Automatic and Precise Localization and Cortical Labeling of Subdural and Depth Intracranial Electrodes

The changes in neural complexity and connectivity in thalamocortical and cortico-cortical systems after propofol-induced unconsciousness in different temporal scales

Existing studies have indicated neural activity across diverse temporal and spatial scales. However, the alterations in complexity, functional connectivity, and directional connectivity within the thalamocortical and corticocortical systems across various scales during propofol-induced unconsciousness remain uncertain. We analyzed the stereo-electroencephalography (SEEG) from wakefulness to unconsciousness among the brain regions of the prefrontal cortex, temporal lobe, and anterior nucleus of the thalamus. The complexity (examined by permutation entropy (PE)), functional connectivity (permutation mutual information (PMI)), and directional connectivity (symbolic conditional mutual information (SCMI) and directionality index (DI)) were calculated across various scales. In the lower-band frequency (0.1-45 Hz) SEEG, after the loss of consciousness, PE significantly decreased (p < 0.001) in all regions and scales, except for the thalamus, which remained relatively unchanged at large scales (τ=32 ms). Following the loss of consciousness, inter-regional PMI either significantly increased or remained stable across different scales (τ=4 ms to 32 ms). During the unconscious state, SCMI between brain regions exhibited inconsistent changes across scales. In the late unconscious stage, the inter-regional DI across all scales indicated a shift from a balanced state of information flow between brain regions to a pattern where the prefrontal cortex and thalamus drive the temporal lobe. Our findings demonstrate that propofol-induced unconsciousness is associated with reduced cortical complexity, diverse functional connectivity, and a disrupted balance of information integration among thalamocortical and cortico-cortical systems. This study enhances the theoretical understanding of anesthetic-induced loss of consciousness by elucidating the scale- and region-specific effects of propofol on thalamocortical and cortico-cortical systems.

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

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

Electrophysiological signatures underlying variability in human memory consolidation

We experience countless pieces of new information each day, but remembering them later depends on firmly instilling memory storage in the brain. Numerous studies have implicated non-rapid eye movement (NREM) sleep in consolidating memories via interactions between hippocampus and cortex. However, the temporal dynamics of this hippocampal-cortical communication and the concomitant neural oscillations during memory reactivations remains unclear. To address this issue, the present study used the procedure of targeted memory reactivation (TMR) following learning of object-location associations to selectively reactivate memories during human NREM sleep. Cortical pattern reactivation and hippocampal-cortical coupling were measured with intracranial EEG recordings in patients with epilepsy. We found that TMR produced variable amounts of memory enhancement across a set of object-location associations. Successful TMR increased hippocampal ripples and cortical spindles, apparent during two discrete sweeps of reactivation. The first reactivation sweep was accompanied by increased hippocampal-cortical communication and hippocampal ripple events coupled to local cortical activity (cortical ripples and high-frequency broadband activity). In contrast, hippocampal-cortical coupling decreased during the second sweep, while increased cortical spindle activity indicated continued cortical processing to achieve long-term storage. Taken together, our findings show how dynamic patterns of item-level reactivation and hippocampal-cortical communication support memory enhancement during NREM sleep.

Connectome-based disentangling of epilepsy networks from insular stereoelectroencephalographic leads

Objective

Epilepsy is considered as a network disorder of interacting brain regions. The propagation of local epileptic activity from the seizure onset zone (SOZ) along neuronal networks determines the semiology of seizures. However, in highly interconnected brain regions such as the insula, the association between the SOZ and semiology is blurred necessitating invasive stereoelectroencephalography (SEEG). Normative connectomes on MRI data enable to link different symptoms and lesion locations to a common functional network. The present study applied connectomics to disentangle epilepsy networks from insular SEEG recordings and to describe their relationship to seizure semiology.

Methods

We retrospectively extracted functional networks by normative connectome analysis from 118 insular contacts depicting epileptic discharges during SEEG in 20 epilepsy patients. The resulting epilepsy networks were correlated to the corresponding semiology by voxel-wise regression and multivariate analyses of variances.

Results

Epileptic foci were found in the posterior insula for somatosensory, other sensory and motor seizures, while cognitive and autonomic symptoms were related to the anterior insula. We identified insular connections to the superior temporal gyrus and heschl gyrus in sensory seizures and projections to the somatosensory cortex in somatosensory seizures. Insula-basal ganglia pathways were found in cognitive seizure manifestations, while insular connectivity to fronto-basal regions were strongest in patients with autonomic seizures.

Conclusion

The semiology of seizures is mirrored in the functional connectivity of insular epileptic discharges. Combining SEEG and connectomics could provide additional information about seizure propagation within the epilepsy network and might enable new treatment options in the future like deep brain stimulation.

Posterior cingulate epilepsy: Seizure semiology and intracranial electrical stimulation using SEEG

PURPOSE

This study aimed to explore seizure semiology and the effects of intracerebral electrical stimulation on the human posterior cingulate cortex (PCC) using Stereoelectroencephalography (SEEG) to deepen our comprehension of posterior cingulate epilepsy (PCE).

METHODS

This study examined the characteristics of seizures through video documentation, by assessing the outcomes of intracranial electrical stimulation (iES) during SEEG. We further identified the connection between the observed semiology and precise anatomical locations within the PCC subregions where seizure onset zones (SOZ) were identified.

RESULTS

Analysis was conducted on 59 seizures from 15 patients recorded via SEEG. Behavioural arrest emerged as the predominant manifestation across the PCC subregions. Where ictal activity extended to both the mesial and lateral temporal cortex, automatism was predominantly observed in seizures originating from the ventral PCC (vPCC). The retrosplenial cortex (RSC) is associated with complex motor behaviour, with seizure discharges spreading to the temporal lobe. Seizures originating from the PCC include axial tonic and autonomic seizures. Only one case of positive clinical seizures was documented. High frequencies of iES within the PCC induced various clinical responses, categorised as vestibular, visual, psychological, and autonomic, with vestibular reactions primarily occurring in the dorsal PCC (dPCC) and RSC, visual responses in the left RSC, and autonomic reactions in the vPCC and RSC.

CONCLUSION

The manifestations of seizures in PCE vary according to the SOZ and the patterns of seizure propagation. The occurrence of seizures induced by iES is exceedingly rare, indicating that mapping of the PCC could pinpoint the primary sector of PCC.

Predictability alters information flow during action observation in human electrocorticographic activity

The action observation network (AON) has been extensively studied using short, isolated motor acts. How activity in the network is altered when these isolated acts are embedded in meaningful sequences of actions remains poorly understood. Here we utilized intracranial electrocorticography to characterize how the exchange of information across key nodes of the AON-the precentral, supramarginal, and visual cortices-is affected by such embedding and the resulting predictability. We found more top-down beta oscillation from precentral to supramarginal contacts during the observation of predictable actions in meaningful sequences compared to the same actions in randomized, and hence less predictable, order. In addition, we find that expectations enabled by the embedding lead to a suppression of bottom-up visual responses in the high-gamma range in visual areas. These results, in line with predictive coding, inform how nodes of the AON integrate information to process the actions of others.

Multi-scale goal distance representations in human hippocampus during virtual spatial navigation

Goal-directed navigation relies on both coarse and fine-grained coding of spatial distance between the current position of a navigating subject and a goal destination. However, the neural signatures underlying goal distance coding remain poorly understood. Using intracranial EEG recordings from the hippocampus of drug-resistant epilepsy patients who performed a virtual spatial navigation task, we found that the right hippocampal theta power was significantly modulated by goal distance and decreased with goal proximity. This modulation varied along the hippocampal longitudinal axis such that theta power in the posterior hippocampus decreased more strongly with goal proximity. Similarly, neural timescale, reflecting the duration across which information can be maintained, increased gradually from the posterior to anterior hippocampus. Taken together, this study provides empirical evidence for multi-scale spatial representations of goal distance in the human hippocampus and links the hippocampal processing of spatial information to its intrinsic temporal dynamics.

Responses of Chemosensory Perception to Stimulation of the Human Brain

OBJECTIVE

Significant advances have been made in our understanding of the neural substrates of human chemosensory processing, involving the piriform cortex, insula, and orbitofrontal cortex. However, the important and challenging issues are to localize the brain regions with high anatomic precision that can causally produce chemosensory perception and further delineate the topography of different classifications of chemosensory perception.

METHODS

We quantitatively measured subjective responses of chemosensory perception to intracranial electrical stimulation over the brain in neurosurgical patients (n = 302) with medically refractory epilepsy.

RESULTS

The chemosensory perceptions including olfaction, gustation, and chemesthesis were elicited in 21 of 302 patients (7%). Chemosensory responses were evoked in 53 (0.2%) of 21,661 stimulated sites. The highest response rate (1.8%) was in the insula (37/2,051 stimulated sites from 15/163 patients). The chemosensory perception emerged predominantly during stimulation of the insula along the central sulcus axis. Notably, there existed a distinct pattern that the anteroventral insula predominately represented orthonasal olfaction, whereas different chemosensory modalities converged in the mid-dorsal insula.

INTERPRETATION

This study provided a detailed characterization of chemosensory perception across the brain, especially in the insula. These results suggest that the cortex along the banks of the central sulcus of the insula may play a role in producing the supramodal sensation of flavor. It also indicates that dysfunction of the central insula should be considered during the evaluation of chemosensory-related epileptic seizures. ANN NEUROL 2023;93:175-183.

The anterior nucleus of the thalamus plays a role in the epileptic network

OBJECTIVES

We investigated both the metabolic differences and interictal/ictal discharges of the anterior nucleus of the thalamus (ANT) in patients with epilepsy to clarify the relationship between the ANT and the epileptic network.

METHODS

Nineteen patients with drug-resistant epilepsy who underwent stereoelectroencephalography were studied. Metabolic differences in ANT were analyzed using [18F] fluorodeoxyglucose-positron emission tomography with three-dimensional (3D) visual and quantitative analyses. Interictal and ictal discharges in the ANT were analyzed using visual and time-frequency analyses. The relationship between interictal discharge and metabolic differences was analyzed.

RESULTS

We found that patients with temporal lobe epilepsy (TLE) showed significant metabolic differences in bilateral ANT compared with extratemporal lobe epilepsy in 3D visual and quantitative analyses. Four types of interictal activities were recorded from the ANT: spike, high-frequency oscillation (HFO), slow-wave, and α-rhythmic activity. Spike and HFO waveforms were recorded mainly in patients with TLE. Two spike patterns were recorded: synchronous and independent. In 83.3% of patients, ANT was involved during seizures. Three seizure onset types of ANT were recorded: low-voltage fast activity, rhythmic spikes, and theta band discharge. The time interval of seizure onset between the seizure onset zone and ANT showed two patterns: immediate and delayed.

INTERPRETATION

ANT can receive either interictal discharges or ictal discharges which propagate from the epileptogenic zones. Independent epileptic discharges can also be recorded from the ANT in some patients. Metabolic anomalies and epileptic discharges in the ANT indicate that the ANT plays a role in the epileptic network in most patients with epilepsy, especially TLE.

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.

Neural Substrates of External and Internal Visual Sensations Induced by Human Intracranial Electrical Stimulation

Offline perceptions are self-generated sensations that do not involve physical stimulus. These perceptions can be induced by external hallucinated objects or internal imagined objects. However, how the brain dissociates these visual sensations remains unclear. We aimed to map the brain areas involved in internal and external visual sensations induced by intracranial electrical stimulation and further investigate their neural differences. In this study, we collected subjective reports of internal and external visual sensations elicited by electrical stimulation in 40 drug-refractory epilepsy during presurgical evaluation. The response rate was calculated and compared to quantify the dissociated distribution of visual responses. We found that internal and external visual sensations could be elicited when different brain areas were stimulated, although there were more overlapping brain areas. Specifically, stimulation of the hippocampus and inferior temporal cortex primarily induces internal visual sensations. In contrast, stimulation of the occipital visual cortex mainly triggers external visual sensations. Furthermore, compared to that of the dorsal visual areas, the ventral visual areas show more overlap between the two visual sensations. Our findings show that internal and external visual sensations may rely on distinct neural representations of the visual pathway. This study indicated that implantation of electrodes in ventral visual areas should be considered during the evaluation of visual sensation aura epileptic seizures.

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.

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.

Theta oscillations coordinate grid-like representations between ventromedial prefrontal and entorhinal cortex

Grid cells and theta oscillations are fundamental constituents of the brain’s navigation system and have been described in the entorhinal cortex (EC). Recent fMRI studies reveal that the ventromedial prefrontal cortex (vmPFC) contains grid-like representations. However, the neural mechanisms underlying human vmPFC grid-like representations and their interactions with EC grid activity have remained unknown. We conducted intracranial electroencephalography (iEEG) recordings from epilepsy patients during a virtual spatial navigation task. Oscillatory theta power in the vmPFC exhibited a sixfold rotational symmetry that was coordinated with grid-like representations in the EC. We found that synchronous theta oscillations occurred between these regions that predicted navigational performance. Analysis of information transfer revealed a unidirectional signal from vmPFC to EC during memory retrieval. Together, this study provides insights into the previously unknown neural signature and functional role of grid-like representations outside the EC and their synchronization with the entorhinal grid during human spatial navigation.

Validation of semi-automated anatomically labeled SEEG contacts in a brain atlas for mapping connectivity in focal epilepsy

OBJECTIVE

Stereotactic electroencephalography (SEEG) has been widely used to explore the epileptic network and localize the epileptic zone in patients with medically intractable epilepsy. Accurate anatomical labeling of SEEG electrode contacts is critically important for correctly interpreting epileptic activity. We present a method for automatically assigning anatomical labels to SEEG electrode contacts using a 3D-segmented cortex and coregistered postoperative CT images.

METHOD

Stereotactic electroencephalography electrode contacts were spatially localized relative to the brain volume using a standard clinical procedure. Each contact was then assigned an anatomical label by clinical epilepsy fellows. Separately, each contact was automatically labeled by coregistering the subject's MRI to the USCBrain atlas using the BrainSuite software and assigning labels from the atlas based on contact locations. The results of both labeling methods were then compared, and a subsequent vetting of the anatomical labels was performed by expert review.

RESULTS

Anatomical labeling agreement between the two methods for over 17 000 SEEG contacts was 82%. This agreement was consistent in patients with and without previous surgery (P = .852). Expert review of contacts in disagreement between the two methods resulted in agreement with the atlas based over manual labels in 48% of cases, agreement with manual over atlas-based labels in 36% of cases, and disagreement with both methods in 16% of cases. Labels deemed incorrect by the expert review were then categorized as either in a region directly adjacent to the correct label or as a gross error, revealing a lower likelihood of gross error from the automated method.

SIGNIFICANCE

The method for semi-automated atlas-based anatomical labeling we describe here demonstrates potential to assist clinical workflow by reducing both analysis time and the likelihood of gross anatomical error. Additionally, it provides a convenient means of intersubject analysis by standardizing the anatomical labels applied to SEEG contact locations across subjects.

Detection of human white matter activation and evaluation of its function in movement decoding using stereo-electroencephalography (SEEG)

Objective. White matter tissue takes up approximately 50% of the human brain volume and it is widely known as a messenger conducting information between areas of the central nervous system. However, the characteristics of white matter neural activity and whether white matter neural recordings can contribute to movement decoding are often ignored and still remain largely unknown. In this work, we make quantitative analyses to investigate these two important questions using invasive neural recordings.Approach. We recorded stereo-electroencephalography (SEEG) data from 32 human subjects during a visually-cued motor task, where SEEG recordings can tap into gray and white matter electrical activity simultaneously. Using the proximal tissue density method, we identified the location (i.e. gray or white matter) of each SEEG contact. Focusing on alpha oscillatory and high gamma activities, we compared the activation patterns between gray matter and white matter. Then, we evaluated the performance of such white matter activation in movement decoding.Main results. The results show that white matter also presents activation under the task, in a similar way with the gray matter but at a significantly lower amplitude. Additionally, this work also demonstrates that combing white matter neural activities together with that of gray matter significantly promotes the movement decoding accuracy than using gray matter signals only.Significance. Taking advantage of SEEG recordings from a large number of subjects, we reveal the response characteristics of white matter neural signals under the task and demonstrate its enhancing function in movement decoding. This study highlights the importance of taking white matter activities into consideration in further scientific research and translational applications.

Theta oscillations synchronize human medial prefrontal cortex and amygdala during fear learning

Numerous animal studies have demonstrated that fear acquisition and expression rely on the coordinated activity of medial prefrontal cortex (mPFC) and amygdala and that theta oscillations support interregional communication within the fear network. However, it remains unclear whether these results can be generalized to fear learning in humans. We addressed this question using intracranial electroencephalography recordings in 13 patients with epilepsy during a fear conditioning paradigm. We observed increased power and inter-regional synchronization of amygdala and mPFC in theta (4 to 8 hertz) oscillations for conditioned stimulus (CS+) versus CS-. Analysis of information flow revealed that the dorsal mPFC (dmPFC) led amygdala activity in theta oscillations. Last, a computational model showed that trial-by-trial changes in amygdala theta oscillations predicted the model-based associability (i.e., learning rate). This study provides compelling evidence that theta oscillations within and between amygdala, ventral mPFC, and dmPFC constitute a general mechanism of fear learning across species.

Anterior thalamic stimulation improves working memory precision judgments

BACKGROUND

The anterior nucleus of thalamus (ANT) has been suggested as an extended hippocampal system. The circuit of ANT and hippocampus has been widely demonstrated to be associated with memory function. Both lesions to each region and disrupting inter-regional information flow can induce working memory impairment. However, the role of this circuit in working memory precision remains unknown.

OBJECTIVE

To test the role of the hippocampal-anterior thalamic pathway in working memory precision, we delivered intracranially electrical stimulation to the ANT. We hypothesize that ANT stimulation can improve working memory precision.

METHODS

Presurgical epilepsy patients with depth electrodes in ANT and hippocampus were recruited to perform a color-recall working memory task. Participants were instructed to point out the color they were supposed to recall by clicking a point on the color wheel, while the intracranial EEG data were synchronously recorded. For randomly selected half trials, a bipolar electrical stimulation was delivered to the ANT electrodes.

RESULTS

We found that compared to non-stimulation trials, working memory precision judgements were significantly improved for stimulation trials. ANT electrical stimulation significantly increased spectral power of gamma (30-100 Hz) oscillations and decreased interictal epileptiform discharges (IED) in the hippocampus. Moreover, the increased gamma power during the pre-stimulus and retrieval period predicted the improvement of working memory precision judgements.

CONCLUSION

ANT electrical stimulation can improve working memory precision judgements and modulate hippocampal gamma activity, providing direct evidence on the role of the human hippocampal-anterior thalamic axis in working memory precision.

MRIES: A Matlab Toolbox for Mapping the Responses to Intracranial Electrical Stimulation

Propose

Directed cortical responses to intracranial electrical stimulation are a good standard for mapping inter-regional direct connectivity. Cortico-cortical evoked potential (CCEP), elicited by single pulse electrical stimulation (SPES), has been widely used to map the normal and abnormal brain effective network. However, automated processing of CCEP datasets and visualization of connectivity results remain challenging for researchers and clinicians. In this study, we develop a Matlab toolbox named MRIES (Mapping the Responses to Intracranial Electrical Stimulation) to automatically process CCEP data and visualize the connectivity results.

Method

The MRIES integrates the processing pipeline of the CCEP datasets and various methods for connectivity calculation based on low- and high-frequency signals with stimulation artifacts removed. The connectivity matrices are saved in different folders for visualization. Different visualization patterns (connectivity matrix, circle map, surface map, and volume map) are also integrated to the graphical user interface (GUI), which makes it easy to intuitively display and compare different connectivity measurements. Furthermore, one sample CCEP data set collected from eight epilepsy patients is used to validate the MRIES toolbox.

Result

We show the GUI and visualization functions of MRIES using one example CCEP data that has been described in a complete tutorial. We applied this toolbox to the sample CCEP data set to investigate the direct connectivity between the medial temporal lobe and the insular cortex. We find bidirectional connectivity between MTL and insular that are consistent with the findings of previous studies.

Conclusion

MRIES has a friendly GUI and integrates the full processing pipeline of CCEP data and various visualization methods. The MRIES toolbox, tutorial, and example data can be freely downloaded. As an open-source package, MRIES is expected to improve the reproducibility of CCEP findings and facilitate clinical translation.

Prestimulus dynamics blend with the stimulus in neural variability quenching

Neural responses to the same stimulus show significant variability over trials, with this variability typically reduced (quenched) after a stimulus is presented. This trial-to-trial variability (TTV) has been much studied, however how this neural variability quenching is influenced by the ongoing dynamics of the prestimulus period is unknown. Utilizing a human intracranial stereo-electroencephalography (sEEG) data set, we investigate how prestimulus dynamics, as operationalized by standard deviation (SD), shapes poststimulus activity through trial-to-trial variability (TTV). We first observed greater poststimulus variability quenching in those real trials exhibiting high prestimulus variability as observed in all frequency bands. Next, we found that the relative effect of the stimulus was higher in the later (300-600ms) than the earlier (0-300ms) poststimulus period. Lastly, we replicate our findings in a separate EEG dataset and extend them by finding that trials with high prestimulus variability in the theta and alpha bands had faster reaction times. Together, our results demonstrate that stimulus-related activity, including its variability, is a blend of two factors: 1) the effects of the external stimulus itself, and 2) the effects of the ongoing dynamics spilling over from the prestimulus period - the state at stimulus onset - with the second dwarfing the influence of the first.

Temporo-Frontal Coherences and High-Frequency iEEG Responses during Spatial Navigation in Patients with Drug-Resistant Epilepsy

The prefrontal cortex and hippocampus function in tight coordination during multiple cognitive processes. During spatial navigation, prefrontal neurons are linked to hippocampal theta oscillations, presumably in order to enhance communication. Hippocampal ripples have been suggested to reflect spatial memory processes. Whether prefrontal-hippocampal-interaction also takes place within the ripple band is unknown. This intracranial EEG study aimed to investigate whether ripple band coherences can also be used to show this communication. Twelve patients with epilepsy and intracranial EEG evaluation completed a virtual spatial navigation task. We calculated ordinary coherence between prefrontal and temporal electrodes during retrieval, re-encoding, and pre-task rest. Coherences were compared between the conditions via permutation testing. Additionally, ripples events were automatically detected and changes in occurrence rates were investigated excluding ripples on epileptic spikes. Ripple-band coherences yielded no general effect of the task on coherences across all patients. Furthermore, we did not find significant effects of task conditions on ripple rates. Subsequent analyses pointed to rather short periods of synchrony as opposed to general task-related changes in ripple-band coherence. Specifically designed tasks and adopted measures might be necessary in order to map these interactions in future studies.

Interpretation of the Intracranial Stereoelectroencephalography Signal

The intracranial electroencephalogram (iEEG) is essential in decision making for epilepsy surgery. Although localization of epileptogenic brain regions by means of iEEG has been the gold standard for surgical decision-making for more than 70 years, established guidelines for what constitutes genuine iEEG epileptic activity and what is normal brain activity are not available. This review provides a summary of the current state of knowledge and understanding on normal iEEG entities and variants, the effects of sleep on regional and lobar iEEG, iEEG patterns of interictal and ictal epileptic activity and their relation to well-described epileptogenic pathologies and surgical outcome.

Semiologic subgroups of insulo-opercular seizures based on connectional architecture atlas

OBJECTIVE

Insulo-opercular seizures are characterized by diverse semiology, related to the insula's multiple functional roles and extensive connectivity. We aimed to identify semiologic subgroups and correlate these with insulo-opercular subregions based on connectional architecture.

METHODS

We retrospectively collected a large series of 37 patients with insulo-opercular seizures explored by stereoelectroencephalography (SEEG) from three epilepsy centers. A new human brain atlas (Brainnetome Atlas, BNA) based on both anatomic and functional connections was employed to segment insulo-opercular cortex. Semiology and SEEG changes were carefully reviewed and quantified. Principal component analysis and cluster analysis were used to correlate semiologic characteristics with insulo-opercular subregions.

RESULTS

Four main semiologic subgroups were identified, organized along an anteroventral to posterodorsal axis based on BNA. Group 1 was characterized by epigastric sensation and/or integrated gestural motor behaviors with or without feelings of fear or rage, involving the anteroventral insular regions and mesial temporal lobes. Group 2 was characterized by auditory sensations and symmetric proximal/axial tonic signs involving the posteroventral temporal operculum. The characteristics of group 3 were orofacial and laryngeal signs, involving the intermediate insulo-opercular regions. The features of group 4 were somatosensory signs followed by nonintegrated gestural motor behaviors and/or asymmetric tonic signs involving the posterodorsal insulo-opercular regions with propagation to the mesial frontal lobes. Thus anteroventral seizure organizations predominantly showed limbic system semiology, whereas more posterodorsal regions were associated with semiology involving mainly the sensorimotor system. Subjective symptoms proved to be particularly discriminating factors.

SIGNIFICANCE

Insulo-opercular seizures can be categorized in terms of clinical semiology and correlate with connectional architecture subregions along an anteroventral-posterodorsal axis in line with the cytoarchitectonic gradient rather than the gyral anatomy of the insula cortex. This provides new insights into facilitating differential diagnosis and presurgical localization but also highlights the importance of considering connectional architecture in determining neural correlates of complex semiologic patterns.

High-frequency stimulation of anterior nucleus of thalamus desynchronizes epileptic network in humans

Epilepsy has been classically seen as a brain disorder resulting from abnormally enhanced neuronal excitability and synchronization. Although it has been described since antiquity, there are still significant challenges achieving the therapeutic goal of seizure freedom. Deep brain stimulation of the anterior nucleus of the thalamus has emerged as a promising therapy for focal drug-resistant epilepsy; the basic mechanism of action, however, remains unclear. Here, we show that desynchronization is a potential mechanism of deep brain stimulation of the anterior nucleus of the thalamus by studying local field potentials recordings from the cortex during high-frequency stimulation (130 Hz) of the anterior nucleus of the thalamus in nine patients with drug-resistant focal epilepsy. We demonstrate that high-frequency stimulation applied to the anterior nucleus of the thalamus desynchronizes ipsilateral hippocampal background electrical activity over a broad frequency range, and reduces pathological epileptic discharges including interictal spikes and high-frequency oscillations. Furthermore, high-frequency stimulation of the anterior nucleus of the thalamus is capable of decoupling large-scale neural activity involving the hippocampus and distributed cortical areas. We found that stimulation frequencies ranging from 15 to 45 Hz were associated with synchronization of hippocampal local field potentials, whereas higher frequencies (>45 Hz) promoted desynchronization of ipsilateral hippocampal activity. Moreover, reciprocal effective connectivity between the anterior nucleus of the thalamus and the hippocampus was demonstrated by hippocampal-thalamic evoked potentials and thalamic-hippocampal evoked potentials. In summary, high-frequency stimulation of the anterior nucleus of the thalamus is shown to desynchronize focal and large-scale epileptic networks, and here is proposed as the mechanism for reducing seizure generation and propagation. Our data also demonstrate position-specific correlation between deep brain stimulation applied to the anterior nucleus of the thalamus and patients with temporal lobe epilepsy and seizure onset zone within the Papaz circuit or limbic system. Our observation may prove useful for guiding electrode implantation to increase clinical efficacy.

Hippocampal theta phases organize the reactivation of large-scale electrophysiological representations during goal-directed navigation

Humans are adept in simultaneously following multiple goals, but the neural mechanisms for maintaining specific goals and distinguishing them from other goals are incompletely understood. For short time scales, working memory studies suggest that multiple mental contents are maintained by theta-coupled reactivation, but evidence for similar mechanisms during complex behaviors such as goal-directed navigation is scarce. We examined intracranial electroencephalography recordings of epilepsy patients performing an object-location memory task in a virtual environment. We report that large-scale electrophysiological representations of objects that cue for specific goal locations are dynamically reactivated during goal-directed navigation. Reactivation of different cue representations occurred at stimulus-specific hippocampal theta phases. Locking to more distinct theta phases predicted better memory performance, identifying hippocampal theta phase coding as a mechanism for separating competing goals. Our findings suggest shared neural mechanisms between working memory and goal-directed navigation and provide new insights into the functions of the hippocampal theta rhythm.

Integrated analysis of anatomical and electrophysiological human intracranial data

Human intracranial electroencephalography (iEEG) recordings provide data with much greater spatiotemporal precision than is possible from data obtained using scalp EEG, magnetoencephalography (MEG), or functional MRI. Until recently, the fusion of anatomical data (MRI and computed tomography (CT) images) with electrophysiological data and their subsequent analysis have required the use of technologically and conceptually challenging combinations of software. Here, we describe a comprehensive protocol that enables complex raw human iEEG data to be converted into more readily comprehensible illustrative representations. The protocol uses an open-source toolbox for electrophysiological data analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible analysis methods that, over the past decade, have been developed and used by a large research community. In this protocol, we describe how to analyze complex iEEG datasets by providing an intuitive and rapid approach that can handle both neuroanatomical information and large electrophysiological datasets. We provide a worked example using an example dataset. We also explain how to automate the protocol and adjust the settings to enable analysis of iEEG datasets with other characteristics. The protocol can be implemented by a graduate student or postdoctoral fellow with minimal MATLAB experience and takes approximately an hour to execute, excluding the automated cortical surface extraction.

Hexadirectional Modulation of Theta Power in Human Entorhinal Cortex during Spatial Navigation

Grid cells and theta oscillations are fundamental components of the brain's navigation system. Grid cells provide animals [1, 2] and humans [3, 4] with a spatial map of the environment by exhibiting multiple firing fields arranged in a regular grid of equilateral triangles. This unique firing pattern presumably constitutes the neural basis for path integration [5-8] and may also enable navigation in visual and conceptual spaces [9-12]. Theta frequency oscillations are a prominent mesoscopic network phenomenon during navigation in both rodents and humans [13, 14] and encode movement speed [15-17], distance traveled [18], and proximity to spatial boundaries [19]. Whether theta oscillations may also carry a grid-like signal remains elusive, however. Capitalizing on previous fMRI studies revealing a macroscopic proxy of sum grid cell activity in human entorhinal cortex (EC) [20-22], we examined intracranial EEG recordings from the EC of epilepsy patients (n = 9) performing a virtual navigation task. We found that the power of theta oscillations (4-8 Hz) exhibits 6-fold rotational modulation by movement direction, reminiscent of grid cell-like representations detected using fMRI. Modulation of theta power was specific to 6-fold rotational symmetry and to the EC. Hexadirectional modulation of theta power by movement direction only emerged during fast movements, stabilized over the course of the experiment, and showed sensitivity to the environmental boundary. Our results suggest that oscillatory power in the theta frequency range carries an imprint of sum grid cell activity potentially enabled by a common grid orientation of neighboring grid cells [23].

ALICE: A tool for automatic localization of intra-cranial electrodes for clinical and high-density grids

BACKGROUND

Electrocorticographic (ECoG) measurements require the accurate localization of implanted electrodes with respect to the subject's neuroanatomy. Electrode localization is particularly relevant to associate structure with function. Several procedures have attempted to solve this problem, namely by co-registering a post-operative computed tomography (CT) scan, with a pre-operative magnetic resonance imaging (MRI) anatomy scan. However, this type of procedure requires a manual and time-consuming detection and transcription of the electrode coordinates from the CT volume scan and restricts the extraction of smaller high-resolution ECoG grid electrodes due to the downsampling of the CT.

NEW METHOD

ALICE automatically detects electrodes on the post-operative high-resolution CT scan, visualizes them in a combined 2D and 3D volume space using AFNI and SUMA software and then projects the electrodes on the individual's cortical surface rendering. The pipeline integrates the multiple-step method into a user-friendly GUI in Matlab^®, thus providing an easy, automated and standard tool for ECoG electrode localization.

RESULTS

ALICE was validated in 13 subjects implanted with clinical ECoG grids by comparing the calculated electrode center-of-mass coordinates with those computed using a commonly used method.

COMPARISON WITH EXISTING METHODS

A novel aspect of ALICE is the combined 2D-3D visualization of the electrodes on the CT scan and the option to also detect high-density ECoG grids. Feasibility was shown in 5 subjects and validated for 2 subjects.

CONCLUSIONS

The ALICE pipeline provides a fast and accurate detection, discrimination and localization of ECoG electrodes spaced down to 4 mm apart.