Utilizing Centromedian Thalamus Connectivity to Personalize Noninvasive Neuromodulation Targets

INTRODUCTION

The centromedian nucleus (CM) of the thalamus is essential for arousal, attention, sensory processing, and motor control. Neuromodulation targeting CM dysfunction has shown efficacy in various neurological disorders. However, its individualized precise transcranial magnetic stimulation (TMS) remains unreported. Using resting-state functional MRI, we mapped CM-based functional connectivity (CM-FC) to develop a personalized TMS scheme for neurological conditions.

METHODS

We first analyzed the CM-FC patterns of healthy subjects via 10 scanning sessions in three MRI scanners spanning two subject groups: one from the Human Connectome Project (n = 20, four sessions) dataset and the other from Hangzhou Normal University (n = 20, three sessions of 3 T MRI and three sessions of 1.5 T MRI). Pearson's correlation was used for CM-FC evaluation. Then, we proposed an overlapping index ranging from 1 to 10, and group-level clusters with the highest overlapping index located 4 cm beneath the scalp were identified. In the individual CM-FC map, watershed image segmentation was used to obtain an individual cluster. The peak voxel with the highest FC value within the individual cluster was defined as a potential individualized target for future TMS.

RESULTS

The spatial FC patterns were remarkably similar between the left and right CMs. CMs have widespread positive connectivity with cortical areas, including the sensorimotor cortex, supplementary motor area, middle frontal cortex, medial temporal cortex, and middle cingulate. Among the group-level FC patterns of the left and right CMs, only the left CM had a group cluster in the left primary sensorimotor cortex (PSMC, cluster size = 51) with an overlapping index of 10, that is, 10 sessions showed significant CM-FC.

CONCLUSIONS

The left PSMC exhibited reproducible FC with the left CM. The individual peak FC location in the left PSMC could be used as a TMS target for indirect modulation of CM activity and aid in the treatment of CM-related neurological disorders.

Transcranial magnetic stimulation and magnetoencephalography are feasible alternatives to invasive methods in optimizing responsive neurostimulation device placement

Responsive neurostimulation (RNS) is a treatment option for patients with refractory epilepsy when surgical resection is not possible due to overlap of the irritative zone and eloquent cortex. Presurgical evaluations for RNS placement typically rely on invasive methods. This study investigated the potential of transcranial magnetic stimulation (TMS) and magnetoencephalography (MEG) to provide key presurgical information non-invasively. We hypothesized that these non-invasive methods may assist in optimizing RNS placement by providing useful information for seizure localization by MEG and eloquent cortex mapping by TMS. A retrospective chart review identified nine patients who underwent RNS placement (mean age = 20.4 years [SD = 5.6], two-thirds were female). Characterization of the irritative zone using MEG was successful in eight of nine patients. Non-invasive mapping of relevant eloquent cortex was attempted in all patients. TMS was successful in eight of nine patients, and MEG was successful in two of six patients. Importantly, patients mapped with non-invasive modalities experienced an average seizure reduction of 77 % at their most recent clinic visit, compared to 75 % seizure reduction in those with invasive evaluations, indicating appropriate RNS placement. These data demonstrate that TMS and MEG can provide key information for RNS and may be feasible alternatives to invasive methods for assisting in decision making regarding RNS placement. Non-invasive methods for determining RNS placement have a high rate of success when data from multiple non-invasive modalities converge and can inform more accurate placement of intracranial electrodes prior to RNS placement or mitigate their need.

'Remote inhibition' of motor cortex in Epileptic encephalopathy with spike-wave activation in sleep (EE-SWAS): A TMS based cortical excitability study

OBJECTIVES

The study compared real-time motor cortex excitability using transcranial magnetic stimulation (TMS)-derived parameters between children with epileptic encephalopathy with spike-wave activation in sleep (EE-SWAS) and age-matched neurotypical controls. The EE-SWAS group received steroids as standard of care and were longitudinally followed for three months.

MATERIALS & METHODS

Children aged 5-12 years with immunotherapy-naive EE-SWAS (spike-wave-index≥50 %) and neurotypical controls were enrolled. Cognitive and behavioral assessments were performed using valid psychometric tools. Real-time motor cortex excitability was assessed by measuring resting motor threshold (RMT), short intra-cortical inhibition (SICI) and long intra-cortical inhibition (LICI) in both groups. In EE-SWAS group, a follow up evaluation with TMS at 4- and 12-week intervals, EEG, and neurobehavioral assessments at 12-weeks were performed to assess the effect of steroids on cortical excitability and to determine electroclinical outcome.

RESULTS

Forty-eight children with suspected EE-SWAS and 26 neurotypical controls were screened; 20 were enrolled in each group. Children with EE-SWAS (mean age: 8.05 ± 1.76 years) had cognitive and behavioral problems (20/20), and ongoing seizures (12/20). At baseline, the dominant motor cortex was significantly inhibited in the EE-SWAS group compared to neurotypical children{RMT(%)[86.3 ± 6.96 vs 58.05 ± 4.71(p < 0.0001)]; LICI(%)[55.05 ± 4.39 vs 73.9 ± 3.75(p < 0.0001)]; SICI(%)[39.2 ± 4.36 vs 55.45 ± 4.78(p < 0.0001)]}. Reversal of motor cortex inhibition was sequentially observed in EE-SWAS group at 4- and 12-week follow-ups{(RMT[4, 12 weeks]: 71.45 ± 9.83, 63.45 ± 8.48); (LICI[4, 12 weeks]: 66.00 ± 6.26, 74.50 ± 5.36); (SICI[4, 12 weeks]: 49.35 ± 6.24, 56.05 ± 5.57)}[repeated-measures ANOVA: p < 0.0001].

CONCLUSION

Motor cortex is remotely inhibited in EE-SWAS, which may contribute to neurobehavioral impairment. Steroids can disinhibit/reverse the epilepsy-induced motor cortex inhibition leading to improvement in neurobehavior.

Measuring the nonselective effects of motor inhibition using isometric force recordings

Inhibition is a key cognitive control mechanism humans use to enable goal-directed behavior. When rapidly exerted, inhibitory control has broad, nonselective motor effects, typically demonstrated using corticospinal excitability measurements (CSE) elicited by transcranial magnetic stimulation (TMS). For example, during rapid action-stopping, CSE is suppressed at both stopped and task-unrelated muscles. While such TMS-based CSE measurements have provided crucial insights into the fronto-basal ganglia circuitry underlying inhibitory control, they have several downsides. TMS is contraindicated in many populations (e.g., epilepsy or deep-brain stimulation patients), has limited temporal resolution, produces distracting auditory and haptic stimulation, is difficult to combine with other imaging methods, and necessitates expensive, immobile equipment. Here, we attempted to measure the nonselective motor effects of inhibitory control using a method unaffected by these shortcomings. Thirty male and female human participants exerted isometric force on a high-precision handheld force transducer while performing a foot-response stop-signal task. Indeed, when foot movements were successfully stopped, force output at the task-irrelevant hand was suppressed as well. Moreover, this nonselective reduction of isometric force was highly correlated with stop-signal performance and showed frequency dynamics similar to established inhibitory signatures typically found in neural and muscle recordings. Together, these findings demonstrate that isometric force recordings can reliably capture the nonselective effects of motor inhibition, opening the door to many applications that are hard or impossible to realize with TMS.

Drug-resistant juvenile myoclonic epilepsy: A literature review

The ILAE's Task Force on Nosology and Definitions revised in 2022 its definition of juvenile myoclonic epilepsy (JME), the most common idiopathic generalized epilepsy disorder, but this definition may well change again in the future. Although good drug response could almost be a diagnostic criterion for JME, drug resistance (DR) is observed in up to a third of patients. It is important to distinguish this from pseudoresistance, which is often linked to psychosocial problems or psychiatric comorbidities. After summarizing these aspects and the various definitions applied to JME, the present review lists the risk factors for DR-JME that have been identified in numerous studies and meta-analyses. The factors most often cited are absence seizures, young age at onset, and catamenial seizures. By contrast, photosensitivity seems to favor good treatment response, at least in female patients. Current hypotheses on DR mechanisms in JME are based on studies of either simple (e.g., cortical excitability) or more complex (e.g., anatomical and functional connectivity) neurophysiological markers, bearing in mind that JME is regarded as a neural network disease. This research has revealed correlations between the intensity of some markers and DR, and above all shed light on the role of these markers in associated neurocognitive and neuropsychiatric disorders in both patients and their siblings. Studies of neurotransmission have mainly pointed to impaired GABAergic inhibition. Genetic studies have generally been inconclusive. Increasing restrictions have been placed on the use of valproate, the standard antiseizure medication for this syndrome, owing to its teratogenic and developmental risks. Levetiracetam and lamotrigine are prescribed as alternatives, as is vagal nerve stimulation, and there are several other promising antiseizure drugs and neuromodulation methods. The development of better alternative treatments is continuing to take place alongside advances in our knowledge of JME, as we still have much to learn and understand.

Functional and structural networks decoupling in generalized tonic-clonic seizures and its reorganization by drugs

OBJECTIVE

To investigate potential functional and structural large-scale network disturbances in untreated patients with generalized tonic-clonic seizures (GTCS) and the effects of antiseizure drugs.

METHODS

In this study, 41 patients with GTCS, comprising 21 untreated patients and 20 patients who received antiseizure medications (ASMs), and 29 healthy controls were recruited to construct large-scale brain networks based on resting-state functional magnetic resonance imaging and diffusion tensor imaging. Structural and functional connectivity and network-level weighted correlation probability (NWCP) were further investigated to identify network features that corresponded to response to ASMs.

RESULTS

Untreated patients showed more extensive enhancement of functional and structural connections than controls. Specifically, we observed abnormally enhanced connections between the default mode network (DMN) and the frontal-parietal network. In addition, treated patients showed similar functional connection strength to that of the control group. However, all patients exhibited similar structural network alterations. Moreover, the NWCP value was lower for connections within the DMN and between the DMN and other networks in the untreated patients; receiving ASMs could reverse this pattern.

SIGNIFICANCE

Our study identified alterations in structural and functional connectivity in patients with GTCS. The influence of ASMs may be more noticeable within the functional network; moreover, abnormalities in both the functional and structural coupling state may be improved by ASM treatment. Therefore, the coupling state of structural and functional connectivity may be used as an indicator of the efficacy of ASMs.

Clinical diagnostic utility of transcranial magnetic stimulation in neurological disorders. Updated report of an IFCN committee

The review provides a comprehensive update (previous report: Chen R, Cros D, Curra A, Di Lazzaro V, Lefaucheur JP, Magistris MR, et al. The clinical diagnostic utility of transcranial magnetic stimulation: report of an IFCN committee. Clin Neurophysiol 2008;119(3):504-32) on clinical diagnostic utility of transcranial magnetic stimulation (TMS) in neurological diseases. Most TMS measures rely on stimulation of motor cortex and recording of motor evoked potentials. Paired-pulse TMS techniques, incorporating conventional amplitude-based and threshold tracking, have established clinical utility in neurodegenerative, movement, episodic (epilepsy, migraines), chronic pain and functional diseases. Cortical hyperexcitability has emerged as a diagnostic aid in amyotrophic lateral sclerosis. Single-pulse TMS measures are of utility in stroke, and myelopathy even in the absence of radiological changes. Short-latency afferent inhibition, related to central cholinergic transmission, is reduced in Alzheimer's disease. The triple stimulation technique (TST) may enhance diagnostic utility of conventional TMS measures to detect upper motor neuron involvement. The recording of motor evoked potentials can be used to perform functional mapping of the motor cortex or in preoperative assessment of eloquent brain regions before surgical resection of brain tumors. TMS exhibits utility in assessing lumbosacral/cervical nerve root function, especially in demyelinating neuropathies, and may be of utility in localizing the site of facial nerve palsies. TMS measures also have high sensitivity in detecting subclinical corticospinal lesions in multiple sclerosis. Abnormalities in central motor conduction time or TST correlate with motor impairment and disability in MS. Cerebellar stimulation may detect lesions in the cerebellum or cerebello-dentato-thalamo-motor cortical pathways. Combining TMS with electroencephalography, provides a novel method to measure parameters altered in neurological disorders, including cortical excitability, effective connectivity, and response complexity.

Drug resistance in idiopathic generalized epilepsies: Evidence and concepts

Although approximately 10%-15% of patients with idiopathic generalized epilepsy (IGE)/genetic generalized epilepsy remain drug-resistant, there is no consensus or established concept regarding the underlying mechanisms and prevalence. This review summarizes the recent data and the current hypotheses on mechanisms that may contribute to drug-resistant IGE. A literature search was conducted in PubMed and Embase for studies on mechanisms of drug resistance published since 1980. The literature shows neither consensus on the definition nor a widely accepted model to explain drug resistance in IGE or one of its subsyndromes. Large-scale genetic studies have failed to identify distinct genetic causes or affected genes involved in pharmacokinetics. We found clinical and experimental evidence in support of four hypotheses: (1) "network hypothesis"-the degree of drug resistance in IGE reflects the severity of cortical network alterations, (2) "minor focal lesion in a predisposed brain hypothesis"-minor cortical lesions are important for drug resistance, (3) "interneuron hypothesis"-impaired functioning of γ-aminobutyric acidergic interneurons contributes to drug resistance, and (4) "changes in drug kinetics"-genetically impaired kinetics of antiseizure medication (ASM) reduce the effectiveness of available ASMs. In summary, the exact definition and cause of drug resistance in IGE is unknown. However, published evidence suggests four different mechanisms that may warrant further investigation.

Generalized Fast Discharges Along the Genetic Generalized Epilepsy Spectrum: Clinical and Prognostic Significance

Objective

To investigate the electroclinical characteristics and the prognostic impact of generalized fast discharges in a large cohort of genetic generalized epilepsy (GGE) patients studied with 24-h prolonged ambulatory electroencephalography (paEEG).

Methods

This retrospective multicenter cohort study included 202 GGE patients. The occurrence of generalized paroxysmal fast activity (GPFA) and generalized polyspike train (GPT) was reviewed. GGE patients were classified as having idiopathic generalized epilepsy (IGE) or another GGE syndrome (namely perioral myoclonia with absences, eyelid myoclonia with absences, epilepsy with myoclonic absences, generalized epilepsy with febrile seizures plus, or GGE without a specific epilepsy syndrome) according to recent classification proposals.

Results

GPFA/GPT was found in overall 25 (12.4%) patients, though it was significantly less frequent in IGE compared with other GGE syndromes (9.3 vs. 25%, p = 0.007). GPFA/GPT was found independently of seizure type experienced during history, the presence of mild intellectual disability/borderline intellectual functioning, or EEG features. At multivariable analysis, GPFA/GPT was significantly associated with drug resistance (p = 0.04) and with a higher number of antiseizure medications (ASMs) at the time of paEEG (p < 0.001) and at the last medical observation (p < 0.001). Similarly, GPFA/GPT, frequent/abundant generalized spike-wave discharges during sleep, and a higher number of seizure types during history were the only factors independently associated with a lower chance of achieving 2-year seizure remission at the last medical observation. Additionally, a greater number of GPFA/GPT discharges significantly discriminated between patients who achieved 2-year seizure remission at the last medical observation and those who did not (area under the curve = 0.77, 95% confidence interval 0.57-0.97, p = 0.02).

Conclusion

We found that generalized fast discharges were more common than expected in GGE patients when considering the entire GGE spectrum. In addition, our study highlighted that GPFA/GPT could be found along the entire GGE continuum, though their occurrence was more common in less benign GGE syndromes. Finally, we confirmed that GPFA/GPT was associated with difficult-to-treat GGE, as evidenced by the multivariable analysis and the higher ASM load during history.

TMS-induced brain connectivity modulation in Genetic Generalized Epilepsy

OBJECTIVE

In epilepsy patients, Transcranial Magnetic Stimulation (TMS) may result in the induction and modulation of epileptiform discharges (EDs). We hereby investigate the modulatory effects of TMS on brain connectivity in Genetic Generalized Epilepsy (GGE) and explore their potential as a diagnostic biomarker in GGE.

METHODS

Patients with GGE (n=18) and healthy controls (n=11) were investigated with a paired-pulse TMS-EEG protocol. The brain network was studied at local and at global levels using Coherence as an EEG connectivity measure. Comparison of patients vs controls was performed in a time-resolved manner by analyzing comparatively pre- vs post-TMS brain networks.

RESULTS

There was statistically significant TMS-induced modulation of connectivity at specific frequency bands within groups and difference in TMS-induced modulation between the two groups. The most significant difference between patients and controls related to connectivity modulation in the γ band at 1-3 sec post-TMS (p=0.004).

CONCLUSIONS

TMS modulates the healthy and epileptic brain connectivity in different ways. Our results indicate that TMS-EEG connectivity analysis can be a basis for a diagnostic biomarker of GGE.

SIGNIFICANCE

The analysis identifies specific time periods and frequency bands of interest of TMS-induced connectivity modulation and elucidates the effect of TMS on the healthy and epileptic brain connectivity.

Transcranial Magnetic Stimulation in Succinic Semialdehyde Dehydrogenase Deficiency: A Measure of Maturational Trajectory of Cortical Excitability

BACKGROUND

Succinic semialdehyde dehydrogenase deficiency (SSADHD) is a disorder of GABA degradation with use-dependent downregulation of postsynaptic GABA_A/B receptors. We aim to measure the resulting cortical excitation: inhibition ratio using transcranial magnetic stimulation.

METHODS

In this single-center observational study, 18 subjects with SSADHD and 8 healthy controls underwent transcranial magnetic stimulation. Resting motor threshold, cortical silent period, and long-interval intracortical inhibition were measured in both groups. Resting motor threshold in focal epilepsy patients from an institutional transcranial magnetic stimulation database were also included.

RESULTS

SSADHD subjects had higher resting motor threshold than healthy controls but lower relative to focal epilepsy patients. Resting motor threshold decreased with age in all groups. Cortical silent period was longer in SSADHD subjects than in healthy controls. No difference was detected in long-interval intracortical inhibition between the 2 groups.

CONCLUSION

Findings suggest abnormal corticospinal tract physiology in SSADHD, but with preserved developmental trajectory for corticospinal tract maturation. Defining features of these transcranial magnetic stimulation metrics in SSADHD will be better elucidated through this ongoing longitudinal study.

Investigating the Effect of Transcutaneous Auricular Vagus Nerve Stimulation on Cortical Excitability in Healthy Males

OBJECTIVES

As a potential treatment for epilepsy, transcutaneous auricular vagus nerve stimulation (taVNS) has yielded inconsistent results. Combining transcranial magnetic stimulation with electromyography (TMS-EMG) and electroencephalography (TMS-EEG) can be used to investigate the effect of interventions on cortical excitability by evaluating changes in motor evoked potentials (MEPs) and TMS-evoked potentials (TEPs). The goal of this study is to objectively evaluate the effect of taVNS on cortical excitability with TMS-EMG and TMS-EEG. These findings are expected to provide insight in the mechanism of action and help identify more optimal stimulation paradigms.

MATERIALS AND METHODS

In this prospective single-blind cross-over study, 15 healthy male subjects underwent active and sham taVNS for 60 min, using a maximum tolerated stimulation current. Single and paired pulse TMS was delivered over the right-sided motor hotspot to evaluate MEPs and TEPs before and after the intervention. MEP statistical analysis was conducted with a two-way repeated measures ANOVA. TEPs were analyzed with a cluster-based permutation analysis. Linear regression analysis was implemented to investigate an association with stimulation current.

RESULTS

MEP and TEP measurements were not affected by taVNS in this study. An association was found between taVNS stimulation current and MEP outcome measures indicating a decrease in cortical excitability in participants who tolerated higher taVNS currents. A subanalysis of participants (n = 8) who tolerated a taVNS current ≥2.5 mA showed a significant increase in the resting motor threshold, decrease in MEP amplitude and modulation of the P60 and P180 TEP components.

CONCLUSIONS

taVNS did not affect cortical excitability measurements in the overall population in this study. However, taVNS has the potential to modulate specific markers of cortical excitability in participants who tolerate higher stimulation levels. These findings indicate the need for adequate stimulation protocols based on the recording of objective outcome parameters.

A Systematic Review of Long-Interval Intracortical Inhibition as a Biomarker in Neuropsychiatric Disorders

Long-interval intracortical inhibition (LICI) is a paired-pulse transcranial magnetic stimulation (TMS) paradigm mediated in part by gamma-aminobutyric acid receptor B (GABA_B) inhibition. Prior work has examined LICI as a putative biomarker in an array of neuropsychiatric disorders. This review conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) sought to examine existing literature focused on LICI as a biomarker in neuropsychiatric disorders. There were 113 articles that met the inclusion criteria. Existing literature suggests that LICI may have utility as a biomarker of GABA_B functioning but more research with increased methodologic rigor is needed. The extant LICI literature has heterogenous methodology and inconsistencies in findings. Existing findings to date are also non-specific to disease. Future research should carefully consider existing methodological weaknesses and implement high-quality test-retest reliability studies.

Review of Transcranial Magnetic Stimulation in Epilepsy

PURPOSE

Despite the availability of numerous pharmacologic and nonpharmacologic antiseizure therapies, a fraction of patients with epilepsy remain refractory to current treatment options, underscoring the need for novel drugs and neuromodulatory therapies. Transcranial magnetic stimulation (TMS), coupled with either electromyography or electroencephalography, enables rapid measurement of the cortical excitation/inhibition ratio, which is pathologically shifted toward excess excitability in patients with epilepsy. In this review, we summarize: (1) TMS protocols that have been deployed to identify promising compounds in the antiepilepsy drug (AED)-development pipeline, and (2) the therapeutic potential of TMS in the treatment of drug-resistant seizures.

METHODS

A focused literature review of the use of TMS in epilepsy, using a PubMed search, was performed. Over 70 articles were included that pertained to: (1) the use of TMS-EMG and TMS-EEG in elucidating the mechanisms of action of AEDs and in discovering potential new AEDs; and (2) the use of repetitive TMS in the treatment of seizures.

FINDINGS

Studies from the literature have reported that AEDs alter TMS-derived metrics, typically by leading to a net increase in cortical inhibition with successful therapy. Preclinical TMS work in rodent models of epilepsy has led to the development of novel antiseizure drug compounds. Clinical translational studies of TMS have been used to determine guidelines on the dosages of other agents in the AED pipeline in preparation for clinical trials. Several studies have described the use of therapeutic repetitive TMS in both the ictal and interictal states of epilepsy, with inconsistent results.

IMPLICATIONS

TMS has diagnostic and therapeutic potential in epilepsy. TMS-derived markers can enable early-stage measures of AED target engagement, and can facilitate studies of the pharmacokinetic and pharmacodynamic properties of AEDs. TMS may also be used in the early prediction of the efficacy of different AEDs in treating patients, and in direct neuromodulation of epileptic networks. From the therapeutics perspective, despite favorable results in some trials, the optimization of treatment paradigms and the determination of ideal candidates for TMS are still needed. Finally, preclinical experiments of TMS have provided mechanistic insight into its effects on the excitation/inhibition ratio, and may facilitate rational drug-device coupling paradigms. Overall, the capacity of TMS in both the modulation and measurement of changes in cortical excitability highlights its unique role in advancing antiepilepsy therapeutics.

Cortical excitability measured with transcranial magnetic stimulation in children with epilepsy before and after antiepileptic drugs

AIM

To evaluate cortical excitability with transcranial magnetic stimulation (TMS) in children with new-onset epilepsy before and after antiepileptic drugs (AEDs).

METHOD

Fifty-five drug-naïve patients (29 females, 26 males; 3-18y), with new-onset epilepsy were recruited from 1st May 2014 to 31st October 2017 at the Child Neurology Department, Queen Silvia's Children's Hospital, Gothenburg, Sweden. We performed TMS in 48 children (23 females, 25 males; mean [SD] age 10y [3y], range 4-15y) with epilepsy (27 generalized and 21 focal) before and after the introduction of AEDs. We used single- and paired-pulse TMS. We used single-pulse TMS to record resting motor thresholds (RMTs), stimulus-response curves, and cortical silent periods (CSPs). We used paired-pulse TMS to record intracortical inhibition and facilitation at short, long, and intermediate intervals.

RESULTS

There were no differences in cortical excitability between children with generalized and focal epilepsy at baseline. After AED treatment, RMTs increased (p=0.001), especially in children receiving sodium valproate (p=0.005). CSPs decreased after sodium valproate was administered (p=0.050). As in previous studies, we noted a negative correlation between RMT and age in our study cohort. Paired-pulse TMS could not be performed in most children because high RMTs made suprathreshold stimulation impossible.

INTERPRETATION

Cortical excitability as measured with RMT decreased after the introduction of AEDs. This was seen in children with both generalized and focal epilepsy who were treated with sodium valproate, although it was most prominent in children with generalized epilepsy. We suggest that TMS might be used as a prognostic tool to predict AED efficacy.

WHAT THIS PAPER ADDS

Resting motor threshold (RMT) correlated negatively with age in children with epilepsy. No differences in cortical excitability were noted between patients with generalized and focal epilepsy. Treatment with antiepileptic drugs decreased cortical excitability as measured with transcranial magnetic stimulation (TMS). Decreased cortical excitability with increased RMT was recorded, especially after sodium valproate treatment. Paired-pulse TMS was difficult to perform because of high RMTs in children.

Seizure pathways change on circadian and slower timescales in individual patients with focal epilepsy

Personalized medicine requires that treatments adapt to not only the patient but also changing factors within each individual. Although epilepsy is a dynamic disorder characterized by pathological fluctuations in brain state, surprisingly little is known about whether and how seizures vary in the same patient. We quantitatively compared within-patient seizure network evolutions using intracranial electroencephalographic (iEEG) recordings of over 500 seizures from 31 patients with focal epilepsy (mean 16.5 seizures per patient). In all patients, we found variability in seizure paths through the space of possible network dynamics. Seizures with similar pathways tended to occur closer together in time, and a simple model suggested that seizure pathways change on circadian and/or slower timescales in the majority of patients. These temporal relationships occurred independent of whether the patient underwent antiepileptic medication reduction. Our results suggest that various modulatory processes, operating at different timescales, shape within-patient seizure evolutions, leading to variable seizure pathways that may require tailored treatment approaches.

Quantifying epileptogenesis in rats with spontaneous and responsive brain state dynamics

There is a crucial need to identify biomarkers of epileptogenesis that will help predict later development of seizures. This work identifies two novel electrophysiological biomarkers that quantify epilepsy progression in a rat model of epileptogenesis. The long-term tetanus toxin rat model was used to show the development and remission of epilepsy over several weeks. We measured the response to periodic electrical stimulation and features of spontaneous seizure dynamics over several weeks. Both biomarkers showed dramatic changes during epileptogenesis. Electrically induced responses began to change several days before seizures began and continued to change until seizures resolved. These changes were consistent across animals and allowed development of an algorithm that could differentiate which animals would later develop epilepsy. Once seizures began, there was a progression of seizure dynamics that closely follows recent theoretical predictions, suggesting that the underlying brain state was changing over time. This research demonstrates that induced electrical responses and seizure onset dynamics are useful biomarkers to quantify dynamical changes in epileptogenesis. These tools hold promise for robust quantification of the underlying epileptogenicity and prediction of later development of seizures.

Automatic Seizure Detection using Fully Convolutional Nested LSTM

The automatic seizure detection system can effectively help doctors to monitor and diagnose epilepsy thus reducing their workload. Many outstanding studies have given good results in the two-class seizure detection problems, but most of them are based on hand-wrought feature extraction. This study proposes an end-to-end automatic seizure detection system based on deep learning, which does not require heavy preprocessing on the EEG data or feature engineering. The fully convolutional network with three convolution blocks is first used to learn the expressive seizure characteristics from EEG data. Then these robust EEG features pertinent to seizures are presented as an input to the Nested Long Short-Term Memory (NLSTM) model to explore the inherent temporal dependencies in EEG signals. Lastly, the high-level features obtained from the NLSTM model are fed into the softmax layer to output predicted labels. The proposed method yields an accuracy range of 98.44–100% in 10 different experiments based on the Bonn University database. A larger EEG database is then used to evaluate the performance of the proposed method in real-life situations. The average sensitivity of 97.47%, specificity of 96.17%, and false detection rate of 0.487 per hour are yielded. For CHB–MIT Scalp EEG database, the proposed model also achieves a segment-level sensitivity of 94.07% with a false detection rate of 0.66 per hour. The excellent results obtained on three different EEG databases demonstrate that the proposed method has good robustness and generalization power under ideal and real-life conditions.

Biomarkers Obtained by Transcranial Magnetic Stimulation of the Motor Cortex in Epilepsy

Epilepsy is associated with numerous neurodevelopmental disorders. Transcranial magnetic stimulation (TMS) of the motor cortex coupled with electromyography (EMG) enables biomarkers that provide measures of cortical excitation and inhibition that are particularly relevant to epilepsy and related disorders. The motor threshold (MT), cortical silent period (CSP), short interval intracortical inhibition (SICI), intracortical facilitation (ICF), and long interval intracortical inhibition (LICI) are among TMS-derived metrics that are modulated by antiepileptic drugs. TMS may have a practical role in optimization of antiepileptic medication regimens, as studies demonstrate dose-dependent relationships between TMS metrics and acute medication administration. A close association between seizure freedom and normalization of cortical excitability with long-term antiepileptic drug use highlights a plausible utility of TMS in measures of anti-epileptic drug efficacy. Finally, TMS-derived biomarkers distinguish patients with various epilepsies from healthy controls and thus may enable development of disorder-specific biomarkers and therapies both within and outside of the epilepsy realm.

Reachability Analysis of Neural Masses and Seizure Control Based on Combination Convolutional Neural Network

Epileptic seizures arise from synchronous firing of multiple spatially separated neural masses; therefore, many synchrony measures are used for seizure detection and characterization. However, synchrony measures reflect only the overall interaction strength among populations of neurons but cannot reveal the coupling strengths among individual populations, which is more important for seizure control. The concepts of reachability and reachable cluster were proposed to denote the coupling strengths of a set of neural masses. Here, we describe a seizure control method based on coupling strengths using combination convolutional neural network (CCNN) modeling. The neurophysiologically based neural mass model (NMM), which can bridge signal processing and neurophysiology, was used to simulate the proposed controller. Although the adjacency matrix and reachability matrix could not be identified perfectly, the vast majority of adjacency values were identified, reaching 95.64% using the CCNN with an optimal threshold. For cases of discrete and continuous coupling strengths, the proposed controller maintained the average reachable cluster strengths at about 0.1, indicating effective seizure control.

Using Dictionary Pair Learning for Seizure Detection

Automatic seizure detection is extremely important in the monitoring and diagnosis of epilepsy. The paper presents a novel method based on dictionary pair learning (DPL) for seizure detection in the long-term intracranial electroencephalogram (EEG) recordings. First, for the EEG data, wavelet filtering and differential filtering are applied, and the kernel function is performed to make the signal linearly separable. In DPL, the synthesis dictionary and analysis dictionary are learned jointly from original training samples with alternating minimization method, and sparse coefficients are obtained by using of linear projection instead of costly [Formula: see text]-norm or [Formula: see text]-norm optimization. At last, the reconstructed residuals associated with seizure and nonseizure sub-dictionary pairs are calculated as the decision values, and the postprocessing is performed for improving the recognition rate and reducing the false detection rate of the system. A total of 530[Formula: see text]h from 20 patients with 81 seizures were used to evaluate the system. Our proposed method has achieved an average segment-based sensitivity of 93.39%, specificity of 98.51%, and event-based sensitivity of 96.36% with false detection rate of 0.236/h.

Cortical excitability affects mood state in patients with idiopathic generalized epilepsies (IGEs)

Previously, we demonstrated an association between cortical hyperexcitability and mood disturbance in healthy adults. Studies have documented hyperexcitability in patients with idiopathic generalized epilepsies (IGEs; long-interval intracortical inhibition [LICI]) and high prevalence of mood comorbidities. This study aimed to investigate the influences of cortical excitability and seizure control on mood state in patients with IGEs. Single and paired-pulse transcranial magnetic stimulation (TMS) was applied to 30 patients with IGEs (16 controlled IGEs [cIGEs], 14 with treatment-resistant IGEs [trIGEs]), and 22 healthy controls (HCs) to assess cortical excitability with LICI. The Profile of Mood Sates (POMS) questionnaire was used to assess total mood disturbance (TMD), as well as, six mood domains: Depression, Confusion, Anger, Anxiety, Fatigue, and Vigor. To assess the effects of seizure control (HC vs. cIGEs vs. trIGEs) and LICI response (inhibitory vs. excitatory) on TMD, a two-way multivariate analysis of variance (MANOVA) was performed. Analyses revealed a significant main effect of long-interval intracortical inhibition (LICI) response on TMD (F(1, 46) = 4.69, p = 0.04), but not seizure control (F(2, 46) = 0.288, p = 0.75). Excitatory responders endorsed significantly higher TMD scores, indicating greater mood disturbance, than inhibitory responders (MD = -2.12; T (50) = -2.47, p = 0.04). Also, excitatory responders endorsed more items than inhibitory responders on the Depression (MD = -2.12; T (50) = -2.47, p = 0.04) and Fatigue (MD = -3.42; T (50) = -2.96, p = 0.03) subscales of the POMS. These findings provide further evidence of a relationship between hyperexcitability and mood disturbance, and indicate that cortical excitability may have greater influence on mood state than seizure control in patients with IGEs. Results also support theories for the underlying role of gamma-aminobutyric acid (GABA) network dysfunction in the etiology of depression. To better understand the clinical relevance and causal nature of these relationships, further investigation is warranted.

Cortical excitability and seizure control influence attention performance in patients with idiopathic generalized epilepsies (IGEs)

We recently found that higher cortical excitability is associated with poorer attention performance in healthy adults. While patients with idiopathic generalized epilepsies (IGEs), previously termed genetic generalized epilepsies, are known to demonstrate increased cortical excitability and cognitive deficits, a relationship between these variables in IGEs has not been investigated. Therefore, we aimed to characterize the effects of cortical excitability and seizure control on cognitive performance in IGEs. We studied 30 patients with IGEs (16 patients with controlled IGEs (cIGEs) and 14 patients with treatment-resistant IGEs (trIGEs)) and 24 healthy controls (HCs). Transcranial magnetic stimulation (TMS) was used to measure cortical excitability, including long-interval intracortical inhibition (LICI). Attention was assessed with the Digit Span Forwards, Digit Span Backwards, Trails A, and Flanker tasks. Executive functioning was assessed using Trails B, Stroop Color and Word, and the Wisconsin Card Sorting Task. Two-way multivariate analyses of variance (MANOVAs) were conducted to assess the influences of seizure control (HCs vs. cIGEs vs. trIGEs) and cortical excitability (inhibitory vs. excitatory) on composite measures of attention and executive functions. Attention performance was significantly affected by cortical excitability and seizure control. Participants with primarily excitatory LICI responses, indicating higher cortical excitability, performed worse than inhibitory responders on composite attention (Wilks' lambda = 0.748, F(4, 44) = 3.72, p = 0.011). While participants with cIGEs and trIGEs did not significantly differ in attention performance, participants with trIGEs performed worse on the Digit Forwards (False Discovery Rate (FDR)p < 0.001), Digit Backwards (FDRp = 0.015), and Flanker (FDRp = 0.0075) tasks compared with HCs. These results provide support for the relationship between cortical excitability and attention dysfunction in IGEs. Further investigation is needed to determine whether there is a causal relationship between these variables and whether intracortical gamma-aminobutyric acid (GABA)_B networks may be targeted to improve attention deficits in clinical populations with decreased LICI. Findings also suggest that additional research directly comparing cognition in patients with cIGEs and trIGEs is warranted.

Accurate Coil Positioning is Important for Single and Paired Pulse TMS on the Subject Level

Function-guided navigation is commonly used when assessing cortical excitability using transcranial magnetic stimulation (TMS). However, the required accuracy, stability and the effect of a change in coil positioning are not entirely known. This study investigates the accuracy of function-guided navigation for determining the hotspot. Furthermore, it evaluates the effect of a change in coil location on the single and paired pulse excitability measures: motor evoked potential (MEP) amplitude, TMS evoked potential (TEP) and long intracortical inhibition (LICI), and of a change in coil orientation on LICI. Eight healthy subjects participated in the single pulse study, and ten in the paired pulse study. A robot-guided navigation system was used to ensure accurate and stable coil positioning at the motor hotspot as determined using function-guided navigation. In addition, we targeted four locations at 2 mm and four at 5 mm distance around the initially defined hotspot, and we increased and decreased the coil orientation by 10°. In none of the subjects, the largest MEP amplitudes were evoked at the originally determined hotspot, resulting in a poor accuracy of function-guided navigation. At the group level, a change in coil location had no significant effect on the MEP amplitude, TEP, or LICI, and a change in coil orientation did not significantly affected LICI. However, at the subject level significant effects on MEP amplitude, TEP, and LICI were found for changes in coil location or orientation, although absolute differences were relatively small and did not show a consistent pattern. This study indicates that a high accuracy in coil positioning is especially required to measure cortical excitability reliably in individual subjects using single or paired pulse TMS.

Clinical and electrocorticographic response to antiepileptic drugs in patients treated with responsive stimulation

OBJECTIVE

The objective of this study was to explore whether chronic electrocorticographic (ECoG) data recorded by a responsive neurostimulation system could be used to assess clinical responses to antiepileptic drugs (AEDs).

METHODS

Antiepileptic drugs initiated and maintained for ≥3 months by patients participating in clinical trials of the RNS^® System were identified. Such "AED Starts" that produced an additional ≥50% reduction in patient-reported clinical seizure frequency were categorized as clinically beneficial, and the remaining as not beneficial. Electrocorticographic features recorded by the RNS^® Neurostimulator were analyzed during three periods: 3 months before the AED Start, first month after the AED Start, and the first 3 months after the AED Start.

RESULTS

The most commonly added medications were clobazam (n = 41), lacosamide (n = 96), levetiracetam (n = 31), and pregabalin (n = 25). Across all four medications, there were sufficient clinical data for 193 AED Starts to be included in the analyses, and 59 AED Starts were considered clinically beneficial. The proportion of AED Starts that qualified as clinically beneficial was higher for clobazam (53.7%) and levetiracetam (51.6%) than for lacosamide (18.8%) and pregabalin (12%). Across all AED Starts for which RNS ECoG detection settings were held constant, the clinically beneficial AED Starts were associated with a significantly greater reduction in the detection of epileptiform activity (p < 0.001) at 1 (n = 33) and 3 months (n = 30) compared with AED Starts that were not beneficial at 1 (n = 71) and 3 months (n = 60). Furthermore, there was a significant reduction in interictal spike rate and spectral power (1-125 Hz) associated with a clinically beneficial response to an AED Start at 1 (n = 32) and 3 months (n = 35) (p < 0.001). These reductions were not observed at either 1 (n = 59) or 3 months (n = 60) for AED Starts that were not clinically beneficial.

CONCLUSIONS

Significant quantitative changes in ECoG data recorded by the RNS System were observed in patients who experienced an additional clinical response to a new AED. While there was variability across patients in the changes observed, the results suggest that quantitative ECoG data may provide useful information when assessing whether a patient may have a favorable clinical response to an AED.

Long-interval intracortical inhibition as biomarker for epilepsy: a transcranial magnetic stimulation study

Cortical excitability, as measured by transcranial magnetic stimulation combined with electromyography, is a potential biomarker for the diagnosis and follow-up of epilepsy. We report on long-interval intracortical inhibition data measured in four different centres in healthy controls (n = 95), subjects with refractory genetic generalized epilepsy (n = 40) and with refractory focal epilepsy (n = 69). Long-interval intracortical inhibition was measured by applying two supra-threshold stimuli with an interstimulus interval of 50, 100, 150, 200 and 250 ms and calculating the ratio between the response to the second (test stimulus) and to the first (conditioning stimulus). In all subjects, the median response ratio showed inhibition at all interstimulus intervals. Using a mixed linear-effects model, we compared the long-interval intracortical inhibition response ratios between the different subject types. We conducted two analyses; one including data from the four centres and one excluding data from Centre 2, as the methods in this centre differed from the others. In the first analysis, we found no differences in long-interval intracortical inhibition between the different subject types. In all subjects, the response ratios at interstimulus intervals 100 and 150 ms showed significantly more inhibition than the response ratios at 50, 200 and 250 ms. Our second analysis showed a significant interaction between interstimulus interval and subject type (P = 0.0003). Post hoc testing showed significant differences between controls and refractory focal epilepsy at interstimulus intervals of 100 ms (P = 0.02) and 200 ms (P = 0.04). There were no significant differences between controls and refractory generalized epilepsy groups or between the refractory generalized and focal epilepsy groups. Our results do not support the body of previous work that suggests that long-interval intracortical inhibition is significantly reduced in refractory focal and genetic generalized epilepsy. Results from the second analysis are even in sharper contrast with previous work, showing inhibition in refractory focal epilepsy at 200 ms instead of facilitation previously reported. Methodological differences, especially shorter intervals between the pulse pairs, may have contributed to our inability to reproduce previous findings. Based on our results, we suggest that long-interval intracortical inhibition as measured by transcranial magnetic stimulation and electromyography is unlikely to have clinical use as a biomarker of epilepsy.

Neurophysiological Analysis of the Genesis Mechanism of EEG During the Interictal and Ictal Periods Using a Multiple Neural Masses Model

Electroencephalography (EEG) is an important method to investigate the neurophysiological mechanism underlying epileptogenesis to identify new therapies for the treatment of epilepsy. The neurophysiologically based neural mass model (NMM) can build a bridge between signal processing and neurophysiology, which can be used as a platform to explore the neurophysiological mechanism of epileptogenesis. Most EEG signals cannot be regarded as the outputs of a single NMM with identical model parameters. The outputs of NMM are simple because the diversity of neural signals in the same NMM is ignored. To improve the simulation of EEG signals, a multiple NMM is proposed, the output of which is the linear combination of the outputs of all NMMs. The NMM number is not fixed and is minimized under the premise of guaranteeing the fitting effect. Orthogonal matching pursuit is used to solve a constrained [Formula: see text] norm minimization problem for NMM number and the strength of every NMM. The results showed that the NMM number was significantly lower during the ictal period than during the interictal period, and the strength of major NMMs increased. This indicates that neural masses fuse into fewer larger neural masses with greater strength. The distribution of excitatory and inhibitory strength during the ictal and interictal periods was similar, whereas the excitation/inhibition ratio was higher during the ictal period than during the interictal period.

Cortical excitability correlates with seizure control and epilepsy duration in chronic epilepsy

Objective

Cortical excitability differs between treatment responders and nonresponders in new‐onset epilepsy. Moreover, during the first 3 years of epilepsy, cortical excitability becomes more abnormal in nonresponders but normalizes in responders. Here, we study chronic active epilepsy, to examine whether cortical excitability continues to evolve over time, in association with epilepsy duration and treatment response.

Methods

We studied 28 normal subjects, 28 patients with moderately controlled epilepsy (≤4 seizures per year) and 40 patients with poorly controlled epilepsy (≥20 or more seizures per year). Resting motor threshold (RMT), active motor threshold (AMT), short‐interval intracortical inhibition (SICI), intracortical facilitation (ICF) and cortical silent period (CSP) were measured, using transcranial magnetic stimulation (TMS). Disease and treatment covariates were collected (age at onset of epilepsy, epilepsy duration, number of drugs prescribed, total drug load, sodium channel drug load).

Results

RMT and AMT were higher in patients than in normal subjects; RMT and AMT were higher in poorly controlled than moderately controlled patients. ICF at 12 msec and 15 msec were lower in poorly controlled patients than in normal subjects. Long‐interval intracortical inhibition (LICI) at 50 msec was higher in poorly controlled compared to moderately controlled patients. These differences were not explained by antiepileptic drug (AED) treatment or duration of epilepsy. RMT and AMT increased with duration in the poorly controlled group, but did not increase with duration in the moderately controlled group.

Interpretation

Cortical excitability differs markedly between moderately controlled and poorly controlled patients with chronic epilepsy, not explained by disease or treatment variables. Moreover, the evolution of cortical excitability over time differs, becoming more abnormal in the poorly controlled group.

Lateralizing Cortical Excitability in Drug Naïve Patients with Generalized or Focal Epilepsy

Background and Purpose:

Numerous transcranial magnetic stimulation (TMS) studies have defined the characteristic features of TMS in epilepsy. TME parameters were expected to classify the epilepsy syndrome or drug responses. However, the results such as cortical silent periods (CSP) are variable according to conditions of patients. Here, we investigate whether specific TMS parameters have localizing or lateralizing values in drug-naïve epilepsy patients.

Methods:

We recruited 148 consecutive untreated patients with epilepsy (idiopathic generalized epilepsy (IGE) 38, focal epilepsy (FE) 110, mean age 31.4 years) and 38 age- and gender-matched normal subjects. We obtained resting motor threshold (RMT), motor-evoked potential (MEP), CSP, short interval intracortical inhibition (SICI, inter-stimuli interval 2–5 ms), and intracortical facilitation (ICF, inter-stimuli interval 10–20 ms). TMS were performed during a seizure-free state of more than 48 h.

Results:

In IGE, no interhemispheric difference in CSP was found (p > 0.05). However, the mean CSP was longer in IGE patients than in normal controls at all stimulus intensities (p < 0.05). The mean CSP in ipsilateral hemisphere (IH) of FE was significantly longer at all stimulus intensities than that in normal controls (p < 0.001). The CSP in IH was longer than that in the contralateral hemisphere of FE. There was no significant difference in CSP between FE and IGE. SICI was significantly reduced only in the IH of FE versus normal subjects. RMT, MEP amplitudes, and ICF did not differ among IGE, FE, and normal controls.

Conclusions:

We found that prolonged CSP and reduced SICI in FE indicate asymmetrically increased cortical inhibition and excitation in the epileptic hemispheres. It suggests that CSP among TMS parameters has a crucial role to lateralize the epileptic hemisphere in FE.

Altered Structural and Functional Feature of Striato-Cortical Circuit in Benign Epilepsy with Centrotemporal Spikes

Benign epilepsy with centrotemporal spikes (BECT) is the most common form of childhood idiopathic focal epilepsy syndrome. We investigated quantitative evidence regarding brain morphology and functional connectivity features to provide insight into the neuroanatomical foundation of this disorder, using high resolution T1-weighted magnetic resonance imaging (MRI) and resting state functional MRI in 21 patients with BECT and in 20 healthy children. The functional connectivity analysis, seeded at the regions with altered gray-matter (GM) volume in voxel-based morphometry (VBM) analysis, was further performed. Then, the observed structural and functional alteration were investigated for their association with the clinical and behavior manifestations. The increased GM volume in the striatum and fronto-temporo-parietal cortex (striato-cortical circuit) was observed in BECT. The decreased connections were found among the motor network and frontostriatal loop, and between the default mode network (DMN) and language regions. Additionally, the GM of striatum was negatively correlated with age at epilepsy onset. The current observations may contribute to the understanding of the altered structural and functional feature of striato-cortical circuit in patients with BECT. The findings also implied alterations of the motor network and DMN, which were associated with the epileptic activity in patients with BECT. This further suggested that the onset of BECT might have enduring structural and functional effects on brain maturation.

Early Seizure Detection Using Neuronal Potential Similarity: A Generalized Low-Complexity and Robust Measure

A novel approach using neuronal potential similarity (NPS) of two intracranial electroencephalogram (iEEG) electrodes placed over the foci is proposed for automated early seizure detection in patients with refractory partial epilepsy. The NPS measure is obtained from the spectral analysis of space-differential iEEG signals. Ratio between the NPS values obtained from two specific frequency bands is then investigated as a robust generalized measure, and reveals invaluable information about seizure initiation trends. A threshold-based classifier is subsequently applied on the proposed measure to generate alarms. The performance of the method was evaluated using cross-validation on a large clinical dataset, involving 183 seizure onsets in 1785 h of long-term continuous iEEG recordings of 11 patients. On average, the results show a high sensitivity of 86.9% (159 out of 183), a very low false detection rate of 1.4 per day, and a mean detection latency of 13.1 s from electrographic seizure onsets, while in average preceding clinical onsets by 6.3 s. These high performance results, specifically the short detection latency, coupled with the very low computational cost of the proposed method make it adequate for using in implantable closed-loop seizure suppression systems.

STRAPS: A Fully Data-Driven Spatio-Temporally Regularized Algorithm for M/EEG Patch Source Imaging

For M/EEG-based distributed source imaging, it has been established that the L2-norm-based methods are effective in imaging spatially extended sources, whereas the L1-norm-based methods are more suited for estimating focal and sparse sources. However, when the spatial extents of the sources are unknown a priori, the rationale for using either type of methods is not adequately supported. Bayesian inference by exploiting the spatio-temporal information of the patch sources holds great promise as a tool for adaptive source imaging, but both computational and methodological limitations remain to be overcome. In this paper, based on state-space modeling of the M/EEG data, we propose a fully data-driven and scalable algorithm, termed STRAPS, for M/EEG patch source imaging on high-resolution cortices. Unlike the existing algorithms, the recursive penalized least squares (RPLS) procedure is employed to efficiently estimate the source activities as opposed to the computationally demanding Kalman filtering/smoothing. Furthermore, the coefficients of the multivariate autoregressive (MVAR) model characterizing the spatial-temporal dynamics of the patch sources are estimated in a principled manner via empirical Bayes. Extensive numerical experiments demonstrate STRAPS's excellent performance in the estimation of locations, spatial extents and amplitudes of the patch sources with varying spatial extents.

Kernel Collaborative Representation-Based Automatic Seizure Detection in Intracranial EEG

Automatic seizure detection is of great significance in the monitoring and diagnosis of epilepsy. In this study, a novel method is proposed for automatic seizure detection in intracranial electroencephalogram (iEEG) recordings based on kernel collaborative representation (KCR). Firstly, the EEG recordings are divided into 4s epochs, and then wavelet decomposition with five scales is performed. After that, detail signals at scales 3, 4 and 5 are selected to be sparsely coded over the training sets using KCR. In KCR, l2-minimization replaces l1-minimization and the sparse coefficients are computed with regularized least square (RLS), and a kernel function is utilized to improve the separability between seizure and nonseizure signals. The reconstructed residuals of each EEG epoch associated with seizure and nonseizure training samples are compared and EEG epochs are categorized as the class that minimizes the reconstructed residual. At last, a multi-decision rule is applied to obtain the final detection decision. In total, 595 h of iEEG recordings from 21 patients with 87 seizures are employed to evaluate the system. The average sensitivity of 94.41%, specificity of 96.97%, and false detection rate of 0.26/h are achieved. The seizure detection system based on KCR yields both a high sensitivity and a low false detection rate for long-term EEG.

Evidence for an excitatory GABAA response in human motor cortex in idiopathic generalised epilepsy

PURPOSE

Impaired GABAergic inhibition has been implicated in the pathophysiology of epilepsy. The possibility of a paradoxical excitatory effect of GABA in epilepsy has been suggested, but has not been investigated in vivo. We investigated pre- and post-synaptic GABAergic mechanisms in patients with idiopathic generalised epilepsy (IGE).

METHOD

In 10 patients and 12 control subjects we explored short- and long-interval intracortical inhibition (SICI, LICI; post-synaptic GABAA and GABAB-mediated respectively) and long-interval intracortical facilitation (LICF; pre-synaptic disinhibition) using transcranial magnetic stimulation.

RESULTS

While post-synaptic GABAB-mediated inhibition was unchanged in IGE (p=0.09), LICF was reduced compared to controls (controls: 141±17% of baseline; untreated patients: 107±12%, p=0.2; treated patients: 79±10%, p=0.003). GABAA-mediated inhibition was reduced in untreated patients (response amplitude 56±4% of baseline vs. 26±6% in controls, p=0.004) and normalised with treatment (37±12%, p=0.5 vs. controls). When measured during LICI, GABAA-mediated inhibition became excitatory in untreated IGE (response amplitude 120±10% of baseline, p=0.017), but not in treated patients.

CONCLUSION

Pre- and post-synaptic GABA-mediated inhibitory mechanisms are altered in IGE. The findings lend in vivo support to evidence from experimental models and in vitro studies of human epileptic brain tissue that GABA may have a paradoxical excitatory role in ictogenesis.

TMS and drugs revisited 2014

The combination of pharmacology and transcranial magnetic stimulation to study the effects of drugs on TMS-evoked EMG responses (pharmaco-TMS-EMG) has considerably improved our understanding of the effects of TMS on the human brain. Ten years have elapsed since an influential review on this topic has been published in this journal (Ziemann, 2004). Since then, several major developments have taken place: TMS has been combined with EEG to measure TMS evoked responses directly from brain activity rather than by motor evoked potentials in a muscle, and pharmacological characterization of the TMS-evoked EEG potentials, although still in its infancy, has started (pharmaco-TMS-EEG). Furthermore, the knowledge from pharmaco-TMS-EMG that has been primarily obtained in healthy subjects is now applied to clinical settings, for instance, to monitor or even predict clinical drug responses in neurological or psychiatric patients. Finally, pharmaco-TMS-EMG has been applied to understand the effects of CNS active drugs on non-invasive brain stimulation induced long-term potentiation-like and long-term depression-like plasticity. This is a new field that may help to develop rationales of pharmacological treatment for enhancement of recovery and re-learning after CNS lesions. This up-dated review will highlight important knowledge and recent advances in the contribution of pharmaco-TMS-EMG and pharmaco-TMS-EEG to our understanding of normal and dysfunctional excitability, connectivity and plasticity of the human brain.

Computer-aided diagnosis of alcoholism-related EEG signals

Alcoholism is a severe disorder that affects the functionality of neurons in the central nervous system (CNS) and alters the behavior of the affected person. Electroencephalogram (EEG) signals can be used as a diagnostic tool in the evaluation of subjects with alcoholism. The neurophysiological interpretation of EEG signals in persons with alcoholism (PWA) is based on observation and interpretation of the frequency and power in their EEGs compared to EEG signals from persons without alcoholism. This paper presents a review of the known features of EEGs obtained from PWA and proposes that the impact of alcoholism on the brain can be determined by computer-aided analysis of EEGs through extracting the minute variations in the EEG signals that can differentiate the EEGs of PWA from those of nonaffected persons. The authors advance the idea of automated computer-aided diagnosis (CAD) of alcoholism by employing the EEG signals. This is achieved through judicious combination of signal processing techniques such as wavelet, nonlinear dynamics, and chaos theory and pattern recognition and classification techniques. A CAD system is cost-effective and efficient and can be used as a decision support system by physicians in the diagnosis and treatment of alcoholism especially those who do not specialize in alcoholism or neurophysiology. It can also be of great value to rehabilitation centers to assess PWA over time and to monitor the impact of treatment aimed at minimizing or reversing the effects of the disease on the brain. A CAD system can be used to determine the extent of alcoholism-related changes in EEG signals (low, medium, high) and the effectiveness of therapeutic plans.

A Wavelet-Statistical Features Approach for Nonconvulsive Seizure Detection

The detection of nonconvulsive seizures (NCSz) is a challenge because of the lack of physical symptoms, which may delay the diagnosis of the disease. Many researchers have reported automatic detection of seizures. However, few investigators have concentrated on detection of NCSz. This article proposes a method for reliable detection of NCSz. The electroencephalography (EEG) signal is usually contaminated by various nonstationary noises. Signal denoising is an important preprocessing step in the analysis of such signals. In this study, a new wavelet-based denoising approach using cubical thresholding has been proposed to reduce noise from the EEG signal prior to analysis. Three statistical features were extracted from wavelet frequency bands, encompassing the frequency range of 0 to 8, 8 to 16, 16 to 32, and 0 to 32 Hz. Extracted features were used to train linear classifier to discriminate between normal and seizure EEGs. The performance of the method was tested on a database of nine patients with 24 seizures in 80 hours of EEG recording. All the seizures were successfully detected, and false positive rate was found to be 0.7 per hour.

Does the region of epileptogenicity influence the pattern of change in cortical excitability?

OBJECTIVE

To investigate whether cortical excitability measures on transcranial magnetic stimulation (TMS) differed between groups of patients with different focal epilepsy syndromes.

METHODS

85 Patients with focal epilepsy syndromes divided into temporal and extra-temporal lobe epilepsy were studied. The cohorts were further divided into drug naïve-new onset, refractory and seizure free groups. Motor threshold (MT) and paired pulse TMS at short (2, 5, 10, 15 ms) and long (100-300 ms) interstimulus intervals (ISIs) were measured. Results were compared to those of 20 controls.

RESULTS

Cortical excitability was higher at 2 & 5 ms and 250, 300 ms ISIs (p<0.01) in focal epilepsy syndromes compared to controls however significant inter-hemispheric differences in MT and the same ISIs were only seen in the drug naïve state early at onset and were much more prominent in temporal lobe epilepsy.

CONCLUSION

Disturbances in cortical excitability are more confined to the affected hemisphere in temporal lobe epilepsy but only early at onset in the drug naïve state.

SIGNIFICANCE

Group TMS studies show that cortical excitability measures are different in temporal lobe epilepsy and can be distinguished from other focal epilepsies early at onset in the drug naïve state. Further studies are needed to determine whether these results can be applied clinically as the utility of TMS in distinguishing between epilepsy syndromes at an individual level remains to be determined.

On the midway to epilepsy: is cortical excitability normal in patients with isolated seizures?

Paired pulse transcranial magnetic stimulation was used to investigate differences in cortical excitability between patients with isolated (unrecurrent, unprovoked) seizures and those with epilepsy. Compared to controls, cortical excitability was higher in the isolated seizure group at 250-300 ms. Compared to epilepsy, cortical excitability was lower in patients with isolated seizures also at 250 and 300 ms. Lowered seizure threshold caused by disturbances within inhibitory circuits is present in patients who experience a seizure even if no further seizures occur.

Computational modeling of transcranial direct current stimulation in the child brain: implications for the treatment of refractory childhood focal epilepsy

Transcranial direct current stimulation (tDCS) was recently proposed for the treatment of epilepsy. However, the electrode arrangement for this case is debated. This paper analyzes the influence of the position of the anodal electrode on the electric field in the brain. The simulation shows that moving the anode from scalp to shoulder does influence the electric field not only in the cortex, but also in deeper brain regions. The electric field decreases dramatically in the brain area without epileptiform activity.