Speech encoding by coupled cortical theta and gamma oscillations
Many environmental stimuli present a quasi-rhythmic structure at different timescales that the brain needs to decompose and integrate. Cortical oscillations have been proposed as instruments of sensory de-multiplexing, i.e., the parallel processing of different frequency streams in sensory signals. Yet their causal role in such a process has never been demonstrated. Here, we used a neural microcircuit model to address whether coupled theta–gamma oscillations, as observed in human auditory cortex, could underpin the multiscale sensory analysis of speech. We show that, in continuous speech, theta oscillations can flexibly track the syllabic rhythm and temporally organize the phoneme-level response of gamma neurons into a code that enables syllable identification. The tracking of slow speech fluctuations by theta oscillations, and its coupling to gamma-spiking activity both appeared as critical features for accurate speech encoding. These results demonstrate that cortical oscillations can be a key instrument of speech de-multiplexing, parsing, and encoding.
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.
Commission of error causes the adjustments in a number of brain systems related to goal-directed behavior. Errors may be caused by failures of motor inhibition or by general attentional lapses, which lead to the different neural adjustments with their specific electrophysiological and behavioral correlates (van Driel et al., 2012; Danielmeier and Ullsperger, 2011). Thus, post-error adjustments may lead both to non-specific increase of motor threshold or to specific improvement of stimulus processing and decision making, with different brain systems involved in these processes (King et al., 2010). In the present study, we aimed at the investigation of error-related theta and alpha band power modulations and of corresponding behavioral adjustments.
An auditory two-choice version of the condensation task was used in the experiment (Posner, 1964; Chernyshev et al., 2015). Subjects were presented with random sequence of tones; each tone was either 500 Hz (‘low’) or 2000 Hz (‘high’), either a pure tone (‘pure’) or the same tone intermixed with broadband noise. The participants were instructed to respond to stimuli with pressing left or right button on a gamepad, according to the memorized rule (see Table 1). Correct responses after stimulus onset were immediately followed by a positive feedback (a schematic smiling face) presented for 500 ms after the response. This task is highly demanding for sustained attention, but implies no to-be-inhibited “automatic” responses. We analyzed modulations of non-phase-locked theta (4 – 7 Hz) and alpha (8 – 12 Hz) EEG power that occurs on erroneous and post-error correct trials. We used data-driven approach with threshold-free cluster enhancement (TFCE)-based permutational correction for multiple spatial-time-frequency bins in order to avoid a priori ROI selection. Also, we analyzed correlations between post-error spectral modulations and behavioral variables (percentage of errors and post-error slowing), using Spearman’s correlation coefficient.
Response times (RT) on erroneous trials was significantly larger than on correct trials (t = 9.48, p < 0.001). No significant post-error slowing (PES) was found (t=-0.53, p=0.60). Errors (compared to correct trials) lead to significantly (p < 0.05) increased frontal midline theta (FMT) power (0 – 400 ms), followed by the enhanced alpha band suppression in the parietal (400 – 700 ms) and the left central regions (500 – 1000 ms) (Fig. 1A, top row). Based on these results, we selected three regions of interest: R1 – frontal midline theta, R2 – posterior alpha, R3 – left central alpha (Fig. 1A, top row). Stronger parietal alpha suppression was associated with better task performance, stronger left central alpha suppression was associated with more pronounced PES, and FMT increase positively correlated with both behavioral variables (Fig. 1B). On post-error correct trials (compared to post-correct ones), the following significant (p < 0.05) effects were found (Fig. 1A, bottom row): stronger pre-response left-central alpha suppression (-1000 – -250 ms); stronger generalized alpha suppression around the response (-150 – 500 ms), weaker post-response FMT power (0 – 600 ms).
We believe that our results suggest the occurrence of the conflict / error detection signal, followed by the signals of attentional reconfiguration and motor threshold adjustment. These adjustments resulted in optimized performance on the subsequent trials, accompanied by the reduced uncertainty of the response and decreased conflict. Our findings presumably indicate post-error adaptations in several brain systems, and extend the literature on sustained attention lapses and cognitive control.
Error commission leads to adaptive adjustments in a number of brain networks that subserve goal-directed behavior, resulting in either enhanced stimulus processing or increased motor threshold depending on the nature of errors committed. Here, we studied these adjustments by analyzing post-error modulations of alpha and theta band activity in the auditory version of the two-choice condensation task, which is highly demanding for sustained attention while involves no inhibition of prepotent responses. Errors were followed by increased frontal midline theta (FMT) activity, as well as by enhanced alpha band suppression in the parietal and the left central regions; parietal alpha suppression correlated with the task performance, left central alpha suppression correlated with the post-error slowing, and FMT increase correlated with both behavioral measures. On post-error correct trials, left-central alpha band suppression started earlier before the response, and the response was followed by weaker FMT activity, as well as by enhanced alpha band suppression distributed over the entire scalp. These findings indicate that several separate neuronal networks are involved in post-error adjustments, including the midfrontal performance monitoring network, the parietal attentional network, and the sensorimotor network. Supposedly, activity within these networks is rapidly modulated after errors, resulting in optimization of their functional state on the subsequent trials, with corresponding changes in behavioral measures.
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.
Cognitive control includes maintenance of task-specific processes related to attention, and non-specific regulation of motor threshold. Generally, two different kinds of errors may occur, with some errors related to attentional lapses and decision uncertainty, and some errors – to failures of sustaining motor threshold. Error commission leads to adaptive adjustments in brain networks that subserve goal-directed behavior, resulting in either enhanced stimulus processing or increased motor threshold depending on the nature of errors committed. We report here two studies using the auditory version of the two-choice condensation task, which is highly demanding for sustained attention while involves no inhibition of prepotent responses. We analyzed power and topography of EEG oscillations in theta, alpha, and beta frequency bands.
Experiment 1. We studied post-error adaptive adjustments resulting in optimized brain processing and behaviour on subsequent trials. Errors were followed by increased frontal midline theta (FMT) activity, as well as by enhanced alpha band suppression in the parietal and the left central regions; parietal alpha suppression correlated with the task performance, left central alpha suppression correlated with the post-error slowing, and FMT increase correlated with both behavioral measures. On post-error correct trials, left-central alpha band suppression started earlier before the response, and the response was followed by weaker FMT activity, as well as by enhanced alpha band suppression distributed over the entire scalp. These findings show the existence of three separate neuronal networks involved in post-error adjustments: the midfrontal performance monitoring network, the parietal attentional network, and the sensorimotor network.
Experiment 2. We studied if response time may be a valid approximation distinguishing trials with high and low levels of sustained attention and decision uncertainty. We found that error-related FMT activity was present only on fast erroneous trials. The feedback-related FMT activity was equally strong on slow erroneous and fast erroneous trials. Late post-response posterior alpha suppression was stronger on erroneous slow trials. Feedbackrelated frontal beta oscillations were present only on slow correct trials. The data obtained cumulatively suggests that response time allows distinguishing the two types of trials, with fast trials related to higher levels of attention and low uncertainty, and slow trials related to lower levels of attention and higher uncertainty.
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.
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.