Individualized localization and cortical surface-based registration of intracranial electrodes

Closed-loop control of theta oscillations enhances human hippocampal network connectivity

Theta oscillations are implicated in regulating information flow within cortico-hippocampal networks to support memory and cognition. However, causal evidence tying theta oscillations to network communication in humans is lacking. Here we report experimental findings using a closed-loop, phase-locking algorithm to apply direct electrical stimulation to neocortical nodes of the hippocampal network precisely timed to ongoing hippocampal theta rhythms in human neurosurgical patients. We show that repetitive stimulation of lateral temporal cortex synchronized to hippocampal theta increases hippocampal theta while it is delivered, suggesting theta entrainment of hippocampal neural activity. After stimulation, network connectivity is persistently increased relative to baseline, as indicated by theta-phase synchrony of hippocampus to neocortex and increased amplitudes of the hippocampal evoked response to isolated neocortical stimulation. These indicators of network connectivity are not affected by control stimulation delivered with approximately the same rhythm but without phase locking to hippocampal theta. These findings support the causal role of theta oscillations in routing neural signals across the hippocampal network and suggest phase-synchronized stimulation as a promising method to modulate theta- and hippocampal-dependent behaviors.

Synchronous Theta Networks Characterize Successful Memory Retrieval

Memory retrieval activates regions across the brain, including not only the hippocampus and medial temporal lobe (MTL), but also frontal, parietal, and lateral temporal cortical regions. What remains unclear, however, is how these regions communicate to organize retrieval-specific processing. Here, we elucidate the role of theta (3-8 Hz) synchronization, broadly implicated in memory function, during the spontaneous retrieval of episodic memories. Analyzing a dataset of 382 neurosurgical patients (213 males, 168 females, and 1 unknown) implanted with intracranial electrodes who completed a free-recall task, we find that synchronous networks of theta phase synchrony span the brain in the moments before spontaneous recall, in comparison to periods of deliberation and incorrect recalls. Hubs of the retrieval network, which systematically synchronize with other regions, appear throughout the prefrontal cortex and lateral and medial temporal lobes, as well as other areas. Theta synchrony increases appear more prominently for slow (3 Hz) theta than for fast (8 Hz) theta in the recall-deliberation contrast, but not in the encoding or recall-intrusion contrasts, and theta power and synchrony correlate positively throughout the theta band. These results implicate diffuse brain-wide synchronization of theta rhythms, especially slow theta, in episodic memory retrieval.

Natural language processing models reveal neural dynamics of human conversation

Through conversation, humans engage in a complex process of alternating speech production and comprehension to communicate. The neural mechanisms that underlie these complementary processes through which information is precisely conveyed by language, however, remain poorly understood. Here, we used pre-trained deep learning natural language processing models in combination with intracranial neuronal recordings to discover neural signals that reliably reflected speech production, comprehension, and their transitions during natural conversation between individuals. Our findings indicate that the neural activities that reflected speech production and comprehension were broadly distributed throughout frontotemporal areas across multiple frequency bands. We also find that these activities were specific to the words and sentences being conveyed and that they were dependent on the word's specific context and order. Finally, we demonstrate that these neural patterns partially overlapped during language production and comprehension and that listener-speaker transitions were associated with specific, time-aligned changes in neural activity. Collectively, our findings reveal a dynamical organization of neural activities that subserve language production and comprehension during natural conversation and harness the use of deep learning models in understanding the neural mechanisms underlying human language.

Neural correlates of minimal recognizable configurations in the human brain

Inferring object identity from incomplete information is a ubiquitous challenge for the visual system. Here, we study the neural mechanisms underlying processing of minimally recognizable configurations (MIRCs) and their subparts, which are unrecognizable (sub-MIRCs). MIRCs and sub-MIRCs are very similar at the pixel level, yet they lead to a dramatic gap in recognition performance. To evaluate how the brain processes such images, we invasively record human neurophysiological responses. Correct identification of MIRCs is associated with a dynamic interplay of feedback and feedforward mechanisms between frontal and temporal areas. Interpretation of sub-MIRC images improves dramatically after exposure to the corresponding full objects. This rapid and unsupervised learning is accompanied by changes in neural responses in the temporal cortex. These results are at odds with purely feedforward models of object recognition and suggest a role for the frontal lobe in providing top-down signals related to object identity in difficult visual tasks.

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.

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.

Differential cortical layer engagement during seizure initiation and spread in humans

Despite decades of research, we still do not understand how spontaneous human seizures start and spread - especially at the level of neuronal microcircuits. In this study, we used laminar arrays of micro-electrodes to simultaneously record the local field potentials and multi-unit neural activities across the six layers of the neocortex during focal seizures in humans. We found that, within the ictal onset zone, the discharges generated during a seizure consisted of current sinks and sources only within the infra-granular and granular layers. Outside of the seizure onset zone, ictal discharges reflected current flow in the supra-granular layers. Interestingly, these patterns of current flow evolved during the course of the seizure - especially outside the seizure onset zone where superficial sinks and sources extended into the deeper layers. Based on these observations, a framework describing cortical-cortical dynamics of seizures is proposed with implications for seizure localization, surgical targeting, and neuromodulation techniques to block the generation and propagation of seizures.

Improving auditory attention decoding by classifying intracranial responses to glimpsed and masked acoustic events

Listeners with hearing loss have trouble following a conversation in multitalker environments. While modern hearing aids can generally amplify speech, these devices are unable to tune into a target speaker without first knowing to which speaker a user aims to attend. Brain-controlled hearing aids have been proposed using auditory attention decoding (AAD) methods, but current methods use the same model to compare the speech stimulus and neural response, regardless of the dynamic overlap between talkers which is known to influence neural encoding. Here, we propose a novel framework that directly classifies event-related potentials (ERPs) evoked by glimpsed and masked acoustic events to determine whether the source of the event was attended. We present a system that identifies auditory events using the local maxima in the envelope rate of change, assesses the temporal masking of auditory events relative to competing speakers, and utilizes masking-specific ERP classifiers to determine if the source of the event was attended. Using intracranial electrophysiological recordings, we showed that high gamma ERPs from recording sites in auditory cortex can effectively decode the attention of subjects. This method of AAD provides higher accuracy, shorter switch times, and more stable decoding results compared with traditional correlational methods, permitting the quick and accurate detection of changes in a listener's attentional focus. This framework also holds unique potential for detecting instances of divided attention and inattention. Overall, we extend the scope of AAD algorithms by introducing the first linear, direct-classification method for determining a listener's attentional focus that leverages the latest research in multitalker speech perception. This work represents another step toward informing the development of effective and intuitive brain-controlled hearing assistive devices.

Exploring Motor Network Connectivity in State-Dependent Transcranial Magnetic Stimulation: A Proof-of-Concept Study

State-dependent non-invasive brain stimulation (NIBS) informed by electroencephalography (EEG) has contributed to the understanding of NIBS inter-subject and inter-session variability. While these approaches focus on local EEG characteristics, it is acknowledged that the brain exhibits an intrinsic long-range dynamic organization in networks. This proof-of-concept study explores whether EEG connectivity of the primary motor cortex (M1) in the pre-stimulation period aligns with the Motor Network (MN) and how the MN state affects responses to the transcranial magnetic stimulation (TMS) of M1. One thousand suprathreshold TMS pulses were delivered to the left M1 in eight subjects at rest, with simultaneous EEG. Motor-evoked potentials (MEPs) were measured from the right hand. The source space functional connectivity of the left M1 to the whole brain was assessed using the imaginary part of the phase locking value at the frequency of the sensorimotor μ-rhythm in a 1 s window before the pulse. Group-level connectivity revealed functional links between the left M1, left supplementary motor area, and right M1. Also, pulses delivered at high MN connectivity states result in a greater MEP amplitude compared to low connectivity states. At the single-subject level, this relation is more highly expressed in subjects that feature an overall high cortico-spinal excitability. In conclusion, this study paves the way for MN connectivity-based NIBS.

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.

Natural language processing models reveal neural dynamics of human conversation

Through conversation, humans relay complex information through the alternation of speech production and comprehension. The neural mechanisms that underlie these complementary processes or through which information is precisely conveyed by language, however, remain poorly understood. Here, we used pretrained deep learning natural language processing models in combination with intracranial neuronal recordings to discover neural signals that reliably reflect speech production, comprehension, and their transitions during natural conversation between individuals. Our findings indicate that neural activities that encoded linguistic information were broadly distributed throughout frontotemporal areas across multiple frequency bands. We also find that these activities were specific to the words and sentences being conveyed and that they were dependent on the word's specific context and order. Finally, we demonstrate that these neural patterns partially overlapped during language production and comprehension and that listener-speaker transitions were associated with specific, time-aligned changes in neural activity. Collectively, our findings reveal a dynamical organization of neural activities that subserve language production and comprehension during natural conversation and harness the use of deep learning models in understanding the neural mechanisms underlying human language.

Anatomical registration of intracranial electrodes. Robust model-based localization and deformable smooth brain-shift compensation methods

BACKGROUND

Intracranial electrodes are typically localized from post-implantation CT artifacts. Automatic algorithms localizing low signal-to-noise ratio artifacts and high-density electrode arrays are missing. Additionally, implantation of grids/strips introduces brain deformations, resulting in registration errors when fusing post-implantation CT and pre-implantation MR images. Brain-shift compensation methods project electrode coordinates to cortex, but either fail to produce smooth solutions or do not account for brain deformations.

NEW METHODS

We first introduce GridFit, a model-based fitting approach that simultaneously localizes all electrodes' CT artifacts in grids, strips, or depth arrays. Second, we present CEPA, a brain-shift compensation algorithm combining orthogonal-based projections, spring-mesh models, and spatial regularization constraints.

RESULTS

We tested GridFit on ∼6000 simulated scenarios. The localization of CT artifacts showed robust performance under difficult scenarios, such as noise, overlaps, and high-density implants (<1 mm errors). Validation with data from 20 challenging patients showed 99% accurate localization of the electrodes (3160/3192). We tested CEPA brain-shift compensation with data from 15 patients. Projections accounted for simple mechanical deformation principles with < 0.4 mm errors. The inter-electrode distances smoothly changed across neighbor electrodes, while changes in inter-electrode distances linearly increased with projection distance.

COMPARISON WITH EXISTING METHODS

GridFit succeeded in difficult scenarios that challenged available methods and outperformed visual localization by preserving the inter-electrode distance. CEPA registration errors were smaller than those obtained for well-established alternatives. Additionally, modeling resting-state high-frequency activity in five patients further supported CEPA.

CONCLUSION

GridFit and CEPA are versatile tools for registering intracranial electrode coordinates, providing highly accurate results even in the most challenging implantation scenarios. The methods are implemented in the iElectrodes open-source toolbox.

iEEG-recon: A fast and scalable pipeline for accurate reconstruction of intracranial electrodes and implantable devices

OBJECTIVE

Clinicians use intracranial electroencephalography (iEEG) in conjunction with noninvasive brain imaging to identify epileptic networks and target therapy for drug-resistant epilepsy cases. Our goal was to promote ongoing and future collaboration by automating the process of "electrode reconstruction," which involves the labeling, registration, and assignment of iEEG electrode coordinates on neuroimaging. We developed a standalone, modular pipeline that performs electrode reconstruction. We demonstrate our tool's compatibility with clinical and research workflows and its scalability on cloud platforms.

METHODS

We created iEEG-recon, a scalable electrode reconstruction pipeline for semiautomatic iEEG annotation, rapid image registration, and electrode assignment on brain magnetic resonance imaging (MRI). Its modular architecture includes a clinical module for electrode labeling and localization, and a research module for automated data processing and electrode contact assignment. To ensure accessibility for users with limited programming and imaging expertise, we packaged iEEG-recon in a containerized format that allows integration into clinical workflows. We propose a cloud-based implementation of iEEG-recon and test our pipeline on data from 132 patients at two epilepsy centers using retrospective and prospective cohorts.

RESULTS

We used iEEG-recon to accurately reconstruct electrodes in both electrocorticography and stereoelectroencephalography cases with a 30-min running time per case (including semiautomatic electrode labeling and reconstruction). iEEG-recon generates quality assurance reports and visualizations to support epilepsy surgery discussions. Reconstruction outputs from the clinical module were radiologically validated through pre- and postimplant T1-MRI visual inspections. We also found that our use of ANTsPyNet deep learning-based brain segmentation for electrode classification was consistent with the widely used FreeSurfer segmentations.

SIGNIFICANCE

iEEG-recon is a robust pipeline for automating reconstruction of iEEG electrodes and implantable devices on brain MRI, promoting fast data analysis and integration into clinical workflows. iEEG-recon's accuracy, speed, and compatibility with cloud platforms make it a useful resource for epilepsy centers worldwide.

Human Hippocampal Ripples Signal Encoding of Episodic Memories

Direct human brain recordings have confirmed the presence of high-frequency oscillatory events, termed ripples, during awake behavior. While many prior studies have focused on medial temporal lobe (MTL) ripples during memory retrieval, here we investigate ripples during memory encoding. Specifically, we ask whether ripples during encoding predict whether and how memories are subsequently recalled. Detecting ripples from MTL electrodes implanted in 116 neurosurgical participants (n = 61 male) performing a verbal episodic memory task, we find that encoding ripples do not distinguish recalled from not recalled items in any MTL region, even as high-frequency activity during encoding predicts recall in these same regions. Instead, hippocampal ripples increase during encoding of items that subsequently lead to recall of temporally and semantically associated items during retrieval, a phenomenon known as clustering. This subsequent clustering effect arises specifically when hippocampal ripples co-occur during encoding and retrieval, suggesting that ripples mediate both encoding and reinstatement of episodic memories.

Neural manifolds carry reactivation of phonetic representations during semantic processing

Traditional models of speech perception posit that neural activity encodes speech through a hierarchy of cognitive processes, from low-level representations of acoustic and phonetic features to high-level semantic encoding. Yet it remains unknown how neural representations are transformed across levels of the speech hierarchy. Here, we analyzed unique microelectrode array recordings of neuronal spiking activity from the human left anterior superior temporal gyrus, a brain region at the interface between phonetic and semantic speech processing, during a semantic categorization task and natural speech perception. We identified distinct neural manifolds for semantic and phonetic features, with a functional separation of the corresponding low-dimensional trajectories. Moreover, phonetic and semantic representations were encoded concurrently and reflected in power increases in the beta and low-gamma local field potentials, suggesting top-down predictive and bottom-up cumulative processes. Our results are the first to demonstrate mechanisms for hierarchical speech transformations that are specific to neuronal population dynamics.

Functional and anatomical connectivity predict brain stimulation's mnemonic effects

Closed-loop direct brain stimulation is a promising tool for modulating neural activity and behavior. However, it remains unclear how to optimally target stimulation to modulate brain activity in particular brain networks that underlie particular cognitive functions. Here, we test the hypothesis that stimulation's behavioral and physiological effects depend on the stimulation target's anatomical and functional network properties. We delivered closed-loop stimulation as 47 neurosurgical patients studied and recalled word lists. Multivariate classifiers, trained to predict momentary lapses in memory function, triggered the stimulation of the lateral temporal cortex (LTC) during the study phase of the task. We found that LTC stimulation specifically improved memory when delivered to targets near white matter pathways. Memory improvement was largest for targets near white matter that also showed high functional connectivity to the brain's memory network. These targets also reduced low-frequency activity in this network, an established marker of successful memory encoding. These data reveal how anatomical and functional networks mediate stimulation's behavioral and physiological effects, provide further evidence that closed-loop LTC stimulation can improve episodic memory, and suggest a method for optimizing neuromodulation through improved stimulation targeting.

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 precise anatomical source 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 intracranial electroencephalography (iEEG). Our data revealed a remarkably distributed change of activity in almost half of the sampled sites. Notably, an orderly successive activation of a set of brain regions - anatomically consistent across subjects-was observed in individual brains. Furthermore, 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 information is partially leaked or transformed along the processing chain. Furthermore, in each activated region, distinct neuronal populations with opposite activity patterns during target and control conditions were juxtaposed in an anatomically orderly manner. Our study complements the 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.

Significance statement

Our study elucidates the spatiotemporal dynamics and anatomical specificity of brain activations across >10,000 sites during arithmetic tasks, as captured by intracranial EEG. We discovered an orderly, successive activation of brain regions, consistent across individuals, and a decrease in functional connectivity as a function of temporal distance between regions. Our findings provide unprecedented insights into the sequence of cognitive processing and regional interactions, offering a novel perspective for enhancing computational models of cognitive symbolic systems.

Direct cortical stimulation induces short-term plasticity of neural oscillations in humans

Patterned brain stimulation is commonly employed as a tool for eliciting plasticity in brain circuits and treating neuropsychiatric disorders. Although widely used in clinical settings, there remains a limited understanding of how stimulation-induced plasticity influences neural oscillations and their interplay with the underlying baseline functional architecture. To address this question, we applied 15 minutes of 10Hz focal electrical simulation, a pattern identical to 'excitatory' repetitive transcranial magnetic stimulation (rTMS), to 14 medically-intractable epilepsy patients undergoing intracranial electroencephalographic (iEEG). We quantified the spectral features of the cortico-cortical evoked potential (CCEPs) in these patients before and after stimulation. We hypothesized that for a given region the temporal and spectral components of the CCEP predicted the location and degree of stimulation-induced plasticity. Across patients, low frequency power (alpha and beta) showed the broadest change, while the magnitude of change was stronger in high frequencies (beta and gamma). Next we demonstrated that regions with stronger baseline evoked spectral responses were more likely to undergo plasticity after stimulation. These findings were specific to a given frequency in a specific temporal window. Post-stimulation power changes were driven by the interaction between direction of change in baseline power and temporal window of change. Finally, regions exhibiting early increases and late decreases in evoked baseline power exhibited power changes after stimulation and were independent of stimulation location. Together, these findings that time-frequency baseline features predict post-stimulation plasticity effects demonstrate properties akin to Hebbian learning in humans and extend this theory to the temporal and spectral window of interest. These findings can help improve our understanding of human brain plasticity and lead to more effective brain stimulation techniques.

Differential cortical network engagement during states of un/consciousness in humans

What happens in the human brain when we are unconscious? Despite substantial work, we are still unsure which brain regions are involved and how they are impacted when consciousness is disrupted. Using intracranial recordings and direct electrical stimulation, we mapped global, network, and regional involvement during wake vs. arousable unconsciousness (sleep) vs. non-arousable unconsciousness (propofol-induced general anesthesia). Information integration and complex processing we`re reduced, while variability increased in any type of unconscious state. These changes were more pronounced during anesthesia than sleep and involved different cortical engagement. During sleep, changes were mostly uniformly distributed across the brain, whereas during anesthesia, the prefrontal cortex was the most disrupted, suggesting that the lack of arousability during anesthesia results not from just altered overall physiology but from a disconnection between the prefrontal and other brain areas. These findings provide direct evidence for different neural dynamics during loss of consciousness compared with loss of arousability.

Functional and anatomical connectivity predict brain stimulation's mnemonic effects

Closed-loop direct brain stimulation is a promising tool for modulating neural activity and behavior. However, it remains unclear how to optimally target stimulation to modulate brain activity in particular brain networks that underlie particular cognitive functions. Here, we test the hypothesis that stimulation's behavioral and physiological effects depend on the stimulation target's anatomical and functional network properties. We delivered closed-loop stimulation as 47 neurosurgical patients studied and recalled word lists. Multivariate classifiers, trained to predict momentary lapses in memory function, triggered stimulation of the lateral temporal cortex (LTC) during the study phase of the task. We found that LTC stimulation specifically improved memory when delivered to targets near white matter pathways. Memory improvement was largest for targets near white matter that also showed high functional connectivity to the brain's memory network. These targets also reduced low-frequency activity in this network, an established marker of successful memory encoding. These data reveal how anatomical and functional networks mediate stimulation's behavioral and physiological effects, provide further evidence that closed-loop LTC stimulation can improve episodic memory, and suggest a method for optimizing neuromodulation through improved stimulation targeting.

Modular pipeline for reconstruction and localization of implanted intracranial ECoG and sEEG electrodes

Implantation of electrodes in the brain has been used as a clinical tool for decades to stimulate and record brain activity. As this method increasingly becomes the standard of care for several disorders and diseases, there is a growing need to quickly and accurately localize the electrodes once they are placed within the brain. We share here a protocol pipeline for localizing electrodes implanted in the brain, which we have applied to more than 260 patients, that is accessible to multiple skill levels and modular in execution. This pipeline uses multiple software packages to prioritize flexibility by permitting multiple different parallel outputs while minimizing the number of steps for each output. These outputs include co-registered imaging, electrode coordinates, 2D and 3D visualizations of the implants, automatic surface and volumetric localizations of the brain regions per electrode, and anonymization and data sharing tools. We demonstrate here some of the pipeline's visualizations and automatic localization algorithms which we have applied to determine appropriate stimulation targets, to conduct seizure dynamics analysis, and to localize neural activity from cognitive tasks in previous studies. Further, the output facilitates the extraction of information such as the probability of grey matter intersection or the nearest anatomic structure per electrode contact across all data sets that go through the pipeline. We expect that this pipeline will be a useful framework for researchers and clinicians alike to localize implanted electrodes in the human brain.

Distinct neural encoding of glimpsed and masked speech in multitalker situations

Humans can easily tune in to one talker in a multitalker environment while still picking up bits of background speech; however, it remains unclear how we perceive speech that is masked and to what degree non-target speech is processed. Some models suggest that perception can be achieved through glimpses, which are spectrotemporal regions where a talker has more energy than the background. Other models, however, require the recovery of the masked regions. To clarify this issue, we directly recorded from primary and non-primary auditory cortex (AC) in neurosurgical patients as they attended to one talker in multitalker speech and trained temporal response function models to predict high-gamma neural activity from glimpsed and masked stimulus features. We found that glimpsed speech is encoded at the level of phonetic features for target and non-target talkers, with enhanced encoding of target speech in non-primary AC. In contrast, encoding of masked phonetic features was found only for the target, with a greater response latency and distinct anatomical organization compared to glimpsed phonetic features. These findings suggest separate mechanisms for encoding glimpsed and masked speech and provide neural evidence for the glimpsing model of speech perception.

Anatomical registration of intracranial electrodes. Robust model-based localization and deformable smooth brain-shift compensation methods

Precise electrode localization is important for maximizing the utility of intracranial EEG data. Electrodes are typically localized from post-implantation CT artifacts, but algorithms can fail due to low signal-to-noise ratio, unrelated artifacts, or high-density electrode arrays. Minimizing these errors usually requires time-consuming visual localization and can still result in inaccurate localizations. In addition, surgical implantation of grids and strips typically introduces non-linear brain deformations, which result in anatomical registration errors when post-implantation CT images are fused with the pre-implantation MRI images. Several projection methods are currently available, but they either fail to produce smooth solutions or do not account for brain deformations. To address these shortcomings, we propose two novel algorithms for the anatomical registration of intracranial electrodes that are almost fully automatic and provide highly accurate results. We first present GridFit, an algorithm that simultaneously localizes all contacts in grids, strips, or depth arrays by fitting flexible models to the electrodes' CT artifacts. We observed localization errors of less than one millimeter (below 8% relative to the inter-electrode distance) and robust performance under the presence of noise, unrelated artifacts, and high-density implants when we ran ~6000 simulated scenarios. Furthermore, we validated the method with real data from 20 intracranial patients. As a second registration step, we introduce CEPA, a brain-shift compensation algorithm that combines orthogonal-based projections, spring-mesh models, and spatial regularization constraints. When tested with real data from 15 patients, anatomical registration errors were smaller than those obtained for well-established alternatives. Additionally, CEPA accounted simultaneously for simple mechanical deformation principles, which is not possible with other available methods. Inter-electrode distances of projected coordinates smoothly changed across neighbor electrodes, while changes in inter-electrode distances linearly increased with projection distance. Moreover, in an additional validation procedure, we found that modeling resting-state high-frequency activity (75-145 Hz ) in five patients further supported our new algorithm. Together, GridFit and CEPA constitute a versatile set of tools for the registration of subdural grid, strip, and depth electrode coordinates that provide highly accurate results even in the most challenging implantation scenarios. The methods presented here are implemented in the iElectrodes open-source toolbox, making their use simple, accessible, and straightforward to integrate with other popular toolboxes used for analyzing electrophysiological data.

Electrochemical and electrophysiological considerations for clinical high channel count neural interfaces

Electrophysiological recording and stimulation are the gold standard for functional mapping during surgical and therapeutic interventions as well as capturing cellular activity in the intact human brain. A critical component probing human brain activity is the interface material at the electrode contact that electrochemically transduces brain signals to and from free charge carriers in the measurement system. Here, we summarize state-of-the-art electrode array systems in the context of translation for use in recording and stimulating human brain activity. We leverage parametric studies with multiple electrode materials to shed light on the varied levels of suitability to enable high signal-to-noise electrophysiological recordings as well as safe electrophysiological stimulation delivery. We discuss the effects of electrode scaling for recording and stimulation in pursuit of high spatial resolution, channel count electrode interfaces, delineating the electrode-tissue circuit components that dictate the electrode performance. Finally, we summarize recent efforts in the connectorization and packaging for high channel count electrode arrays and provide a brief account of efforts toward wireless neuronal monitoring systems.

Closed-loop enhancement and neural decoding of cognitive control in humans

Deficits in cognitive control-that is, in the ability to withhold a default pre-potent response in favour of a more adaptive choice-are common in depression, anxiety, addiction and other mental disorders. Here we report proof-of-concept evidence that, in participants undergoing intracranial epilepsy monitoring, closed-loop direct stimulation of the internal capsule or striatum, especially the dorsal sites, enhances the participants' cognitive control during a conflict task. We also show that closed-loop stimulation upon the detection of lapses in cognitive control produced larger behavioural changes than open-loop stimulation, and that task performance for single trials can be directly decoded from the activity of a small number of electrodes via neural features that are compatible with existing closed-loop brain implants. Closed-loop enhancement of cognitive control might remediate underlying cognitive deficits and aid the treatment of severe mental disorders.

An open-source application to identify the three-dimensional locations of electrodes implanted into the rat brain from computed tomography images

Electrophysiological recordings using metal electrodes implanted into the brains have been widely utilized to evaluate neuronal circuit dynamics related to behavior and external stimuli. The most common method for identifying implanted electrode tracks in the brain tissue has been histological examination following postmortem slicing and staining of the brain tissue, which consumes time and resources and occasionally fails to identify the tracks because the brain preparations have been damaged during processing. Recent studies have proposed the use of a promising alternative method, consisting of computed tomography (CT) scanning that can directly reconstruct the three-dimensional arrangements of electrodes in the brains of living animals. In this study, we developed an open-source Python-based application that estimates the location of an implanted electrode from CT image sequences in a rat. After the user manually sets reference coordinates and an area from a sequence of CT images, this application automatically overlays an estimated location of an electrode tip on a histological template image; the estimates are highly accurate, with less than 135μm of error, irrespective of the depth of the brain region. The estimation of an electrode location can be completed within a few minutes. Our simple and user-friendly application extends beyond currently available CT-based electrode localization methods and opens up the possibility of applying this technique to various electrophysiological recording paradigms.

Characterizing brain dynamics during ketamine-induced dissociation and subsequent interactions with propofol using human intracranial neurophysiology

Ketamine produces antidepressant effects in patients with treatment-resistant depression, but its usefulness is limited by its psychotropic side effects. Ketamine is thought to act via NMDA receptors and HCN1 channels to produce brain oscillations that are related to these effects. Using human intracranial recordings, we found that ketamine produces gamma oscillations in prefrontal cortex and hippocampus, structures previously implicated in ketamine's antidepressant effects, and a 3 Hz oscillation in posteromedial cortex, previously proposed as a mechanism for its dissociative effects. We analyzed oscillatory changes after subsequent propofol administration, whose GABAergic activity antagonizes ketamine's NMDA-mediated disinhibition, alongside a shared HCN1 inhibitory effect, to identify dynamics attributable to NMDA-mediated disinhibition versus HCN1 inhibition. Our results suggest that ketamine engages different neural circuits in distinct frequency-dependent patterns of activity to produce its antidepressant and dissociative sensory effects. These insights may help guide the development of brain dynamic biomarkers and novel therapeutics for depression.

Propofol disrupts alpha dynamics in functionally distinct thalamocortical networks during loss of consciousness

During propofol-induced general anesthesia, alpha rhythms measured using electroencephalography undergo a striking shift from posterior to anterior, termed anteriorization, where the ubiquitous waking alpha is lost and a frontal alpha emerges. The functional significance of alpha anteriorization and the precise brain regions contributing to the phenomenon are a mystery. While posterior alpha is thought to be generated by thalamocortical circuits connecting nuclei of the sensory thalamus with their cortical partners, the thalamic origins of the propofol-induced alpha remain poorly understood. Here, we used human intracranial recordings to identify regions in sensory cortices where propofol attenuates a coherent alpha network, distinct from those in the frontal cortex where it amplifies coherent alpha and beta activities. We then performed diffusion tractography between these identified regions and individual thalamic nuclei to show that the opposing dynamics of anteriorization occur within two distinct thalamocortical networks. We found that propofol disrupted a posterior alpha network structurally connected with nuclei in the sensory and sensory associational regions of the thalamus. At the same time, propofol induced a coherent alpha oscillation within prefrontal cortical areas that were connected with thalamic nuclei involved in cognition, such as the mediodorsal nucleus. The cortical and thalamic anatomy involved, as well as their known functional roles, suggests multiple means by which propofol dismantles sensory and cognitive processes to achieve loss of consciousness.

Direct intracranial recordings in the human angular gyrus during arithmetic processing

The role of angular gyrus (AG) in arithmetic processing remains a subject of debate. In the present study, we recorded from the AG, supramarginal gyrus (SMG), intraparietal sulcus (IPS), and superior parietal lobule (SPL) across 467 sites in 30 subjects performing addition or multiplication with digits or number words. We measured the power of high-frequency-broadband (HFB) signal, a surrogate marker for regional cortical engagement, and used single-subject anatomical boundaries to define the location of each recording site. Our recordings revealed the lowest proportion of sites with activation or deactivation within the AG compared to other subregions of the inferior parietal cortex during arithmetic processing. The few activated AG sites were mostly located at the border zones between AG and IPS, or AG and SMG. Additionally, we found that AG sites were more deactivated in trials with fast compared to slow response times. The increase or decrease of HFB within specific AG sites was the same when arithmetic trials were presented with number words versus digits and during multiplication as well as addition trials. Based on our findings, we conclude that the prior neuroimaging findings of so-called activations in the AG during arithmetic processing could have been due to group-based analyses that might have blurred the individual anatomical boundaries of AG or the subtractive nature of the neuroimaging methods in which lesser deactivations compared to the control condition have been interpreted as "activations". Our findings offer a new perspective with electrophysiological data about the engagement of AG during arithmetic processing.

Advances in human intracranial electroencephalography research, guidelines and good practices

Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.

Modeling intracranial electrodes. A simulation platform for the evaluation of localization algorithms

Introduction

Intracranial electrodes are implanted in patients with drug-resistant epilepsy as part of their pre-surgical evaluation. This allows the investigation of normal and pathological brain functions with excellent spatial and temporal resolution. The spatial resolution relies on methods that precisely localize the implanted electrodes in the cerebral cortex, which is critical for drawing valid inferences about the anatomical localization of brain function. Multiple methods have been developed to localize the electrodes, mainly relying on pre-implantation MRI and post-implantation computer tomography (CT) images. However, they are hard to validate because there is no ground truth data to test them and there is no standard approach to systematically quantify their performance. In other words, their validation lacks standardization. Our work aimed to model intracranial electrode arrays and simulate realistic implantation scenarios, thereby providing localization algorithms with new ways to evaluate and optimize their performance.

Results

We implemented novel methods to model the coordinates of implanted grids, strips, and depth electrodes, as well as the CT artifacts produced by these. We successfully modeled realistic implantation scenarios, including different sizes, inter-electrode distances, and brain areas. In total, ∼3,300 grids and strips were fitted over the brain surface, and ∼850 depth electrode arrays penetrating the cortical tissue were modeled. Realistic CT artifacts were simulated at the electrode locations under 12 different noise levels. Altogether, ∼50,000 thresholded CT artifact arrays were simulated in these scenarios, and validated with real data from 17 patients regarding the coordinates' spatial deformation, and the CT artifacts' shape, intensity distribution, and noise level. Finally, we provide an example of how the simulation platform is used to characterize the performance of two cluster-based localization methods.

Conclusion

We successfully developed the first platform to model implanted intracranial grids, strips, and depth electrodes and realistically simulate thresholded CT artifacts and their noise. These methods provide a basis for developing more complex models, while simulations allow systematic evaluation of the performance of electrode localization techniques. The methods described in this article, and the results obtained from the simulations, are freely available via open repositories. A graphical user interface implementation is also accessible via the open-source iElectrodes toolbox.

Patient-specific solution of the electrocorticography forward problem in deforming brain

Invasive intracranial electroencephalography (iEEG), or electrocorticography (ECoG), measures electric potential directly on the surface of the brain and can be used to inform treatment planning for epilepsy surgery. Combined with numerical modeling it can further improve accuracy of epilepsy surgery planning. Accurate solution of the iEEG forward problem, which is a crucial prerequisite for solving the iEEG inverse problemin epilepsy seizure onset zone localization, requires accurate representation of the patient's brain geometry and tissue electrical conductivity after implantation of electrodes. However, implantation of subdural grid electrodes causes the brain to deform, which invalidates preoperatively acquired image data. Moreover, postoperative magnetic resonance imaging (MRI) is incompatible with implanted electrodes and computed tomography (CT) has insufficient range of soft tissue contrast, which precludes both MRI and CT from being used to obtain the deformed postoperative geometry. In this paper, we present a biomechanics-based image warping procedure using preoperative MRI for tissue classification and postoperative CT for locating implanted electrodes to perform non-rigid registration of the preoperative image data to the postoperative configuration. We solve the iEEG forward problem on the predicted postoperative geometry using the finite element method (FEM) which accounts for patient-specific inhomogeneity and anisotropy of tissue conductivity. Results for the simulation of a current source in the brain show large differences in electric potential predicted by the models based on the original images and the deformed images corresponding to the brain geometry deformed by placement of invasive electrodes. Computation of the lead field matrix (useful for solution of the iEEG inverse problem) also showed significant differences between the different models. The results suggest that rapid and accurate solution of the forward problem in a deformed brain for a given patient is achievable.

Interaction of bottom-up and top-down neural mechanisms in spatial multi-talker speech perception

How the human auditory cortex represents spatially separated simultaneous talkers and how talkers' locations and voices modulate the neural representations of attended and unattended speech are unclear. Here, we measured the neural responses from electrodes implanted in neurosurgical patients as they performed single-talker and multi-talker speech perception tasks. We found that spatial separation between talkers caused a preferential encoding of the contralateral speech in Heschl's gyrus (HG), planum temporale (PT), and superior temporal gyrus (STG). Location and spectrotemporal features were encoded in different aspects of the neural response. Specifically, the talker's location changed the mean response level, whereas the talker's spectrotemporal features altered the variation of response around response's baseline. These components were differentially modulated by the attended talker's voice or location, which improved the population decoding of attended speech features. Attentional modulation due to the talker's voice only appeared in the auditory areas with longer latencies, but attentional modulation due to location was present throughout. Our results show that spatial multi-talker speech perception relies upon a separable pre-attentive neural representation, which could be further tuned by top-down attention to the location and voice of the talker.

Neural mass model-based study of frontal-temporal theta oscillations in human subjects during the performance of a cognitive control task

Cognitive control, the ability to rapidly shift one's attention and behavioral strategy in response to environmental changes, is often compromised across psychiatric disorders. One of the well-validated behavioral paradigms for tapping into the cognitive control circuits is a cognitive interference task, where subjects must suppress a natural response to follow a less intuitive rule. Slower response times on these tasks indicate difficulty exerting control to overcome response conflict. Conflict evokes robust electrophysiological signatures, such as theta (4-8 Hz) oscillations in the prefrontal cortex (PFC). However, the underlying neural mechanisms of conflict-evoked theta oscillations in the PFC are not clear. The objective of this work is to use a neural mass model (NMM) to find feasible cortical networks generating theta oscillations during conflict processing in human subjects. We used intracranial EEG (iEEG) recorded from dorsolateral PFC (dIPFC) and lateral temporal lobe (LTL) of human subjects with intractable epilepsy undergoing invasive monitoring, while they performed a multi-source interference task (MSIT). We used a dynamic causal modeling (DCM) framework to simulate dIPFC-LTL theta using a Jansen-Rit NMM. We found significant evidence for an LTL input into the dlPFC during the initial 500 ms of conflict processing compared to a bidirectional connection between the dlPFC and LTL. We conclude that a neural mass modeling framework can be used to elucidate candidate mechanisms of neural oscillations underlying conflict resolution in human subjects. Clinical Relevance- This can be used to find feasible target mechanisms for designing therapy in patients with compromised cognitive control. This framework can also be expanded to serve as an in-silico test bed for designing and testing neuromodulatory interventions such as electrical stimulation for improving cognitive control in mood/anxiety disorders.

Human Spindle Variability

In humans, sleep spindles are 10- to 16-Hz oscillations lasting approximately 0.5-2 s. Spindles, along with cortical slow oscillations, may facilitate memory consolidation by enabling synaptic plasticity. Early recordings of spindles at the scalp found anterior channels had overall slower frequency than central-posterior channels. This robust, topographical finding led to dichotomizing spindles as "slow" versus "fast," modeled as two distinct spindle generators in frontal versus posterior cortex. Using a large dataset of intracranial stereoelectroencephalographic (sEEG) recordings from 20 patients (13 female, 7 male) and 365 bipolar recordings, we show that the difference in spindle frequency between frontal and parietal channels is comparable to the variability in spindle frequency within the course of individual spindles, across different spindles recorded by a given site, and across sites within a given region. Thus, fast and slow spindles only capture average differences that obscure a much larger underlying overlap in frequency. Furthermore, differences in mean frequency are only one of several ways that spindles differ. For example, compared with parietal, frontal spindles are smaller, tend to occur after parietal when both are engaged, and show a larger decrease in frequency within-spindles. However, frontal and parietal spindles are similar in being longer, less variable, and more widespread than occipital, temporal, and Rolandic spindles. These characteristics are accentuated in spindles which are highly phase-locked to posterior hippocampal spindles. We propose that rather than a strict parietal-fast/frontal-slow dichotomy, spindles differ continuously and quasi-independently in multiple dimensions, with variability due about equally to within-spindle, within-region, and between-region factors.SIGNIFICANCE STATEMENT Sleep spindles are 10- to 16-Hz neural oscillations generated by cortico-thalamic circuits that promote memory consolidation. Spindles are often dichotomized into slow-anterior and fast-posterior categories for cognitive and clinical studies. Here, we show that the anterior-posterior difference in spindle frequency is comparable to that observed between different cycles of individual spindles, between spindles from a given site, or from different sites within a region. Further, we show that spindles vary on other dimensions such as duration, amplitude, spread, primacy and consistency, and that these multiple dimensions vary continuously and largely independently across cortical regions. These findings suggest that multiple continuous variables rather than a strict frequency dichotomy may be more useful biomarkers for memory consolidation or psychiatric disorders.

Electrocorticographic evidence of a common neurocognitive sequence for mentalizing about the self and others

Neuroimaging studies of mentalizing (i.e., theory of mind) consistently implicate the default mode network (DMN). Nevertheless, the social cognitive functions of individual DMN regions remain unclear, perhaps due to limited spatiotemporal resolution in neuroimaging. Here we use electrocorticography (ECoG) to directly record neuronal population activity while 16 human participants judge the psychological traits of themselves and others. Self- and other-mentalizing recruit near-identical cortical sites in a common spatiotemporal sequence. Activations begin in the visual cortex, followed by temporoparietal DMN regions, then finally in medial prefrontal regions. Moreover, regions with later activations exhibit stronger functional specificity for mentalizing, stronger associations with behavioral responses, and stronger self/other differentiation. Specifically, other-mentalizing evokes slower and longer activations than self-mentalizing across successive DMN regions, implying lengthier processing at higher levels of representation. Our results suggest a common neurocognitive pathway for self- and other-mentalizing that follows a complex spatiotemporal gradient of functional specialization across DMN and beyond.

Hetero-Integration of Silicon Nanomembranes with 2D Materials for Bioresorbable, Wireless Neurochemical System

Although neurotransmitters are key substances closely related to evaluating degenerative brain diseases as well as regulating essential functions in the body, many research efforts have not been focused on direct observation of such biochemical messengers, rather on monitoring relatively associated physical, mechanical, and electrophysiological parameters. Here, a bioresorbable silicon-based neurochemical analyzer incorporated with 2D transition metal dichalcogenides is introduced as a completely implantable brain-integrated system that can wirelessly monitor time-dynamic behaviors of dopamine and relevant parameters in a simultaneous mode. An extensive range of examinations of molybdenum/tungsten disulfide (MoS₂ /WS₂ ) nanosheets and catalytic iron nanoparticles (Fe NPs) highlights the underlying mechanisms of strong chemical and target-specific responses to the neurotransmitters, along with theoretical modeling tools. Systematic characterizations demonstrate reversible, stable, and long-term operational performances of the degradable bioelectronics with excellent sensitivity and selectivity over those of non-dissolvable counterparts. A complete set of in vivo experiments with comparative analysis using carbon-fiber electrodes illustrates the capability for potential use as a clinically accessible tool to associated neurodegenerative diseases.

Local and distant cortical responses to single pulse intracranial stimulation in the human brain are differentially modulated by specific stimulation parameters

BACKGROUND

Electrical neuromodulation via direct electrical stimulation (DES) is an increasingly common therapy for a wide variety of neuropsychiatric diseases. Unfortunately, therapeutic efficacy is inconsistent, likely due to our limited understanding of the relationship between the massive stimulation parameter space and brain tissue responses.

OBJECTIVE

To better understand how different parameters induce varied neural responses, we systematically examined single pulse-induced cortico-cortico evoked potentials (CCEP) as a function of stimulation amplitude, duration, brain region, and whether grey or white matter was stimulated.

METHODS

We measured voltage peak amplitudes and area under the curve (AUC) of intracranially recorded stimulation responses as a function of distance from the stimulation site, pulse width, current injected, location relative to grey and white matter, and brain region stimulated (N = 52, n = 719 stimulation sites).

RESULTS

Increasing stimulation pulse width increased responses near the stimulation location. Increasing stimulation amplitude (current) increased both evoked amplitudes and AUC nonlinearly. Locally (<15 mm), stimulation at the boundary between grey and white matter induced larger responses. In contrast, for distant sites (>15 mm), white matter stimulation consistently produced larger responses than stimulation in or near grey matter. The stimulation location-response curves followed different trends for cingulate, lateral frontal, and lateral temporal cortical stimulation.

CONCLUSION

These results demonstrate that a stronger local response may require stimulation in the grey-white boundary while stimulation in the white matter could be needed for network activation. Thus, stimulation parameters tailored for a specific anatomical-functional outcome may be key to advancing neuromodulatory therapy.

Large-scale neural recordings with single neuron resolution using Neuropixels probes in human cortex

Recent advances in multi-electrode array technology have made it possible to monitor large neuronal ensembles at cellular resolution in animal models. In humans, however, current approaches restrict recordings to a few neurons per penetrating electrode or combine the signals of thousands of neurons in local field potential (LFP) recordings. Here we describe a new probe variant and set of techniques that enable simultaneous recording from over 200 well-isolated cortical single units in human participants during intraoperative neurosurgical procedures using silicon Neuropixels probes. We characterized a diversity of extracellular waveforms with eight separable single-unit classes, with differing firing rates, locations along the length of the electrode array, waveform spatial spread and modulation by LFP events such as inter-ictal discharges and burst suppression. Although some challenges remain in creating a turnkey recording system, high-density silicon arrays provide a path for studying human-specific cognitive processes and their dysfunction at unprecedented spatiotemporal resolution.

BrainQuake: An Open-Source Python Toolbox for the Stereoelectroencephalography Spatiotemporal Analysis

Intracranial stereoelectroencephalography (SEEG) is broadly used in the presurgical evaluation of intractable epilepsy, due to its high temporal resolution in neural activity recording and high spatial resolution within suspected epileptogenic zones. Neurosurgeons or technicians face the challenge of conducting a workflow of post-processing operations with the multimodal data (e.g., MRI, CT, and EEG) after the implantation surgery, such as brain surface reconstruction, electrode contact localization, and SEEG data analysis. Several software or toolboxes have been developed to take one or more steps in the workflow but without an end-to-end solution. In this study, we introduced BrainQuake, an open-source Python software for the SEEG spatiotemporal analysis, integrating modules and pipelines in surface reconstruction, electrode localization, seizure onset zone (SOZ) prediction based on ictal and interictal SEEG analysis, and final visualizations, each of which is highly automated with a user-friendly graphical user interface (GUI). BrainQuake also supports remote communications with a public server, which is facilitated with automated and standardized preprocessing pipelines, high-performance computing power, and data curation management to provide a time-saving and compatible platform for neurosurgeons and researchers.

LeGUI: A Fast and Accurate Graphical User Interface for Automated Detection and Anatomical Localization of Intracranial Electrodes

Accurate anatomical localization of intracranial electrodes is important for identifying the seizure foci in patients with epilepsy and for interpreting effects from cognitive studies employing intracranial electroencephalography. Localization is typically performed by coregistering postimplant computed tomography (CT) with preoperative magnetic resonance imaging (MRI). Electrodes are then detected in the CT, and the corresponding brain region is identified using the MRI. Many existing software packages for electrode localization chain together separate preexisting programs or rely on command line instructions to perform the various localization steps, making them difficult to install and operate for a typical user. Further, many packages provide solutions for some, but not all, of the steps needed for confident localization. We have developed software, Locate electrodes Graphical User Interface (LeGUI), that consists of a single interface to perform all steps needed to localize both surface and depth/penetrating intracranial electrodes, including coregistration of the CT to MRI, normalization of the MRI to the Montreal Neurological Institute template, automated electrode detection for multiple types of electrodes, electrode spacing correction and projection to the brain surface, electrode labeling, and anatomical targeting. The software is written in MATLAB, core image processing is performed using the Statistical Parametric Mapping toolbox, and standalone executable binaries are available for Windows, Mac, and Linux platforms. LeGUI was tested and validated on 51 datasets from two universities. The total user and computational time required to process a single dataset was approximately 1 h. Automatic electrode detection correctly identified 4362 of 4695 surface and depth electrodes with only 71 false positives. Anatomical targeting was verified by comparing electrode locations from LeGUI to locations that were assigned by an experienced neuroanatomist. LeGUI showed a 94% match with the 482 neuroanatomist-assigned locations. LeGUI combines all the features needed for fast and accurate anatomical localization of intracranial electrodes into a single interface, making it a valuable tool for intracranial electrophysiology research.

Size of the spatial correlation between ECoG and fMRI activity

Electrocorticography (ECoG) is typically employed to accurately identify the seizure focus as well as the location of brain functions to be spared during surgical resection in participants with drug-resistant epilepsy. Increasingly, this technique has become a powerful tool to map cognitive functions onto brain regions. Cortical mapping is more commonly investigated with functional MRI (fMRI), which measures blood-oxygen level dependent (BOLD) changes induced by neuronal activity. The multimodal integration between typical 3T fMRI activity maps and ECoG measurements can provide unique insight into the spatiotemporal aspects of cognition. However, the optimal integration of fMRI and ECoG requires fundamental insight into the spatial smoothness of the BOLD signal under each electrode. Here we use ECoG as ground truth for the extent of activity, as each electrode is thought to record from the cortical tissue directly underneath the contact, to estimate the spatial smoothness of the associated BOLD response at 3T fMRI. We compared the high-frequency broadband (HFB) activity recorded with ECoG while participants performed a motor task. Activity maps were obtained with fMRI at 3T for the same task in the same participant prior to surgery. We then correlated HFB power with the fMRI BOLD signal change in the area around each electrode. This latter measure was quantified by applying a 3D Gaussian kernel of varying width (sigma between 1 mm and 20 mm) to the fMRI maps including only gray-matter. We found that the correlation between HFB and BOLD activity increased sharply up to the point when the kernel width was set to 4 mm, which we defined as the kernel width of maximal spatial specificity. After this point, as the kernel width increased, the highest level of explained variance was reached at a kernel width of 9 mm for most participants. Intriguingly, maximal specificity was also limited to 4 mm for low-frequency bands, such as alpha and beta, but the kernel width with the highest explained variance was less spatially limited than the HFB. In summary, spatial specificity is limited to a kernel width of 4 mm but explained variance keeps on increasing as you average over more and more voxels containing the relatively noisy BOLD signal. Future multimodal studies should choose the kernel width based on their research goal. For maximal spatial specificity, ECoG electrodes are best compared to 3T fMRI with a kernel width of 4 mm. When optimizing the correlation between modalities, highest explained variance can be obtained at larger kernel widths of 9 mm, at the expense of spatial specificity. Finally, we release the complete pipeline so that researchers can estimate the most appropriate kernel width from their multimodal datasets.

An engram of intentionally forgotten information

Successful forgetting of unwanted memories is crucial for goal-directed behavior and mental wellbeing. While memory retention strengthens memory traces, it is unclear what happens to memory traces of events that are actively forgotten. Using intracranial EEG recordings from lateral temporal cortex, we find that memory traces for actively forgotten information are partially preserved and exhibit unique neural signatures. Memory traces of successfully remembered items show stronger encoding-retrieval similarity in gamma frequency patterns. By contrast, encoding-retrieval similarity of item-specific memory traces of actively forgotten items depend on activity at alpha/beta frequencies commonly associated with functional inhibition. Additional analyses revealed selective modification of item-specific patterns of connectivity and top-down information flow from dorsolateral prefrontal cortex to lateral temporal cortex in memory traces of intentionally forgotten items. These results suggest that intentional forgetting relies more on inhibitory top-down connections than intentional remembering, resulting in inhibitory memory traces with unique neural signatures and representational formats.

High γ Activity in Cortex and Hippocampus Is Correlated with Autonomic Tone during Sleep

Studies in animals have demonstrated a strong relationship between cortical and hippocampal activity, and autonomic tone. However, the extent, distribution, and nature of this relationship have not been investigated with intracranial recordings in humans during sleep. Cortical and hippocampal population neuronal firing was estimated from high γ band activity (HG) from 70 to 110 Hz in local field potentials (LFPs) recorded from 15 subjects (nine females) during nonrapid eye movement (NREM) sleep. Autonomic tone was estimated from heart rate variability (HRV). HG and HRV were significantly correlated in the hippocampus and multiple cortical sites in NREM stages N1-N3. The average correlation between HG and HRV could be positive or negative across patients given anatomic location and sleep stage and was most profound in lateral temporal lobe in N3, suggestive of greater cortical activity associated with sympathetic tone. Patient-wide correlation was related to δ band activity (1-4 Hz), which is known to be correlated with high γ activity during sleep. The percentage of statistically correlated channels was weaker in N1 and N2 as compared with N3, and was strongest in regions that have previously been associated with autonomic processes, such as anterior hippocampus and insula. The anatomic distribution of HRV-HG correlations during sleep did not reproduce those usually observed with positron emission tomography (PET) or functional magnetic resonance imaging (fMRI) during waking. This study aims to characterize the relationship between autonomic tone and neuronal firing rate during sleep and further studies are needed to investigate finer temporal resolutions, denser coverages, and different frequency bands in both waking and sleep.

Association Between Burst-Suppression Latency and Burst-Suppression Ratio Under Isoflurane or Adjuvant Drugs With Isoflurane Anesthesia in Mice

The same doses of anesthesia may yield varying depths of anesthesia in different patients. Clinical studies have revealed a possible causal relationship between deep anesthesia and negative short- and long-term patient outcomes. However, a reliable index and method of the clinical monitoring of deep anesthesia and detecting latency remain lacking. As burst-suppression is a characteristic phenomenon of deep anesthesia, the present study investigated the relationship between burst-suppression latency (BSL) and the subsequent burst-suppression ratio (BSR) to find an improved detection for the onset of intraoperative deep anesthesia. The mice were divided young, adult and old group treated with 1.0% or 1.5% isoflurane anesthesia alone for 2 h. In addition, the adult mice were pretreated with intraperitoneal injection of ketamine, dexmedetomidine, midazolam or propofol before they were anesthetized by 1.0% isoflurane for 2 h. Continuous frontal, parietal and occipital electroencephalogram (EEG) were acquired during anesthesia. The time from the onset of anesthesia to the first occurrence of burst-suppression was defined as BSL, while BSR was calculated as percentage of burst-suppression time that was spent in suppression periods. Under 1.0% isoflurane anesthesia, we found a negative correlation between BSL and BSR for EEG recordings obtained from the parietal lobes of young mice, from the parietal and occipital lobes of adult mice, and the occipital lobes of old mice. Under 1.5% isoflurane anesthesia, only the BSL calculated from EEG data obtained from the occipital lobe was negatively correlated with BSR in all mice. Furthermore, in adult mice receiving 1.0% isoflurane anesthesia, the co-administration of ketamine and midazolam, but not dexmedetomidine and propofol, significantly decreased BSL and increased BSR. Together, these data suggest that BSL can detect burst-suppression and predict the subsequent BSR under isoflurane anesthesia used alone or in combination with anesthetics or adjuvant drugs. Furthermore, the consistent negative correlation between BSL and BSR calculated from occipital EEG recordings recommends it as the optimal position for monitoring burst-suppression.

Region-Level Functional and Effective Network Analysis of Human Brain During Cognitive Task Engagement

Mental disorders are a major source of disability, with few effective treatments. It has recently been argued that these diseases might be effectively treated by focusing on decision-making, and specifically remediating decision-making deficits that act as "ingredients" in these disorders. Prior work showed that direct electrical brain stimulation can enhance human cognitive control, and consequently decision-making. This raises a challenge of detecting cognitive control lapses directly from electrical brain activity. Here, we demonstrate approaches to overcome that challenge. We propose a novel method, referred to as maximal variance node merging (MVNM), that merges nodes within a brain region to construct informative inter-region brain networks. We employ this method to estimate functional (correlational) and effective (causal) networks using local field potentials (LFP) during a cognitive behavioral task. The effective networks computed using convergent cross mapping differentiate task engagement from background neural activity with 85% median classification accuracy. We also derive task engagement networks (TENs): networks that constitute the most discriminative inter-region connections. Subsequent graph analysis illustrates the crucial role of the dorsolateral prefrontal cortex (dlPFC) in task engagement, consistent with a widely accepted model for cognition. We also show that task engagement is linked to prefrontal cortex theta (4-8 Hz) oscillations. We, therefore, identify objective biomarkers associated with task engagement. These approaches may generalize to other cognitive functions, forming the basis of a network-based approach to detecting and rectifying decision deficits.

Validating Patient-Specific Finite Element Models of Direct Electrocortical Stimulation

Direct electrocortical stimulation (DECS) with electrocorticography electrodes is an established therapy for epilepsy and an emerging application for stroke rehabilitation and brain-computer interfaces. However, the electrophysiological mechanisms that result in a therapeutic effect remain unclear. Patient-specific computational models are promising tools to predict the voltages in the brain and better understand the neural and clinical response to DECS, but the accuracy of such models has not been directly validated in humans. A key hurdle to modeling DECS is accurately locating the electrodes on the cortical surface due to brain shift after electrode implantation. Despite the inherent uncertainty introduced by brain shift, the effects of electrode localization parameters have not been investigated. The goal of this study was to validate patient-specific computational models of DECS against in vivo voltage recordings obtained during DECS and quantify the effects of electrode localization parameters on simulated voltages on the cortical surface. We measured intracranial voltages in six epilepsy patients during DECS and investigated the following electrode localization parameters: principal axis, Hermes, and Dykstra electrode projection methods combined with 0, 1, and 2 mm of cerebral spinal fluid (CSF) below the electrodes. Greater CSF depth between the electrode and cortical surface increased model errors and decreased predicted voltage accuracy. The electrode localization parameters that best estimated the recorded voltages across six patients with varying amounts of brain shift were the Hermes projection method and a CSF depth of 0 mm (r = 0.92 and linear regression slope = 1.21). These results are the first to quantify the effects of electrode localization parameters with in vivo intracranial recordings and may serve as the basis for future studies investigating the neuronal and clinical effects of DECS for epilepsy, stroke, and other emerging closed-loop applications.

Distinct cortical systems reinstate the content and context of episodic memories

Episodic recall depends upon the reinstatement of cortical activity present during the formation of a memory. Evidence from functional neuroimaging and invasive recordings in humans suggest that reinstatement organizes our memories by time or content, yet the neural systems involved in reinstating these unique types of information remain unclear. Here, combining computational modeling and intracranial recordings from 69 epilepsy patients, we show that two cortical systems uniquely reinstate the semantic content and temporal context of previously studied items during free recall. Examining either the posterior medial or anterior temporal networks, we find that forward encoding models trained on the brain's response to the temporal and semantic attributes of items can predict the serial position and semantic category of unseen items. During memory recall, these models uniquely link reinstatement of temporal context and semantic content to these posterior and anterior networks, respectively. These findings demonstrate how specialized cortical systems enable the human brain to target specific memories.

Microscale Physiological Events on the Human Cortical Surface

Despite ongoing advances in our understanding of local single-cellular and network-level activity of neuronal populations in the human brain, extraordinarily little is known about their "intermediate" microscale local circuit dynamics. Here, we utilized ultra-high-density microelectrode arrays and a rare opportunity to perform intracranial recordings across multiple cortical areas in human participants to discover three distinct classes of cortical activity that are not locked to ongoing natural brain rhythmic activity. The first included fast waveforms similar to extracellular single-unit activity. The other two types were discrete events with slower waveform dynamics and were found preferentially in upper cortical layers. These second and third types were also observed in rodents, nonhuman primates, and semi-chronic recordings from humans via laminar and Utah array microelectrodes. The rates of all three events were selectively modulated by auditory and electrical stimuli, pharmacological manipulation, and cold saline application and had small causal co-occurrences. These results suggest that the proper combination of high-resolution microelectrodes and analytic techniques can capture neuronal dynamics that lay between somatic action potentials and aggregate population activity. Understanding intermediate microscale dynamics in relation to single-cell and network dynamics may reveal important details about activity in the full cortical circuit.