A sensor-weighted overlapping-sphere head model and exhaustive head model comparison for MEG

Huang, Mingxiong; Mosher, John C.; Leahy, Richard M.

doi:10.1088/0031-9155/44/2/010

Cited by 577 publications

(417 citation statements)

References 16 publications

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“…Fiduciary locations were verified afterward using high-resolution digital photographs. For source localization, a multiple local-spheres (46) forward model was derived by fitting spheres to the brain surface extracted by FSL's Brain Extraction Tool (47). Estimates of source power were at 4 mm isotropic voxel resolution for each participant and frequency-band.…”

Section: Meg Methodsmentioning

confidence: 99%

Resting GABA concentration predicts peak gamma frequency and fMRI amplitude in response to visual stimulation in humans

Muthukumaraswamy

Edden

Jones

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

529

490

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Functional imaging of the human brain is an increasingly important technique for clinical and cognitive neuroscience research, with functional MRI (fMRI) of the blood oxygen level-dependent (BOLD) response and electroencephalography or magnetoencephalography (MEG) recordings of neural oscillations being 2 of the most popular approaches. However, the neural and physiological mechanisms that generate these responses are only partially understood and sources of interparticipant variability in these measures are rarely investigated. Here, we test the hypothesis that the properties of these neuroimaging metrics are related to individual levels of cortical inhibition by combining magnetic resonance spectroscopy to quantify resting GABA concentration in the visual cortex, MEG to measure stimulus-induced visual gamma oscillations and fMRI to measure the BOLD response to a simple visual grating stimulus. Our results demonstrate that across individuals gamma oscillation frequency is positively correlated with resting GABA concentration in visual cortex (R ‫؍‬ 0.68; P < 0.02), BOLD magnitude is inversely correlated with resting GABA (R ‫؍‬ ؊0.64; P < 0.05) and that gamma oscillation frequency is strongly inversely correlated with the magnitude of the BOLD response (R ‫؍‬ ؊0.88; P < 0.001). Our results are therefore supportive of recent theories suggesting that these functional neuroimaging metrics are dependent on the excitation/inhibition balance in an individual's cortex and have important implications for the interpretation of functional imaging results, particularly when making between-group comparisons in clinical research.functional magnetic resonance imaging ͉ magnetic resonance spectroscopy ͉ magnetoencephalography ͉ oscillations T he high spatial resolution and noninvasive nature of blood oxygen level-dependent (BOLD) functional MRI (fMRI) (1) have led to it becoming one of the most popular tools for measuring brain function in human neuroscience. However, fMRI provides only an indirect measure of neural activity by measuring task-related changes in cerebral haemodynamics that are coupled by a complex and only partially understood mechanism to changes in underlying neural activity. Recent evidence suggests that oscillations in the gamma frequency range, loosely defined as approximately 30-100 Hz, are well correlated temporally, spatially, and functionally with haemodynamic changes in cortex, suggesting a close relationship with the BOLD response (2-6). There is also increasing evidence that the most plausible mechanism for the generation of gamma oscillations is a neuronal network containing a mixture of interconnected pyramidal cells and GABAergic inhibitory interneurons (7-10) with the balance of excitation-inhibition setting the peak gamma oscillation frequency of the network (11). Similarly, it has recently been argued that the magnitude of the BOLD response is also sensitive to this excitation-inhibition balance (12, 13).In the current study, we tested the hypothesis that the local level of inhibition in an i...

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Section: Meg Methodsmentioning

confidence: 99%

Resting GABA concentration predicts peak gamma frequency and fMRI amplitude in response to visual stimulation in humans

Muthukumaraswamy

Edden

Jones

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

529

490

View full text Add to dashboard Cite

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“…An additional 29 reference channels were recorded for noise cancellation purposes, and the primary sensors were analyzed as synthetic third‐order gradiometers [Vrba and Robinson, 2001]. For source localization, a multiple local‐spheres forward model [Huang et al, 1999] was derived by fitting spheres to the brain surface extracted by FSL's Brain Extraction Tool [Smith, 2002]. Estimates of the three‐dimensional distribution of source power were derived for the whole head at 4 mm isotropic voxel resolution for each participant.…”

Section: Experimental Design and Proceduresmentioning

confidence: 99%

Intersubject variability and induced gamma in the visual cortex: DCM with empirical Bayes and neural fields

Pinotsis

Perry

Singh

et al. 2016

Human Brain Mapping

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This article describes the first application of a generic (empirical) Bayesian analysis of between‐subject effects in the dynamic causal modeling (DCM) of electrophysiological (MEG) data. It shows that (i) non‐invasive (MEG) data can be used to characterize subject‐specific differences in cortical microcircuitry and (ii) presents a validation of DCM with neural fields that exploits intersubject variability in gamma oscillations. We find that intersubject variability in visually induced gamma responses reflects changes in the excitation‐inhibition balance in a canonical cortical circuit. Crucially, this variability can be explained by subject‐specific differences in intrinsic connections to and from inhibitory interneurons that form a pyramidal‐interneuron gamma network. Our approach uses Bayesian model reduction to evaluate the evidence for (large sets of) nested models—and optimize the corresponding connectivity estimates at the within and between‐subject level. We also consider Bayesian cross‐validation to obtain predictive estimates for gamma‐response phenotypes, using a leave‐one‐out procedure. Hum Brain Mapp 37:4597–4614, 2016. © The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

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“…The brain anatomy and head location were based on a real experimental recording session and the simulated sampling rate was 600Hz. In all cases a multiple local sphere volume conductor head model (Huang, 1999) was employed and the forward solution was based on the dipole model derived by Sarvas (Sarvas, 1987).…”

Section: ) Simulationsmentioning

confidence: 99%

Measuring temporal, spectral and spatial changes in electrophysiological brain network connectivity

et al. 2014

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ABSTRACT:The topic of functional connectivity in neuroimaging is expanding rapidly and many studies now focus on coupling between spatially separate brain regions. These studies show that a relatively small number of large scale networks exist within the brain, and that healthy function of these networks is disrupted in many clinical populations. To date, the vast majority of studies probing connectivity employ techniques that compute time averaged correlation over several minutes, and between specific pre-defined brain locations. However, increasing evidence suggests that functional connectivity is non-stationary in time. Further, electrophysiological measurements show that connectivity is dependent on the frequency band of neural oscillations. It is also conceivable that networks exhibit a degree of spatial inhomogeneity, i.e. the large scale networks that we observe may result from the time average of multiple transiently synchronised sub-networks, each with their own spatial signature. This means that the next generation of neuroimaging tools to compute functional connectivity must account for spatial inhomogeneity, spectral non-uniformity and temporal non-stationarity. Here, we present a means to achieve this via application of windowed canonical correlation analysis (CCA) to source space projected MEG data. We describe generation of time-frequency connectivity plots, showing the temporal and spectral distribution of coupling between brain regions. Moreover, CCA over voxels provides a means to assess spatial nonuniformity within short time-frequency windows. The feasibility of this technique is demonstrated in simulation and in a resting state MEG experiment where we elucidate multiple distinct spatiotemporal-spectral modes of covariation between the left and right sensorimotor areas. 3 1) INTRODUCTION:Traditional analysis of neuroimaging data has focussed on the identification of significant changes in some metric of interest that are time locked to a particular task. Such methodologies usually rely on knowledge of task timing, and in some cases accurate models of the temporal evolution of neuroimaging signals which are then compared to measured data. These techniques have proved effective in highlighting brain regions that are involved in sensory and cognitive tasks. However, the last decade has seen a 'paradigm shift' in functional brain imaging (Raichle, 2009), with traditional analyses increasingly complemented by analysis of functional connectivity (Biswal et al., 1995, Beckmann et al., 2005, Fox et al., 2005, Fox and Raichle, 2007, Deco and Corbetta, 2011. Here, researchers seek to elucidate spatial patterns of temporal covariation between brain regions.Significant statistical interdependency (e.g. assessed via temporal correlation (Biswal et al., 1995) or independent component analysis (Beckmann et al., 2005)) between signals originating in two or more spatially separate anatomical regions is usually taken to mean that those regions are 'connected'. Functional magnetic resonance imaging (fMRI) has...

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A sensor-weighted overlapping-sphere head model and exhaustive head model comparison for MEG

Cited by 577 publications

References 16 publications

Resting GABA concentration predicts peak gamma frequency and fMRI amplitude in response to visual stimulation in humans

Resting GABA concentration predicts peak gamma frequency and fMRI amplitude in response to visual stimulation in humans

Intersubject variability and induced gamma in the visual cortex: DCM with empirical Bayes and neural fields

Measuring temporal, spectral and spatial changes in electrophysiological brain network connectivity

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