Multivariate time–frequency analysis of electromagnetic brain activity during bimanual motor learning

Boonstra, Tjeerd W.; Daffertshofer, Andreas; Breakspear, Michael; Beek, Peter J.

doi:10.1016/j.neuroimage.2007.03.012

Cited by 101 publications

(77 citation statements)

References 58 publications

(66 reference statements)

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“…The observation of super-Gaussian (double-exponential) PDFs primarily in the beta range is also of importance, particularly because this frequency range underlies critical cognitive and behavioral processes, such as motor learning and coordination (Boonstra et al, 2007). This finding suggests the presence of nontrivial spatial and temporal correlations among macroscopic neuronal populations.…”

Section: Discussionmentioning

confidence: 99%

Bistability and Non-Gaussian Fluctuations in Spontaneous Cortical Activity

Freyer¹,

Aquino²,

Robinson³

et al. 2009

Journal of Neuroscience

162

156

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The brain is widely assumed to be a paradigmatic example of a complex, self-organizing system. As such, it should exhibit the classic hallmarks of nonlinearity, multistability, and "nondiffusivity" (large coherent fluctuations). Surprisingly, at least at the very large scale of neocortical dynamics, there is little empirical evidence to support this, and hence most computational and methodological frameworks for healthy brain activity have proceeded very reasonably from a purely linear and diffusive perspective. By studying the temporal fluctuations of power in human resting-state electroencephalograms, we show that, although these simple properties may hold true at some temporal scales, there is strong evidence for bistability and nondiffusivity in key brain rhythms. Bistability is manifest as nonclassic bursting between high-and low-amplitude modes in the alpha rhythm. Nondiffusivity is expressed through the irregular appearance of high amplitude "extremal" events in beta rhythm power fluctuations. The statistical robustness of these observations was confirmed through comparison with Gaussian-rendered phase-randomized surrogate data. Although there is a good conceptual framework for understanding bistability in cortical dynamics, the implications of the extremal events challenge existing frameworks for understanding large-scale brain systems.

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Section: Discussionmentioning

confidence: 99%

Bistability and Non-Gaussian Fluctuations in Spontaneous Cortical Activity

Freyer¹,

Aquino²,

Robinson³

et al. 2009

Journal of Neuroscience

162

156

View full text Add to dashboard Cite

show abstract

“…To obtain time-frequency spectra for each reconstructed source signal, we used complex Morlet wavelets with a center frequency of 1 Hz and a bandwidth of 6 cycles (Boonstra et al, 2007). The magnitude squared time-frequency spectra were averaged over time to estimate wavelet-based power spectra and then averaged across participants to yield the spatial distribution of power spectral density at the group level.…”

Section: Spectral Decompositionmentioning

confidence: 99%

Intrinsic Coupling Modes in Source-Reconstructed Electroencephalography

et al. 2014

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Intrinsic coupling of neuronal assemblies constitutes a key feature of ongoing brain activity, yielding the rich spatiotemporal patterns observed in neuroimaging data and putatively supporting cognitive processes. Intrinsic coupling has been investigated in electrophysiological recordings using two types of functional connectivity measures: amplitude and phase coupling. These two coupling modes differ in their likely causes and functions, and have been proposed to provide complementary insights into intrinsic neuronal interactions. Here, we investigate the relationship between amplitude and phase coupling in source-reconstructed electroencephalography (EEG). Volume conduction is a key obstacle for connectivity analysis in EEG-we therefore also test the envelope correlation of orthogonalized signals and the phase lag index. Functional connectivity between six seed source regions (bilateral visual, sensorimotor, and auditory cortices) and all other cortical voxels was computed. For all four measures, coupling between homologous sensory areas in both hemispheres was significantly higher than with other voxels at the same physical distance. The frequency of significant coupling differed between sensory areas: 10 Hz for visual, 30 Hz for auditory, and 40 Hz for sensorimotor cortices. By contrasting envelope correlations and phase locking values, we observed two distinct clusters of voxels showing a different relationship between amplitude and phase coupling. Large clusters contiguous to the seed regions showed an identity (1:1) relationship between amplitude and phase coupling, whereas a cluster located around the contralateral homologous regions showed higher phase than amplitude coupling. These results show a relationship between intrinsic coupling modes that is distinct from the effect of volume conduction.

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“…Several MEG studies have shown changes in beta-band synchronization in the motor cortex during motor learning, reflecting a modulation in cortical excitability (Boonstra et al, 2007;Houweling et al, 2008;Pollok et al, 2014). However, few studies have investigated connectivity changes in the distributed motor system using EEG or MEG.…”

Section: Introductionmentioning

confidence: 99%

Upregulation of cortico-cerebellar functional connectivity after motor learning

et al. 2016

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Interactions between the cerebellum and primary motor cortex are crucial for the acquisition of new motor skills. Recent neuroimaging studies indicate that learning motor skills is associated with subsequent modulation of resting-state functional connectivity in the cerebellar and cerebral cortices. The neuronal processes underlying the motor-learning induced plasticity are not well understood. Here, we investigate changes in functional connectivity in source-reconstructed electroencephalography (EEG) following the performance of a single session of a dynamic force task in twenty young adults. Source activity was reconstructed in 112 regions of interest (ROIs) and the functional connectivity between all ROIs was estimated using imaginary part of the coherence. Significant changes in resting-state connectivity were assessed using partial least squares (PLS). We found that subjects adapted their motor performance during the training session and showed improved accuracy but with slower movement times. A number of connections were significantly upregulated after motor training, principally involving connections within the cerebellum and between the cerebellum and motor cortex. Increased connectivity was confined to specific frequency ranges in the mu and beta bands. Post-hoc analysis of the phase spectra of these cerebellar and corticocerebellar connections revealed an increased phase-lag between motor cortical and cerebellar activity following motor practice. These findings show a reorganization of intrinsic corticocerebellar connectivity related to motor adaption and demonstrate the potential of EEG connectivity analysis in source-space to reveal the neuronal processes that underpin neural plasticity.

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Multivariate time–frequency analysis of electromagnetic brain activity during bimanual motor learning

Cited by 101 publications

References 58 publications

Bistability and Non-Gaussian Fluctuations in Spontaneous Cortical Activity

Bistability and Non-Gaussian Fluctuations in Spontaneous Cortical Activity

Intrinsic Coupling Modes in Source-Reconstructed Electroencephalography

Upregulation of cortico-cerebellar functional connectivity after motor learning

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