Dynamics of task-related electrophysiological networks: a benchmarking study

Tabbal, Judie; Kabbara, Aya; Khalil, Mohamad; Benquet, Pascal; Hassan, Mahmoud

doi:10.1016/j.neuroimage.2021.117829

Cited by 13 publications

(20 citation statements)

References 83 publications

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“…et al, 2010), particularly in EEG and MEG (Debener et al, 2005;Delorme et al, 2002;Malinowska et al, 2014;Onton et al, 2005;Tang et al, 2002). Moreover, a recent study has compared the effectiveness of different BSS algorithms when analyzing dynamical functional connectivity on MEG (Tabbal et al, 2021). It has been argued…”

Section: Detectability Of Deep Sources With Bss On Simultaneous Meg-seegmentioning

confidence: 99%

Magnetoencephalography can reveal deep brain network activities linked to memory processes

López‐Madrona

Villalon

Badier

et al. 2022

Human Brain Mapping

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Recording from deep neural structures such as hippocampus noninvasively and yet with high temporal resolution remains a major challenge for human neuroscience. Although it has been proposed that deep neuronal activity might be recordable during cognitive tasks using magnetoencephalography (MEG), this remains to be demonstrated as the contribution of deep structures to MEG recordings may be too small to be detected or might be eclipsed by the activity of large‐scale neocortical networks. In the present study, we disentangled mesial activity and large‐scale networks from the MEG signals thanks to blind source separation (BSS). We then validated the MEG BSS components using intracerebral EEG signals recorded simultaneously in patients during their presurgical evaluation of epilepsy. In the MEG signals obtained during a memory task involving the recognition of old and new images, we identified with BSS a putative mesial component, which was present in all patients and all control subjects. The time course of the component selectively correlated with stereo‐electroencephalography signals recorded from hippocampus and rhinal cortex, thus confirming its mesial origin. This finding complements previous studies with epileptic activity and opens new possibilities for using MEG to study deep brain structures in cognition and in brain disorders.

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Section: Detectability Of Deep Sources With Bss On Simultaneous Meg-seegmentioning

confidence: 99%

Magnetoencephalography can reveal deep brain network activities linked to memory processes

López‐Madrona

Villalon

Badier

et al. 2022

Human Brain Mapping

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show abstract

“…Due to its versatility, ICA has been widely used to remove artifacts (Jung et al, 2000) and to separate neuronal sources in local field potentials (Herreras et al, 2015;Makarov et al, 2010), particularly in EEG and MEG (Debener et al, 2005;Delorme et al, 2002;Malinowska et al, 2014;Onton et al, 2005;Tang et al, 2002). Moreover, a recent study has compared the effectiveness of different BSS algorithms when analyzing dynamical functional connectivity on MEG (Tabbal et al, 2021). It has been argued that ICA relies not only on the independence of the sources, but also on their sparsity (Daubechies et al, 2009), which has been recently…”

Section: Detectability Of Deep Sources With Bss On Simultaneous Meg-seegmentioning

confidence: 99%

Magnetoencephalography can reveal deep brain network activities linked to memory processes

López‐Madrona

Villalon

Badier

et al. 2022

Preprint

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Recording from deep neural structures such as hippocampus non-invasively and yet with high temporal resolution remains a major challenge for human neuroscience. Although it has been proposed that deep neuronal activity might be recordable during cognitive tasks using magnetoencephalography (MEG), this remains to be demonstrated as the contribution of deep structures to MEG recordings may be too small to be detected or might be eclipsed by the activity of large-scale neocortical networks. In the present study, we disentangled mesial activity and large-scale networks from the MEG signals thanks to blind source separation (BSS). We then validated the MEG BSS components using intracerebral EEG signals recorded simultaneously in patients during their presurgical evaluation of epilepsy. In the MEG signals obtained during a memory task involving the recognition of old and new images, we identified with BSS a putative mesial component, which was present in all patients and all control subjects. The time course of the component selectively correlated with SEEG signals recorded from hippocampus and rhinal cortex, thus confirming its mesial origin. This finding complements previous studies with epileptic activity and opens new possibilities for using MEG to study deep brain structures in cognition and in brain disorders.

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“…Surprisingly, studies using electrophysiological neuroimaging techniques such as magneto/ electro-encephalography (M/EEG) are rare, and most of the few that do exist have investigated motor adaptation (e.g., Gentili et al, 2015;Mehrkanoon et al, 2016;Miraglia et al, 2018) and, to a lesser extent, motor skill learning (e.g., Schubert et al, 2021;Tzvi et al, 2016Tzvi et al, , 2018 during very short periods of practice (from single sessions to very few days). This is despite the flexibility and ease of use of these neuroimaging techniques and the recent development and adaptation of existing brain dynamics methodologies to M/EEG data (Hassan & Wendling, 2018;O'Neill et al, 2017;Tabbal et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Dynamic rewiring of electrophysiological brain networks during learning

Ruggeri

Miehlbradt

Kabbara

et al. 2023

Network Neuroscience

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Human learning is an active and complex process. However, the brain mechanisms underlying human skill learning and the effect of learning on the communication between brain regions, at different frequency bands, are still largely unknown. Here, we tracked changes in large-scale electrophysiological networks over a 6-week training period during which participants practiced a series of motor sequences during 30 home training sessions. Our findings showed that brain networks become more flexible with learning in all the frequency bands from theta to gamma ranges. We found consistent increase of flexibility in the prefrontal and limbic areas in the theta and alpha band, and over somatomotor and visual areas in the alpha band. Specific to the beta rhythm, we revealed that higher flexibility of prefrontal regions during the early stage of learning strongly correlated with better performance measured during home training sessions. Our findings provide novel evidence that prolonged motor skill practice results in higher, frequency-specific, temporal variability in brain network structure.

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Dynamics of task-related electrophysiological networks: a benchmarking study

Cited by 13 publications

References 83 publications

Magnetoencephalography can reveal deep brain network activities linked to memory processes

Magnetoencephalography can reveal deep brain network activities linked to memory processes

Magnetoencephalography can reveal deep brain network activities linked to memory processes

Dynamic rewiring of electrophysiological brain networks during learning

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