Stereoelectroencephalography Recordings Research Articles

Interictal stereo-electroencephalography features below 45 Hz are sufficient for correct localization of the epileptogenic zone and postsurgical outcome prediction.

Evidence suggests that the most promising results in interictal localization of the epileptogenic zone (EZ) are achieved by a combination of multiple stereo-electroencephalography (SEEG) biomarkers in machine learning models. These biomarkers usually include SEEG features calculated in standard frequency bands, but also high-frequency (HF) bands. Unfortunately, HF features require extra effort to record, store, and process. Here we investigate the added value of these HF features for EZ localization and postsurgical outcome prediction. In 50 patients we analyzed 30 min of SEEG recorded during non-rapid eye movement sleep and tested a logistic regression model with three different sets of features. The first model used broadband features (1-500 Hz); the second model used low-frequency features up to 45 Hz; and the third model used HF features above 65 Hz. The EZ localization by each model was evaluated by various metrics including the area under the precision-recall curve (AUPRC) and the positive predictive value (PPV). The differences between the models were tested by the Wilcoxon signed-rank tests and Cliff's Delta effect size. The differences in outcome predictions based on PPV values were further tested by the McNemar test. The AUPRC score of the random chance classifier was .098. The models (broad-band, low-frequency, high-frequency) achieved median AUPRCs of .608, .582, and .522, respectively, and correctly predicted outcomes in 38, 38, and 33 patients. There were no statistically significant differences in AUPRC or any other metric between the three models. Adding HF features to the model did not have any additional contribution. Low-frequency features are sufficient for correct localization of the EZ and outcome prediction with no additional value when considering HF features. This finding allows significant simplification of the feature calculation process and opens the possibility of using these models in SEEG recordings with lower sampling rates, as commonly performed in clinical routines.

Impact of transcranial electrical stimulation on simultaneous stereoelectroencephalography recordings: A randomized sham-controlled study

ObjectiveClinical exploitation of transcranial electrical stimulation for focal epilepsy treatment lacks quantification of the underlying neurophysiological changes. This study explores the immediate effects of transcranial alternating (tACS) and direct (tDCS) current stimulation on local and network brain activity using simultaneous stereoelectroencephalography (SEEG) recordings. MethodsPatients were randomized for personalized tACS (n = 5) or tDCS (n = 6). Active stimulation (20 min) was preceded by sham stimulation (20 min). Changes in interictal epileptiform discharges (IED), functional connectivity (FC) and power spectral density (PSD) were quantified against baseline. ResultsResults demonstrated variable responses. Spike rate decreased in 2/6 subjects following sham and tDCS, while 2/6 showed an increase. Alpha power and aperiodic PSD components generally increased during and after tDCS but decreased following tACS. FC changes varied among subjects and were detectable even during sham sessions. ConclusionsStrong variability suggests that tES does not have a univocal effect on immediate changes in IED or FC, possibly due to the single session format and challenges in affecting subcortical areas. SignificanceThis is the first study to examine intracranial FC changes during tACS and tDCS, highlighting the importance of sham comparisons and individual variability in tES response, offering valuable insights into its application for epilepsy treatment.

Functional mapping of movement and speech using task-based electrophysiological changes in stereoelectroencephalography.

Stereoelectroencephalography (SEEG) has become the predominant method for intracranial seizure localization. When imaging, semiology, and scalp EEG findings are not in full agreement or definitively localizing, implanted SEEG recordings are used to test candidate seizure onset zones (SOZs). Discovered SOZs may then be targeted for resection, laser ablation, or neurostimulation. If an SOZ is eloquent, resection and ablation are both contraindicated, so identifying functional representation is crucial for therapeutic decision-making. The authors present a novel functional brain mapping technique that utilizes task-based electrophysiological changes in SEEG during behavioral tasks and test this in pediatric and adult patients. SEEG was recorded in 20 patients with epilepsy who ranged in age from 6 to 39 years (12 female, 18 of 20 patients < 21 years of age) and underwent implanted monitoring to identify seizure onset. Each performed 1) visually cued simple repetitive movements of the hand, foot, or tongue while electromyography was recorded; and 2) simple picture-naming or verb-generation speech tasks while audio was recorded. Broadband changes in the power spectrum of the SEEG recording were compared between behavior and rest. Electrophysiological functional mapping of movement and/or speech areas was completed in all 20 patients. Eloquent representation was identified in both cortex and white matter and generally corresponded to classically described functional anatomical organization as well as other clinical mapping results. Robust maps of brain activity were identified in healthy brain, regions of developmental or acquired structural abnormality, and SOZs. Task-based electrophysiological mapping using broadband changes in the SEEG signal reliably identifies movement and speech representation in pediatric and adult epilepsy patients.

Localizing hidden Interictal Epileptiform Discharges with simultaneous intracerebral and scalp high-density EEG recordings

BackgroundScalp EEG is one of the main tools in the clinical evaluation of epilepsy. In some cases intracranial Interictal Epileptiform Discharges (IEDs) are not visible from the scalp. Recent studies have shown the feasibility of revealing them in the EEG if their timings are extracted from simultaneous intracranial recordings, but their potential for the localization of the epileptogenic zone is not yet well defined. New methodWe recorded simultaneous high-density EEG (HD-EEG) and stereo-electroencephalography (SEEG) during interictal periods in 8 patients affected by drug-resistant focal epilepsy. We identified IEDs in the SEEG and systematically analyzed the time-locked signals on the EEG by means of evoked potentials, topographical analysis and Electrical Source Imaging (ESI). The dataset has been standardized and is being publicly shared. ResultsOur results showed that IEDs that were not clearly visible at single-trials could be uncovered by averaging, in line with previous reports. They also showed that their topographical voltage distributions matched the position of the SEEG electrode where IEDs had been identified, and that ESI techniques can reconstruct it with an accuracy of ∼2 cm. Finally, the present dataset provides a reference to test the accuracy of different methods and parameters. Comparison with existing methodsOur study is the first to systematically compare ESI methods on simultaneously recorded IEDs, and to share a public resource with in-vivo data for their evaluation. ConclusionsSimultaneous HD-EEG and SEEG recordings can unveil hidden IEDs whose origins can be reconstructed using topographical and ESI analyses, but results depend on the selected methods and parameters.

Depth versus surface: A critical review of subdural and depth electrodes in intracranial electroencephalographic studies.

Intracranial electroencephalographic (IEEG) recording, using subdural electrodes (SDEs) and stereoelectroencephalography (SEEG), plays a pivotal role in localizing the epileptogenic zone (EZ). SDEs, employed for superficial cortical seizure foci localization, provide information on two-dimensional seizure onset and propagation. In contrast, SEEG, with its three-dimensional sampling, allows exploration of deep brain structures, sulcal folds, and bihemispheric networks. SEEG offers the advantages of fewer complications, better tolerability, and coverage of sulci. Although both modalities allow electrical stimulation, SDE mapping can tessellate cortical gyri, providing the opportunity for a tailored resection. With SEEG, both superficial gyri and deep sulci can be stimulated, and there is a lower risk of afterdischarges and stimulation-induced seizures. Most systematic reviews and meta-analyses have addressed the comparative effectiveness of SDEs and SEEG in localizing the EZ and achieving seizure freedom, although discrepancies persist in the literature. The combination of SDEs and SEEG could potentially overcome the limitations inherent to each technique individually, better delineating seizure foci. This review describes the strengths and limitations of SDE and SEEG recordings, highlighting their unique indications in seizure localization, as evidenced by recent publications. Addressing controversies in the perceived usefulness of the two techniques offers insights that can aid in selecting the most suitable IEEG in clinical practice.

Interictal discharge traveling waves recorded from stereoelectroencephalography electrodes.

Interictal epileptiform discharges (IEDs) are intermittent high-amplitude electrical signals that occur between seizures. They have been shown to propagate through the brain as traveling waves when recorded with epicortical grid-type electrodes and small penetrating microelectrode arrays. However, little work has been done to translate experimental IED analyses to more clinically relevant platforms such as stereoelectroencephalography (SEEG). In this pilot study, the authors aimed to define a computational method to identify and characterize IEDs recorded from clinical SEEG electrodes and leverage the directionality of IED traveling waves to localize the seizure onset zone (SOZ). Continuous SEEG recordings from 15 patients with medically refractory epilepsy were collected, and IEDs were detected by identifying overlapping peaks of a minimum prominence. IED pathways of propagation were defined and compared to the SOZ location determined by a clinical neurologist based on the ictal recordings. For further analysis of the IED pathways of propagation, IED detections were divided into triplets, defined as a set of 3 consecutive contacts within the same IED detection. Univariate and multivariate linear regression models were employed to associate IED characteristics with colocalization to the SOZ. A median (range) of 22.6 (4.4-183.9) IEDs were detected per hour from 15 patients over a mean of 23.2 hours of recording. Depending on the definition of the SOZ, a median (range) of 20.8% (0.0%-54.5%) to 62.1% (19.2%-99.4%) of IEDs per patient traversed the SOZ. IEDs passing through the SOZ followed discrete pathways that had little overlap with those of the IEDs passing outside the SOZ. Contact triplets that occurred more than once were significantly more likely to be detected in an IED passing through the SOZ (p < 0.001). Per our multivariate model, patients with a greater proportion of IED traveling waves had a significantly greater proportion of IEDs that localized to the SOZ (β = 0.64, 95% CI 0.01-1.27, p = 0.045). By using computational methods, IEDs can be meaningfully detected from clinical-grade SEEG recordings of patients with epilepsy. In some patients, a high proportion of IEDs are traveling waves according to multiple metrics that colocalize to the SOZ, offering hope that IED detection, with further refinement, could serve as an alternative method for SOZ localization.

Diagnostic value of high frequency oscillation in localization of type Ⅱ focal cortical dysplasia epilepsy

Retrospective analysis was conducted on 9 patients with type Ⅱ focal cortical dysplasia (FCD) who underwent stereo-electroencephalography (SEEG) implantation in the Department of Neurosurgery of the First Affiliated Hospital of Fujian Medical University from November 2020 to February 2023. The onset area, onset time, and frequency of high-frequency oscillations (HFO) were analyzed and the correlation of HFOs with interictal, preictal, and ictal periods. SEEG recordings of 80-500 Hz HFOs were observed in both interictal and ictal periods in 9 patients, with 6 patients exhibiting fast ripples (FR) in the range of 250-500 Hz. Surgical resection of the seizure onset area and FR-generating electrodes was performed, and postoperative follow-up for over 2 years indicated Engel I in 5 cases. 6 patients showed continuous discharge during the preictal period, and the distribution index of continuous discharge was positively correlated with seizure frequency. HFOs in the range of 80-500 Hz were present in all four seizure onset patterns during the ictal period. The onset area and FR-emitting electrode were surgically removed in 6 patients with continuous discharge and overlapping HFOs during the preictal period, with 5 cases of Engel I. Type Ⅱ FCD discharges exhibited complexity, high discharge indices, and a close association with HFOs. Compared with the spike wave, the electrode range of HF is more limited, and the incidence of HF before attack is significantly increased, which is closely correlated with the onset area. The simultaneous occurrence of HFO and the spike waves has higher diagnostic value than the individual occurrence, effectively enhancing surgical efficacy.

Combining independent component analysis and source localization for improving spatial sampling of stereoelectroencephalography in epilepsy

Stereoelectroencephalography is a powerful intracerebral EEG recording method for the presurgical evaluation of epilepsy. It consists in implanting depth electrodes in the patient’s brain to record electrical activity and map the epileptogenic zone, which should be resected to render the patient seizure-free. Stereoelectroencephalography has high spatial accuracy and signal-to-noise ratio but remains limited in the coverage of the explored brain regions. Thus, the implantation might provide a suboptimal sampling of epileptogenic regions. We investigate the potential of improving a suboptimal stereoelectroencephalography recording by performing source localization on stereoelectroencephalography signals. We propose combining independent component analysis, connectivity measures to identify components of interest, and distributed source modelling. This approach was tested on two patients with two implantations each, the first failing to characterize the epileptogenic zone and the second giving a better diagnosis. We demonstrate that ictal and interictal source localization performed on the first stereoelectroencephalography recordings matches the findings of the second stereo-EEG exploration. Our findings suggest that independent component analysis followed by source localization on the topographies of interest is a promising method for retrieving the epileptogenic zone in case of suboptimal implantation.

Deep Learning Approaches for Imaging-Based Automated Segmentation of Tuberous Sclerosis Complex

The present study presents a novel approach for identifying epileptogenic tubers in patients with tuberous sclerosis complex (TSC) and automating tuber segmentation using a three-dimensional convolutional neural network (3D CNN). The study retrospectively included 31 TSC patients whose lesions were manually annotated from multiparametric neuroimaging data. Epileptogenic tubers were determined via presurgical evaluation and stereoelectroencephalography recording. Neuroimaging metrics were extracted and compared between epileptogenic and non-epileptogenic tubers. Additionally, five datasets with different preprocessing strategies were used to construct and train 3D CNNs for automated tuber segmentation. The normalized positron emission tomography (PET) metabolic value was significantly lower in epileptogenic tubers defined via presurgical evaluation (p = 0.001). The CNNs showed high performance for localizing tubers, with an accuracy between 0.992 and 0.994 across the five datasets. The automated segmentations were highly correlated with clinician-based features. The neuroimaging characteristics for epileptogenic tubers were demonstrated, increasing surgical confidence in clinical practice. The validated deep learning detection algorithm yielded a high performance in determining tubers with an excellent agreement with reference clinician-based segmentation. Collectively, when coupled with our investigation of minimal input requirements, the approach outlined in this study represents a clinically invaluable tool for the management of TSC.

Channel Selection for Stereo-electroencephalography (SEEG)-based Invasive Brain-Computer Interfaces using Deep Learning Methods.

Brain-computer interfaces (BCIs) can enable direct communication with assistive devices by recording and decoding signals from the brain. To achieve high performance, many electrodes will be used, such as the recently developed invasive BCIs with channel numbers up to hundreds or even thousands. For those high-throughput BCIs, channel selection is important to reduce signal redundancy and invasiveness while maintaining decoding performance. However, such endeavour is rarely reported for invasive BCIs, especially those using deep learning methods. Two deep learning-based methods, referred to as Gumbel and STG, were proposed in this paper. They were evaluated using the Stereo-electroencephalography (SEEG) signals, and compared with three other methods, including manual selection, mutual information-based method (MI), and all channels (all channels without selection). The task is to classify the SEEG signals into five movements using channels selected by each method. When 10 channels were selected, the mean classification accuracies using Gumbel, STG (referred to as STG-10), manual selection, and MI selection were 65%, 60%, 60%, and 47%, respectively, whilst the accuracy was 59% using all channels (no selection). In addition, an investigation of the selected channels showed that Gumbel and STG have successfully identified the pre-central and post-central areas, which are closely related to motor control. Both Gumbel and STG successfully selected the informative channels in SEEG recordings while maintaining decoding accuracy. This study enables future high-throughput BCIs using deep learning methods, to identify useful channels and reduce computing and wireless transmission pressure.

Diffusion spectrum imaging based semi-automatic optic radiation tractography for vision preservation in SEEG-guided radiofrequency thermocoagulation

ObjectiveTo assess the efficacy and safety of stereoelectroencephalography (SEEG)-guided radiofrequency thermocoagulation (RFTC), using diffusion spectrum imaging (DSI) tractography to preoperatively delineate the optic radiation (OR) and reduce the risk of visual field defects (VFDs) where the epileptogenic zones (EZs) are located in or close to the eloquent visual areas. MethodsWe prospectively followed up twenty-four consecutive patients (12 males and 12 females) who underwent SEEG-guided RFTC in or near the OR pathway. A distance of ≥ 3.5 mm away from the OR on the targeted electrodes contacts that exhibited relevant ictal onset patterns, IEDs and EES during SEEG recordings, was required as our selection criterion prior to performing RFTC, enough to theoretically prevent VFDs. Using default tracking parameters, the optic radiation was tracked semi-automatically in DSI-studio. ResultsThere were 12 male and 12 female patients ranging in age from 6 to 57 years, with follow-up period ranging from 6 to 37 months. Nineteen patients responded to RFTC (R+, 79.16 %), and 5 patients did not benefit from RFTC (R-, 20.83 %). The preoperative application of DSI semi-automatic based OR tractography was successful in the protection of the OR in all 24 patients. Three patients experienced a neurologic deficit following RFTC, and five patients had a partial quadrant visual field deficit prior to surgery that did not worsen, and none of the remaining nineteen patients had a quadrant visual field deficit. ConclusionOur study validates the safety and efficacy of SEEG-RFTC as a viable therapeutic approach for epileptic foci situated in or adjacent to the visual eloquent regions. We demonstrate that DSI-based tractography offers superior precision in delineating the OR compared to DTI. We establish that implementing a criterion of a minimum distance of ≥ 3.5 mm in radius from the OR on the targeted electrode contacts prior to conducting RFTC can effectively mitigate the risk of VFDs.

Lack of spontaneous typical seizures during intracranial monitoring with stereo-electroencephalography.

In the presurgical evaluation of patients with drug-resistant epilepsy (DRE), occasionally, patients do not experience spontaneous typical seizures (STS) during a stereo-electroencephalography (SEEG) study, which limits its effectiveness. We sought to identify risk factors for patients who did not have STS during SEEG and to analyze the clinical outcomes for this particular set of patients. We conducted a retrospective analysis of all patients with DRE who underwent depth electrode implantation and SEEG recordings between January 2013 and December 2018. SEEG was performed in 155 cases during this period. Eleven (7.2%) did not experience any clinical seizures (non-STS group), while 143 experienced at least one patient-typical seizure during admission (STS group). No significant differences were found between STS and non-STS groups in terms of patient demographics, lesional/non-lesional epilepsy ratio, pre-SEEG seizure frequency, number of ASMs used, electrographic seizures or postoperative seizure outcome in those who underwent resective surgery. Statistically significant differences were found in the average number of electrodes implanted (7.0 in the non-STS group vs. 10.2 in STS), days in Epilepsy Monitoring Unit (21.8 vs. 12.8 days) and the number of cases that underwent resective surgery following SEEG (27.3% vs 60.8%), respectively. The three non-STS patients (30%) who underwent surgery, all had their typical seizures triggered during ECS studies. Three cases were found to have psychogenic non-epileptic seizures. None of the patients in the non-STS group were offered neurostimulation devices. Five of the non-STS patients experienced transient seizure improvement following SEEG. We were unable to identify any factors that predicted lack of seizures during SEEG recordings. Resective surgery was only offered in cases where ECS studies replicated patient-typical seizures. Larger data sets are required to be able to identify factors that predict which patients will fail to develop seizures during SEEG.

Theta Oscillations Support Prefrontal-hippocampal Interactions in Sequential Working Memory.

The prefrontal cortex and hippocampus may support sequential working memory beyond episodic memory and spatial navigation. This stereoelectroencephalography (SEEG) study investigated how the dorsolateral prefrontal cortex (DLPFC) interacts with the hippocampus in the online processing of sequential information. Twenty patients with epilepsy (eight women, age 27.6 ± 8.2 years) completed a line ordering task with SEEG recordings over the DLPFC and the hippocampus. Participants showed longer thinking times and more recall errors when asked to arrange random lines clockwise (random trials) than to maintain ordered lines (ordered trials) before recalling the orientation of a particular line. First, the ordering-related increase in thinking time and recall error was associated with a transient theta power increase in the hippocampus and a sustained theta power increase in the DLPFC (3-10 Hz). In particular, the hippocampal theta power increase correlated with the memory precision of line orientation. Second, theta phase coherences between the DLPFC and hippocampus were enhanced for ordering, especially for more precisely memorized lines. Third, the theta band DLPFC → hippocampus influence was selectively enhanced for ordering, especially for more precisely memorized lines. This study suggests that theta oscillations may support DLPFC-hippocampal interactions in the online processing of sequential information.

Interictal High-Frequency Oscillations, Spikes, and Connectivity Profiles: a Fingerprint of Epileptogenic Brain Pathologies.

Focal cortical dysplasia (FCD), hippocampal sclerosis (HS), nonspecific gliosis (NG), and normal tissue (NT) comprise the majority of histopathological results of surgically treated drug-resistant epilepsy patients. Epileptic spikes, high-frequency oscillations (HFOs), and connectivity measures are valuable biomarkers of epileptogenicity. The question remains whether they could also be utilized for pre-resective differentiation of the underlying brain pathology. This study explored spikes and HFOs together with functional connectivity in various epileptogenic pathologies. Interictal awake SEEG recordings of 33 patients with focal drug-resistant epilepsy with seizure-free postoperative outcomes were analyzed (15 FCD, 8 HS, 6 NT, and 4 NG). Interictal spikes and HFOs were automatically identified in the channels contained in the overlap of seizure onset zone (SOZ) and resected tissue. Functional connectivity measures (relative entropy, linear correlation, cross-correlation, and phase consistency) were computed for neighboring electrode pairs. Statistically significant differences were found between the individual pathologies in HFO rates, spikes, and their characteristics, together with functional connectivity measures with the highest values in the case of HS and NG/NT. A model to predict brain pathology based on all interictal measures achieved up to 84.0% prediction accuracy. The electrophysiological profile of the various epileptogenic lesions in epilepsy surgery patients was analyzed. Based on this profile, a predictive model was developed. This model offers excellent potential to identify the nature of the underlying lesion prior to resection. If validated, this model may be particularly valuable for counseling patients, as depending on the lesion type, different outcomes are achieved after epilepsy surgery.

0044 Thalamocortical functional connectivity increases during REM sleep sawtooth waves

Abstract Introduction Whereas sawtooth waves have been described as phasic bursts of slow oscillations in the scalp EEG during rapid-eye-movement (REM) sleep sixty years ago, little is known about their generators and functional role. Stereo-electroencephalography recordings (SEEG) performed for presurgical evaluation in drug-resistant focal epilepsy have allowed to demonstrate a peculiar slow activity in the thalamic medial pulvinar nucleus during REM sleep, contrasting with the fast desynchronized cortical activity. However, the link between this slow thalamic activity and sawtooth waves remains unexplored. Here, we used combined polysomnography and SEEG to investigate the dynamics of thalamic activity and thalamocortical functional connectivity at the time of scalp EEG sawtooth waves. Methods Night sleep recordings from six patients investigated with SEEG including thalamic recording in the Epilepsy Department of Lyon University Hospital were analyzed. Sawtooth waves were visually detected in the scalp EEG and compared with control segments selected in REM sleep. Thalamic SEEG and cortical EEG spectral power as well as thalamo-cortical functional connectivity assessed by the phase locking value were compared between sawtooth waves and control segments using paired t-tests. The effect size was assessed with Cohen’s d. Results A significant increase in the delta-theta (2-5 Hz, p&amp;lt; 0.003, d=0.31) and gamma (30-100 Hz, p&amp;lt; 0.003, d=0.31) power bands was found in posterior nuclei of the thalamus at the time of scalp-detected sawtooth waves. Functional thalamocortical connectivity in the 2-5 Hz band was enhanced during sawtooth waves as compared to control segments, with the thalamus driving the cortex (p&amp;lt; 0.001; d=0.84, average +/- SD time lag: 23 +/- 22 ms). Conclusion Our results provide evidence that sawtooth waves involve not only cortical areas but also the thalamus, suggesting a relation with ponto-geniculo-occipital waves. They may hence represent phasic brainstem-triggered short windows of an increased thalamocortical dialogue during REM sleep. Support (if any) B.F was supported by the Natural Sciences and Engineering Research Council of Canada RGPIN2020-04127 and RGPAS-2020-00021, and by the Fonds de Recherche du Québec–Santé 2021-2025 Salary Award “Chercheur-boursier clinicien Senior”. T.A. was supported by the “Healthy Brains, Healthy Lives initiative at McGill University” from the Canada First Research Excellence Fund and Fonds de recherche du Québec 2021-2022.

N°291 – ERC Galvani – Pilot study 2: Impact of transcranial current stimulation on brain networks by simultaneous stereoelectroencephalography recordings in drug-resistant focal epilepsy patients

N°291 – ERC Galvani – Pilot study 2: Impact of transcranial current stimulation on brain networks by simultaneous stereoelectroencephalography recordings in drug-resistant focal epilepsy patients

Integrative roles of human amygdala subdivisions: Insight from direct intracerebral stimulations via stereotactic EEG.

Substantial studies of human amygdala function have revealed its importance in processing emotional experience, autonomic regulation, and sensory information; however, the neural substrates and circuitry subserving functions have not been directly mapped at the level of the subnuclei in humans. We provide a useful overview of amygdala functional characterization by using direct electrical stimulation to various amygdala regions in 48 patients with drug-resistant epilepsy undergoing stereoelectroencephalography recordings. This stimulation extends beyond the anticipated emotional, neurovegetative, olfactory, and somatosensory responses to include visual, auditory, and vestibular sensations, which may be explained by the functional connectivity with cortical and subcortical regions due to evoked amygdala-cortical potentials. Among the physiological symptom categories for each subnucleus, the most frequently evoked neurovegetative symptoms were distributed in almost every subnucleus. Laterobasal subnuclei are mainly associated with emotional responses, somatosensory responses, and vestibular sensations. Superficial subnuclei are mainly associated with emotional responses and olfactory and visual hallucinations. Our findings contribute to a better understanding of the functional architecture of the human amygdala at the subnuclei level and as a mechanistic basis for the clinical practice of amygdala stimulation in treating patients with neuropsychiatric disorders.

Studying memory processes at different levels with simultaneous depth and surface EEG recordings.

Investigating cognitive brain functions using non-invasive electrophysiology can be challenging due to the particularities of the task-related EEG activity, the depth of the activated brain areas, and the extent of the networks involved. Stereoelectroencephalographic (SEEG) investigations in patients with drug-resistant epilepsy offer an extraordinary opportunity to validate information derived from non-invasive recordings at macro-scales. The SEEG approach can provide brain activity with high spatial specificity during tasks that target specific cognitive processes (e.g., memory). Full validation is possible only when performing simultaneous scalp SEEG recordings, which allows recording signals in the exact same brain state. This is the approach we have taken in 12 subjects performing a visual memory task that requires the recognition of previously viewed objects. The intracranial signals on 965 contact pairs have been compared to 391 simultaneously recorded scalp signals at a regional and whole-brain level, using multivariate pattern analysis. The results show that the task conditions are best captured by intracranial sensors, despite the limited spatial coverage of SEEG electrodes, compared to the whole-brain non-invasive recordings. Applying beamformer source reconstruction or independent component analysis does not result in an improvement of the multivariate task decoding performance using surface sensor data. By analyzing a joint scalp and SEEG dataset, we investigated whether the two types of signals carry complementary information that might improve the machine-learning classifier performance. This joint analysis revealed that the results are driven by the modality exhibiting best individual performance, namely SEEG.

Event-related causality in stereo-EEG discriminates syntactic processing of noun phrases and verb phrases

Objective. Syntax involves complex neurobiological mechanisms, which are difficult to disentangle for multiple reasons. Using a protocol able to separate syntactic information from sound information we investigated the neural causal connections evoked by the processing of homophonous phrases, i.e. with the same acoustic information but with different syntactic content. These could be either verb phrases (VP) or noun phrases. Approach. We used event-related causality from stereo-electroencephalographic recordings in ten epileptic patients in multiple cortical and subcortical areas, including language areas and their homologous in the non-dominant hemisphere. The recordings were made while the subjects were listening to the homophonous phrases. Main results. We identified the different networks involved in the processing of these syntactic operations (faster in the dominant hemisphere) showing that VPs engage a wider cortical and subcortical network. We also present a proof-of-concept for the decoding of the syntactic category of a perceived phrase based on causality measures. Significance. Our findings help unravel the neural correlates of syntactic elaboration and show how a decoding based on multiple cortical and subcortical areas could contribute to the development of speech prostheses for speech impairment mitigation.

Temporal-spatial deciphering mental subtraction in the human brain

Mental subtraction, involving numerical processing and operation, requires a complex interplay among several brain regions. Diverse studies have utilized scalp electroencephalograph, electrocorticogram, or functional magnetic resonance imaging to resolve the structure pattern and functional activity during subtraction operation. However, a high resolution of the spatial-temporal understanding of the neural mechanisms involved in mental subtraction is unavailable. Thus, this study obtained intracranial stereoelectroencephalography recordings from 20 patients with pharmacologically resistant epilepsy. Specifically, two sample-delayed mismatch paradigms of numeric comparison and subtracting results comparison were used to help reveal the time frame of mental subtraction. The brain sub-regions were chronologically screened using the stereoelectroencephalography recording for mental subtraction. The results indicated that the anterior cortex, containing the frontal, insular, and parahippocampous, worked for preparing for mental subtraction; moreover, the posterior cortex, such as parietal, occipital, limbic, and temporal regions, cooperated during subtraction. Especially, the gamma band activities in core regions within the parietal-cingulate-temporal cortices mediated the critical mental subtraction. Overall, this research is the first to describe the spatiotemporal activities underlying mental subtraction in the human brain. It provides a comprehensive insight into the cognitive control activity underlying mental arithmetic.