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.

A review of signal processing and machine learning techniques for interictal epileptiform discharge detection

Brain interictal epileptiform discharges (IEDs), as one of the hallmarks of epileptic brain, are transient events captured by electroencephalogram (EEG). IEDs are generated by seizure networks, and they occur between seizures (interictal periods). The development of a robust method for IED detection could be highly informative for clinical treatment procedures and epileptic patient management. Since 1972, different machine learning techniques, from template matching to deep learning, have been developed to automatically detect IEDs from scalp EEG (scEEG) and intracranial EEG (iEEG). While the scEEG signals suffer from low information details and high attenuation of IEDs due to the high skull electrical impedance, the iEEG signals recorded using implanted electrodes enjoy higher details and are more suitable for identifying the IEDs. In this review paper, we group IED detection techniques into six categories: (1) template matching, (2) feature representation (mimetic, time-frequency, and nonlinear features), (3) matrix decomposition, (4) tensor factorization, (5) neural networks, and (6) estimation of the iEEG from the concurrent scEEG followed by detection and classification. The methods are compared quantitatively (e.g., in terms of accuracy, sensitivity, and specificity), and their general advantages and limitations are described. Finally, current limitations and possible future research paths related to this field are mentioned.

Neuromodulation Techniques in Children with Super-Refractory Status Epilepticus

Status epilepticus (SE) is a life-threatening condition and medical emergency which can have lifelong consequences, including neuronal death and alteration of neuronal networks, resulting in long-term neurologic and cognitive deficits in children. When standard pharmacological treatment for SE is not successful in controlling seizures, the condition evolves to refractory SE (rSE) and finally to super-refractory SE (srSE) if it exceeds 24 h despite using anaesthetics. In this systematic review, we present literature data on the potential uses of clinical neuromodulation techniques for the management of srSE in children, including electroconvulsive therapy, vagus nerve stimulation, and deep brain stimulation. The evaluation of these techniques is limited by the small number of published paediatric cases (n = 25, one with two techniques) in peer-reviewed articles (n = 18). Although neuromodulation strategies have not been tested through randomised, prospective controlled clinical trials, this review presents the existing data and the potential benefits of neuromodulation therapy, suggesting that these techniques, when available, could be considered at earlier stages within the course of srSE intending to prevent long-term neurologic complications. Clinical trials aiming to establish whether early intervention can prevent long-term sequelae are necessary in order to establish the potential clinical value of neuromodulation techniques for the treatment of srSE in children.

Localization of Epileptic Brain Responses to Single-Pulse Electrical Stimulation by Developing an Adaptive Iterative Linearly Constrained Minimum Variance Beamformer

Delayed responses (DRs) to single pulse electrical stimulation (SPES) in patients with severe refractory epilepsy, from their intracranial recordings, can help to identify regions associated with epileptogenicity. Automatic DR localization is a large step in speeding up the identification of epileptogenic focus. Here, for the first time, an adaptive iterative linearly constrained minimum variance beamformer (AI-LCMV) is developed and employed to localize the DR sources from intracranial electroencephalogram (EEG) recorded using subdural electrodes. The prime objective here is to accurately localize the regions for the corresponding DRs using an adaptive localization method that exploits the morphology of DRs as the desired sources. The traditional closed-form linearly constrained minimum variance (CF-LCMV) solution is meant for tracking the sources with dominating power. Here, by incorporating the morphology of DRs, as a constraint, to an iterative linearly constrained minimum variance (LCMV) solution, the array of subdural electrodes is used to localize the low-power DRs, some not even visible in any of the electrode signals. The results from the cases included in this study also indicate more distinctive locations compared to those achievable by conventional beamformers. Most importantly, the proposed AI-LCMV is able to localize the DRs invisible over other electrodes.

VMAT-IGRT Hypofractionated Radiotherapy for Locally Advanced Thyroid Cancer in Resource-Limited Settings: A Retrospective Report from Bolivia

PURPOSE/OBJECTIVE(S)

This study evaluated the efficacy and safety of hypofractionated external beam irradiation with VMAT-IGRT in patients with locally advanced thyroid cancer (LATC) who are ineligible for surgery or I-131 in a resource-limited setting such as Bolivia.

MATERIALS/METHODS

Thirty-three patients were treated with H-VMAT-IGRT between August 2018 and November 2022. Axial CT images were acquired every 3 mm from the base of the skull to the middle of the chest. GTV was determined based on contrast-enhanced tumor on T1 MRI or CT scans. CTV 55 Gy at 2.75 Gy was defined based on visible residual tumor and included dissected nodal stations with pathologically positive nodes. CTV 44 at 2.2 Gy for non-dissected nodal stations with low risk of recurrence, we escalated the dose up to 60 Gy at 3 Gy for small tumors up to 20 cc. PTVs were created by adding a 0.3 cm margin around CTVs, with a reduced margin of minus 3-5 mm for critical organs such as esophagus, pharynx, and brachial plexus. Equivalent dose in 2Gy (EQD2 10-3) was reported for tumor control and OAR constraints. Acute toxicity was reported according to RTOG criteria, and response to treatment was assessed at baseline, end of treatment, and every third month thereafter.

RESULTS

The mean time since last follow-up was 24 months (r: 8-40). Patients were 80% female and 20% male. The mean age was 50 years (r: 29-72). Local recurrence was treated in 52% (13) of patients and LATC in 48% (12). Papillary carcinomas accounted for 68% of patients, followed by anaplastic (24%) and follicular (8%) carcinomas. Tumors were classified according to the AJCC classification. Of the patients treated for LATC, 25% were at stage II, 25% at stage III, 16.67% at stage IVA, and 33.33% at stage IVB. Among patients treated for local recurrence, the initial stage before progression was stage I in 61.5%, stage II in 30.8%, and stage IVA in 7.7%. The median PTV was 383.8 cc (r: 51.7 - 627.3) and the median CTV55 was 189.2 cc (r: 39.6 - 519.8). Grade 1 (10%), Grade 2 (90%) dysphagia, Grade 1 (40%), Grade 2 (60%) acute pharyngeal mucositis, and no Grade 3 acute toxicity were observed. No reports of chronic upper damage GI, 30% xerostomia G2. Two-year local control was 70% and overall survival was also 70%.

CONCLUSION

We suggest that reducing treatment duration while maintaining efficacy is particularly beneficial in resource-limited areas with a shortage of trained personnel and overburdened treatment centers. We report good local control rates with no detrimental effects on quality of life with hypofractionation. No evidence of delayed damage, such as spinal cord dysfunction, mandibular osteoradionecrosis, or brachial plexopathy, was noted. We emphasize the importance of using VMAT with IGRT for reliable and accurate daily treatment of the target area, while minimizing side effects. However, these results should be confirmed with a larger sample of patients.

Separating Inhibitory and Excitatory Responses of Epileptic Brain to Single-Pulse Electrical Stimulation

To enable an accurate recognition of neuronal excitability in an epileptic brain for modeling or localization of epileptic zone, here the brain response to single-pulse electrical stimulation (SPES) has been decomposed into its constituent components using adaptive singular spectrum analysis (SSA). Given the response at neuronal level, these components are expected to be the inhibitory and excitatory components. The prime objective is to thoroughly investigate the nature of delayed responses (elicited between 100[Formula: see text]ms-1 s after SPES) for localization of the epileptic zone. SSA is a powerful subspace signal analysis method for separation of single channel signals into their constituent uncorrelated components. The consistency in the results for both early and delayed brain responses verifies the usability of the approach.

Higher-order tensor decomposition based scalp-to-intracranial EEG projection for detection of interictal epileptiform discharges

Objective.Interictal epileptiform discharges (IEDs) occur between two seizures onsets. IEDs are mainly captured by intracranial recordings and are often invisible over the scalp. This study proposes a model based on tensor factorization to map the time-frequency (TF) features of scalp EEG (sEEG) to the TF features of intracranial EEG (iEEG) in order to detect IEDs from over the scalp with high sensitivity.Approach.Continuous wavelet transform is employed to extract the TF features. Time, frequency, and channel modes of IED segments from iEEG recordings are concatenated into a four-way tensor. Tucker and CANDECOMP/PARAFAC decomposition techniques are employed to decompose the tensor into temporal, spectral, spatial, and segmental factors. Finally, TF features of both IED and non-IED segments from scalp recordings are projected onto the temporal components for classification.Main results.The model performance is obtained in two different approaches: within- and between-subject classification approaches. Our proposed method is compared with four other methods, namely a tensor-based spatial component analysis method, TF-based method, linear regression mapping model, and asymmetric-symmetric autoencoder mapping model followed by convolutional neural networks. Our proposed method outperforms all these methods in both within- and between-subject classification approaches by respectively achieving 84.2% and 72.6% accuracy values.Significance.The findings show that mapping sEEG to iEEG improves the performance of the scalp-based IED detection model. Furthermore, the tensor-based mapping model outperforms the autoencoder- and regression-based mapping models.

24-Hour video EEG in the evaluation of the first unprovoked seizure

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In the evaluation of a first seizure, the capture of epileptiform discharges is increased with 24-hour EEG recordings.

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This likely stems from a combination of increased sampling and robust sleep recording.

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Subtle seizures were also recorded by prolonging the EEG in a minority of first seizure presentations.

Objective

To assess the gain in detection of epileptiform abnormalities in 24-hour EEG recordings following the first seizure.

Methods

We identified patients who underwent 24-hour video EEG (VEEG) with “first seizure” as an indication. We noted the presence or absence of epileptiform discharges (EDs) in the VEEG study and the latency for the appearance of such discharges. We compared the rate of EDs during the initial 60 min with those occurring only later during the recording.

Results

Data from 25 patients, aged 15 to 59, were included. Of the 11 patients with EDs, eight (73%) appeared only after 60 min of recording. This equates to a 32% absolute increase in the detection of EDs across all patients. The latency to first EDs varied from one to 1080 min with a median of 170 min. In four cases, actual subtle seizures were recorded.

Conclusion

This study suggests an increase in the detection of EDs with the 24-hour studies compared to the traditional shorter recordings, in the context of a first seizure.

Significance

A standard EEG can be performed close to the seizure, followed by a longer up to 24-hour recording if the initial shorter study is unrevealing.

Incorporating Uncertainty in Data Labeling into Automatic Detection of Interictal Epileptiform Discharges from Concurrent Scalp-EEG via Multi-way Analysis

Interictal epileptiform discharges (IEDs) are elicited from an epileptic brain, whereas they can also be due to other neurological abnormalities. The diversity in their morphologies, their strengths, and their sources within the brain cause a great deal of uncertainty in their labeling by clinicians. The aim of this study is therefore to exploit and incorporate this uncertainty (the probability of the waveform being an IED) in the IED detection system which combines spatial component analysis (SCA) with the IED probabilities referred to as SCA-IEDP-based method. For comparison, we also propose and study SCA-based method in which probability of the waveform being an IED is ignored. The proposed models are employed to detect IEDs in two different classification approaches: (1) subject-dependent and (2) subject-independent classification approaches. The proposed methods are compared with two other state-of-the-art methods namely, time-frequency features and tensor factorization methods. The proposed SCA-IEDP model has achieved superior performance in comparison with the traditional SCA and other competing methods. It achieved 79.9% and 63.4% accuracy values in subject-dependent and subject-independent classification approaches, respectively. This shows that considering the IED probabilities in designing an IED detection system can boost its performance.

Encoding of long-term associations through neural unitization in the human medial temporal lobe

Besides decades of research showing the role of the medial temporal lobe (MTL) in memory and the encoding of associations, the neural substrates underlying these functions remain unknown. We identified single neurons in the human MTL that responded to multiple and, in most cases, associated stimuli. We observed that most of these neurons exhibit no differences in their spike and local field potential (LFP) activity associated with the individual response-eliciting stimuli. In addition, LFP responses in the theta band preceded single neuron responses by ~70 ms, with the single trial phase providing fine tuning of the spike response onset. We postulate that the finding of similar neuronal responses to associated items provides a simple and flexible way of encoding memories in the human MTL, increasing the effective capacity for memory storage and successful retrieval.

In this work, the authors recorded single neurons and field potentials from the human medial temporal lobe (MTL) and show indistinguishable responses to associated stimuli. This coding mechanism provides a simple and flexible way of encoding memories in the human MTL.

Deep Neural Architectures for Mapping Scalp to Intracranial EEG

Data is often plagued by noise which encumbers machine learning of clinically useful biomarkers and electroencephalogram (EEG) data is no exemption. Intracranial EEG (iEEG) data enhances the training of deep learning models of the human brain, yet is often prohibitive due to the invasive recording process. A more convenient alternative is to record brain activity using scalp electrodes. However, the inherent noise associated with scalp EEG data often impedes the learning process of neural models, achieving substandard performance. Here, an ensemble deep learning architecture for nonlinearly mapping scalp to iEEG data is proposed. The proposed architecture exploits the information from a limited number of joint scalp-intracranial recording to establish a novel methodology for detecting the epileptic discharges from the sEEG of a general population of subjects. Statistical tests and qualitative analysis have revealed that the generated pseudo-intracranial data are highly correlated with the true intracranial data. This facilitated the detection of IEDs from the scalp recordings where such waveforms are not often visible. As a real-world clinical application, these pseudo-iEEGs are then used by a convolutional neural network for the automated classification of intracranial epileptic discharges (IEDs) and non-IED of trials in the context of epilepsy analysis. Although the aim of this work was to circumvent the unavailability of iEEG and the limitations of sEEG, we have achieved a classification accuracy of 68% an increase of 6% over the previously proposed linear regression mapping.

Mechanisms underlying different onset patterns of focal seizures

Focal seizures are episodes of pathological brain activity that appear to arise from a localised area of the brain. The onset patterns of focal seizure activity have been studied intensively, and they have largely been distinguished into two types-low amplitude fast oscillations (LAF), or high amplitude spikes (HAS). Here we explore whether these two patterns arise from fundamentally different mechanisms. Here, we use a previously established computational model of neocortical tissue, and validate it as an adequate model using clinical recordings of focal seizures. We then reproduce the two onset patterns in their most defining properties and investigate the possible mechanisms underlying the different focal seizure onset patterns in the model. We show that the two patterns are associated with different mechanisms at the spatial scale of a single ECoG electrode. The LAF onset is initiated by independent patches of localised activity, which slowly invade the surrounding tissue and coalesce over time. In contrast, the HAS onset is a global, systemic transition to a coexisting seizure state triggered by a local event. We find that such a global transition is enabled by an increase in the excitability of the "healthy" surrounding tissue, which by itself does not generate seizures, but can support seizure activity when incited. In our simulations, the difference in surrounding tissue excitability also offers a simple explanation of the clinically reported difference in surgical outcomes. Finally, we demonstrate in the model how changes in tissue excitability could be elucidated, in principle, using active stimulation. Taken together, our modelling results suggest that the excitability of the tissue surrounding the seizure core may play a determining role in the seizure onset pattern, as well as in the surgical outcome.

Epilepsy in Qatar: Causes, treatment, and outcome

OBJECTIVE

Qatar is a small country on the Eastern coast of the Arabian Peninsula. Its population is a unique mixture of native citizens and immigrants. We aimed to describe the features of epilepsy in Qatar as such information is virtually lacking from the current literature.

METHODS

We summarized information retrospectively collected from 468 patients with epilepsy seen through the national health system adult neurology clinic.

RESULTS

Epilepsy was classified as focal in 65.5% of the cases and generalized in 23%. Common causes of epilepsy were as follows: stroke (9%), hippocampal sclerosis (7%), infections (6%), and trauma (6%). Sixty-six percent of patients were receiving a single antiepileptic drug, with levetiracetam being the most frequently prescribed drug (41% of subjects). When the patients were divided by geographical background, remote infections caused the epilepsy in 15% of Asian patients (with neurocysticercosis accounting for 10%) but only in 1% of Qatari and 3% of Middle East/North African subjects (with no reported neurocysticercosis) (p<0.001). Cerebrovascular and neurodegenerative etiologies were the most prominent in Qataris, accounting for 14% (p=0.005) and 4% (p=0.03) of cases, respectively. The choice of antiepileptic drugs varied also according to the regional background, but the seizure freedom rate did not, averaging at 54% on the last clinic visit.

SIGNIFICANCE

To our knowledge, this is the first detailed information about epilepsy in Qatar. The geographical origin of patients adds to the heterogeneity of this disorder. Neurocysticercosis should be in the etiological differential diagnosis of epilepsy in patients coming from Southeast Asian countries, despite the fact that it is not endemic to Qatar. The choice of antiepileptic drugs is influenced by the availability of individual agents in the patients' native countries but had no bearing on the final seizure outcome.

Differences in globus pallidus neuronal firing rates and patterns relate to different disease biology in children with dystonia

BACKGROUND

The pathophysiology underlying different types of dystonia is not yet understood. We report microelectrode data from the globus pallidus interna (GPi) and globus pallidus externa (GPe) in children undergoing deep brain stimulation (DBS) for dystonia and investigate whether GPi and GPe firing rates differ between dystonia types.

METHODS

Single pass microelectrode data were obtained to guide electrode position in 44 children (3.3-18.1 years, median 10.7) with the following dystonia types: 14 primary, 22 secondary Static and 8 progressive secondary to neuronal brain iron accumulation (NBIA). Preoperative stereotactic MRI determined coordinates for the GPi target. Digitised spike trains were analysed offline, blind to clinical data. Electrode placement was confirmed by a postoperative stereotactic CT scan.

FINDINGS

We identified 263 GPi and 87 GPe cells. Both GPi and GPe firing frequencies differed significantly with dystonia aetiology. The median GPi firing frequency was higher in the primary group than in the secondary static group (13.5 Hz vs 9.6 Hz; p=0.002) and higher in the NBIA group than in either the primary (25 Hz vs 13.5 Hz; p=0.006) or the secondary static group (25 Hz vs 9.6 Hz; p=0.00004). The median GPe firing frequency was higher in the NBIA group than in the secondary static group (15.9 Hz vs 7 Hz; p=0.013). The NBIA group also showed a higher proportion of regularly firing GPi cells compared with the other groups (p<0.001). A higher proportion of regular GPi cells was also seen in patients with fixed/tonic dystonia compared with a phasic/dynamic dystonia phenotype (p<0.001). The GPi firing frequency showed a positive correlation with 1-year outcome from DBS measured by improvement in the Burke-Fahn-Marsden Dystonia Rating Scale (BFMDRS-m) score (p=0.030). This association was stronger for the non-progressive patients (p=0.006).

INTERPRETATION

Pallidal firing rates and patterns differ significantly with dystonia aetiology and phenotype. Identification of specific firing patterns may help determine targets and patient-specific protocols for neuromodulation therapy.

FUNDING

National Institute of Health Research, Guy's and St. Thomas' Charity, Dystonia Society UK, Action Medical Research, German National Academic Foundation.

Clinical manifestations and coronary artery disease risk factors at diagnosis of systemic lupus erythematosus: data from an international inception cohort

Systemic Lupus International Collaborating Clinics (SLICC) comprises 27 centres from 11 countries. An inception cohort of 918 SLE patients has been assembled according to a standardized protocol between 2000 and 2006. Clinical features, classic coronary artery disease (CAD) risk factors, as well as other potential risk factors were collected. Of the 918 patients 89% were females, and of multi racial origin. Less than half the patients were living in a permanent relationship, 58% had post secondary education and 51% were employed. Eight percent had family history of SLE. At enrolment, with at mean age of diagnosis of 34.5 years, a significant number of patients already had CAD risk factors, such as hypertension (33%) and hypercholesterolemia (36%). Only 15% of the patients were postmenopausal, 16% were current smokers and 3.6% had diabetes at entry to the SLICC-RAS (Registry for Atherosclerosis). A number of patients in this multi-racial, multi-ethnic inception cohort of lupus patients have classic CAD risk factors within a mean of 5.4 months from diagnosis. This cohort will be increased to 1500 patients to be followed yearly for 10 years. This will provide a unique opportunity to evaluate risk factors for accelerated atherosclerosis in SLE. Lupus (2007) 16, 731—735.

Single-cell recordings in the human medial temporal lobe

Recordings from individual neurons in patients who are implanted with depth electrodes for clinical reasons have opened the possibility to narrow down the gap between neurophysiological studies in animals and non-invasive (e.g. functional magnetic resonance imaging, electroencephalogram, magnetoencephalography) investigations in humans. Here we provide a description of the main procedures for electrode implantation and recordings, the experimental paradigms used and the main steps for processing the data. We also present key characteristics of the so-called 'concept cells', neurons in the human medial temporal lobe with selective and invariant responses that represent the meaning of the stimulus, and discuss their proposed role in declarative memory. Finally, we present novel results dealing with the stability of the representation given by these neurons, by studying the effect of stimulus repetition in the strength of the responses. In particular, we show that, after an initial decay, the response strength reaches an asymptotic value after approximately 15 presentations that remains above baseline for the whole duration of the experiment.

Epilepsia partialis continua responsive to neocortical electrical stimulation

Epilepsia partialis continua (EPC), defined as a syndrome of continuous focal jerking, is a rare form of focal status epilepticus that usually affects a distal limb, and when prolonged, can produce long-lasting deficits in limb function. Substantial electrophysiologic evidence links the origin of EPC to the motor cortex; thus surgical resection carries the risk of significant handicap. We present two patients with focal, drug-resistant EPC, who were admitted for intracranial video-electroencephalography monitoring to elucidate the location of the epileptogenic focus and identification of eloquent motor cortex with functional mapping. In both cases, the focus resided at or near eloquent motor cortex and therefore precluded resective surgery. Chronic cortical stimulation delivered through subdural strips at the seizure focus (continuous stimulation at 60-130 Hz, 2-3 mA) resulted in >90% reduction in seizures and abolition of the EPC after a follow-up of 22 months in both patients. Following permanent implantation of cortical stimulators, no adverse effects were noted. EPC restarted when intensity was reduced or batteries depleted. Battery replacement restored previous improvement. This two-case report opens up avenues for the treatment of this debilitating condition.

[Congenital insensitivity to pain: clinical and neurophysiological study in three sisters of a Moroccan family]

Congenital insensitivity to pain is a rare hereditary sensory and autonomic neuropathy (HSAN). This disorder is an autosomal recessive condition: since 1996, mutations attributed to this entity have been found in the neurotrophin tyrosine-kinase gene receptor on chromosome 1. The authors report 3 cases of congenital insensitivity to pain. In these 3 sisters of consanguineous parents, the clinical investigation showed total absence of pain and temperature sensations with preservation of all other sensory modalities, mental retardation, but in contrast to HSAN type IV, there was no anhidrosis. The neurophysiological investigation revealed an isolated axonal sensory polyneuropathy in the 3 patients. The clinical and neurophysiological investigations were normal in both parents and the brother. The physiopathology of this entity is discussed. We suggest a particular form of HSAN type IV with preservation of transpiration or a new entity of congenital insensitivity to pain, which should be analyzed genetically.