Intracranial Electroencephalography Research Articles

Incomplete resection of the intracranial electroencephalographic seizure onset zone is not associated with postsurgical outcomes.

Delineation of seizure onset regions using intracranial electroencephalography (icEEG) is vital in the surgical workup of drug-resistant epilepsy cases. However, it is unknown whether the complete resection of these regions is necessary for seizure freedom, or whether postsurgical seizure recurrence can be attributed to the incomplete removal of seizure onset regions. To address this gap, we retrospectively analyzed icEEG recordings from 63 subjects, identifying seizure onset regions visually and algorithmically. We assessed onset region resection and correlated this with postsurgical seizure control. The majority of subjects had more than half of their onset regions resected (82.46% and 80.65% of subjects using visual and algorithmic methods, respectively). There was no association between the proportion of the seizure onset zone (SOZ) that was subsequently resected and better surgical outcomes (area under the receiver operating characteristic curve [AUC] < .7). Investigating the spatial extent of onset regions, we found no substantial evidence of an association with postsurgical seizure control (all AUC < .7). Although seizure onset regions are typically resected completely or in large part, incomplete resection is not associated with worse postsurgical outcomes. We conclude that postsurgical seizure recurrence cannot be attributed to an incomplete resection of the icEEG SOZ alone. Other network mechanisms beyond icEEG seizure onset likely contribute.

Frequency-specific directed connectivity between the hippocampus and parietal cortex during verbal and spatial episodic memory: an intracranial EEG replication.

Hippocampus-parietal cortex circuits are thought to play a crucial role in memory and attention, but their neural basis remains poorly understood. We employed intracranial intracranial electroencephalography (iEEG) to investigate the neurophysiological underpinning of these circuits across three memory tasks spanning verbal and spatial domains. We uncovered a consistent pattern of higher causal directed connectivity from the hippocampus to both lateral parietal cortex (supramarginal and angular gyrus) and medial parietal cortex (posterior cingulate cortex) in the delta-theta band during memory encoding and recall. This connectivity was independent of activation or suppression states in the hippocampus or parietal cortex. Crucially, directed connectivity from the supramarginal gyrus to the hippocampus was enhanced in participants with higher memory recall, highlighting its behavioral significance. Our findings align with the attention-to-memory model, which posits that attention directs cognitive resources toward pertinent information during memory formation. The robustness of these results was demonstrated through Bayesian replication analysis of the memory encoding and recall periods across the three tasks. Our study sheds light on the neural basis of casual signaling within hippocampus-parietal circuits, broadening our understanding of their critical roles in human cognition.

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.

Temporal dynamics of short-term neural adaptation across human visual cortex.

Neural responses in visual cortex adapt to prolonged and repeated stimuli. While adaptation occurs across the visual cortex, it is unclear how adaptation patterns and computational mechanisms differ across the visual hierarchy. Here we characterize two signatures of short-term neural adaptation in time-varying intracranial electroencephalography (iEEG) data collected while participants viewed naturalistic image categories varying in duration and repetition interval. Ventral- and lateral-occipitotemporal cortex exhibit slower and prolonged adaptation to single stimuli and slower recovery from adaptation to repeated stimuli compared to V1-V3. For category-selective electrodes, recovery from adaptation is slower for preferred than non-preferred stimuli. To model neural adaptation we augment our delayed divisive normalization (DN) model by scaling the input strength as a function of stimulus category, enabling the model to accurately predict neural responses across multiple image categories. The model fits suggest that differences in adaptation patterns arise from slower normalization dynamics in higher visual areas interacting with differences in input strength resulting from category selectivity. Our results reveal systematic differences in temporal adaptation of neural population responses between lower and higher visual brain areas and show that a single computational model of history-dependent normalization dynamics, fit with area-specific parameters, accounts for these differences.

Dichotomous frequency-dependent phase synchrony in the sensorimotor network characterizes simplistic movement

It is hypothesized that disparate brain regions interact via synchronous activity to control behavior. The nature of these interconnected ensembles remains an area of active investigation, and particularly the role of high frequency synchronous activity in simplistic behavior is not well known. Using intracranial electroencephalography, we explored the spectral dynamics and network connectivity of sensorimotor cortical activity during a simple motor task in seven epilepsy patients. Confirming prior work, we see a “spectral tilt” (increased high-frequency (HF, 70–100 Hz) and decreased low-frequency (LF, 3–33 Hz) broadband oscillatory activity) in motor regions during movement compared to rest, as well as an increase in LF synchrony between these regions using time-resolved phase-locking. We then explored this phenomenon in high frequency and found a robust but opposite effect, where time-resolved HF broadband phase-locking significantly decreased during movement. This “connectivity tilt” (increased LF synchrony and decreased HF synchrony) displayed a graded anatomical dependency, with the most robust pattern occurring in primary sensorimotor cortical interactions and less robust pattern occurring in associative cortical interactions. Connectivity in theta (3–7 Hz) and high beta (23–27 Hz) range had the most prominent low frequency contribution during movement, with theta synchrony building gradually while high beta having the most prominent effect immediately following the cue. There was a relatively sharp, opposite transition point in both the spectral and connectivity tilt at approximately 35 Hz. These findings support the hypothesis that task-relevant high-frequency spectral activity is stochastic and that the decrease in high-frequency synchrony may facilitate enhanced low frequency phase coupling and interregional communication. Thus, the “connectivity tilt” may characterize behaviorally meaningful cortical interactions.

Text and image generation from intracranial electroencephalography using an embedding space for text and images

Objective. Invasive brain–computer interfaces (BCIs) are promising communication devices for severely paralyzed patients. Recent advances in intracranial electroencephalography (iEEG) coupled with natural language processing have enhanced communication speed and accuracy. It should be noted that such a speech BCI uses signals from the motor cortex. However, BCIs based on motor cortical activities may experience signal deterioration in users with motor cortical degenerative diseases such as amyotrophic lateral sclerosis. An alternative approach to using iEEG of the motor cortex is necessary to support patients with such conditions. Approach. In this study, a multimodal embedding of text and images was used to decode visual semantic information from iEEG signals of the visual cortex to generate text and images. We used contrastive language-image pretraining (CLIP) embedding to represent images presented to 17 patients implanted with electrodes in the occipital and temporal cortices. A CLIP image vector was inferred from the high-γ power of the iEEG signals recorded while viewing the images. Main results. Text was generated by CLIPCAP from the inferred CLIP vector with better-than-chance accuracy. Then, an image was created from the generated text using StableDiffusion with significant accuracy. Significance. The text and images generated from iEEG through the CLIP embedding vector can be used for improved communication.

Electrophysiological signatures of veridical head direction in humans.

Information about heading direction is critical for navigation as it provides the means to orient ourselves in space. However, given that veridical head-direction signals require physical rotation of the head and most human neuroimaging experiments depend upon fixing the head in position, little is known about how the human brain is tuned to such heading signals. Here we adress this by asking 52 healthy participants undergoing simultaneous electroencephalography and motion tracking recordings (split into two experiments) and 10 patients undergoing simultaneous intracranial electroencephalography and motion tracking recordings to complete a series of orientation tasks in which they made physical head rotations to target positions. We then used a series of forward encoding models and linear mixed-effects models to isolate electrophysiological activity that was specifically tuned to heading direction. We identified a robust posterior central signature that predicts changes in veridical head orientation after regressing out confounds including sensory input and muscular activity. Both source localization and intracranial analysis implicated the medial temporal lobe as the origin of this effect. Subsequent analyses disentangled head-direction signatures from signals relating to head rotation and those reflecting location-specific effects. Lastly, when directly comparing head direction and eye-gaze-related tuning, we found that the brain maintains both codes while actively navigating, with stronger tuning to head direction in the medial temporal lobe. Together, these results reveal a taxonomy of population-level head-direction signals within the human brain that is reminiscent of those reported in the single units of rodents.

Scan-associated anxiety (scanxiety): the enigma of emotional breathing oscillations at 0.32 Hz (19 bpm).

MRI-related anxiety in healthy participants is often characterized by a dominant breathing frequency at around 0.32 Hz (19 breaths per minute, bpm) at the beginning but in a few cases also at the end of scanning. Breathing waves at 19 bpm are also observed in patients with anxiety independently of the scanned body part. In patients with medically intractable epilepsy and intracranial electroencephalography (iEEG), spontaneous breathing through the nose varied between 0.24 and 0.37 Hz (~19 bpm). Remarkable is the similarity of the observed breathing rates at around 0.32 Hz during different types of anxiety states (e.g., epilepsy, cancer, claustrophobia) with the preferred breathing frequency of 0.32 Hz (19 bpm), which is predicted by the binary hierarchy model of Klimesch. This elevated breathing frequency most likely reflects an emotional processing state, in which energy demands are minimized due to a harmonic coupling ratio with other brain-body oscillations.

What do brain oscillations tell about the human sense of smell?

Brain activity may manifest itself as oscillations which are repetitive rhythms of neuronal firing. These local field potentials can be measured via intracranial electroencephalography (iEEG). This review focuses on iEEG used to map human brain structures involved in olfaction. After presenting the methodology of the review, a summary of the brain structures involved in olfaction is given, followed by a review of the literature on human olfactory oscillations in different contexts. A single case is provided as an illustration of the olfactory oscillations. Overall, the timing and sequence of oscillations found in the different structures of the olfactory system seem to play an important role for olfactory perception.

146 Intracranial Correlates of Fear Memory in Freely Moving Humans During Virtual Reality

INTRODUCTION: Distressing reactions to perceived threats in safe environments is a hallmark feature of anxiety-related disorders such as post-traumatic stress disorder (PTSD) and generalized anxiety disorder (GAD). The development of targeted neural circuit therapies requires an understanding of how neurophysiological activity relates to behavior and subjective emotional experience. METHODS: The task induced memories fear memories, a critical component of anxiety-related disorders etiology, using a novel virtual reality platform. Here, participants freely explored a virtual reality environment via walking or joystick. Participants were instructed to stop movement following color change of the room. When the room turned green (CS+), a noxious virtual reality stimulus (insect) attacked the participant (US) 6 seconds following color change onset. When the room turned blue (CS-), no noxious VR stimulus occurred. In addition to intracranial electroencephalography (iEEG) activity, we recorded physiological and behavioral metrics of fear. RESULTS: We found that the same neural signature indicative of fear memory retrieval in rodents (increased amygdala theta oscillations) was significantly elevated for the fear compared to the neutral cue in freely behaving humans. CONCLUSIONS: These findings represent an important step in translating decades of research in rodents to humans and provide putative electrophysiological signatures of fear behavior that can be used as putative targets for treatment with closed loop brain stimulation therapies. We believe that this combined collection of simultaneous autonomic responses to subjective emotional (fear ratings and physiology), clinical symptoms (anxiety scales) and neural activity has immense transdiagnostic clinical potential for anxiety-related disorders and can inform neurosurgical approaches to treating severe psychopathology.

A personalized earbud for non-invasive long-term EEG monitoring

Objective. The primary objective of this study was to evaluate the reliability, comfort, and performance of a custom-fit, non-invasive long-term electrophysiologic headphone, known as Aware Hearable, for the ambulatory recording of brain activities. These recordings play a crucial role in diagnosing neurological disorders such as epilepsy and in studying neural dynamics during daily activities. Approach. The study uses commercial manufacturing processes common to the hearing aid industry, such as 3D scanning, computer-aided design modeling, and 3D printing. These processes enable the creation of the Aware Hearable with a personalized, custom-fit, thereby ensuring complete and consistent contact with the inner surfaces of the ear for high-quality data recordings. Additionally, the study employs a machine learning data analysis approach to validate the recordings produced by Aware Hearable, by comparing them to the gold standard intracranial electroencephalography recordings in epilepsy patients. Main results. The results indicate the potential of Aware Hearable to expedite the diagnosis of epilepsy by enabling extended periods of ambulatory recording. Significance. This offers significant reductions in burden to patients and their families. Furthermore, the device’s utility may extend to a broader spectrum, making it suitable for other applications involving neurophysiological recordings in real-world settings.

Spatiotemporal Dynamics of Successive Activations across the Human Brain during Simple Arithmetic Processing.

Previous neuroimaging studies have offered unique insights about the spatial organization of activations and deactivations across the brain; however, these were not powered to explore the exact timing of events at the subsecond scale combined with a precise anatomical source of information at the level of individual brains. As a result, we know little about the order of engagement across different brain regions during a given cognitive task. Using experimental arithmetic tasks as a prototype for human-unique symbolic processing, we recorded directly across 10,076 brain sites in 85 human subjects (52% female) using the intracranial electroencephalography. Our data revealed a remarkably distributed change of activity in almost half of the sampled sites. In each activated brain region, we found juxtaposed neuronal populations preferentially responsive to either the target or control conditions, arranged in an anatomically orderly manner. Notably, an orderly successive activation of a set of brain regions-anatomically consistent across subjects-was observed in individual brains. The temporal order of activations across these sites was replicable across subjects and trials. Moreover, the degree of functional connectivity between the sites decreased as a function of temporal distance between regions, suggesting that the information is partially leaked or transformed along the processing chain. Our study complements prior imaging studies by providing hitherto unknown information about the timing of events in the brain during arithmetic processing. Such findings can be a basis for developing mechanistic computational models of human-specific cognitive symbolic systems.

High-performance prediction of epilepsy surgical outcomes based on the genetic neural networks and hybrid iEEG marker

Accurately identification of the seizure onset zone (SOZ) is pivotal for successful surgery in patients with medically refractory epilepsy. The purpose of this study is to improve the performance of model predicting the epilepsy surgery outcomes using genetic neural network (GNN) model based on a hybrid intracranial electroencephalography (iEEG) marker. We extracted 21 SOZ related markers based on iEEG data from 79 epilepsy patients. The least absolute shrinkage and selection operator (LASSO) regression was employed to integrated seven markers, selected after testing in pairs with all 21 biomarkers and 7 machine learning models, into a hybrid marker. Based on the hybrid marker, we devised a GNN model and compared its predictive performance for surgical outcomes with six other mainstream machine-learning models. Compared to the mainstream models, underpinning the GNN with the hybrid iEEG marker resulted in a better prediction of surgical outcomes, showing a significant increase of the prediction accuracy from approximately 87% to 94.3% (P = 0.0412). This study suggests that the hybrid iEEG marker can improve the performance of model predicting the epilepsy surgical outcomes, and validates the effectiveness of the GNN in characterizing and analyzing complex relationships between clinical data variables.

Safety of intracranial electrodes in an MRI environment: a technical report.

Intracranial electroencephalography (iEEG) involves placing intracranial electrodes to localise seizures in patients with medically refractory epilepsy. While magnetic resonance imaging (MRI) enables visualisation of electrodes within patient-specific anatomy, the safety of these electrodes must be confirmed prior to routine clinical utilisation. Therefore, the purpose of this study was to evaluate the safety of iEEG electrodes from a particular manufacturer in a 3.0-Tesla (3.0T) MRI environment. Measurements of magnetically induced displacement force and torque were determined for each of the 10 test articles using standardised techniques. Test articles were subsequently evaluated for radiofrequency-induced heating using a Perspex phantom in both open and 'fault' conditions. Additionally, we assessed radiofrequency (RF)-induced heating with all test articles placed into the phantom simultaneously to simulate an implantation, again in both open and 'fault' conditions. Finally, each test article was evaluated for MRI artefacts. The magnetically induced displacement force was found to be less than the force on the article due to gravity for all test articles. Similarly, the maximum magnetically induced torque was less than the worst-case torque due to gravity for all test articles apart from the 8-contact strip - for which it was 11% greater - and the depthalon cap. The maximum temperature change for any portion of any test article assessed individually was 1.7°C, or 1.2°C for any device component meant to be implanted intracranially. In the implantation configuration, the maximum recorded temperature change was 0.7°C. MRI may be safely performed for localising iEEG electrodes at 3.0T under certain conditions.

Temporally organized representations of reward and risk in the human brain

The value and uncertainty associated with choice alternatives constitute critical features relevant for decisions. However, the manner in which reward and risk representations are temporally organized in the brain remains elusive. Here we leverage the spatiotemporal precision of intracranial electroencephalography, along with a simple card game designed to elicit the unfolding computation of a set of reward and risk variables, to uncover this temporal organization. Reward outcome representations across wide-spread regions follow a sequential order along the anteroposterior axis of the brain. In contrast, expected value can be decoded from multiple regions at the same time, and error signals in both reward and risk domains reflect a mixture of sequential and parallel encoding. We further highlight the role of the anterior insula in generalizing between reward prediction error and risk prediction error codes. Together our results emphasize the importance of neural dynamics for understanding value-based decisions under uncertainty.

Face-Specific Activity in the Ventral Stream Visual Cortex Linked to Conscious Face Perception.

When presented with visual stimuli of face images, the ventral stream visual cortex of the human brain exhibits face-specific activity that is modulated by the physical properties of the input images. However, it is still unclear whether this activity relates to conscious face perception. We explored this issue by using the human intracranial electroencephalography technique. Our results showed that face-specific activity in the ventral stream visual cortex was significantly higher when the subjects subjectively saw faces than when they did not, even when face stimuli were presented in both conditions. In addition, the face-specific neural activity exhibited a more reliable neural response and increased posterior-anterior direction information transfer in the "seen" condition than the "unseen" condition. Furthermore, the face-specific neural activity was significantly correlated with performance. These findings support the view that face-specific activity in the ventral stream visual cortex is linked to conscious face perception.

Consciousness transitions during epilepsy seizures through the lens of integrated information theory

Consciousness is one of the most complex aspects of human experience. Studying the mechanisms involved in the transitions among different levels of consciousness remains as one of the greatest challenges in neuroscience. In this study we use a measure of integrated information (ΦAR) to evaluate dynamic changes during consciousness transitions. We applied the measure to intracranial electroencephalography (SEEG) recordings collected from 6 patients that suffer from refractory epilepsy, taking into account inter-ictal, pre-ictal and ictal periods. We analyzed the dynamical evolution of ΦAR in groups of electrode contacts outside the epileptogenic region and compared it with the Consciousness Seizure Scale (CCS). We show that changes on ΦAR are significantly correlated with changes in the reported states of consciousness.

The impact of simultaneous intracranial recordings on scalp EEG: A finite element analysis

BackgroundIn this study, we examined the utility of simultaneous scalp and stereotactic intracranial electroencephalography (SSIEEG) in epilepsy patients. Although SSIEEG offers valuable insights into epilepsy and cognitive function, its routine use is uncommon. Challenges include interpreting post-craniotomy scalp EEG due to surgically implanted electrodes. New methodWe describe our methodology for conducting SSIEEG recordings. To simulate the potential impact on EEG interpretation, we computed the leadfield of scalp electrodes with and without burrholes using Finite Element Analysis to compare the resulting sensitivity volume and waveforms of simulated intracranial signals between skulls with and without burrholes. ResultsThe presence of burr holes in the skull layer of the leadfield models did not discernibly modify simulated waveforms or scalp EEG topology. Using realistic SEEG burr hole diameter, the difference in the average leadfield of scalp electrodes was 0.12% relative to the effect of switching two nearby electrodes, characterized by the cosine similarity difference. No patients experienced adverse events related to SSIEEG. Comparison with existing methodsAlthough there is increasing acceptance and interest in SSIEEG, few studies have characterized the technical feasibility. Here, we demonstrate through modelling that scalp recordings from SSIEEG are comparable to that through an intact skull. ConclusionThe placement and simultaneous acquisition of scalp EEG during invasive monitoring through stereotactically inserted EEG electrodes is routinely performed at the Hospital for Sick Children. Scalp EEG recordings may assist with clinical interpretation. Burr holes in the skull layer did not discernibly alter EEG waveforms or topology.

Intracranial electroencephalography reveals effector-independent evidence accumulation dynamics in multiple human brain regions.

Neural representations of perceptual decision formation that are abstracted from specific motor requirements have previously been identified in humans using non-invasive electrophysiology; however, it is currently unclear where these originate in the brain. Here we capitalized on the high spatiotemporal precision of intracranial EEG to localize such abstract decision signals. Participants undergoing invasive electrophysiological monitoring for epilepsy were asked to judge the direction of random-dot stimuli and respond either with a speeded button press (N = 24), or vocally, after a randomized delay (N = 12). We found a widely distributed motor-independent network of regions where high-frequency activity exhibited key characteristics consistent with evidence accumulation, including a gradual buildup that was modulated by the strength of the sensory evidence, and an amplitude that predicted participants' choice accuracy and response time. Our findings offer a new view on the brain networks governing human decision-making.

Utilizing Excitatory and Inhibitory Activity Derived from Interictal Intracranial Electroencephalography as Potential Biomarkers for Epileptogenicity.

Epileptogenic zones (EZs), where epileptic seizures cease after resection, are localized by assessing the seizure-onset zone using ictal electroencephalography (EEG). Owing to the difficulty in capturing unpredictable seizures, biomarkers capable of identifying EZs from interictal EEG are anticipated. Recent studies using intracranial EEG have identified several potential candidate biomarkers for epileptogenicity. High-frequency oscillation (HFO) was initially expected to be a robust biomarker of abnormal excitatory activity in the ictogenic region. However, HFO-guided resection failed to improve seizure prognosis. Meanwhile, the regularity of low-gamma oscillations (30-80 Hz) indicates inhibitory interneurons' hypersynchronization, which could be used to localize the EZ. Besides resting-state EEG assessments, evoked potentials elicited by single-pulse electrical stimulation, such as corticocortical evoked potentials (CCEP), became valuable tools for assessing epileptogenic regions. CCEP responses recorded in the cortex remote from the stimulation site indicate functional connectivity, revealing increased internal connectivity within the ictogenic region and elevated inhibitory input from the non-involved regions to the ictogenic region. Conversely, large responses close to the stimulation site reflect local excitability, manifesting as an increased N1 amplitude and overriding HFO. Further research is required to establish whether these novel electrophysiological methods, either individually or in combination, can function as robust biomarkers of epileptogenicity and hold promise for improving seizure prognosis.