetal-neonatal brain connectome using variational autoencoder. eLife 2023; 12:e80878. [PMID: 37184067 PMCID: PMC10241511 DOI: 10.7554/elife.80878] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 05/09/2023] [Indexed: 05/16/2023] Open
Abstract
Recent advances in functional magnetic resonance imaging (fMRI) have helped elucidate previously inaccessible trajectories of early-life prenatal and neonatal brain development. To date, the interpretation of fetal-neonatal fMRI data has relied on linear analytic models, akin to adult neuroimaging data. However, unlike the adult brain, the fetal and newborn brain develops extraordinarily rapidly, far outpacing any other brain development period across the life span. Consequently, conventional linear computational models may not adequately capture these accelerated and complex neurodevelopmental trajectories during this critical period of brain development along the prenatal-neonatal continuum. To obtain a nuanced understanding of fetal-neonatal brain development, including nonlinear growth, for the first time, we developed quantitative, systems-wide representations of brain activity in a large sample (>500) of fetuses, preterm, and full-term neonates using an unsupervised deep generative model called variational autoencoder (VAE), a model previously shown to be superior to linear models in representing complex resting-state data in healthy adults. Here, we demonstrated that nonlinear brain features, that is, latent variables, derived with the VAE pretrained on rsfMRI of human adults, carried important individual neural signatures, leading to improved representation of prenatal-neonatal brain maturational patterns and more accurate and stable age prediction in the neonate cohort compared to linear models. Using the VAE decoder, we also revealed distinct functional brain networks spanning the sensory and default mode networks. Using the VAE, we are able to reliably capture and quantify complex, nonlinear fetal-neonatal functional neural connectivity. This will lay the critical foundation for detailed mapping of healthy and aberrant functional brain signatures that have their origins in fetal life.
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Affiliation(s)
- Jung-Hoon Kim
- Developing Brain Institute, Children's National HospitalWashingtonUnited States
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3
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Wu Y, Zhang Y, Mao Y, Feng K, Wei D, Song L. Reconstructing sources location of visual color cortex by the task-irrelevant visual stimuli through machine learning decoding. Heliyon 2022; 8:e12287. [PMID: 36582686 PMCID: PMC9792758 DOI: 10.1016/j.heliyon.2022.e12287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 10/15/2022] [Accepted: 12/05/2022] [Indexed: 12/23/2022] Open
Abstract
Visual color sensing is generated by electrical discharges from endocranial neuronal sources that penetrate the skull and reach to the cerebral cortex. However, the space location of the source generated by this neural mechanism remains elusive. In this paper, we emulate the generation of visual color signal by task-irrelevant stimuli to activate brain neurons, where its consequences over the cerebral cortex is experimentally tracked. We first document the changes to brain color sensing using electroencephalography (EEG), and find that the sensing classification accuracy of primary visual cortex (V1) regions was positively correlated with the space correlation of visual evoked potential (VEP) power distribution under machine learning decoding. We then explore the decoded results to trace the brain activity neural source location of EEG inversion problem and assess its reconstructive possibility. We show that visual color EEG in V1 can reconstruct endocranial neuronal source location, through the machine learning decoding of channel location.
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Affiliation(s)
- Yijia Wu
- Academy for Engineering & Technology, Fudan University, Shang Hai, China,Shanghai East-bund Institute on Networking Systems of AI, Shang Hai, China,Corresponding author.
| | - Yanni Zhang
- Shanghai East-bund Institute on Networking Systems of AI, Shang Hai, China
| | - Yanjing Mao
- Academy for Engineering & Technology, Fudan University, Shang Hai, China
| | - Kaiqiang Feng
- Academy for Engineering & Technology, Fudan University, Shang Hai, China
| | - Donglai Wei
- Academy for Engineering & Technology, Fudan University, Shang Hai, China
| | - Liang Song
- Academy for Engineering & Technology, Fudan University, Shang Hai, China,Shanghai East-bund Institute on Networking Systems of AI, Shang Hai, China
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4
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Himmelberg MM, Gardner JL, Winawer J. What has vision science taught us about functional MRI? Neuroimage 2022; 261:119536. [PMID: 35931310 PMCID: PMC9756767 DOI: 10.1016/j.neuroimage.2022.119536] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 07/21/2022] [Accepted: 08/02/2022] [Indexed: 10/31/2022] Open
Abstract
In the domain of human neuroimaging, much attention has been paid to the question of whether and how the development of functional magnetic resonance imaging (fMRI) has advanced our scientific knowledge of the human brain. However, the opposite question is also important; how has our knowledge of the brain advanced our understanding of fMRI? Here, we discuss how and why scientific knowledge about the human and animal visual system has been used to answer fundamental questions about fMRI as a brain measurement tool and how these answers have contributed to scientific discoveries beyond vision science.
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Affiliation(s)
- Marc M Himmelberg
- Department of Psychology, New York University, NY, USA; Center for Neural Science, New York University, NY, USA.
| | | | - Jonathan Winawer
- Department of Psychology, New York University, NY, USA; Center for Neural Science, New York University, NY, USA
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5
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Zhao Y, Gao Y, Zu Z, Li M, Schilling KG, Anderson AW, Ding Z, Gore JC. Detection of functional activity in brain white matter using fiber architecture informed synchrony mapping. Neuroimage 2022; 258:119399. [PMID: 35724855 PMCID: PMC9388229 DOI: 10.1016/j.neuroimage.2022.119399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 06/13/2022] [Accepted: 06/17/2022] [Indexed: 01/12/2023] Open
Abstract
A general linear model is widely used for analyzing fMRI data, in which the blood oxygenation-level dependent (BOLD) signals in gray matter (GM) evoked in response to neural stimulation are modeled by convolving the time course of the expected neural activity with a canonical hemodynamic response function (HRF) obtained a priori. The maps of brain activity produced reflect the magnitude of local BOLD responses. However, detecting BOLD signals in white matter (WM) is more challenging as the BOLD signals are weaker and the HRF is different, and may vary more across the brain. Here we propose a model-free approach to detect changes in BOLD signals in WM by measuring task-evoked increases of BOLD signal synchrony in WM fibers. The proposed approach relies on a simple assumption that, in response to a functional task, BOLD signals in relevant fibers are modulated by stimulus-evoked neural activity and thereby show greater synchrony than when measured in a resting state, even if their magnitudes do not change substantially. This approach is implemented in two technical stages. First, for each voxel a fiber-architecture-informed spatial window is created with orientation distribution functions constructed from diffusion imaging data. This provides the basis for defining neighborhoods in WM that share similar local fiber architectures. Second, a modified principal component analysis (PCA) is used to estimate the synchrony of BOLD signals in each spatial window. The proposed approach is validated using a 3T fMRI dataset from the Human Connectome Project (HCP) at a group level. The results demonstrate that neural activity can be reliably detected as increases in fMRI signal synchrony within WM fibers that are engaged in a task with high sensitivities and reproducibility.
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Affiliation(s)
- Yu Zhao
- Vanderbilt University Institute of Imaging Science, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, United States.
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, United States,Department of Biomedical Engineering, Vanderbilt University, United States
| | - Zhongliang Zu
- Vanderbilt University Institute of Imaging Science, United States,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, United States
| | - Muwei Li
- Vanderbilt University Institute of Imaging Science, United States,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, United States
| | - Kurt G. Schilling
- Vanderbilt University Institute of Imaging Science, United States,Department of Biomedical Engineering, Vanderbilt University, United States
| | - Adam W. Anderson
- Vanderbilt University Institute of Imaging Science, United States,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, United States,Department of Biomedical Engineering, Vanderbilt University, United States
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, United States; Department of Biomedical Engineering, Vanderbilt University, United States; Department of Electrical and Computer Engineering, Vanderbilt University, United States.
| | - John C. Gore
- Vanderbilt University Institute of Imaging Science, United States,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, United States,Department of Biomedical Engineering, Vanderbilt University, United States,Department of Molecular Physiology and Biophysics, Vanderbilt University, United States,Department of Physics and Astronomy, Vanderbilt University, United States
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6
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Taylor AJ, Kim JH, Ress D. Temporal stability of the hemodynamic response function across the majority of human cerebral cortex. Hum Brain Mapp 2022; 43:4924-4942. [PMID: 35965416 PMCID: PMC9582369 DOI: 10.1002/hbm.26047] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 07/13/2022] [Accepted: 07/25/2022] [Indexed: 12/23/2022] Open
Abstract
The hemodynamic response function (HRF) measured with functional magnetic resonance imaging is generated by vascular and metabolic responses evoked by brief (<4 s) stimuli. It is known that the human HRF varies across cortex, between subjects, with stimulus paradigms, and even between different measurements in the same cortical location. However, our results demonstrate that strong HRFs are remarkably repeatable across sessions separated by time intervals up to 3 months. In this study, a multisensory stimulus was used to activate and measure the HRF across the majority of cortex (>70%, with lesser reliability observed in some areas of prefrontal cortex). HRFs were measured with high spatial resolution (2‐mm voxels) in central gray matter to minimize variations caused by partial‐volume effects. HRF amplitudes and temporal dynamics were highly repeatable across four sessions in 20 subjects. Positive and negative HRFs were consistently observed across sessions and subjects. Negative HRFs were generally weaker and, thus, more variable than positive HRFs. Statistical measurements showed that across‐session variability is highly correlated to the variability across events within a session; these measurements also indicated a normal distribution of variability across cortex. The overall repeatability of the HRFs over long time scales generally supports the long‐term use of event‐related functional magnetic resonance imaging protocols.
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Affiliation(s)
- Amanda J Taylor
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas, USA
| | - Jung Hwan Kim
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas, USA
| | - David Ress
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas, USA
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7
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Stickland RC, Zvolanek KM, Moia S, Caballero-Gaudes C, Bright MG. Lag-Optimized Blood Oxygenation Level Dependent Cerebrovascular Reactivity Estimates Derived From Breathing Task Data Have a Stronger Relationship With Baseline Cerebral Blood Flow. Front Neurosci 2022; 16:910025. [PMID: 35801183 PMCID: PMC9254683 DOI: 10.3389/fnins.2022.910025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 05/11/2022] [Indexed: 11/13/2022] Open
Abstract
Cerebrovascular reactivity (CVR), an important indicator of cerebrovascular health, is commonly studied with the Blood Oxygenation Level Dependent functional MRI (BOLD-fMRI) response to a vasoactive stimulus. Theoretical and empirical evidence suggests that baseline cerebral blood flow (CBF) modulates BOLD signal amplitude and may influence BOLD-CVR estimates. We address how acquisition and modeling choices affect the relationship between baseline cerebral blood flow (bCBF) and BOLD-CVR: whether BOLD-CVR is modeled with the inclusion of a breathing task, and whether BOLD-CVR amplitudes are optimized for hemodynamic lag effects. We assessed between-subject correlations of average GM values and within-subject spatial correlations across cortical regions. Our results suggest that a breathing task addition to a resting-state acquisition, alongside lag-optimization within BOLD-CVR modeling, can improve BOLD-CVR correlations with bCBF, both between- and within-subjects, likely because these CVR estimates are more physiologically accurate. We report positive correlations between bCBF and BOLD-CVR, both between- and within-subjects. The physiological explanation of this positive correlation is unclear; research with larger samples and tightly controlled vasoactive stimuli is needed. Insights into what drives variability in BOLD-CVR measurements and related measurements of cerebrovascular function are particularly relevant when interpreting results in populations with altered vascular and/or metabolic baselines or impaired cerebrovascular reserve.
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Affiliation(s)
- Rachael C. Stickland
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Kristina M. Zvolanek
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, United States
| | - Stefano Moia
- Basque Center on Cognition, Brain and Language, Donostia, Spain
- University of the Basque Country EHU/UPV, Donostia, Spain
| | | | - Molly G. Bright
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, United States
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8
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Hermann KL, Singh SR, Rosenthal IA, Pantazis D, Conway BR. Temporal dynamics of the neural representation of hue and luminance polarity. Nat Commun 2022; 13:661. [PMID: 35115511 PMCID: PMC8814185 DOI: 10.1038/s41467-022-28249-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 01/12/2022] [Indexed: 11/09/2022] Open
Abstract
Hue and luminance contrast are basic visual features. Here we use multivariate analyses of magnetoencephalography data to investigate the timing of the neural computations that extract them, and whether they depend on common neural circuits. We show that hue and luminance-contrast polarity can be decoded from MEG data and, with lower accuracy, both features can be decoded across changes in the other feature. These results are consistent with the existence of both common and separable neural mechanisms. The decoding time course is earlier and more temporally precise for luminance polarity than hue, a result that does not depend on task, suggesting that luminance contrast is an updating signal that separates visual events. Meanwhile, cross-temporal generalization is slightly greater for representations of hue compared to luminance polarity, providing a neural correlate of the preeminence of hue in perceptual grouping and memory. Finally, decoding of luminance polarity varies depending on the hues used to obtain training and testing data. The pattern of results is consistent with observations that luminance contrast is mediated by both L-M and S cone sub-cortical mechanisms.
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Affiliation(s)
- Katherine L Hermann
- Laboratory of Sensorimotor Research, National Eye Institute, Bethesda, MD, 20892, USA
- Department of Psychology, Stanford University, Stanford, CA, 94305, USA
| | - Shridhar R Singh
- Laboratory of Sensorimotor Research, National Eye Institute, Bethesda, MD, 20892, USA
| | - Isabelle A Rosenthal
- Laboratory of Sensorimotor Research, National Eye Institute, Bethesda, MD, 20892, USA
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Bevil R Conway
- Laboratory of Sensorimotor Research, National Eye Institute, Bethesda, MD, 20892, USA.
- National Institute of Mental Health, Bethesda, MD, 20892, USA.
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9
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Bobadilla-Suarez S, Jones M, Love BC. Robust priors for regularized regression. Cogn Psychol 2022; 132:101444. [PMID: 34861584 PMCID: PMC8903146 DOI: 10.1016/j.cogpsych.2021.101444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 10/26/2021] [Accepted: 10/29/2021] [Indexed: 11/15/2022]
Abstract
Induction benefits from useful priors. Penalized regression approaches, like ridge regression, shrink weights toward zero but zero association is usually not a sensible prior. Inspired by simple and robust decision heuristics humans use, we constructed non-zero priors for penalized regression models that provide robust and interpretable solutions across several tasks. Our approach enables estimates from a constrained model to serve as a prior for a more general model, yielding a principled way to interpolate between models of differing complexity. We successfully applied this approach to a number of decision and classification problems, as well as analyzing simulated brain imaging data. Models with robust priors had excellent worst-case performance. Solutions followed from the form of the heuristic that was used to derive the prior. These new algorithms can serve applications in data analysis and machine learning, as well as help in understanding how people transition from novice to expert performance.
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Affiliation(s)
| | - Matt Jones
- Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO 80309-0345, USA
| | - Bradley C Love
- Department of Experimental Psychology, University College London, 26 Bedford Way, London, WC1H 0AP, UK; The Alan Turing Institute, 96 Euston Road, London, NW1 2DB, UK
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10
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Cohen MX. A tutorial on generalized eigendecomposition for denoising, contrast enhancement, and dimension reduction in multichannel electrophysiology. Neuroimage 2021; 247:118809. [PMID: 34906717 DOI: 10.1016/j.neuroimage.2021.118809] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 11/20/2021] [Accepted: 12/10/2021] [Indexed: 10/19/2022] Open
Abstract
The goal of this paper is to present a theoretical and practical introduction to generalized eigendecomposition (GED), which is a robust and flexible framework used for dimension reduction and source separation in multichannel signal processing. In cognitive electrophysiology, GED is used to create spatial filters that maximize a researcher-specified contrast. For example, one may wish to exploit an assumption that different sources have different frequency content, or that sources vary in magnitude across experimental conditions. GED is fast and easy to compute, performs well in simulated and real data, and is easily adaptable to a variety of specific research goals. This paper introduces GED in a way that ties together myriad individual publications and applications of GED in electrophysiology, and provides sample MATLAB and Python code that can be tested and adapted. Practical considerations and issues that often arise in applications are discussed.
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Affiliation(s)
- Michael X Cohen
- Donders Centre for Medical Neuroscience, Radboud University Medical Center, the Netherlands.
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11
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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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12
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Wu GR, Colenbier N, Van Den Bossche S, Clauw K, Johri A, Tandon M, Marinazzo D. rsHRF: A toolbox for resting-state HRF estimation and deconvolution. Neuroimage 2021; 244:118591. [PMID: 34560269 DOI: 10.1016/j.neuroimage.2021.118591] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 06/25/2021] [Accepted: 09/16/2021] [Indexed: 10/20/2022] Open
Abstract
The hemodynamic response function (HRF) greatly influences the intra- and inter-subject variability of brain activation and connectivity, and might confound the estimation of temporal precedence in connectivity analyses, making its estimation necessary for a correct interpretation of neuroimaging studies. Additionally, the HRF shape itself is a useful local measure. However, most algorithms for HRF estimation are specific for task-related fMRI data, and only a few can be directly applied to resting-state protocols. Here we introduce rsHRF, a Matlab and Python toolbox that implements HRF estimation and deconvolution from the resting-state BOLD signal. We first provide an overview of the main algorithm, practical implementations, and then demonstrate the feasibility and usefulness of rsHRF by validation experiments with a publicly available resting-state fMRI dataset. We also provide tools for statistical analyses and visualization. We believe that this toolbox may significantly contribute to a better analysis and understanding of the components and variability of BOLD signals.
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Affiliation(s)
- Guo-Rong Wu
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing 400715, China; Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium.
| | - Nigel Colenbier
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium; Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven 3001, Belgium; Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice 30126, Italy
| | - Sofie Van Den Bossche
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium
| | - Kenzo Clauw
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium
| | - Amogh Johri
- International Institute of Information Technology, Bangalore 560100, India
| | - Madhur Tandon
- Indraprastha Institute of Information Technology, Delhi 110020, India
| | - Daniele Marinazzo
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium; Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice 30126, Italy
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13
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Lin Y, Zhou X, Naya Y, Gardner JL, Sun P. Voxel-Wise Linearity Analysis of Increments and Decrements in BOLD Responses in Human Visual Cortex Using a Contrast Adaptation Paradigm. Front Hum Neurosci 2021; 15:541314. [PMID: 34531731 PMCID: PMC8439421 DOI: 10.3389/fnhum.2021.541314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Accepted: 08/09/2021] [Indexed: 11/13/2022] Open
Abstract
The linearity of BOLD responses is a fundamental presumption in most analysis procedures for BOLD fMRI studies. Previous studies have examined the linearity of BOLD signal increments, but less is known about the linearity of BOLD signal decrements. The present study assessed the linearity of both BOLD signal increments and decrements in the human primary visual cortex using a contrast adaptation paradigm. Results showed that both BOLD signal increments and decrements kept linearity to long stimuli (e.g., 3 s, 6 s), yet, deviated from linearity to transient stimuli (e.g., 1 s). Furthermore, a voxel-wise analysis showed that the deviation patterns were different for BOLD signal increments and decrements: while the BOLD signal increments demonstrated a consistent overestimation pattern, the patterns for BOLD signal decrements varied from overestimation to underestimation. Our results suggested that corrections to deviations from linearity of transient responses should consider the different effects of BOLD signal increments and decrements.
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Affiliation(s)
- Yun Lin
- Department of Psychology, School of Social Sciences, Tsinghua University, Beijing, China
| | - Xi Zhou
- Department of Psychology, School of Social Sciences, Tsinghua University, Beijing, China
| | - Yuji Naya
- School of Psychological and Cognitive Sciences, Peking University, Beijing, China
| | - Justin L Gardner
- Department of Psychology, Stanford University, Stanford, CA, United States
| | - Pei Sun
- Department of Psychology, School of Social Sciences, Tsinghua University, Beijing, China.,Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China.,Laboratory for Cognitive Brain Mapping, RIKEN Center for Brain Sciences, Wako, Japan
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14
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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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15
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Kassinopoulos M, Mitsis GD. Physiological noise modeling in fMRI based on the pulsatile component of photoplethysmograph. Neuroimage 2021; 242:118467. [PMID: 34390877 DOI: 10.1016/j.neuroimage.2021.118467] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 07/21/2021] [Accepted: 08/10/2021] [Indexed: 02/06/2023] Open
Abstract
The blood oxygenation level-dependent (BOLD) contrast mechanism allows the noninvasive monitoring of changes in deoxyhemoglobin content. As such, it is commonly used in functional magnetic resonance imaging (fMRI) to study brain activity since levels of deoxyhemoglobin are indirectly related to local neuronal activity through neurovascular coupling mechanisms. However, the BOLD signal is severely affected by physiological processes as well as motion. Due to this, several noise correction techniques have been developed to correct for the associated confounds. The present study focuses on cardiac pulsatility fMRI confounds, aiming to refine model-based techniques that utilize the photoplethysmograph (PPG) signal. Specifically, we propose a new technique based on convolution filtering, termed cardiac pulsatility model (CPM) and compare its performance with the cardiac-related RETROICOR (Card-RETROICOR), which is a technique commonly used to model fMRI fluctuations due to cardiac pulsatility. Further, we investigate whether variations in the amplitude of the PPG pulses (PPG-Amp) covary with variations in amplitude of pulse-related fMRI fluctuations, as well as with the systemic low frequency oscillations (SLFOs) component of the fMRI global signal (GS - defined as the mean signal across all gray matter voxels). Capitalizing on 3T fMRI data from the Human Connectome Project, CPM was found to explain a significantly larger fraction of the fMRI signal variance compared to Card-RETROICOR, particularly for subjects with larger heart rate variability during the scan. The amplitude of the fMRI pulse-related fluctuations did not covary with PPG-Amp; however, PPG-Amp explained significant variance in the GS that was not attributed to variations in heart rate or breathing patterns. Our results suggest that the proposed approach can model high-frequency fluctuations due to pulsation as well as low-frequency physiological fluctuations more accurately compared to model-based techniques commonly employed in fMRI studies.
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Affiliation(s)
- Michalis Kassinopoulos
- Graduate Program in Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada.
| | - Georgios D Mitsis
- Department of Bioengineering, McGill University, Montreal, QC, Canada
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16
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Zhang Q, Gheres KW, Drew PJ. Origins of 1/f-like tissue oxygenation fluctuations in the murine cortex. PLoS Biol 2021; 19:e3001298. [PMID: 34264930 PMCID: PMC8282088 DOI: 10.1371/journal.pbio.3001298] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 05/24/2021] [Indexed: 01/07/2023] Open
Abstract
The concentration of oxygen in the brain spontaneously fluctuates, and the distribution of power in these fluctuations has a 1/f-like spectra, where the power present at low frequencies of the power spectrum is orders of magnitude higher than at higher frequencies. Though these oscillations have been interpreted as being driven by neural activity, the origin of these 1/f-like oscillations is not well understood. Here, to gain insight of the origin of the 1/f-like oxygen fluctuations, we investigated the dynamics of tissue oxygenation and neural activity in awake behaving mice. We found that oxygen signal recorded from the cortex of mice had 1/f-like spectra. However, band-limited power in the local field potential did not show corresponding 1/f-like fluctuations. When local neural activity was suppressed, the 1/f-like fluctuations in oxygen concentration persisted. Two-photon measurements of erythrocyte spacing fluctuations and mathematical modeling show that stochastic fluctuations in erythrocyte flow could underlie 1/f-like dynamics in oxygenation. These results suggest that the discrete nature of erythrocytes and their irregular flow, rather than fluctuations in neural activity, could drive 1/f-like fluctuations in tissue oxygenation.
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Affiliation(s)
- Qingguang Zhang
- Center for Neural Engineering, Department of Engineering Science and Mechanics, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- * E-mail: (QZ); (PJD)
| | - Kyle W. Gheres
- Graduate Program in Molecular Cellular and Integrative Biosciences, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Patrick J. Drew
- Center for Neural Engineering, Department of Engineering Science and Mechanics, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- Department of Neurosurgery, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- * E-mail: (QZ); (PJD)
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17
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Iliski, a software for robust calculation of transfer functions. PLoS Comput Biol 2021; 17:e1008614. [PMID: 34125846 PMCID: PMC8224889 DOI: 10.1371/journal.pcbi.1008614] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 06/24/2021] [Accepted: 05/18/2021] [Indexed: 12/27/2022] Open
Abstract
Understanding the relationships between biological processes is paramount to unravel pathophysiological mechanisms. These relationships can be modeled with Transfer Functions (TFs), with no need of a priori hypotheses as to the shape of the transfer function. Here we present Iliski, a software dedicated to TFs computation between two signals. It includes different pre-treatment routines and TF computation processes: deconvolution, deterministic and non-deterministic optimization algorithms that are adapted to disparate datasets. We apply Iliski to data on neurovascular coupling, an ensemble of cellular mechanisms that link neuronal activity to local changes of blood flow, highlighting the software benefits and caveats in the computation and evaluation of TFs. We also propose a workflow that will help users to choose the best computation according to the dataset. Iliski is available under the open-source license CC BY 4.0 on GitHub (https://github.com/alike-aydin/Iliski) and can be used on the most common operating systems, either within the MATLAB environment, or as a standalone application. Iliski is a software helping the user to find the relationship between two sets of data, namely transfer functions. Although transfer functions are widely used in many scientific fields to link two signals, their computation can be tricky due to data features such as multisource noise, or to specific shape requirements imposed by the nature of the signals, e.g. in biological data. Iliski offers a user-friendly graphical interface to ease the computation of transfer functions for both experienced and users with no coding skills. It proposes several signal pre-processing methods and allows rapid testing of different computing approaches, either based on deconvolution or on optimization of multi-parametric functions. This article, combined with a User Manual, provides a detailed description of Iliski functionalities and a thorough description of the advantages and drawbacks of each computing method using experimental biological data. In the era of Big Data, scientists strive to find new models for patho-physiological mechanisms, and Iliski fulfils the requirements of rigorous, flexible, and fast data driven hypothesis testing.
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18
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Zhao Y, Liu P, Turner MP, Abdelkarim D, Lu H, Rypma B. The neural-vascular basis of age-related processing speed decline. Psychophysiology 2021; 58:e13845. [PMID: 34115388 DOI: 10.1111/psyp.13845] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 04/14/2021] [Accepted: 04/19/2021] [Indexed: 12/12/2022]
Abstract
Most studies examining neurocognitive aging are based on the blood-oxygen level-dependent signal obtained during functional magnetic resonance imaging (fMRI). The physiological basis of this signal is neural-vascular coupling, the process by which neurons signal cerebrovasculature to dilate in response to an increase in active neural metabolism due to stimulation. These fMRI studies of aging rely on the hemodynamic equivalence assumption that this process is not disrupted by physiologic deterioration associated with aging. Studies of neural-vascular coupling challenge this assumption and show that neural-vascular coupling is closely related to cognition. In this review, we put forward a theory of processing speed decline in aging and how it is related to age-related neural-vascular coupling changes based on the results of studies elucidating the relationships between cognition, cerebrovascular dynamics, and aging.
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Affiliation(s)
- Yuguang Zhao
- School of Behavioral and Brain Sciences, Center for Brain Health, University of Texas at Dallas, Richardson, TX, USA
| | - Peiying Liu
- School of Medicine, Department of Radiology, Johns Hopkins University, Baltimore, MD, USA
| | - Monroe P Turner
- School of Behavioral and Brain Sciences, Center for Brain Health, University of Texas at Dallas, Richardson, TX, USA
| | - Dema Abdelkarim
- School of Behavioral and Brain Sciences, Center for Brain Health, University of Texas at Dallas, Richardson, TX, USA
| | - Hanzhang Lu
- School of Medicine, Department of Radiology, Johns Hopkins University, Baltimore, MD, USA
| | - Bart Rypma
- School of Behavioral and Brain Sciences, Center for Brain Health, University of Texas at Dallas, Richardson, TX, USA
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19
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Hütel M, Antonelli M, Melbourne A, Ourselin S. Hemodynamic matrix factorization for functional magnetic resonance imaging. Neuroimage 2021; 231:117814. [PMID: 33549748 PMCID: PMC8210649 DOI: 10.1016/j.neuroimage.2021.117814] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 01/10/2021] [Accepted: 01/24/2021] [Indexed: 11/30/2022] Open
Abstract
The General Linear Model (GLM) used in task-fMRI relates activated brain areas to extrinsic task conditions. The translation of resulting neural activation into a hemodynamic response is commonly approximated with a linear convolution model using a hemodynamic response function (HRF). There are two major limitations in GLM analysis. Firstly, the GLM assumes that neural activation is either on or off and matches the exact stimulus duration in the corresponding task timings. Secondly, brain networks observed in resting-state fMRI experiments present also during task experiments, but the GLM approach models these task-unrelated brain activity as noise. A novel kernel matrix factorization approach, called hemodynamic matrix factorization (HMF), is therefore proposed that addresses both limitations by assuming that task-related and task-unrelated brain activity can be modeled with the same convolution model as in GLM analysis. By contrast to the GLM, the proposed HMF is a blind source separation (BSS) technique, which decomposes fMRI data into modes. Each mode comprises of a neural activation time course and a spatial mapping. Two versions of HMF are proposed in which the neural activation time course of each mode is convolved with either the canonical HRF or predetermined subject-specific HRFs. Firstly, HMF with the canonical HRF is applied to two open-source cohorts. These cohorts comprise of several task experiments including motor, incidental memory, spatial coherence discrimination, verbal discrimination task and a very short localization task, engaging multiple parts of the eloquent cortex. HMF modes were obtained whose neural activation time course followed original task timings and whose corresponding spatial map matched cortical areas known to be involved in the respective task processing. Secondly, the alignment of these neural activation time courses to task timings were further improved by replacing the canonical HRF with subject-specific HRFs during HMF mode computation. In addition to task-related modes, HMF also produced seemingly task-unrelated modes whose spatial maps matched known resting-state networks. The validity of a fMRI task experiment relies on the assumption that the exposure to a stimulus for a given time causes an imminent increase in neural activation of equal duration. The proposed HMF is an attempt to falsify this assumption and allows to identify subject task participation that does not comply with the experiment instructions.
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Affiliation(s)
- Michael Hütel
- Department of Medical Physics and Biomedical Engineering, UCL, United Kingdom; School of Biomedical Engineering & Imaging Sciences, KCL, United Kingdom.
| | - Michela Antonelli
- School of Biomedical Engineering & Imaging Sciences, KCL, United Kingdom
| | - Andrew Melbourne
- School of Biomedical Engineering & Imaging Sciences, KCL, United Kingdom
| | - Sebastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, KCL, United Kingdom
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20
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van Dijk JA, Fracasso A, Petridou N, Dumoulin SO. Validating Linear Systems Analysis for Laminar fMRI: Temporal Additivity for Stimulus Duration Manipulations. Brain Topogr 2021; 34:88-101. [PMID: 33210193 PMCID: PMC7803719 DOI: 10.1007/s10548-020-00808-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 11/05/2020] [Indexed: 11/26/2022]
Abstract
Advancements in ultra-high field (7 T and higher) magnetic resonance imaging (MRI) scanners have made it possible to investigate both the structure and function of the human brain at a sub-millimeter scale. As neuronal feedforward and feedback information arrives in different layers, sub-millimeter functional MRI has the potential to uncover information processing between cortical micro-circuits across cortical depth, i.e. laminar fMRI. For nearly all conventional fMRI analyses, the main assumption is that the relationship between local neuronal activity and the blood oxygenation level dependent (BOLD) signal adheres to the principles of linear systems theory. For laminar fMRI, however, directional blood pooling across cortical depth stemming from the anatomy of the cortical vasculature, potentially violates these linear system assumptions, thereby complicating analysis and interpretation. Here we assess whether the temporal additivity requirement of linear systems theory holds for laminar fMRI. We measured responses elicited by viewing stimuli presented for different durations and evaluated how well the responses to shorter durations predicted those elicited by longer durations. We find that BOLD response predictions are consistently good predictors for observed responses, across all cortical depths, and in all measured visual field maps (V1, V2, and V3). Our results suggest that the temporal additivity assumption for linear systems theory holds for laminar fMRI. We thus show that the temporal additivity assumption holds across cortical depth for sub-millimeter gradient-echo BOLD fMRI in early visual cortex.
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Affiliation(s)
- Jelle A van Dijk
- Spinoza Centre for Neuroimaging, Amsterdam, The Netherlands.
- Experimental Psychology, Utrecht University, Utrecht, The Netherlands.
| | - Alessio Fracasso
- Spinoza Centre for Neuroimaging, Amsterdam, The Netherlands
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, G12 8QB, UK
- Radiology Department, Imaging Division, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Natalia Petridou
- Radiology Department, Imaging Division, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Serge O Dumoulin
- Spinoza Centre for Neuroimaging, Amsterdam, The Netherlands
- Experimental Psychology, Utrecht University, Utrecht, The Netherlands
- Experimental and Applied Psychology, VU University, Amsterdam, The Netherlands
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21
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Oliveira ÍAF, van der Zwaag W, Raimondo L, Dumoulin SO, Siero JCW. Comparing hand movement rate dependence of cerebral blood volume and BOLD responses at 7T. Neuroimage 2020; 226:117623. [PMID: 33301935 DOI: 10.1016/j.neuroimage.2020.117623] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 11/06/2020] [Accepted: 11/27/2020] [Indexed: 11/17/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) based on the Blood Oxygenation Level Dependent (BOLD) contrast takes advantage of the coupling between neuronal activity and the hemodynamics to allow a non-invasive localisation of the neuronal activity. In general, fMRI experiments assume a linear relationship between neuronal activation and the observed hemodynamics. However, the relationship between BOLD responses, neuronal activity, and behaviour are often nonlinear. In addition, the nonlinearity between BOLD responses and behaviour may be related to neuronal process rather than a neurovascular uncoupling. Further, part of the nonlinearity may be driven by vascular nonlinearity effects in particular from large vessel contributions. fMRI based on cerebral blood volume (CBV), promises a higher microvascular specificity, potentially without vascular nonlinearity effects and reduced contamination of the large draining vessels compared to BOLD. In this study, we aimed to investigate differences in BOLD and VASO-CBV signal changes during a hand movement task over a broad range of movement rates. We used a double readout 3D-EPI sequence at 7T to simultaneously measure VASO-CBV and BOLD responses in the sensorimotor cortex. The measured BOLD and VASO-CBV responses increased very similarly in a nonlinear fashion, plateauing for movement rates larger than 1 Hz. Our findings show a tight relationship between BOLD and VASO-CBV responses, indicating that the overall interplay of CBV and BOLD responses are similar for the assessed range of movement rates. These results suggest that the observed nonlinearity of neuronal origin is already present in VASO-CBV measurements, and consequently shows relatively unchanged BOLD responses.
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Affiliation(s)
- Ícaro A F Oliveira
- Spinoza Centre for Neuroimaging, Amsterdam, the Netherlands; Experimental and Applied Psychology, VU University, Amsterdam, the Netherlands.
| | | | - Luisa Raimondo
- Spinoza Centre for Neuroimaging, Amsterdam, the Netherlands; Experimental and Applied Psychology, VU University, Amsterdam, the Netherlands
| | - Serge O Dumoulin
- Spinoza Centre for Neuroimaging, Amsterdam, the Netherlands; Experimental and Applied Psychology, VU University, Amsterdam, the Netherlands; Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, the Netherlands
| | - Jeroen C W Siero
- Spinoza Centre for Neuroimaging, Amsterdam, the Netherlands; Radiology, Utrecht Center for Image Sciences, University Medical Center Utrecht, Utrecht, the Netherlands
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22
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Wilzén J, Eklund A, Villani M. Physiological Gaussian process priors for the hemodynamics in fMRI analysis. J Neurosci Methods 2020; 342:108778. [PMID: 32473943 DOI: 10.1016/j.jneumeth.2020.108778] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 04/22/2020] [Accepted: 05/11/2020] [Indexed: 11/30/2022]
Abstract
BACKGROUND Inference from fMRI data faces the challenge that the hemodynamic system that relates neural activity to the observed BOLD fMRI signal is unknown. NEW METHOD We propose a new Bayesian model for task fMRI data with the following features: (i) joint estimation of brain activity and the underlying hemodynamics, (ii) the hemodynamics is modeled nonparametrically with a Gaussian process (GP) prior guided by physiological information and (iii) the predicted BOLD is not necessarily generated by a linear time-invariant (LTI) system. We place a GP prior directly on the predicted BOLD response, rather than on the hemodynamic response function as in previous literature. This allows us to incorporate physiological information via the GP prior mean in a flexible way, and simultaneously gives us the nonparametric flexibility of the GP. RESULTS Results on simulated data show that the proposed model is able to discriminate between active and non-active voxels also when the GP prior deviates from the true hemodynamics. Our model finds time varying dynamics when applied to real fMRI data. COMPARISON WITH EXISTING METHOD(S) The proposed model is better at detecting activity in simulated data than standard models, without inflating the false positive rate. When applied to real fMRI data, our GP model in several cases finds brain activity where previously proposed LTI models does not. CONCLUSIONS We have proposed a new non-linear model for the hemodynamics in task fMRI, that is able to detect active voxels, and gives the opportunity to ask new kinds of questions related to hemodynamics.
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Affiliation(s)
- Josef Wilzén
- Division of Statistics & Machine Learning, Department of Computer and Information Science, Linköping University, Linköping, Sweden.
| | - Anders Eklund
- Division of Statistics & Machine Learning, Department of Computer and Information Science, Linköping University, Linköping, Sweden; Division of Medical Informatics, Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Mattias Villani
- Division of Statistics & Machine Learning, Department of Computer and Information Science, Linköping University, Linköping, Sweden; Department of Statistics, Stockholm University, Sweden
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23
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Steber S, Rossi S. So young, yet so mature? Electrophysiological and vascular correlates of phonotactic processing in 18-month-olds. Dev Cogn Neurosci 2020; 43:100784. [PMID: 32510350 PMCID: PMC7184260 DOI: 10.1016/j.dcn.2020.100784] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 04/08/2020] [Accepted: 04/09/2020] [Indexed: 11/30/2022] Open
Abstract
The present study investigated neural correlates of implicit phonotactic processing in 18-month-old children that just reached an important step in language development: the vocabulary spurt. Pseudowords, either phonotactically legal or illegal with respect to their native language, were acoustically presented to monolingually German raised infants. Neural activity was simultaneously assessed by means of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). The former method excellently tracks fast processing mechanisms, whereas the latter reveals brain areas recruited. Results of the present study indicate that 18-month-olds recognize the linguistic properties of their native language based on phonotactics. This manifested in an increased N400 for legal compared to illegal pseudowords in the EEG conforming to adult-like mechanisms. Unfortunately, fNIRS findings did not support this discrimination ability. Possible methodological and brain maturational reasons might explain this null finding. This study provides evidence for the advantage of a multi-methodological approach in order to get a clear picture on neural language development.
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Affiliation(s)
- Sarah Steber
- ICONE - Innsbruck Cognitive Neuroscience, Department for Hearing, Speech, and Voice Disorders, Medical University of Innsbruck, 6020, Innsbruck, Austria; Department of Psychology, University of Innsbruck, 6020, Innsbruck, Austria.
| | - Sonja Rossi
- ICONE - Innsbruck Cognitive Neuroscience, Department for Hearing, Speech, and Voice Disorders, Medical University of Innsbruck, 6020, Innsbruck, Austria.
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24
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Steber S, König N, Stephan F, Rossi S. Uncovering electrophysiological and vascular signatures of implicit emotional prosody. Sci Rep 2020; 10:5807. [PMID: 32242032 PMCID: PMC7118077 DOI: 10.1038/s41598-020-62761-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 03/18/2020] [Indexed: 11/13/2022] Open
Abstract
The capability of differentiating between various emotional states in speech displays a crucial prerequisite for successful social interactions. The aim of the present study was to investigate neural processes underlying this differentiating ability by applying a simultaneous neuroscientific approach in order to gain both electrophysiological (via electroencephalography, EEG) and vascular (via functional near-infrared-spectroscopy, fNIRS) responses. Pseudowords conforming to angry, happy, and neutral prosody were presented acoustically to participants using a passive listening paradigm in order to capture implicit mechanisms of emotional prosody processing. Event-related brain potentials (ERPs) revealed a larger P200 and an increased late positive potential (LPP) for happy prosody as well as larger negativities for angry and neutral prosody compared to happy prosody around 500 ms. FNIRS results showed increased activations for angry prosody at right fronto-temporal areas. Correlation between negativity in the EEG and activation in fNIRS for angry prosody suggests analogous underlying processes resembling a negativity bias. Overall, results indicate that mechanisms of emotional and phonological encoding (P200), emotional evaluation (increased negativities) as well as emotional arousal and relevance (LPP) are present during implicit processing of emotional prosody.
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Affiliation(s)
- Sarah Steber
- ICONE - Innsbruck Cognitive Neuroscience, Department for Hearing, Speech, and Voice Disorders, Medical University of Innsbruck, 6020, Innsbruck, Austria
- Department of Psychology, University of Innsbruck, 6020, Innsbruck, Austria
| | - Nicola König
- ICONE - Innsbruck Cognitive Neuroscience, Department for Hearing, Speech, and Voice Disorders, Medical University of Innsbruck, 6020, Innsbruck, Austria
- Department of Psychology, University of Innsbruck, 6020, Innsbruck, Austria
| | - Franziska Stephan
- ICONE - Innsbruck Cognitive Neuroscience, Department for Hearing, Speech, and Voice Disorders, Medical University of Innsbruck, 6020, Innsbruck, Austria
- Department of Educational Psychology, Faculty of Education, University of Leipzig, 04109, Leipzig, Germany
| | - Sonja Rossi
- ICONE - Innsbruck Cognitive Neuroscience, Department for Hearing, Speech, and Voice Disorders, Medical University of Innsbruck, 6020, Innsbruck, Austria.
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25
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van Dijk JA, Fracasso A, Petridou N, Dumoulin SO. Linear systems analysis for laminar fMRI: Evaluating BOLD amplitude scaling for luminance contrast manipulations. Sci Rep 2020; 10:5462. [PMID: 32214136 PMCID: PMC7096513 DOI: 10.1038/s41598-020-62165-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 03/09/2020] [Indexed: 01/18/2023] Open
Abstract
A fundamental assumption of nearly all functional magnetic resonance imaging (fMRI) analyses is that the relationship between local neuronal activity and the blood oxygenation level dependent (BOLD) signal can be described as following linear systems theory. With the advent of ultra-high field (7T and higher) MRI scanners, it has become possible to perform sub-millimeter resolution fMRI in humans. A novel and promising application of sub-millimeter fMRI is measuring responses across cortical depth, i.e. laminar imaging. However, the cortical vasculature and associated directional blood pooling towards the pial surface strongly influence the cortical depth-dependent BOLD signal, particularly for gradient-echo BOLD. This directional pooling may potentially affect BOLD linearity across cortical depth. Here we assess whether the amplitude scaling assumption for linear systems theory holds across cortical depth. For this, we use stimuli with different luminance contrasts to elicit different BOLD response amplitudes. We find that BOLD amplitude across cortical depth scales with luminance contrast, and that this scaling is identical across cortical depth. Although nonlinearities may be present for different stimulus configurations and acquisition protocols, our results suggest that the amplitude scaling assumption for linear systems theory across cortical depth holds for luminance contrast manipulations in sub-millimeter laminar BOLD fMRI.
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Affiliation(s)
- Jelle A van Dijk
- Experimental Psychology, Utrecht University, Utrecht, NL, Netherlands.
- Spinoza Centre for Neuroimaging, Amsterdam, NL, Netherlands.
| | - Alessio Fracasso
- Spinoza Centre for Neuroimaging, Amsterdam, NL, Netherlands
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, G12 8QB, UK
- Radiology Department, Imaging Division, Center for Image Sciences, University Medical Center Utrecht, Utrecht, NL, Netherlands
| | - Natalia Petridou
- Radiology Department, Imaging Division, Center for Image Sciences, University Medical Center Utrecht, Utrecht, NL, Netherlands
| | - Serge O Dumoulin
- Experimental Psychology, Utrecht University, Utrecht, NL, Netherlands
- Spinoza Centre for Neuroimaging, Amsterdam, NL, Netherlands
- Experimental and Applied Psychology, VU University, Amsterdam, NL, Netherlands
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26
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Rossi S, Gugler MF, Rungger M, Galvan O, Zorowka PG, Seebacher J. How the Brain Understands Spoken and Sung Sentences. Brain Sci 2020; 10:E36. [PMID: 31936356 PMCID: PMC7017195 DOI: 10.3390/brainsci10010036] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 12/19/2019] [Accepted: 01/06/2020] [Indexed: 11/24/2022] Open
Abstract
The present study investigates whether meaning is similarly extracted from spoken and sung sentences. For this purpose, subjects listened to semantically correct and incorrect sentences while performing a correctness judgement task. In order to examine underlying neural mechanisms, a multi-methodological approach was chosen combining two neuroscientific methods with behavioral data. In particular, fast dynamic changes reflected in the semantically associated N400 component of the electroencephalography (EEG) were simultaneously assessed with the topographically more fine-grained vascular signals acquired by the functional near-infrared spectroscopy (fNIRS). EEG results revealed a larger N400 for incorrect compared to correct sentences in both spoken and sung sentences. However, the N400 was delayed for sung sentences, potentially due to the longer sentence duration. fNIRS results revealed larger activations for spoken compared to sung sentences irrespective of semantic correctness at predominantly left-hemispheric areas, potentially suggesting a greater familiarity with spoken material. Furthermore, the fNIRS revealed a widespread activation for correct compared to incorrect sentences irrespective of modality, potentially indicating a successful processing of sentence meaning. The combined results indicate similar semantic processing in speech and song.
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Affiliation(s)
- Sonja Rossi
- ICONE-Innsbruck Cognitive Neuroscience, Department for Hearing, Speech, and Voice Disorders, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Manfred F Gugler
- Department for Medical Psychology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Markus Rungger
- Department for Hearing, Speech, and Voice Disorders, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Oliver Galvan
- Department for Hearing, Speech, and Voice Disorders, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Patrick G Zorowka
- Department for Hearing, Speech, and Voice Disorders, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Josef Seebacher
- Department for Hearing, Speech, and Voice Disorders, Medical University of Innsbruck, 6020 Innsbruck, Austria
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27
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Drew PJ. Vascular and neural basis of the BOLD signal. Curr Opin Neurobiol 2019; 58:61-69. [PMID: 31336326 DOI: 10.1016/j.conb.2019.06.004] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Accepted: 06/22/2019] [Indexed: 12/26/2022]
Abstract
Neural activity in the brain is usually coupled to increases in local cerebral blood flow, leading to the increase in oxygenation that generates the BOLD fMRI signal. Recent work has begun to elucidate the vascular and neural mechanisms underlying the BOLD signal. The dilatory response is distributed throughout the vascular network. Arteries actively dilate within a second following neural activity increases, while venous distensions are passive and have a time course that last tens of seconds. Vasodilation, and thus local blood flow, is controlled by the activity of both neurons and astrocytes via multiple different pathways. The relationship between sensory-driven neural activity and the vascular dynamics in sensory areas are well-captured with a linear convolution model. However, depending on the behavioral state or brain region, the coupling between neural activity and hemodynamic signals can be weak or even inverted.
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Affiliation(s)
- Patrick J Drew
- Departments of Engineering Science and Mechanics, Biomedical Engineering and Neurosurgery, Pennsylvania State University, University Park, PA 16802, United States.
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28
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Mesoscopic and microscopic imaging of sensory responses in the same animal. Nat Commun 2019; 10:1110. [PMID: 30846689 PMCID: PMC6405955 DOI: 10.1038/s41467-019-09082-4] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Accepted: 02/20/2019] [Indexed: 01/09/2023] Open
Abstract
Imaging based on blood flow dynamics is widely used to study sensory processing. Here we investigated the extent to which local neuronal and capillary responses (two-photon microscopy) are correlated to mesoscopic responses detected with fast ultrasound (fUS) and BOLD-fMRI. Using a specialized chronic olfactory bulb preparation, we report that sequential imaging of the same mouse allows quantitative comparison of odour responses, imaged at both microscopic and mesoscopic scales. Under these conditions, functional hyperaemia occurred at the threshold of neuronal activation and fUS-CBV signals could be detected at the level of single voxels with activation maps varying according to blood velocity. Both neuronal and vascular responses increase non-linearly as a function of odour concentration, whereas both microscopic and mesoscopic vascular responses are linearly correlated to local neuronal calcium. These data establish strengths and limits of mesoscopic imaging techniques to report neural activity. Neuronal activity leads to a local increase in blood flow and volume, a process termed hyperaemia. Here, the authors employ multiple imaging approaches of neuronal and vascular activity at varying resolution to delineate the spatiotemporal dynamics of neurovascular coupling evoked by odours in the olfactory bulb.
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29
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Groba A, De Houwer A, Obrig H, Rossi S. Bilingual and Monolingual First Language Acquisition Experience Differentially Shapes Children's Property Term Learning: Evidence from Behavioral and Neurophysiological Measures. Brain Sci 2019; 9:E40. [PMID: 30759804 PMCID: PMC6406634 DOI: 10.3390/brainsci9020040] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Accepted: 02/10/2019] [Indexed: 11/17/2022] Open
Abstract
Studies of novel noun learning show bilingual children rely less on the Mutual Exclusivity Constraint (MEC) for word learning than monolinguals. Shifting the focus to learning novel property terms (adjectives), the present study compared 3.5- and five-year-old bilingual and monolingual preschoolers' adherence to the MEC. We found no bilingual-monolingual differences on a behavioral forced-choice task for the 3.5-year-olds, but five-year-old monolinguals adhered more to the MEC than bilinguals did. Older bilinguals adhered less to the MEC than younger ones, while there was no difference in MEC adherence between the younger and older monolinguals. In the 5-year-olds, we additionally acquired neurophysiological data using functional near-infrared spectroscopy (fNIRS) to allow for a first explorative look at potential neuronal underpinnings. The data show that, compared to bilinguals, monolinguals reveal higher activation over three brain regions (right frontal, left temporo-parietal, and left prefrontal) that may be involved in exploiting the MEC, building on conflict detection, inhibition, solution of a disjunction, and working memory processes. Taken together, our behavioral and neurophysiological findings reveal different paths towards novel property term learning depending on children's language acquisition context.
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Affiliation(s)
- Agnes Groba
- Institute of Special Education, University of Leipzig, Marschnerstr. 29 e, 04109 Leipzig, Germany.
- Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103 Leipzig, Germany.
- Department of Linguistics, University of Erfurt, Nordhäuser Straße 63, 99089 Erfurt, Germany.
| | - Annick De Houwer
- Department of Linguistics, University of Erfurt, Nordhäuser Straße 63, 99089 Erfurt, Germany.
| | - Hellmuth Obrig
- Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103 Leipzig, Germany.
- Clinic for Cognitive Neurology, Medical Faculty, University of Leipzig, Liebigstraße 16, 04103 Leipzig, Germany.
| | - Sonja Rossi
- Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103 Leipzig, Germany.
- Department for Hearing, Speech, and Voice Disorders, Medical University of Innsbruck, Anichstraße 35, A-6020 Innsbruck, Austria.
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30
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Baxter L, Fitzgibbon S, Moultrie F, Goksan S, Jenkinson M, Smith S, Andersson J, Duff E, Slater R. Optimising neonatal fMRI data analysis: Design and validation of an extended dHCP preprocessing pipeline to characterise noxious-evoked brain activity in infants. Neuroimage 2019; 186:286-300. [PMID: 30414984 PMCID: PMC6347570 DOI: 10.1016/j.neuroimage.2018.11.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Revised: 10/16/2018] [Accepted: 11/06/2018] [Indexed: 11/21/2022] Open
Abstract
The infant brain is unlike the adult brain, with considerable differences in morphological, neurodynamic, and haemodynamic features. As the majority of current MRI analysis tools were designed for use in adults, a primary objective of the Developing Human Connectome Project (dHCP) is to develop optimised methodological pipelines for the analysis of neonatal structural, resting state, and diffusion MRI data. Here, in an independent neonatal dataset we have extended and optimised the dHCP fMRI preprocessing pipeline for the analysis of stimulus-response fMRI data. We describe and validate this extended dHCP fMRI preprocessing pipeline to analyse changes in brain activity evoked following an acute noxious stimulus applied to the infant's foot. We compare the results obtained from this extended dHCP pipeline to results obtained from a typical FSL FEAT-based analysis pipeline, evaluating the pipelines' outputs using a wide range of tests. We demonstrate that a substantial increase in spatial specificity and sensitivity to signal can be attained with a bespoke neonatal preprocessing pipeline through optimised motion and distortion correction, ICA-based denoising, and haemodynamic modelling. The improved sensitivity and specificity, made possible with this extended dHCP pipeline, will be paramount in making further progress in our understanding of the development of sensory processing in the infant brain.
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Affiliation(s)
- Luke Baxter
- Department of Paediatrics, University of Oxford, Oxford, United Kingdom
| | - Sean Fitzgibbon
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Fiona Moultrie
- Department of Paediatrics, University of Oxford, Oxford, United Kingdom
| | - Sezgi Goksan
- Department of Paediatrics, University of Oxford, Oxford, United Kingdom
| | - Mark Jenkinson
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Stephen Smith
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Jesper Andersson
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Eugene Duff
- Department of Paediatrics, University of Oxford, Oxford, United Kingdom
| | - Rebeccah Slater
- Department of Paediatrics, University of Oxford, Oxford, United Kingdom.
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31
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Chen CF, Kreutz-Delgado K, Sereno MI, Huang RS. Unraveling the spatiotemporal brain dynamics during a simulated reach-to-eat task. Neuroimage 2019; 185:58-71. [PMID: 30315910 PMCID: PMC6325169 DOI: 10.1016/j.neuroimage.2018.10.028] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 09/11/2018] [Accepted: 10/09/2018] [Indexed: 01/17/2023] Open
Abstract
The reach-to-eat task involves a sequence of action components including looking, reaching, grasping, and feeding. While cortical representations of individual action components have been mapped in human functional magnetic resonance imaging (fMRI) studies, little is known about the continuous spatiotemporal dynamics among these representations during the reach-to-eat task. In a periodic event-related fMRI experiment, subjects were scanned while they reached toward a food image, grasped the virtual food, and brought it to their mouth within each 16-s cycle. Fourier-based analysis of fMRI time series revealed periodic signals and noise distributed across the brain. Independent component analysis was used to remove periodic or aperiodic motion artifacts. Time-frequency analysis was used to analyze the temporal characteristics of periodic signals in each voxel. Circular statistics was then used to estimate mean phase angles of periodic signals and select voxels based on the distribution of phase angles. By sorting mean phase angles across regions, we were able to show the real-time spatiotemporal brain dynamics as continuous traveling waves over the cortical surface. The activation sequence consisted of approximately the following stages: (1) stimulus related activations in occipital and temporal cortices; (2) movement planning related activations in dorsal premotor and superior parietal cortices; (3) reaching related activations in primary sensorimotor cortex and supplementary motor area; (4) grasping related activations in postcentral gyrus and sulcus; (5) feeding related activations in orofacial areas. These results suggest that phase-encoded design and analysis can be used to unravel sequential activations among brain regions during a simulated reach-to-eat task.
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Affiliation(s)
- Ching-Fu Chen
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Kenneth Kreutz-Delgado
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, 92093, USA; Institute for Neural Computation, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Martin I Sereno
- Department of Psychology and Neuroimaging Center, San Diego State University, San Diego, CA, 92182, USA; Experimental Psychology, University College London, London, WC1H 0AP, UK
| | - Ruey-Song Huang
- Institute for Neural Computation, University of California, San Diego, La Jolla, CA, 92093, USA.
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32
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Razlighi QR. Task-Evoked Negative BOLD Response in the Default Mode Network Does Not Alter Its Functional Connectivity. Front Comput Neurosci 2018; 12:67. [PMID: 30177878 PMCID: PMC6109759 DOI: 10.3389/fncom.2018.00067] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Accepted: 07/23/2018] [Indexed: 11/16/2022] Open
Abstract
While functional connectivity networks are often extracted from resting-state fMRI scans, they have been shown to be active during task performance as well. However, the effect of an in-scanner task on functional connectivity networks is not completely understood. While there is evidence that task-evoked positive BOLD response can alter functional connectivity networks, particularly in the primary sensorimotor cortices, the effect of task-evoked negative BOLD response on the functional connectivity of the Default mode network (DMN) is somewhat ambiguous. In this study, we aim to investigate whether task performance, which is associated with negative BOLD response in the DMN regions, alters the time-course of functional connectivity in the same regions obtained by independent component analysis (ICA). ICA has been used to effectively extract functional connectivity networks during task performance and resting-state. We first demonstrate that performing a simple visual-motor task alters the temporal time-course of the network extracted from the primary visual cortex. Then we show that despite detecting a robust task-evoked negative BOLD response in the DMN regions, a simple visual-motor task does not alter the functional connectivity of the DMN regions. Our findings suggest that different mechanisms may underlie the relationship between task-related activation/deactivation networks and the overlapping functional connectivity networks in the human large-scale brain networks.
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Affiliation(s)
- Qolamreza R. Razlighi
- Department of Neurology, Collage of Physician and Surgeons, Columbia University, New York, NY, United States
- Taub Institute for Research on Alzheimer's Disease and The Aging, Columbia University, New York, NY, United States
- Biomedical Engineering Department, Columbia University, New York, NY, United States
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33
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Becker CO, Bassett DS, Preciado VM. Large-scale dynamic modeling of task-fMRI signals via subspace system identification. J Neural Eng 2018; 15:066016. [PMID: 30088476 DOI: 10.1088/1741-2552/aad8c7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE We analyze task-based fMRI time series to produce large-scale dynamical models that are capable of approximating the observed signal with good accuracy. APPROACH We extend subspace system identification methods for deterministic and stochastic state-space models with external inputs. The dynamic behavior of the generated models is characterized using control-theoretic analysis tools. To validate their effectiveness, we perform a probabilistic inversion of the identified input-output relationships via joint state-input maximum likelihood estimation. Our experimental setup explores a large dataset generated using state-of-the-art acquisition and pre-processing methods from the Human Connectome Project. MAIN RESULTS We analyze both anatomically parcellated and spatially dense time series, and propose an efficient algorithm to address the high-dimensional optimization problem resulting from the latter. Our results enable the quantification of input-output transfer functions between each task condition and each region of the cortex, as exemplified by a motor task. Further, the identified models produce impulse response functions between task conditions and cortical regions that are compatible with typical hemodynamic response functions. We then extend subspace methods to account for multi-subject experimental configurations, identifying models that capture common dynamical characteristics across subjects. Finally, we show that system inversion via maximum-likelihood allows the time-of-occurrence of the task stimuli to be estimated from the observed outputs. SIGNIFICANCE The ability to produce dynamical input-output models might have an impact in the expanding field of neurofeedback. In particular, the models we produce allow the partial quantification of the effect of external task-related inputs on the metabolic response of the brain, conditioned on its current state. Such a notion provides a basis for leveraging control-theoretic approaches to neuromodulation and self-regulation in therapeutic applications.
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34
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Birman D, Gardner JL. A quantitative framework for motion visibility in human cortex. J Neurophysiol 2018; 120:1824-1839. [PMID: 29995608 DOI: 10.1152/jn.00433.2018] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Despite the central use of motion visibility to reveal the neural basis of perception, perceptual decision making, and sensory inference there exists no comprehensive quantitative framework establishing how motion visibility parameters modulate human cortical response. Random-dot motion stimuli can be made less visible by reducing image contrast or motion coherence, or by shortening the stimulus duration. Because each of these manipulations modulates the strength of sensory neural responses they have all been extensively used to reveal cognitive and other nonsensory phenomena such as the influence of priors, attention, and choice-history biases. However, each of these manipulations is thought to influence response in different ways across different cortical regions and a comprehensive study is required to interpret this literature. Here, human participants observed random-dot stimuli varying across a large range of contrast, coherence, and stimulus durations as we measured blood-oxygen-level dependent responses. We developed a framework for modeling these responses that quantifies their functional form and sensitivity across areas. Our framework demonstrates the sensitivity of all visual areas to each parameter, with early visual areas V1-V4 showing more parametric sensitivity to changes in contrast and V3A and the human middle temporal area to coherence. Our results suggest that while motion contrast, coherence, and duration share cortical representation, they are encoded with distinct functional forms and sensitivity. Thus, our quantitative framework serves as a reference for interpretation of the vast perceptual literature manipulating these parameters and shows that different manipulations of visibility will have different effects across human visual cortex and need to be interpreted accordingly. NEW & NOTEWORTHY Manipulations of motion visibility have served as a key tool for understanding the neural basis for visual perception. Here we measured human cortical response to changes in visibility across a comprehensive range of motion visibility parameters and modeled these with a quantitative framework. Our quantitative framework can be used as a reference for linking human cortical response to perception and underscores that different manipulations of motion visibility can have greatly different effects on cortical representation.
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Affiliation(s)
- Daniel Birman
- Department of Psychology, Stanford University , Stanford, California
| | - Justin L Gardner
- Department of Psychology, Stanford University , Stanford, California
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35
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Dobs K, Schultz J, Bülthoff I, Gardner JL. Task-dependent enhancement of facial expression and identity representations in human cortex. Neuroimage 2018; 172:689-702. [PMID: 29432802 DOI: 10.1016/j.neuroimage.2018.02.013] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Revised: 02/02/2018] [Accepted: 02/06/2018] [Indexed: 11/24/2022] Open
Abstract
What cortical mechanisms allow humans to easily discern the expression or identity of a face? Subjects detected changes in expression or identity of a stream of dynamic faces while we measured BOLD responses from topographically and functionally defined areas throughout the visual hierarchy. Responses in dorsal areas increased during the expression task, whereas responses in ventral areas increased during the identity task, consistent with previous studies. Similar to ventral areas, early visual areas showed increased activity during the identity task. If visual responses are weighted by perceptual mechanisms according to their magnitude, these increased responses would lead to improved attentional selection of the task-appropriate facial aspect. Alternatively, increased responses could be a signature of a sensitivity enhancement mechanism that improves representations of the attended facial aspect. Consistent with the latter sensitivity enhancement mechanism, attending to expression led to enhanced decoding of exemplars of expression both in early visual and dorsal areas relative to attending identity. Similarly, decoding identity exemplars when attending to identity was improved in dorsal and ventral areas. We conclude that attending to expression or identity of dynamic faces is associated with increased selectivity in representations consistent with sensitivity enhancement.
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Affiliation(s)
- Katharina Dobs
- Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max-Planck-Ring 8, 72076 Tübingen, Germany; Laboratory for Human Systems Neuroscience, RIKEN Brain Science Institute, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, 43 Vassar Street, Cambridge, MA 02139, USA.
| | - Johannes Schultz
- Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max-Planck-Ring 8, 72076 Tübingen, Germany; Division of Medical Psychology and Department of Psychiatry, University of Bonn, Sigmund Freud Str. 25, 53105 Bonn, Germany
| | - Isabelle Bülthoff
- Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max-Planck-Ring 8, 72076 Tübingen, Germany
| | - Justin L Gardner
- Laboratory for Human Systems Neuroscience, RIKEN Brain Science Institute, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan; Department of Psychology, Stanford University, 450 Serra Mall, Stanford, CA 94305, USA
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36
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Abstract
How is temporal information processed in human visual cortex? Visual input is relayed to V1 through segregated transient and sustained channels in the retina and lateral geniculate nucleus (LGN). However, there is intense debate as to how sustained and transient temporal channels contribute to visual processing beyond V1. The prevailing view associates transient processing predominately with motion-sensitive regions and sustained processing with ventral stream regions, while the opposing view suggests that both temporal channels contribute to neural processing beyond V1. Using fMRI, we measured cortical responses to time-varying stimuli and then implemented a two temporal channel-encoding model to evaluate the contributions of each channel. Different from the general linear model of fMRI that predicts responses directly from the stimulus, the encoding approach first models neural responses to the stimulus from which fMRI responses are derived. This encoding approach not only predicts cortical responses to time-varying stimuli from milliseconds to seconds but also, reveals differential contributions of temporal channels across visual cortex. Consistent with the prevailing view, motion-sensitive regions and adjacent lateral occipitotemporal regions are dominated by transient responses. However, ventral occipitotemporal regions are driven by both sustained and transient channels, with transient responses exceeding the sustained. These findings propose a rethinking of temporal processing in the ventral stream and suggest that transient processing may contribute to rapid extraction of the content of the visual input. Importantly, our encoding approach has vast implications, because it can be applied with fMRI to decipher neural computations in millisecond resolution in any part of the brain.
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37
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Compressive Temporal Summation in Human Visual Cortex. J Neurosci 2017; 38:691-709. [PMID: 29192127 DOI: 10.1523/jneurosci.1724-17.2017] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Revised: 10/23/2017] [Accepted: 11/17/2017] [Indexed: 01/23/2023] Open
Abstract
Combining sensory inputs over space and time is fundamental to vision. Population receptive field models have been successful in characterizing spatial encoding throughout the human visual pathways. A parallel question, how visual areas in the human brain process information distributed over time, has received less attention. One challenge is that the most widely used neuroimaging method, fMRI, has coarse temporal resolution compared with the time-scale of neural dynamics. Here, via carefully controlled temporally modulated stimuli, we show that information about temporal processing can be readily derived from fMRI signal amplitudes in male and female subjects. We find that all visual areas exhibit subadditive summation, whereby responses to longer stimuli are less than the linear prediction from briefer stimuli. We also find fMRI evidence that the neural response to two stimuli is reduced for brief interstimulus intervals (indicating adaptation). These effects are more pronounced in visual areas anterior to V1-V3. Finally, we develop a general model that shows how these effects can be captured with two simple operations: temporal summation followed by a compressive nonlinearity. This model operates for arbitrary temporal stimulation patterns and provides a simple and interpretable set of computations that can be used to characterize neural response properties across the visual hierarchy. Importantly, compressive temporal summation directly parallels earlier findings of compressive spatial summation in visual cortex describing responses to stimuli distributed across space. This indicates that, for space and time, cortex uses a similar processing strategy to achieve higher-level and increasingly invariant representations of the visual world.SIGNIFICANCE STATEMENT Combining sensory inputs over time is fundamental to seeing. Two important temporal phenomena are summation, the accumulation of sensory inputs over time, and adaptation, a response reduction for repeated or sustained stimuli. We investigated these phenomena in the human visual system using fMRI. We built predictive models that operate on arbitrary temporal patterns of stimulation using two simple computations: temporal summation followed by a compressive nonlinearity. Our new temporal compressive summation model captures (1) subadditive temporal summation, and (2) adaptation. We show that the model accounts for systematic differences in these phenomena across visual areas. Finally, we show that for space and time, the visual system uses a similar strategy to achieve increasingly invariant representations of the visual world.
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38
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Inverted Encoding Models of Human Population Response Conflate Noise and Neural Tuning Width. J Neurosci 2017; 38:398-408. [PMID: 29167406 DOI: 10.1523/jneurosci.2453-17.2017] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Revised: 11/08/2017] [Accepted: 11/10/2017] [Indexed: 01/02/2023] Open
Abstract
Channel-encoding models offer the ability to bridge different scales of neuronal measurement by interpreting population responses, typically measured with BOLD imaging in humans, as linear sums of groups of neurons (channels) tuned for visual stimulus properties. Inverting these models to form predicted channel responses from population measurements in humans seemingly offers the potential to infer neuronal tuning properties. Here, we test the ability to make inferences about neural tuning width from inverted encoding models. We examined contrast invariance of orientation selectivity in human V1 (both sexes) and found that inverting the encoding model resulted in channel response functions that became broader with lower contrast, thus apparently violating contrast invariance. Simulations showed that this broadening could be explained by contrast-invariant single-unit tuning with the measured decrease in response amplitude at lower contrast. The decrease in response lowers the signal-to-noise ratio of population responses that results in poorer population representation of orientation. Simulations further showed that increasing signal to noise makes channel response functions less sensitive to underlying neural tuning width, and in the limit of zero noise will reconstruct the channel function assumed by the model regardless of the bandwidth of single units. We conclude that our data are consistent with contrast-invariant orientation tuning in human V1. More generally, our results demonstrate that population selectivity measures obtained by encoding models can deviate substantially from the behavior of single units because they conflate neural tuning width and noise and are therefore better used to estimate the uncertainty of decoded stimulus properties.SIGNIFICANCE STATEMENT It is widely recognized that perceptual experience arises from large populations of neurons, rather than a few single units. Yet, much theory and experiment have examined links between single units and perception. Encoding models offer a way to bridge this gap by explicitly interpreting population activity as the aggregate response of many single neurons with known tuning properties. Here we use this approach to examine contrast-invariant orientation tuning of human V1. We show with experiment and modeling that due to lower signal to noise, contrast-invariant orientation tuning of single units manifests in population response functions that broaden at lower contrast, rather than remain contrast-invariant. These results highlight the need for explicit quantitative modeling when making a reverse inference from population response profiles to single-unit responses.
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39
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Grinband J, Steffener J, Razlighi QR, Stern Y. BOLD neurovascular coupling does not change significantly with normal aging. Hum Brain Mapp 2017; 38:3538-3551. [PMID: 28419680 DOI: 10.1002/hbm.23608] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Revised: 03/28/2017] [Accepted: 03/28/2017] [Indexed: 12/11/2022] Open
Abstract
Studies of cognitive function that compare the blood oxygenation level dependent (BOLD) signal across age groups often require the assumption that neurovascular coupling does not change with age. Tests of this assumption have produced mixed results regarding the strength of the coupling and its relative time course. Using deconvolution, we found that age does not have a significant effect on the time course of the hemodynamic impulse response function or on the slope of the BOLD versus stimulus duration relationship. These results suggest that in cognitive studies of healthy aging, group differences in BOLD activation are likely due to age-related changes in cognitive-neural interactions and information processing rather than to impairments in neurovascular coupling. Hum Brain Mapp 38:3538-3551, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Jack Grinband
- Department of Radiology, Columbia University, New York
| | - Jason Steffener
- Interdisciplinary School of Health Sciences, University of Ottawa, Ontario
| | - Qolamreza R Razlighi
- Department of Neurology, Columbia University, New York.,Department of Biomedical Engineering, Columbia University, New York
| | - Yaakov Stern
- Department of Neurology, Columbia University, New York
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40
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Rudolph MD, Miranda-Domínguez O, Cohen AO, Breiner K, Steinberg L, Bonnie RJ, Scott ES, Taylor-Thompson K, Chein J, Fettich KC, Richeson JA, Dellarco DV, Galván A, Casey BJ, Fair DA. At risk of being risky: The relationship between "brain age" under emotional states and risk preference. Dev Cogn Neurosci 2017; 24:93-106. [PMID: 28279917 PMCID: PMC5849238 DOI: 10.1016/j.dcn.2017.01.010] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Revised: 01/23/2017] [Accepted: 01/26/2017] [Indexed: 11/28/2022] Open
Abstract
Developmental differences regarding decision making are often reported in the absence of emotional stimuli and without context, failing to explain why some individuals are more likely to have a greater inclination toward risk. The current study (N=212; 10-25y) examined the influence of emotional context on underlying functional brain connectivity over development and its impact on risk preference. Using functional imaging data in a neutral brain-state we first identify the "brain age" of a given individual then validate it with an independent measure of cortical thickness. We then show, on average, that "brain age" across the group during the teen years has the propensity to look younger in emotional contexts. Further, we show this phenotype (i.e. a younger brain age in emotional contexts) relates to a group mean difference in risk perception - a pattern exemplified greatest in young-adults (ages 18-21). The results are suggestive of a specified functional brain phenotype that relates to being at "risk to be risky."
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Affiliation(s)
- Marc D Rudolph
- Department of Behavioral Neuroscience, Department of Psychiatry, Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, United States
| | - Oscar Miranda-Domínguez
- Department of Behavioral Neuroscience, Department of Psychiatry, Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, United States
| | - Alexandra O Cohen
- Sackler Institute for Developmental Psychobiology, Department of Psychiatry, Weill Cornell Medical College, New York, NY, United States
| | - Kaitlyn Breiner
- Department of Psychology, University of California, Los Angeles, CA, United States
| | - Laurence Steinberg
- Department of Psychology, Temple University, Philadelphia, PA, United States
| | - Richard J Bonnie
- University of Virginia School of Law, Charlottesville, VA, United States
| | | | | | - Jason Chein
- Department of Psychology, Temple University, Philadelphia, PA, United States
| | - Karla C Fettich
- Department of Psychology, Temple University, Philadelphia, PA, United States
| | - Jennifer A Richeson
- Department of Psychology and Institute for Policy Research, Northwestern University, Evanston, IL, United States; Department of Psychology, Yale University, New Haven CT, United States
| | - Danielle V Dellarco
- Sackler Institute for Developmental Psychobiology, Department of Psychiatry, Weill Cornell Medical College, New York, NY, United States
| | - Adriana Galván
- Department of Psychology, University of California, Los Angeles, CA, United States
| | - B J Casey
- Sackler Institute for Developmental Psychobiology, Department of Psychiatry, Weill Cornell Medical College, New York, NY, United States; Department of Psychology, Yale University, New Haven CT, United States
| | - Damien A Fair
- Department of Behavioral Neuroscience, Department of Psychiatry, Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, United States.
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Chen CF, Kreutz-Delgado K, Sereno MI, Huang RS. Validation of periodic fMRI signals in response to wearable tactile stimulation. Neuroimage 2017; 150:99-111. [PMID: 28193488 DOI: 10.1016/j.neuroimage.2017.02.024] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Revised: 01/24/2017] [Accepted: 02/09/2017] [Indexed: 01/22/2023] Open
Abstract
To map cortical representations of the body, we recently developed a wearable technology for automatic tactile stimulation in human functional magnetic resonance imaging (fMRI) experiments. In a two-condition block design experiment, air puffs were delivered to the face and hands periodically. Surface-based regions of interest (S-ROIs) were initially identified by thresholding a linear statistical measure of signal-to-noise ratio of periodic response. Across subjects, S-ROIs were found in the frontal, primary sensorimotor, posterior parietal, insular, temporal, cingulate, and occipital cortices. To validate and differentiate these S-ROIs, we develop a measure of temporal stability of response based on the assumption that a periodic stimulation evokes stable (low-variance) periodic fMRI signals throughout the entire scan. Toward this end, we apply time-frequency analysis to fMRI time series and use circular statistics to characterize the distribution of phase angles for data selection. We then assess the temporal variability of a periodic signal by measuring the path length of its trajectory in the complex plane. Both within and outside the primary sensorimotor cortex, S-ROIs with high temporal variability and deviant phase angles are rejected. A surface-based probabilistic group-average map is constructed for spatial screening of S-ROIs with low to moderate temporal variability in non-sensorimotor regions. Areas commonly activated across subjects are also summarized in the group-average map. In summary, this study demonstrates that analyzing temporal characteristics of the entire fMRI time series is essential for second-level selection and interpretation of S-ROIs initially defined by an overall linear statistical measure.
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Affiliation(s)
- Ching-Fu Chen
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Kenneth Kreutz-Delgado
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093, USA; Institute for Neural Computation, University of California, San Diego, La Jolla, CA 92093, USA
| | - Martin I Sereno
- Birkbeck/UCL Centre for NeuroImaging (BUCNI), London WC1E 7HX, UK; Department of Psychology and Neuroimaging Center, San Diego State University, San Diego, CA 92182, USA
| | - Ruey-Song Huang
- Institute for Neural Computation, University of California, San Diego, La Jolla, CA 92093, USA.
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Denisova K, Zhao G, Wang Z, Goh S, Huo Y, Peterson BS. Cortical interactions during the resolution of information processing demands in autism spectrum disorders. Brain Behav 2017; 7:e00596. [PMID: 28239517 PMCID: PMC5318360 DOI: 10.1002/brb3.596] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2015] [Revised: 07/27/2016] [Accepted: 09/11/2016] [Indexed: 12/15/2022] Open
Abstract
INTRODUCTION Our flexible and adaptive interactions with the environment are guided by our individual representation of the physical world, estimated through sensation and evaluation of available information against prior knowledge. When linking sensory evidence with higher-level expectations for action, the central nervous system (CNS) in typically developing (TD) individuals relies in part on distributed and interacting cortical regions to communicate neuronal signals flexibly across the brain. Increasing evidence suggests that the balance between levels of signal and noise during information processing may be disrupted in individuals with Autism Spectrum Disorders (ASD). METHODS Participants with and without ASD performed a visuospatial interference task while undergoing functional Magnetic Resonance Imaging (fMRI). We empirically estimated parameters characterizing participants' latencies and their subtle fluctuations (noise accumulation) over the 16-min scan. We modeled hemodynamic activation and used seed-based analyses of neural coupling to study dysfunction in interference-specific connectivity in a subset of ASD participants who were nonparametrically matched to TD participants on age, male-to-female ratio, and magnitude of movement during the scan. RESULTS Stochastic patterns of response fluctuations reveal significantly higher noise-to-signal levels and a more random and noisy structure in ASD versus TD participants, and in particular ASD adults who have the greatest clinical autistic deficits. While individuals with ASD show an overall weaker modulation of interference-specific functional connectivity relative to TD individuals, in particular between the seeds of Anterior Cingulate Cortex (ACC) and Inferior Parietal Sulcus (IPS) and the rest of the brain, we found that in ASD, higher uncertainty during the task is linked to increased interference-specific coupling between bilateral anterior insula and prefrontal cortex. CONCLUSIONS Subtle and informative differences in the structure of experiencing information exist between ASD and TD individuals. Our findings reveal in ASD an atypical capacity to apply previously perceived information in a manner optimal for adaptive functioning, plausibly revealing suboptimal message-passing across the CNS.
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Affiliation(s)
- Kristina Denisova
- Department of Psychiatry Center for Developmental Neuropsychiatry Columbia University College of Physicians and Surgeons New York NY USA; Division of Developmental Neuroscience New York State Psychiatric Institute New York NY USA; Sackler Institute for Developmental Psychobiology Columbia University College of Physicians and Surgeons New York NY USA
| | - Guihu Zhao
- Department of Psychiatry Center for Developmental Neuropsychiatry Columbia University College of Physicians and Surgeons New York NY USA; School of Information Science and Engineering Central South University Changsha China
| | - Zhishun Wang
- Department of Psychiatry Center for Developmental Neuropsychiatry Columbia University College of Physicians and Surgeons New York NY USA
| | - Suzanne Goh
- Department of Psychiatry Center for Developmental Neuropsychiatry Columbia University College of Physicians and Surgeons New York NY USA
| | - Yuankai Huo
- Department of Psychiatry Center for Developmental Neuropsychiatry Columbia University College of Physicians and Surgeons New York NY USA
| | - Bradley S Peterson
- Children's Hospital Los Angeles Keck School of Medicine of the University of Southern California Los Angeles CA USA
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Puckett AM, Aquino KM, Robinson P, Breakspear M, Schira MM. The spatiotemporal hemodynamic response function for depth-dependent functional imaging of human cortex. Neuroimage 2016; 139:240-248. [DOI: 10.1016/j.neuroimage.2016.06.019] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2016] [Revised: 05/27/2016] [Accepted: 06/10/2016] [Indexed: 11/15/2022] Open
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The hemodynamic response to somatosensory stimulation in mice depends on the anesthetic used: Implications on analysis of mouse fMRI data. Neuroimage 2015; 116:40-9. [DOI: 10.1016/j.neuroimage.2015.05.013] [Citation(s) in RCA: 66] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2014] [Revised: 04/30/2015] [Accepted: 05/05/2015] [Indexed: 11/23/2022] Open
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Modeling Neurovascular Coupling from Clustered Parameter Sets for Multimodal EEG-NIRS. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015; 2015:830849. [PMID: 26089979 PMCID: PMC4452306 DOI: 10.1155/2015/830849] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2014] [Revised: 02/03/2015] [Accepted: 02/04/2015] [Indexed: 11/17/2022]
Abstract
Despite significant improvements in neuroimaging technologies and analysis methods, the fundamental relationship between local changes in cerebral hemodynamics and the underlying neural activity remains largely unknown. In this study, a data driven approach is proposed for modeling this neurovascular coupling relationship from simultaneously acquired electroencephalographic (EEG) and near-infrared spectroscopic (NIRS) data. The approach uses gamma transfer functions to map EEG spectral envelopes that reflect time-varying power variations in neural rhythms to hemodynamics measured with NIRS during median nerve stimulation. The approach is evaluated first with simulated EEG-NIRS data and then by applying the method to experimental EEG-NIRS data measured from 3 human subjects. Results from the experimental data indicate that the neurovascular coupling relationship can be modeled using multiple sets of gamma transfer functions. By applying cluster analysis, statistically significant parameter sets were found to predict NIRS hemodynamics from EEG spectral envelopes. All subjects were found to have significant clustered parameters (P < 0.05) for EEG-NIRS data fitted using gamma transfer functions. These results suggest that the use of gamma transfer functions followed by cluster analysis of the resulting parameter sets may provide insights into neurovascular coupling in human neuroimaging data.
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Puckett AM, Mathis JR, DeYoe EA. An investigation of positive and inverted hemodynamic response functions across multiple visual areas. Hum Brain Mapp 2014; 35:5550-64. [PMID: 25044672 DOI: 10.1002/hbm.22569] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2013] [Revised: 05/29/2014] [Accepted: 06/24/2014] [Indexed: 01/16/2023] Open
Abstract
Recent studies have demonstrated significant regional variability in the hemodynamic response function (HRF), highlighting the difficulty of correctly interpreting functional MRI (fMRI) data without proper modeling of the HRF. The focus of this study was to investigate the HRF variability within visual cortex. The HRF was estimated for a number of cortical visual areas by deconvolution of fMRI blood oxygenation level dependent (BOLD) responses to brief, large-field visual stimulation. Significant HRF variation was found across visual areas V1, V2, V3, V4, VO-1,2, V3AB, IPS-0,1,2,3, LO-1,2, and TO-1,2. Additionally, a subpopulation of voxels was identified that exhibited an impulse response waveform that was similar, but not identical, to an inverted version of the commonly described and modeled positive HRF. These voxels were found within the retinotopic confines of the stimulus and were intermixed with those showing positive responses. The spatial distribution and variability of these HRFs suggest a vascular origin for the inverted waveforms. We suggest that the polarity of the HRF is a separate factor that is independent of the suppressive or activating nature of the underlying neuronal activity. Correctly modeling the polarity of the HRF allows one to recover an estimate of the underlying neuronal activity rather than discard the responses from these voxels on the assumption that they are artifactual. We demonstrate this approach on phase-encoded retinotopic mapping data as an example of the benefits of accurately modeling the HRF during the analysis of fMRI data.
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Affiliation(s)
- Alexander M Puckett
- Department of Radiology, Medical College of Wisconsin; Milwaukee, Wisconsin, 53226; School of Psychology, University of Wollongong; Wollongong, New South Wales, 2522, Australia
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Uga M, Dan I, Sano T, Dan H, Watanabe E. Optimizing the general linear model for functional near-infrared spectroscopy: an adaptive hemodynamic response function approach. NEUROPHOTONICS 2014; 1:015004. [PMID: 26157973 PMCID: PMC4478847 DOI: 10.1117/1.nph.1.1.015004] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2014] [Revised: 05/29/2014] [Accepted: 06/02/2014] [Indexed: 05/15/2023]
Abstract
An increasing number of functional near-infrared spectroscopy (fNIRS) studies utilize a general linear model (GLM) approach, which serves as a standard statistical method for functional magnetic resonance imaging (fMRI) data analysis. While fMRI solely measures the blood oxygen level dependent (BOLD) signal, fNIRS measures the changes of oxy-hemoglobin (oxy-Hb) and deoxy-hemoglobin (deoxy-Hb) signals at a temporal resolution severalfold higher. This suggests the necessity of adjusting the temporal parameters of a GLM for fNIRS signals. Thus, we devised a GLM-based method utilizing an adaptive hemodynamic response function (HRF). We sought the optimum temporal parameters to best explain the observed time series data during verbal fluency and naming tasks. The peak delay of the HRF was systematically changed to achieve the best-fit model for the observed oxy- and deoxy-Hb time series data. The optimized peak delay showed different values for each Hb signal and task. When the optimized peak delays were adopted, the deoxy-Hb data yielded comparable activations with similar statistical power and spatial patterns to oxy-Hb data. The adaptive HRF method could suitably explain the behaviors of both Hb parameters during tasks with the different cognitive loads during a time course, and thus would serve as an objective method to fully utilize the temporal structures of all fNIRS data.
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Affiliation(s)
- Minako Uga
- Jichi Medical University, Center for Development of Advanced Medical Technology, 3311-1 Yakushiji, Shimotsuke, Tochigi 329-0498, Japan
- Chuo University, Applied Cognitive Neuroscience Laboratory, 1-13-27 Kasuga, Bunkyo, Tokyo 112-8551, Japan
| | - Ippeita Dan
- Jichi Medical University, Center for Development of Advanced Medical Technology, 3311-1 Yakushiji, Shimotsuke, Tochigi 329-0498, Japan
- Chuo University, Applied Cognitive Neuroscience Laboratory, 1-13-27 Kasuga, Bunkyo, Tokyo 112-8551, Japan
- Address all correspondence to: Ippeita Dan, E-mail:
| | - Toshifumi Sano
- Jichi Medical University, Center for Development of Advanced Medical Technology, 3311-1 Yakushiji, Shimotsuke, Tochigi 329-0498, Japan
- Chuo University, Applied Cognitive Neuroscience Laboratory, 1-13-27 Kasuga, Bunkyo, Tokyo 112-8551, Japan
| | - Haruka Dan
- Jichi Medical University, Center for Development of Advanced Medical Technology, 3311-1 Yakushiji, Shimotsuke, Tochigi 329-0498, Japan
- Chuo University, Applied Cognitive Neuroscience Laboratory, 1-13-27 Kasuga, Bunkyo, Tokyo 112-8551, Japan
| | - Eiju Watanabe
- Jichi Medical University, Center for Development of Advanced Medical Technology, 3311-1 Yakushiji, Shimotsuke, Tochigi 329-0498, Japan
- Jichi Medical University, Department of Neurosurgery, 3311-1 Yakushiji, Shimotsuke, Tochigi 329-0498, Japan
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Lei Y, Tong L, Yan B. A mixed L2 norm regularized HRF estimation method for rapid event-related fMRI experiments. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2013; 2013:643129. [PMID: 23762193 PMCID: PMC3665251 DOI: 10.1155/2013/643129] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2013] [Revised: 03/26/2013] [Accepted: 04/08/2013] [Indexed: 11/18/2022]
Abstract
Brain state decoding or "mind reading" via multivoxel pattern analysis (MVPA) has become a popular focus of functional magnetic resonance imaging (fMRI) studies. In brain decoding, stimulus presentation rate is increased as fast as possible to collect many training samples and obtain an effective and reliable classifier or computational model. However, for extremely rapid event-related experiments, the blood-oxygen-level-dependent (BOLD) signals evoked by adjacent trials are heavily overlapped in the time domain. Thus, identifying trial-specific BOLD responses is difficult. In addition, voxel-specific hemodynamic response function (HRF), which is useful in MVPA, should be used in estimation to decrease the loss of weak information across voxels and obtain fine-grained spatial information. Regularization methods have been widely used to increase the efficiency of HRF estimates. In this study, we propose a regularization framework called mixed L2 norm regularization. This framework involves Tikhonov regularization and an additional L2 norm regularization term to calculate reliable HRF estimates. This technique improves the accuracy of HRF estimates and significantly increases the classification accuracy of the brain decoding task when applied to a rapid event-related four-category object classification experiment. At last, some essential issues such as the impact of low-frequency fluctuation (LFF) and the influence of smoothing are discussed for rapid event-related experiments.
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Affiliation(s)
- Yu Lei
- China National Digital Switching System Engineering and Technological Research Center, Zhengzhou 450002, China
| | - Li Tong
- China National Digital Switching System Engineering and Technological Research Center, Zhengzhou 450002, China
| | - Bin Yan
- China National Digital Switching System Engineering and Technological Research Center, Zhengzhou 450002, China
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Litvak V, Jha A, Flandin G, Friston K. Convolution models for induced electromagnetic responses. Neuroimage 2012; 64:388-98. [PMID: 22982359 PMCID: PMC3518783 DOI: 10.1016/j.neuroimage.2012.09.014] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2012] [Revised: 09/03/2012] [Accepted: 09/06/2012] [Indexed: 11/01/2022] Open
Abstract
In Kilner et al. [Kilner, J.M., Kiebel, S.J., Friston, K.J., 2005. Applications of random field theory to electrophysiology. Neurosci. Lett. 374, 174-178.] we described a fairly general analysis of induced responses-in electromagnetic brain signals-using the summary statistic approach and statistical parametric mapping. This involves localising induced responses-in peristimulus time and frequency-by testing for effects in time-frequency images that summarise the response of each subject to each trial type. Conventionally, these time-frequency summaries are estimated using post-hoc averaging of epoched data. However, post-hoc averaging of this sort fails when the induced responses overlap or when there are multiple response components that have variable timing within each trial (for example stimulus and response components associated with different reaction times). In these situations, it is advantageous to estimate response components using a convolution model of the sort that is standard in the analysis of fMRI time series. In this paper, we describe one such approach, based upon ordinary least squares deconvolution of induced responses to input functions encoding the onset of different components within each trial. There are a number of fundamental advantages to this approach: for example; (i) one can disambiguate induced responses to stimulus onsets and variably timed responses; (ii) one can test for the modulation of induced responses-over peristimulus time and frequency-by parametric experimental factors and (iii) one can gracefully handle confounds-such as slow drifts in power-by including them in the model. In what follows, we consider optimal forms for convolution models of induced responses, in terms of impulse response basis function sets and illustrate the utility of deconvolution estimators using simulated and real MEG data.
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Affiliation(s)
- Vladimir Litvak
- The Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, WC1N 3BG, UK.
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