MEG/EEG Group Analysis With Brainstorm

Tadel, François; Bock, Elizabeth; Niso, Guiomar; Mosher, John C.; Cousineau, Martin; Pantazis, Dimitrios; Leahy, Richard M.; Baillet, Sylvain

doi:10.3389/fnins.2019.00076

Cited by 180 publications

(156 citation statements)

References 28 publications

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“…We projected scalp EEG signals to the cortex model consisting of 15000 voxels. We averaged all projected source activity on the individual cortex model across trials and projected it onto the template cortex model (FSAverage, 15000 voxels) for group-level analysis[64].…”

Section: Discussionmentioning

confidence: 99%

Pinging the Brain with Transcranial Magnetic Stimulation Reveals Cortical Reactivity in Time and Space

Ahn

Fröhlich

2019

Preprint

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17 Single-pulse transcranial magnetic stimulation (TMS) elicits an evoked 18 electroencephalography (EEG) potential (TMS-evoked potential, TEP), which is interpreted 19 as direct evidence of cortical reactivity to TMS. Thus, combining TMS with EEG may enable 20 the mechanistic investigation of how TMS treatment paradigms engage network targets in 21 the brain. However, there remains a central controversy about whether the TEP is a genuine 22 marker of cortical reactivity to TMS or the TEP is contaminated by responses to peripheral 23 somatosensory and auditory inputs. Resolving this controversy is of great significance for 24 the field and will validate TMS as a tool to probe networks of interest in cognitive and clinical 25 neuroscience. Here, we delineated the TEP's cortical origins by localizing successive TEP 26 components in time and space and modulating them subsequently with transcranial direct 27 current stimulation (tDCS). We collected both motor evoked potentials (MEPs) and TEPs 28 elicited by suprathreshold single-pulse TMS to the left primary motor cortex (M1). We found 29 that the earliest TEP component (P25) was localized on the TMS target location (left M1) 30 and the following TEP components (N45 and P60) largely were localized on the primary 31 somatosensory cortex, which may reflect afferent input by hand-muscle twitches. The later 32 TEP components (N100, P180, and N280) largely were localized to the auditory cortex. To 33 casually test that these components reflect cortical and corticospinal excitability, we applied 34 tDCS to the left M1. As hypothesized, we found that tDCS modulated cortical and 35 corticospinal excitability selectively by modulating the pre-stimulus mu-rhythm oscillatory 36 power. Together, our findings provide causal evidence that the early TEP components 37 reflect cortical reactivity to TMS. 38 39 tDCS 3 41 Introduction 42 Combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) 43 provide an opportunity to quantify brain network dynamics by pinging them with TMS[1]. The 44 TMS-evoked potential (TEP), which is considered a reflection of cortical reactivity to TMS, 45 has been shown to have diagnostic value in a variety of neurological and psychiatric 46 disorders[2]. However, there is ongoing controversy about the origin of the TEP. A recent 47 study claimed that the stimulation of peripheral nerves and the TMS coil's loud clicking 48 sound may confound the TEP amplitude[3]. Specifically, sham TMS elicited EEG potentials 49 that were correlated highly with those by real TMS, despite the use of sophisticated 50 procedures to attenuate the somatosensory and auditory confounds. In rebuttal of this 51 publication, it was suggested that insufficient TMS intensity and incomplete auditory 52 masking may explain the sensory-dominant evoked potentials in the experiment[4]. 53 Nonetheless, residual auditory input is unavoidable in TMS studies[5] because of air and 54 bone conduction from the TMS clicking sound[6,7]. Thus, it continues to be debated whether...

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Section: Discussionmentioning

confidence: 99%

Pinging the Brain with Transcranial Magnetic Stimulation Reveals Cortical Reactivity in Time and Space

Ahn

Fröhlich

2019

Preprint

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“…A z-score normalization was applied to each cortical source trace with respect to the baseline period (-200, 0 ms): this standardization replaces the raw source amplitude value with a dimensionless statistic that is suitable for hypothesis testing. Such normalization reduces the influence of inter-individual fluctuations in neural current intensity that is due to anatomical or physiological differences of no (primary) relevance (Tadel et al, 2019).…”

Section: Data Analysis -Cortical Source Reconstructionmentioning

confidence: 99%

Altered beta-band oscillations and connectivity underlie detail-oriented visual processing in autism

Ronconi

Vitale

Federici

et al. 2020

Preprint

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Sensory and perceptual anomalies have been increasingly recognized as core phenotypic markers for autism spectrum disorders (ASD). A neurophysiological characterization of these anomalies is of utmost importance to understand more complex behavioural manifestations within the spectrum. The present study employed electroencephalography (EEG) to test whether detail-oriented visual perception, a recognized hallmark of ASD, is associated with altered neural oscillations and functional connectivity in beta (and alpha) frequency bands, considering their role in feedback and top-down reentrant signalling in the typical population. A sample of children with diagnosis of ASD (N=18) and typically developing peers (TD; N=20) performed a visual crowding task, where they had to discriminate a peripheral target letter surrounded by flankers at different distances, together with a control condition with no flankers. In TD participants the amplitude of the target-locked N1 component and its cortical sources was significantly modulated as a function of visual crowding, whereas such modulation was absent in the ASD group, suggesting that their visual scene analysis takes place without a flexible neural computation. The comparison between groups showed a decreased activity in the ASD group in occipital, infero-temporal and inferior/middle frontal regions in conditions requiring detail-oriented perception as opposed to conditions requiring the discrimination of target in isolation. Moreover, in TD participants detail-oriented perception was associated with an event-related beta power reduction (15-30 Hz), which was not evident in the ASD group. A data-driven functional connectivity analysis highlighted in the ASD sample an increased connectivity in the beta frequency range between occipital and infero-temporal regions. Notably, individual hyperconnectivity indexes correlated to less severe ASD symptomatology and to a diminished detail-oriented perception, suggesting a potential compensatory mechanism. Overall, these results show that altered communication in the beta frequency band may explain atypical perception in ASD, reflecting aberrant feedback connectivity within the visual system with potential cascade effects in visual scene parsing and higher-order functions.

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“…In the current study, to be able to investigate the subcortical sources as well as cortical ones, a mixed head-model was generated consisting of cortical (cortex) and subcortical regions (thalamus and brainstem). This headmodel was generated based on the template anatomy ICBM152 (non-linear average of 152 individual magnetic resonance scans, Fonov et al, 2011) and the default channel location file in the Brainstorm application (Tadel et al, 2019(Tadel et al, , 2011. The realistic head-model was developed using the boundary element method (BEM) as implemented in OpenMEEG (Gramfort Alexandre et al, 2010), and consisted of 3 compartments i.e., brain (cortical and subcortical), skull, and scalp with conductivity values of 0.33, 0.0041, and 0.33 S/m, respectively.…”

Section: Mixed Head-modelmentioning

confidence: 99%

“…The realistic head-model was developed using the boundary element method (BEM) as implemented in OpenMEEG (Gramfort Alexandre et al, 2010), and consisted of 3 compartments i.e., brain (cortical and subcortical), skull, and scalp with conductivity values of 0.33, 0.0041, and 0.33 S/m, respectively. Using the Brainstorm application (Tadel et al, 2019(Tadel et al, , 2011, the surface model of the cortex (triangulation of the cortical surface) was combined with the volume model (three-dimensional dipole grid) of the thalamus and brainstem. For the surface model, a dipole orthogonal to the surface was used at each grid point, while for the volume model, three dipoles with orthogonal orientations were considered at each grid point.…”

Section: Mixed Head-modelmentioning

confidence: 99%

Improving source modeling of auditory steady-state responses with frequency-specific brain maps

Farahani

Wouters

Wieringen

2019

Preprint

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Auditory steady-state responses (ASSRs) are evoked brain responses to modulated or repetitive acoustic stimuli. Due to a wide range of clinical and research applications, there is a great (clinical) interest to investigate the underlying neural generators of ASSRs. The cortical sources of ASSRs mostly are located in the auditory cortex (AC), although some studies avoiding prior assumptions regarding the number and location of the sources have also reported activity of sources outside of the AC. However, little is known about the number and location of these sources. In this study, we present a novel extension to minimum-norm imaging (MNI) which facilitates ASSR source reconstruction and provides a comprehensive and consistent picture of sources in response to low-as well as high modulation frequencies, monaurally presented to the left and right ears. Results demonstrate that the proposed MNI approach is successful in reconstructing sources located both within (primary) and outside (non-primary) of the AC. The locations of the non-primary sources are consistent with the literature. Primary sources are detected in every experimental condition, thereby corroborating the robustness of the approach. Moreover, we show that the MNI approach is capable of reconstructing the subcortical activities of ASSRs. In summary, the results indicate that the MNI approach outperforms the previously used method of group-ICA, in terms of detection of sources in the AC, reconstructing the subcortical activities and reducing computational load.

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MEG/EEG Group Analysis With Brainstorm

Cited by 180 publications

References 28 publications

Pinging the Brain with Transcranial Magnetic Stimulation Reveals Cortical Reactivity in Time and Space

Pinging the Brain with Transcranial Magnetic Stimulation Reveals Cortical Reactivity in Time and Space

Altered beta-band oscillations and connectivity underlie detail-oriented visual processing in autism

Improving source modeling of auditory steady-state responses with frequency-specific brain maps

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