Spatial and temporal relationships of electrocorticographic alpha and gamma activity during auditory processing

Electrocorticographic Activation Patterns of Electroencephalographic Microstates

Electroencephalography (EEG) microstates are short successive periods of stable scalp field potentials representing spontaneous activation of brain resting-state networks. EEG microstates are assumed to mediate local activity patterns. To test this hypothesis, we correlated momentary global EEG microstate dynamics with the local temporo-spectral evolution of electrocorticography (ECoG) and stereotactic EEG (SEEG) depth electrode recordings. We hypothesized that these correlations involve the gamma band. We also hypothesized that the anatomical locations of these correlations would converge with those of previous studies using either combined functional magnetic resonance imaging (fMRI)-EEG or EEG source localization. We analyzed resting-state data (5 min) of simultaneous noninvasive scalp EEG and invasive ECoG and SEEG recordings of two participants. Data were recorded during the presurgical evaluation of pharmacoresistant epilepsy using subdural and intracranial electrodes. After standard preprocessing, we fitted a set of normative microstate template maps to the scalp EEG data. Using covariance mapping with EEG microstate timelines and ECoG/SEEG temporo-spectral evolutions as inputs, we identified systematic changes in the activation of ECoG/SEEG local field potentials in different frequency bands (theta, alpha, beta, and high-gamma) based on the presence of particular microstate classes. We found significant covariation of ECoG/SEEG spectral amplitudes with microstate timelines in all four frequency bands (p = 0.001, permutation test). The covariance patterns of the ECoG/SEEG electrodes during the different microstates of both participants were similar. To our knowledge, this is the first study to demonstrate distinct activation/deactivation patterns of frequency-domain ECoG local field potentials associated with simultaneous EEG microstates.

Short-Term Effect of Auditory Stimulation on Neural Activities: A Scoping Review of Longitudinal Electroencephalography and Magnetoencephalography Studies

Explored through EEG/MEG, auditory stimuli function as a suitable research probe to reveal various neural activities, including event-related potentials, brain oscillations and functional connectivity. Accumulating evidence in this field stems from studies investigating neuroplasticity induced by long-term auditory training, specifically cross-sectional studies comparing musicians and non-musicians as well as longitudinal studies with musicians. In contrast, studies that address the neural effects of short-term interventions whose duration lasts from minutes to hours are only beginning to be featured. Over the past decade, an increasing body of evidence has shown that short-term auditory interventions evoke rapid changes in neural activities, and oscillatory fluctuations can be observed even in the prestimulus period. In this scoping review, we divided the extracted neurophysiological studies into three groups to discuss neural activities with short-term auditory interventions: the pre-stimulus period, during stimulation, and a comparison of before and after stimulation. We show that oscillatory activities vary depending on the context of the stimuli and are greatly affected by the interplay of bottom-up and top-down modulational mechanisms, including attention. We conclude that the observed rapid changes in neural activitiesin the auditory cortex and the higher-order cognitive part of the brain are causally attributed to short-term auditory interventions.

Music can be reconstructed from human auditory cortex activity using nonlinear decoding models

Music is core to human experience, yet the precise neural dynamics underlying music perception remain unknown. We analyzed a unique intracranial electroencephalography (iEEG) dataset of 29 patients who listened to a Pink Floyd song and applied a stimulus reconstruction approach previously used in the speech domain. We successfully reconstructed a recognizable song from direct neural recordings and quantified the impact of different factors on decoding accuracy. Combining encoding and decoding analyses, we found a right-hemisphere dominance for music perception with a primary role of the superior temporal gyrus (STG), evidenced a new STG subregion tuned to musical rhythm, and defined an anterior-posterior STG organization exhibiting sustained and onset responses to musical elements. Our findings show the feasibility of applying predictive modeling on short datasets acquired in single patients, paving the way for adding musical elements to brain-computer interface (BCI) applications.

Automatic auditory processing features in distinct subtypes of patients at clinical high risk for psychosis: Forecasting remission with mismatch negativity

Individuals at clinical high risk (CHR) for psychosis exhibit a compromised mismatch negativity (MMN) response, which indicates dysfunction of pre-attentive deviance processing. Event-related potential and time-frequency (TF) information, in combination with clinical and cognitive profiles, may provide insight into the pathophysiology and psychopathology of the CHR stage and predict the prognosis of CHR individuals. A total of 92 individuals with CHR were recruited and followed up regularly for up to 3 years. Individuals with CHR were classified into three clinical subtypes demonstrated previously, specifically 28 from Cluster 1 (characterized by extensive negative symptoms and cognitive deficits), 31 from Cluster 2 (characterized by thought and behavioral disorganization, with moderate cognitive impairment), and 33 from Cluster 3 (characterized by the mildest symptoms and cognitive deficits). Auditory MMN to frequency and duration deviants was assessed. The event-related spectral perturbation (ERSP) and inter-trial coherence (ITC) were acquired using TF analysis. Predictive indices for remission were identified using logistic regression analyses. As expected, reduced frequency MMN (fMMN) and duration MMN (dMMN) responses were noted in Cluster 1 relative to the other two clusters. In the TF analysis, Cluster 1 showed decreased theta and alpha ITC in response to deviant stimuli. The regression analyses revealed that dMMN latency and alpha ERSP to duration deviants, theta ITC to frequency deviants and alpha ERSP to frequency deviants, and fMMN latency were significant MMN predictors of remission for the three clusters. MMN variables outperformed behavioral variables in predicting remission of Clusters 1 and 2. Our findings indicate relatively disrupted automatic auditory processing in a certain CHR subtype and a close affinity between these electrophysiological indexes and clinical profiles within different clusters. Furthermore, MMN indexes may serve as predictors of subsequent remission from the CHR state. These findings suggest that the auditory MMN response is a potential neurophysiological marker for distinct clinical subtypes of CHR.

Effects of spatialized water-sound sequences for traffic noise masking on brain activities

Informational masking of water sounds has been proven effective in mitigating traffic noise perception with different sound levels and signal-to-noise ratios, but less is known about the effects of the spatial distribution of water sounds on the perception of the surrounding environment and corresponding psychophysical responses. Three different spatial settings of water-sound sequences with a traffic noise condition were used to investigate the role of spatialization of water-sound sequences on traffic noise perception. The neural responses of 20 participants were recorded by a portable electroencephalogram (EEG) device during the spatial sound playback time. The mental effects and attention process related to informational masking were assessed by the analysis of the EEG spectral power distribution and sensor-level functional connectivity along with subjective assessments. The results showed higher relative power of the alpha band and greater alpha-beta ratio among water-sound sequence conditions compared to traffic noise conditions, which confirmed the increased relaxation on the mental state induced by the introduction of water sounds. Moreover, different spatial settings of water-sound sequences evoked different cognitive network responses. The setting of two-position switching water brought more attentional network activations than other water sequences related to the information masking process along with more positive subjective feelings.

Heksor: The CNS substrate of an adaptive behavior

Over the past half‐century, the largely hardwired central nervous system (CNS) of 1970 has become the ubiquitously plastic CNS of today, in which change is the rule not the exception. This transformation complicates a central question in neuroscience: how are adaptive behaviours – behaviours that serve the needs of the individual – acquired and maintained through life? It poses a more basic question: how do many adaptive behaviours share the ubiquitously plastic CNS? This question compels neuroscience to adopt a new paradigm. The core of this paradigm is a CNS entity with unique properties, here given the name heksor from the Greek hexis. A heksor is a distributed network of neurons and synapses that changes itself as needed to maintain the key features of an adaptive behaviour, the features that make the behaviour satisfactory. Through their concurrent changes, the numerous heksors that share the CNS negotiate the properties of the neurons and synapses that they all use. Heksors keep the CNS in a state of negotiated equilibrium that enables each heksor to maintain the key features of its behaviour. The new paradigm based on heksors and the negotiated equilibrium they create is supported by animal and human studies of interactions among new and old adaptive behaviours, explains otherwise inexplicable results, and underlies promising new approaches to restoring behaviours impaired by injury or disease. Furthermore, the paradigm offers new and potentially important answers to extant questions, such as the generation and function of spontaneous neuronal activity, the aetiology of muscle synergies, and the control of homeostatic plasticity.

Alpha and broadband high-frequency activity track task dynamics and predict performance in controlled decision-making

Intracranial recordings in human subjects provide a unique, fine-grained temporal and spatial resolution inaccessible to conventional non-invasive methods. A prominent signal in these recordings is broadband high-frequency activity (approx. 70-150 Hz), generally considered to reflect neuronal excitation. Here we explored the use of this broadband signal to track, on a single-trial basis, the temporal and spatial distribution of task-engaged areas involved in decision-making. We additionally focused on the alpha rhythm (8-14 Hz), thought to regulate the (dis)engagement of neuronal populations based on task demands. Using these signals, we characterized activity across cortex using intracranial recordings in patients with intractable epilepsy performing the Multi-Source Interference Task, a Stroop-like decision-making paradigm. We analyzed recordings both from grid electrodes placed over cortical areas including frontotemporal and parietal cortex, and depth electrodes in prefrontal regions, including cingulate cortex. We found a widespread negative relationship between alpha power and broadband activity, substantiating the gating role of alpha in regions beyond sensory/motor cortex. Combined, these signals reflect the spatio-temporal pattern of task-engagement, with alpha decrease signifying task-involved regions and broadband increase temporally locking to specific task aspects, distributed over cortical sites. We report sites that only respond to stimulus presentation or to the decision report and, interestingly, sites that reflect the time-on-task. The latter predict the subject's reaction times on a trial-by-trial basis. A smaller subset of sites showed modulation with task condition. Taken together, alpha and broadband signals allow tracking of neuronal population dynamics across cortex on a fine temporal and spatial scale.

Spontaneous State Detection Using Time-Frequency and Time-Domain Features Extracted From Stereo-Electroencephalography Traces

As a minimally invasive recording technique, stereo-electroencephalography (SEEG) measures intracranial signals directly by inserting depth electrodes shafts into the human brain, and thus can capture neural activities in both cortical layers and subcortical structures. Despite gradually increasing SEEG-based brain-computer interface (BCI) studies, the features utilized were usually confined to the amplitude of the event-related potential (ERP) or band power, and the decoding capabilities of other time-frequency and time-domain features have not been demonstrated for SEEG recordings yet. In this study, we aimed to verify the validity of time-domain and time-frequency features of SEEG, where classification performances served as evaluating indicators. To do this, using SEEG signals under intermittent auditory stimuli, we extracted features including the average amplitude, root mean square, slope of linear regression, and line-length from the ERP trace and three traces of band power activities (high-gamma, beta, and alpha). These features were used to detect the active state (including activations to two types of names) against the idle state. Results suggested that valid time-domain and time-frequency features distributed across multiple regions, including the temporal lobe, parietal lobe, and deeper structures such as the insula. Among all feature types, the average amplitude, root mean square, and line-length extracted from high-gamma (60–140 Hz) power and the line-length extracted from ERP were the most informative. Using a hidden Markov model (HMM), we could precisely detect the onset and the end of the active state with a sensitivity of 95.7 ± 1.3% and a precision of 91.7 ± 1.6%. The valid features derived from high-gamma power and ERP in this work provided new insights into the feature selection procedure for further SEEG-based BCI applications.

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.

Canonical EEG microstates transitions reflect switching among BOLD resting state networks and predict fMRI signal

Objective.Electroencephalography (EEG) microstates (MSs), which reflect a large topographical representation of coherent electrophysiological brain activity, are widely adopted to study cognitive processes mechanisms and aberrant alterations in brain disorders. MS topographies are quasi-stable lasting between 60-120 ms. Some evidence suggests that MS are the electrophysiological signature of resting-state networks (RSNs). However, the spatial and functional interpretation of MS and their association with functional magnetic resonance imaging (fMRI) remains unclear.Approach. In a cohort of healthy subjects (n= 52), we conducted several statistical and machine learning (ML) approaches analyses on the association among MS spatio-temporal dynamics and the blood-oxygenation-level dependent (BOLD) simultaneous EEG-fMRI data using statistical and ML approaches.Main results.Our results using a generalized linear model showed that MS transitions were largely and negatively associated with BOLD signals in the somatomotor, visual, dorsal attention, and ventral attention fMRI networks with limited association within the default mode network. Additionally, a novel recurrent neural network (RNN) confirmed the association between MS transitioning and fMRI signal while revealing that MS dynamics can model BOLD signals and vice versa.Significance.Results suggest that MS transitions may represent the deactivation of fMRI RSNs and provide evidence that both modalities measure common aspects of undergoing brain neuronal activities. These results may help to better understand the electrophysiological interpretation of MS.

Respiration aligns perception with neural excitability

Recent studies from the field of interoception have highlighted the link between bodily and neural rhythms during action, perception, and cognition. The mechanisms underlying functional body-brain coupling, however, are poorly understood, as are the ways in which they modulate behavior. We acquired respiration and human magnetoencephalography data from a near-threshold spatial detection task to investigate the trivariate relationship between respiration, neural excitability, and performance. Respiration was found to significantly modulate perceptual sensitivity as well as posterior alpha power (8-13 Hz), a well-established proxy of cortical excitability. In turn, alpha suppression prior to detected versus undetected targets underscored the behavioral benefits of heightened excitability. Notably, respiration-locked excitability changes were maximized at a respiration phase lag of around -30° and thus temporally preceded performance changes. In line with interoceptive inference accounts, these results suggest that respiration actively aligns sampling of sensory information with transient cycles of heightened excitability to facilitate performance.

Ongoing neural oscillations influence behavior and sensory representations by suppressing neuronal excitability

The ability to process and respond to external input is critical for adaptive behavior. Why, then, do neural and behavioral responses vary across repeated presentations of the same sensory input? Ongoing fluctuations of neuronal excitability are currently hypothesized to underlie the trial-by-trial variability in sensory processing. To test this, we capitalized on intracranial electrophysiology in neurosurgical patients performing an auditory discrimination task with visual cues: specifically, we examined the interaction between prestimulus alpha oscillations, excitability, task performance, and decoded neural stimulus representations. We found that strong prestimulus oscillations in the alpha+ band (i.e., alpha and neighboring frequencies), rather than the aperiodic signal, correlated with a low excitability state, indexed by reduced broadband high-frequency activity. This state was related to slower reaction times and reduced neural stimulus encoding strength. We propose that the alpha+ rhythm modulates excitability, thereby resulting in variability in behavior and sensory representations despite identical input.

Investigating Data Cleaning Methods to Improve Performance of Brain-Computer Interfaces Based on Stereo-Electroencephalography

Stereo-electroencephalography (SEEG) utilizes localized and penetrating depth electrodes to directly measure electrophysiological brain activity. The implanted electrodes generally provide a sparse sampling of multiple brain regions, including both cortical and subcortical structures, making the SEEG neural recordings a potential source for the brain–computer interface (BCI) purpose in recent years. For SEEG signals, data cleaning is an essential preprocessing step in removing excessive noises for further analysis. However, little is known about what kinds of effect that different data cleaning methods may exert on BCI decoding performance and, moreover, what are the reasons causing the differentiated effects. To address these questions, we adopted five different data cleaning methods, including common average reference, gray–white matter reference, electrode shaft reference, bipolar reference, and Laplacian reference, to process the SEEG data and evaluated the effect of these methods on improving BCI decoding performance. Additionally, we also comparatively investigated the changes of SEEG signals induced by these different methods from multiple-domain (e.g., spatial, spectral, and temporal domain). The results showed that data cleaning methods could improve the accuracy of gesture decoding, where the Laplacian reference produced the best performance. Further analysis revealed that the superiority of the data cleaning method with excellent performance might be attributed to the increased distinguishability in the low-frequency band. The findings of this work highlighted the importance of applying proper data clean methods for SEEG signals and proposed the application of Laplacian reference for SEEG-based BCI.

Detection of human white matter activation and evaluation of its function in movement decoding using stereo-electroencephalography (SEEG)

Objective. White matter tissue takes up approximately 50% of the human brain volume and it is widely known as a messenger conducting information between areas of the central nervous system. However, the characteristics of white matter neural activity and whether white matter neural recordings can contribute to movement decoding are often ignored and still remain largely unknown. In this work, we make quantitative analyses to investigate these two important questions using invasive neural recordings.Approach. We recorded stereo-electroencephalography (SEEG) data from 32 human subjects during a visually-cued motor task, where SEEG recordings can tap into gray and white matter electrical activity simultaneously. Using the proximal tissue density method, we identified the location (i.e. gray or white matter) of each SEEG contact. Focusing on alpha oscillatory and high gamma activities, we compared the activation patterns between gray matter and white matter. Then, we evaluated the performance of such white matter activation in movement decoding.Main results. The results show that white matter also presents activation under the task, in a similar way with the gray matter but at a significantly lower amplitude. Additionally, this work also demonstrates that combing white matter neural activities together with that of gray matter significantly promotes the movement decoding accuracy than using gray matter signals only.Significance. Taking advantage of SEEG recordings from a large number of subjects, we reveal the response characteristics of white matter neural signals under the task and demonstrate its enhancing function in movement decoding. This study highlights the importance of taking white matter activities into consideration in further scientific research and translational applications.

Self-Related Stimuli Decoding With Auditory and Visual Modalities Using Stereo-Electroencephalography

Name recognition plays important role in self-related cognitive processes and also contributes to a variety of clinical applications, such as autism spectrum disorder diagnosis and consciousness disorder analysis. However, most previous name-related studies usually adopted noninvasive EEG or fMRI recordings, which were limited by low spatial resolution and temporal resolution, respectively, and thus millisecond-level response latencies in precise brain regions could not be measured using these noninvasive recordings. By invasive stereo-electroencephalography (SEEG) recordings that have high resolution in both the spatial and temporal domain, the current study distinguished the neural response to one's own name or a stranger's name, and explored common active brain regions in both auditory and visual modalities. The neural activities were classified using spatiotemporal features of high-gamma, beta, and alpha band. Results showed that different names could be decoded using multi-region SEEG signals, and the best classification performance was achieved at high gamma (60–145 Hz) band. In this case, auditory and visual modality-based name classification accuracies were 84.5 ± 8.3 and 79.9 ± 4.6%, respectively. Additionally, some single regions such as the supramarginal gyrus, middle temporal gyrus, and insula could also achieve remarkable accuracies for both modalities, supporting their roles in the processing of self-related information. The average latency of the difference between the two responses in these precise regions was 354 ± 63 and 285 ± 59 ms in the auditory and visual modality, respectively. This study suggested that name recognition was attributed to a distributed brain network, and the subsets with decoding capabilities might be potential implanted regions for awareness detection and cognition evaluation.

Within-subject reaction time variability: Role of cortical networks and underlying neurophysiological mechanisms

Variations in reaction time are a ubiquitous characteristic of human behavior. Extensively documented, they have been successfully modeled using parameters of the subject or the task, but the neural basis of behavioral reaction time that varies within the same subject and the same task has been minimally studied. In this paper, we investigate behavioral reaction time variance using 28 datasets of direct cortical recordings in humans who engaged in four different types of simple sensory-motor reaction time tasks. Using a previously described technique that can identify the onset of population-level cortical activity and a novel functional connectivity algorithm described herein, we show that the cumulative latency difference of population-level neural activity across the task-related cortical network can explain up to 41% of the trial-by-trial variance in reaction time. Furthermore, we show that reaction time variance may primarily be due to the latencies in specific brain regions and demonstrate that behavioral latency variance is accumulated across the whole task-related cortical network. Our results suggest that population-level neural activity monotonically increases prior to movement execution, and that trial-by-trial changes in that increase are, in part, accounted for by inhibitory activity indexed by low-frequency oscillations. This pre-movement neural activity explains 19% of the measured variance in neural latencies in our data. Thus, our study provides a mechanistic explanation for a sizable fraction of behavioral reaction time when the subject's task is the same from trial to trial.

Conflict processing networks: A directional analysis of stimulus-response compatibilities using MEG

The suppression of distracting information in order to focus on an actual cognitive goal is a key feature of executive functions. The use of brain imaging methods to investigate the underlying neurobiological brain activations that occur during conflict processing have demonstrated a strong involvement of the fronto-parietal attention network (FPAN). Surprisingly, the directional interconnections, their time courses and activations at different frequency bands remain to be elucidated, and thus, this constitutes the focus of this study. The shared information flow between brain areas of the FPAN is provided for frequency bands ranging from the theta to the lower gamma band (4–40 Hz). We employed an adaptation of the Simon task utilizing Magnetoencephalography (MEG). Granger causality was applied to investigate interconnections between the active brain regions, as well as their directionality. Following stimulus onset, the middle frontal precentral cortex and superior parietal cortex were significantly activated during conflict processing in a time window of between 300 to 600ms. Important differences in causality were found across frequency bands between processing of conflicting stimuli in the left as compared to the right visual hemifield. The exchange of information from and to the FPAN was most prominent in the beta band. Moreover, the anterior cingulate cortex and the anterior insula represented key areas for conflict monitoring, either by receiving input from other areas of the FPAN or by generating output themselves. This indicates that the salience network is at least partly involved in processing conflict information. The present study provides detailed insights into the underlying neural mechanisms of the FPAN, especially regarding its temporal characteristics and directional interconnections.

Who said what? The effects of speech tempo on target detection and information extraction in a multi-talker situation: An ERP and functional connectivity study

People with normal hearing can usually follow one of the several concurrent speakers. Speech tempo affects both the separation of concurrent speech streams and information extraction from them. The current study varied the tempo of two concurrent speech streams to investigate these processes in a multi-talker situation. Listeners performed a target-detection and a content-tracking task, while target-related ERPs and functional brain networks sensitive to speech tempo were extracted from the EEG signal. At slower than normal speech tempo, building the two streams required longer processing times, and possibly the utilization of higher-order, for example, syntactic and semantic cues. The observed longer reaction times and higher connectivity strength in a theta band network associated with frontal control over auditory/speech processing are compatible with this notion. With increasing tempo, target detection performance decreased and the N2b and the P3b amplitudes increased. These data suggest an increased need for strictly allocating target-detection-related resources at higher tempo. This was also reflected by the observed increase in the strength of gamma-band networks within and between frontal, temporal, and cingular areas. At the fastest tested speech tempo, there was a sharp drop in recognition memory performance, while target detection performance increased compared to the normal speech tempo. This was accompanied by a significant increase in the strength of a low alpha network associated with the suppression of task-irrelevant speech. These results suggest that participants prioritized the immediate target detection task over the continuous content tracking, likely due to some capacity limit reached the fastest speech tempo.

Differences in Clinical Characteristics and Brain Activity between Patients with Low- and High-Frequency Tinnitus

This study was aimed at delineating and comparing differences in clinical characteristics and brain activity between patients with low- and high-frequency tinnitus (LFT and HFT, respectively) using high-density electroencephalography (EEG). This study enrolled 3217 patients with subjective tinnitus who were divided into LFT (frequency < 4000 Hz) and HFT (≥4000 Hz) groups. Data regarding medical history, Tinnitus Handicap Inventory, tinnitus matching, and hearing threshold were collected from all patients. Twenty tinnitus patients and 20 volunteers were subjected to 256-channel EEG, and neurophysiological differences were evaluated using standardized low-resolution brain electromagnetic tomography (sLORETA) source-localized EEG recordings. Significant differences in sex (p < 0.001), age (p = 0.022), laterality (p < 0.001), intensity (p < 0.001), tinnitus type (p < 0.001), persistent tinnitus (p = 0.04), average threshold (p < 0.001), and hearing loss (p = 0.028) were observed between LFT and HFT groups. The tinnitus pitch only appeared to be correlated with the threshold of the worst hearing loss in the HFT group. Compared with the controls, the LFT group exhibited increased gamma power (p < 0.05), predominantly in the posterior cingulate cortex (PCC, BA31), whereas the HFT group had significantly decreased alpha1 power (p < 0.05) in the angular gyrus (BA39) and auditory association cortex (BA22). Higher gamma linear connectivity between right BA39 and right BA41 was observed in the HFT group relative to controls (t = 3.637, p = 0.027). Significant changes associated with increased gamma in the LFT group and decreased alpha1 in the HFT group indicate that tinnitus pitch is crucial for matching between the tinnitus and control groups. Differences of band frequency energy in brain activity levels may contribute to the clinical characteristics and internal tinnitus “spectrum” differences.

Towards a Pragmatic Approach to a Psychophysiological Unit of Analysis for Mental and Brain Disorders: An EEG-Copeia for Neurofeedback

This article proposes what we call an "EEG-Copeia" for neurofeedback, like the "Pharmacopeia" for psychopharmacology. This paper proposes to define an "EEG-Copeia" as an organized list of scientifically validated EEG markers, characterized by a specific association with an identified cognitive process, that define a psychophysiological unit of analysis useful for mental or brain disorder evaluation and treatment. A characteristic of EEG neurofeedback for mental and brain disorders is that it targets a EEG markers related to a supposed cognitive process, whereas conventional treatments target clinical manifestations. This could explain why EEG neurofeedback studies encounter difficulty in achieving reproducibility and validation. The present paper suggests that a first step to optimize EEG neurofeedback protocols and future research is to target a valid EEG marker. The specificity of the cognitive skills trained and learned during real time feedback of the EEG marker could be enhanced and both the reliability of neurofeedback training and the therapeutic impact optimized. However, several of the most well-known EEG markers have seldom been applied for neurofeedback. Moreover, we lack a reliable and valid EEG targets library for further RCT to evaluate the efficacy of neurofeedback in mental and brain disorders. With the present manuscript, our aim is to foster dialogues between cognitive neuroscience and EEG neurofeedback according to a psychophysiological perspective. The primary objective of this review was to identify the most robust EEG target. EEG markers linked with one or several clearly identified cognitive-related processes will be identified. The secondary objective was to organize these EEG markers and related cognitive process in a psychophysiological unit of analysis matrix inspired by the Research Domain Criteria (RDoC) project.

Gene expression meta-analysis reveals the down-regulation of three GABA receptor subunits in the superior temporal gyrus of patients with schizophrenia

One of the main theories accounting for the underlying pathophysiology of schizophrenia posits alterations in GABAergic neurotransmission. While previous gene expression studies of postmortem brain samples typically report the down-regulation of GABA related genes in schizophrenia, the results are often inconsistent and not uniform across studies. We performed a systematic gene expression analysis of 22 GABA related genes in postmortem superior temporal gyrus (STG) samples of 19 elderly subjects with schizophrenia (mean age: 77) and 14 matched controls from the Icahn school of Medicine at Mount Sinai (MSSM) cohort. To test the validity and robustness of the resulting differentially expressed genes, we then conducted a meta-analysis of the MSSM and an independent dataset from the Stanley Consortium of 14 STG samples of relatively young subjects with schizophrenia (mean age: 44) and 15 matched controls. For the first time, the findings showed the down-regulation of three GABA-receptor subunits of type A, GABRA1, GABRA2 and GABRB3, in the STG samples of subjects with schizophrenia, in both the elderly and the relatively young patients. These findings, as well as previous results, lend weight to the notion of a common upstream pathology that alters GABAergic neurotransmission in schizophrenia. GABRA1, GABRA2 and GABRB3 down-regulation may contribute to the pathophysiology and clinical manifestations of schizophrenia through altered oscillation synchronization in the STG.

Oscillations in the auditory system and their possible role

GOURÉVITCH, B., C. Martin, O. Postal, J.J. Eggermont. Oscillations in the auditory system, their possible role. NEUROSCI BIOBEHAV REV XXX XXX-XXX, 2020. - Neural oscillations are thought to have various roles in brain processing such as, attention modulation, neuronal communication, motor coordination, memory consolidation, decision-making, or feature binding. The role of oscillations in the auditory system is less clear, especially due to the large discrepancy between human and animal studies. Here we describe many methodological issues that confound the results of oscillation studies in the auditory field. Moreover, we discuss the relationship between neural entrainment and oscillations that remains unclear. Finally, we aim to identify which kind of oscillations could be specific or salient to the auditory areas and their processing. We suggest that the role of oscillations might dramatically differ between the primary auditory cortex and the more associative auditory areas. Despite the moderate presence of intrinsic low frequency oscillations in the primary auditory cortex, rhythmic components in the input seem crucial for auditory processing. This allows the phase entrainment between the oscillatory phase and rhythmic input, which is an integral part of stimulus selection within the auditory system.

Natural music evokes correlated EEG responses reflecting temporal structure and beat

The brain activity of multiple subjects has been shown to synchronize during salient moments of natural stimuli, suggesting that correlation of neural responses indexes a brain state operationally termed 'engagement'. While past electroencephalography (EEG) studies have considered both auditory and visual stimuli, the extent to which these results generalize to music-a temporally structured stimulus for which the brain has evolved specialized circuitry-is less understood. Here we investigated neural correlation during natural music listening by recording EEG responses from N=48 adult listeners as they heard real-world musical works, some of which were temporally disrupted through shuffling of short-term segments (measures), reversal, or randomization of phase spectra. We measured correlation between multiple neural responses (inter-subject correlation) and between neural responses and stimulus envelope fluctuations (stimulus-response correlation) in the time and frequency domains. Stimuli retaining basic musical features, such as rhythm and melody, elicited significantly higher behavioral ratings and neural correlation than did phase-scrambled controls. However, while unedited songs were self-reported as most pleasant, time-domain correlations were highest during measure-shuffled versions. Frequency-domain measures of correlation (coherence) peaked at frequencies related to the musical beat, although the magnitudes of these spectral peaks did not explain the observed temporal correlations. Our findings show that natural music evokes significant inter-subject and stimulus-response correlations, and suggest that the neural correlates of musical 'engagement' may be distinct from those of enjoyment.

Abnormal Neural Activity in Children With Diffuse Intrinsic Pontine Glioma Had Manifested Deficit in Behavioral Inhibition: A Resting-State Functional MRI Study

PURPOSE

The purpose of this study was to investigate whether alterations of regional neural function in children with diffuse intrinsic pontine glioma (DIPG) had manifested deficit in behavioral inhibition using resting-state functional MRI (rs-fMRI).

METHODS

There were 17 participants with DIPG who took part in the study. Eight children were with deficit in behavioral inhibition, whereas the other 9 children did not obtain deficit in behavioral inhibition. Five healthy children with age, sex, and education matched to the study group also participated as the control group. These 3 groups underwent rs-fMRI, and the results were then converted to amplitude of low-frequency fluctuation (ALFF) data. Amplitude of low-frequency fluctuation data were further analyzed by single-factor analysis of variance comparing among 3 groups based on the whole brain levels. Amplitude of low-frequency fluctuation results were subjected to t test of voxel-wised comparison to derive the rs-fMRI brain function differences between the 2 DIPG groups. The Pearson correlation between ALFF values of abnormal regions found in 3 groups and the scores obtained according to the Child Behavior Checklist were analyzed.

RESULTS

The 3 groups had shown significant differences in terms of the ALFF results, with the ALFF increased in several brain regions (P < 0.05, corrected with AlphaSim, clusters >59 voxels), which include left supramarginal gyrus, left dorsolateral superior frontal gyrus, right precentral gyrus, and right middle frontal gyrus. Participants with deficit in behavioral inhibition had shown significant differences (ALFF decreased) in several brain regions, including left dorsolateral superior frontal gyrus and right fusiform gyrus (P < 0.05, corrected with AlphaSim, clusters >123 voxels), whereas other brain regions had shown ALFF increased, including left supramarginal gyrus, left middle frontal gyrus, and right medial superior frontal gyrus (P < 0.05, corrected with AlphaSim, clusters >123 voxels). There was no significant correlation between ALFF values and Child Behavior Checklist scores (P > 0.05).

CONCLUSIONS

These findings of focal spontaneous hyperfunction and hypofunction, which correlate with deficit in behavioral inhibition processing, and the abnormal brain regions are considered to be inefficient (in regions of the brain that may relate to compensatory brain and behavioral functioning, and it may be that the brain region needs to exert extra energy to perform a task to the same degree as the control group) or inability (inability in a certain region, or underpowered), pointing to a pathophysiologic process in executive dysfunction.

Neuronal Correlates of Informational and Energetic Masking in the Human Brain in a Multi-Talker Situation

Human listeners can follow the voice of one speaker while several others are talking at the same time. This process requires segregating the speech streams from each other and continuously directing attention to the target stream. We investigated the functional brain networks underlying this ability. Two speech streams were presented simultaneously to participants, who followed one of them and detected targets within it (target stream). The loudness of the distractor speech stream varied on five levels: moderately softer, slightly softer, equal, slightly louder, or moderately louder than the attended. Performance measures showed that the most demanding task was the moderately softer distractors condition, which indicates that a softer distractor speech may receive more covert attention than louder distractors and, therefore, they require more cognitive resources. EEG-based measurement of functional connectivity between various brain regions revealed frequency-band specific networks: (1) energetic masking (comparing the louder distractor conditions with the equal loudness condition) was predominantly associated with stronger connectivity between the frontal and temporal regions at the lower alpha (8–10 Hz) and gamma (30–70 Hz) bands; (2) informational masking (comparing the softer distractor conditions with the equal loudness condition) was associated with a distributed network between parietal, frontal, and temporal regions at the theta (4–8 Hz) and beta (13–30 Hz) bands. These results suggest the presence of distinct cognitive and neural processes for solving the interference from energetic vs. informational masking.

A cognitive brain-computer interface monitoring sustained attentional variations during a continuous task

We introduce a cognitive brain–computer interface based on a continuous performance task for the monitoring of variations of visual sustained attention, i.e. the self-directed maintenance of cognitive focus in non-arousing conditions while possibly ignoring distractors and avoiding mind wandering. We introduce a visual sustained attention continuous performance task with three levels of task difficulty. Pairwise discrimination of these task difficulties from electroencephalographic features was performed using a leave-one-subject-out cross validation approach. Features were selected using the orthogonal forward regression supervised feature selection method. Cognitive load was best predicted using a combination of prefrontal theta power, broad spatial range gamma power, fronto-central beta power, and fronto-central alpha power. Generalization performance estimates for pairwise classification of task difficulty using these features reached 75% for 5 s epochs, and 85% for 30 s epochs.

Optimal referencing for stereo-electroencephalographic (SEEG) recordings

Stereo-electroencephalography (SEEG) is an intracranial recording technique in which depth electrodes are inserted in the brain as part of presurgical assessments for invasive brain surgery. SEEG recordings can tap into neural signals across the entire brain and thereby sample both cortical and subcortical sites. However, even though signal referencing is important for proper assessment of SEEG signals, no previous study has comprehensively evaluated the optimal referencing method for SEEG. In our study, we recorded SEEG data from 15 human subjects during a motor task, referencing them against the average of two white matter contacts (monopolar reference). We then subjected these signals to 5 different re-referencing approaches: common average reference (CAR), gray-white matter reference (GWR), electrode shaft reference (ESR), bipolar reference, and Laplacian reference. The results from three different signal quality metrics suggest the use of the Laplacian re-reference for study of local population-level activity and low-frequency oscillatory activity.

Rodent Mismatch Negativity/theta Neuro-Oscillatory Response as a Translational Neurophysiological Biomarker for N-Methyl-D-Aspartate Receptor-Based New Treatment Development in Schizophrenia

Deficits in the generation of auditory mismatch negativity (MMN) generation are among the most widely replicated neurophysiological abnormalities in schizophrenia and are linked to underlying dysfunction of N-methyl-D-aspartate receptor (NMDAR)-mediated neurotransmission. Here, we evaluate physiological properties of rodent MMN, along with sensitivity to NMDAR agonist and antagonist treatments, relative to known patterns of dysfunction in schizophrenia. Epidural neurophysiological responses to frequency and duration deviants, along with responses to standard stimuli, were obtained at baseline and following 2 and 4 weeks' treatment in rats treated with saline, phencyclidine (PCP, 15 mg/kg/d by osmotic minipump), or PCP+glycine (16% by weight diet) interventions. Responses were analyzed using both event-related potential (ERP) and neuro-oscillatory (evoked power) approaches. At baseline, rodent duration MMN was associated with increased theta (θ)-frequency response similar to that observed in humans. PCP significantly reduced rodent duration MMN (p<0.001) and θ-band (p<0.01) response. PCP effects were prevented by concurrent glycine treatment (p<0.01 vs PCP alone). Effects related to stimulus-specific adaptation (SSA) were observed primarily in the alpha (α) and beta (β) frequency ranges. PCP treatment also significantly reduced α-frequency response to standard stimuli while increasing θ-band response, reproducing the pattern of deficit observed in schizophrenia. Overall, we demonstrate that rodent duration MMN shows neuro-oscillatory signature similar to human MMN, along with sensitivity to the NMDAR antagonist and agonist administration. These findings reinforce recent human studies linking MMN deficits to θ-band neuro-oscillatory dysfunction and support utility of rodent duration MMN as a translational biomarker for investigation of mechanisms underlying impaired local circuit function in schizophrenia.

Real-time detection and discrimination of visual perception using electrocorticographic signals

OBJECTIVE

Several neuroimaging studies have demonstrated that the ventral temporal cortex contains specialized regions that process visual stimuli. This study investigated the spatial and temporal dynamics of electrocorticographic (ECoG) responses to different types and colors of visual stimulation that were presented to four human participants, and demonstrated a real-time decoder that detects and discriminates responses to untrained natural images.

APPROACH

ECoG signals from the participants were recorded while they were shown colored and greyscale versions of seven types of visual stimuli (images of faces, objects, bodies, line drawings, digits, and kanji and hiragana characters), resulting in 14 classes for discrimination (experiment I). Additionally, a real-time system asynchronously classified ECoG responses to faces, kanji and black screens presented via a monitor (experiment II), or to natural scenes (i.e. the face of an experimenter, natural images of faces and kanji, and a mirror) (experiment III). Outcome measures in all experiments included the discrimination performance across types based on broadband γ activity.

MAIN RESULTS

Experiment I demonstrated an offline classification accuracy of 72.9% when discriminating among the seven types (without color separation). Further discrimination of grey versus colored images reached an accuracy of 67.1%. Discriminating all colors and types (14 classes) yielded an accuracy of 52.1%. In experiment II and III, the real-time decoder correctly detected 73.7% responses to face, kanji and black computer stimuli and 74.8% responses to presented natural scenes.

SIGNIFICANCE

Seven different types and their color information (either grey or color) could be detected and discriminated using broadband γ activity. Discrimination performance maximized for combined spatial-temporal information. The discrimination of stimulus color information provided the first ECoG-based evidence for color-related population-level cortical broadband γ responses in humans. Stimulus categories can be detected by their ECoG responses in real time within 500 ms with respect to stimulus onset.

Micro-calibration of space and motion by photoreceptors synchronized in parallel with cortical oscillations: A unified theory of visual perception

A fundamental function of the visual system is detecting motion, yet visual perception is poorly understood. Current research has determined that the retina and ganglion cells elicit responses for motion detection; however, the underlying mechanism for this is incompletely understood. Previously we proposed that retinogeniculo-cortical oscillations and photoreceptors work in parallel to process vision. Here we propose that motion could also be processed within the retina, and not in the brain as current theory suggests. In this paper, we discuss: 1) internal neural space formation; 2) primary, secondary, and tertiary roles of vision; 3) gamma as the secondary role; and 4) synchronization and coherence. Movement within the external field is instantly detected by primary processing within the space formed by the retina, providing a unified view of the world from an internal point of view. Our new theory begins to answer questions about: 1) perception of space, erect images, and motion, 2) purpose of lateral inhibition, 3) speed of visual perception, and 4) how peripheral color vision occurs without a large population of cones located peripherally in the retina. We explain that strong oscillatory activity influences on brain activity and is necessary for: 1) visual processing, and 2) formation of the internal visuospatial area necessary for visual consciousness, which could allow rods to receive precise visual and visuospatial information, while retinal waves could link the lateral geniculate body with the cortex to form a neural space formed by membrane potential-based oscillations and photoreceptors. We propose that vision is tripartite, with three components that allow a person to make sense of the world, terming them "primary, secondary, and tertiary roles" of vision. Finally, we propose that Gamma waves that are higher in strength and volume allow communication among the retina, thalamus, and various areas of the cortex, and synchronization brings cortical faculties to the retina, while the thalamus is the link that couples the retina to the rest of the brain through activity by gamma oscillations. This novel theory lays groundwork for further research by providing a theoretical understanding that expands upon the functions of the retina, photoreceptors, and retinal plexus to include parallel processing needed to form the internal visual space that we perceive as the external world.

A tale of two sites: Differential impairment of frequency and duration mismatch negativity across a primarily inpatient versus a primarily outpatient site in schizophrenia

Deficits in mismatch negativity (MMN) generation are among the best replicated neurophysiological deficits in schizophrenia, with reduced amplitude reflecting impaired information processing at the level of supratemporal auditory cortex. Differential patterns of MMN dysfunction according to deviant types have been reported across studies, with some research groups showing impairment in duration MMN but not frequency MMN, and other research groups reporting both findings. We evaluate the hypothesis that recruitment setting, reflecting current functional status, might be an important determinant of the pattern of MMN dysfunction. Here, we evaluated patterns of MMN dysfunction, along with tone matching and neuropsychological performance in subjects drawn from 1) a predominant inpatient/residential care setting (Nathan Kline Institute) and 2) a predominant outpatient setting (Columbia University). As predicted, compared to healthy controls, deficits in duration MMN were observed across sites, whereas deficits in frequency MMN/tone matching were confined to the chronic inpatient setting. Within patients, the frequency MMN deficit was highly correlated with impairments in tone matching ability across sites (r=-0.52, p<0.0001), as well as impairments in verbal learning (r=-0.54, p<0.0001). Responses to standard stimuli in the MMN paradigm were assessed using measures of alpha evoked power and inter-trial coherence (ITC). While deficits in alpha ITC were observed across sites (both p<0.05), deficits in alpha power were observed at the inpatient (p=0.001) but not outpatient (p=0.2) site. Overall, these finding indicate that impairments of frequency MMN generation and response power to standard stimuli could be particularly linked to forms of schizophrenia that are associated with poor functional outcome.

Improvement in mismatch negativity generation during d-serine treatment in schizophrenia: Correlation with symptoms

BACKGROUND

Deficits in N-methyl-d-aspartate-type (NMDAR) function contribute to symptoms and cognitive dysfunction in schizophrenia. The efficacy of NMDAR agonists in the treatment of persistent symptoms of schizophrenia has been variable, potentially reflecting limitations in functional target engagement. We recently demonstrated significant improvement in auditory mismatch negativity (MMN) with once-weekly treatment with d-serine, a naturally occurring NMDAR glycine-site agonist. This study investigates effects of continuous (daily) NMDAR agonists in schizophrenia/schizoaffective disorder.

METHODS

Primary analysis was on MMN after double-blind crossover (60mg/kg/d, n=16, 6weeks) treatment with d-serine/placebo. Secondary measures included clinical symptoms, neurocognition, and the effects of open-label (30-120mg/kg/d, n=21) d-serine and bitopertin/placebo (10mg, n=29), a glycine transport inhibitor.

RESULTS

Double-blind d-serine treatment led to significant improvement in MMN frequency (p=0.001, d=2.3) generation and clinical symptoms (p=0.023, d=0.80). MMN frequency correlated significantly with change in symptoms (r=-0.63, p=0.002) following co-variation for treatment type. d-Serine treatment led to a significant, large effect size increase vs. placebo in evoked α-power in response to standards (p=0.036, d=0.81), appearing to normalize evoked α power relative to previous findings with controls. While similar results were seen with open-label d-serine, no significant effects of bitopertin were observed for symptoms or MMN.

CONCLUSIONS

These findings represent the first randomized double-blind placebo-controlled study with 60mg/kg d-serine in schizophrenia, and are consistent with meta-analyses showing significant effects of d-serine in schizophrenia. Results overall support suggest that MMN may have negative, as well as positive, predictive value in predicting efficacy of novel compounds.

CLINICAL TRIALS REGISTRATION

Clinicaltrials.gov: NCT00322023/NCT00817336 (d-serine); NCT01116830 (bitopertin).

Task relevance modulates the behavioural and neural effects of sensory predictions

The brain is thought to generate internal predictions to optimize behaviour. However, it is unclear whether predictions signalling is an automatic brain function or depends on task demands. Here, we manipulated the spatial/temporal predictability of visual targets, and the relevance of spatial/temporal information provided by auditory cues. We used magnetoencephalography (MEG) to measure participants’ brain activity during task performance. Task relevance modulated the influence of predictions on behaviour: spatial/temporal predictability improved spatial/temporal discrimination accuracy, but not vice versa. To explain these effects, we used behavioural responses to estimate subjective predictions under an ideal-observer model. Model-based time-series of predictions and prediction errors (PEs) were associated with dissociable neural responses: predictions correlated with cue-induced beta-band activity in auditory regions and alpha-band activity in visual regions, while stimulus-bound PEs correlated with gamma-band activity in posterior regions. Crucially, task relevance modulated these spectral correlates, suggesting that current goals influence PE and prediction signalling.

As natural environments change, animals need to continuously learn and update predictions about their current context to optimize behaviour. According to predictive coding, a general principle of brain function is the propagation of both neural predictions from hierarchically higher to lower brain regions and of the ensuing prediction-errors back up the cortical hierarchy. We show that the neural activity that signals internal predictions and prediction-errors depends on the current task or goals. We applied magnetoencephalography and computational modelling of behavioural data to a study in which human participants could generate spatial and temporal predictions about upcoming stimuli, while performing spatial or temporal tasks. We found that current context (task relevance) modulated the influence of predictions on behavioural and neural responses. At the level of behavioural responses, only the task-relevant predictions led to improvement in task performance. At the level of neural responses, we found that predictions and prediction-errors correlated with activity in different brain regions and in dissociable frequency bands—reflecting synchronized neural activity. Crucially, these specific neural signatures of prediction and prediction-error signalling were strongly modulated by their contextual relevance. Thus, our results show that current goals influence prediction and prediction-error signalling in the brain.

Neural mechanisms of mismatch negativity dysfunction in schizophrenia

Schizophrenia is associated with cognitive deficits that reflect impaired cortical information processing. Mismatch negativity (MMN) indexes pre-attentive information processing dysfunction at the level of primary auditory cortex. This study investigates mechanisms underlying MMN impairments in schizophrenia using event-related potential, event-related spectral decomposition (ERSP) and resting state functional connectivity (rsfcMRI) approaches. For this study, MMN data to frequency, intensity and duration-deviants were analyzed from 69 schizophrenia patients and 38 healthy controls. rsfcMRI was obtained from a subsample of 38 patients and 23 controls. As expected, schizophrenia patients showed highly significant, large effect size (P=0.0004, d=1.0) deficits in MMN generation across deviant types. In ERSP analyses, responses to deviants occurred primarily the theta (4-7 Hz) frequency range consistent with distributed corticocortical processing, whereas responses to standards occurred primarily in alpha (8-12 Hz) range consistent with known frequencies of thalamocortical activation. Independent deficits in schizophrenia were observed in both the theta response to deviants (P=0.021) and the alpha-response to standards (P=0.003). At the single-trial level, differential patterns of response were observed for frequency vs duration/intensity deviants, along with At the network level, MMN deficits engaged canonical somatomotor, ventral attention and default networks, with a differential pattern of engagement across deviant types (P<0.0001). Findings indicate that deficits in thalamocortical, as well as corticocortical, connectivity contribute to auditory dysfunction in schizophrenia. In addition, differences in ERSP and rsfcMRI profiles across deviant types suggest potential differential engagement of underlying generator mechanisms.

Interbrain phase synchronization during turn-taking verbal interaction-a hyperscanning study using simultaneous EEG/MEG

Recently, neurophysiological findings about social interaction have been investigated widely, and hardware has been developed that can measure multiple subjects' brain activities simultaneously. These hyperscanning studies have enabled us to discover new and important evidences of interbrain interactions. Yet, very little is known about verbal interaction without any visual input. Therefore, we conducted a new hyperscanning study based on verbal, interbrain turn-taking interaction using simultaneous EEG/MEG, which measures rapidly changing brain activities. To establish turn-taking verbal interactions between a pair of subjects, we set up two EEG/MEG systems (19 and 146 channels of EEG and MEG, respectively) located ∼100 miles apart. Subjects engaged in verbal communication via condenser microphones and magnetic-compatible earphones, and a network time protocol synchronized the two systems. Ten subjects participated in this experiment and performed verbal interaction and noninteraction tasks separately. We found significant oscillations in EEG alpha and MEG alpha/gamma bands in several brain regions for all subjects. Furthermore, we estimated phase synchronization between two brains using the weighted phase lag index and found statistically significant synchronization in EEG and MEG data. Our novel paradigm and neurophysiological findings may foster a basic understanding of the functional mechanisms involved in human social interactions. Hum Brain Mapp 39:171-188, 2018. © 2017 Wiley Periodicals, Inc.

Dynamic Correlations between Intrinsic Connectivity and Extrinsic Connectivity of the Auditory Cortex in Humans

The arrival of sound signals in the auditory cortex (AC) triggers both local and inter-regional signal propagations over time up to hundreds of milliseconds and builds up both intrinsic functional connectivity (iFC) and extrinsic functional connectivity (eFC) of the AC. However, interactions between iFC and eFC are largely unknown. Using intracranial stereo-electroencephalographic recordings in people with drug-refractory epilepsy, this study mainly investigated the temporal dynamic of the relationships between iFC and eFC of the AC. The results showed that a Gaussian wideband-noise burst markedly elicited potentials in both the AC and numerous higher-order cortical regions outside the AC (non-auditory cortices). Granger causality analyses revealed that in the earlier time window, iFC of the AC was positively correlated with both eFC from the AC to the inferior temporal gyrus and that to the inferior parietal lobule. While in later periods, the iFC of the AC was positively correlated with eFC from the precentral gyrus to the AC and that from the insula to the AC. In conclusion, dual-directional interactions occur between iFC and eFC of the AC at different time windows following the sound stimulation and may form the foundation underlying various central auditory processes, including auditory sensory memory, object formation, integrations between sensory, perceptional, attentional, motor, emotional, and executive processes.

Instantaneous voltage as an alternative to power- and phase-based interpretation of oscillatory brain activity

For decades, oscillatory brain activity has been characterized primarily by measurements of power and phase. While many studies have linked those measurements to cortical excitability, their relationship to each other and to the physiological underpinnings of excitability is unclear. The recently proposed Function-through-Biased-Oscillations (FBO) hypothesis (Schalk, 2015) addressed these issues by suggesting that the voltage potential at the cortical surface directly reflects the excitability of cortical populations, that this voltage is rhythmically driven away from a low resting potential (associated with depolarized cortical populations) towards positivity (associated with hyperpolarized cortical populations). This view explains how oscillatory power and phase together influence the instantaneous voltage potential that directly regulates cortical excitability. This implies that the alternative measurement of instantaneous voltage of oscillatory activity should better predict cortical excitability compared to either of the more traditional measurements of power or phase. Using electrocorticographic (ECoG) data from 28 human subjects, the results of our study confirm this prediction: compared to oscillatory power and phase, the instantaneous voltage explained 20% and 31% more of the variance in broadband gamma, respectively, and power and phase together did not produce better predictions than the instantaneous voltage. These results synthesize the previously separate power- and phase-based interpretations and associate oscillatory activity directly with a physiological interpretation of cortical excitability. This alternative view has implications for the interpretation of studies of oscillatory activity and for current theories of cortical information transmission.

Multi-scale visual analysis of time-varying electrocorticography data via clustering of brain regions

Background

There exists a need for effective and easy-to-use software tools supporting the analysis of complex Electrocorticography (ECoG) data. Understanding how epileptic seizures develop or identifying diagnostic indicators for neurological diseases require the in-depth analysis of neural activity data from ECoG. Such data is multi-scale and is of high spatio-temporal resolution. Comprehensive analysis of this data should be supported by interactive visual analysis methods that allow a scientist to understand functional patterns at varying levels of granularity and comprehend its time-varying behavior.

Results

We introduce a novel multi-scale visual analysis system, ECoG ClusterFlow, for the detailed exploration of ECoG data. Our system detects and visualizes dynamic high-level structures, such as communities, derived from the time-varying connectivity network. The system supports two major views: 1) an overview summarizing the evolution of clusters over time and 2) an electrode view using hierarchical glyph-based design to visualize the propagation of clusters in their spatial, anatomical context. We present case studies that were performed in collaboration with neuroscientists and neurosurgeons using simulated and recorded epileptic seizure data to demonstrate our system’s effectiveness.

Conclusion

ECoG ClusterFlow supports the comparison of spatio-temporal patterns for specific time intervals and allows a user to utilize various clustering algorithms. Neuroscientists can identify the site of seizure genesis and its spatial progression during various the stages of a seizure. Our system serves as a fast and powerful means for the generation of preliminary hypotheses that can be used as a basis for subsequent application of rigorous statistical methods, with the ultimate goal being the clinical treatment of epileptogenic zones.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1633-9) contains supplementary material, which is available to authorized users.

Brain network dynamics in the human articulatory loop

OBJECTIVE

The articulatory loop is a fundamental component of language function, involved in the short-term buffer of auditory information followed by its vocal reproduction. We characterized the network dynamics of the human articulatory loop, using invasive recording and stimulation.

METHODS

We measured high-gamma activity_{70-110 Hz} recorded intracranially when patients with epilepsy either only listened to, or listened to and then reproduced two successive tones by humming. We also conducted network analyses, and analyzed behavioral responses to cortical stimulation.

RESULTS

Presentation of the initial tone elicited high-gamma augmentation bilaterally in the superior-temporal gyrus (STG) within 40ms, and in the precentral and inferior-frontal gyri (PCG and IFG) within 160ms after sound onset. During presentation of the second tone, high-gamma augmentation was reduced in STG but enhanced in IFG. The task requiring tone reproduction further enhanced high-gamma augmentation in PCG during and after sound presentation. Event-related causality (ERC) analysis revealed dominant flows within STG immediately after sound onset, followed by reciprocal interactions involving PCG and IFG. Measurement of cortico-cortical evoked-potentials (CCEPs) confirmed connectivity between distant high-gamma sites in the articulatory loop. High-frequency stimulation of precentral high-gamma sites in either hemisphere induced speech arrest, inability to control vocalization, or forced vocalization. Vocalization of tones was accompanied by high-gamma augmentation over larger extents of PCG.

CONCLUSIONS

Bilateral PCG rapidly and directly receives feed-forward signals from STG, and may promptly initiate motor planning including sub-vocal rehearsal for short-term buffering of auditory stimuli. Enhanced high-gamma augmentation in IFG during presentation of the second tone may reflect high-order processing of the tone sequence.

SIGNIFICANCE

The articulatory loop employs sustained reciprocal propagation of neural activity across a network of cortical sites with strong neurophysiological connectivity.

Frequency-specific electrophysiologic correlates of resting state fMRI networks

Resting state functional MRI (R-fMRI) studies have shown that slow (< 0.1 Hz), intrinsic fluctuations of the blood oxygen level dependent (BOLD) signal are temporally correlated within hierarchically organized functional systems known as resting state networks (RSNs) (Doucet et al., 2011). Most broadly, this hierarchy exhibits a dichotomy between two opposed systems (Fox et al., 2005). One system engages with the environment and includes the visual, auditory, and sensorimotor (SMN) networks as well as the dorsal attention network (DAN), which controls spatial attention. The other system includes the default mode network (DMN) and the fronto-parietal control system (FPC), RSNs that instantiate episodic memory and executive control, respectively. Here, we test the hypothesis, based on the spectral specificity of electrophysiologic responses to perceptual vs. memory tasks (Klimesch, 1999; Pfurtscheller and Lopes da Silva, 1999), that these two large-scale neural systems also manifest frequency specificity in the resting state. We measured the spatial correspondence between electrocorticographic (ECoG) band-limited power (BLP) and R-fMRI correlation patterns in awake, resting, human subjects. Our results show that, while gamma BLP correspondence was common throughout the brain, theta (4–8 Hz) BLP correspondence was stronger in the DMN and FPC, whereas alpha (8–12 Hz) correspondence was stronger in the SMN and DAN. Thus, the human brain, at rest, exhibits frequency specific electrophysiology, respecting both the spectral structure of task responses and the hierarchical organization of RSNs.

Alpha oscillations and their impairment in affective and post-traumatic stress disorders

Affective and anxiety disorders are debilitating conditions characterized by impairments in cognitive and social functioning. Elucidating their neural underpinnings may assist in improving diagnosis and developing targeted interventions. Neural oscillations are fundamental for brain functioning. Specifically, oscillations in the alpha frequency range (alpha rhythms) are prevalent in the awake, conscious brain and play an important role in supporting perceptual, cognitive, and social processes. We review studies utilizing various alpha power measurements to assess abnormalities in brain functioning in affective and anxiety disorders as well as obsessive compulsive and post-traumatic stress disorders. Despite some inconsistencies, studies demonstrate associations between aberrant alpha patterns and these disorders both in response to specific cognitive and emotional tasks and during a resting state. We conclude by discussing methodological considerations and future directions, and underscore the need for much further research on the role of alpha functionality in social contexts. As social dysfunction accompanies most psychiatric conditions, research on alpha's involvement in social processes may provide a unique window into the neural mechanisms underlying these disorders.

A method to establish the spatiotemporal evolution of task-related cortical activity from electrocorticographic signals in single trials

BACKGROUND

Progress in neuroscience depends substantially on the ability to establish the detailed spatial and temporal sequence of neuronal population-level activity across large areas of the brain. Because there is substantial inter-trial variability in neuronal activity, traditional techniques that rely on signal averaging obscure where and when neuronal activity occurs. Thus, up to the present, it has been difficult to examine the detailed progression of neuronal activity across large areas of the brain.

NEW METHOD

Here we describe a method for establishing the spatiotemporal evolution of neuronal population-level activity across large brain regions by determining exactly where and when neural activity occurs during a behavioral task in individual trials. We validate the efficacy of the method, examine the effects of its parameterization, and demonstrate its utility by highlighting two sets of results that could not readily be achieved with traditional methods.

RESULTS

Our results reveal the precise spatiotemporal evolution of neuronal population activity that unfolds during a sensorimotor task in individual trials, and establishes the relationship between neuronal oscillations and the onset of this activity.

CONCLUSIONS

The ability to identify the spatiotemporal evolution of neuronal population activity onsets in single trials gives investigators a powerful new tool with which to study large-scale cortical processes.

Alpha power indexes task-related networks on large and small scales: A multimodal ECoG study in humans and a non-human primate

Performing different tasks, such as generating motor movements or processing sensory input, requires the recruitment of specific networks of neuronal populations. Previous studies suggested that power variations in the alpha band (8-12Hz) may implement such recruitment of task-specific populations by increasing cortical excitability in task-related areas while inhibiting population-level cortical activity in task-unrelated areas (Klimesch et al., 2007; Jensen and Mazaheri, 2010). However, the precise temporal and spatial relationships between the modulatory function implemented by alpha oscillations and population-level cortical activity remained undefined. Furthermore, while several studies suggested that alpha power indexes task-related populations across large and spatially separated cortical areas, it was largely unclear whether alpha power also differentially indexes smaller networks of task-related neuronal populations. Here we addressed these questions by investigating the temporal and spatial relationships of electrocorticographic (ECoG) power modulations in the alpha band and in the broadband gamma range (70-170Hz, indexing population-level activity) during auditory and motor tasks in five human subjects and one macaque monkey. In line with previous research, our results confirm that broadband gamma power accurately tracks task-related behavior and that alpha power decreases in task-related areas. More importantly, they demonstrate that alpha power suppression lags population-level activity in auditory areas during the auditory task, but precedes it in motor areas during the motor task. This suppression of alpha power in task-related areas was accompanied by an increase in areas not related to the task. In addition, we show for the first time that these differential modulations of alpha power could be observed not only across widely distributed systems (e.g., motor vs. auditory system), but also within the auditory system. Specifically, alpha power was suppressed in the locations within the auditory system that most robustly responded to particular sound stimuli. Altogether, our results provide experimental evidence for a mechanism that preferentially recruits task-related neuronal populations by increasing cortical excitability in task-related cortical areas and decreasing cortical excitability in task-unrelated areas. This mechanism is implemented by variations in alpha power and is common to humans and the non-human primate under study. These results contribute to an increasingly refined understanding of the mechanisms underlying the selection of the specific neuronal populations required for task execution.

Untangling the relatedness among correlations, part I: Nonparametric approaches to inter-subject correlation analysis at the group level

FMRI data acquisition under naturalistic and continuous stimuli (e.g., watching a video or listening to music) has become popular recently due to the fact that it entails less manipulation and more realistic/complex contexts involved in the task, compared to the conventional task-based experimental designs. The synchronization or response similarities among subjects are typically measured through inter-subject correlation (ISC) between any pair of subjects. At the group level, summarizing the collection of ISC values is complicated by their intercorrelations, which necessarily lead to the violation of independence assumed in typical parametric approaches such as Student's t-test. Nonparametric methods, such as bootstrapping and permutation testing, have previously been adopted for testing purposes by resampling the time series of each subject, but the quantitative validity of these specific approaches in terms of controllability of false positive rate (FPR) has never been explored before. Here we survey the methods of ISC group analysis that have been employed in the literature, and discuss the issues involved in those methods. We then propose less computationally intensive nonparametric methods that can be performed at the group level (for both one- and two-sample analyses), as compared to the popular method of circularly shifting the EPI time series at the individual level. As part of the new approaches, subject-wise (SW) resampling is adopted instead of element-wise (EW) resampling, so that exchangeability and independence assumptions are satisfied, and the patterned correlation structure among the ISC values can be more accurately captured. We examine the FPR controllability and power achievement of all the methods through simulations, as well as their performance when applied to a real experimental dataset.

Unsupervised Decoding of Long-Term, Naturalistic Human Neural Recordings with Automated Video and Audio Annotations

Fully automated decoding of human activities and intentions from direct neural recordings is a tantalizing challenge in brain-computer interfacing. Implementing Brain Computer Interfaces (BCIs) outside carefully controlled experiments in laboratory settings requires adaptive and scalable strategies with minimal supervision. Here we describe an unsupervised approach to decoding neural states from naturalistic human brain recordings. We analyzed continuous, long-term electrocorticography (ECoG) data recorded over many days from the brain of subjects in a hospital room, with simultaneous audio and video recordings. We discovered coherent clusters in high-dimensional ECoG recordings using hierarchical clustering and automatically annotated them using speech and movement labels extracted from audio and video. To our knowledge, this represents the first time techniques from computer vision and speech processing have been used for natural ECoG decoding. Interpretable behaviors were decoded from ECoG data, including moving, speaking and resting; the results were assessed by comparison with manual annotation. Discovered clusters were projected back onto the brain revealing features consistent with known functional areas, opening the door to automated functional brain mapping in natural settings.

Electrocorticographic mapping of expressive language function without requiring the patient to speak: A report of three cases

Objective

Patients requiring resective brain surgery often undergo functional brain mapping during perioperative planning to localize expressive language areas. Currently, all established protocols to perform such mapping require substantial time and patient participation during verb generation or similar tasks. These issues can make language mapping impractical in certain clinical circumstances (e.g., during awake craniotomies) or with certain populations (e.g., pediatric patients). Thus, it is important to develop new techniques that reduce mapping time and the requirement for active patient participation. Several neuroscientific studies reported that the mere auditory presentation of speech stimuli can engage not only receptive but also expressive language areas. Here, we tested the hypothesis that submission of electrocorticographic (ECoG) recordings during a short speech listening task to an appropriate analysis procedure can identify eloquent expressive language cortex without requiring the patient to speak.

Methods

Three patients undergoing temporary placement of subdural electrode grids passively listened to stories while we recorded their ECoG activity. We identified those sites whose activity in the broadband gamma range (70–170 Hz) changed immediately after presentation of the speech stimuli with respect to a prestimulus baseline.

Results

Our analyses revealed increased broadband gamma activity at distinct locations in the inferior frontal cortex, superior temporal gyrus, and/or perisylvian areas in all three patients and premotor and/or supplementary motor areas in two patients. The sites in the inferior frontal cortex that we identified with our procedure were either on or immediately adjacent to locations identified using electrical cortical stimulation (ECS) mapping.

Conclusions

The results of this study provide encouraging preliminary evidence that it may be possible that a brief and practical protocol can identify expressive language areas without requiring the patient to speak. This protocol could provide the clinician with a map of expressive language cortex within a few minutes. This may be useful as an adjunct to ECS interrogation or as an alternative to mapping using functional magnetic resonance imaging (fMRI). In conclusion, with further development and validation in more subjects, the approach presented here could help in identifying expressive language areas in situations where patients cannot speak in response to task instructions.

The primary motor cortex is involved in the control of a non-motor cognitive action

OBJECTIVE

Adaptive interactions with the outer world necessitate effective connections between cognitive and executive functions. The primary motor cortex (M1) with its control of the spinal cord motor apparatus and its involvement in the processing of cognitive information related to motor functions is one of the best suited structures of this cognition-action connection. The question arose whether M1 might be involved also in situations where no overt or covered motor action is present.

METHODS

The EEG data analyzed were recorded during an oddball task in one epileptic patient (19 years) with depth multilead electrodes implanted for diagnostic reasons into the M1 and several prefrontal areas.

RESULTS

The main result was the finding of an evoked response to non-target stimuli with a pronounced late component in all frontal areas explored, including three loci of the M1. The late component was implicated in the evaluation of predicted and actual action and was synchronized in all three precentral loci and in the majority of prefrontal loci.

CONCLUSION

The finding is considered as direct evidence of functional involvement of the M1 in cognitive activity not related to motor function.

SIGNIFICANCE

Our results contribute to better understanding of neural mechanisms underlying cognition.

Proceedings of the Seventh International Workshop on Advances in Electrocorticography

The Seventh International Workshop on Advances in Electrocorticography (ECoG) convened in Washington, DC, on November 13-14, 2014. Electrocorticography-based research continues to proliferate widely across basic science and clinical disciplines. The 2014 workshop highlighted advances in neurolinguistics, brain-computer interface, functional mapping, and seizure termination facilitated by advances in the recording and analysis of the ECoG signal. The following proceedings document summarizes the content of this successful multidisciplinary gathering.

Identifying the Attended Speaker Using Electrocorticographic (ECoG) Signals

People affected by severe neuro-degenerative diseases (e.g., late-stage amyotrophic lateral sclerosis (ALS) or locked-in syndrome) eventually lose all muscular control. Thus, they cannot use traditional assistive communication devices that depend on muscle control, or brain-computer interfaces (BCIs) that depend on the ability to control gaze. While auditory and tactile BCIs can provide communication to such individuals, their use typically entails an artificial mapping between the stimulus and the communication intent. This makes these BCIs difficult to learn and use. In this study, we investigated the use of selective auditory attention to natural speech as an avenue for BCI communication. In this approach, the user communicates by directing his/her attention to one of two simultaneously presented speakers. We used electrocorticographic (ECoG) signals in the gamma band (70-170 Hz) to infer the identity of attended speaker, thereby removing the need to learn such an artificial mapping. Our results from twelve human subjects show that a single cortical location over superior temporal gyrus or pre-motor cortex is typically sufficient to identify the attended speaker within 10 s and with 77% accuracy (50% accuracy due to chance). These results lay the groundwork for future studies that may determine the real-time performance of BCIs based on selective auditory attention to speech.