Spectral organization of focal seizures within the thalamotemporal network

The Reciprocal Relationship between Sleep and Epilepsy

The relationship between sleep and epilepsy is bidirectional. Certain epilepsy syndromes predominantly or exclusively manifest during sleep, with seizures frequently originating from non-rapid eye movement (NREM) sleep. Interictal epileptiform discharges observed on electroencephalograms are most likely to be activated during the deep NREM sleep stage known as N3. Conversely, epileptiform discharges, anti-seizure medications (ASMs), as well as other anti-seizure therapies can exert detrimental effects on sleep architecture. Moreover, the co-occurrence of sleep disorders has the potential to exacerbate seizure control. Understating the relationship between sleep and epilepsy is crucial for healthcare providers. Addressing and managing sleep-related problems in individuals with epilepsy can potentially contribute to improved seizure control and overall well-being. At the same time, improving seizure control can improve sleep quality and quantity, thus further improving the health of individuals with epilepsy.

Evolution of Stereo-Electroencephalography at Massachusetts General Hospital

The practice of invasive monitoring for presurgical epilepsy workup has evolved at Massachusetts General Hospital (MGH) in parallel to the evolution in the field's understanding of epilepsy as a network disorder. Implantations have shifted from an emphasis on singularly finding single foci for the purpose of resection to a network-hypothesis-driven approach aiming to delineate patients' seizure networks with the goal of developing surgical interventions that disrupt critical nodes of these networks. Here, the authors review all invasive monitoring cases at MGH from April 2016 through June 2023 to describe how this paradigm shift has taken form.

Sensing-Enabled Deep Brain Stimulation in Epilepsy

Deep brain stimulation has demonstrated efficacy in reducing seizure frequency in patients with drug-resistant epilepsy who may otherwise not be candidates for other surgical procedures. Recently, a clinical device that can monitor neural activity in the form of local field potentials around the deep brain stimulator lead implant site has been introduced. While this technology has been clinically adopted in other disorders treated with deep brain stimulation, such as Parkinson's disease, its application in epilepsy remains unclear. Previous research using investigational devices has suggested that specific frequency bands may correlate with clinical response to deep brain stimulation in epilepsy, but features of the clinical device may prevent its use. The authors present their experience with using this technology in epilepsy patients and describe some of its limitations. Ultimately, novel biomarkers will need to be identified to elucidate how neural activity at deep brain stimulation sites may change with clinical response.

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.

Thalamic stereoelectroencephalography in epilepsy surgery: a scoping literature review

OBJECTIVE

Stereoelectroencephalography (sEEG) is a well-established surgical method for defining the epileptogenic network. Traditionally reserved for identifying discrete cortical regions for resection or ablation, sEEG in current practice is also used for identifying more broadly involved subcortical epileptic network components, driven by the availability of brain-based neuromodulation strategies. In particular, sEEG investigations including thalamic nuclei are becoming more frequent in parallel with the increase in therapeutic strategies involving thalamic targets such as deep brain stimulation (DBS) and responsive neurostimulation (RNS). The objective to this study was to evaluate existing evidence and trends regarding the purpose, techniques, and relevant electrographic findings of thalamic sEEG.

METHODS

MEDLINE and Embase databases were systematically queried for eligible peer-reviewed studies involving sEEG electrode implantation into thalamic nuclei of patients with epilepsy. Available data were abstracted concerning preoperative workup and purpose for implanting the thalamus, thalamic targets and trajectories, and electrophysiological methodology and findings.

RESULTS

sEEG investigations have included thalamic targets for both basic and clinical research purposes. Medial pulvinar, dorsomedial, anterior, and centromedian nuclei have been the most frequently studied. Few studies have reported any complications with thalamic sEEG implantation, and no studies have reported long-term complications. Various methods have been utilized to characterize thalamic activity in epileptic disorders including evoked potentials, power spectrograms, synchronization indices, and the epileptogenicity index. Thalamic intracranial recordings are beginning to be used to guide neuromodulation strategies including RNS and DBS, as well as to understand complex, network-dependent seizure disorders.

CONCLUSIONS

Inclusion of thalamic coverage during sEEG evaluation in drug-resistant epilepsy is a growing practice and is amenable to various methods of electrographic data analysis. Further study is required to establish well-defined criteria for thalamic implantation during invasive investigations as well as safety and ethical considerations.

Closed-Loop Brain Stimulation and Paradigm Shifts in Epilepsy Surgery

Advances in device technology have created greater flexibility in treating seizures as emergent properties of networks that exist on a local to global continuum. All patients with drug-resistant epilepsy are potential surgical candidates, given that intracranial neuromodulation through deep brain stimulation and responsive neurostimulation can reduce seizures and improve quality of life, even in multifocal and generalized epilepsies. To achieve this goal, indications and strategies for diagnostic epilepsy surgery are evolving. This article describes the state-of-the-art in epilepsy surgery and related changes in how we define indications for diagnostic and therapeutic surgical intervention.

Time-Series Generative Adversarial Network Approach of Deep Learning Improves Seizure Detection From the Human Thalamic SEEG

Seizure detection algorithms are often optimized to detect seizures from the epileptogenic cortex. However, in non-localizable epilepsies, the thalamus is frequently targeted for neuromodulation. Developing a reliable seizure detection algorithm from thalamic SEEG may facilitate the translation of closed-loop neuromodulation. Deep learning algorithms promise reliable seizure detectors, but the major impediment is the lack of larger samples of curated ictal thalamic SEEG needed for training classifiers. We aimed to investigate if synthetic data generated by temporal Generative Adversarial Networks (TGAN) can inflate the sample size to improve the performance of a deep learning classifier of ictal and interictal states from limited samples of thalamic SEEG. Thalamic SEEG from 13 patients (84 seizures) was obtained during stereo EEG evaluation for epilepsy surgery. Overall, TGAN generated synthetic data augmented the performance of the bidirectional Long-Short Term Memory (BiLSTM) performance in classifying thalamic ictal and baseline states. Adding synthetic data improved the accuracy of the detection model by 18.5%. Importantly, this approach can be applied to classify electrographic seizure onset patterns or develop patient-specific seizure detectors from implanted neuromodulation devices.

Anterior nucleus of the thalamus seizure detection in ambulatory humans

There is a paucity of data to guide anterior nucleus of the thalamus (ANT) deep brain stimulation (DBS) with brain sensing. The clinical Medtronic Percept DBS device provides constrained brain sensing power within a frequency band (power-in-band [PIB]), recorded in 10-min averaged increments. Here, four patients with temporal lobe epilepsy were implanted with an investigational device providing full bandwidth chronic intracranial electroencephalogram (cEEG) from bilateral ANT and hippocampus (Hc). ANT PIB-based seizure detection was assessed. Detection parameters were cEEG PIB center frequency, bandwidth, and epoch duration. Performance was evaluated against epileptologist-confirmed Hc seizures, and assessed by area under the precision-recall curve (PR-AUC). Data included 99 days of cEEG, and 20, 278, 3, and 18 Hc seizures for Subjects 1-4. The best detector had 7-Hz center frequency, 5-Hz band width, and 10-s epoch duration (group PR-AUC = .90), with 75% sensitivity and .38 false alarms per day for Subject 1, and 100% and .0 for Subjects 3 and 4. Hc seizures in Subject 2 did not propagate to ANT. The relative change of ANT PIB was maximal ipsilateral to seizure onset for all detected seizures. Chronic ANT and Hc recordings provide direct guidance for ANT DBS with brain sensing.

Functional connectivity of the thalamocortical circuit in patients with seizure relapse after antiseizure medication withdrawal

OBJECTIVE

To characterize the features of thalamocortical functional connectivity during seizure recurrence at the time of antiseizure medication (ASM) withdrawal.

METHODS

Patients with chronic epilepsy who attempted to discontinue medications were prospectively registered and followed up; 19 patients remained seizure-free (SF-group), 18 patients had seizure relapses (SR-group) after ASM withdrawal, and 28 healthy controls were recruited. Resting-state functional magnetic resonance imaging was performed before ASM withdrawal. Thalamus subdivisions were set as seeds to calculate voxelwise functional connectivity. Partial correlation analysis between functional connectivity and clinical variables was performed. A support vector machine was used to assess the predictive ability of the specific functional connectivity for seizure relapse.

RESULTS

The within-group comparison indicated that the SR-group had more extensive functional connectivity than the SF-group; the left inferior pulvinar, left medial pulvinar, and right anterior pulvinar showed a significantly stronger functional connection with the precuneus in the SR-group than in the SF-group (Gaussian random field correction, voxel-level p < .001 and cluster-level p < .05). In the SR-group, a positive correlation was found between the left inferior pulvinar-precuneus connectivity and the active period (r = .46, p = .05), seizure-free period (r = .67, p = .002), and disease duration (r = .53, p = .02), and between the left medial pulvinar-precuneus connectivity and the seizure-free period (r = .58, p = .01). The combination of these thalamocortical connections showed a high predictive ability, with an area under the curve of .92 and accuracy of .90 (p = .01).

SIGNIFICANCE

This study determined distinct features of thalamocortical functional connectivity at the time of ASM withdrawal in patients with and without seizure relapse, showing a potential for predicting seizure outcomes following ASM withdrawal.

Invasive Electrophysiology for Circuit Discovery and Study of Comorbid Psychiatric Disorders in Patients With Epilepsy: Challenges, Opportunities, and Novel Technologies

Intracranial electroencephalographic (iEEG) recordings from patients with epilepsy provide distinct opportunities and novel data for the study of co-occurring psychiatric disorders. Comorbid psychiatric disorders are very common in drug-resistant epilepsy and their added complexity warrants careful consideration. In this review, we first discuss psychiatric comorbidities and symptoms in patients with epilepsy. We describe how epilepsy can potentially impact patient presentation and how these factors can be addressed in the experimental designs of studies focused on the electrophysiologic correlates of mood. Second, we review emerging technologies to integrate long-term iEEG recording with dense behavioral tracking in naturalistic environments. Third, we explore questions on how best to address the intersection between epilepsy and psychiatric comorbidities. Advances in ambulatory iEEG and long-term behavioral monitoring technologies will be instrumental in studying the intersection of seizures, epilepsy, psychiatric comorbidities, and their underlying circuitry.

Robot-assisted stereoelectroencephalography exploration of the limbic thalamus in human focal epilepsy: implantation technique and complications in the first 24 patients

OBJECTIVE

Despite numerous imaging studies highlighting the importance of the thalamus in a patient's surgical prognosis, human electrophysiological studies involving the limbic thalamic nuclei are limited. The objective of this study was to evaluate the safety and accuracy of robot-assisted stereotactic electrode placement in the limbic thalamic nuclei of patients with suspected temporal lobe epilepsy (TLE).

METHODS

After providing informed consent, 24 adults with drug-resistant, suspected TLE undergoing evaluation with stereoelectroencephalography (SEEG) were enrolled in the prospective study. The trajectory of one electrode planned for clinical sampling of the operculoinsular cortex was modified to extend it to the thalamus, thereby preventing the need for additional electrode placement for research. The anterior nucleus of the thalamus (ANT) (n = 13) and the medial group of thalamic nuclei (MED) (n = 11), including the mediodorsal and centromedian nuclei, were targeted. The postimplantation CT scan was coregistered to the preoperative MR image, and Morel's thalamic atlas was used to confirm the accuracy of implantation.

RESULTS

Ten (77%) of 13 patients in the ANT group and 10 (91%) of 11 patients in the MED group had electrodes accurately placed in the thalamic nuclei. None of the patients had a thalamic hemorrhage. However, trace asymptomatic hemorrhages at the cortical-level entry site were noted in 20.8% of patients, who did not require additional surgical intervention. SEEG data from all the patients were interpretable and analyzable. The trajectories for the ANT implant differed slightly from those of the MED group at the entry point-i.e., the precentral gyrus in the former and the postcentral gyrus in the latter.

CONCLUSIONS

Using judiciously planned robot-assisted SEEG, the authors demonstrate the safety of electrophysiological sampling from various thalamic nuclei for research recordings, presenting a technique that avoids implanting additional depth electrodes or compromising clinical care. With these results, we propose that if patients are fully informed of the risks involved, there are potential benefits of gaining mechanistic insights to seizure genesis, which may help to develop neuromodulation therapies.

Directed Connectivity Analysis of the Neuro-Cardio- and Respiratory Systems Reveals Novel Biomarkers of Susceptibility to SUDEP

Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality and its pathophysiological mechanisms remain unknown. Goal: We set to record and analyze for the first time concurrent electroencephalographic (EEG), electrocardiographic (ECG), and unrestrained whole-body plethysmographic (Pleth) signals from control (WT - wild type) and SUDEP-prone mice (KO- knockout Kcna1 animal model). Methods: Employing multivariate autoregressive models (MVAR) we measured all tri-organ effective directional interactions by the generalized partial directed coherence (GPDC) in the frequency domain over time (hours). Results: When compared to the control (WT) animals, the SUDEP-prone (KO) animals exhibited (p < 0.001) reduced afferent and efferent interactions between the heart and the brain over the full frequency spectrum (0-200Hz), enhanced efferent interactions from the brain to the lungs and from the heart to the lungs at high (>90 Hz) frequencies (especially during periods with seizure activity), and decreased feedback from the lungs to the brain at low (<40 Hz) frequencies. Conclusions: These results show that impairment in the afferent and efferent pathways in the holistic neuro-cardio-respiratory network could lead to SUDEP, and effective connectivity measures and their dynamics could serve as novel biomarkers of susceptibility to SUDEP and seizures respectively.

Machine learning approach to detect focal-onset seizures in the human anterior nucleus of the thalamus

OBJECTIVE

There is an unmet need to develop seizure detection algorithms from brain regions outside the epileptogenic cortex. The study aimed to demonstrate the feasibility of classifying seizures and interictal states from local field potentials (LFPs) recorded from the human thalamus- a subcortical region remote to the epileptogenic cortex. We tested the hypothesis that spectral and entropy-based features extracted from LFPs recorded from the anterior nucleus of the thalamus (ANT) can distinguish its state of ictal recruitment from other interictal states (including awake, sleep).

APPROACH

Two supervised machine learning tools (random forest and the random kitchen sink) were used to evaluate the performance of spectral (discrete wavelet transform-DWT), and time-domain (multiscale entropy-MSE) features in classifying seizures from interictal states in patients undergoing stereo EEG evaluation for epilepsy surgery. Under the supervision of IRB, field potentials were recorded from the ANT in consenting adults with drug-resistant temporal lobe epilepsy. Seizures were confirmed in the ANT using line-length and visual inspection. Wilcoxon rank-sum (WRS) method was used to test the differences in spectral patterns between seizure and interictal (awake and sleep) states.

MAIN RESULTS

79 seizures (10 patients) and 158 segments (approx. 4 hours) of interictal stereo EEG data were analyzed. The mean seizure detection latencies with line length in the ANT varied between seizure types (range 5-34 seconds). However, the DWT and MSE in the ANT showed significant changes for all seizure types within the first 20 seconds after seizure onset. The random forest (accuracy 93.9 % and false-positive 4.6%) and the random kitchen sink (accuracy 97.3% and false-positive 1.8%) classified seizures and interictal states.

SIGNIFICANCE

These results suggest that features extracted from the thalamic LFPs can be trained to detect seizures that can be used for monitoring seizure counts and for closed-loop seizure abortive interventions.

Localization of the epileptogenic zone based on ictal stereo-electroencephalogram: Brain network and single-channel signal feature analysis

Accurate localization of the epileptogenic zone (EZ) is crucial for refractory focal epilepsy patients to achieve freedom from seizures following epilepsy surgery. In this study, ictal stereo-electroencephalography data from 35 patients with refractory focal epilepsy were analyzed. Effective networks based on partial directed coherence were analyzed, and a gray level co-occurrence matrix was applied to extract the time-varying features of the in-degree. These features, combined with the single-channel signal time-frequency features, including approximate entropy and line length, were used to localize the EZ based on a cluster algorithm. For all seizure-free patients (n = 23), the proposed method was effective in identifying the clinical-EZ-contacts and clinical-EZ-blocks, with an F1-score of 62.47 % and 72.18 %, respectively. The sensitivity was 96.00 % for the clinical-EZ-block identification, which provided the information for the decision-making of clinicians, prompting clinicians to focus on the identified EZ-blocks and their nearby contacts. The agreement between the EZ identified by the proposed method and the clinical-EZ was worse for non-seizure-free patients (n = 12) than for seizure-free patients. Furthermore, our method provided better results than using only brain network or single-channel signal features. This suggests that combining these complementary features can facilitate more accurate localization of the EZ.

Acute modulation of the limbic network with low and high-frequency stimulation of the human fornix

Targeted stimulation of white matter has opened newer perspectives in the field of neuromodulation, towards an attempt to improve memory or as a therapy for epilepsy. Stimulation of the fornix, being a part of the Papez circuit, is likely to modulate the limbic network excitability. However, the stimulation-frequency dependent variability in network excitability is unknown. In the case study, which involved stereo electroencephalographic (SEEG) recording of field potentials in a 48-year old left-handed woman with suspected temporal lobe epilepsy, we demonstrated the network effects of acute low (1 and 10 Hz) and high (50 Hz) frequency electrical stimulation of fornix. Mapping the short-latency evoked responses to forniceal stimulation confirmed the SEEG target localization within the Papez circuit. Low and high-frequency stimulation of the fornix produced opposite effects in the post-stimuli excitability, with the latter causing increased excitability in the limbic network that culminated in a clinical seizure. A distinct spectral peak around 8 Hz confirmed that sensing field potentials from the forniceal white matter is feasible. This is the first case study that provided an insight into how the temporal patterning of forniceal stimulation altered the downstream limbic network excitability.