Human Electrocorticography Research Articles

Mapping cognitive activity from electrocorticography field potentials in humans performing NBack task

Objective. Advancements in data science and assistive technologies have made invasive brain-computer interfaces (iBCIs) increasingly viable for enhancing the quality of life in physically disabled individuals. Intracortical microelectrode implants are a common choice for such a communication system due to their fine temporal and spatial resolution. The small size of these implants makes the implantation plan critical for the successful exfiltration of information, particularly when targeting representations of task goals that lack robust anatomical correlates. Approach. Working memory processes including encoding, retrieval, and maintenance are observed in many areas of the brain. Using human electrocorticography (ECoG) recordings during a working memory experiment, we provide proof that it is possible to localize cognitive activity associated with the task and to identify key locations involved with executive memory functions. Results. From the analysis, we could propose an optimal iBCI implant location with the desired features. The general approach is not limited to working memory but could also be used to map other goal-encoding factors such as movement intentions, decision-making, and visual-spatial attention. Significance. Deciphering the intended action of a BCI user is a complex challenge that involves the extraction and integration of cognitive factors such as movement planning, working memory, visual-spatial attention, and the decision state. Examining field potentials from ECoG electrodes while participants engaged in tailored cognitive tasks can pinpoint location with valuable information related to anticipated actions. This manuscript demonstrates the feasibility of identifying electrodes involved in cognitive activity related to working memory during user engagement in the NBack task. Devoting time in meticulous preparation to identify the optimal brain regions for BCI implant locations will increase the likelihood of rich signal outcomes, thereby improving the overall BCI user experience.

Auditory prediction errors in sound frequency and duration generated different cortical activation patterns in the human brain: an ECoG study.

Sound frequency and duration are essential auditory components. The brain perceives deviations from the preceding sound context as prediction errors, allowing efficient reactions to the environment. Additionally, prediction error response to duration change is reduced in the initial stages of psychotic disorders. To compare the spatiotemporal profiles of responses to prediction errors, we conducted a human electrocorticography study with special attention to high gamma power in 13 participants who completed both frequency and duration oddball tasks. Remarkable activation in the bilateral superior temporal gyri in both the frequency and duration oddball tasks were observed, suggesting their association with prediction errors. However, the response to deviant stimuli in duration oddball task exhibited a second peak, which resulted in a bimodal response. Furthermore, deviant stimuli in frequency oddball task elicited a significant response in the inferior frontal gyrus that was not observed in duration oddball task. These spatiotemporal differences within the Parasylvian cortical network could account for our efficient reactions to changes in sound properties. The findings of this study may contribute to unveiling auditory processing and elucidating the pathophysiology of psychiatric disorders.

Consistent spectro-spatial features of human ECoG successfully decode naturalistic behavioral states.

Understanding the neural correlates of naturalistic behavior is critical for extending and confirming the results obtained from trial-based experiments and designing generalizable brain-computer interfaces that can operate outside laboratory environments. In this study, we aimed to pinpoint consistent spectro-spatial features of neural activity in humans that can discriminate between naturalistic behavioral states. We analyzed data from five participants using electrocorticography (ECoG) with broad spatial coverage. Spontaneous and naturalistic behaviors such as "Talking" and "Watching TV" were labeled from manually annotated videos. Linear discriminant analysis (LDA) was used to classify the two behavioral states. The parameters learned from the LDA were then used to determine whether the neural signatures driving classification performance are consistent across the participants. Spectro-spatial feature values were consistently discriminative between the two labeled behavioral states across participants. Mainly, θ, α, and low and high γ in the postcentral gyrus, precentral gyrus, and temporal lobe showed significant classification performance and feature consistency across participants. Subject-specific performance exceeded 70%. Combining neural activity from multiple cortical regions generally does not improve decoding performance, suggesting that information regarding the behavioral state is non-additive as a function of the cortical region. To the best of our knowledge, this is the first attempt to identify specific spectro-spatial neural correlates that consistently decode naturalistic and active behavioral states. The aim of this work is to serve as an initial starting point for developing brain-computer interfaces that can be generalized in a realistic setting and to further our understanding of the neural correlates of naturalistic behavior in humans.

448 Analysis of Bipolar Re-Referencing on Spectral Composition and Spike Detection in Human Electrocorticography

INTRODUCTION: Electrocorticography (ECoG) enables the localization of epileptic seizure foci prior to surgical resection. Improving the signal-to-noise ratio is paramount, and can be accomplished with bipolar re-referencing. METHODS: Intracranial recordings for drug-resistant epilepsy were performed at UCSF using high-density ECoG grid (4 mm inter-electrode pitch), strip (10 mm), and penetrating depth (5 mm) electrodes. Raw voltages for each participant (n = 11) were systematically re-referenced (bipolar subtraction) at increasing scales (4 mm-50 mm) and a multi-taper power spectrum (2-200 Hz) was generated around spike-free baseline windows. Cluster based permutation tests (p < 0.05) were used to compare spectral composition features after z-scoring log power features by frequency and binning by bipolar distance. High-density and low-density (4mm vs 8mm) re-referencing schemes were used for spike localization by applying a line length transform to highlight epileptiform features in peri-spike windows and comparing to baseline windows using cluster-based permutation tests. RESULTS: Bipolar pair distances <10 mm decreased low-frequency (<30 Hz) power, whereas both low and high frequency (30-200 Hz) power increased by longer bipolar distances. These findings were consistent across different components (grids, strips, depths). The detection of high gamma (50-200 Hz) neural activity in the STG related to an audio stimulus was enhanced at bipolar distances of <45 mm but attenuated at higher distances. Similar spike detection (duration and number of channels involved) was seen between high- vs. low-density bipolar re-referencing. CONCLUSIONS: Re-referencing at <10 mm bipolar distance preferentially attenuated low-frequency signal while standard clinical and larger distances systematically augmented broadband (2-200 Hz) signals. Shorter distances, especially high-density, augmented spatial delineation of neural activity when compared to more widely spaced arrays, however, standard clinical arrays capture the general spatio-temporal extent of spike waveforms.

Recording of pig neuronal activity in the comparative context of the awake human brain

Gyriform mammals display neurophysiological and neural network activity that other species exhibit only in rudimentary or dissimilar form. However, neural recordings from large mammals such as the pig can be anatomically hindered and pharmacologically suppressed by anesthetics. This curtails comparative inferences. To mitigate these limitations, we set out to modify electrocorticography, intracerebral depth and intracortical recording methods to study the anesthetized pig. In the process, we found that common forms of infused anesthesia such as pentobarbital or midazolam can be neurophysiologic suppressants acting in dose-independent fashion relative to anesthetic dose or brain concentration. Further, we corroborated that standard laboratory conditions may impose electrical interference with specific neural signals. We thus aimed to safeguard neural network integrity and recording fidelity by developing surgical, anesthesia and noise reduction methods and by working inside a newly designed Faraday cage, and evaluated this from the point of view of neurophysiological power spectral density and coherence analyses. We also utilized novel silicon carbide electrodes to minimize mechanical disruption of single-neuron activity. These methods allowed for the preservation of native neurophysiological activity for several hours. Pig electrocorticography recordings were essentially indistinguishable from awake human recordings except for the small segment of electrical activity associated with vision in conscious persons. In addition, single-neuron and paired-pulse stimulation recordings were feasible simultaneously with electrocorticography and depth electrode recordings. The spontaneous and stimulus-elicited neuronal activities thus surveyed can be recorded with a degree of precision similar to that achievable in rodent or any other animal studies and prove as informative as unperturbed human electrocorticography.

350 Investigating the Architecture of Speech Processing Pathways in the Brain

OBJECTIVES/GOALS: Speech production requires mapping between sound-based and motor-based neural representations of a word – accomplished by learning internal models. However, the neural bases of these internal models remain unclear. The aim of this study is to provide experimental evidence for these internal models in the brain during speech production. METHODS/STUDY POPULATION: 16 healthy human adults were recruited for this electrooculography speech study. 20 English pseudowords were designed to vary on confusability along specific features of articulation (place vs manner). All words were controlled for length and voicing. Three task conditions were performed: speech perception, covert and overt speech production. EEG was recorded using a 64-channel Biosemi ActiveTwo system. EMG was recorded on the orbicularis orbis inferior and neck strap muscles. Overt productions were recorded with a high-quality microphone to determine overt production onset. EMG during was used to determine covert production onset. Neuroimaging: Representational Similarity Analysis (RSA), was used to probe the sound- and motor-based neural representations over sensors and time for each task. RESULTS/ANTICIPATED RESULTS: Production (motor) and perception (sound) neural representations were calculated using a cross-validated squared Euclidean distance metric. The RSA results in the speech perception task show a strong selectivity around 150ms, which is compatible with recent human electrocorticography findings in human superior temporal gyrus. Parietal sensors showed a large difference for motor-based neural representations, indicating a strong encoding for production related processes, as hypothesized by previous studies on the ventral and dorsal stream model of language. Temporal sensors, however, showed a large change for both motor- and sound-based neural representations. This is a surprising result since temporal regions are believed to be primarily engaged in perception (sound-based) processes. DISCUSSION/SIGNIFICANCE: This study used neuroimaging (EEG) and advanced multivariate pattern analysis (RSA) to test models of production (motor-) and perception (sound-) based neural representations in three different speech task conditions. These results show strong feasibility of this approach to map how the perception and production processes interact in the brain.

Enhanced phase-amplitude coupling of human electrocorticography selectively in the posterior cortical region during rapid eye movement sleep.

The spatiotemporal dynamics of interaction between slow (delta or infraslow) waves and fast (gamma) activities during wakefulness and sleep are yet to be elucidated in human electrocorticography (ECoG). We evaluated phase-amplitude coupling (PAC), which reflects neuronal coding in information processing, using ECoG in 11 patients with intractable focal epilepsy. PAC was observed between slow waves of 0.5-0.6Hz and gamma activities, not only during light sleep and slow-wave sleep (SWS) but even during wakefulness and rapid eye movement (REM) sleep. While PAC was high over a large region during SWS, it was stronger in the posterior cortical region around the temporoparietal junction than in the frontal cortical region during REM sleep. PAC tended to be higher in the posterior cortical region than in the frontal cortical region even during wakefulness. Our findings suggest that the posterior cortical region has a functional role in REM sleep and may contribute to the maintenance of the dreaming experience.

Representing the dynamics of high-dimensional data with non-redundant wavelets

SummaryA crucial question in data science is to extract meaningful information embedded in high-dimensional data into a low-dimensional set of features that can represent the original data at different levels. Wavelet analysis is a pervasive method for decomposing time-series signals into a few levels with detailed temporal resolution. However, obtained wavelets are intertwined and over-represented across levels for each sample and across different samples within one population. Here, using neuroscience data of simulated spikes, experimental spikes, calcium imaging signals, and human electrocorticography signals, we leveraged conditional mutual information between wavelets for feature selection. The meaningfulness of selected features was verified to decode stimulus or condition with high accuracy yet using only a small set of features. These results provide a new way of wavelet analysis for extracting essential features of the dynamics of spatiotemporal neural data, which then enables to support novel model design of machine learning with representative features.

Detecting attention-related rhythms: When is behavior not enough? (Commentary on van der Werf et al. 2021).

Recent research suggests that environmental sampling is a fundamentally rhythmic process (Benedetto, Morrone, & Tomassini, 2019; Fiebelkorn & Kastner, 2019; Schroeder, Wilson, Radman, Scharfman, & Lakatos, 2010). In primates, both attention-related sampling and exploratory eye movements are seemingly shaped by theta-band activity (3-8 Hz) in the large-scale network that directs both spatial attention and goal-directed eye movements (i.e., the 'attention network').

A Platform for Virtual Reality Task Design with Intracranial Electrodes.

Research with human intracranial electrodes has traditionally been constrained by the limitations of the inpatient clinical setting. Immersive virtual reality (VR), however, can transcend setting and enable novel task design with precise control over visual and auditory stimuli. This control over visual and auditory feedback makes VR an exciting platform for new in-patient, human electrocorticography (ECOG) and stereo-electroencephalography (sEEG) research. The integration of intracranial electrode recording and stimulation with VR task dynamics required foundational systems engineering. In this work, we present a custom API that bridges Unity, the leading VR game development engine, and Synapse, the proprietary software of the Tucker Davis Technologies (TDT) neural recording and stimulation platform. To demonstrate the functionality and efficiency of our API, we developed a closed-loop brain-computer interface (BCI) task in which filtered neural signals controlled the movement of a virtual object and virtual object dynamics triggered neural stimulation. This closed-loop VR-BCI task confirmed the utility, safety, and efficacy of our API and its readiness for human task deployment.

Evidence for a deep, distributed and dynamic code for animacy in human ventral anterior temporal cortex

How does the human brain encode semantic information about objects? This paper reconciles two seemingly contradictory views. The first proposes that local neural populations independently encode semantic features; the second, that semantic representations arise as a dynamic distributed code that changes radically with stimulus processing. Combining simulations with a well-known neural network model of semantic memory, multivariate pattern classification, and human electrocorticography, we find that both views are partially correct: information about the animacy of a depicted stimulus is distributed across ventral temporal cortex in a dynamic code possessing feature-like elements posteriorly but with elements that change rapidly and nonlinearly in anterior regions. This pattern is consistent with the view that anterior temporal lobes serve as a deep cross-modal 'hub' in an interactive semantic network, and more generally suggests that tertiary association cortices may adopt dynamic distributed codes difficult to detect with common brain imaging methods.

Interpreting wide-band neural activity using convolutional neural networks.

Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data requires considerable knowledge about the nature of the representation and often depends on manual operations. Decoding provides a means to infer the information content of such recordings but typically requires highly processed data and prior knowledge of the encoding scheme. Here, we developed a deep-learning framework able to decode sensory and behavioral variables directly from wide-band neural data. The network requires little user input and generalizes across stimuli, behaviors, brain regions, and recording techniques. Once trained, it can be analyzed to determine elements of the neural code that are informative about a given variable. We validated this approach using electrophysiological and calcium-imaging data from rodent auditory cortex and hippocampus as well as human electrocorticography (ECoG) data. We show successful decoding of finger movement, auditory stimuli, and spatial behaviors - including a novel representation of head direction - from raw neural activity.

Prediction of Successful Memory Encoding Based on Lateral Temporal Cortical Gamma Power.

Prediction of successful memory encoding is important for learning. High-frequency activity (HFA), such as gamma frequency activity (30–150 Hz) of cortical oscillations, is induced during memory tasks and is thought to reflect underlying neuronal processes. Previous studies have demonstrated that medio-temporal electrophysiological characteristics are related to memory formation, but the effects of neocortical neural activity remain underexplored. The main aim of the present study was to evaluate the ability of gamma activity in human electrocorticography (ECoG) signals to differentiate memory processes into remembered and forgotten memories. A support vector machine (SVM) was employed, and ECoG recordings were collected from six subjects during verbal memory recognition task performance. Two-class classification using an SVM was performed to predict subsequently remembered vs. forgotten trials based on individually selected frequencies (low gamma, 30–60 Hz; high gamma, 60–150 Hz) at time points during pre- and during stimulus intervals. The SVM classifier distinguished memory performance between remembered and forgotten trials with a mean maximum accuracy of 87.5% using temporal cortical gamma activity during the 0- to 1-s interval. Our results support the functional relevance of ECoG for memory formation and suggest that lateral temporal cortical HFA may be utilized for memory prediction.

Oscillatory correlates of auditory working memory examined with human electrocorticography

This work examines how sounds are held in auditory working memory (AWM) in humans by examining oscillatory local field potentials (LFPs) in candidate brain regions. Previous fMRI studies by our group demonstrated blood oxygenation level-dependent (BOLD) response increases during maintenance in auditory cortex, inferior frontal cortex and the hippocampus using a paradigm with a delay period greater than 10s. The relationship between such BOLD changes and ensemble activity in different frequency bands is complex, and the long delay period raised the possibility that long-term memory mechanisms were engaged. Here we assessed LFPs in different frequency bands in six subjects with recordings from all candidate brain regions using a paradigm with a short delay period of 3 s. Sustained delay activity was demonstrated in all areas, with different patterns in the different areas. Enhancement in low frequency (delta) power and suppression across higher frequencies (beta/gamma) were demonstrated in primary auditory cortex in medial Heschl’s gyrus (HG) whilst non-primary cortex showed patterns of enhancement and suppression that altered at different levels of the auditory hierarchy from lateral HG to superior- and middle-temporal gyrus. Inferior frontal cortex showed increasing suppression with increasing frequency. The hippocampus and parahippocampal gyrus showed low frequency increases and high frequency decreases in oscillatory activity. This work demonstrates sustained activity patterns during AWM maintenance, with prominent low-frequency increases in medial temporal lobe regions.

Unsupervised Sleep and Wake State Identification in Long-Term Electrocorticography Recordings.

Studying the neural correlates of sleep can lead to revelations in our understanding of sleep and its interplay with different neurological disorders. Sleep research relies on manual annotation of sleep stages based on rules developed for healthy adults. Automating sleep stage annotation can expedite sleep research and enable us to better understand atypical sleep patterns. Our goal was to create a fully unsupervised approach to label sleep and wake states in human electro-corticography (ECoG) data from epilepsy patients. Here, we demonstrate that with continuous data from a single ECoG electrode, hidden semi-Markov models (HSMM) perform best in classifying sleep/wake states without excessive transitions, with a mean accuracy (n=4) of 85.2% compared to using K-means clustering (72.2%) and hidden Markov models (81.5%). Our results confirm that HSMMs produce meaningful labels for ECoG data and establish the groundwork to apply this model to cluster sleep stages and potentially other behavioral states.

Spatiotemporal Differentiation of MMN From N1 Adaptation: A Human ECoG Study

Auditory mismatch negativity (MMN) is an electrophysiological response to a deviation from regularity. This response is considered pivotal to understanding auditory processing, particularly in the pre-attentive phase. However, previous findings suggest that MMN is a product of N1 adaptation/enhancement, which reflects lower-order auditory processing. The separability of these two components remains unclear and is considered an important issue in the field of neuroscience. The aim of the present study was to spatiotemporally differentiate MMN from N1 adaptation using human electrocorticography (ECoG). Auditory evoked potentials under the classical oddball (OD) task as well as the many standards (MS) task were recorded in three patients with epilepsy whose lateral cortices were widely covered with high-density electrodes. Close observation identified an electrode at which N1 adaptation was temporally separated from MMN, whereas N1 adaptation was partially incorporated into MMN at other electrodes. Since N1 adaptation occurs in the N1 population, we spatially compared MMN with N1 obtained from the MS task instead of N1 adaptation. As a result, N1 was observed in a limited area around the Sylvian fissure adjacent to A1, whereas MMN was noted in wider areas, including the temporal, frontal, and parietal lobes. MMN was thus considered to be differentiated from N1 adaptation. The results suggest that MMN is not merely a product of the neural adaptation of N1 and instead represents higher-order processes in auditory deviance detection. These results will contribute to strengthening the foundation of future research in this field.

Synchronisation of Neural Oscillations and Cross-modal Influences.

At any given moment, we receive multiple signals from our different senses. Prior research has shown that signals in one sensory modality can influence neural activity and behavioural performance associated with another sensory modality. Recent human and nonhuman primate studies suggest that such cross-modal influences in sensory cortices are mediated by the synchronisation of ongoing neural oscillations. In this review, we consider two mechanisms proposed to facilitate cross-modal influences on sensory processing, namely cross-modal phase resetting and neural entrainment. We consider how top-down processes may further influence cross-modal processing in a flexible manner, and we highlight fruitful directions for further research.

Heterogeneity of Preictal Dynamics in Human Epileptic Seizures.

It is generally understood that there is a preictal phase in the development of a seizure and this precictal period is the basis for seizure prediction attempts. The focus of this study is the preictal global spatiotemporal dynamics and its intra-patient variability. We analyzed preictal broadband brain connectivity from human electrocorticography (ECoG) recordings of 185 seizures (which included 116 clinical seizures) collected from 12 patients. ECoG electrodes record from only a part of the cortex, leaving large regions of the brain unobserved. Brain connectivity was therefore estimated using the sparse-plus-latent-regularized precision matrix (SLRPM) method, which calculates connectivity from partial correlations of the conditional statistics of the observed regions given the unobserved latent regions. Brain connectivity was quantified using eigenvector centrality (EC), from which a degree of heterogeneity was calculated for the preictal periods of all seizures in each patient. Results from the SLRPM method are compared to those from the sparse-regularized precision matrix (SRPM) and correlation methods, which do not account for the unobserved inputs when estimating brain connectivity. The degree of heterogeneity estimated by the SLRPM method is higher than those estimated by the SRPM and correlation methods for the preictal periods in most patients. These results reveal substantial heterogeneity or desynchronization among brain areas in the preictal period of human epileptic seizures. Furthermore, the SLRPM method identifies more onset channels from the preictal active electrodes compared to the SRPM and correlation methods. Finally, the correlation between the degree of heterogeneity and seizure severity of patients for SLRPM and SRPM methods were lower than that obtained from the correlation method. These results support recent findings suggesting that inhibitory neurons can have anti-seizure effects by inducing variability or heterogeneity across seizures. Understanding how this variability is linked to seizure initiation may lead to better predictions and controlling therapies.

Electrocorticographic dissociation of alpha and beta rhythmic activity in the human sensorimotor system.

This study uses electrocorticography in humans to assess how alpha- and beta-band rhythms modulate excitability of the sensorimotor cortex during psychophysically-controlled movement imagery. Both rhythms displayed effector-specific modulations, tracked spectral markers of action potentials in the local neuronal population, and showed spatially systematic phase relationships (traveling waves). Yet, alpha- and beta-band rhythms differed in their anatomical and functional properties, were weakly correlated, and traveled along opposite directions across the sensorimotor cortex. Increased alpha-band power in the somatosensory cortex ipsilateral to the selected arm was associated with spatially-unspecific inhibition. Decreased beta-band power over contralateral motor cortex was associated with a focal shift from relative inhibition to excitation. These observations indicate the relevance of both inhibition and disinhibition mechanisms for precise spatiotemporal coordination of movement-related neuronal populations, and illustrate how those mechanisms are implemented through the substantially different neurophysiological properties of sensorimotor alpha- and beta-band rhythms.

P1-02-01. Phase amplitude coupling of human electrocorticography reveals enhanced neuronal coding in posterior cortical regions during rapid eye movement sleep and wakefulness

P1-02-01. Phase amplitude coupling of human electrocorticography reveals enhanced neuronal coding in posterior cortical regions during rapid eye movement sleep and wakefulness