Long-Range Temporal Correlations in the amplitude of alpha oscillations predict and reflect strength of intracortical facilitation: Combined TMS and EEG study
While variability of the motor responses to transcranial magnetic stimulation (TMS) is widely acknowledged, little is known about its central origin. One plausible explanation for such variability may relate to different neuronal states defining the reactivity of the cortex to TMS. In this study intrinsic spatio-temporal neuronal dynamics were estimated with Long-Range Temporal Correlations (LRTC) in order to predict the inter-individual differences in the strength of intra-cortical facilitation (ICF) and short-interval intracortical inhibition (SICI) produced by paired-pulse TMS (ppTMS) of the left primary motor cortex. LRTC in the alpha frequency range were assessed from multichannel electroencephalography (EEG) obtained at rest before and after the application of and single-pulse TMS (spTMS) and ppTMS protocols. For the EEG session, preceding TMS application, we showed a positive correlation across subjects between the strength of ICF and LRTC in the fronto-central and parietal areas. This in turn attests to the existence of subject-specific neuronal phenotypes defining the reactivity of the brain to ppTMS. In addition, we also showed that ICF was associated with the changes in neuronal dynamics in the EEG session after the application of the stimulation. This result provides a complementary evidence for the recent findings demonstrating that the cortical stimulation with sparse non-regular stimuli might have considerable long-lasting effects on the cortical activity.
With a view to early detection of statistical instability of water quality, it is necessary to analyze the probability of deviations of parameters under inspection from their most probable values. It is shown the outlook of using in this case time-frame inspection charts enabling to fix with high reliability the exit of the system from sustainable state, and on this basis to make management decisions, such as on the transfer of water management into emergency mode of operation or on the need to find the source of instability.
Increasing evidence suggests that neuronal communication is a defining property of functionally specialized brain networks and that it is implemented through synchronization between population activities of distinct brain areas. The detection of long-range coupling in electroencephalography (EEG) and magnetoencephalography (MEG) data using conventional metrics (such as coherence or phase-locking value) is by definition contaminated by spatial leakage. Methods such as imaginary coherence, phase-lag index or orthogonalized amplitude correlations tackle spatial leakage by ignoring zero-phase interactions. Although useful, these metrics will by construction lead to false negatives in cases where true zero-phase coupling exists in the data and will underestimate interactions with phase lags in the vicinity of zero. Yet, empirically observed neuronal synchrony in invasive recordings indicates that it is not uncommon to find zero or close-to-zero phase lag between the activity profiles of coupled neuronal assemblies. Here, we introduce a novel method that allows us to mitigate the undesired spatial leakage effects and detect zero and near zero phase interactions. To this end, we propose a projection operation that operates on sensor-space cross-spectrum and suppresses the spatial leakage contribution but retains the true zero-phase interaction component. We then solve the network estimation task as a source estimation problem defined in the product space of interacting source topographies. We show how this framework provides reliable interaction detection for all phase-lag values and we thus refer to the method as Phase Shift Invariant Imaging of Coherent Sources (PSIICOS). Realistic simulations demonstrate that PSIICOS has better detector characteristics than existing interaction metrics. Finally, we illustrate the performance of PSIICOS by applying it to real MEG dataset recorded during a standard mental rotation task. Taken together, using analytical derivations, data simulations and real brain data, this study presents a novel source-space MEG/EEG connectivity method that overcomes previous limitations and for the first time allows for the estimation of true zero-phase coupling via non-invasive electrophysiological recordings.
In this research we compare the performance of different data mining techniques in the analysis of electroencephalogram (EEG) data. We study the question od predicting post-comatose neuro-developmental scores based mainly on statistical features of the EEG recordings. We compare results from applying different data mining techniques, such as the Elastic Net, Lasso, Gaussian Support Vector Regression and Random Forest Regression. We also compare the results produced with different matrix completion methods.
The Abstract book contains the abstracts of the posters presentations of the participants of the Methodological school: Methods of data processing in EEg and MEG, Moscow, 16-30th of April, 2013. The School was devoted to the theoretical and practical aspects of the contemporary methods of the dynamic mapping of brain activity by analysis of multichannel MEG and EEG.
The Abstract book contains the abstracts of the posters presentations of the participants of the Methodological school: Methods of data processing in EEG and MEG, Moscow, 16-30th of April, 2013. The School was devoted to the theoretical and practical aspects of the contemporary methods of the dynamic mapping of brain activity by analysis of multichannel MEG and EEG.
The Ustja dialect belongs to the Vologda dialect group. The latter has a well documented realization of the etymological *ê as [i] between palatalized consonants, under stress. Among contemporary speakers, *ê in this context may be realized either as [i] (the dialectal variant) or as [e] (standard Russian). No speaker who only uses the dialectal variant has been recorded. The paper focuses on how particular wordforms and the speaker’s age correlate with the variation between the dialectal vs. standard realization.