Phase–amplitude coupling and the BOLD signal: A simultaneous intracranial EEG (icEEG) - fMRI study in humans performing a finger-tapping task

Murta, Teresa; Chaudhary, Umair J.; Tierney, Tim M.; Dias, Afonso; Leite, Marco; Carmichael, David W.; Figueiredo, Patrícia; Lemieux, Louis

doi:10.1016/j.neuroimage.2016.08.036

Cited by 41 publications

(26 citation statements)

References 68 publications

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“…The PAC p values were adjusted with Benjamini and Hochberg FDR procedure (Benjamini and Hochberg, 1995;Benjamini et al, 2001;Genovese et al, 2002) to address the multiple-comparison problem. PAC within the motor cortex was also consistent with previous findings (Miller et al, 2012;Murta et al, 2017). Both corticocortical and thalamocortical PAC were absent during movement of either hand at a group level; however, in 3 of 11 subjects, ipsilateral coupling decreased without being completely suppressed (Fig.…”

Section: Pac Coupling During Rest and Suppression During Movement Exesupporting

confidence: 91%

The Functional Role of Thalamocortical Coupling in the Human Motor Network

Opri¹,

Cernera²,

Okun

et al. 2019

J. Neurosci.

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The amplitude of high broadband activity in human cortical field potentials indicates local processing and has repeatedly been shown to reflect motor control in the primary motor cortex. In a group of male and female subjects affected by essential tremor and undergoing deep brain stimulation surgery, ventral intermediate nucleus low-frequency oscillations (Ͻ30 Hz) entrain the corticomotor high broadband activity (Ͼ40 Hz) during rest, relinquishing that role during movement execution. This finding suggests that there is significant cross-rhythm communication between thalamocortical regions, and motor behavior corresponds to changes in thalamocortical phaseamplitude coupling profiles. Herein, we demonstrate that thalamocortical coupling is a crucial mechanism for gating motor behavior.

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Section: Pac Coupling During Rest and Suppression During Movement Exesupporting

confidence: 91%

The Functional Role of Thalamocortical Coupling in the Human Motor Network

Opri¹,

Cernera²,

Okun

et al. 2019

J. Neurosci.

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show abstract

“…In contrast gamma activity was highly localised in motor regions and only present during motor activity. It is possible that the relatively low power of the gamma activity made it more difficult to measure in the context of greater noise levels in ECoG recordings during fMRI although we have previously performed evaluation of this data (28).…”

Section: Spatial Coupling Aspectsmentioning

confidence: 99%

Measurement of the mapping between intracranial EEG and fMRI recordings in the human brain

Carmichael

Vulliémoz

Murta³

et al. 2017

Preprint

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(150 non-technical)There are considerable gaps in our understanding of the relationship between human brain activity measured at different temporal and spatial scales by intracranial electroencephalography and fMRI. By comparing individual features and summary descriptions of intracranial EEG activity we determined which best predict fMRI changes in the sensorimotor cortex in two brain states: at rest and during motor performance. We also then examine the specificity of this relationship to spatial colocalisation of the two signals.We acquired electrocorticography and fMRI simultaneously (ECoG-fMRI) in the sensorimotor cortex of 3 patients with epilepsy. During motor activity, high gamma power was the only frequency band where the electrophysiological response was colocalised with fMRI measures across all subjects. The best model of fMRI changes was its principal components, a parsimonious description of the entire ECoG spectrogram. This model performed much better than a model based on the classical frequency bands both during task and rest periods or models derived on a summary of cross spectral changes (e.g. 'root mean squared EEG frequency' ). This suggests that the region specific fMRI signal is reflected in spatially and spectrally distributed EEG activity.

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“…phase-amplitude coupling (PAC), in both cortical and subcortical regions for a variety of brain states [97][98][99][100][101][102] . This feature has also been used for the correlation analysis of the concurrent EEG and rs-fMRI signal recordings from animals and humans 18,19,21,23,[103][104][105] . Our analysis confirms that the phases of the slow oscillatory rs-fMRI signal carry critical dynamic brain state features 3 .…”

Section: Discussionmentioning

confidence: 99%

Predicting the fMRI signal fluctuation with echo-state neural networks trained on vascular network dynamics

Sobczak

Sejnowski

et al. 2019

Preprint

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Resting-state functional MRI (rs-fMRI) studies have revealed specific low-frequency hemodynamic signal fluctuations (<0.1 Hz) in the brain, which could be related to oscillations in neural activity through several mechanisms. Although the vascular origin of the fMRI signal is well established, the neural correlates of global rs-fMRI signal fluctuations are difficult to separate from other confounding sources. Recently, we reported that single-vessel fMRI slow oscillations are directly coupled to brain state changes. Here, we used an echo-state network (ESN) to predict the future temporal evolution of the rs-fMRI slow oscillatory feature from both rodent and human brains. rs-fMRI signals from individual blood vessels that were strongly correlated with neural calcium oscillations were used to train an ESN to predict brain state-specific rs-fMRI signal fluctuations. The ESN-based prediction model was also applied to recordings from the Human Connectome Project (HCP), which classified variance-independent brain states based on global fluctuations of rs-fMRI features. The ESN revealed brain states with global synchrony and decoupled internal correlations within the default-mode network.This phenomenon has been observed at the level of single-vessel fMRI dynamic mapping with concurrent calcium recordings, which show stronger neural correlation with the fMRI signal detected from individual penetrating vessels than the rest of voxels through the whole rodent cortex 24 . This highly coherent vessel-specific fMRI signal fluctuation is a direct signal source that is closely linked to global brain state changes. Here, we applied the artificial state-encoding neural network system in a prediction scheme to better model the brain state-specific coherent oscillatory features from the vessel voxels.The echo state network 47 (ESN), a recurrent neural network (RNN) based on reservoir computing 48, 49 , provides a computational framework for temporally predicting dynamic brain signals. The ESN's main component is a dynamic reservoir consisting of recurrently connected

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Phase–amplitude coupling and the BOLD signal: A simultaneous intracranial EEG (icEEG) - fMRI study in humans performing a finger-tapping task

Cited by 41 publications

References 68 publications

The Functional Role of Thalamocortical Coupling in the Human Motor Network

The Functional Role of Thalamocortical Coupling in the Human Motor Network

Measurement of the mapping between intracranial EEG and fMRI recordings in the human brain

Predicting the fMRI signal fluctuation with echo-state neural networks trained on vascular network dynamics

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