Probing the Human Brain Functional Connectome with Simultaneous EEG and fMRI

Abbott, David F.

doi:10.3389/fnins.2016.00302

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…EEG (Lei et al, 2015 ; Abbott, 2016 ) has been widely used to construct BCI systems for exploring hMNS activations due to its high temporal resolution. In our study, the experimental movements were made by a humanoid robot at four different speed modes for AO tasks.…”

Section: Introductionmentioning

confidence: 99%

“…Both experiments are designed by a BCI form to validate the relationships between speed mode of AO and the hMNS activations. Due to the high temporal resolution, quantitative EEG (Lei et al, 2015 ; Abbott, 2016 ) is used as a convenient and cheap method to explore hMNS activations by measuring ERD suppressions (Oberman et al, 2007 ; Orgs et al, 2008 ). The ERD suppressions in EEG data are analyzed using a series of BCI methods and spatio-spectral-temporal characteristics.…”

Section: Introductionmentioning

confidence: 99%

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Effect of Different Movement Speed Modes on Human Action Observation: An EEG Study

Luo

Chao

et al. 2018

Front. Neurosci.

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Action observation (AO) generates event-related desynchronization (ERD) suppressions in the human brain by activating partial regions of the human mirror neuron system (hMNS). The activation of the hMNS response to AO remains controversial for several reasons. Therefore, this study investigated the activation of the hMNS response to a speed factor of AO by controlling the movement speed modes of a humanoid robot's arm movements. Since hMNS activation is reflected by ERD suppressions, electroencephalography (EEG) with BCI analysis methods for ERD suppressions were used as the recording and analysis modalities. Six healthy individuals were asked to participate in experiments comprising five different conditions. Four incremental-speed AO tasks and a motor imagery (MI) task involving imaging of the same movement were presented to the individuals. Occipital and sensorimotor regions were selected for BCI analyses. The experimental results showed that hMNS activation was higher in the occipital region but more robust in the sensorimotor region. Since the attended information impacts the activations of the hMNS during AO, the pattern of hMNS activations first rises and subsequently falls to a stable level during incremental-speed modes of AO. The discipline curves suggested that a moderate speed within a decent inter-stimulus interval (ISI) range produced the highest hMNS activations. Since a brain computer/machine interface (BCI) builds a path-way between human and computer/mahcine, the discipline curves will help to construct BCIs made by patterns of action observation (AO-BCI). Furthermore, a new method for constructing non-invasive brain machine brain interfaces (BMBIs) with moderate AO-BCI and motor imagery BCI (MI-BCI) was inspired by this paper.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effect of Different Movement Speed Modes on Human Action Observation: An EEG Study

Luo

Chao

et al. 2018

Front. Neurosci.

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“…The present framework at its core combines a segmentation and identification method for EEG features with a multivariate correlation approach, which is robust and capable of capturing event-related as well as spontaneous covariation between electrophysiological and hemodynamic time series. Given its ability to address these issues with a mathematically straightforward approach, the present framework may represent a valuable first step towards identifying symmetrical BOLD-EEG dependencies, which can be followed up by means of more complex fusion methods (see Abbott, 2016, for a review).…”

Section: Significancementioning

confidence: 99%

Cross multivariate correlation coefficients as screening tool for analysis of concurrent EEG‐fMRI recordings

Ji¹,

Petro

Chen³

et al. 2018

J of Neuroscience Research

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Over the past decade, the simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) data has garnered growing interest because it may provide an avenue towards combining the strengths of both imaging modalities. Given their pronounced differences in temporal and spatial statistics, the combination of EEG and fMRI data is however methodologically challenging. Here, we propose a novel screening approach that relies on a Cross Multivariate Correlation Coefficient (xMCC) framework. This approach accomplishes three tasks:(1) It provides a measure for testing multivariate correlation and multivariate uncorrelation of the two modalities; (2) it provides criterion for the selection of EEG features; (3) it performs a screening of relevant EEG information by grouping the EEG channels into clusters to improve efficiency and to reduce computational load when searching for the best predictors of the BOLD signal. The present report applies this approach to a data set with concurrent recordings of steady-state-visual evoked potentials (ssVEPs) and fMRI, recorded while observers viewed phase-reversing Gabor patches. We test the hypothesis that fluctuations in visuo-cortical mass potentials systematically covary with BOLD fluctuations not only in visual cortical, but also in anterior temporal and prefrontal areas. Results supported the hypothesis and showed that the xMCC-based analysis provides straightforward identification of neurophysiological plausible brain regions with EEG-fMRI covariance. Furthermore xMCC converged with other extant methods for EEG-fMRI analysis.

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“…For years, scientists have been seeking alternative solutions to successfully manage disorders concerning the central nervous system (CNS) based on the theory of brain plasticity in Parkinson's Disease (PD) and ASD [1][2][3] and the relationship between the gut microbiota and the CNS in ASD [4] or by using statistical tools for the early diagnosis and prediction of AD [5][6][7][8]. In this direction, EEG systems were invented for recording electrical activities on the scalp surface to reach conclusions for the functionality of the human brain [9][10][11]. From its very existence till today, EEG testing is the most common and simple imaging method for the identification of electrophysiology abnormalities in the human brain and their correlation to ASD and other related disorders.…”

Section: Introductionmentioning

confidence: 99%