Oscillatory Neural Activity Research Articles

Regularity of background auditory scene and selective attention: A brain oscillatory study

There are scarce data related to the facilitative effects of regularity encoding on auditory selective attention during every day listening conditions. Therefore, present study aimed to investigate how temporal and spectral regularities of background auditory stream affect auditory selective attention at both levels of brain neural oscillatory activities and involved cortical locations. EEG was recorded in healthy young adults listening to two concurrent auditory streams including background and foreground ones in three conditions differing for background auditory stream which was characterized by having spectral or temporal sound regularities and random structure. The neural sources of EEG bands during recognizing target sounds in the foreground stream were determined via standardized Low-Resolution brain Electromagnetic Tomography (sLORETA). Sound regularities of the background auditory stream had no significant effect on the EEG relative power during the task of selective attention. In all conditions, there was a significant increase in the relative power of EEG alpha and beta frequency bands. sLORETA localized significant increase of mentioned bands in the precuneus of parietal lobe; medial frontal gyrus of frontal lobe and insula of sub-lobar in temporal regularity, spectral regularity and random conditions respectively. These results revealed that although temporal and spectral acoustic regularities of competitor auditory stream had no facilitative effect on alpha-related brain processing during selective attention, different brain cortical locations were activated with the introduction of these regularities. This result might provide preliminary evidence for some degree of brain neural specialization in the processing of temporal and spectral regularities during auditory selective attention tasks.

Detection and analysis of cortical beta bursts in developmental EEG data

Developmental EEG research often involves analyzing signals within various frequency bands, based on the assumption that these signals represent oscillatory neural activity. However, growing evidence suggests that certain frequency bands are dominated by transient burst events in single trials rather than sustained oscillations. This is especially true for the beta band, with adult ‘beta burst’ timing a better predictor of motor behavior than slow changes in average beta amplitude. No developmental research thus far has looked at beta bursts, with techniques used to investigate frequency-specific activity structure rarely even applied to such data. Therefore, we aimed to: i) provide a tutorial for developmental EEG researchers on the application of methods for evaluating the rhythmic versus transient nature of frequency-specific activity; and ii) use these techniques to investigate the existence of sensorimotor beta bursts in infants. We found that beta activity in 12-month-olds did occur in bursts, however differences were also revealed in terms of duration, amplitude, and rate during grasping compared to adults. Application of the techniques illustrated here will be critical for clarifying the functional roles of frequency-specific activity across early development, including the role of beta activity in motor processing and its contribution to differing developmental motor trajectories.

Aging-Related Changes in Cortical Sources of Sleep Oscillatory Neural Activity Following Motor Learning Reflect Contributions of Cortical Thickness and Pre-sleep Functional Activity.

Oscillatory neural activity during sleep, such as that in the delta and sigma bands, is important for motor learning consolidation. This activity is reduced with typical aging, and this reduction may contribute to aging-related declines in motor learning consolidation. Evidence suggests that brain regions involved in motor learning contribute to oscillatory neural activity during subsequent sleep. However, aging-related differences in regional contributions to sleep oscillatory activity following motor learning are unclear. To characterize these differences, we estimated the cortical sources of consolidation-related oscillatory activity using individual anatomical information in young and older adults during non-rapid eye movement sleep after motor learning and analyzed them in light of cortical thickness and pre-sleep functional brain activation. High-density electroencephalogram was recorded from young and older adults during a midday nap, following completion of a functional magnetic resonance imaged serial reaction time task as part of a larger experimental protocol. Sleep delta activity was reduced with age in a left-weighted motor cortical network, including premotor cortex, primary motor cortex, supplementary motor area, and pre-supplementary motor area, as well as non-motor regions in parietal, temporal, occipital, and cingulate cortices. Sleep theta activity was reduced with age in a similar left-weighted motor network, and in non-motor prefrontal and middle cingulate regions. Sleep sigma activity was reduced with age in left primary motor cortex, in a non-motor right-weighted prefrontal-temporal network, and in cingulate regions. Cortical thinning mediated aging-related sigma reductions in lateral orbitofrontal cortex and frontal pole, and partially mediated delta reductions in parahippocampal, fusiform, and lingual gyri. Putamen, caudate, and inferior parietal cortex activation prior to sleep predicted frontal and motor cortical contributions to sleep delta and theta activity in an age-moderated fashion, reflecting negative relationships in young adults and positive or absent relationships in older adults. Overall, these results support the local sleep hypothesis that brain regions active during learning contribute to consolidation-related neural activity during subsequent sleep and demonstrate that sleep oscillatory activity in these regions is reduced with aging.

Visual discrimination accuracy does not differ between nasal inhalation and exhalation when stimuli are voluntarily aligned to breathing phase

This study investigated the possible enhancement of visual discrimination accuracy by voluntarily adjusting the timing of stimulus presentation to a specific respiratory phase. Previous research has suggested that respiratory phases modulate perceptual and cognitive processing. For instance, a fearful face was identified faster when presented during nasal inhalation than during nasal exhalation, which could be related to changes in neural oscillatory activity synchronized with breathing in through one's nose. Based on such findings, the present study asked 40 young adults to perform an emotional discrimination task consisting of distinguishing fearful vs. neutral faces and a physical discrimination task consisting of distinguishing high- vs. low-contrast Gabor patches during nasal respiration. Participants presented themselves with the stimuli to be judged in a designated respiratory phase by pressing a button. It was hypothesized that fear discrimination accuracy would be higher during inhalation than exhalation if sensitivity to emotional stimuli increased during inhalation. Conversely, if overall visual sensitivity was enhanced during inhalation, the identical effect was expected for contrast discrimination. The results indicated that discrimination accuracy did not differ between inhalation and exhalation phases in either task. This result provided no evidence that the respiratory phase affected visual discrimination accuracy when people adjusted the timing of stimulus presentation to the onset of inhalation or exhalation.

State anxiety alters the neural oscillatory correlates of predictions and prediction errors during reward-based learning

Anxiety influences how the brain estimates and responds to uncertainty. The consequences of these processes on behaviour have been described in theoretical and empirical studies, yet the associated neural correlates remain unclear. Rhythm-based accounts of Bayesian predictive coding propose that predictions in generative models of perception are represented in alpha (8–12 Hz) and beta oscillations (13–30 Hz). Updates to predictions are driven by prediction errors weighted by precision (inverse variance) encoded in gamma oscillations (>30 Hz) and associated with the suppression of beta activity. We tested whether state anxiety alters the neural oscillatory activity associated with predictions and precision-weighted prediction errors (pwPE) during learning. Healthy human participants performed a probabilistic reward-based learning task in a volatile environment. In our previous work, we described learning behaviour in this task using a hierarchical Bayesian model, revealing more precise (biased) beliefs about the tendency of the reward contingency in state anxiety, consistent with reduced learning in this group. The model provided trajectories of predictions and pwPEs for the current study, allowing us to assess their parametric effects on the time-frequency representations of EEG data. Using convolution modelling for oscillatory responses, we found that, relative to a control group, state anxiety increased beta activity in frontal and sensorimotor regions during processing of pwPE, and in fronto-parietal regions during encoding of predictions. No effects of state anxiety on gamma modulation were found. Our findings expand prior evidence on the oscillatory representations of predictions and pwPEs into the reward-based learning domain. The results suggest that state anxiety modulates beta-band oscillatory correlates of pwPE and predictions in generative models, providing insights into the neural processes associated with biased belief updating and poorer learning.

Evidence for Three Subgroups of Female FMR1 Premutation Carriers Defined by Distinct Neuropsychiatric Features: A Pilot Study.

Over 200 Cytosine-guanine-guanine (CGG) trinucleotide repeats in the 5′ untranslated region of the Fragile X mental retardation 1 (FMR1) gene results in a “full mutation,” clinically Fragile X Syndrome (FXS), whereas 55 – 200 repeats result in a “premutation.” FMR1 premutation carriers (PMC) are at an increased risk for a range of psychiatric, neurocognitive, and physical conditions. Few studies have examined the variable expression of neuropsychiatric features in female PMCs, and whether heterogeneous presentation among female PMCs may reflect differential presentation of features in unique subgroups. In the current pilot study, we examined 41 female PMCs (ages 17–78 years) and 15 age-, sex-, and IQ-matched typically developing controls (TDC) across a battery of self-report, eye tracking, expressive language, neurocognitive, and resting state EEG measures to determine the feasibility of identifying discrete clusters. Secondly, we sought to identify the key features that distinguished these clusters of female PMCs. We found a three cluster solution using k-means clustering. Cluster 1 represented a psychiatric feature group (27% of our sample); cluster 2 represented a group with executive dysfunction and elevated high frequency neural oscillatory activity (32%); and cluster 3 represented a relatively unaffected group (41%). Our findings indicate the feasibility of using a data-driven approach to identify naturally occurring clusters in female PMCs using a multi-method assessment battery. CGG repeat count and its association with neuropsychiatric features differ across clusters. Together, our findings provide important insight into potential diverging pathophysiological mechanisms and risk factors for each female PMC cluster, which may ultimately help provide novel and individualized targets for treatment options.

Temporal dynamics of decision making: A synthesis of computational and neurophysiological approaches.

As interest in the temporal dynamics of decision-making has grown, researchers have increasingly turned to computational approaches such as the drift diffusion model (DDM) to identify how cognitive processes unfold during choice. At the same time, technological advances in noninvasive neurophysiological methods such as electroencephalography and magnetoencephalography now allow researchers to map the neural time course of decision making with millisecond precision. Combining these approaches can potentially yield important new insights into how choices emerge over time. Here we review recent research on the computational and neurophysiological correlates of perceptual and value-based decision making, from DDM parameters to scalp potentials and oscillatory neural activity. Starting with motor response preparation, the most well-understood aspect of the decision process, we discuss evidence that urgency signals and shifts in baseline activation, rather than shifts in the physiological value of the choice-triggering response threshold, are responsible for adjusting response times under speeded choice scenarios. Research on the neural correlates of starting point bias suggests that prestimulus activity can predict biases in motor choice behavior. Finally, studies examining the time dynamics of evidence construction and evidence accumulation have identified signals at frontocentral and centroparietal electrodes associated respectively with these processes, emerging 300-500 ms after stimulus onset. These findings can inform psychological theories of decision-making, providing empirical support for attribute weighting in value-based choice while suggesting theoretical alternatives to dual-process accounts. Further research combining computational and neurophysiological approaches holds promise for providing greater insight into the moment-by-moment evolution of the decision process. This article is categorized under: Psychology > Reasoning and Decision Making Neuroscience > Cognition Economics > Individual Decision-Making.

The impact of mild cognitive impairment on the neural dynamics serving verbal working memory

AbstractBackgroundIn healthy adults, neural oscillatory activity within a predominantly left‐lateralized network of brain regions underlies verbal working memory (VWM) performance, but how mild cognitive impairment (MCI) impacts these oscillatory dynamics is not well characterized. The present study utilized the spatiotemporal precision of magnetoencephalographic (MEG) brain imaging to investigate the effects of MCI on the neural oscillations serving specific phases (i.e., encoding, maintenance) of VWM. In congruence with the compensation‐related utilization of neural circuits hypothesis (CRUNCH), we hypothesized that MCI patients would recruit greater neural resources during VWM.MethodSixty‐one adults (13 clinically‐diagnosed MCI, 48 controls; 28 females; M age: 63.5) completed a Sternberg‐type VWM task during MEG. Only correct trials were analyzed, and the average number of trials included did not differ between groups. All MEG data underwent standard preprocessing, were transformed into the time‐frequency domain, and significant neural oscillatory responses relative to baseline were imaged using a beamformer. To determine the effect of group (MCI vs. controls), ANCOVAs were performed on the resulting encoding and maintenance whole‐brain maps with age as a covariate.ResultThroughout encoding and maintenance, decreases in alpha/beta (9‐16 Hz) oscillatory activity were seen in left fronto‐temporal regions, which were significantly larger in MCI patients during the encoding phase (p < .05, corrected). Additionally, significant group differences were detected in right inferior frontal and superior temporal cortices throughout both encoding and maintenance, such that MCI patients exhibited decreased alpha/beta activity within these regions, while these responses were largely absent in controls (p < .05, corrected). Interestingly, the strength of the alpha/beta response within the right inferior frontal cortex significantly predicted behavioral performance on the task, such that individuals who demonstrated stronger decreases in alpha/beta within this region during encoding and maintenance, tended to have faster reaction times (p < .01).ConclusionWhile both groups recruited a similar left‐hemispheric cortical network during VWM encoding and maintenance, individuals with MCI recruited some nodes more strongly and additionally recruited right fronto‐temporal regions. Additionally, stronger recruitment of the right inferior frontal cortex was beneficial for task performance. These results offer supporting evidence for CRUNCH.

Occipital neural dynamics in cannabis and alcohol use: independent effects of addiction

Alcohol and cannabis use disorder (AUD/CUD) are two of the most common addictive disorders. While studies are beginning to understand the neural changes related to acute and chronic use, few studies have examined the independent effects of AUD and CUD on neural oscillatory activity. We examined 45 adults who reported current use of both cannabis and alcohol. Participants underwent the SCID-V to determine whether they met criteria for AUD and/or CUD. Participants also completed a visual-spatial processing task while undergoing magnetoencephalography (MEG). ANCOVA with a 2 × 2 design was then used to identify the main effects of AUD and CUD on source-level oscillatory activity. Of the 45 adults, 17 met criteria for AUD, and 26 met criteria for CUD. All participants, including comparison groups, reported use of both cannabis and alcohol. Statistical analyses showed a main effect of AUD, such that participants with AUD displayed a blunted occipital alpha (8–16 Hz) response. Post-hoc testing showed this decreased alpha response was related to increased AUD symptoms, above and beyond amount of use. No effects of AUD or CUD were identified in visual theta or gamma activity. In conclusion, AUD was associated with reduced alpha responses and scaled with increasing severity, independent of CUD. These findings indicate that alpha oscillatory activity may play an integral part in networks affected by alcohol addiction.

Respiration modulates oscillatory neural network activity at rest.

Despite recent advances in understanding how respiration affects neural signalling to influence perception, cognition, and behaviour, it is yet unclear to what extent breathing modulates brain oscillations at rest. We acquired respiration and resting state magnetoencephalography (MEG) data from human participants to investigate if, where, and how respiration cyclically modulates oscillatory amplitudes (2 to 150 Hz). Using measures of phase-amplitude coupling, we show respiration-modulated brain oscillations (RMBOs) across all major frequency bands. Sources of these modulations spanned a widespread network of cortical and subcortical brain areas with distinct spectrotemporal modulation profiles. Globally, delta and gamma band modulations varied with distance to the head centre, with stronger modulations at distal (versus central) cortical sites. Overall, we provide the first comprehensive mapping of RMBOs across the entire brain, highlighting respiration-brain coupling as a fundamental mechanism to shape neural processing within canonical resting state and respiratory control networks (RCNs).

Predictive Feedback, Early Sensory Representations, and Fast Responses to Predicted Stimuli Depend on NMDA Receptors.

Learned associations between stimuli allow us to model the world and make predictions, crucial for efficient behavior (e.g., hearing a siren, we expect to see an ambulance and quickly make way). While there are theoretical and computational frameworks for prediction, the circuit and receptor-level mechanisms are unclear. Using high-density EEG, Bayesian modeling, and machine learning, we show that inferred "causal" relationships between stimuli and frontal alpha activity account for reaction times (a proxy for predictions) on a trial-by-trial basis in an audiovisual delayed match-to-sample task which elicited predictions. Predictive β feedback activated sensory representations in advance of predicted stimuli. Low-dose ketamine, an NMDAR blocker, but not the control drug dexmedetomidine, perturbed behavioral indices of predictions, their representation in higher-order cortex, feedback to posterior cortex, and pre-activation of sensory templates in higher-order sensory cortex. This study suggests that predictions depend on alpha activity in higher-order cortex, β feedback, and NMDARs, and ketamine blocks access to learned predictive information.SIGNIFICANCE STATEMENT We learn the statistical regularities around us, creating associations between sensory stimuli. These associations can be exploited by generating predictions, which enable fast and efficient behavior. When predictions are perturbed, it can negatively influence perception and even contribute to psychiatric disorders, such as schizophrenia. Here we show that the frontal lobe generates predictions and sends them to posterior brain areas, to activate representations of predicted sensory stimuli before their appearance. Oscillations in neural activity (α and β waves) are vital for these predictive mechanisms. The drug ketamine blocks predictions and the underlying mechanisms. This suggests that the generation of predictions in the frontal lobe, and the feedback pre-activating sensory representations in advance of stimuli, depend on NMDARs.

Interpretable SincNet-based Deep Learning for Emotion Recognition from EEG brain activity.

Machine learning methods, such as deep learning, show promising results in the medical domain. However, the lack of interpretability of these algorithms may hinder their applicability to medical decision support systems. This paper studies an interpretable deep learning technique, called SincNet. SincNet is a convolutional neural network that efficiently learns customized band-pass filters through trainable sinc-functions. In this study, we use SincNet to analyze the neural activity of individuals with Autism Spectrum Disorder (ASD), who experience characteristic differences in neural oscillatory activity. In particular, we propose a novel SincNet-based neural network for detecting emotions in ASD patients using EEG signals. The learned filters can be easily inspected to detect which part of the EEG spectrum is used for predicting emotions. We found that our system automatically learns the high-α (9-13 Hz) and β (13-30 Hz) band suppression often present in individuals with ASD. This result is consistent with recent neuroscience studies on emotion recognition, which found an association between these band suppressions and the behavioral deficits observed in individuals with ASD. The improved interpretability of SincNet is achieved without sacrificing performance in emotion recognition.

Aberrant inhibitory processing in the somatosensory cortices of cannabis-users

Delta-9 tetrahydrocannabinol (THC) is a major exogenous psychoactive agent, which acts as a partial agonist on cannabinoid (CB1) receptors. THC is known to inhibit presynaptic neurotransmission and has been repeatedly linked to acute decrements in cognitive function across multiple domains. Previous electrophysiological studies of sensory gating have shown specific deficits in inhibitory processing in cannabis-users, but to date these findings have been limited to the auditory cortices, and the degree to which these aberrations extend to other brain regions remains largely unknown. We used magnetoencephalography (MEG) and a paired-pulse somatosensory stimulation paradigm to probe inhibitory processing in 29 cannabis-users (i.e. at least four times per month) and 41 demographically matched non-user controls. MEG responses to each stimulation were imaged in both the oscillatory and time domain, and voxel time-series data were extracted to quantify the dynamics of sensory gating, oscillatory gamma activity, evoked responses, and spontaneous neural activity. We observed robust somatosensory responses following both stimulations, which were used to compute sensory gating ratios. Cannabis-users exhibited significantly impaired gating relative to non-users in somatosensory cortices, as well as decreased spontaneous neural activity. In contrast, oscillatory gamma activity did not appear to be affected by cannabis use. We observed impaired gating of redundant somatosensory information and altered spontaneous activity in the same cortical tissue in cannabis-users compared to non-users. These data suggest that cannabis use is associated with a decline in the brain's ability to properly filter repetitive information and impairments in cortical inhibitory processing.

Neural oscillatory activity serving sensorimotor control is predicted by superoxide-sensitive mitochondrial redox environments

Motor control requires a coordinated ensemble of spatiotemporally precise neural oscillations across a distributed motor network, particularly in the beta range (15 to 30 Hz) to successfully plan and execute volitional actions. While substantial evidence implicates beta activity as critical to motor control, the molecular processes supporting these microcircuits and their inherent oscillatory dynamics remain poorly understood. Among these processes are mitochondrial integrity and the associated redox environments, although their direct impact on human neurophysiological function is unknown. Herein, 40 healthy adults completed a motor sequence paradigm during magnetoencephalography (MEG). MEG data were imaged in the time-frequency domain using a beamformer to evaluate beta oscillatory profiles during distinct phases of motor control (i.e., planning and execution) and subsequent behavior. To comprehensively quantify features of the mitochondrial redox environment, we used state-of-the-art systems biology approaches including Seahorse Analyzer to assess mitochondrial respiration and electron paramagnetic resonance spectroscopy to measure superoxide levels in whole blood as well as antioxidant activity assays. Using structural equation modeling, we tested the relationship between mitochondrial function and sensorimotor brain-behavior dynamics through alterations in the redox environment (e.g., generation of superoxide and alteration in antioxidant defenses). Our results indicated that superoxide-sensitive but not hydrogen peroxide-sensitive features of the redox environment had direct and mediating effects on the bioenergetic-neural pathways serving motor performance in healthy adults. Importantly, our results suggest that alterations in the redox environment may directly impact behavior above and beyond mitochondrial respiratory capacities alone and further may be effective targets for age- and disease-related declines in cognitive-motor function.

Estimating Directed Phase-Amplitude Interactions from EEG Data through Kernel-Based Phase Transfer Entropy

Cross-frequency interactions, a form of oscillatory neural activity, are thought to play an essential role in the integration of distributed information in the brain. Indeed, phase-amplitude interactions are believed to allow for the transfer of information from large-scale brain networks, oscillating at low frequencies, to local, rapidly oscillating neural assemblies. A promising approach to estimating such interactions is the use of transfer entropy (TE), a non-linear, information-theory-based effective connectivity measure. The conventional method involves feeding instantaneous phase and amplitude time series, extracted at the target frequencies, to a TE estimator. In this work, we propose that the problem of directed phase-amplitude interaction detection is recast as a phase TE estimation problem, under the hypothesis that estimating TE from data of the same nature, i.e., two phase time series, will improve the robustness to the common confounding factors that affect connectivity measures, such as the presence of high noise levels. We implement our proposal using a kernel-based TE estimator, defined in terms of Renyi’s α entropy, which has successfully been used to compute single-trial phase TE. We tested our approach on the synthetic data generated through a simulation model capable of producing a time series with directed phase-amplitude interactions at two given frequencies, and on EEG data from a cognitive task designed to activate working memory, a memory system whose underpinning mechanisms are thought to include phase–amplitude couplings. Our proposal detected statistically significant interactions between the simulated signals at the desired frequencies for the synthetic data, identifying the correct direction of the interaction. It also displayed higher robustness to noise than the alternative methods. The results attained for the working memory data showed that the proposed approach codes connectivity patterns based on directed phase–amplitude interactions, that allow for the different cognitive load levels of the working memory task to be differentiated.

Theta and Alpha Oscillatory Activity During Working Memory Maintenance in Long-Term Cannabis Users: The Importance of the Polydrug Use Context.

Background: Impairments in various subdomains of memory have been associated with chronic cannabis use, but less is known about their neural underpinnings, especially in the domain of the brain’s oscillatory activity.Aims: To investigate neural oscillatory activity supporting working memory (WM) in regular cannabis users and non-using controls. We focused our analyses on frontal midline theta and posterior alpha asymmetry as oscillatory fingerprints for the WM’s maintenance process.Methods: 30 non-using controls (CG) and 57 regular cannabis users—27 exclusive cannabis users (CU) and 30 polydrug cannabis users (PU) completed a Sternberg modified WM task with a concurrent electroencephalography recording. Theta, alpha and beta frequency bands were examined during WM maintenance.Results: When compared to non-using controls, the PU group displayed increased frontal midline theta (FMT) power during WM maintenance, which was positively correlated with RT. The posterior alpha asymmetry during the maintenance phase, on the other hand, was negatively correlated with RT in the CU group. WM performance did not differ between groups.Conclusions: Both groups of cannabis users (CU and PU), when compared to the control group, displayed differences in oscillatory activity during WM maintenance, unique for each group (in CU posterior alpha and in PU FMT correlated with performance). We interpret those differences as a reflection of compensatory strategies, as there were no differences between groups in task performance. Understanding the psychophysiological processes in regular cannabis users may provide insight on how chronic use may affect neural networks underlying cognitive processes, however, a polydrug use context (i.e., combining cannabis with other illegal substances) seems to be an important factor.

Dynamic large-scale connectivity of intrinsic cortical oscillations supports adaptive listening in challenging conditions.

In multi-talker situations, individuals adapt behaviorally to this listening challenge mostly with ease, but how do brain neural networks shape this adaptation? We here establish a long-sought link between large-scale neural communications in electrophysiology and behavioral success in the control of attention in difficult listening situations. In an age-varying sample of N = 154 individuals, we find that connectivity between intrinsic neural oscillations extracted from source-reconstructed electroencephalography is regulated according to the listener's goal during a challenging dual-talker task. These dynamics occur as spatially organized modulations in power-envelope correlations of alpha and low-beta neural oscillations during approximately 2-s intervals most critical for listening behavior relative to resting-state baseline. First, left frontoparietal low-beta connectivity (16 to 24 Hz) increased during anticipation and processing of a spatial-attention cue before speech presentation. Second, posterior alpha connectivity (7 to 11 Hz) decreased during comprehension of competing speech, particularly around target-word presentation. Connectivity dynamics of these networks were predictive of individual differences in the speed and accuracy of target-word identification, respectively, but proved unconfounded by changes in neural oscillatory activity strength. Successful adaptation to a listening challenge thus latches onto two distinct yet complementary neural systems: a beta-tuned frontoparietal network enabling the flexible adaptation to attentive listening state and an alpha-tuned posterior network supporting attention to speech.

Communication channel, emotion category and attentional requirement modulate emotional speech perception: Electrophysiological and behavioral evidence

The present study employed electrophysiological and behavioral measures to examine the modulatory effects of communication channels, emotion categories and attentional requirements on the timing, neural oscillatory activities and behavioral performances of emotional speech processing. Thirty participants (15 women and 15 men) were presented with spoken words denoting happiness, neutrality and sadness in prosody or semantics. They were asked to attend to the emotional content (explicit tasks) and speakers’ gender (implicit tasks) of the stimuli. Auditory event-related potential (AERP), inter-trial phase coherence (ITPC), accuracy, and reaction time data were recorded and analyzed. Results indicated that prosody (relative to semantics) and happiness (relative to neutrality and sadness) enjoyed greater processing advantages during the sensory processing of acoustic signals, initial derivation of emotional significance, and cognitive evaluation of emotional speech. However, these channel and category-specific effects were attenuated in implicit tasks and sadness processing. Explicit tasks also displayed greater perceptual sensitivities in early auditory processing, but exhibited greater processing difficulty during decision making. In addition, ITPC values served as significant predictors of AERP amplitudes across the three contextual factors. These findings reveal the neural and behavioral underpinnings of emotional speech processing, which is mediated by the dynamic interplay among channels, emotions and attention.

Neural processing of phoneme, affect, and gender information in spoken words: A cross-modal priming study

Human speech contains not only the linguistic content but also important information about speaker identity and affect. This study employed whole-head magnetoencephalography (MEG) to examine how brain activities were modulated by selective listening of phoneme, affect and gender information with different degrees of task difficulty. The participants were 10 male Japanese adults with normal hearing. The words were ‘right’ and ‘light’ recorded from native English speakers and binaurally presented at 50dB SL. The participants were asked to judge congruency between the visual prime and the spoken word for each trial. The experiment started with a familiarization phase, which was immediately followed by the test phase with 200 trials in each condition. Behavioral results confirmed an increasing order of difficulty from gender to affect to phoneme conditions. Significant priming effects were found only for the affect and gender conditions. In line with the behavioral results, the MEG data revealed distinct patterns of hemispheric and regional involvement and neural oscillatory activities for evaluating the cross-modal congruency in the three conditions. These results demonstrate the neural dynamics and complexity in processing linguistic and paralinguistic information in spoken words with differential influences of language experience.

Exploring Ripple Waves in the Human Brain.

Ripples are brief (<150 ms) high-frequency oscillatory neural activities in the brain with a range of 140 to 200 Hz in rodents and 80 to 140 Hz in humans. Ripples are regarded as playing an essential role in several aspects of memory function, mainly in the hippocampus. This type of ripple generally occurs with sharp waves and is called a sharp-wave ripple (SPW-R). Extensive research of SPW-Rs in the rodent brain while actively awake has also linked the function of these SPW-Rs to navigation and decision making. Although many studies with rodents unveiled SPW-R function, research in humans on this subject is still sparse. Therefore, unveiling SPW-R function in the human hippocampus is warranted. A certain type of ripples may also be a biomarker of epilepsy. This type of ripple is called a pathological ripple (p-ripple). p-ripples have a wider range of frequency (80-500 Hz) than SPW-Rs, and the range of frequency is especially higher in brain regions that are intrinsically linked to epilepsy onset. Brain regions producing ripples are too small for scalp electrode recording, and intracranial recording is typically needed to detect ripples. In addition, SPW-Rs in the human hippocampus have been recorded from patients with epilepsy who may have p-ripples. Differentiating SPW-Rs and p-ripples is often not easy. We need to develop more sophisticated methods to record SPW-Rs to differentiate them from p-ripples. This paper reviews the general features and roles of ripple waves.