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.

Predictive modeling of evoked intracranial EEG response to medial temporal lobe stimulation in patients with epilepsy.

Despite promising advancements, closed-loop neurostimulation for drug-resistant epilepsy (DRE) still relies on manual tuning and produces variable outcomes, while automated predictable algorithms remain an aspiration. As a fundamental step towards addressing this gap, here we study predictive dynamical models of human intracranial EEG (iEEG) response under parametrically rich neurostimulation. Using data from n = 13 DRE patients, we find that stimulation-triggered switched-linear models with ~300 ms of causal historical dependence best explain evoked iEEG dynamics. These models are highly consistent across different stimulation amplitudes and frequencies, allowing for learning a generalizable model from abundant STIM OFF and limited STIM ON data. Further, evoked iEEG in nearly all subjects exhibited a distance-dependent pattern, whereby stimulation directly impacts the actuation site and nearby regions (≲ 20 mm), affects medium-distance regions (20 ~ 100 mm) through network interactions, and hardly reaches more distal areas (≳ 100 mm). Peak network interaction occurs at 60 ~ 80 mm from the stimulation site. Due to their predictive accuracy and mechanistic interpretability, these models hold significant potential for model-based seizure forecasting and closed-loop neurostimulation design.

Speech-induced suppression and vocal feedback sensitivity in human cortex.

Across the animal kingdom, neural responses in the auditory cortex are suppressed during vocalization, and humans are no exception. A common hypothesis is that suppression increases sensitivity to auditory feedback, enabling the detection of vocalization errors. This hypothesis has been previously confirmed in non-human primates, however a direct link between auditory suppression and sensitivity in human speech monitoring remains elusive. To address this issue, we obtained intracranial electroencephalography (iEEG) recordings from 35 neurosurgical participants during speech production. We first characterized the detailed topography of auditory suppression, which varied across superior temporal gyrus (STG). Next, we performed a delayed auditory feedback (DAF) task to determine whether the suppressed sites were also sensitive to auditory feedback alterations. Indeed, overlapping sites showed enhanced responses to feedback, indicating sensitivity. Importantly, there was a strong correlation between the degree of auditory suppression and feedback sensitivity, suggesting suppression might be a key mechanism that underlies speech monitoring. Further, we found that when participants produced speech with simultaneous auditory feedback, posterior STG was selectively activated if participants were engaged in a DAF paradigm, suggesting that increased attentional load can modulate auditory feedback sensitivity.

The brain lowers its response to inputs we generate ourselves, such as moving or speaking. Essentially, our brain 'knows' what will happen next when we carry out these actions, and therefore does not need to react as strongly as it would to unexpected events. This is why we cannot tickle ourselves, and why the brain does not react as much to our own voice as it does to someone else's. Quieting down the brain's response also allows us to focus on things that are new or important without getting distracted by our own movements or sounds. Studies in non-human primates showed that neurons in the auditory cortex (the region of the brain responsible for processing sound) displayed suppressed levels of activity when the animals made sounds. Interestingly, when the primates heard an altered version of their own voice, many of these same neurons became more active. But it was unclear whether this also happens in humans. To investigate, Ozker et al. used a technique called electrocorticography to record neural activity in different regions of the human brain while participants spoke. The results showed that most areas of the brain involved in auditory processing showed suppressed activity when individuals were speaking. However, when people heard an altered version of their own voice which had an unexpected delay, those same areas displayed increased activity. In addition, Ozker et al. found that the higher the level of suppression in the auditory cortex, the more sensitive these areas were to changes in a person's speech. These findings suggest that suppressing the brain's response to self-generated speech may help in detecting errors during speech production. Speech deficits are common in various neurological disorders, such as stuttering, Parkinson's disease, and aphasia. Ozker et al. hypothesize that these deficits may arise because individuals fail to suppress activity in auditory regions of the brain, causing a struggle when detecting and correcting errors in their own speech. However, further experiments are needed to test this theory.

Novel cyclic homogeneous oscillation detection method for high accuracy and specific characterization of neural dynamics.

Determining the presence and frequency of neural oscillations is essential to understanding dynamic brain function. Traditional methods that detect peaks over 1/f noise within the power spectrum fail to distinguish between the fundamental frequency and harmonics of often highly non-sinusoidal neural oscillations. To overcome this limitation, we define fundamental criteria that characterize neural oscillations and introduce the cyclic homogeneous oscillation (CHO) detection method. We implemented these criteria based on an autocorrelation approach to determine an oscillation's fundamental frequency. We evaluated CHO by verifying its performance on simulated non-sinusoidal oscillatory bursts and validated its ability to determine the fundamental frequency of neural oscillations in electrocorticographic (ECoG), electroencephalographic (EEG), and stereoelectroencephalographic (SEEG) signals recorded from 27 human subjects. Our results demonstrate that CHO outperforms conventional techniques in accurately detecting oscillations. In summary, CHO demonstrates high precision and specificity in detecting neural oscillations in time and frequency domains. The method's specificity enables the detailed study of non-sinusoidal characteristics of oscillations, such as the degree of asymmetry and waveform of an oscillation. Furthermore, CHO can be applied to identify how neural oscillations govern interactions throughout the brain and to determine oscillatory biomarkers that index abnormal brain function.

Maintenance and transformation of representational formats during working memory prioritization.

Visual working memory depends on both material-specific brain areas in the ventral visual stream (VVS) that support the maintenance of stimulus representations and on regions in the prefrontal cortex (PFC) that control these representations. How executive control prioritizes working memory contents and whether this affects their representational formats remains an open question, however. Here, we analyzed intracranial EEG (iEEG) recordings in epilepsy patients with electrodes in VVS and PFC who performed a multi-item working memory task involving a retro-cue. We employed Representational Similarity Analysis (RSA) with various Deep Neural Network (DNN) architectures to investigate the representational format of prioritized VWM content. While recurrent DNN representations matched PFC representations in the beta band (15-29 Hz) following the retro-cue, they corresponded to VVS representations in a lower frequency range (3-14 Hz) towards the end of the maintenance period. Our findings highlight the distinct coding schemes and representational formats of prioritized content in VVS and PFC.

Cortical sites critical to language function act as connectors between language subnetworks.

Historically, eloquent functions have been viewed as localized to focal areas of human cerebral cortex, while more recent studies suggest they are encoded by distributed networks. We examined the network properties of cortical sites defined by stimulation to be critical for speech and language, using electrocorticography from sixteen participants during word-reading. We discovered distinct network signatures for sites where stimulation caused speech arrest and language errors. Both demonstrated lower local and global connectivity, whereas sites causing language errors exhibited higher inter-community connectivity, identifying them as connectors between modules in the language network. We used machine learning to classify these site types with reasonably high accuracy, even across participants, suggesting that a site's pattern of connections within the task-activated language network helps determine its importance to function. These findings help to bridge the gap in our understanding of how focal cortical stimulation interacts with complex brain networks to elicit language deficits.

AB073. Electrocorticography high-gamma dynamics during intraoperative hand movement mapping.

BACKGROUND: Intraoperative functional mapping for glioma resection often necessitates awake craniotomies, requiring active patient participation. This procedure presents challenges for both the surgical team and the patient. Thus, minimizing mapping time becomes crucial. Passive mapping utilizing electrocorticography (ECoG) presents a promising approach to reduce intraoperative mapping efforts via direct electrical stimulation. This study aims to identify an efficient mapping protocol for hand movement by optimizing mapping duration and localization accuracy. METHODS: Three glioma patients (two males, one female) underwent awake craniotomy for tumor resection at Asahikawa Medical University Hospital and Kindai University in Osaka. Patients were maintained at a bispectral index (BIS) level above 90 to ensure wakefulness during mapping. Data were collected using a DC-coupled g.HIamp biosignal amplifier, digitized with 24-bit resolution at a minimum sampling rate of 1,200 Hz. Each session comprised ten runs, each lasting 250 seconds, consisting of a 12-second rest phase (baseline) followed by a 12-second grasping period containing ten grasping movements. High-gamma activity (HGA, 60-170 Hz) was recorded from ECoG locations on the pre- and postcentral gyrus. Locations exhibiting significant grasping-related HGA, with stronger responses during early trials within a run, were classified as "attenuated". RESULTS: Among 37 electrodes on the sensorimotor cortex, 16 exhibited significant HGA during grasping. Three locations demonstrated significant attenuation after three runs, with one location showing attenuation after the first three trials within a run. CONCLUSIONS: The observed attenuation effect of short-term repeated movements during intraoperative monitoring is relatively modest initially. However, as the number of repeated grasping blocks increases, the number of attenuated locations also rises. Consequently, minimizing overall mapping time, rather than reducing the number of tasks per block, is paramount. For statistical analysis, a minimum of 20 grasping trials (two runs of ten movements) or 48 seconds of motor mapping is recommended. Alternatively, a mapping protocol involving a third run or 30 grasping trials (72 seconds) may enhance data robustness. These preliminary findings, though based on a limited patient cohort, warrant confirmation and further investigation, particularly in epilepsy patients.

Symbolic information approach applied to human intracranial data to characterize and distinguish different congnitive processes.

How the human brain processes information during different cognitive tasks is one of the greatest questions in contemporary neuroscience. Understanding the statistical properties of brain signals during specific activities is one promising way to address this question. Here we analyze freely available data from implanted electrocorticography (ECoG) in five human subjects during two different cognitive tasks in the light of information theory quantifiers ideas. We employ a symbolic information approach to determine the probability distribution function associated with the time series from different cortical areas. Then we utilize these probabilities to calculate the associated Shannon entropy and a statistical complexity measure based on the disequilibrium between the actual time series and one with a uniform probability distribution function. We show that an Euclidian distance in the complexity-entropy plane and an asymmetry index for complexity are useful for comparing the two conditions. We show that our method can distinguish visual search epochs from blank screen intervals in different electrodes and patients. By using a multiscale approach and embedding time delays to downsample the data, we find important timescales in which the relevant information is being processed. We also determine cortical regions and time intervals along the 2-s-long trials that present more pronounced differences between the two cognitive tasks. Finally, we show that the method is useful to distinguish cognitive processes using brain activity on a trial-by-trial basis.

Primary somatosensory cortex CB1 and 5­HT1A receptors interaction in the penicillin model of epilepsy.

Cannabinoid and serotonin systems regulate many biological processes. The aim of the present study was to investigate the functional interaction between the cannabinoid and serotonergic systems of the primary somatosensory region (S1) of the brain in epileptiform activity caused by penicillin. The ACEA (an agonist of CB1 receptor), AM­251 (an antagonist of CB1 receptor), 8­OH­DPAT (an agonist of 5­HT1A receptor) and WAY­100635 (an antagonist of 5­HT1A receptor) were administered into the S1 after the same site administration of penicillin in urethane­anesthetized rats. Electrocorticographic recording was done for a 90­min period. The spike waves number and amplitude were recorded in 15­min intervals. Areas under the curve (AUC) of the above­mentioned spike alterations was calculated in 90 min. Spike waves with frequency of 30/min and amplitude of 1.3 mV were appeared after penicillin microinjection. The ACEA (50 ng), 8­OH­DPAT (500 ng) and ACEA (10 ng) plus 8­OH­DPAT (100 ng) reduced epileptiform activity. The AM­251 (50 ng) and WAY­100365 (500 ng) prevented the reducing effects of ACEA (50 ng) and 8­OH­DPAT (500 ng). The AM­251 alone increased spike waves frequency. The AUC results supported the effects of the above­mentioned treatments. The results showed that activating CB1 and 5­HT1A receptors in the S1 may reduce the epileptiform activity caused by penicillin. Therefore, alone and together activation of central CB1 and 5­HT1A receptors might be considered in the management of epilepsy treatment.

Oscillatory mechanisms of intrinsic human brain networks.

Non-invasive neuroimaging has revealed specific network-based resting-state dynamics in the human brain, yet the underlying neurophysiological mechanism remains unclear. We employed intracranial electroencephalography to characterize local field potentials within the default mode network (DMN), frontoparietal network (FPN), and salience network (SN) in 42 participants. We identified stronger within-network phase coherence at low frequencies (θ and α band) within the DMN, and at high frequencies (γ band) within the FPN. Hidden Markov modeling indicated that the DMN exhibited preferential low frequency phase coupling. Phase-amplitude coupling (PAC) analysis revealed that the low-frequency phase in the DMN modulated the high-frequency amplitude envelopes of the FPN, suggesting frequency-dependent characterizations of intrinsic brain networks at rest. These findings provide intracranial electrophysiological evidence in support of the network model for intrinsic organization of human brain and shed light on the way brain networks communicate at rest.