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

Rodent and human seizures demonstrate a dynamic interplay with spreading depolarizations

Seizure termination has been linked to spreading depolarizations (SDs) in experimental models of epilepsy, and SDs have been suggested to protect against seizures. However, the precise mechanisms of seizure-associated SDs remain unclear. Additionally, the co-occurrence of SDs with human seizures remains debated. In this study, we found that SDs are a prominent feature following ictal events in both human clinical recordings and in a rodent model of ictogenesis. Approximately one-third of rodent seizure-like events (SLEs) associated with SDs, while all human seizures analyzed associated with propagating infraslow shifts, indicative of SDs. In rodents, SDs clustered towards the end of ictal events, resulting in significantly shorter SLEs and delayed onset of subsequent SLEs. Interestingly, SLEs with SDs displayed significantly more low gamma activity during ictal events than SLEs that did not end in SDs. Furthermore, we found no significant correlation between [K+]o levels and the likelihood of SLEs ending in SDs, questioning the role of [K+]o in SD induction during seizures. Interestingly, the human data demonstrate clear SD propagation during seizures and show that SDs appear and propagate in multiple brain regions simultaneously with ictal events. Collectively, these results indicate that SDs are a hallmark of ictal activity associated with seizure termination. Furthermore, these findings provide unique insight into the neuronal dynamics that promote SD induction by showing that increased low gamma activity during SLEs is more predictive of SD induction than [K+]o levels. Taken together, these findings provide rationale for further exploration of SDs to prematurely terminate life-threatening seizures.

VocalMind: A Stereotactic EEG Dataset for Vocalized, Mimed, and Imagined Speech in Tonal Language

Speech BCIs based on implanted electrodes hold significant promise for enhancing spoken communication through high temporal resolution and invasive neural sensing. Despite the potential, acquiring such data is challenging due to its invasive nature, and publicly available datasets, particularly for tonal languages, are limited. In this study, we introduce VocalMind, a stereotactic electroencephalography (sEEG) dataset focused on Mandarin Chinese, a tonal language. This dataset includes sEEG-speech parallel recordings from three distinct speech modes, namely vocalized speech, mimed speech, and imagined speech, at both word and sentence levels, totaling over one hour of intracranial neural recordings related to speech production. This paper also presents a baseline model as the reference model for future studies, at the same time, ensuring the integrity of the dataset. The diversity of tasks and the substantial data volume provide a valuable resource for developing advanced algorithms for speech decoding, thereby advancing BCI research for spoken communication.

At-Home Movement State Classification Using Totally Implantable Bidirectional Cortical-Basal Ganglia Neural Interface

Movement decoding from invasive human recordings typically relies on a distributed system employing advanced machine learning algorithms programmed into an external computer for state classification. These brain-computer interfaces are limited to short-term studies in laboratory settings that may not reflect behavior and neural states in the real world. The development of implantable devices with sensing capabilities is revolutionizing the study and treatment of brain circuits. However, it is unknown whether these devices can decode natural movement state from recorded neural activity or accurately classify states in real-time using on-board algorithms. Here, using a totally implanted sensing-enabled neurostimulator to perform long-term, at-home recordings from the motor cortex and pallidum of four subjects with Parkinson's disease, we successfully identified highly sensitive and specific personalized signatures of gait state, as determined by wearable sensors. Additionally, we demonstrated the feasibility of using at-home data to generate biomarkers compatible with the classifier embedded on-board the neurostimulator. These findings offer a pipeline for ecologically valid movement biomarker identification that can advance therapy across a variety of diseases.

Deep brain stimulation-entrained gamma oscillations in chronic home recordings in Parkinson's disease

BACKGROUND

In Parkinson's disease, invasive brain recordings show that dopaminergic medication can induce narrowband gamma rhythms in the motor cortex and subthalamic nucleus, which co-fluctuate with dyskinesia scores. Deep brain stimulation can entrain these gamma oscillations to a subharmonic stimulation frequency. However, the incidence of entrainment during chronic therapeutic stimulation, its relationship to the basal ganglia stimulation site, and its effect on dyskinesia remain unknown.

OBJECTIVE

Determine whether the behavioral effects and statistical properties of levodopa-induced gamma oscillations are altered when entrained with deep brain stimulation.

METHODS

We used a sensing-enabled deep brain stimulator system, attached to both motor cortex and subthalamic (n = 15) or pallidal (n = 5) leads, to record 993 h of multisite field potentials, with 656 h recorded prior to initiating stimulation. 13 subjects (20 hemispheres) with Parkinson's disease (1/13 female, mean age 59 ± 9 years) streamed data while at home on their usual antiparkinsonian medication. Recordings during stimulation occurred at least five months after initiating stimulation.

RESULTS

Cortical entrained gamma oscillations were detected in 4/5 hemispheres undergoing pallidal stimulation and 12/15 hemispheres undergoing subthalamic stimulation. Entraining levodopa-induced gamma oscillations at either site reduced their prodyskinetic effects. Cortical entrained gamma oscillations had reduced variance in peak frequency, increased spectral power, and higher variance in spectral power than levodopa-induced gamma oscillations.

CONCLUSION

Stimulation-entrained gamma oscillations are functionally and physiologically distinct from levodopa-induced gamma oscillations that occur in the absence of deep brain stimulation. Understanding the discrepancies between types of gamma oscillations may improve programming protocols.

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.

Decoding speech intent from non-frontal cortical areas

Objective. Brain machine interfaces (BMIs) that can restore speech have predominantly focused on decoding speech signals from the speech motor cortices. A few studies have shown some information outside the speech motor cortices, such as in parietal and temporal lobes, that also may be useful for BMIs. The ability to use information from outside the frontal lobe could be useful not only for people with locked-in syndrome, but also to people with frontal lobe damage, which can cause nonfluent aphasia or apraxia of speech. However, temporal and parietal lobes are predominantly involved in perceptive speech processing and comprehension. Therefore, to be able to use signals from these areas in a speech BMI, it is important to ascertain that they are related to production. Here, using intracranial recordings, we sought evidence for whether, when and where neural information related to speech intent could be found in the temporal and parietal corticesApproach. Using intracranial recordings, we examined neural activity across temporal and parietal cortices to identify signals associated with speech intent. We employed causal information to distinguish speech intent from resting states and other language-related processes, such as comprehension and working memory. Neural signals were analyzed for their spatial distribution and temporal dynamics to determine their relevance to speech production.Main results. Causal information enabled us to distinguish speech intent from resting state and other processes involved in language processing or working memory. Information related to speech intent was distributed widely across the temporal and parietal lobes, including superior temporal, medial temporal, angular, and supramarginal gyri.Significance. Loss of communication due to neurological diseases can be devastating. While speech BMIs have made strides in decoding speech from frontal lobe signals, our study reveals that the temporal and parietal cortices contain information about speech production intent that can be causally decoded prior to the onset of voice. This information is distributed across a large network. This information can be used to improve current speech BMIs and potentially expand the patient population for speech BMIs to include people with frontal lobe damage from stroke or traumatic brain injury.

Lack of context modulation in human single neuron responses in the medial temporal lobe

In subjects implanted with intracranial electrodes, we use two different stories involving the same person (or place) to evaluate whether and to what extent context modulates human single-neuron responses. Nearly all neurons (97% during encoding and 100% during recall) initially responding to a person/place do not modulate their response with context. Likewise, nearly none (<1%) of the initially non-responsive neurons show conjunctive coding, responding to particular persons/places in a particular context during the tasks. In line with these findings, taking all neurons together it is possible to decode the person/place being depicted in each story, but not the particular story. Moreover, the neurons show consistent results across encoding and recall of the stories and during passive viewing of pictures. These results suggest a context invariant, non-conjunctive coding of memories at the single-neuron level in the human hippocampus and amygdala, in contrast to what has been described in other species.

Chronic adaptive deep brain stimulation versus conventional stimulation in Parkinson's disease: a blinded randomized feasibility trial

Deep brain stimulation (DBS) is a widely used therapy for Parkinson's disease (PD) but lacks dynamic responsiveness to changing clinical and neural states. Feedback control might improve therapeutic effectiveness, but the optimal control strategy and additional benefits of 'adaptive' neurostimulation are unclear. Here we present the results of a blinded randomized cross-over pilot trial aimed at determining the neural correlates of specific motor signs in individuals with PD and the feasibility of using these signals to drive adaptive DBS. Four male patients with PD were recruited from a population undergoing DBS implantation for motor fluctuations, with each patient receiving adaptive DBS and continuous DBS. We identified stimulation-entrained gamma oscillations in the subthalamic nucleus or motor cortex as optimal markers of high versus low dopaminergic states and their associated residual motor signs in all four patients. We then demonstrated improved motor symptoms and quality of life with adaptive compared to clinically optimized standard stimulation. The results of this pilot trial highlight the promise of personalized adaptive neurostimulation in PD based on data-driven selection of neural signals. Furthermore, these findings provide the foundation for further larger clinical trials to evaluate the efficacy of personalized adaptive neurostimulation in PD and other neurological disorders. ClinicalTrials.gov registration: NCT03582891 .

Modeling and Optimizing Deep Brain Stimulation to Enhance Gait in Parkinson's Disease: Personalized Treatment with Neurophysiological Insights

Although high-frequency deep brain stimulation (DBS) is effective at relieving many motor symptoms of Parkinson's disease (PD), its effects on gait can be variable and unpredictable. This is due to 1) a lack of standardized and robust metrics for gait assessment in PD patients, 2) the challenges of performing a thorough evaluation of all the stimulation parameters space that can alter gait, and 3) a lack of understanding for impacts of stimulation on the neurophysiological signatures of walking. In this study, our goal was to develop a data-driven approach to identify optimal, personalized DBS stimulation parameters to improve gait in PD patients and identify the neurophysiological signature of improved gait. Local field potentials from the globus pallidus and electrocorticography from the motor cortex of three PD patients were recorded using an implanted bidirectional neural stimulator during overground walking. A walking performance index (WPI) was developed to assess gait metrics with high reliability. DBS frequency, amplitude, and pulse width on the "clinically-optimized" stimulation contact were then systemically changed to study their impacts on gait metrics and underlying neural dynamics. We developed a Gaussian Process Regressor (GPR) model to map the relationship between DBS settings and the WPI. Using this model, we identified and validated personalized DBS settings that significantly improved gait metrics. Linear mixed models were employed to identify neural spectral features associated with enhanced walking performance. We demonstrated that improved walking performance was linked to the modulation of neural activity in specific frequency bands, with reduced beta band power in the pallidum and increased alpha band pallidal-motor cortex coherence synchronization during key moments of the gait cycle. Integrating WPI and GPR to optimize DBS parameters underscores the importance of developing and understanding personalized, data-driven interventions for gait improvement in PD.

The human claustrum tracks slow waves during sleep

Slow waves are a distinguishing feature of non-rapid-eye-movement (NREM) sleep, an evolutionarily conserved process critical for brain function. Non-human studies suggest that the claustrum, a small subcortical nucleus, coordinates slow waves. We show that, in contrast to neurons from other brain regions, claustrum neurons in the human brain increase their spiking activity and track slow waves during NREM sleep, suggesting that the claustrum plays a role in coordinating human sleep architecture.

Single-neuron representations of odours in the human brain

Olfaction is a fundamental sensory modality that guides animal and human behaviour1,2. However, the underlying neural processes of human olfaction are still poorly understood at the fundamental-that is, the single-neuron-level. Here we report recordings of single-neuron activity in the piriform cortex and medial temporal lobe in awake humans performing an odour rating and identification task. We identified odour-modulated neurons within the piriform cortex, amygdala, entorhinal cortex and hippocampus. In each of these regions, neuronal firing accurately encodes odour identity. Notably, repeated odour presentations reduce response firing rates, demonstrating central repetition suppression and habituation. Different medial temporal lobe regions have distinct roles in odour processing, with amygdala neurons encoding subjective odour valence, and hippocampal neurons predicting behavioural odour identification performance. Whereas piriform neurons preferably encode chemical odour identity, hippocampal activity reflects subjective odour perception. Critically, we identify that piriform cortex neurons reliably encode odour-related images, supporting a multimodal role of the human piriform cortex. We also observe marked cross-modal coding of both odours and images, especially in the amygdala and piriform cortex. Moreover, we identify neurons that respond to semantically coherent odour and image information, demonstrating conceptual coding schemes in olfaction. Our results bridge the long-standing gap between animal models and non-invasive human studies and advance our understanding of odour processing in the human brain by identifying neuronal odour-coding principles, regional functional differences and cross-modal integration.

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

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

SEEG4D: a tool for 4D visualization of stereoelectroencephalography data

Epilepsy is a prevalent and serious neurological condition which impacts millions of people worldwide. Stereoelectroencephalography (sEEG) is used in cases of drug resistant epilepsy to aid in surgical resection planning due to its high spatial resolution and ability to visualize seizure onset zones. For accurate localization of the seizure focus, sEEG studies combine pre-implantation magnetic resonance imaging, post-implant computed tomography to visualize electrodes, and temporally recorded sEEG electrophysiological data. Many tools exist to assist in merging multimodal spatial information; however, few allow for an integrated spatiotemporal view of the electrical activity. In the current work, we present SEEG4D, an automated tool to merge spatial and temporal data into a complete, four-dimensional virtual reality (VR) object with temporal electrophysiology that enables the simultaneous viewing of anatomy and seizure activity for seizure localization and presurgical planning. We developed an automated, containerized pipeline to segment tissues and electrode contacts. Contacts are aligned with electrical activity and then animated based on relative power. SEEG4D generates models which can be loaded into VR platforms for viewing and planning with the surgical team. Automated contact segmentation locations are within 1 mm of trained raters and models generated show signal propagation along electrodes. Critically, spatial-temporal information communicated through our models in a VR space have potential to enhance sEEG pre-surgical planning.

Do Interictal Epileptiform Discharges and Brain Responses to Electrical Stimulation Come From the Same Location? An Advanced Source Localization Solution

Identification of seizure sources in the brain is of paramount importance, particularly for drug-resistant epilepsy patients who may require surgical operation. Interictal epileptiform discharges (IEDs), which may or may not be frequent, are known to originate from seizure networks. Delayed responses (DRs) to brain electrical stimulation have been recently discovered. If DRs and IEDs come from the same location and the DRs can be accurately localized, there will be a significant step in identification of the seizure sources. The solution to this important question has been investigated in this paper. For this, we have exploited the morphology of these spike-type events, as well as the variability in their temporal locations, to develop new constraints for an adaptive Bayesian beamformer that outperforms the conventional and recently proposed beamformers even for identifying correlated sources. This beamformer is applied to an array (a.k.a mat) of cortical EEG electrodes. The developed approach has been tested on 300 data segments from five epileptic patients included in this study, which clinically represent a large population of candidates for surgical treatment. As the significant outcome of applying this beamformer, it is very likely (if not certain) that for an epileptic subject, the IEDs and DRs originate from the same location in the brain. This paves the way for a quick identification of the source(s) of seizure in the brain.

On the speech envelope in the cortical tracking of speech

The synchronization between the speech envelope and neural activity in auditory regions, referred to as cortical tracking of speech (CTS), plays a key role in speech processing. The method selected for extracting the envelope is a crucial step in CTS measurement, and the absence of a consensus on best practices among the various methods can influence analysis outcomes and interpretation. Here, we systematically compare five standard envelope extraction methods the absolute value of Hilbert transform (absHilbert), gammatone filterbanks, heuristic approach, Bark scale, and vocalic energy), analyzing their impact on the CTS. We present performance metrics for each method based on the recording of brain activity from participants listening to speech in clear and noisy conditions, utilizing intracranial EEG, MEG and EEG data. As expected, we observed significant CTS in temporal brain regions below 10 Hz across all datasets, regardless of the extraction methods. In general, the gammatone filterbanks approach consistently demonstrated superior performance compared to other methods. Results from our study can guide scientists in the field to make informed decisions about the optimal analysis to extract the CTS, contributing to advancing the understanding of the neuronal mechanisms implicated in CTS.

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.

Motor network gamma oscillations in chronic home recordings predict dyskinesia in Parkinson's disease

In Parkinson's disease, imbalances between 'antikinetic' and 'prokinetic' patterns of neuronal oscillatory activity are related to motor dysfunction. Invasive brain recordings from the motor network have suggested that medical or surgical therapy can promote a prokinetic state by inducing narrowband gamma rhythms (65-90 Hz). Excessive narrowband gamma in the motor cortex promotes dyskinesia in rodent models, but the relationship between narrowband gamma and dyskinesia in humans has not been well established. To assess this relationship, we used a sensing-enabled deep brain stimulator system, attached to both motor cortex and basal ganglia (subthalamic or pallidal) leads, paired with wearable devices that continuously tracked motor signs in the contralateral upper limbs. We recorded 984 h of multisite field potentials in 30 hemispheres of 16 subjects with Parkinson's disease (2/16 female, mean age 57 ± 12 years) while at home on usual antiparkinsonian medications. Recordings were done 2-4 weeks after implantation, prior to starting therapeutic stimulation. Narrowband gamma was detected in the precentral gyrus, subthalamic nucleus or both structures on at least one side of 92% of subjects with a clinical history of dyskinesia. Narrowband gamma was not detected in the globus pallidus. Narrowband gamma spectral power in both structures co-fluctuated similarly with contralateral wearable dyskinesia scores (mean correlation coefficient of ρ = 0.48 with a range of 0.12-0.82 for cortex, ρ = 0.53 with a range of 0.5-0.77 for subthalamic nucleus). Stratification analysis showed the correlations were not driven by outlier values, and narrowband gamma could distinguish 'on' periods with dyskinesia from 'on' periods without dyskinesia. Time lag comparisons confirmed that gamma oscillations herald dyskinesia onset without a time lag in either structure when using 2-min epochs. A linear model incorporating the three oscillatory bands (beta, theta/alpha and narrowband gamma) increased the predictive power of dyskinesia for several subject hemispheres. We further identified spectrally distinct oscillations in the low gamma range (40-60 Hz) in three subjects, but the relationship of low gamma oscillations to dyskinesia was variable. Our findings support the hypothesis that excessive oscillatory activity at 65-90 Hz in the motor network tracks with dyskinesia similarly across both structures, without a detectable time lag. This rhythm may serve as a promising control signal for closed-loop deep brain stimulation using either cortical or subthalamic detection.

More widespread and rigid neuronal representation of reward expectation underlies impulsive choices

Impulsive choices prioritize smaller, more immediate rewards over larger, delayed, or potentially uncertain rewards. Impulsive choices are a critical aspect of substance use disorders and maladaptive decision-making across the lifespan. Here, we sought to understand the neuronal underpinnings of expected reward and risk estimation on a trial-by-trial basis during impulsive choices. To do so, we acquired electrical recordings from the human brain while participants carried out a risky decision-making task designed to measure choice impulsivity. Behaviorally, we found a reward-accuracy tradeoff, whereby more impulsive choosers were more accurate at the task, opting for a more immediate reward while compromising overall task performance. We then examined how neuronal populations across frontal, temporal, and limbic brain regions parametrically encoded reinforcement learning model variables, namely reward and risk expectation and surprise, across trials. We found more widespread representations of reward value expectation and prediction error in more impulsive choosers, whereas less impulsive choosers preferentially represented risk expectation. A regional analysis of reward and risk encoding highlighted the anterior cingulate cortex for value expectation, the anterior insula for risk expectation and surprise, and distinct regional encoding between impulsivity groups. Beyond describing trial-by-trial population neuronal representations of reward and risk variables, these results suggest impaired inhibitory control and model-free learning underpinnings of impulsive choice. These findings shed light on neural processes underlying reinforced learning and decision-making in uncertain environments and how these processes may function in psychiatric disorders.

High-Gamma Activity Is Coupled to Low-Gamma Oscillations in Precentral Cortices and Modulates with Movement and Speech

Planning and executing motor behaviors requires coordinated neural activity among multiple cortical and subcortical regions of the brain. Phase-amplitude coupling between the high-gamma band amplitude and the phase of low frequency oscillations (theta, alpha, beta) has been proposed to reflect neural communication, as has synchronization of low-gamma oscillations. However, coupling between low-gamma and high-gamma bands has not been investigated. Here, we measured phase-amplitude coupling between low- and high-gamma in monkeys performing a reaching task and in humans either performing finger-flexion or word-reading tasks. We found significant coupling between low-gamma phase and high-gamma amplitude in multiple sensorimotor and premotor cortices of both species during all tasks. This coupling modulated with the onset of movement. These findings suggest that interactions between the low and high gamma bands are markers of network dynamics related to movement and speech generation.

The human claustrum tracks slow waves during sleep

Slow waves are a distinguishing feature of non-rapid-eye-movement (NREM) sleep, an evolutionarily conserved process critical for brain function. Non-human studies posit that the claustrum, a small subcortical nucleus, coordinates slow waves. We recorded claustrum neurons in humans during sleep. In contrast to neurons from other brain regions, claustrum neurons increased their activity and tracked slow waves during NREM sleep suggesting that the claustrum plays a role in human sleep architecture.

Characterization of spatiotemporal dynamics of binary and graded tonic pain in humans using intracranial recordings

Pain is a complex experience involving sensory, emotional, and cognitive aspects, and multiple networks manage its processing in the brain. Examining how pain transforms into a behavioral response can shed light on the networks' relationships and facilitate interventions to treat chronic pain. However, studies using high spatial and temporal resolution methods to investigate the neural encoding of pain and its psychophysical correlates have been limited. We recorded from intracranial stereo-EEG (sEEG) electrodes implanted in sixteen different brain regions of twenty patients who underwent psychophysical pain testing consisting of a tonic thermal stimulus to the hand. Broadband high-frequency local field potential amplitude (HFA; 70-150 Hz) was isolated to investigate the relationship between the ongoing neural activity and the resulting psychophysical pain evaluations. Two different generalized linear mixed-effects models (GLME) were employed to assess the neural representations underlying binary and graded pain psychophysics. The first model examined the relationship between HFA and whether the patient responded "yes" or "no" to whether the trial was painful. The second model investigated the relationship between HFA and how painful the stimulus was rated on a visual analog scale. GLMEs revealed that HFA in the inferior temporal gyrus (ITG), superior frontal gyrus (SFG), and superior temporal gyrus (STG) predicted painful responses at stimulus onset. An increase in HFA in the orbitofrontal cortex (OFC), SFG, and striatum predicted pain responses at stimulus offset. Numerous regions, including the anterior cingulate cortex, hippocampus, IFG, MTG, OFC, and striatum, predicted the pain rating at stimulus onset. However, only the amygdala and fusiform gyrus predicted increased pain ratings at stimulus offset. We characterized the spatiotemporal representations of binary and graded painful responses during tonic pain stimuli. Our study provides evidence from intracranial recordings that the neural encoding of psychophysical pain changes over time during a tonic thermal stimulus, with different brain regions being predictive of pain at the beginning and end of the stimulus.

YAEL: Your Advanced Electrode Localizer

Intracranial electroencephalography (iEEG) provides a unique opportunity to record and stimulate neuronal populations in the human brain. A key step in neuroscience inference from iEEG is localizing the electrodes relative to individual subject anatomy and identified regions in brain atlases. We describe a new software tool, Your Advanced Electrode Localizer (YAEL), that provides an integrated solution for every step of the electrode localization process. YAEL is compatible with all common data formats to provide an easy-to-use, drop-in replacement for problematic existing workflows that require users to grapple with multiple programs and interfaces. YAEL's automatic extrapolation and interpolation functions speed localization, especially important in patients with many implanted stereotactic (sEEG) electrode shafts. The graphical user interface is presented in a web browser for broad compatibility and includes an interactive 3D viewer for easier localization of nearby sEEG contacts. After localization is complete, users may enter or import data into YAEL's 3D viewer to create publication-ready visualizations of electrodes and brain anatomy, including identified brain areas from atlases; the response to experimental tasks measured with iEEG; and clinical measures such as epileptiform activity or the results of electrical stimulation mapping. YAEL is free and open source and does not depend on any commercial software. Installation instructions for Mac, Windows, and Linux are available at https://yael.wiki.

Personalized chronic adaptive deep brain stimulation outperforms conventional stimulation in Parkinson's disease

Deep brain stimulation is a widely used therapy for Parkinson's disease (PD) but currently lacks dynamic responsiveness to changing clinical and neural states. Feedback control has the potential to improve therapeutic effectiveness, but optimal control strategy and additional benefits of "adaptive" neurostimulation are unclear. We implemented adaptive subthalamic nucleus stimulation, controlled by subthalamic or cortical signals, in three PD patients (five hemispheres) during normal daily life. We identified neurophysiological biomarkers of residual motor fluctuations using data-driven analyses of field potentials over a wide frequency range and varying stimulation amplitudes. Narrowband gamma oscillations (65-70 Hz) at either site emerged as the best control signal for sensing during stimulation. A blinded, randomized trial demonstrated improved motor symptoms and quality of life compared to clinically optimized standard stimulation. Our approach highlights the promise of personalized adaptive neurostimulation based on data-driven selection of control signals and may be applied to other neurological disorders.

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.

Psychophysical pain encoding in the cingulate cortex predicts responsiveness of electrical stimulation

Background

The anterior cingulate cortex (ACC) plays an important role in the cognitive and emotional processing of pain. Prior studies have used deep brain stimulation (DBS) to treat chronic pain, but results have been inconsistent. This may be due to network adaptation over time and variable causes of chronic pain. Identifying patient-specific pain network features may be necessary to determine patient candidacy for DBS.

Hypothesis

Cingulate stimulation would increase patients' hot pain thresholds if non-stimulation 70-150 Hz activity encoded psychophysical pain responses.

Methods

In this study, four patients who underwent intracranial monitoring for epilepsy monitoring participated in a pain task. They placed their hand on a device capable of eliciting thermal pain for five seconds and rated their pain. We used these results to determine the individual's thermal pain threshold with and without electrical stimulation. Two different types of generalized linear mixed-effects models (GLME) were employed to assess the neural representations underlying binary and graded pain psychophysics.

Results

The pain threshold for each patient was determined from the psychometric probability density function. Two patients had a higher pain threshold with stimulation than without, while the other two patients had no difference. We also evaluated the relationship between neural activity and pain responses. We found that patients who responded to stimulation had specific time windows where high-frequency activity was associated with increased pain ratings.

Conclusion

Stimulation of cingulate regions with increased pain-related neural activity was more effective at modulating pain perception than stimulating non-responsive areas. Personalized evaluation of neural activity biomarkers could help identify the best target for stimulation and predict its effectiveness in future studies evaluating DBS.

Characterization of spatiotemporal dynamics of binary and graded tonic pain in humans using intracranial recordings

Pain is a complex experience involving sensory, emotional, and cognitive aspects, and multiple networks manage its processing in the brain. Examining how pain transforms into a behavioral response can shed light on the networks' relationships and facilitate interventions to treat chronic pain. However, studies using high spatial and temporal resolution methods to investigate the neural encoding of pain and its psychophysical correlates have been limited. We recorded from intracranial stereo-EEG (sEEG) electrodes implanted in sixteen different brain regions of twenty patients who underwent psychophysical pain testing consisting of a tonic thermal stimulus to the hand. Broadband high-frequency local field potential amplitude (HFA; 70-150 Hz) was isolated to investigate the relationship between the ongoing neural activity and the resulting psychophysical pain evaluations. Two different generalized linear mixed-effects models (GLME) were employed to assess the neural representations underlying binary and graded pain psychophysics. The first model examined the relationship between HFA and whether the patient responded "yes" or "no" to whether the trial was painful. The second model investigated the relationship between HFA and how painful the stimulus was rated on a visual analog scale. GLMEs revealed that HFA in the inferior temporal gyrus (ITG), superior frontal gyrus (SFG), and superior temporal gyrus (STG) predicted painful responses at stimulus onset. An increase in HFA in the orbitofrontal cortex (OFC), SFG, and striatum predicted pain responses at stimulus offset. Numerous regions including the anterior cingulate cortex, hippocampus, IFG, MTG, OFC, and striatum, predicted the pain rating at stimulus onset. However, only the amygdala and fusiform gyrus predicted increased pain ratings at stimulus offset. We characterized the spatiotemporal representations of binary and graded painful responses during tonic pain stimuli. Our study provides evidence from intracranial recordings that the neural encoding of psychophysical pain changes over time during a tonic thermal stimulus, with different brain regions being predictive of pain at the beginning and end of the stimulus.

High-gamma activity is coupled to low-gamma oscillations in precentral cortices and modulates with movement and speech

Planning and executing motor behaviors requires coordinated neural activity among multiple cortical and subcortical regions of the brain. Phase-amplitude coupling between the high-gamma band amplitude and the phase of low frequency oscillations (theta, alpha, beta) has been proposed to reflect neural communication, as has synchronization of low-gamma oscillations. However, coupling between low-gamma and high-gamma bands has not been investigated. Here, we measured phase-amplitude coupling between low- and high-gamma in monkeys performing a reaching task and in humans either performing finger movements or speaking words aloud. We found significant coupling between low-gamma phase and high-gamma amplitude in multiple sensorimotor and premotor cortices of both species during all tasks. This coupling modulated with the onset of movement. These findings suggest that interactions between the low and high gamma bands are markers of network dynamics related to movement and speech generation.

Using chronic recordings from a closed-loop neurostimulation system to capture seizures across multiple thalamic nuclei

We report the case of a patient with unilateral diffuse frontotemporal epilepsy in whom we implanted a responsive neurostimulation system with leads spanning the anterior and centromedian nucleus of the thalamus. During chronic recording, ictal activity in the centromedian nucleus consistently preceded the anterior nucleus, implying a temporally organized seizure network involving the thalamus. With stimulation, the patient had resolution of focal impaired awareness seizures and secondarily generalized seizures. This report describes chronic recordings of seizure activity from multiple thalamic nuclei within a hemisphere and demonstrates the potential efficacy of closed-loop neurostimulation of multiple thalamic nuclei to control seizures.

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 structural connectivity informs outcomes of responsive neurostimulation for temporal lobe epilepsy

OBJECTIVE

Responsive neurostimulation is an effective therapy for patients with refractory mesial temporal lobe epilepsy. However, clinical outcomes are variable, few patients become seizure-free, and the optimal stimulation location is currently undefined. The aim of this study was to quantify responsive neurostimulation in the mesial temporal lobe, identify stimulation-dependent networks associated with seizure reduction, and determine if stimulation location or stimulation-dependent networks inform outcomes.

METHODS

We modeled patient-specific volumes of tissue activated and created probabilistic stimulation maps of local regions of stimulation across a retrospective cohort of 22 patients with mesial temporal lobe epilepsy. We then mapped the network stimulation effects by seeding tractography from the volume of tissue activated with both patient-specific and normative diffusion-weighted imaging. We identified networks associated with seizure reduction across patients using the patient-specific tractography maps and then predicted seizure reduction across the cohort.

RESULTS

Patient-specific stimulation-dependent connectivity was correlated with responsive neurostimulation effectiveness after cross-validation (p = .03); however, normative connectivity derived from healthy subjects was not (p = .44). Increased connectivity from the volume of tissue activated to the medial prefrontal cortex, cingulate cortex, and precuneus was associated with greater seizure reduction.

SIGNIFICANCE

Overall, our results suggest that the therapeutic effect of responsive neurostimulation may be mediated by specific networks connected to the volume of tissue activated. In addition, patient-specific tractography was required to identify structural networks correlated with outcomes. It is therefore likely that altered connectivity in patients with epilepsy may be associated with the therapeutic effect and that utilizing patient-specific imaging could be important for future studies. The structural networks identified here may be utilized to target stimulation in the mesial temporal lobe and to improve seizure reduction for patients treated with responsive neurostimulation.

A hierarchical anatomical framework and workflow for organizing stereotactic encephalography in epilepsy

Stereotactic electroencephalography (SEEG) is an increasingly utilized method for invasive monitoring in patients with medically intractable epilepsy. Yet, the lack of standardization for labeling electrodes hinders communication among clinicians. A rational clustering of contacts based on anatomy rather than arbitrary physical leads may help clinical neurophysiologists interpret seizure networks. We identified SEEG electrodes on post‐implant CTs and registered them to preoperative MRIs segmented according to an anatomical atlas. Individual contacts were automatically assigned to anatomical areas independent of lead. These contacts were then organized using a hierarchical anatomical schema for display and interpretation. Bipolar‐referenced signal cross‐correlations were used to compare the similarity of grouped signals within a conventional montage versus this anatomical montage. As a result, we developed a hierarchical organization for SEEG contacts using well‐accepted, free software that is based solely on their post‐implant anatomical location. When applied to three example SEEG cases for epilepsy, clusters of contacts that were anatomically related collapsed into standardized groups. Qualitatively, seizure events organized using this framework were better visually clustered compared to conventional schemes. Quantitatively, signals grouped by anatomical region were more similar to each other than electrode‐based groups as measured by Pearson correlation. Further, we uploaded visualizations of SEEG reconstructions into the electronic medical record, rendering them durably useful given the interpretable electrode labels. In conclusion, we demonstrate a standardized, anatomically grounded approach to the organization of SEEG neuroimaging and electrophysiology data that may enable improved communication among and across surgical epilepsy teams and promote a clearer view of individual seizure networks.