Source Activity Correlation Effects on LCMV Beamformers in a Realistic Measurement Environment

In EEG and MEG studies on brain functional connectivity and source interactions can be performed at sensor or source level. Beamformers are well-established source-localization tools for MEG/EEG signals, being employed in source connectivity studies both in time and frequency domain. However, it has been demonstrated that beamformers suffer from a localization bias due to correlation between source time courses. This phenomenon has been ascertained by means of theoretical proofs and simulations. Nonetheless, the impact of correlated sources on localization outputs with real data has been disputed for a long time. In this paper, by means of a phantom, we address the correlation issue in a realistic MEG environment. Localization performances in the presence of simultaneously active sources are studied as a function of correlation degree and distance between sources. A linear constrained minimum variance (LCMV) beamformer is applied to the oscillating signals generated by the current dipoles within the phantom. Results show that high correlation affects mostly dipoles placed at small distances (1, 5 centimeters). In this case the sources merge. If the dipoles lie 3 centimeters apart, the beamformer localization detects attenuated power amplitudes and blurred sources as the correlation level raises

Prefrontal Theta-Phase Synchronized Brain Stimulation With Real-Time EEG-Triggered TMS

BackgroundTheta-band neuronal oscillations in the prefrontal cortex are associated with several cognitive functions. Oscillatory phase is an important correlate of excitability and phase synchrony mediates information transfer between neuronal populations oscillating at that frequency. The ability to extract and exploit the prefrontal theta rhythm in real time in humans would facilitate insight into neurophysiological mechanisms of cognitive processes involving the prefrontal cortex, and development of brain-state-dependent stimulation for therapeutic applications.ObjectivesWe investigate individual source-space beamforming-based estimation of the prefrontal theta oscillation as a method to target specific phases of the ongoing theta oscillations in the human dorsomedial prefrontal cortex (DMPFC) with real-time EEG-triggered transcranial magnetic stimulation (TMS). Different spatial filters for extracting the prefrontal theta oscillation from EEG signals are compared and additional signal quality criteria are assessed to take into account the dynamics of this cortical oscillation.MethodsTwenty two healthy participants were recruited for anatomical MRI scans and EEG recordings with 18 composing the final analysis. We calculated individual spatial filters based on EEG beamforming in source space. The extracted EEG signal was then used to simulate real-time phase-detection and quantify the accuracy as compared to post-hoc phase estimates. Different spatial filters and triggering parameters were compared. Finally, we validated the feasibility of this approach by actual real-time triggering of TMS pulses at different phases of the prefrontal theta oscillation.ResultsHigher phase-detection accuracy was achieved using individualized source-based spatial filters, as compared to an average or standard Laplacian filter, and also by detecting and avoiding periods of low theta amplitude and periods containing a phase reset. Using optimized parameters, prefrontal theta-phase synchronized TMS of DMPFC was achieved with an accuracy of ±55°.ConclusionThis study demonstrates the feasibility of triggering TMS pulses during different phases of the ongoing prefrontal theta oscillation in real time. This method is relevant for brain state-dependent stimulation in human studies of cognition. It will also enable new personalized therapeutic repetitive TMS protocols for more effective treatment of neuropsychiatric disorders

TMS- EEG Signatures of GABAergic Neurotransmission in the Human Cortex

Combining transcranial magnetic stimulation (TMS) and electroencephalography (EEG) constitutes a powerful tool to directly assess human cortical excitability and connectivity. TMS of the primary motor cortex elicits a sequence of TMS-evoked EEG potentials (TEPs). It is thought that inhibitory neurotransmission through GABA-A receptors (GABAAR) modulates early TEPs (<50 ms after TMS), whereas GABA-B receptors (GABABR) play a role for later TEPs (at ∼100 ms after TMS). However, the physiological underpinnings of TEPs have not been clearly elucidated yet. Here, we studied the role of GABAA/B-ergic neurotransmission for TEPs in healthy subjects using a pharmaco-TMS-EEG approach. In Experiment 1, we tested the effects of a single oral dose of alprazolam (a classical benzodiazepine acting as allosteric-positive modulator at α1, α2, α3, and α5 subunit-containing GABAARs) and zolpidem (a positive modulator mainly at the α1 GABAAR) in a double-blind, placebo-controlled, crossover study. In Experiment 2, we tested the influence of baclofen (a GABABR agonist) and diazepam (a classical benzodiazepine) versus placebo on TEPs. Alprazolam and diazepam increased the amplitude of the negative potential at 45 ms after stimulation (N45) and decreased the negative component at 100 ms (N100), whereas zolpidem increased the N45 only. In contrast, baclofen specifically increased the N100 amplitude. These results provide strong evidence that the N45 represents activity of α1-subunit-containing GABAARs, whereas the N100 represents activity of GABABRs. Findings open a novel window of opportunity to study alteration of GABAA-/GABAB-related inhibition in disorders, such as epilepsy or schizophrenia

μ-Rhythm Extracted With Personalized EEG Filters Correlates With Corticospinal Excitability in Real-Time Phase-Triggered EEG-TMS

Ongoing brain activity has been implicated in the modulation of cortical excitability. The combination of electroencephalography (EEG) and transcranial magnetic stimulation (TMS) in a real-time triggered setup is a novel method for testing hypotheses about the relationship between spontaneous neuronal oscillations, cortical excitability, and synaptic plasticity. For this method, a reliable real-time extraction of the neuronal signal of interest from scalp EEG with high signal-to-noise ratio (SNR) is of crucial importance. Here we compare individually tailored spatial filters as computed by spatial-spectral decomposition (SSD), which maximizes SNR in a frequency band of interest, against established local C3-centered Laplacian filters for the extraction of the sensorimotor μ-rhythm. Single-pulse TMS over the left primary motor cortex was synchronized with the surface positive or negative peak of the respective extracted signal, and motor evoked potentials (MEP) were recorded with electromyography (EMG) of a contralateral hand muscle. Both extraction methods led to a comparable degree of MEP amplitude modulation by phase of the sensorimotor μ-rhythm at the time of stimulation. This could be relevant for targeting other brain regions with no working benchmark such as the local C3-centered Laplacian filter, as sufficient SNR is an important prerequisite for reliable real-time single-trial detection of EEG features

A meta-analysis of job satisfaction correlates in the public administration literature

This study reports the findings of the first quantitative review of the public administration literature on job satisfaction. We retrieved quantitative data from primary studies published in 42 public administration journals since 1969 and performed a meta-analysis of the relationships between job satisfaction and 43 correlates

A New Framework to Interpret Individual Inter-Hemispheric Compensatory Communication after Stroke

Stroke constitutes the main cause of adult disability worldwide. Even after application of standard rehabilitation protocols, the majority of patients still show relevant motor impairment. Outcomes of standard rehabilitation protocols have led to mixed results, suggesting that relevant factors for brain re-organization after stroke have not been considered in explanatory models. Therefore, finding a comprehensive model to optimally define patient-dependent rehabilitation protocols represents a crucial topic in clinical neuroscience. In this context, we first report on the rehabilitation models conceived thus far in the attempt of predicting stroke rehabilitation outcomes. Then, we propose a new framework to interpret results in stroke literature in the light of the latest evidence regarding: (1) the role of the callosum in inter-hemispheric communication, (2) the role of prefrontal cortices in exerting a control function, and (3) diaschisis mechanisms. These new pieces of evidence on the role of callosum can help to understand which compensatory mechanism may take place following a stroke. Moreover, depending on the individual impairment, the prefrontal control network will play different roles according to the need of high-level motor control. We believe that our new model, which includes crucial overlooked factors, will enable clinicians to better define individualized motor rehabilitation protocols