Drug-resistant Epilepsy Patients Research Articles

Cannabidiol-enriched oil for adult patients with drug-resistant epilepsy: Prospective clinical and electrophysiological study.

Cannabidiol-enriched oil (CBDO) is being used increasingly to improve seizure control in adult patients with drug-resistant epilepsy (DRE), despite the lack of large-scale studies supporting its efficacy in this patient population. We aimed to assess the effects of add-on CBDO on seizure frequency as well as on gait, cognitive, affective, and sleep-quality metrics, and to explore the electrophysiological changes in responder and non-responder DRE patients treated with add-on CBDO. We prospectively recruited adult DRE patients who were treated with add-on CBDO. Patients were evaluated prior to treatment and following 4 weeks of a maintenance daily dose of ≈260 mg CBD and ≈12 mg Δ9-tetrahydrocannabinol (THC). The outcome measures included seizure response to CBDO (defined as ≥50% decrease in seizures compared to pre-CBDO baseline), gait testing, Montreal Cognitive Assessment (MoCA), Hospital Anxiety and Depression Scale (HADS), and sleep-quality questionnaire assessments. Patients underwent electroencephalography (EEG) recording during rest as well as event-related potentials (ERPs) during visual Go/NoGo task while sitting and while walking. Nineteen patients were recruited, of which 16 finished pre- and post-CBDO assessments. Seven patients (43.75%) were responders demonstrating an average reduction of 82.4% in seizures, and nine patients (56.25%) were non-responders with an average seizure increase of 30.1%. No differences in demographics and clinical parameters were found between responders and non-responders at baseline. However, responders demonstrated better performance in the dual-task walking post-treatment (p = .015), and correlation between increase in MoCA and seizure reduction (r = .810, p = .027). Post-CBDO P300 amplitude was lower during No/Go-sitting in non-responders (p = .028) and during No/Go-walking in responders (p = .068). CBDO treatment can reduce seizures in a subset of patients with DRE, but could aggravate seizure control in a minority of patients; yet we found no specific baseline clinical or electrophysiological characteristics that are associated with response to CBDO. However, changes in ERPs in response to treatment could be a promising direction to better identify patients who could benefit from CBDO treatment.

Tolerability of tariquidar – A third generation P-gp inhibitor as add-on medication to antiseizure medications in drug-resistant epilepsy

PurposeP-glycoprotein (P-gp) has been hypothesized to be involved in drug-resistance of epilepsy by actively extruding antiseizure medications (ASMs) from the brain. The P-gp inhibitor tariquidar (TQD) has been shown to effectively inhibit P-gp at the human blood-brain barrier, improving brain entry of several ASMs. A potential strategy to overcome drug-resistance is the co-administration of P-gp inhibitors such as TQD to ASMs. Here we present data on the tolerability of single-dose TQD as a potential add-on medication to ASMs. MethodsWe performed a multi-centre cohort study including drug-resistant epilepsy patients and healthy controls from the United Kingdom and Austria. TQD was administered intravenously at five different doses (2 mg/kg or 3 mg/kg of TQD were given to drug-resistant epilepsy patients and healthy controls, higher doses of TQD at 4 mg/kg, 6 mg/kg and 8 mg/kg as well as a prolonged infusion aiming at a dose of 6 mg/kg were only given to healthy controls). Adverse events were recorded and graded using the Common Terminology Criteria (CTCAE) scale. Additionally, TQD plasma concentration levels were measured and compared between drug-resistant patients and healthy controls. ResultsIn total, 108 participants received TQD once at variable doses and it was overall well tolerated. At doses of 2 or 3 mg/kg TQD, only two of the 19 drug-resistant epilepsy patients and a third of the healthy controls (n = 14/42) reported adverse events probably related to TQD. The majority of those adverse events (96 %) were reported as mild. One drug-resistant epilepsy patient reported adverse events 24-hours after TQD administration possibly related to TQD-induced increased ASMs levels in the brain. ConclusionsTQD is an effective and well tolerated P-gp inhibitor as a single dose and could potentially be used intermittently in conjunction with ASMs to improve efficacy. This promising strategy to overcome drug-resistance in epilepsy should be investigated further in clinical randomised controlled trials.

Neuronal synchrony and critical bistability: Mechanistic biomarkers for localizing the epileptogenic network.

Postsurgical seizure freedom in drug-resistant epilepsy (DRE) patients varies from 30% to 80%, implying that in many cases the current approaches fail to fully map the epileptogenic zone (EZ). We aimed to advance a novel approach to better characterize epileptogenicity and investigate whether the EZ encompasses a broader epileptogenic network (EpiNet) beyond the seizure zone (SZ) that exhibits seizure activity. We first used computational modeling to test putative complex systems-driven and systems neuroscience-driven mechanistic biomarkers for epileptogenicity. We then used these biomarkers to extract features from resting-state stereoelectroencephalograms recorded from DRE patients and trained supervised classifiers to localize the SZ against gold standard clinical localization. To further explore the prevalence of pathological features in an extended brain network outside of the clinically identified SZ, we also used unsupervised classification. Supervised SZ classification trained on individual features achieved accuracies of .6-.7 area under the receiver operating characteristic curve (AUC). Combining all criticality and synchrony features further improved the AUC to .85. Unsupervised classification discovered an EpiNet-like cluster of brain regions, in which 51% of brain regions were outside of the SZ. Brain regions in the EpiNet-like cluster engaged in interareal hypersynchrony and locally exhibited high-amplitude bistability and excessive inhibition, which was strikingly similar to the high seizure risk regime revealed by our computational modeling. The finding that combining biomarkers improves SZ localization accuracy indicates that the novel mechanistic biomarkers for epileptogenicity employed here yield synergistic information. On the other hand, the discovery of SZ-like brain dynamics outside of the clinically defined SZ provides empirical evidence of an extended pathophysiological EpiNet.

Individualized epidemic spreading models predict epilepsy surgery outcomes: A pseudo-prospective study

Abstract Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients, but up to 50% of patients continue to have seizures one year after the resection. In order to aid presurgical planning and predict postsurgical outcome on a patient-by-patient basis, we developed a framework of individualized computational models that combines epidemic spreading with patient-specific connectivity and epileptogeneity maps: the Epidemic Spreading Seizure and Epilepsy Surgery framework (ESSES). ESSES parameters were fitted in a retrospective study (N = 15) to reproduce invasive electroencephalography (iEEG)-recorded seizures. ESSES reproduced the iEEG-recorded seizures, and significantly better so for patients with good (seizure-free, SF) than bad (nonseizure-free, NSF) outcome. We illustrate here the clinical applicability of ESSES with a pseudo-prospective study (N = 34) with a blind setting (to the resection strategy and surgical outcome) that emulated presurgical conditions. By setting the model parameters in the retrospective study, ESSES could be applied also to patients without iEEG data. ESSES could predict the chances of good outcome after any resection by finding patient-specific model-based optimal resection strategies, which we found to be smaller for SF than NSF patients, suggesting an intrinsic difference in the network organization or presurgical evaluation results of NSF patients. The actual surgical plan overlapped more with the model-based optimal resection, and had a larger effect in decreasing modeled seizure propagation, for SF patients than for NSF patients. Overall, ESSES could correctly predict 75% of NSF and 80.8% of SF cases pseudo-prospectively. Our results show that individualised computational models may inform surgical planning by suggesting alternative resections and providing information on the likelihood of a good outcome after a proposed resection. This is the first time that such a model is validated with a fully independent cohort and without the need for iEEG recordings.

DeepFLAIR: A neural network approach to mitigate signal and contrast loss in temporal lobes at 7 Tesla FLAIR images

Background and purposeHigher magnetic field strength introduces stronger magnetic field inhomogeneities in the brain, especially within temporal lobes, leading to image artifacts. Particularly, T2-weighted fluid-attenuated inversion recovery (FLAIR) images can be affected by these artifacts. Here, we aimed to improve the FLAIR image quality in temporal lobe regions through image processing of multiple contrast images via machine learning using a neural network. MethodsThirteen drug-resistant MR-negative epilepsy patients (age 29.2 ± 9.4y, 5 females) were scanned on a 7 T MRI scanner. Magnetization-prepared (MP2RAGE) and saturation-prepared with 2 rapid gradient echoes, multi-echo gradient echo with four echo times, and the FLAIR sequence were acquired. A voxel-wise neural network was trained on extratemporal-lobe voxels from the acquired structural scans to generate a new FLAIR-like image (i.e., deepFLAIR) with reduced temporal lobe inhomogeneities. The deepFLAIR was evaluated in temporal lobes through signal-to-noise (SNR), contrast-to-noise (CNR) ratio, the sharpness of the gray-white matter boundary and joint-histogram analysis. Saliency mapping demonstrated the importance of each input image per voxel. ResultsSNR and CNR in both gray and white matter were significantly increased (p < 0.05) in the deepFLAIR's temporal ROIs, compared to the FLAIR. The gray-white matter boundary sharpness was either preserved or improved in 10/13 right-sided temporal regions and was found significantly increased in the ROIs. Multiple image contrasts were influential for the deepFLAIR reconstruction with the MP2RAGE second inversion image being the most important. ConclusionsThe deepFLAIR network showed promise to restore the FLAIR signal and reduce contrast attenuation in temporal lobe areas. This may yield a valuable tool, especially when artifact-free FLAIR images are not available.

Enhancement of Subjective Quality of Life Following Surgical Intervention in Patients With Drug-Resistant Epilepsy: A Depressive Symptom-Independent Outcome.

To evaluate the impact of depressive symptoms on the subjective perception of quality of life in patients with drug-resistant epilepsy (DRE) after surgical treatment for seizures. Methods: A case-control study with DRE patients who received surgical treatment (n=19) and matched non-operated patients (n=23). We assessed the quality of life using the Subjective Handicap of Epilepsy (SHE) scale, alongside measuring depressive symptoms using the Beck Depression Inventory (BDI). The mean age of the participants was 45 years, with females constituting 52.4% of the patients. A majority (73.8%) had been diagnosed with temporal lobe epilepsy. Those who had undergone surgical intervention showed significantly enhanced performance across all quality-of-life domains on the SHE scale independently of depressive symptoms. The domains of "Work and Activity,""Physical Health,"and "Self-Perception" displayed the greatest improvements, with the surgical group's averages outperforming the control group by factors of 1.87, 2.53, and 2.81, respectively. Influential differences impacting the quality-of-life scores included seizure frequency, the quantity of antiepileptic drugs utilized, and the incidence of convulsive seizures. The findings suggest that surgical control of seizures in drug-resistant focal epilepsy is associated with improvement in quality of life across various domains, independently of the depressive symptoms of the patients.

Optimized microburst VNS elicits fMRI responses beyond thalamic-specific response from standard VNS.

In parallel to standard vagus nerve stimulation (VNS), microburst stimulation delivery has been developed. We evaluated the fMRI-related signal changes associated with standard and optimized microburst stimulation in a proof-of-concept study (NCT03446664). Twenty-nine drug-resistant epilepsy patients were prospectively implanted with VNS. Three 3T fMRI scans were collected 2 weeks postimplantation. The maximum tolerated VNS intensity was determined prior to each scan starting at 0.125 mA with 0.125 mA increments. FMRI scans were block-design with alternating 30 sec stimulation [ON] and 30 sec no stimulation [OFF]: Scan 1 utilized standard VNS and Scan 3 optimized microburst parameters to determine target settings. Semi-automated on-site fMRI data processing utilized ON-OFF block modeling to determine VNS-related fMRI activation per stimulation setting. Anatomical thalamic mask was used to derive highest mean thalamic t-value for determination of microburst stimulation parameters. Paired t-tests corrected at P < 0.05 examined differences in fMRI responses to each stimulation type. Standard and microburst stimulation intensities at Scans 1 and 3 were similar (P = 0.16). Thalamic fMRI responses were obtained in 28 participants (19 with focal; 9 with generalized seizures). Group activation maps showed standard VNS elicited thalamic activation while optimized microburst VNS showed widespread activation patterns including thalamus. Comparison of stimulation types revealed significantly greater cerebellar, midbrain, and parietal fMRI signal changes in microburst compared to standard VNS. These differences were not associated with seizure responses. While standard and optimized microburst VNS elicited thalamic activation, microburst also engaged other brain regions. Relationship between these fMRI activation patterns and clinical response warrants further investigation. The study was registered with clinicaltrials.gov (NCT03446664).

Serum biomarkers in patients with drug-resistant epilepsy: a proteomics-based analysis.

To investigate the serum biomarkers in patients with drug-resistant epilepsy (DRE). A total of 9 DRE patients and 9 controls were enrolled. Serum from DRE patients was prospectively collected and analyzed for potential serum biomarkers using TMT18-labeled proteomics. After fine quality control, bioinformatics analysis was conducted to find differentially expressed proteins. Pathway enrichment analysis identified some biological features shared by differential proteins. Protein-protein interaction (PPI) network analysis was further performed to discover the core proteins. A total of 117 serum differential proteins were found in our study, of which 44 were revised upwards and 73 downwards. The up-regulated proteins mainly include UGGT2, PDIA4, SEMG1, KIAA1191, CCT7 etc. and the down-regulated proteins mainly include ROR1, NIF3L1, ITIH4, CFP, COL11A2 etc. Pathway enrichment analysis identified that the upregulated proteins were mainly enriched in processes such as immune response, extracellular exosome, serine-type endopeptidase activity and complement and coagulation cascades, and the down-regulated proteins were enriched in signal transduction, extracellular exosome, zinc/calcium ion binding and metabolic pathways. PPI network analysis revealed that the core proteins nodes include PRDX6, CAT, PRDX2, SOD1, PARK7, GSR, TXN, ANXA1, HINT1, and S100A8 etc. The discovery of these differential proteins enriched our understanding of serum biomarkers in patients with DRE and potentially provides guidance for future targeted therapy.

Neuropsychological characteristics of drug resistant epilepsy patients with and without comorbid functional seizures

IntroductionNeuropsychological testing is a mandatory component in the evaluation of drug resistant epilepsy. The results of testing may assist with both the localization of an epilepsy as well as assessment of surgical risk. Previous studies have demonstrated differences in the neuropsychological performance of patients with epilepsy and functional seizures. We hypothesized that comorbid functional seizures could potentially influence neuropsychological test performance. Therefore, we evaluated whether there is a difference in the neuropsychological test results between drug resistant epilepsy patients with and without comorbid functional seizures. MethodNeuropsychological test results were compared between 25 patients with drug resistant focal epilepsy and 25 patients that also had documented functional seizures. Univariate analyses and multiple logistic regression models were used to both assess performance differences between the groups and to assess whether test results could be used to accurately identify which patients had comorbid functional seizures. ResultsEpilepsy patients with comorbid functional seizures performed significantly worse on the FAS Verbal Fluency Test compared to ES patients (p = 0.047). Digit Span Backwards (p = 0.10), Digit Span Forwards (p = 0.14) and Working Memory Index (p = 0.10) tended to be lower in the epilepsy and functional seizures group but was not statistically significant. A multiple logistic regression model using the results of four neuropsychological tests was able to identify patients with comorbid functional seizures with 83.33% accuracy. ConclusionsThere are appeared to be some differences in the neuropsychological performance among drug resistant epilepsy patients based on whether they have comorbid functional seizures. These findings may have relevant implications for the interpretation of neuropsychological test results.

Mapping hippocampal and thalamic atrophy in epilepsy: A 7-T magnetic resonance imaging study.

Epilepsy patients are often grouped together by clinical variables. Quantitative neuroimaging metrics can provide a data-driven alternative for grouping of patients. In this work, we leverage ultra-high-field 7-T structural magnetic resonance imaging (MRI) to characterize volumetric atrophy patterns across hippocampal subfields and thalamic nuclei in drug-resistant focal epilepsy. Forty-two drug-resistant epilepsy patients and 13 controls with 7-T structural neuroimaging were included in this study. We measured hippocampal subfield and thalamic nuclei volumetry, and applied an unsupervised machine learning algorithm, Latent Dirichlet Allocation (LDA), to estimate atrophy patterns across the hippocampal subfields and thalamic nuclei of patients. We studied the association between predefined clinical groups and the estimated atrophy patterns. Additionally, we used hierarchical clustering on the LDA factors to group patients in a data-driven approach. In patients with mesial temporal sclerosis (MTS), we found a significant decrease in volume across all ipsilateral hippocampal subfields (false discovery rate-corrected p [pFDR] < .01) as well as in some ipsilateral (pFDR < .05) and contralateral (pFDR < .01) thalamic nuclei. In left temporal lobe epilepsy (L-TLE) we saw ipsilateral hippocampal and some bilateral thalamic atrophy (pFDR < .05), whereas in right temporal lobe epilepsy (R-TLE) extensive bilateral hippocampal and thalamic atrophy was observed (pFDR < .05). Atrophy factors demonstrated that our MTS cohort had two atrophy phenotypes: one that affected the ipsilateral hippocampus and one that affected the ipsilateral hippocampus and bilateral anterior thalamus. Atrophy factors demonstrated posterior thalamic atrophy in R-TLE, whereas an anterior thalamic atrophy pattern was more common in L-TLE. Finally, hierarchical clustering of atrophy patterns recapitulated clusters with homogeneous clinical properties. Leveraging 7-T MRI, we demonstrate widespread hippocampal and thalamic atrophy in epilepsy. Through unsupervised machine learning, we demonstrate patterns of volumetric atrophy that vary depending on disease subtype. Incorporating these atrophy patterns into clinical practice could help better stratify patients to surgical treatments and specific device implantation strategies.

Hippocampal sclerosis is associated with celiac disease type immunity in patients with drug-resistant temporal lobe epilepsy

Abstract Background A prior small-scale single center study suggested an association between celiac disease (CD)-type immunity and refractory temporal lobe epilepsy (TLE) with hippocampal sclerosis (HS). The present study addresses this putative association in a large, well-characterized group of drug-resistant epilepsy (DRE) patients. These patients were grouped based on the spectrum of CD and gluten sensitivity-associated antibodies. Methods In this cross-sectional study, 253 consecutive adult epilepsy patients (135 females, 118 males; age 16–76 years) were categorized into three groups: (i) CD-positive group with either prior diagnosis of CD or CD-specific TG2/EmA antibodies, (ii) AGA-positive group with antigliadin antibodies (AGA) but without CD, and (iii) CD/AGA-negative group without any gluten sensitivity-associated antibodies or CD. Clinical and immunological findings were then compared among the groups. Results TLE with HS was more common in the CD-positive group compared to CD/AGA-negative group (31.8% versus 11.9%, P = 0.019). Autoimmune disorders were more common in the AGA-positive group than in the CD/AGA-negative group (P = 0.025). Considering HS lateralization; left lateralization was more common in CD-positive group compared to CD/AGA-negative group (71.4% versus 25%, P = 0.030). TG6 seropositivity did not differ among the groups (P &gt; 0.05). Conclusions This study provides further evidence linking TLE with HS and CD-type autoimmunity suggesting that CD-type immune response to gluten can be one potential mechanism as a disease modifier leading to DRE and HS. Understanding these immunological factors is imperative for developing immunomodulatory or dietary treatments for DRE potentially preventing HS progression.

The Association Between Trajectory-Skull Angle and Accuracy of Stereoelectroencephalography Electrode Implantation in Drug-Resistant Epilepsy

OBJECTIVETo analyze the relationship between trajectory-skull angle and stereoelectroencephalography (SEEG) electrode implantation accuracy in drug-resistant epilepsy patients, aiming to guide clinical electrode placement and enhance surgical precision and safety. METHODSWe conducted a retrospective analysis of medical records and surgical characteristics of 32 consecutive patients diagnosed with drug-resistant epilepsy, who underwent SEEG procedures at our center from June 2020 to June 2023. To evaluate the accuracy of electrode implantation, we utilized preoperative and postoperative CT scans fused with SinoPlan software-planned trajectories. Entry radial error and target vector error were assessed as measurements of electrode implantation accuracy. RESULTSAfter adjusting for confounders, we found a significant positive correlation between trajectory-skull angle and entry radial error (β=0.02, 95% CI: 0.01–0.03, P<0.001). Likewise, a significant positive correlation existed between trajectory-skull angle and target vector error in all three models (β=0.03, 95% CI: 0.01–0.04, P<0.001). Additionally, a U-shaped relationship between trajectory-skull angle and target vector error was identified using smooth curve fitting. This U-shaped pattern persisted in both frame-based and robot-guided stereotactic techniques. According to the two-piecewise linear regression model, the inflection points were 9° in the frame-based group and 16° in the robot-guided group. CONCLUSIONSThis study establishes a significant positive linear correlation between trajectory-skull angle and entry radial error, along with a distinctive U-shaped pattern in the relationship between trajectory-skull angle and target vector error. Our findings suggest that trajectory-skull angles of 9° (frame-based) and 16° (robot-guided) may optimize the accuracy of target vector error.

Intrinsic brain activity differences in perampanel-responsive and non-responsive drug-resistant epilepsy patients: an EEG microstate analysis.

Drug-resistant epilepsy (DRE) patients exhibit aberrant large-scale brain networks. Perampanel may be a therapeutic option for controlling seizures in these patients. We aim to explore the differences of resting-state electroencephalogram (EEG) microstate in perampanel-responsive and non-responsive DRE patients. Retrospective study. Clinical data were collected from DRE patients who received perampanel treatment at the Fujian Medical University Union Hospital from June 2020 to September 2021, with a minimum follow-up of 6 months. Patients were classified into three groups based on the extent of reduction in seizure frequency: non-responsive (seizure reduction <50%), responsive (seizure reduction >50% but not seizure-free), and seizure-free. Resting-state EEG data sets of all participants were subjected to EEG microstate analysis. The study comprehensively compared the mean duration, frequency per second, and temporal coverage of each microstate among the three groups. A total of 76 perampanel-treated DRE patients were categorized into three groups based on their response to treatment: non-responsive (n = 20), responsive (n = 36), and seizure-free (n = 20), according to the degree of seizure frequency reduction. The results of EEG microstate analysis revealed no statistically significant distinctions in frequency, duration, and coverage of microstate D in these DRE patients. However, the seizure-free group showed significantly increased duration and coverage of microstate A, frequency and coverage of microstate B, and significantly decreased duration, frequency, and coverage of microstate C when compared with the other groups. Microstate A, B, and D is associated with the sensorimotor network, visual network, salience network, and attention network, respectively. This study demonstrates statistically significant differences in the sensorimotor, visual, and salience networks, but not in the attention network, between perampanel-responsive and non-responsive DRE patients.

Do Interictal Epileptiform Discharges and Brain Responses to Electrical Stimulation Come from the Same Location? An Advanced Source Localization Solution.

Identification of seizure sources in the brain is of paramount importance, particularly for drug-resistant epilepsy patients who may require surgical operation. Interictal epileptiform discharges (IEDs), which may or may not be frequent, are known to originate from seizure networks. Delayed responses (DRs) to brain electrical stimulation have been recently discovered. If DRs and IEDs come from the same location and the DRs can be accurately localized, there will be a significant step in identification of the seizure sources. The solution to this important question has been investigated in this paper. For this, we have exploited the morphology of these spike-type events, as well as the variability in their temporal locations, to develop new constraints for an adaptive Bayesian beamformer that outperforms the conventional and recently proposed beamformers even for identifying correlated sources. This beamformer is applied to an array (a.k.a mat) of cortical EEG electrodes. The developed approach has been tested on 300 data segments from five epileptic patients included in this study, which clinically represent a large population of candidates for surgical treatment. As the significant outcome of applying this beamformer, it is very likely (if not certain) that for an epileptic subject, the IEDs and DRs originate from the same location in the brain. This paves the way for a quick identification of the source(s) of seizure in the brain.

Who are the Better Candidates for Epilepsy Surgery?

The resective epilepsy surgery can be the effective procedure to get seizure-free outcome in these drug resistant epilepsy (DRE) patients. Class I evidence firmly establishes the superiority of epilepsy surgery over medical treatments in both seizure control and quality of life for DRE patients. For the effective identification of optimal surgical candidates, it's essential to understand the prognostic factors of epilepsy surgery based on the surgical methods employed. Established positive prognostic indicators for temporal resection include the presence of hippocampal sclerosis on magnetic resonance imaging (MRI), focal lesions on MRI, unilateral temporal spikes, concordant ictal electroencephalography (EEG), and a history of prolonged febrile convulsion. Potential negative predictors encompass preoperative secondary generalized tonic-clonic seizures, a normal MRI, postoperative EEG spikes, and age at the time of surgery. For neocortical epilepsy, the prognostic factors identified through multivariate analysis were the presence of a discrete lesion, localized hypometabolism on Fluorodeoxyglucose positron emission tomography (FDG-PET), and localized ictal EEG. A significant correlation was found between achieving a seizure-free outcome in no visible lesion on MRI (MR-negative) epilepsy patients and having concordance in two or more presurgical evaluations, specifically in interictal EEG, ictal EEG, FDG-PET, and ictal single-photon emission computed tomography. There was a marked improvement in the seizure-free outcome in MR-negative temporal lobe epilepsy (TLE) by the application of this strategy. The better surgical candidates for epilepsy surgery are the followings: patients displaying a discrete lesion on MRI with concordant video-EEG monitoring (VEM) results, patients diagnosed with unilateral hippocampal sclerosis who have concordant VEM results, patients with unilateral hippocampal sclerosis but discordant VEM results, patients with focal cortical dysplasia and concordant VEM results, and patients diagnosed with MR-negative TLE who exhibit two or more consistent results from presurgical evaluations.

Efficacy and safety of modified Atkins diet therapy for drug‐resistant epilepsy in children and adults: A systematic review and meta‐analysis

AbstractIntroductionDiet‐based treatments have been proposed for drug‐resistant epilepsy (DRE), and the Modified Atkins Diet (MAD) is an alternative. This study aimed to assess the efficacy and safety of MAD as an adjunctive therapy for reducing seizures in patients with drug‐resistant epilepsy.MethodsA literature search was done on six databases. Randomized controlled trial (RCT) studies were included, comparing DRE patients of all ages on standard antiseizure medications (ASD) who received MAD compared to no‐dietary or other dietary interventions. The outcomes are the seizure frequency reduction and the adverse events. Cochrane risk‐of‐bias tool and GRADE were used to assess study quality and the overall certainty of evidence. The effect size of the efficacy of MAD was computed as risk ratio (95% CI). Subgroup analysis based on each type of comparator was done.ResultsTen RCT studies (905 participants) were included, comparing MAD to no dietary intervention, ketogenic diet (KD), and low glycemic index treatment (LGIT). Overall, MAD is constantly and significantly more efficacious than no dietary intervention in reducing seizures ≥50% (RR 7.90, 95% CI 4.59–13.62), ≥90% (RR 4.61, 95% CI 2.05–10.36), and inducing complete seizure‐free (RR 7.79, 95% 2.61–23.22) with high certainty. Other dietary therapies and MAD did not differ significantly from one another in terms of efficacy. The risk of adverse events in MAD is insignificantly higher than in others (RR 1.19, 95% 0.92–1.55).ConclusionThe Modified Atkins Diet has better seizure reduction than no diet intervention but is comparable to other dietary interventions with a negligible increased risk of adverse events.

Surgical complications of vagus nerve stimulation surgery: A 14-years single-center experience

IntroductionVagus nerve stimulation (VNS) is the most frequently used neuromodulation treatment for Drug-Resistant Epilepsy (DRE) patients. Complications of VNS surgery include surgical site infection and unilateral vocal cord paresis. Complication rates vary across studies. Research questionWhat is the safety profile of VNS related surgeries? Materials and methodsRetrospective cohort study using patient files of DRE-patients who had undergone primary implantation of a VNS-system, replacement of the VNS pulse generator, replacement of the lead, replacement of both pulse generator and lead, or VNS removal surgery in the Maastricht UMC+. Multiple Imputation was used for missing data. Univariable and multivariable logistic regression analysis were performed to analyze possible risk factors, in case of a small sample size, an independent-samples t-test and Fisher's exact test or Pearson's X2-test were used. The complication rate was calculated as percentage. ResultsThis study included a total of 606 VNS surgical procedures, leading to 67 complications of which 3 permanent complications. Complication rate after primary implantation was 13.4%; 2,5% for pulse generator replacement; 21.4% for lead revision and 27.3% for complete VNS removal. No statistically significant results were found when analyzing the results of adults and children <18 years separately. Discussion and conclusionComplication rates of VNS-related surgeries in our own institutional series are low and comparable to previously reported series. VNS surgery is a relatively safe procedure. The complication rate differs per type of surgery and mean surgery duration was longer for patients with complications after lead revision surgery compared to patients without complications.

Alterations in brain network functional connectivity and topological properties in DRE patients.

The study aimed to find the difference in functional network topology on interictal electroencephalographic (EEG) between patients with drug-resistant epilepsy (DRE) and healthy people. We retrospectively analyzed the medical records as well as EEG data of ten patients with DRE and recruited five sex-age-matched healthy controls (HC group). Each participant remained awake while undergoing video-electroencephalography (vEEG) monitoring. After excluding data that contained abnormal discharges, we screened EEG segments that were free of artifacts and put them together into 20-min segments. The screened data was bandpass filtered to different frequency bands (delta, theta, alpha, beta, and gamma). The weighted phase lag index (wPLI) and the network properties were calculated to evaluate changes in the topology of the functional network. Finally, the results were statistically analyzed, and the false discovery rate (FDR) was used to correct for differences after multiple comparisons. In the full frequency band (0.5-45 Hz), the functional connectivity in the DRE group during the interictal period was significantly lower than that in the HC group (p < 0.05). Compared to the HC group, in the full frequency band, the DRE group exhibited significantly decreased clustering coefficient (CC), node degree (D), and global efficiency (GE), while the characteristic path length (CPL) significantly increased (p < 0.05). In the sub-frequency bands, the functional connectivity of the DRE group was significantly lower than that of the HC group in the delta band but higher in the alpha, beta, and gamma bands (p < 0.05). The statistical results of network properties revealed that in the delta band, the DRE group had significantly decreased values for D, CC, and GE, but in the alpha, beta, and gamma bands, these values were significantly increased (p < 0.05). Additionally, the CPL of the DRE group significantly increased in the delta and theta bands but significantly decreased in the alpha, beta, and gamma bands (p < 0.05). The topology structure of the functional network in DRE patients was significantly changed compared with healthy people, which was reflected in different frequency bands. It provided a theoretical basis for understanding the pathological network alterations of DRE.

Assessment of psychiatric comorbidities in DRE (drug -resistant epilepsy) patients

Assessment of psychiatric comorbidities in DRE (drug -resistant epilepsy) patients

Network-motif delay differential analysis of brain activity during seizures.

Delay Differential Analysis (DDA) is a nonlinear method for analyzing time series based on principles from nonlinear dynamical systems. DDA is extended here to incorporate network aspects to improve the dynamical characterization of complex systems. To demonstrate its effectiveness, DDA with network capabilities was first applied to the well-known Rössler system under different parameter regimes and noise conditions. Network-motif DDA, based on cortical regions, was then applied to invasive intracranial electroencephalographic data from drug-resistant epilepsy patients undergoing presurgical monitoring. The directional network motifs between brain areas that emerge from this analysis change dramatically before, during, and after seizures. Neural systems provide a rich source of complex data, arising from varying internal states generated by network interactions.