Advances in human intracranial electroencephalography research, guidelines and good practices

Proper reference selection and re-referencing to mitigate bias in single pulse electrical stimulation data

BACKGROUND

Single pulse electrical stimulation experiments produce brain stimulation evoked potentials used to infer brain connectivity. The choice of recording reference for intracranial electrodes remains non-standardized and can significantly impact data interpretation. When the reference electrode is affected by stimulation or evoked brain activity, it can contaminate the brain stimulation evoked potentials recorded at all other electrodes and influence interpretation of findings.

NEW METHOD

This specific issue is highlighted in intracranial EEG datasets from two subjects recorded at separate institutions. We present several intuitive metrics to detect the presence of reference contamination, based on artificial similarity between all channels. We also offer practical guidance on mitigating contamination, by switching to a more neutral reference electrode, or by post hoc re-referencing, per stimulation site, to an adjusted common average that is optimized for bias and noise.

RESULTS

Either switching the reference electrode or re-referencing to an adjusted common average effectively mitigated the reference contamination issue. This was evidenced by metrics that indicated increased variability in the latencies and response durations of brain stimulation evoked potentials across the brain, and by increased similarity between experimental runs after re-referencing.

CONCLUSION

Overall, this study demonstrates the necessity of clear quality checks and preprocessing steps to ensure accurate interpretation of single pulse electrical stimulation data, and it provides a set of statistics and tools to achieve this.

Spatiotemporal dynamics of reading Kana (syllabograms) and Kanji (morphograms)

Reading engages complex neural networks integrating visual, phonological, and semantic information. The dual-stream model posits ventral and dorsal pathways for lexical and sublexical processing in the left hemisphere and is well-supported in alphabetic languages. However, its applicability to non-alphabetic scripts remains unclear. The Japanese writing system, comprising Kana (syllabograms) and Kanji (morphograms) with distinct orthographic, phonological, and semantic properties, provides a unique framework to investigate neural dissociation between phonological and orthographic-semantic processing. Previous studies suggest that Kanji relies on the ventral route for whole-word recognition and semantic processing, whereas Kana depends mainly on the dorsal route for phonological decoding via grapheme-to-phoneme conversion; however, their spatiotemporal dynamics remain unknown. Using high-gamma power analysis from electrocorticography recordings in 14 patients with epilepsy and subdural implants, we examined the spatiotemporal neural dynamics of Kana and Kanji reading. Participants completed a visual lexical decision task with Kana and Kanji words and pseudowords. Across 912 electrodes, differential high-gamma power analysis showed that Kanji activated bilateral occipitotemporal fusiform regions early (120-550 ms) and the left inferior temporal gyrus (150-240 ms). Conversely, Kana showed prolonged late activation (270-750 ms) in the left-lateralised superior temporal, supramarginal, and inferior frontal gyri, especially during pseudoword processing. These findings indicate that Kanji relies on bilateral ventral stream earlier, while Kana depends on the left dorsal stream, with slower processing reflecting the extra grapheme-to-phoneme conversion. This underscores the value of non-alphabetic languages in elucidating both universal and script-specific neural mechanisms, advancing a cross-linguistic understanding of the reading network.

Stereoelectroencephalography at Sainte-Anne Hospital, Paris, France

Stereoelectroencephalography (SEEG), which combines the exploration of identified intracerebral structures using depth electrodes and provides direct recording of local field potentials from multiple brain sites, was designed and developed in the 1950s by Jean Talairach and Jean Bancaud, in Sainte-Anne Hospital, Paris. For patients with focal drug-resistant epilepsy, when the non-invasive phase is insufficiently concordant or when relationships between the epileptogenic network and eloquent areas remain to be defined, the main purpose of SEEG is the optimal electrode implantation based on a main hypothesis and questions formulated during the non-invasive phase. Following an initial historical overview, the different steps of this non-invasive phase are described. Some of these steps, like semiology analysis, have remained relatively preserved, while others have considerably evolved, such as positron emission tomography combined with 3 Tesla magnetic resonance imaging (MRI), functional MRI (fMRI) and high-resolution EEG. We then outline the different steps of the SEEG procedure as performed in our institution. Here also, some steps remain quite unchanged such as intracerebral stimulation, amitriptyline and benzodiazepine tests while some others have strikingly evolved such as frameless robot-assisted, MRI-based implantation, depth-signal analyses and quantifications, and radio-frequency thermocoagulation.

Open multi-center intracranial electroencephalography dataset with task probing conscious visual perception

We introduce an intracranial EEG (iEEG) dataset collected as part of an adversarial collaboration between proponents of two theories of consciousness: Global Neuronal Workspace Theory and Integrated Information Theory. The data were recorded from 38 patients undergoing intracranial monitoring of epileptic seizures across three research centers using the same experimental protocol. Participants were presented with suprathreshold visual stimuli belonging to four different categories (faces, objects, letters, false fonts) in three orientations (front, left, right view), and for three durations (0.5, 1.0, 1.5 s). Participants engaged in a non-speeded Go/No-Go target detection task to identify infrequent targets with some stimuli becoming task-relevant and others task-irrelevant. Participants also engaged in a motor localizer task. The data were checked for its quality and converted to Brain Imaging Data Structure (BIDS). The de-identified dataset contains demographics, clinical information, electrode reconstruction, behavioral performance, and eye-tracking data. We also provide code to preprocess and analyze the data. This dataset holds promise for reuse in consciousness science and vision neuroscience to answer questions related to stimulus processing, target detection, and task-relevance, among many others.

Smart Bioelectronics for Real-Time Diagnosis and Therapy of Body Organ Functions

Noncommunicable diseases (NCDs) associated with cardiovascular, neurological, and gastrointestinal disorders remain a leading cause of global mortality, sounding the alarm for the urgent need for better diagnostic and therapeutic solutions. Wearable and implantable biointegrated electronics offer a groundbreaking solution, combining real-time, high-resolution monitoring with innovative treatment capabilities tailored to specific organ functions. In this comprehensive review, we focus on the diseases affecting the brain, heart, gastrointestinal organs, bladder, and adrenal gland, along with their associated physiological parameters. Additionally, we provide an overview of the characteristics of these parameters and explore the potential of bioelectronic devices for in situ sensing and therapeutic applications and highlight the recent advancements in their deployment across specific organs. Finally, we analyze the current challenges and prospects of implementing closed-loop feedback control systems in integrated sensor-therapy applications. By emphasizing organ-specific applications and advocating for closed-loop systems, this review highlights the potential of future bioelectronics to address physiological needs and serves as a guide for researchers navigating the interdisciplinary fields of diagnostics, therapeutics, and personalized medicine.

Macroscale Traveling Waves Evoked by Single-Pulse Stimulation of the Human Brain

Understanding the spatiotemporal dynamics of neural signal propagation is fundamental to unraveling the complexities of brain function. Emerging evidence suggests that corticocortical-evoked potentials (CCEPs) resulting from single-pulse electrical stimulation (SPES) may be used to characterize the patterns of information flow between and within brain networks. At present, the basic spatiotemporal dynamics of CCEP propagation cortically and subcortically are incompletely understood. We hypothesized that SPES evokes neural traveling waves detectable in the three-dimensional space sampled by intracranial stereoelectroencephalography. Across a cohort of 21 adult males and females with intractable epilepsy, we delivered 17,631 stimulation pulses and recorded CCEP responses in 1,019 electrode contacts. The distance between each pair of electrode contacts was approximated using three different metrics (Euclidean distance, path length, and geodesic distance), representing direct, tractographic, and transcortical propagation, respectively. For each robust CCEP, we extracted amplitude-, spectral-, and phase-based features to identify traveling waves emanating from the site of stimulation. Many evoked responses to stimulation appear to propagate as traveling waves (∼14-28%, ∼5-19% with false discovery rate correction), despite sparse sampling throughout the brain. These stimulation-evoked traveling waves exhibited biologically plausible propagation velocities (range, 0.1-9.6 m/s). Our results reveal that direct electrical stimulation elicits neural activity with variable spatiotemporal dynamics that can be modeled as a traveling wave.

Conscious tactile perception entails distinct neural dynamics within somatosensory areas

Distilling the neural correlates of consciousness (NCCs) in humans is challenging due to limitations in the spatiotemporal resolution of recording techniques and confounds related to pre- and post-perceptual processes. In this study, we leveraged the detailed insights provided by human intracortical recordings to elucidate how somatosensory responses to simple tactile stimuli vary across different stimulus intensities and reporting conditions. Among the various spatiotemporal components of somatosensory processing, we observed tonic responses in posterior perisylvian regions that exhibited all the key characteristics of somatosensory NCCs. These responses remained invariant regardless of reporting, displayed an all-or-nothing pattern at the verge of the sensory threshold, and showed the most pronounced divergence between perceived and non-perceived stimuli. Overall, our findings indicate that conscious perception of simple tactile stimuli depends on higher-order somatosensory regions and that sustained neural dynamics in these areas may serve as an organizational principle of somatosensory awareness.

Corollary discharge signals during production are domain general: An intracerebral EEG case study with a professional musician

As measured by event-related potentials, self-produced sounds elicit an overall reduced response in the auditory cortex compared to identical externally presented stimuli. This study examines this modulatory effect with high-precision recordings in naturalistic settings and explores whether it is domain-general across speech or music. Using stereotactic EEG with a professional musician undergoing presurgical epilepsy evaluation, we recorded auditory cortical activity during music and speech production and perception tasks. Compared to externally presented sounds, self-produced sounds induce modulation of activity in the auditory cortex which vary across frequency and spatial location but is consistent across cognitive domains (speech/music) and different stimuli. Self-produced music and speech were associated with widespread low-frequency (4-8 Hz) suppression, mid-frequency (8-80 Hz) enhancement, and decreased encoding of acoustic features. These findings reveal the domain-general nature of motor-driven corollary discharge modulatory signals and their frequency-specific effects in auditory regions.

Epileptic seizures recorded with microelectrodes: A persistent multiscale gap between neuronal activity, micro-, and macro-LFP?

The cascade of events that occur in the human brain, from neurons to local circuits and global network dynamics during epileptic seizures, is barely understood. Ictogenesis in humans has been described in relation to electrophysiological concepts based on local field potentials (LFP) recorded by standard macroelectrodes (macro-LFP). Microelectrodes, however, record at the cellular scale. Despite over four decades of such recordings in patients with epilepsy, there remains a significant gap between these scales. This narrative review explores the contribution of microelectrode recordings of seizures in humans. By focusing closely on neuronal activity, researchers often overlook that microelectrodes also allow recording LFP at the micro-electrode level (micro-LFP). Above all, there is a gap between local circuits recorded at the micro-LFP level and large-scale network dynamics at the macro-LFP level, with little theoretical work to reconcile these two scales. Consequently, to date, analyses of seizures have been coarse, incomplete, and based on small numbers of patients. In particular, most multiscale seizure analyses have not included all three levels of scales (single units, micro-LFP, and macro-LFP) simultaneously, but doing so is key to providing a synthesis of ictal genesis. This review highlights the various challenges that face researchers using microelectrodes: (1) carrying out a systematic descriptive and quantitative analysis of the micro-LFP seizure signal, (2) improving the spatial correspondence between micro- and macroelectrodes in order to achieve better comparability between the two scales, (3) improving brain sampling thanks to specific devices, in particular deep electrodes with microwires, (4) reporting the reference electrode used in each study and how it may impact the results, (5) long duration of recordings over hours and days, and (6) shared simultaneous micro-LFP/macro-LFP databases.

Visualization of functional and effective connectivity underlying auditory descriptive naming

OBJECTIVE

We visualized functional and effective connectivity within specific white matter networks in response to auditory descriptive questions.

METHODS

We investigated 40 Japanese-speaking patients with focal epilepsy and estimated connectivity measures using cortical high-gamma dynamics and MRI tractography.

RESULTS

Hearing a wh-interrogative at question onset enhanced inter-hemispheric functional connectivity, with left-to-right callosal facilitatory flows between the superior-temporal gyri, contrasted by functional connectivity diminution with right-to-left callosal suppressive flows between dorsolateral prefrontal regions. Processing verbs associated with concrete objects or adverbs increased left intra-hemispheric connectivity, with bidirectional facilitatory flows through extensive white matter pathways. Questions beginning with what, compared to where, induced greater neural engagement in the left posterior inferior-frontal gyrus at question offset, linked to enhanced functional connectivity and bidirectional facilitatory flows to the temporal lobe neocortex via the arcuate fasciculus. During overt responses, inter-hemispheric functional connectivity was enhanced, with bidirectional callosal flows between Rolandic areas, and individuals with higher IQ scores exhibited less prolonged neural engagement in the left posterior middle frontal gyrus.

CONCLUSIONS

Visualization of directional neural interactions within white matter networks during overt naming is feasible.

SIGNIFICANCE

Phrase order may influence network dynamics in listeners, even when presented with auditory descriptive questions conveying similar meanings.

Theta oscillations between the ventromedial prefrontal cortex and amygdala support dynamic representations of threat and safety

The amygdala exhibits distinct different activity patterns to threat and safety stimuli. Animal studies have demonstrated that the fear (i.e., threat) and extinction (i.e., safety) memory are encoded by the amygdala and its interaction with the ventromedial prefrontal cortex (vmPFC). Recent studies in both animals and humans suggest that the inter-regional interaction between amygdala and vmPFC can be supported by theta oscillations during fear processing. However, the mechanism by which the human vmPFC-amygdala pathway dynamically supports neural representations of the same stimulus remains elusive, as it alternatively reflects threat and safety situations. To investigate this phenomenon, we conducted intracranial EEG recordings in drug-resistant epilepsy patients (n = 8) with implanted depth electrodes who performed a fear conditioning and extinction task. This task was designed with a fixed structure whereby specific CS+ stimulus could be either safe (never paired with US) or threatening (possibly paired with US) based on an implicit rule during fear acquisition. Our findings showed that the stimulus embodying potential threat information was accompanied by increased theta activities in amygdala during both fear acquisition and early extinction. Furthermore, the learning of safety information was associated with enhanced theta-related direction from the vmPFC to the amygdala. This study provided directly electrophysiological evidence supporting the dynamic oscillatory modulation of threat and safety representations in the human amygdala-vmPFC circuit, and suggests that amygdala safety processing depends on theta inputs from the vmPFC in both fear acquisition and extinction.

Unsupervised learning from EEG data for epilepsy: A systematic literature review

BACKGROUND AND OBJECTIVES

Epilepsy is a neurological disorder characterized by recurrent epileptic seizures, whose neurophysiological signature is altered electroencephalographic (EEG) activity. The use of artificial intelligence (AI) methods on EEG data can positively impact the management of the disease, significantly improving diagnostic and prognostic accuracy as well as treatment outcomes. Our work aims to systematically review the available literature on the use of unsupervised machine learning methods on EEG data in epilepsy, focusing on methodological and clinical differences in terms of algorithms used and clinical applications.

METHODS

Following the PRISMA guidelines, a systematic literature search was performed in several databases for papers published in the last 10 years. Studies employing both unsupervised and self-supervised methods for the classification of EEG data in epilepsy patients were included. The main outcomes of the study were: (i) to provide an overview of the datasets used as input to train the algorithms; (ii) to identify trends in pre-processing, algorithm architectures, validation, and metrics for performance estimation; (iii) to identify and review the clinical applications of AI in epilepsy patients.

RESULTS

A total of 108 studies met the inclusion criteria. Of them, 86 (79.6 %) have been published in the last 5 years and 60 (55.5 %) in the last two years. The most used validation methods were: hold-out in 37 (34.2 %), k-fold-cross validation in 35 (32.4 %), and leave-one-out in 19 (17.6 %) studies, respectively. Accuracy, sensitivity, and specificity were the most used performance metrics being reported in 71 (65.7 %), 62 (57.4 %), and 42 (39.8 %) studies, respectively, followed by F1-score (27 studies; 25 %), precision (26 studies; 24 %), area under the curve (25 studies; 23.1 %), and false positive rate (22 studies; 20.3 %). Furthermore, 42 (38.9 %) compared to 63 (58.3 %) studies used individual patient versus multiple patients models, respectively. Finally, concerning the clinical applications of unsupervised learning methods on epilepsy patients, we identified six main fields of interest: seizure detection (69 studies; 63.9 %), seizure prediction (27 studies; 25 %), signal propagation and characterization (2 studies; 1.8 %), seizure localization (4 studies; 3.7 %), and seizure classification (22 studies; 20.3 %), respectively.

CONCLUSION

The results of this review suggest that the interest in the use of unsupervised learning methods in epilepsy has significantly increased in recent years. From a methodological perspective, the input EEG datasets used for training and testing the algorithms remain the hardest challenge. From a clinical standpoint, the vast majority of studies addressed seizure detection, prediction, and classification whereas studies focusing on seizure characterization and localization are lacking. Future work that can potentially improve the performance of these algorithms includes the use of context information via reinforcement learning and a focus on model explainability.

Protocol for detecting and analyzing non-oscillatory traveling waves from high-spatiotemporal-resolution human electrophysiological recordings

Innovations in electrophysiological recordings and computational analytic techniques enable high-resolution analysis of neural traveling waves. Here, we present a protocol for the detection and analysis of traveling waves from multi-day microelectrode array human electrophysiological recordings through a multi-linear regression statistical approach using point estimator data. We describe steps for traveling wave detection, feature characterization, and propagation pattern analysis. This protocol may improve our understanding of the coordination of neurons during non-oscillatory neural dynamics. For complete details on the use and execution of this protocol, please refer to Smith et al.1.

Normative high-frequency oscillation phase-amplitude coupling and effective connectivity under sevoflurane

Resective surgery for pediatric drug-resistant focal epilepsy often requires extraoperative intracranial electroencephalography recording to accurately localize the epileptogenic zone. This procedure entails multiple neurosurgeries, intracranial electrode implantation and explantation, and days of invasive inpatient evaluation. There is a need for methods to reduce diagnostic burden and introduce objective epilepsy biomarkers. Our preliminary studies aimed to address these issues by using sevoflurane anesthesia to rapidly and reversibly activate intraoperative phase-amplitude coupling between delta and high-frequency activities, as well as high-frequency activity-based effective connectivity. Phase-amplitude coupling can serve as a proxy for spike-and-wave discharges, and effective connectivity describes the spatiotemporal dynamics of neural information flow among regions. Notably, sevoflurane activated these interictal electrocorticography biomarkers most robustly in areas whose resection led to seizure freedom. However, they were also increased in normative brain regions that did not require removal for seizure control. Before using these electrocorticography biomarkers prospectively to guide resection, we should understand their endogenous distribution and propagation pathways, at different anesthetic stages. In the current study, we highlighted the normative distribution of delta and high-frequency activity phase-amplitude coupling and effective connectivity under sevoflurane. Normative data was derived from nineteen patients, whose ages ranged from four to eighteen years and included eleven males. All achieved seizure control following focal resection. Electrocorticography was recorded at an isoflurane baseline, during stepwise increases in sevoflurane concentration, and also during extraoperative slow-wave sleep without anesthesia. Normative electrode sites were then mapped onto a standard cortical surface for anatomical visualization. Dynamic tractography traced white matter pathways that connected sites with significantly augmented biomarkers. Finally, we analyzed all sites -regardless of normal or abnormal status - to determine whether sevoflurane-enhanced biomarker values could intraoperatively localize the epileptogenic sites. We found that normative electrocorticography biomarkers increased as a function of sevoflurane concentration, especially in bilateral frontal and parietal lobe regions (Bonferroni-corrected p-values <0.05). Callosal fibers directly connected homotopic Rolandic regions exhibiting elevated phase-amplitude coupling. The superior longitudinal fasciculus linked frontal and parietal association cortices showing augmented effective connectivity. Higher biomarker values, particularly at three to four volume percent sevoflurane, characterized epileptogenicity and seizure-onset zone status (Bonferroni-corrected p-values <0.05). Supplementary analysis showed that epileptogenic sites exhibited less augmentation in delta-based effective connectivity. This study helps clarify the normative distribution of, and plausible propagation pathways supporting, sevoflurane enhanced electrocorticographic biomarkers. Future work should confirm that sevoflurane-activated electrocorticography biomarkers can predict postoperative seizure outcomes in larger cohorts, to establish their clinical utility.

Circuit dynamics of approach-avoidance conflict in humans

Debilitating anxiety is pervasive in the modern world. Choices to approach or avoid are common in everyday life and excessive avoidance is a cardinal feature of anxiety disorders. Here, we used intracranial EEG to define a distributed prefrontal-limbic circuit supporting approach and avoidance. Presurgical epilepsy patients (n=20) performed a continuous-choice, approach-avoidance conflict decision-making task inspired by the arcade game Pac-Man, where patients trade-off harvesting rewards against potential losses from attack by the ghost. As patients approached increasing rewards and threats, we found evidence of a limbic circuit mediated by increased theta power in the hippocampus, amygdala, orbitofrontal cortex (OFC) and anterior cingulate cortex (ACC), that drops rapidly during avoidance. Theta band connectivity within this circuit and with the lateral prefrontal cortex increases during approach and falls during avoidance, and amygdala and lateral frontal activity granger-caused the theta oscillations in both the OFC and ACC. Importantly, the degree of network connectivity predicted how long patients approach, with enhanced network synchronicity extending approach times. Finally, when threat is imminent, the system dynamically switches to a sustained increase in high-frequency activity (70-150Hz) in the middle frontal gyrus (MFG), tracking the degree of threat. The results provide evidence for a distributed prefrontal-limbic circuit, mediated by theta oscillations and high frequency activity, underlying approach-avoidance conflict in humans.

Dynamical intracranial EEG functional network controllability localizes the seizure onset zone and predicts the epilepsy surgical outcome

Objective. Seizure onset zone (SOZ) localization and SOZ resection outcome prediction are critical for the surgical treatment of drug-resistant epilepsy but have mainly relied on manual inspection of intracranial electroencephalography (iEEG) monitoring data, which can be both inaccurate and time-consuming. Therefore, automating SOZ localization and surgical outcome prediction by using appropriate iEEG neural features and machine learning models has become an emerging topic. However, current channel-wise local features, graph-theoretic network features, and system-theoretic network features cannot fully capture the spatial, temporal, and neural dynamical aspects of epilepsy, hindering accurate SOZ localization and surgical outcome prediction.Approach. Here, we develop a method for computing dynamical functional network controllability from multi-channel iEEG signals, which from a control-theoretic viewpoint, has the ability to simultaneously capture the spatial, temporal, functional, and dynamical aspects of epileptic brain networks. We then apply multiple machine learning models to use iEEG functional network controllability for localizing SOZ and predicting surgical outcomes in drug-resistant epilepsy patients and compare with existing neural features. We finally combine iEEG functional network controllability with representative local, graph-theoretic, and system-theoretic features to leverage complementary information for further improving performance.Main results. We find that iEEG functional network controllability at SOZ channels is significantly higher than that of other channels. We further show that machine learning models using iEEG functional network controllability successfully localize SOZ and predict surgical outcomes, significantly outperforming existing local, graph-theoretic, and system-theoretic features. We finally demonstrate that there exists complementary information among different types of neural features and fusing them further improves performance.Significance. Our results suggest that iEEG functional network controllability is an effective feature for automatic SOZ localization and surgical outcome prediction in epilepsy treatment.

Complexity in speech and music listening via neural manifold flows

Understanding the complex neural mechanisms underlying speech and music perception remains a multifaceted challenge. In this study, we investigated neural dynamics using human intracranial recordings. Employing a novel approach based on low-dimensional reduction techniques, the Manifold Density Flow (MDF), we quantified the complexity of brain dynamics during naturalistic speech and music listening and during resting state. Our results reveal higher complexity in patterns of interdependence between different brain regions during speech and music listening compared with rest, suggesting that the cognitive demands of speech and music listening drive the brain dynamics toward states not observed during rest. Moreover, speech listening has more complexity than music, highlighting the nuanced differences in cognitive demands between these two auditory domains. Additionally, we validated the efficacy of the MDF method through experimentation on a toy model and compared its effectiveness in capturing the complexity of brain dynamics induced by cognitive tasks with another established technique in the literature. Overall, our findings provide a new method to quantify the complexity of brain activity by studying its temporal evolution on a low-dimensional manifold, suggesting insights that are invisible to traditional methodologies in the contexts of speech and music perception.

The neural activity of auditory conscious perception

Although recent work has made headway in understanding the neural temporospatial dynamics of conscious perception, much of that work has focused on visual paradigms. To determine whether there are shared mechanisms for perceptual consciousness across sensory modalities, here we test within the auditory domain. Participants completed an auditory threshold task while undergoing intracranial electroencephalography. Recordings from >2,800 grey matter electrodes were analyzed for broadband gamma power (a range which reflects local neural activity). For perceived trials, we find nearly simultaneous activity in early auditory regions, the right caudal middle frontal gyrus, and the non-auditory thalamus; followed by a wave of activity that sweeps through auditory association regions into parietal and frontal cortices. For not perceived trials, significant activity is restricted to early auditory regions. These findings show the cortical and subcortical networks involved in auditory perception are similar to those observed with vision, suggesting shared mechanisms for conscious perception.

Naturalistic Audiovisual Illusions Reveal the Cortical Sites Involved in the Multisensory Processing of Speech

Audiovisual speech illusions are a spectacular illustration of the effect of visual cues on the perception of speech. Because they allow dissociating perception from the physical characteristics of the sensory inputs, these illusions are useful to investigate the cerebral processing of audiovisual speech. However, the meaningless, monosyllabic utterances typically used to induce illusions are far removed from natural communication through speech. We developed naturalistic speech stimuli that embed mismatched auditory and visual cues within grammatically correct sentences to induce illusory perceptions in controlled fashion. Using intracranial EEG, we confirmed that the cortical processing of audiovisual speech recruits an ensemble of areas, from auditory and visual cortices to multisensory and associative regions. Importantly, we were able to resolve which cortical areas are driven more by the auditory or the visual contents of the speech stimulus or by the eventual perceptual report. Our results suggest that higher order sensory and associative areas, rather than early sensory cortices, are key loci for illusory perception. Naturalistic audiovisual speech illusions represent a powerful tool to dissect the specific roles of individual cortical areas in the processing of audiovisual speech.

Human single-neuron activity is modulated by intracranial theta burst stimulation of the basolateral amygdala

Direct electrical stimulation of the human brain has been used for numerous clinical and scientific applications. Previously, we demonstrated that intracranial theta burst stimulation (TBS) of the basolateral amygdala (BLA) can enhance declarative memory, likely by modulating hippocampal-dependent memory consolidation. At present, however, little is known about how intracranial stimulation affects activity at the microscale. In this study, we recorded intracranial EEG data from a cohort of patients with medically refractory epilepsy as they completed a visual recognition memory task. During the memory task, brief trains of TBS were delivered to the BLA. Using simultaneous microelectrode recordings, we isolated neurons in the hippocampus, amygdala, orbitofrontal cortex, and anterior cingulate cortex and tested whether stimulation enhanced or suppressed firing rates. Additionally, we characterized the properties of modulated neurons, patterns of firing rate coactivity, and the extent to which modulation affected memory task performance. We observed a subset of neurons (~30%) whose firing rate was modulated by TBS, exhibiting highly heterogeneous responses with respect to onset latency, duration, and direction of effect. Notably, location and baseline activity predicted which neurons were most susceptible to modulation, although the impact of this neuronal modulation on memory remains unclear. These findings advance our limited understanding of how focal electrical fields influence neuronal firing at the single-cell level and motivate future neuromodulatory therapies that aim to recapitulate specific patterns of activity implicated in cognition and memory.

Functional segregation of rostral and caudal hippocampus in associative memory

Introduction

The hippocampus plays a crucial role in episodic memory. Given its complexity, the hippocampus participates in multiple aspects of higher cognitive functions, among which are semantics-based encoding and retrieval. However, the "where," "when" and "how" of distinct aspects of memory processing in the hippocampus are still under debate.

Methods

Here, we employed a visual associative memory task that involved encoding three levels of subjective congruence to delineate the differential involvement of the rostral and caudal portions (also referred as anterior/posterior portions) of the human hippocampus during memory encoding, recognition and associative recall.

Results

Through stereo-EEG recordings in epilepsy patients we show that associative memory is reflected by rostral hippocampal activity during encoding, and caudal hippocampal activity during retrieval. In contrast, recognition memory encoding selectively activates the rostral hippocampus. The temporal dynamics of memory processing are manifested by gamma power increase, which partially overlaps with low-frequency power decrease during encoding and retrieval. Congruence levels modulate low-frequency activity prominently in the caudal hippocampus.

Discussion

These findings highlight an anatomical segregation in the hippocampus in accordance with the contributions of its partitions to associative and recognition memory.

Intracranial closed-loop neuromodulation as an intervention for neuropsychiatric disorders: an overview

Recent technological advances in intracranial brain stimulation have enhanced the potential of neuromodulation for addressing neuropsychiatric disorders. We present a review of the methodology and the preliminary outcomes of the pioneering studies exploring intracranial biomarker detection and closed-loop neuromodulation to modulate high-symptom severity states in neuropsychiatric disorders. We searched PubMed, Scopus, Web of Science, Embase, and PsycINFO/PsycNet, followed by the reference and citation lists of retrieved articles. This search strategy yielded a total of 583 articles, of which 5 articles met the inclusion criteria, focusing on depression, obsessive-compulsive disorder, post-traumatic stress disorder, and binge eating disorder. We discuss the methodology of biomarker identification, the biomarkers identified, and the preliminary treatment outcomes for closed-loop neuromodulation. Successful biomarker identification hinges on investigating across various setting. Targeted neuromodulation, either directed at the biomarker or within its associated neural network, offers a promising treatment approach. Future research should seek to understand the mechanisms underlying the effects of neuromodulation as well as the long-term viability of these treatment effects across different neuropsychiatric conditions.

Unravelling the origin of reward positivity: a human intracranial event-related brain potential study

Reward positivity (RewP) is an event-related brain potential component that emerges ∼250-350 ms after receiving reward-related feedback stimuli and is believed to be important for reinforcement learning and reward processing. Although numerous localization studies have indicated that the anterior cingulate cortex (ACC) is the neural generator of this component, other studies have identified sources outside of the ACC, fuelling a debate about its origin. Because the results of EEG and magnetoencephalography source-localization studies are severely limited by the inverse problem, we addressed this question by leveraging the high spatial and temporal resolution of intracranial EEG. We predicted that we would identify a neural generator of rthe RewP in the caudal ACC. We recorded intracranial EEG in 19 patients with refractory epilepsy who underwent invasive video-EEG monitoring at Ghent University Hospital, Belgium. Participants engaged in the virtual T-maze task, a trial-and-error task known to elicit a canonical RewP, while scalp and intracranial EEG were recorded simultaneously. The RewP was identified using a difference wave approach for both scalp and intracranial EEG. The data were aggregated across participants to create a virtual 'meta-participant' that contained all the recorded intracranial event-related brain potentials with respect to their intracranial contact locations. We used both hypothesis-driven (focused on ACC) and exploratory (whole-brain analysis) approaches to segment the brain into regions of interest. For each region of interest, we evaluated the degree to which the time course of the absolute current density (ACD) activity mirrored the time course of the RewP, and we confirmed the statistical significance of the results using permutation analysis. The grand average waveform of the scalp data revealed a RewP at 309 ms after reward feedback with a frontocentral scalp distribution, consistent with the identification of this component as the RewP. The meta-participant contained intracranial event-related brain potentials recorded from 582 intracranial contacts in total. The ACD activity of the aggregated intracranial event-related brain potentials was most similar to the RewP in the left caudal ACC, left dorsolateral prefrontal cortex, left frontomedial cortex and left white matter, with the highest score attributed to caudal ACC, as predicted. To our knowledge, this is the first study to use intracranial EEG aggregated across multiple human epilepsy patients and current source density analysis to identify the neural generator(s) of the RewP. These results provide direct evidence that the ACC is a neural generator of the RewP.

Intracranial Voltage Profiles from Untangled Human Deep Sources Reveal Multisource Composition and Source Allocation Bias

Intracranial potentials are used as functional biomarkers of neural networks. As potentials spread away from the source populations, they become mixed in the recordings. In humans, interindividual differences in the gyral architecture of the cortex pose an additional challenge, as functional areas vary in location and extent. We used source separation techniques to disentangle mixing potentials obtained by exploratory deep arrays implanted in epileptic patients of either sex to gain access to the number, location, relative contribution, and dynamics of coactive sources. The unique spatial profiles of separated generators made it possible to discern dozens of independent cortical areas for each patient, whose stability maintained even during seizure, enabling the follow up of activity for days and across states. Through matching these profiles to MRI, we associated each with limited portions of sulci and gyri and determined the local or remote origin of the corresponding sources. We also plotted source-specific 3D coverage across arrays. In average, individual recording sites are contributed to by 3-5 local and distant generators from areas up to several centimeters apart. During seizure, 13-85% of generators were involved, and a few appeared anew. Significant bias in location assignment using raw potentials is revealed, including numerous false positives when determining the site of origin of a seizure. This is not amended by bipolar montage, which introduce additional errors of its own. In this way, source disentangling reveals the multisource nature and far intracranial spread of potentials in humans, while efficiently addressing patient-specific anatomofunctional cortical divergence.

The human auditory cortex concurrently tracks syllabic and phonemic timescales via acoustic spectral flux

Dynamical theories of speech processing propose that the auditory cortex parses acoustic information in parallel at the syllabic and phonemic timescales. We developed a paradigm to independently manipulate both linguistic timescales, and acquired intracranial recordings from 11 patients who are epileptic listening to French sentences. Our results indicate that (i) syllabic and phonemic timescales are both reflected in the acoustic spectral flux; (ii) during comprehension, the auditory cortex tracks the syllabic timescale in the theta range, while neural activity in the alpha-beta range phase locks to the phonemic timescale; (iii) these neural dynamics occur simultaneously and share a joint spatial location; (iv) the spectral flux embeds two timescales-in the theta and low-beta ranges-across 17 natural languages. These findings help us understand how the human brain extracts acoustic information from the continuous speech signal at multiple timescales simultaneously, a prerequisite for subsequent linguistic processing.

A Case Study on EEG Signal Correlation Towards Potential Epileptic Foci Triangulation

The precise localization of epileptic foci with the help of EEG or iEEG signals is still a clinical challenge with current methodology, especially if the foci are not close to individual electrodes. On the research side, dipole reconstruction for focus localization is a topic of recent and current developments. Relatively low numbers of recording electrodes cause ill-posed and ill-conditioned problems in the inversion of lead-field matrices to calculate the focus location. Estimations instead of tissue conductivity measurements further deteriorate the precision of location tasks. In addition, time-resolved phase shifts are used to describe connectivity. We hypothesize that correlations over runtime approaches might be feasible to predict seizure foci with adequate precision. In a case study on EEG correlation in a healthy subject, we found repetitive periods of alternating high correlation in the short (20 ms) and long (300 ms) range. During these periods, a numerical determination of proportions of predominant latency and, newly established here, directionality is possible, which supports the identification of loops that, according to current opinion, manifest themselves in epileptic seizures. In the future, this latency and directionality analysis could support focus localization via dipole reconstruction using new triangulation calculations.

Trends and hotspots of stereoelectroencephalogram from 2002 to 2023: a bibliometric analysis

Background

Stereoelectroencephalography (SEEG), as a minimally invasive method that can stably collect intracranial electroencephalographic information over long periods, has increasingly been applied in the diagnosis and treatment of intractable epilepsy in recent years. Over the past 20 years, with the advancement of materials science and computer science, the application scenarios of SEEG have greatly expanded. Bibliometrics, as a method of scientifically analyzing published literature, can summarize the evolutionary process in the SEEG field and offer insights into its future development prospects.

Methods

This article selected all the literature records retrieved on November 4, 2024, from the Web of Science Core Collection (WoSCC). The search terms were as follows: "Stereo-electroencephalography" or "Stereo electroencephalography" or "Stereo-EEG" or "Stereo EEG" or "SEEG." The document types included were research articles and reviews. For analysis, VOSviewer, CiteSpace, and the R package "bibliometrix" were employed to analyze various aspects of the SEEG field, including authors, institutions, countries and regions, and research hotspots.

Results

We reviewed a total of 1,383 non-duplicate literature records from 2002 to 2023, including 1,241 research articles, 116 review articles and 26 letters. Observing the annual publication trends, there has been an overall increase since 2002. The most influential journal in this field is Epilepsia. Other journals with considerable impact include Clinical Neurophysiology, Epileptic Disorders, Epilepsy Research, NeuroImage, and Epilepsy & Behavior. The top 5 most influential scholars are Bartolomei F, Tassi L, Nobili L, Russo GL, and Mc Gonigal A. As for the analysis of countries and regions, France occupies a leading position in this field with its early start, while China and the United States have also emerged as focal points since 2020. Research on SEEG has expanded beyond its initial use for localizing epileptic foci and thermo-coagulation treatments and have been employed as a medium to facilitate real-time prediction of epileptic seizures and enabling the exploration of brain network connectivity.

Conclusion

As a minimally invasive tool for collecting intracranial electroencephalographic signals, SEEG continues to offer vast potential for development and application. Advances in electrode materials and robotic-assisted stereotactic techniques, have enabled SEEG to simultaneously sample multiple brain regions, acquire electrical signals from deep brain structures. These advantages significantly enhance the precision of epileptic focus localization in diagnosis and treatment, addressing the limitations of subdural electrodes. Through bibliometric analysis, this paper traces the developmental trajectory of SEEG and identifying key technological milestones, thereby providing a reference for scholarly research directions.

M/EEG source localization for both subcortical and cortical sources using a convolutional neural network with a realistic head conductivity model

While electroencephalography (EEG) and magnetoencephalography (MEG) are well-established noninvasive methods in neuroscience and clinical medicine, they suffer from low spatial resolution. Electrophysiological source imaging (ESI) addresses this by noninvasively exploring the neuronal origins of M/EEG signals. Although subcortical structures are crucial to many brain functions and neuronal diseases, accurately localizing subcortical sources of M/EEG remains particularly challenging, and the feasibility is still a subject of debate. Traditional ESIs, which depend on explicitly defined regularization priors, have struggled to set optimal priors and accurately localize brain sources. To overcome this, we introduced a data-driven, deep learning-based ESI approach without the need for these priors. We proposed a four-layered convolutional neural network (4LCNN) designed to locate both subcortical and cortical sources underlying M/EEG signals. We also employed a sophisticated realistic head conductivity model using the state-of-the-art segmentation method of ten different head tissues from individual MRI data to generate realistic training data. This is the first attempt at deep learning-based ESI targeting subcortical regions. Our method showed excellent accuracy in source localization, particularly in subcortical areas compared to other methods. This was validated through M/EEG simulations, evoked responses, and invasive recordings. The potential for accurate source localization of the 4LCNNs demonstrated in this study suggests future contributions to various research endeavors such as the clinical diagnosis, understanding of the pathophysiology of various neuronal diseases, and basic brain functions.

Direct visualization of microwires in hybrid depth electrodes using high-resolution photon-counting CT

Hybrid depth electrodes are increasingly being used for epilepsy monitoring and human neurophysiology research. Microwires extending from the tip of the Behnke-Fried (BF) electrode into (sub)cortical areas allow to isolate single neurons and perform microstimulation. Conventional CT or MRI visualize the entire microwire bundle as an artifact extending from the BF electrode tip with low resolution, without proper identification of individual microwires. We illustrate the first direct visualization method of individual microwires using high-resolution photon-counting CT (PCCT). Coregistration of the PCCT scan with a preoperative MRI can visualize individual wires directly in cortex, which is an advantage as it provides feedback on the accuracy of the implantation method and can guide future implantations. This PCCT technique allows for accurately depicting individual microwires which could be relevant for neuroscientific research through improved visualization and implantation of specific cortical and subcortical brain areas. PLAIN LANGUAGE SUMMARY: Researchers are using hybrid depth electrodes to study epilepsy and brain activity. These electrodes, called Behnke-Fried (BF) electrodes, have microwires at the tip that can record single neurons and stimulate brain areas. Regular CT or MRI scans do not show the individual microwires clearly. The authors use a new high-resolution photon-counting CT (PCCT) technique, which can show each individual microwire in the brain. By combining PCCT with MRI, the authors can precisely see where the microwires are located. This could improve future implantation surgeries and brain research.

The BRAIN Initiative data-sharing ecosystem: Characteristics, challenges, benefits, and opportunities

In this paper, we provide an overview and analysis of the BRAIN Initiative data-sharing ecosystem. First, we compare and contrast the characteristics of the seven BRAIN Initiative data archives germane to data sharing and reuse, namely data submission and access procedures and aspects of interoperability. Second, we discuss challenges, benefits, and future opportunities, focusing on issues largely specific to sharing human data and drawing on N = 34 interviews with diverse stakeholders. The BRAIN Initiative-funded archive ecosystem faces interoperability and data stewardship challenges, such as achieving and maintaining interoperability of data and archives and harmonizing research participants' informed consents for tiers of access for human data across multiple archives. Yet, a benefit of this distributed archive ecosystem is the ability of more specialized archives to adapt to the needs of particular research communities. Finally, the multiple archives offer ample raw material for network evolution in response to the needs of neuroscientists over time. Our first objective in this paper is to provide a guide to the BRAIN Initiative data-sharing ecosystem for readers interested in sharing and reusing neuroscience data. Second, our analysis supports the development of empirically informed policy and practice aimed at making neuroscience data more findable, accessible, interoperable, and reusable.

Spatiotemporal Mapping of Auditory Onsets during Speech Production

The human auditory cortex is organized according to the timing and spectral characteristics of speech sounds during speech perception. During listening, the posterior superior temporal gyrus is organized according to onset responses, which segment acoustic boundaries in speech, and sustained responses, which further process phonological content. When we speak, the auditory system is actively processing the sound of our own voice to detect and correct speech errors in real time. This manifests in neural recordings as suppression of auditory responses during speech production compared with perception, but whether this differentially affects the onset and sustained temporal profiles is not known. Here, we investigated this question using intracranial EEG recorded from seventeen pediatric, adolescent, and adult patients with medication-resistant epilepsy while they performed a reading/listening task. We identified onset and sustained responses to speech in the bilateral auditory cortex and observed a selective suppression of onset responses during speech production. We conclude that onset responses provide a temporal landmark during speech perception that is redundant with forward prediction during speech production and are therefore suppressed. Phonological feature tuning in these "onset suppression" electrodes remained stable between perception and production. Notably, auditory onset responses and phonological feature tuning were present in the posterior insula during both speech perception and production, suggesting an anatomically and functionally separate auditory processing zone that we believe to be involved in multisensory integration during speech perception and feedback control.

Proper reference selection and re-referencing to mitigate bias in single pulse electrical stimulation data

Single pulse electrical stimulation experiments produce pulse-evoked potentials used to infer brain connectivity. The choice of recording reference for intracranial electrodes remains non-standardized and can significantly impact data interpretation. When the reference electrode is affected by stimulation or evoked brain activity, it can contaminate the pulse-evoked potentials recorded at all other electrodes and influence interpretation of findings. We highlight this specific issue in intracranial EEG datasets from two subjects recorded at separate institutions. We present several intuitive metrics to detect the presence of reference contamination and offer practical guidance on different mitigation strategies. Either switching the reference electrode or re-referencing to an adjusted common average effectively mitigated the reference contamination issue, as evidenced by increased variability in pulse-evoked potentials across the brain. Overall, we demonstrate the importance of clear quality checks and preprocessing steps that should be performed before analysis of single pulse electrical stimulation data.

Electrophysiological dynamics of salience, default mode, and frontoparietal networks during episodic memory formation and recall: A multi-experiment iEEG replication

Dynamic interactions between large-scale brain networks underpin human cognitive processes, but their electrophysiological mechanisms remain elusive. The triple network model, encompassing the salience (SN), default mode (DMN), and frontoparietal (FPN) networks, provides a framework for understanding these interactions. We analyzed intracranial EEG recordings from 177 participants across four diverse episodic memory experiments, each involving encoding as well as recall phases. Phase transfer entropy analysis revealed consistently higher directed information flow from the anterior insula (AI), a key SN node, to both DMN and FPN nodes. This directed influence was significantly stronger during memory tasks compared to resting-state, highlighting the AI's task-specific role in coordinating large-scale network interactions. This pattern persisted across externally-driven memory encoding and internally-governed free recall. Control analyses using the inferior frontal gyrus (IFG) showed an inverse pattern, with DMN and FPN exerting higher influence on IFG, underscoring the AI's unique role. We observed task-specific suppression of high-gamma power in the posterior cingulate cortex/precuneus node of the DMN during memory encoding, but not recall. Crucially, these results were replicated across all four experiments spanning verbal and spatial memory domains with high Bayes replication factors. Our findings advance understanding of how coordinated neural network interactions support memory processes, highlighting the AI's critical role in orchestrating large-scale brain network dynamics during both memory encoding and retrieval. By elucidating the electrophysiological basis of triple network interactions in episodic memory, our study provides insights into neural circuit dynamics underlying memory function and offer a framework for investigating network disruptions in memory-related disorders.

The Neurostimulationist will see you now: prescribing direct electrical stimulation therapies for the human brain in epilepsy and beyond

As the pace of research in implantable neurotechnology increases, it is important to take a step back and see if the promise lives up to our intentions. While direct electrical stimulation applied intracranially has been used for the treatment of various neurological disorders, such as Parkinson's, epilepsy, clinical depression, and Obsessive-compulsive disorder, the effectiveness can be highly variable. One perspective is that the inability to consistently treat these neurological disorders in a standardized way is due to multiple, interlaced factors, including stimulation parameters, location, and differences in underlying network connectivity, leading to a trial-and-error stimulation approach in the clinic. An alternate view, based on a growing knowledge from neural data, is that variability in this input (stimulation) and output (brain response) relationship may be more predictable and amenable to standardization, personalization, and, ultimately, therapeutic implementation. In this review, we assert that the future of human brain neurostimulation, via direct electrical stimulation, rests on deploying standardized, constrained models for easier clinical implementation and informed by intracranial data sets, such that diverse, individualized therapeutic parameters can efficiently produce similar, robust, positive outcomes for many patients closer to a prescriptive model. We address the pathway needed to arrive at this future by addressing three questions, namely: (1) why aren't we already at this prescriptive future?; (2) how do we get there?; (3) how far are we from this Neurostimulationist prescriptive future? We first posit that there are limited and predictable ways, constrained by underlying networks, for direct electrical stimulation to induce changes in the brain based on past literature. We then address how identifying underlying individual structural and functional brain connectivity which shape these standard responses enable targeted and personalized neuromodulation, bolstered through large-scale efforts, including machine learning techniques, to map and reverse engineer these input-output relationships to produce a good outcome and better identify underlying mechanisms. This understanding will not only be a major advance in enabling intelligent and informed design of neuromodulatory therapeutic tools for a wide variety of neurological diseases, but a shift in how we can predictably, and therapeutically, prescribe stimulation treatments the human brain.

Anatomo-functional basis of emotional and motor resonance elicited by facial expressions

Simulation theories predict that the observation of other's expressions modulates neural activity in the same centres controlling their production. This hypothesis has been developed by two models, postulating that the visual input is directly projected either to the motor system for action recognition (motor resonance) or to emotional/interoceptive regions for emotional contagion and social synchronization (emotional resonance). Here we investigated the role of frontal/insular regions in the processing of observed emotional expressions by combining intracranial recording, electrical stimulation and effective connectivity. First, we intracranially recorded from prefrontal, premotor or anterior insular regions of 44 patients during the passive observation of emotional expressions, finding widespread modulations in prefrontal/insular regions (anterior cingulate cortex, anterior insula, orbitofrontal cortex and inferior frontal gyrus) and motor territories (Rolandic operculum and inferior frontal junction). Subsequently, we electrically stimulated the activated sites, finding that (i) in the anterior cingulate cortex and anterior insula, the stimulation elicited emotional/interoceptive responses, as predicted by the 'emotional resonance model'; (ii) in the Rolandic operculum it evoked face/mouth sensorimotor responses, in line with the 'motor resonance' model; and (iii) all other regions were unresponsive or revealed functions unrelated to the processing of facial expressions. Finally, we traced the effective connectivity to sketch a network-level description of these regions, finding that the anterior cingulate cortex and the anterior insula are reciprocally interconnected while the Rolandic operculum is part of the parieto-frontal circuits and poorly connected with the former. These results support the hypothesis that the pathways hypothesized by the 'emotional resonance' and the 'motor resonance' models work in parallel, differing in terms of spatio-temporal fingerprints, reactivity to electrical stimulation and connectivity patterns.

Introducing HiBoP: a Unity-based visualization software for large iEEG datasets

BACKGROUND

Intracranial EEG data offer a unique spatio-temporal precision to investigate human brain functions. Large datasets have become recently accessible thanks to new iEEG data-sharing practices and tighter collaboration with clinicians. Yet, the complexity of such datasets poses new challenges, especially regarding the visualization and anatomical display of iEEG.

NEW METHOD

We introduce HiBoP, a multi-modal visualization software specifically designed for large groups of patients and multiple experiments. Its main features include the dynamic display of iEEG responses induced by tasks/stimulations, the definition of Regions and electrodes Of Interest, and the shift between group-level and individual-level 3D anatomo-functional data.

RESULTS

We provide a use-case with data from 36 patients to reveal the global cortical dynamics following tactile stimulation. We used HiBoP to visualize high-gamma responses [50-150 Hz], and define three major response components in primary somatosensory and premotor cortices and parietal operculum.

COMPARISON WITH EXISTING METHODS(S)

Several iEEG softwares are now publicly available with outstanding analysis features. Yet, most were developed in languages (Python/Matlab) chosen to facilitate the inclusion of new analysis by users, rather than the quality of the visualization. HiBoP represents a visualization tool developed with videogame standards (Unity/C#), and performs detailed anatomical analysis rapidly, across multiple conditions, patients, and modalities with an easy export toward third-party softwares.

CONCLUSION

HiBoP provides a user-friendly environment that greatly facilitates the exploration of large iEEG datasets, and helps users decipher subtle structure/function relationships.

Intracranial neural representation of phenomenal and access consciousness in the human brain

After more than 30 years of extensive investigation, impressive progress has been made in identifying the neural correlates of consciousness (NCC). However, the functional role of spatiotemporally distinct consciousness-related neural activity in conscious perception is debated. An influential framework proposed that consciousness-related neural activities could be dissociated into two distinct processes: phenomenal and access consciousness. However, though hotly debated, its authenticity has not been examined in a single paradigm with more informative intracranial recordings. In the present study, we employed a visual awareness task and recorded the local field potential (LFP) of patients with electrodes implanted in cortical and subcortical regions. Overall, we found that the latency of visual awareness-related activity exhibited a bimodal distribution, and the recording sites with short and long latencies were largely separated in location, except in the lateral prefrontal cortex (lPFC). The mixture of short and long latencies in the lPFC indicates that it plays a critical role in linking phenomenal and access consciousness. However, the division between the two is not as simple as the central sulcus, as proposed previously. Moreover, in 4 patients with electrodes implanted in the bilateral prefrontal cortex, early awareness-related activity was confined to the contralateral side, while late awareness-related activity appeared on both sides. Finally, Granger causality analysis showed that awareness-related information flowed from the early sites to the late sites. These results provide the first LFP evidence of neural correlates of phenomenal and access consciousness, which sheds light on the spatiotemporal dynamics of NCC in the human brain.

Theta-burst direct electrical stimulation remodels human brain networks

Theta-burst stimulation (TBS), a patterned brain stimulation technique that mimics rhythmic bursts of 3-8 Hz endogenous brain rhythms, has emerged as a promising therapeutic approach for treating a wide range of brain disorders, though the neural mechanism of TBS action remains poorly understood. We investigated the neural effects of TBS using intracranial EEG (iEEG) in 10 pre-surgical epilepsy participants undergoing intracranial monitoring. Here we show that individual bursts of direct electrical TBS at 29 frontal and temporal sites evoked strong neural responses spanning broad cortical regions. These responses exhibited dynamic local field potential voltage changes over the course of stimulation presentations, including either increasing or decreasing responses, suggestive of short-term plasticity. Stronger stimulation augmented the mean TBS response amplitude and spread with more recording sites demonstrating short-term plasticity. TBS responses were stimulation site-specific with stronger TBS responses observed in regions with strong baseline stimulation effective (cortico-cortical evoked potentials) and functional (low frequency phase locking) connectivity. Further, we could use these measures to predict stable and varying (e.g. short-term plasticity) TBS response locations. Future work may integrate pre-treatment connectivity alongside other biophysical factors to personalize stimulation parameters, thereby optimizing induction of neuroplasticity within disease-relevant brain networks.

Macroscale traveling waves evoked by single-pulse stimulation of the human brain

Understanding the spatiotemporal dynamics of neural signal propagation is fundamental to unraveling the complexities of brain function. Emerging evidence suggests that cortico-cortical evoked potentials (CCEPs) resulting from single-pulse electrical stimulation may be used to characterize the patterns of information flow between and within brain networks. At present, the basic spatiotemporal dynamics of CCEP propagation cortically and subcortically are incompletely understood. We hypothesized that single-pulse electrical stimulation evokes neural traveling waves detectable in the three-dimensional space sampled by intracranial stereoelectroencephalography. Across a cohort of 21 adult patients with intractable epilepsy, we delivered 17,631 stimulation pulses and recorded CCEP responses in 1,019 electrode contacts. The distance between each pair of electrode contacts was approximated using three different metrics (Euclidean distance, path length, and geodesic distance), representing direct, tractographic, and transcortical propagation, respectively. For each robust CCEP, we extracted amplitude-, spectral-, and phase-based features to identify traveling waves emanating from the site of stimulation. Many evoked responses to stimulation appear to propagate as traveling waves (~14-28%), despite sparse sampling throughout the brain. These stimulation-evoked traveling waves exhibited biologically plausible propagation velocities (range 0.1-9.6 m/s). Our results reveal that direct electrical stimulation elicits neural activity with variable spatiotemporal dynamics, including the initiation of neural traveling waves.

Simultaneous invasive and non-invasive recordings in humans: A novel Rosetta stone for deciphering brain activity

Simultaneous noninvasive and invasive electrophysiological recordings provide a unique opportunity to achieve a comprehensive understanding of human brain activity, much like a Rosetta stone for human neuroscience. In this review we focus on the increasingly-used powerful combination of intracranial electroencephalography (iEEG) with scalp electroencephalography (EEG) or magnetoencephalography (MEG). We first provide practical insight on how to achieve these technically challenging recordings. We then provide examples from clinical research on how simultaneous recordings are advancing our understanding of epilepsy. This is followed by the illustration of how human neuroscience and methodological advances could benefit from these simultaneous recordings. We conclude with a call for open data sharing and collaboration, while ensuring neuroethical approaches and argue that only with a true collaborative approach the promises of simultaneous recordings will be fulfilled.

Data processing techniques impact quantification of cortico-cortical evoked potentials

BACKGROUND

Cortico-cortical evoked potentials (CCEPs) are a common tool for probing effective connectivity in intracranial human electrophysiology. As with all human electrophysiology data, CCEP data are highly susceptible to noise. To address noise, filters and re-referencing are often applied to CCEP data, but different processing strategies are used from study to study.

NEW METHOD

We systematically compare how common average re-referencing and filtering CCEP data impacts quantification.

RESULTS

We show that common average re-referencing and filters, particularly filters that cut out more frequencies, can significantly impact the quantification of CCEP magnitude and morphology. We identify that high cutoff high pass filters (> 0.5 Hz), low cutoff low pass filters (< 200 Hz), and common average re-referencing impact quantification across subjects. However, we also demonstrate that the presence of noise may impact CCEP quantification, and preprocessing is necessary to mitigate this. We show that filtering is more effective than re-referencing or averaging across trials for reducing most common types of noise.

COMPARISON WITH EXISTING METHODS

These results suggest that existing CCEP processing methods must be applied with care to maximize noise reduction and minimize changes to the data. We do not test every available processing strategy; rather we demonstrate that processing can influence the results of CCEP studies. We emphasize the importance of reporting all processing methods, particularly re-referencing methods.

CONCLUSIONS

We propose a general framework for choosing an appropriate processing pipeline for CCEP data, taking into consideration the noise levels of a specific dataset. We suggest that minimal gentle filtering is preferable.

Speech and music recruit frequency-specific distributed and overlapping cortical networks

To what extent does speech and music processing rely on domain-specific and domain-general neural networks? Using whole-brain intracranial EEG recordings in 18 epilepsy patients listening to natural, continuous speech or music, we investigated the presence of frequency-specific and network-level brain activity. We combined it with a statistical approach in which a clear operational distinction is made between shared, preferred, and domain-selective neural responses. We show that the majority of focal and network-level neural activity is shared between speech and music processing. Our data also reveal an absence of anatomical regional selectivity. Instead, domain-selective neural responses are restricted to distributed and frequency-specific coherent oscillations, typical of spectral fingerprints. Our work highlights the importance of considering natural stimuli and brain dynamics in their full complexity to map cognitive and brain functions.

Approaches of wearable and implantable biosensor towards of developing in precision medicine

In the relentless pursuit of precision medicine, the intersection of cutting-edge technology and healthcare has given rise to a transformative era. At the forefront of this revolution stands the burgeoning field of wearable and implantable biosensors, promising a paradigm shift in how we monitor, analyze, and tailor medical interventions. As these miniature marvels seamlessly integrate with the human body, they weave a tapestry of real-time health data, offering unprecedented insights into individual physiological landscapes. This log embarks on a journey into the realm of wearable and implantable biosensors, where the convergence of biology and technology heralds a new dawn in personalized healthcare. Here, we explore the intricate web of innovations, challenges, and the immense potential these bioelectronics sentinels hold in sculpting the future of precision medicine.

CARLA: Adjusted common average referencing for cortico-cortical evoked potential data

Human brain connectivity can be mapped by single pulse electrical stimulation during intracranial EEG measurements. The raw cortico-cortical evoked potentials (CCEP) are often contaminated by noise. Common average referencing (CAR) removes common noise and preserves response shapes but can introduce bias from responsive channels. We address this issue with an adjusted, adaptive CAR algorithm termed "CAR by Least Anticorrelation (CARLA)". CARLA was tested on simulated CCEP data and real CCEP data collected from four human participants. In CARLA, the channels are ordered by increasing mean cross-trial covariance, and iteratively added to the common average until anticorrelation between any single channel and all re-referenced channels reaches a minimum, as a measure of shared noise. We simulated CCEP data with true responses in 0-45 of 50 total channels. We quantified CARLA's error and found that it erroneously included 0 (median) truly responsive channels in the common average with ≤42 responsive channels, and erroneously excluded ≤2.5 (median) unresponsive channels at all responsiveness levels. On real CCEP data, signal quality was quantified with the mean R2 between all pairs of channels, which represents inter-channel dependency and is low for well-referenced data. CARLA re-referencing produced significantly lower mean R2 than standard CAR, CAR using a fixed bottom quartile of channels by covariance, and no re-referencing. CARLA minimizes bias in re-referenced CCEP data by adaptively selecting the optimal subset of non-responsive channels. It showed high specificity and sensitivity on simulated CCEP data and lowered inter-channel dependency compared to CAR on real CCEP data.

Voxeloc: A time-saving graphical user interface for localizing and visualizing stereo-EEG electrodes

BACKGROUND

Thanks to its unrivalled spatial and temporal resolutions and signal-to-noise ratio, intracranial EEG (iEEG) is becoming a valuable tool in neuroscience research. To attribute functional properties to cortical tissue, it is paramount to be able to determine precisely the localization of each electrode with respect to a patient's brain anatomy. Several software packages or pipelines offer the possibility to localize manually or semi-automatically iEEG electrodes. However, their reliability and ease of use may leave to be desired.

NEW METHOD

Voxeloc (voxel electrode locator) is a Matlab-based graphical user interface to localize and visualize stereo-EEG electrodes. Voxeloc adopts a semi-automated approach to determine the coordinates of each electrode contact, the user only needing to indicate the deep-most contact of each electrode shaft and another point more proximally.

RESULTS

With a deliberately streamlined functionality and intuitive graphical user interface, the main advantages of Voxeloc are ease of use and inter-user reliability. Additionally, oblique slices along the shaft of each electrode can be generated to facilitate the precise localization of each contact. Voxeloc is open-source software and is compatible with the open iEEG-BIDS (Brain Imaging Data Structure) format.

COMPARISON WITH EXISTING METHODS

Localizing full patients' iEEG implants was faster using Voxeloc than two comparable software packages, and the inter-user agreement was better.

CONCLUSIONS

Voxeloc offers an easy-to-use and reliable tool to localize and visualize stereo-EEG electrodes. This will contribute to democratizing neuroscience research using iEEG.

Intracranial EEG signals disentangle multi-areal neural dynamics of vicarious pain perception

Empathy enables understanding and sharing of others' feelings. Human neuroimaging studies have identified critical brain regions supporting empathy for pain, including the anterior insula (AI), anterior cingulate (ACC), amygdala, and inferior frontal gyrus (IFG). However, to date, the precise spatio-temporal profiles of empathic neural responses and inter-regional communications remain elusive. Here, using intracranial electroencephalography, we investigated electrophysiological signatures of vicarious pain perception. Others' pain perception induced early increases in high-gamma activity in IFG, beta power increases in ACC, but decreased beta power in AI and amygdala. Vicarious pain perception also altered the beta-band-coordinated coupling between ACC, AI, and amygdala, as well as increased modulation of IFG high-gamma amplitudes by beta phases of amygdala/AI/ACC. We identified a necessary combination of neural features for decoding vicarious pain perception. These spatio-temporally specific regional activities and inter-regional interactions within the empathy network suggest a neurodynamic model of human pain empathy.

A scalable platform for acquisition of high-fidelity human intracranial EEG with minimal clinical burden

Human neuroscience research has been significantly advanced by neuroelectrophysiological studies from people with refractory epilepsy-the only routine clinical intervention that acquires multi-day, multi-electrode human intracranial electroencephalography (iEEG). While a sampling rate below 2 kHz is sufficient for manual iEEG review by epileptologists, computational methods and research studies may benefit from higher resolution, which requires significant technical development. At adult and pediatric Stanford hospitals, research ports of commercial clinical acquisition systems were configured to collect 10 kHz iEEG of up to 256 electrodes simultaneously with the clinical data. The research digital stream was designed to be acquired post-digitization, resulting in no loss in clinical signal quality. This novel framework implements a near-invisible research platform to facilitate the secure, routine collection of high-resolution iEEG that minimizes research hardware footprint and clinical workflow interference. The addition of a pocket-sized router in the patient room enabled an encrypted tunnel to securely transmit research-quality iEEG across hospital networks to a research computer within the hospital server room, where data was coded, de-identified, and uploaded to cloud storage. Every eligible patient undergoing iEEG clinical evaluation at both hospitals since September 2017 has been recruited; participant recruitment is ongoing. Over 350+ terabytes (representing 1000+ days) of neuroelectrophysiology were recorded across 200+ participants of diverse demographics. To our knowledge, this is the first report of such a research integration within a hospital setting. It is a promising approach to promoting equitable participant enrollment and building comprehensive data repositories with consistent, high-fidelity specifications towards new discoveries in human neuroscience.

Cortical and white matter substrates supporting visuospatial working memory

OBJECTIVE

In tasks involving new visuospatial information, we rely on working memory, supported by a distributed brain network. We investigated the dynamic interplay between brain regions, including cortical and white matter structures, to understand how neural interactions change with different memory loads and trials, and their subsequent impact on working memory performance.

METHODS

Patients undertook a task of immediate spatial recall during intracranial EEG monitoring. We charted the dynamics of cortical high-gamma activity and associated functional connectivity modulations in white matter tracts.

RESULTS

Elevated memory loads were linked to enhanced functional connectivity via occipital longitudinal tracts, yet decreased through arcuate, uncinate, and superior-longitudinal fasciculi. As task familiarity grew, there was increased high-gamma activity in the posterior inferior-frontal gyrus (pIFG) and diminished functional connectivity across a network encompassing frontal, parietal, and temporal lobes. Early pIFG high-gamma activity was predictive of successful recall. Including this metric in a logistic regression model yielded an accuracy of 0.76.

CONCLUSIONS

Optimizing visuospatial working memory through practice is tied to early pIFG activation and decreased dependence on irrelevant neural pathways.

SIGNIFICANCE

This study expands our knowledge of human adaptation for visuospatial working memory, showing the spatiotemporal dynamics of cortical network modulations through white matter tracts.

Artificial intelligence in epilepsy - applications and pathways to the clinic

Artificial intelligence (AI) is rapidly transforming health care, and its applications in epilepsy have increased exponentially over the past decade. Integration of AI into epilepsy management promises to revolutionize the diagnosis and treatment of this complex disorder. However, translation of AI into neurology clinical practice has not yet been successful, emphasizing the need to consider progress to date and assess challenges and limitations of AI. In this Review, we provide an overview of AI applications that have been developed in epilepsy using a variety of data modalities: neuroimaging, electroencephalography, electronic health records, medical devices and multimodal data integration. For each, we consider potential applications, including seizure detection and prediction, seizure lateralization, localization of the seizure-onset zone and assessment for surgical or neurostimulation interventions, and review the performance of AI tools developed to date. We also discuss methodological considerations and challenges that must be addressed to successfully integrate AI into clinical practice. Our goal is to provide an overview of the current state of the field and provide guidance for leveraging AI in future to improve management of epilepsy.

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.