Phase synchronization among neuronal oscillations within the same frequency band has been hypothesized to be a major mechanism for communication between different brain areas. On the other hand, cross-frequency communications are more flexible allowing interactions between oscillations with different frequencies. Among such cross-frequency interactions amplitude-to-amplitude interactions are of a special interest as they show how the strength of spatial synchronization in different neuronal populations relates to each other during a given task. While, previously, amplitude-to-amplitude correlations were studied primarily on the sensor level, we present a source separation approach using spatial filters which maximize the correlation between the envelopes of brain oscillations recorded with electro-/magnetoencephalography (EEG/MEG) or intracranial multichannel recordings. Our approach, which is called canonical source power correlation analysis (cSPoC), is thereby capable of extracting genuine brain oscillations solely based on their assumed coupling behavior even when the signal-to-noise ratio of the signals is low. In addition to using cSPoC for the analysis of cross-frequency interactions in the same subject, we show that it can also be utilized for studying amplitude dynamics of neuronal oscillations across subjects. We assess the performance of cSPoC in simulations as well as in three distinctively different analysis scenarios of real EEG data, each involving several subjects. In the simulations, cSPoC outperforms unsupervised state-of-the-art approaches. In the analysis of real EEG recordings, we demonstrate excellent unsupervised discovery of meaningful power-to-power couplings, within as well as across subjects and frequency bands.
Neural oscillations are ubiquitously observed in the mammalian brain, but it has proven difficult to tie oscillatory patterns to specific cognitive operations. Notably, the coupling between neural oscillations at different timescales has recently received much attention, both from experimentalists and theoreticians. We review the mechanisms underlying various forms of this cross-frequency coupling. We show that different types of neural oscillators and cross-frequency interactions yield distinct signatures in neural dynamics. Finally, we associate these mechanisms with several putative functions of cross-frequency coupling, including neural representations of multiple environmental items, communication over distant areas, internal clocking of neural processes, and modulation of neural processing based on temporal predictions.
Although thalamic deep brain stimulation is an effective treatment for patients with essential tremor, little is known about its effect on cortical neural dynamics. Therefore, we investigated long-range temporal correlations and spectral power in electroencephalographic recordings of patients during OFF versus ON bilateral thalamic deep brain stimulation in comparison with healthy controls. Cortical dynamics were analyzed in the range of 6-30 Hz. We found the presence of long-range temporal correlations up to 20 s in patients and controls. Thalamic deep brain stimulation was associated with increased long-range temporal correlations in the high beta band (21-30 Hz) and decreased power in the low beta band (13-20 Hz) compared with OFF stimulation and healthy controls. Long-range temporal correlations in the 6-10 Hz range were increased with OFF stimulation compared with the controls. Importantly, deep brain stimulation-induced changes in long-range temporal correlations within 6-10 Hz and in the beta ranges (13-20, 21-30 Hz) were correlated with OFF-ON changes in the tremor severity and with the disease duration, respectively. The differential reactivity of long-range temporal correlations and spectral power to deep brain stimulation might suggest that both measures reflect distinct aspects of cortical dynamics and might represent biomarkers for stimulation-induced modulations of neural dynamics in electroencephalography. The fact that long-range temporal correlations, but not spectral power, were correlated with clinical information might suggest long-range temporal correlations as a potential marker for disease severity in essential tremor.
This article describes the expierence of studying factors influencing the social well-being of educational migrants as mesured by means of a psychological well-being scale (A. Perrudet-Badoux, G.A. Mendelsohn, J.Chiche, 1988) previously adapted for Russian by M.V. Sokolova. A statistical analysis of the scale's reliability is performed. Trends in dynamics of subjective well-being are indentified on the basis the correlations analysis between the condbtbions of adaptation and its success rate, and potential mechanisms for developing subjective well-being among student migrants living in student hostels are described. Particular attention is paid to commuting as a factor of adaptation.
The distractive effects on attentional task performance in different paradigms are analyzed in this paper. I demonstrate how distractors may negatively affect (interference effect), positively (redundancy effect) or neutrally (null effect). Distractor effects described in literature are classified in accordance with their hypothetical source. The general rule of the theory is also introduced. It contains the formal prediction of the particular distractor effect, based on entropy and redundancy measures from the mathematical theory of communication (Shannon, 1948). Single- vs dual-process frameworks are considered for hypothetical mechanisms which underpin the distractor effects. Distractor profiles (DPs) are also introduced for the formalization and simple visualization of experimental data concerning the distractor effects. Typical shapes of DPs and their interpretations are discussed with examples from three frequently cited experiments. Finally, the paper introduces hierarchical hypothesis that states the level-fashion modulating interrelations between distractor effects of different classes.