Electrocorticography Data Research Articles

Investigating unilateral and bilateral motor imagery control using electrocorticography and fMRI in awake craniotomy

BackgroundThe rapid development of neurosurgical techniques, such as awake craniotomy, has increased opportunities to explore the mysteries of the brain. This is crucial for deepening our understanding of motor control and imagination processes, especially in developing brain–computer interface (BCI) technologies and improving neurorehabilitation strategies for neurological disorders. ObjectiveThis study aimed to analyze brain activity patterns in patients undergoing awake craniotomy during actual movements and motor imagery, mainly focusing on the motor control processes of the bilateral limbs. MethodsWe conducted detailed observations of patients undergoing awake craniotomies. The experimenter requested participants to perform and imagine a series of motor tasks involving their hands and tongues. Brain activity during these tasks was recorded using functional magnetic resonance imaging (fMRI) and intraoperative electrocorticography (ECoG). The study included left and right finger tapping, tongue protrusion, hand clenching, and imagined movements corresponding to these actions. ResultsfMRI revealed significant activation in the brain's motor areas during task performance, mainly involving bilateral brain regions during imagined movement. ECoG data demonstrated a marked desynchronization pattern in the ipsilateral motor cortex during bilateral motor imagination, especially in bilateral coordination tasks. This finding suggests a potential controlling role of the unilateral cerebral cortex in bilateral motor imagination. ConclusionOur study highlights the unilateral cerebral cortex's significance in controlling bilateral limb motor imagination, offering new insights into future brain network remodeling in patients with hemiplegia. Additionally, these findings provide important insights into understanding motor imagination and its impact on BCI and neurorehabilitation.

Differential neural mechanisms underlie cortical gating of visual spatial attention mediated by alpha-band oscillations

Selective attention relies on neural mechanisms that facilitate processing of behaviorally relevant sensory information while suppressing irrelevant information, consistently linked to alpha-band oscillations in human M/EEG studies. We analyzed cortical alpha responses from intracranial electrodes implanted in eight epilepsy patients, who performed a visual spatial attention task. Electrocorticographic data revealed a spatiotemporal dissociation between attention-modulated alpha desynchronization, associated with the enhancement of sensory processing, and alpha synchronization, associated with the suppression of sensory processing, during the cue-target interval. Dorsal intraparietal areas contralateral to the attended hemifield primarily exhibited a delayed and sustained alpha desynchronization, while ventrolateral extrastriatal areas ipsilateral to the attended hemifield primarily exhibited an earlier and sustained alpha synchronization. Analyses of cross-frequency coupling between alpha phase and broadband high-frequency activity (HFA) further revealed cross-frequency interactions along the visual hierarchy contralateral to the attended locations. Directionality analyses indicate that alpha phase in early and extrastriatal visual areas modulated HFA power in downstream visual areas, thus potentially facilitating the feedforward processing of an upcoming, spatially predictable target. In contrast, in areas ipsilateral to the attended locations, HFA power modulated local alpha phase in early and extrastriatal visual areas, with suppressed interareal interactions, potentially attenuating the processing of distractors. Our findings reveal divergent alpha-mediated neural mechanisms underlying target enhancement and distractor suppression during the deployment of spatial attention, reflecting enhanced functional connectivity at attended locations, while suppressed functional connectivity at unattended locations. The collective dynamics of these alpha-mediated neural mechanisms play complementary roles in the efficient gating of sensory information.

Iterative alignment discovery of speech-associated neural activity.

Objective. Brain-computer interfaces (BCIs) have the potential to preserve or restore speech in patients with neurological disorders that weaken the muscles involved in speech production. However, successful training of low-latency speech synthesis and recognition models requires alignment of neural activity with intended phonetic or acoustic output with high temporal precision. This is particularly challenging in patients who cannot produce audible speech, as ground truth with which to pinpoint neural activity synchronized with speech is not available.Approach. In this study, we present a new iterative algorithm for neural voice activity detection (nVAD) called iterative alignment discovery dynamic time warping (IAD-DTW) that integrates DTW into the loss function of a deep neural network (DNN). The algorithm is designed to discover the alignment between a patient's electrocorticographic (ECoG) neural responses and their attempts to speak during collection of data for training BCI decoders for speech synthesis and recognition.Main results. To demonstrate the effectiveness of the algorithm, we tested its accuracy in predicting the onset and duration of acoustic signals produced by able-bodied patients with intact speech undergoing short-term diagnostic ECoG recordings for epilepsy surgery. We simulated a lack of ground truth by randomly perturbing the temporal correspondence between neural activity and an initial single estimate for all speech onsets and durations. We examined the model's ability to overcome these perturbations to estimate ground truth. IAD-DTW showed no notable degradation (<1% absolute decrease in accuracy) in performance in these simulations, even in the case of maximal misalignments between speech and silence.Significance. IAD-DTW is computationally inexpensive and can be easily integrated into existing DNN-based nVAD approaches, as it pertains only to the final loss computation. This approach makes it possible to train speech BCI algorithms using ECoG data from patients who are unable to produce audible speech, including those with Locked-In Syndrome.

Decoding micro-electrocorticographic signals by using explainable 3D convolutional neural network to predict finger movements

BackgroundElectroencephalography (EEG) and electrocorticography (ECoG) recordings have been used to decode finger movements by analyzing brain activity. Traditional methods focused on single bandpass power changes for movement decoding, utilizing machine learning models requiring manual feature extraction. New methodThis study introduces a 3D convolutional neural network (3D-CNN) model to decode finger movements using ECoG data. The model employs adaptive, explainable AI (xAI) techniques to interpret the physiological relevance of brain signals. ECoG signals from epilepsy patients during awake craniotomy were processed to extract power spectral density across multiple frequency bands. These data formed a 3D matrix used to train the 3D-CNN to predict finger trajectories. ResultsThe 3D-CNN model showed significant accuracy in predicting finger movements, with root-mean-square error (RMSE) values of 0.26–0.38 for single finger movements and 0.20–0.24 for combined movements. Explainable AI techniques, Grad-CAM and SHAP, identified the high gamma (HG) band as crucial for movement prediction, showing specific cortical regions involved in different finger movements. These findings highlighted the physiological significance of the HG band in motor control. Comparison with existing methodsThe 3D-CNN model outperformed traditional machine learning approaches by effectively capturing spatial and temporal patterns in ECoG data. The use of xAI techniques provided clearer insights into the model's decision-making process, unlike the "black box" nature of standard deep learning models. ConclusionsThe proposed 3D-CNN model, combined with xAI methods, enhances the decoding accuracy of finger movements from ECoG data. This approach offers a more efficient and interpretable solution for brain-computer interface (BCI) applications, emphasizing the HG band's role in motor control.

Responsive neurostimulation as a therapy for epilepsy following new-onset refractory status epilepticus: Case series and review of the literature

ObjectiveTo report clinical outcomes of patients who presented with new-onset refractory status epilepticus (NORSE), developed drug-resistant epilepsy (DRE), and were treated with responsive neurostimulation (RNS). MethodsWe performed a retrospective review of patients implanted with RNS at our institution and identified three who originally presented with NORSE. Through chart review, we retrieved objective and subjective information related to their presentation, workup, and outcomes including patient-reported seizure frequency. We reviewed electrocorticography (ECoG) data to estimate seizure burden at 3, 6, 12, and 24 months following RNS implantation. We performed a review of literature concerning neurostimulation in NORSE. ResultsUse of RNS to treat DRE following NORSE was associated with reduced seizure burden and informed care by differentiating epileptic from non-epileptic events. ConclusionsOur single-center experience of three cases suggests that RNS is a safe and potentially effective treatment for DRE following NORSE. SignificanceThis article reports outcomes of the largest case series of NORSE patients treated with RNS. Since patients with NORSE are at high risk of adverse neuropsychiatric and cognitive sequelae beyond seizures, a unique strength of RNS over other surgical options is the ability to distinguish ictal or peri-ictal from non-epileptic events.

Auditory prediction and prediction error responses evoked through a novel cascade roving paradigm: a human ECoG study.

Auditory sensory processing is assumed to occur in a hierarchical structure including the primary auditory cortex (A1), superior temporal gyrus, and frontal areas. These areas are postulated to generate predictions for incoming stimuli, creating an internal model of the surrounding environment. Previous studies on mismatch negativity have indicated the involvement of the superior temporal gyrus in this processing, whereas reports have been mixed regarding the contribution of the frontal cortex. We designed a novel auditory paradigm, the "cascade roving" paradigm, which incorporated complex structures (cascade sequences) into a roving paradigm. We analyzed electrocorticography data from six patients with refractory epilepsy who passively listened to this novel auditory paradigm and detected responses to deviants mainly in the superior temporal gyrus and inferior frontal gyrus. Notably, the inferior frontal gyrus exhibited broader distribution and sustained duration of deviant-elicited responses, seemingly differing in spatio-temporal characteristics from the prediction error responses observed in the superior temporal gyrus, compared with conventional oddball paradigms performed on the same participants. Moreover, we observed that the deviant responses were enhanced through stimulus repetition in the high-gamma range mainly in the superior temporal gyrus. These features of the novel paradigm may aid in our understanding of auditory predictive coding.

Altered dynamical integration/segregation balance during anesthesia-induced loss of consciousness.

In recent years, brain imaging studies have begun to shed light on the neural correlates of physiologically-reversible altered states of consciousness such as deep sleep, anesthesia, and psychedelic experiences. The emerging consensus is that normal waking consciousness requires the exploration of a dynamical repertoire enabling both global integration i.e., long-distance interactions between brain regions, and segregation, i.e., local processing in functionally specialized clusters. Altered states of consciousness have notably been characterized by a tipping of the integration/segregation balance away from this equilibrium. Historically, functional MRI (fMRI) has been the modality of choice for such investigations. However, fMRI does not enable characterization of the integration/segregation balance at sub-second temporal resolution. Here, we investigated global brain spatiotemporal patterns in electrocorticography (ECoG) data of a monkey (Macaca fuscata) under either ketamine or propofol general anesthesia. We first studied the effects of these anesthetics from the perspective of band-specific synchronization across the entire ECoG array, treating individual channels as oscillators. We further aimed to determine whether synchrony within spatially localized clusters of oscillators was differently affected by the drugs in comparison to synchronization over spatially distributed subsets of ECoG channels, thereby quantifying changes in integration/segregation balance on physiologically-relevant time scales. The findings reflect global brain dynamics characterized by a loss of long-range integration in multiple frequency bands under both ketamine and propofol anesthesia, most pronounced in the beta (13-30Hz) and low-gamma bands (30-80Hz), and with strongly preserved local synchrony in all bands.

Towards Implantable Brain-Computer Interface for Communication in Locked-In Syndrome patients

Abstract Locked-in Syndrome (LIS) severely restricts the communication abilities of individuals due to extensive paralysis. The Intracranial Neuro Telemetry to Restore communication (INTRECOM) project aims to aid patients in overcoming these limitations by developing a fully implantable brain computer interface (BCI) system based on state-of-the-art technology holding great promise in revolutionizing the lives of LIS patients. In this project, the Graz BCI group, with its expertise in understanding brain dynamics associated with movement, focuses on the algorithmic development of advanced decoders that enable the user to execute specific commands by simply attempting corresponding movements. Our preliminary findings using electrocorticography (ECoG) data obtained from one individual with LIS that underwent implantation of a BCI communication system demonstrate the applicability of a ‘brain switch’ function that detects brain signals associated with attempted movements. This switch can then be used to translate the intention of the user into a click/select function on a screen.

36 Impact of Neuropsychological Performance and Anxiety on Meditation in Drug Resistant Epilepsy (DRE) Patients Implanted with a Responsive Neurostimulation (RNS) Device

Objective:Responsive neurostimulation (RNS) is a surgical intervention to reduce the frequency of seizures as an adjunctive therapy for patients with drug-resistant epilepsy (DRE). Presurgical neuropsychological evaluations capture symptoms of anxiety and depression, which occur in higher rates within the epilepsy population than in the general population; however, the effects of mood are commonly overlooked or underappreciated in the conceptualization of cognitive functioning and overall quality of life. Previous studies have shown the effects of attentional control and executive functioning on engagement in meditative states. The present study examines pre and post-meditation self-reported anxiety symptoms and the electrophysiological changes captured intracranially during meditation sessions in patients implanted with an RNS device. This study seeks to utilize presurgical neuropsychological evaluations to explore relationships between cognitive profiles and meditative state changes, and reductions in anxiety.Participants and Methods:This study presents a series of 10 patients who underwent RNS device implantation for the treatment of DRE at Mount Sinai Hospital. All patients had at least one contact in the basolateral amygdala. Prior to surgical implantation of the RNS device, all patients completed a comprehensive neuropsychological evaluation based on the NIH Common Data Elements Battery for Epilepsy Patients. Patients in this study completed a 17-and 22-minute meditation protocol based on loving-kindness and Focal Awareness (FA) meditation. Control points and mind-wandering phases were utilized to distinguish the meditative portion of the study during intracranial recordings. All patients completed a pre- and post-meditation questionnaire adapted from the PROMIS Anxiety Short Form as well as self-ratings on meditation depth and satisfaction.Results:Presurgical neuropsychological evaluation of patients showed elevated levels of anxiety on the BAI (M = 18.14, SD = 12.03) and depression on the BDI-II (M = 15.57, SD = 6.92). Neuropsychological findings localized to frontal or frontotemporal deficits in 80% of the patients were captured in this study. Regarding lateralization, 50% of patients presented with bilateral weakness on neuropsychological evaluation, with the rest showing unilateral profiles. A negative correlation was observed between patient responses on pre-meditation anxiety measures and self-reported depth of engagement in meditation, r = -0.65, p = .043. When all meditation sessions were evaluated, patients displayed a reduction in anxiety levels pre- and post- meditation, t = 2.3, p = .03.Conclusions:Present findings suggest a reduction in anxiety symptoms following completion of a meditation paradigm. Additionally, a relationship between anxiety and depth of engagement in meditation was identified. During each meditation session, electrocorticography data was collected and analyzed. Given the high comorbidities of anxiety and depression as well as cognitive symptoms common for individuals with epilepsy, a systems-based approach may enhance conceptualization of neuropsychological and neuropsychiatric evaluations, which may have a significant clinical impact. Evaluation of neuropsychological profiles, meditation effects, and anxiety in this population may support cross-discipline understanding of cognitive and psychiatric profiles to better inform treatment recommendations.

Enhanced sensitivity of electrocorticography during awake craniotomy using a novel circular grid electrode.

Awake craniotomy with intraoperative functional brain mapping (FBM) bedside neurological testing is an important technique used to optimize resective brain surgeries near eloquent cortex. Awake craniotomy performed with electrocorticography (ECoG) and direct electrical stimulation (DES) for FBM can delineate eloquent cortex from lesions and epileptogenic regions. However, current electrode technology demonstrates spatial limitations. Our group has developed a novel circular grid with the goal of improving spatial recording of ECoG to enhance detection of ictal and interictal activity. This retrospective study was approved by the institutional review board at Mayo Clinic Florida. We analyzed patients undergoing awake craniotomy with ECoG and DES and compared ECoG data obtained using the 22 contact circular grid to standard 6 contact strip electrode. We included 144 cases of awake craniotomy with ECoG, 73 using circular grid and 71 with strip electrode. No significant differences were seen regarding preoperative clinical and demographic data, duration of ECoG recording (p = 0.676) and use of DES (p = 0.926). Circular grid was more sensitive in detecting periodic focal epileptiform discharges (PFEDs) (p = 0.004), PFEDs plus (p = 0.032), afterdischarges (ADs) per case (p = 0.022) at lower minimum (p = 0.012) and maximum (p < 0.0012) intensity stimulation, and seizures (p = 0.048). PFEDs (p < 0.001), PFEDs plus (p < 0.001), and HFOs (p < 0.001) but not ADs (p = 0.255) predicted electrographic seizures. We demonstrate higher sensitivity in detecting ictal and interictal activity on ECoG during awake craniotomy with a novel circular grid compared to strip electrode, likely due to better spatial sampling during ECoG. We also found association between PFEDs and intraoperative seizures.

Early Postoperative Seizures Following Awake Craniotomy and Functional Brain Mapping for Lesionectomy

Awake craniotomy with electrocorticography (ECoG) and direct electrical stimulation (DES) facilitates lesionectomy while avoiding adverse effects. Early postoperative seizures (EPS), occurring within 7 days following surgery, can lead to morbidity. However, risk factors for EPS after awake craniotomy including clinical and ECoG data are not well defined. We retrospectively studied the incidence and risk factors of EPS following awake craniotomy for lesionectomy, and report short-term outcomes between January 1, 2020, and December 31,2022. We included 138 patients (56 female) who underwent 142 awake craniotomies, average age was 50.78 ± 15.97 years. Eighty-eight (63.7%) patients had a preoperative history of tumor-related epilepsy treated with antiseizure medication (ASM), 12 (13.6%) with drug-resistance. All others (36.3%) received ASM prophylaxis with levetiracetam perioperatively and continued for 14 days. An equal number of cases (71) each utilized a novel circle grid or strip electrodes for ECoG. There were 31 (21.8%) cases of intraoperative seizures, 16 with EPS (11.3%). Acute abnormality on early postoperative neuroimaging (P= 0.01), subarachnoid hemorrhage (P= 0.01), young age (P= 0.01), and persistent postoperative neurologic deficits (P= 0.013) were associated with EPS. Acute abnormality on neuroimaging remained significant in multivariate analysis. Outcomes during hospitalization and early outpatient follow up were worse with EPS. We report novel findings using ECoG and clinical features to predict EPS, including acute perioperative brain injury, persistent postoperative deficits and young age. Given worse outcomes with EPS, clinical indicators for EPS should alert clinicians of potential need for early postoperative EEG monitoring and perioperative ASM adjustment.

In vivo laser speckle contrast imaging of 4-aminopyridine- or pentylenetetrazole-induced seizures.

Clinical and preclinical studies on epileptic seizures are closely linked to the study of neurovascular coupling. Obtaining reliable information about cerebral blood flow (CBF) in the area of epileptic activity through minimally invasive techniques is crucial for research in this field. In our studies, we used laser speckle contrast imaging (LSCI) to gather information about the local blood circulation in the area of epileptic activity. We used two models of epileptic seizures: one based on 4-aminopyridine (4-AP) and another based on pentylenetetrazole (PTZ). We verified the duration of an epileptic seizure using electrocorticography (ECoG). We applied the antiepileptic drug topiramate (TPM) to both models, but its effect was different in each case. However, in both models, TPM had an effect on neurovascular coupling in the area of epileptic activity, as shown by both LSCI and ECoG data. We demonstrated that TPM significantly reduced the amplitude of 4-AP-induced epileptic seizures (4-AP+TPM: 0.61 ± 0.13 mV vs 4-AP: 1.08 ± 0.19 mV; p < 0.05), and it also reduced gamma power in ECoG in PTZ-induced epileptic seizures (PTZ+TPM: 38.5% ± 11.9% of the peak value vs PTZ: 59.2% ± 3.0% of peak value; p < 0.05). We also captured the pattern of CBF changes during focal epileptic seizures induced by 4-AP. Our data confirm that the system of simultaneous cortical LSCI and registration of ECoG makes it possible to evaluate the effectiveness of pharmacological agents in various types of epileptic seizures in in vivo models and provides spatial and temporal information on the process of ictogenesis.

Intracranial intraoperative radiotherapy (IORT): evaluation of electrocorticography and peri-operative seizure risk.

Intra-operative radiotherapy (IORT) for brain metastases (BMs) and primary brain tumors has emerged as an adjuvant radiation modality that allows for consolidation of care into a single anesthetic episode with surgical resection. Yet, there is a paucity of data regarding the impact that IORT may have on peri-operative and long-term seizure risk. A retrospective analysis of patients receiving IORT during tumor resection was performed via registry including data regarding peri-operative anti-seizure medications and anesthetic agents. Intra-operative neuromonitoring was performed using electrocorticography (ECoG) captured before-, during-, and after-IORT then analyzed for evidence of seizure or significant baseline changes. Kaplan-Meir estimations were used for overall survival analysis relative to documented clinical seizure incidence post-IORT. Of the 24 consecutive patients treated with IORT during tumor resection included, 18 (75%) patients were diagnosed with BMs while 6 (25%) had newly-diagnosed glioblastoma. Mean and median survival times were 487 and 372 days, respectively. Clinical seizures occurred in 3 patients post-IORT, 2 BMs patients within 9 months and 1 glioblastoma patient at 14 months. IORT time represented 9.5% of anesthetic time. ECoG recordings were available for 5 patients (4 BMs; 1 glioblastoma), with mean recording durations of 13% of the total anesthetic time and no evidence of high-frequency oscillations or seizure activity. IORT is an option for delivery of definitive radiation in surgically resected brain tumors without increasing the peri-operative or long-term risk of seizure. ECoG data during the delivery of radiation fail to demonstrate any electrophysiological changes in response to ionizing radiation.

High gamma coherence between task-responsive sensory-motor cortical regions in a motor reaction-time task.

Electrical activity at high gamma frequencies (70-170 Hz) is thought to reflect the activity of small cortical ensembles. For example, high gamma activity (often quantified by spectral power) can increase in sensory-motor cortex in response to sensory stimuli or movement. On the other hand, synchrony of neural activity between cortical areas (often quantified by coherence) has been hypothesized as an important mechanism for inter-areal communication, thereby serving functional roles in cognition and behavior. Currently, high gamma activity has primarily been studied as a local amplitude phenomenon. We investigated the synchronization of high gamma activity within sensory-motor cortex and the extent to which underlying high gamma activity can explain coherence during motor tasks. We characterized high gamma coherence in sensory-motor networks and the relationship between coherence and power by analyzing electrocorticography (ECoG) data from human subjects as they performed a motor response to sensory cues. We found greatly increased high gamma coherence during the motor response compared with the sensory cue. High gamma power poorly predicted high gamma coherence, but the two shared a similar time course. However, high gamma coherence persisted longer than high gamma power. The results of this study suggest that high gamma coherence is a physiologically distinct phenomenon during a sensory-motor task, the emergence of which may require active task participation.NEW & NOTEWORTHY Motor action after auditory stimulus elicits high gamma responses in sensory-motor and auditory cortex, respectively. We show that high gamma coherence reliably and greatly increased during motor response, but not after auditory stimulus. Underlying high gamma power could not explain high gamma coherence. Our results indicate that high gamma coherence is a physiologically distinct sensory-motor phenomenon that may serve as an indicator of increased synaptic communication on short timescales (∼1 s).

Simple method for detecting sleep episodes in rats ECoG using machine learning

In this paper we propose a new method for the automatic recognition of the state of behavioral sleep (BS) and waking state (WS) in freely moving rats using their electrocorticographic (ECoG) data. Three-channels ECoG signals were recorded from frontal left, frontal right and occipital right cortical areas. We employed a simple artificial neural network (ANN), in which the mean values and standard deviations of ECoG signals from two or three channels were used as inputs for the ANN. Results of wavelet-based recognition of BS/WS in the same data were used to train the ANN and evaluate correctness of our classifier. We tested different combinations of ECoG channels for detecting BS/WS.Our results showed that the accuracy of ANN classification did not depend on ECoG-channel. For any ECoG-channel, networks were trained on one rat and applied to another rat with an accuracy of at least 80 %. Itis important that we used a very simple network topology to achieve a relatively high accuracy of classification. Our classifier was based on a simple linear combination of input signals with some weights, and these weights could be replaced by the averaged weights of all trained ANNs without decreases in classification accuracy. In all, we introduce a new sleep recognition method that does not require additional network training. It is enough to know the coefficients and the equations suggested in this paper. The proposed method showed very fast performance and simple computations, therefore it could be used in real time experiments. It might be of high demand in preclinical studies in rodents that require vigilance control or monitoring of sleep-wake patterns.

Insights From Chronic ECoG by RNS.

The NeuroPace responsive neurostimulation system (RNS) has revolutionized the care of patients suffering from focal epilepsy since its approval in 2014. One major advantage of this device is its innate ability to gather long-term electrocorticographic (ECoG) data that the device uses in its novel closed-loop treatment paradigm. Beyond the standard stimulation treatments, which have been demonstrated to be safe and well-tolerated, the data collected by the RNS provide valuable information, such as the long-term circadian and ultradian variations that affect seizure risk, obtained under naturalistic conditions. Additionally, these data inform future surgical procedures, supplementing clinically reported seizures by patients, assessing the response to newly added anti-seizure medications, helping to forecast the risk of future seizures, and understanding the mechanisms of certain long-term outcomes in patients with postsurgical epilepsy. By leveraging these data, the delivery of high-quality clinical care for patients with epilepsy can only be enhanced. Finally, these data open significant avenues of research, including machine learning and artificial intelligence algorithms, which may also translate to improved outcomes in patients who struggle with recurrent seizures.

Attractor-Like Dynamics Extracted from Human Electrocorticographic Recordings Underlie Computational Principles of Auditory Bistable Perception.

In bistable perception, observers experience alternations between two interpretations of an unchanging stimulus. Neurophysiological studies of bistable perception typically partition neural measurements into stimulus-based epochs and assess neuronal differences between epochs based on subjects' perceptual reports. Computational studies replicate statistical properties of percept durations with modeling principles like competitive attractors or Bayesian inference. However, bridging neuro-behavioral findings with modeling theory requires the analysis of single-trial dynamic data. Here, we propose an algorithm for extracting nonstationary timeseries features from single-trial electrocorticography (ECoG) data. We applied the proposed algorithm to 5-min ECoG recordings from human primary auditory cortex obtained during perceptual alternations in an auditory triplet streaming task (six subjects: four male, two female). We report two ensembles of emergent neuronal features in all trial blocks. One ensemble consists of periodic functions that encode a stereotypical response to the stimulus. The other comprises more transient features and encodes dynamics associated with bistable perception at multiple time scales: minutes (within-trial alternations), seconds (duration of individual percepts), and milliseconds (switches between percepts). Within the second ensemble, we identified a slowly drifting rhythm that correlates with the perceptual states and several oscillators with phase shifts near perceptual switches. Projections of single-trial ECoG data onto these features establish low-dimensional attractor-like geometric structures invariant across subjects and stimulus types. These findings provide supporting neural evidence for computational models with oscillatory-driven attractor-based principles. The feature extraction techniques described here generalize across recording modality and are appropriate when hypothesized low-dimensional dynamics characterize an underlying neural system.SIGNIFICANCE STATEMENT Irrespective of the sensory modality, neurophysiological studies of multistable perception have typically investigated events time-locked to the perceptual switching rather than the time course of the perceptual states per se. Here, we propose an algorithm that extracts neuronal features of bistable auditory perception from largescale single-trial data while remaining agnostic to the subject's perceptual reports. The algorithm captures the dynamics of perception at multiple timescales, minutes (within-trial alternations), seconds (durations of individual percepts), and milliseconds (timing of switches), and distinguishes attributes of neural encoding of the stimulus from those encoding the perceptual states. Finally, our analysis identifies a set of latent variables that exhibit alternating dynamics along a low-dimensional manifold, similar to trajectories in attractor-based models for perceptual bistability.

Intra– and inter–hemispheric network dynamics supporting object recognition and speech production

We built normative brain atlases that animate millisecond-scale intra- and inter-hemispheric white matter-level connectivity dynamics supporting object recognition and speech production. We quantified electrocorticographic modulations during three naming tasks using event-related high-gamma activity from 1,114 nonepileptogenic intracranial electrodes (i.e., non-lesional areas unaffected by epileptiform discharges). Using this electrocorticography data, we visualized functional connectivity modulations defined as significant naming-related high-gamma modulations occurring simultaneously at two sites connected by direct white matter streamlines on diffusion-weighted imaging tractography. Immediately after stimulus onset, intra- and inter-hemispheric functional connectivity enhancements were confined mainly across modality-specific perceptual regions. During response preparation, left intra-hemispheric connectivity enhancements propagated in a posterior-to-anterior direction, involving the left precentral and prefrontal areas. After overt response onset, inter- and intra-hemispheric connectivity enhancements mainly encompassed precentral, postcentral, and superior-temporal (STG) gyri. We found task-specific connectivity enhancements during response preparation as follows. Picture naming enhanced activity along the left arcuate fasciculus between the inferior-temporal and precentral/posterior inferior-frontal (pIFG) gyri. Nonspeech environmental sound naming augmented functional connectivity via the left inferior longitudinal and fronto-occipital fasciculi between the medial-occipital and STG/pIFG. Auditory descriptive naming task enhanced usage of the left frontal U-fibers, involving the middle-frontal gyrus. Taken together, the commonly observed network enhancements include inter-hemispheric connectivity optimizing perceptual processing exerted in each hemisphere, left intra-hemispheric connectivity supporting semantic and lexical processing, and inter-hemispheric connectivity for symmetric oral movements during overt speech. Our atlases improve the currently available models of object recognition and speech production by adding neural dynamics via direct intra- and inter-hemispheric white matter tracts.

Propofol anesthesia-induced spatiotemporal changes in cortical activity with loss of external and internal awareness: An electrocorticography study

ObjectiveTo understand the underlying mechanism of consciousness, investigating spatiotemporal changes in the cortical activity during the induction phase of unconsciousness is important. Loss of consciousness induced by general anesthesia is not necessarily accompanied by a uniform inhibition of all cortical activities. We hypothesized that cortical regions involved in internal awareness would be suppressed after disruption of cortical regions involved in external awareness. Thus, we investigated temporal changes in cortex during induction of unconsciousness. MethodsWe recorded electrocorticography data of 16 epilepsy patients and investigated power spectral changes during induction phase from awake state to unconsciousness. Temporal changes were assessed at 1) the start point and 2) the interval of normalized time between start and end of power change (Δ tnormalized). ResultsWe found that the power increased at frequencies < 46 Hz, and decreased in range of 62–150 Hz, in global channels. In temporal changes of power change, superior parietal lobule and dorsolateral prefrontal cortex started to change early, but the changes were completed over a prolonged interval, whereas angular gyrus and associative visual cortex showed a delayed change and rapid completion. ConclusionsLoss of consciousness induced by general anesthesia results first from disrupted communication between self and external world, followed by disrupted communication within self, with decreased activities of superior parietal lobule and dorsolateral prefrontal cortex, and later, attenuated activities of angular gyrus. SignificanceOur findings provided neurophysiological evidence for the temporal changes in consciousness components induced by general anesthesia.

A New Nonlinear Autoregressive Exogenous (NARX)-based Intra-spinal Stimulation Approach to Decode Brain Electrical Activity for Restoration of Leg Movement in Spinally-injured Rabbits.

This study aimed at investigating the stimulation by intra-spinal signals decoded from electrocorticography (ECoG) assessments to restore the movements of the leg in an animal model of spinal cord injury (SCI). The present work is comprised of three steps. First, ECoG signals and the associated leg joint changes (hip, knee, and ankle) in sedated healthy rabbits were recorded in different trials. Second, an appropriate set of intra-spinal electric stimuli was discovered to restore natural leg movements, using the three leg joint movements under a fuzzy-controlled strategy in spinally-injured rabbits under anesthesia. Third, a nonlinear autoregressive exogenous (NARX) neural network model was developed to produce appropriate intra-spinal stimulation developed from decoded ECoG information. The model was able to correlate the ECoG signal data to the intra-spinal stimulation data and finally, induced desired leg movements. In this study, leg movements were also developed from offline ECoG signals (deciphered from rabbits that were not injured) as well as online ECoG data (extracted from the same rabbit after SCI induction). Based on our data, the correlation coefficient was 0.74±0.15 and the normalized root means square error of the brain-spine interface was 0.22±0.10. Overall, we found that using NARX, appropriate information from ECoG recordings can be extracted and used for the generation of proper intra-spinal electric stimulations for restoration of natural leg movements lost due to SCI.