Auditory deviance detection in the human insula: An intracranial EEG study

Language and sensory characteristics are reflected in voice-evoked responses in low birth weight children

BACKGROUND

Children born with very low birth weight (VLBW) are at higher risk for cognitive impairment, including language deficits and sensorimotor difficulties. Voice-evoked response (P1m), which has been suggested as a language development biomarker in young children, remains unexplored for its efficacy in VLBW children. Furthermore, the relation between P1m and sensory difficulties in VLBW children remains unclear.

METHODS

40 children with VLBW were recruited at 5-to-6 years old (26 male, 14 female, mean age of months ± SD, 80.0 ± 4.9). We measured their voice-evoked brain response using child-customized magnetoencephalography (MEG) and examined the relation between P1m and language conceptual inference ability and sensory characteristics.

RESULTS

The final sample comprised 36 children (23 boys, 13 girls; ages 61-86 months; gestational ages 24-36 weeks). As a result of multiple regression analysis, voice-evoked P1m in the left hemisphere was correlated significantly with language ability (β = 0.414 P = 0.015) and sensory hypersensitivity (β = 0.471 P = 0.005).

CONCLUSION

Our findings indicate that the relation between P1m and language conceptual inference ability observed in term children in earlier studies is replicated in VLBW children, and suggests P1m intensity as a biomarker of sensory sensitivity characteristics.

IMPACT

We investigated brain functions related to language development and sensory problems in very low birth-weight children. In very low birth weight children at early school age, brain responses to human voices are associated with language conceptual inference ability and sensory hypersensitivity. These findings promote a physiological understanding of both language development and sensory characteristics in very low birth weight children.

Distributed representations of prediction error signals across the cortical hierarchy are synergistic

A relevant question concerning inter-areal communication in the cortex is whether these interactions are synergistic. Synergy refers to the complementary effect of multiple brain signals conveying more information than the sum of each isolated signal. Redundancy, on the other hand, refers to the common information shared between brain signals. Here, we dissociated cortical interactions encoding complementary information (synergy) from those sharing common information (redundancy) during prediction error (PE) processing. We analyzed auditory and frontal electrocorticography (ECoG) signals in five common awake marmosets performing two distinct auditory oddball tasks and investigated to what extent event-related potentials (ERP) and broadband (BB) dynamics encoded synergistic and redundant information about PE processing. The information conveyed by ERPs and BB signals was synergistic even at lower stages of the hierarchy in the auditory cortex and between auditory and frontal regions. Using a brain-constrained neural network, we simulated the synergy and redundancy observed in the experimental results and demonstrated that the emergence of synergy between auditory and frontal regions requires the presence of strong, long-distance, feedback, and feedforward connections. These results indicate that distributed representations of PE signals across the cortical hierarchy can be highly synergistic.

Aberrant connectivity in the hippocampus, bilateral insula and temporal poles precedes treatment resistance in first-episode psychosis: a prospective resting-state functional magnetic resonance imaging study with connectivity concordance mapping

Functional connectivity resting-state functional magnetic resonance imaging has been proposed to predict antipsychotic treatment response in schizophrenia. However, only a few prospective studies have examined baseline resting-state functional magnetic resonance imaging data in drug-naïve first-episode schizophrenia patients with regard to subsequent treatment response. Data-driven approaches to conceptualize and measure functional connectivity patterns vary broadly, and model-free, voxel-wise, whole-brain analysis techniques are scarce. Here, we apply such a method, called connectivity concordance mapping to resting-state functional magnetic resonance imaging data acquired from an Asian sample (n = 60) with first-episode psychosis, prior to pharmaceutical treatment. Using a longitudinal design, 12 months after the resting-state functional magnetic resonance imaging, we measured and classified patients into two groups based on psychometric testing: treatment responsive and treatment resistant. Next, we compared the two groups' connectivity concordance maps that were derived from the resting-state functional magnetic resonance imaging data at baseline. We have identified consistently higher functional connectivity in the treatment-resistant group in a network including the left hippocampus, bilateral insula and temporal poles. These data-driven novel findings can help researchers to consider new regions of interest and facilitate biomarker development in order to identify treatment-resistant schizophrenia patients early, in advance of treatment and at the time of their first psychotic episode.

Anatomical registration of intracranial electrodes. Robust model-based localization and deformable smooth brain-shift compensation methods

BACKGROUND

Intracranial electrodes are typically localized from post-implantation CT artifacts. Automatic algorithms localizing low signal-to-noise ratio artifacts and high-density electrode arrays are missing. Additionally, implantation of grids/strips introduces brain deformations, resulting in registration errors when fusing post-implantation CT and pre-implantation MR images. Brain-shift compensation methods project electrode coordinates to cortex, but either fail to produce smooth solutions or do not account for brain deformations.

NEW METHODS

We first introduce GridFit, a model-based fitting approach that simultaneously localizes all electrodes' CT artifacts in grids, strips, or depth arrays. Second, we present CEPA, a brain-shift compensation algorithm combining orthogonal-based projections, spring-mesh models, and spatial regularization constraints.

RESULTS

We tested GridFit on ∼6000 simulated scenarios. The localization of CT artifacts showed robust performance under difficult scenarios, such as noise, overlaps, and high-density implants (<1 mm errors). Validation with data from 20 challenging patients showed 99% accurate localization of the electrodes (3160/3192). We tested CEPA brain-shift compensation with data from 15 patients. Projections accounted for simple mechanical deformation principles with < 0.4 mm errors. The inter-electrode distances smoothly changed across neighbor electrodes, while changes in inter-electrode distances linearly increased with projection distance.

COMPARISON WITH EXISTING METHODS

GridFit succeeded in difficult scenarios that challenged available methods and outperformed visual localization by preserving the inter-electrode distance. CEPA registration errors were smaller than those obtained for well-established alternatives. Additionally, modeling resting-state high-frequency activity in five patients further supported CEPA.

CONCLUSION

GridFit and CEPA are versatile tools for registering intracranial electrode coordinates, providing highly accurate results even in the most challenging implantation scenarios. The methods are implemented in the iElectrodes open-source toolbox.

Predictable and unpredictable deviance detection in the human hippocampus and amygdala

Our brains extract structure from the environment and form predictions given past experience. Predictive circuits have been identified in wide-spread cortical regions. However, the contribution of medial temporal structures in predictions remains under-explored. The hippocampus underlies sequence detection and is sensitive to novel stimuli, sufficient to gain access to memory, while the amygdala to novelty. Yet, their electrophysiological profiles in detecting predictable and unpredictable deviant auditory events remain unknown. Here, we hypothesized that the hippocampus would be sensitive to predictability, while the amygdala to unexpected deviance. We presented epileptic patients undergoing presurgical monitoring with standard and deviant sounds, in predictable or unpredictable contexts. Onsets of auditory responses and unpredictable deviance effects were detected earlier in the temporal cortex compared with the amygdala and hippocampus. Deviance effects in 1-20 Hz local field potentials were detected in the lateral temporal cortex, irrespective of predictability. The amygdala showed stronger deviance in the unpredictable context. Low-frequency deviance responses in the hippocampus (1-8 Hz) were observed in the predictable but not in the unpredictable context. Our results reveal a distributed network underlying the generation of auditory predictions and suggest that the neural basis of sensory predictions and prediction error signals needs to be extended.

Characteristics of brain glucose metabolism and metabolic connectivity in noise-induced hearing loss

The purpose of this study was to evaluate the differences in cerebral glucose metabolism and metabolic connectivity between noise-induced hearing loss (NIHL) subjects and normal subjects. Eighty-nine subjects who needed close observation for NIHL or were diagnosed with NIHL and 89 normal subjects were enrolled. After pre-processing of positron emission tomography images including co-registration, spatial normalization, and smoothing, a two-sample t-test was conducted to compare cerebral glucose metabolism between the two groups. To evaluate metabolic connectivity between two groups, BRAPH-BRain Analysis using graPH theory, a software package to perform graph theory analysis of the brain connectome was used. NIHL subjects showed hypometabolism compared to normal subjects in both insulae (x - 38, y - 18, z 4; × 42, y - 12, z 4) and right superior temporal gyrus (× 44, y 16, z - 20). No brain regions showed hypermetabolism in the NIHL subjects. In metabolic connectivity analysis, NIHL subjects showed decreased average strength, global efficiency, local efficiency, and mean clustering coefficient when compared with normal subjects. Decreased glucose metabolism and metabolic connectivity in NIHL subject might reflect decreased auditory function. It might be characteristic of sensorineural hearing loss.

Decision and response monitoring during working memory are sequentially represented in the human insula

Emerging research supports a role of the insula in human cognition. Here, we used intracranial EEG to investigate the spatiotemporal dynamics in the insula during a verbal working memory (vWM) task. We found robust effects for theta, beta, and high frequency activity (HFA) during probe presentation requiring a decision. Theta band activity showed differential involvement across left and right insulae while sequential HFA modulations were observed along the anteroposterior axis. HFA in anterior insula tracked decision making and subsequent HFA was observed in posterior insula after the behavioral response. Our results provide electrophysiological evidence of engagement of different insula subregions in both decision-making and response monitoring during vWM and expand our knowledge of the role of the insula in complex human behavior.

Quantifying evoked responses through information-theoretical measures

Information theory is a viable candidate to advance our understanding of how the brain processes information generated in the internal or external environment. With its universal applicability, information theory enables the analysis of complex data sets, is free of requirements about the data structure, and can help infer the underlying brain mechanisms. Information-theoretical metrics such as Entropy or Mutual Information have been highly beneficial for analyzing neurophysiological recordings. However, a direct comparison of the performance of these methods with well-established metrics, such as the t-test, is rare. Here, such a comparison is carried out by evaluating the novel method of Encoded Information with Mutual Information, Gaussian Copula Mutual Information, Neural Frequency Tagging, and t-test. We do so by applying each method to event-related potentials and event-related activity in different frequency bands originating from intracranial electroencephalography recordings of humans and marmoset monkeys. Encoded Information is a novel procedure that assesses the similarity of brain responses across experimental conditions by compressing the respective signals. Such an information-based encoding is attractive whenever one is interested in detecting where in the brain condition effects are present.

Anatomical registration of intracranial electrodes. Robust model-based localization and deformable smooth brain-shift compensation methods

Precise electrode localization is important for maximizing the utility of intracranial EEG data. Electrodes are typically localized from post-implantation CT artifacts, but algorithms can fail due to low signal-to-noise ratio, unrelated artifacts, or high-density electrode arrays. Minimizing these errors usually requires time-consuming visual localization and can still result in inaccurate localizations. In addition, surgical implantation of grids and strips typically introduces non-linear brain deformations, which result in anatomical registration errors when post-implantation CT images are fused with the pre-implantation MRI images. Several projection methods are currently available, but they either fail to produce smooth solutions or do not account for brain deformations. To address these shortcomings, we propose two novel algorithms for the anatomical registration of intracranial electrodes that are almost fully automatic and provide highly accurate results. We first present GridFit, an algorithm that simultaneously localizes all contacts in grids, strips, or depth arrays by fitting flexible models to the electrodes' CT artifacts. We observed localization errors of less than one millimeter (below 8% relative to the inter-electrode distance) and robust performance under the presence of noise, unrelated artifacts, and high-density implants when we ran ~6000 simulated scenarios. Furthermore, we validated the method with real data from 20 intracranial patients. As a second registration step, we introduce CEPA, a brain-shift compensation algorithm that combines orthogonal-based projections, spring-mesh models, and spatial regularization constraints. When tested with real data from 15 patients, anatomical registration errors were smaller than those obtained for well-established alternatives. Additionally, CEPA accounted simultaneously for simple mechanical deformation principles, which is not possible with other available methods. Inter-electrode distances of projected coordinates smoothly changed across neighbor electrodes, while changes in inter-electrode distances linearly increased with projection distance. Moreover, in an additional validation procedure, we found that modeling resting-state high-frequency activity (75-145 Hz ) in five patients further supported our new algorithm. Together, GridFit and CEPA constitute a versatile set of tools for the registration of subdural grid, strip, and depth electrode coordinates that provide highly accurate results even in the most challenging implantation scenarios. The methods presented here are implemented in the iElectrodes open-source toolbox, making their use simple, accessible, and straightforward to integrate with other popular toolboxes used for analyzing electrophysiological data.

Modeling intracranial electrodes. A simulation platform for the evaluation of localization algorithms

Introduction

Intracranial electrodes are implanted in patients with drug-resistant epilepsy as part of their pre-surgical evaluation. This allows the investigation of normal and pathological brain functions with excellent spatial and temporal resolution. The spatial resolution relies on methods that precisely localize the implanted electrodes in the cerebral cortex, which is critical for drawing valid inferences about the anatomical localization of brain function. Multiple methods have been developed to localize the electrodes, mainly relying on pre-implantation MRI and post-implantation computer tomography (CT) images. However, they are hard to validate because there is no ground truth data to test them and there is no standard approach to systematically quantify their performance. In other words, their validation lacks standardization. Our work aimed to model intracranial electrode arrays and simulate realistic implantation scenarios, thereby providing localization algorithms with new ways to evaluate and optimize their performance.

Results

We implemented novel methods to model the coordinates of implanted grids, strips, and depth electrodes, as well as the CT artifacts produced by these. We successfully modeled realistic implantation scenarios, including different sizes, inter-electrode distances, and brain areas. In total, ∼3,300 grids and strips were fitted over the brain surface, and ∼850 depth electrode arrays penetrating the cortical tissue were modeled. Realistic CT artifacts were simulated at the electrode locations under 12 different noise levels. Altogether, ∼50,000 thresholded CT artifact arrays were simulated in these scenarios, and validated with real data from 17 patients regarding the coordinates' spatial deformation, and the CT artifacts' shape, intensity distribution, and noise level. Finally, we provide an example of how the simulation platform is used to characterize the performance of two cluster-based localization methods.

Conclusion

We successfully developed the first platform to model implanted intracranial grids, strips, and depth electrodes and realistically simulate thresholded CT artifacts and their noise. These methods provide a basis for developing more complex models, while simulations allow systematic evaluation of the performance of electrode localization techniques. The methods described in this article, and the results obtained from the simulations, are freely available via open repositories. A graphical user interface implementation is also accessible via the open-source iElectrodes toolbox.

Complexity-based Encoded Information Quantification in Neurophysiological Recordings

Brain activity differs vastly between sleep, cognitive tasks, and action. Information theory is an appropriate concept to analytically quantify these brain states. Based on neurophysiological recordings, this concept can handle complex data sets, is free of any requirements about the data structure, and can infer the present underlying brain mechanisms. Specifically, by utilizing algorithmic information theory, it is possible to estimate the absolute information contained in brain responses. While current approaches that apply this theory to neurophysiological recordings can discriminate between different brain states, they are limited in directly quantifying the degree of similarity or encoded information between brain responses. Here, we propose a method grounded in algorithmic information theory that affords direct statements about responses' similarity by estimating the encoded information through a compression-based scheme. We validated this method by applying it to both synthetic and real neurophysiological data and compared its efficiency to the mutual information measure. This proposed procedure is especially suited for task paradigms contrasting different event types because it can precisely quantify the similarity of neuronal responses.

Early beta oscillations in multisensory association areas underlie crossmodal performance enhancement

The combination of signals from different sensory modalities can enhance perception and facilitate behavioral responses. While previous research described crossmodal influences in a wide range of tasks, it remains unclear how such influences drive performance enhancements. In particular, the neural mechanisms underlying performance-relevant crossmodal influences, as well as the latency and spatial profile of such influences are not well understood. Here, we examined data from high-density electroencephalography (N = 30) recordings to characterize the oscillatory signatures of crossmodal facilitation of response speed, as manifested in the speeding of visual responses by concurrent task-irrelevant auditory information. Using a data-driven analysis approach, we found that individual gains in response speed correlated with larger beta power difference (13-25 Hz) between the audiovisual and the visual condition, starting within 80 ms after stimulus onset in the secondary visual cortex and in multisensory association areas in the parietal cortex. In addition, we examined data from electrocorticography (ECoG) recordings in four epileptic patients in a comparable paradigm. These ECoG data revealed reduced beta power in audiovisual compared with visual trials in the superior temporal gyrus (STG). Collectively, our data suggest that the crossmodal facilitation of response speed is associated with reduced early beta power in multisensory association and secondary visual areas. The reduced early beta power may reflect an auditory-driven feedback signal to improve visual processing through attentional gating. These findings improve our understanding of the neural mechanisms underlying crossmodal response speed facilitation and highlight the critical role of beta oscillations in mediating behaviorally relevant multisensory processing.

Neural Substrates and Models of Omission Responses and Predictive Processes

Predictive coding theories argue that deviance detection phenomena, such as mismatch responses and omission responses, are generated by predictive processes with possibly overlapping neural substrates. Molecular imaging and electrophysiology studies of mismatch responses and corollary discharge in the rodent model allowed the development of mechanistic and computational models of these phenomena. These models enable translation between human and non-human animal research and help to uncover fundamental features of change-processing microcircuitry in the neocortex. This microcircuitry is characterized by stimulus-specific adaptation and feedforward inhibition of stimulus-selective populations of pyramidal neurons and interneurons, with specific contributions from different interneuron types. The overlap of the substrates of different types of responses to deviant stimuli remains to be understood. Omission responses, which are observed both in corollary discharge and mismatch response protocols in humans, are underutilized in animal research and may be pivotal in uncovering the substrates of predictive processes. Omission studies comprise a range of methods centered on the withholding of an expected stimulus. This review aims to provide an overview of omission protocols and showcase their potential to integrate and complement the different models and procedures employed to study prediction and deviance detection.This approach may reveal the biological foundations of core concepts of predictive coding, and allow an empirical test of the framework’s promise to unify theoretical models of attention and perception.

The Effects of Working Memory Load on Auditory Distraction in Adults With Attention Deficit Hyperactivity Disorder

Cognitive control provides us with the ability to inter alia, regulate the locus of attention and ignore environmental distractions in accordance with our goals. Auditory distraction is a frequently cited symptom in adults with attention deficit hyperactivity disorder (aADHD)-yet few task-based fMRI studies have explored whether deficits in cognitive control (associated with the disorder) impedes on the ability to suppress/compensate for exogenously evoked cortical responses to noise in this population. In the current study, we explored the effects of auditory distraction as function of working memory (WM) load. Participants completed two tasks: an auditory target detection (ATD) task in which the goal was to actively detect salient oddball tones amidst a stream of standard tones in noise, and a visual n-back task consisting of 0-, 1-, and 2-back WM conditions whilst concurrently ignoring the same tonal signal from the ATD task. Results indicated that our sample of young aADHD (n = 17), compared to typically developed controls (n = 17), had difficulty attenuating auditory cortical responses to the task-irrelevant sound when WM demands were high (2-back). Heightened auditory activity to task-irrelevant sound was associated with both poorer WM performance and symptomatic inattentiveness. In the ATD task, we observed a significant increase in functional communications between auditory and salience networks in aADHD. Because performance outcomes were on par with controls for this task, we suggest that this increased functional connectivity in aADHD was likely an adaptive mechanism for suboptimal listening conditions. Taken together, our results indicate that aADHD are more susceptible to noise interference when they are engaged in a primary task. The ability to cope with auditory distraction appears to be related to the WM demands of the task and thus the capacity to deploy cognitive control.

Ictal semiology of epileptic seizures with insulo-opercular genesis

Objective

Epileptic seizures with insular genesis are often difficult to distinguish from those originating in the temporal lobe due to their complex and variable semiology. Here, we analyzed differentiating characteristics in the clinical spectrum of insulo-opercular seizures.

Methods

Ictal semiology in patients with a diagnosis of insulo-opercular epilepsy (IOE) based on imaging of epileptogenic lesions or electrophysiological evidence of an insulo-opercular seizure origin was retrospectively analyzed and compared to age-matched controls with mesial temporal lobe epilepsy (MTE).

Results

Forty-six IOE and 46 matched MTE patients were included. The most prominent ictal features in IOE were focal motor phenomena in 80.4% of these patients. Somatosensory sensations, version, tonic and clonic features, when present, were more frequent contralateral to the SOZ in MTE patients, while they occurred about equally often ipsilateral and contralateral to the SOZ in IOE patients. Ipsilateral manual automatisms were significantly more frequent in MTE patients than in IOE (p = 0.010). Multivariate analysis correctly identified IOE in 78.3% and MTE in 84.8% using five semiologic features (Chi-square = 53.79 with 5 degrees of freedom, p < 0.0001). A subanalysis comparing patients with purely insular lesions with MTE patients using only the earliest ictal signs showed that somatosensory sensations are significantly more frequent in insular epilepsy (p = 0.010), while automatisms were significantly more frequent in MTE patients (p = 0.06).

Significance

Our study represents the first in-depth analysis of ictal semiology in IOE compared to MTE. Use of these differentiating characteristics can serve for a correct syndrome classification and to steer appropriate diagnostic and local therapeutic procedures.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00415-021-10911-0.

The Benefits of Music Listening for Induced State Anxiety: Behavioral and Physiological Evidence

BACKGROUND

Some clinical studies have indicated that neutral and happy music may relieve state anxiety. However, the brain mechanisms by which these effective interventions in music impact state anxiety remain unknown.

METHODS

In this study, we selected music with clinical effects for therapy, and 62 subjects were included using the evoked anxiety paradigm. After evoking anxiety with a visual stimulus, all subjects were randomly divided into three groups (listening to happy music, neutral music and a blank stimulus), and EEG signals were acquired.

RESULTS

We found that different emotional types of music might have different mechanisms in state anxiety interventions. Neutral music had the effect of alleviating state anxiety. The brain mechanisms supported that neutral music ameliorating state anxiety was associated with decreased power spectral density of the occipital lobe and increased brain functional connectivity between the occipital lobe and frontal lobe. Happy music also had the effect of alleviating state anxiety, and the brain mechanism was associated with enhanced brain functional connectivity between the occipital lobe and right temporal lobe.

CONCLUSIONS

This study may be important for a deep understanding of the mechanisms associated with state anxiety music interventions and may further contribute to future clinical treatment using nonpharmaceutical interventions.

Systematic Review of the Neural Effect of Electroconvulsive Therapy in Patients with Schizophrenia: Hippocampus and Insula as the Key Regions of Modulation

OBJECTIVE

Electroconvulsive therapy (ECT) has been the most potent treatment option for treatment-resistant schizophrenia (TRS). However, the underlying neural mechanisms of ECT in schizophrenia remain largely unclear. This paper examines studies that investigated structural and functional changes after ECT in patients with schizophrenia.

METHODS

We carried out a systematic review with following terms: 'ECT', 'schizophrenia', and the terms of various neuroimaging modalities.

RESULTS

Among the 325 records available from the initial search in May 2020, 17 studies were included. Cerebral blood flow in the frontal, temporal, and striatal structures was shown to be modulated (n=3), although the results were divergent. Magnetic resonance spectroscopy (MRS) studies suggested that the ratio of N-acetyl-aspartate/creatinine was increased in the left prefrontal cortex (PFC; n=2) and left thalamus (n=1). The hippocampus and insula (n=6, respectively) were the most common regions of structural/functional modulation, which also showed symptom associations. Functional connectivity of the default mode network (DMN; n=5), PFC (n=4), and thalamostriatal system (n=2) were also commonly modulated.

CONCLUSION

Despite proven effectiveness, there has been a dearth of studies investigating the neurobiological mechanisms underlying ECT. There is preliminary evidence of structural and functional modulation of the hippocampus and insula, functional changes in the DMN, PFC, and thalamostriatal system after ECT in patients with schizophrenia. We discuss the rationale and implications of these findings and the potential mechanism of action of ECT. More studies evaluating the mechanisms of ECT are needed, which could provide a unique window into what leads to treatment response in the otherwise refractory TRS population.

Common and distinct neural correlates of music and food-induced pleasure: A coordinate-based meta-analysis of neuroimaging studies

Neuroimaging studies have shown that, despite the abstractness of music, it may mimic biologically rewarding stimuli (e.g., food) in its ability to engage the brain's reward circuitry. However, due to the lack of research comparing music and other types of reward, it is unclear to what extent the recruitment of reward-related structures overlaps among domains. To achieve this goal, we performed a coordinate-based meta-analysis of 38 neuroimaging studies (703 subjects) comparing the brain responses specifically to music and food-induced pleasure. Both engaged a common set of brain regions, including the ventromedial prefrontal cortex, ventral striatum, and insula. Yet, comparative analyses indicated a partial dissociation in the engagement of the reward circuitry as a function of the type of reward, as well as additional reward type-specific activations in brain regions related to perception, sensory processing, and learning. These results support the idea that hedonic reactions rely on the engagement of a common reward network, yet through specific routes of access depending on the modality and nature of the reward.

Electrophysiology of the Human Superior Temporal Sulcus during Speech Processing

The superior temporal sulcus (STS) is a crucial hub for speech perception and can be studied with high spatiotemporal resolution using electrodes targeting mesial temporal structures in epilepsy patients. Goals of the current study were to clarify functional distinctions between the upper (STSU) and the lower (STSL) bank, hemispheric asymmetries, and activity during self-initiated speech. Electrophysiologic properties were characterized using semantic categorization and dialog-based tasks. Gamma-band activity and alpha-band suppression were used as complementary measures of STS activation. Gamma responses to auditory stimuli were weaker in STSL compared with STSU and had longer onset latencies. Activity in anterior STS was larger during speaking than listening; the opposite pattern was observed more posteriorly. Opposite hemispheric asymmetries were found for alpha suppression in STSU and STSL. Alpha suppression in the STS emerged earlier than in core auditory cortex, suggesting feedback signaling within the auditory cortical hierarchy. STSL was the only region where gamma responses to words presented in the semantic categorization tasks were larger in subjects with superior task performance. More pronounced alpha suppression was associated with better task performance in Heschl's gyrus, superior temporal gyrus, and STS. Functional differences between STSU and STSL warrant their separate assessment in future studies.

Insights into human cognition from intracranial EEG: A review of audition, memory, internal cognition, and causality

By recording neural activity directly from the human brain, researchers gain unprecedented insight into how neurocognitive processes unfold in real time. We first briefly discuss how intracranial electroencephalography (iEEG) recordings, performed for clinical practice, are used to study human cognition with the spatiotemporal and single-trial precision traditionally limited to non-human animal research. We then delineate how studies using iEEG have informed our understanding of issues fundamental to human cognition: auditory prediction, working and episodic memory, and internal cognition. We also discuss the potential of iEEG to infer causality through the manipulation or 'engineering' of neurocognitive processes via spatiotemporally precise electrical stimulation. We close by highlighting limitations of iEEG, potential of burgeoning techniques to further increase spatiotemporal precision, and implications for future research using intracranial approaches to understand, restore, and enhance human cognition.

The brain tracks auditory rhythm predictability independent of selective attention

The brain responds to violations of expected rhythms, due to extraction- and prediction of the temporal structure in auditory input. Yet, it is unknown how probability of rhythm violations affects the overall rhythm predictability. Another unresolved question is whether predictive processes are independent of attention processes. In this study, EEG was recorded while subjects listened to rhythmic sequences. Predictability was manipulated by changing the stimulus-onset-asynchrony (SOA deviants) for given tones in the rhythm. When SOA deviants were inserted rarely, predictability remained high, whereas predictability was lower with more frequent SOA deviants. Dichotic tone-presentation allowed for independent manipulation of attention, as specific tones of the rhythm were presented to separate ears. Attention was manipulated by instructing subjects to attend to tones in one ear only, while keeping the rhythmic structure of tones constant. The analyses of event-related potentials revealed an attenuated N1 for tones when rhythm predictability was high, while the N1 was enhanced by attention to tones. Bayesian statistics revealed no interaction between predictability and attention. A right-lateralization of attention effects, but not predictability effects, suggested potentially different cortical processes. This is the first study to show that probability of rhythm violation influences rhythm predictability, independent of attention.