The interictal suppression hypothesis is the dominant differentiator of seizure onset zones in focal epilepsy.

Successful surgical treatment of drug-resistant epilepsy traditionally relies on the identification of seizure onset zones (SOZs). Connectome-based analyses of electrographic data from stereo electroencephalography (SEEG) may empower improved detection of SOZs. Specifically, connectome-based analyses based on the interictal suppression hypothesis posit that when the patient is not having a seizure, SOZs are inhibited by non-SOZs through high inward connectivity and low outward connectivity. However, it is not clear whether there are other motifs that can better identify potential SOZs. Thus, we sought to use unsupervised machine learning to identify network motifs that elucidate SOZs and investigate if there is another motif that outperforms the ISH. Resting-state SEEG data from 81 patients with drug-resistant epilepsy undergoing a pre-surgical evaluation at Vanderbilt University Medical Center were collected. Directed connectivity matrices were computed using the alpha band (8-13 Hz). Principal component analysis (PCA) was performed on each patient's connectivity matrix. Each patient's components were analysed qualitatively to identify common patterns across patients. A quantitative definition was then used to identify the component that most closely matched the observed pattern in each patient. A motif characteristic of the interictal suppression hypothesis (high-inward and low-outward connectivity) was present in all individuals and found to be the most robust motif for identification of SOZs in 64/81 (79%) patients. This principal component demonstrated significant differences in SOZs compared to non-SOZs. While other motifs for identifying SOZs were present in other patients, they differed for each patient, suggesting that seizure networks are patient specific, but the ISH is present in nearly all networks. We discovered that a potentially suppressive motif based on the interictal suppression hypothesis was present in all patients, and it was the most robust motif for SOZs in 79% of patients. Each patient had additional motifs that further characterized SOZs, but these motifs were not common across all patients. This work has the potential to augment clinical identification of SOZs to improve epilepsy treatment.

Structural and functional abnormalities and cognitive profiles in older adults with early-onset and late-onset focal epilepsy.

This study aimed to determine the patterns of changes in structure, function, and cognitive ability in early-onset and late-onset older adults with focal epilepsy (OFE). This study first utilized the deformation-based morphometry analysis to identify structural abnormalities, which were used as the seed region to investigate the functional connectivity with the whole brain. Next, a correlation analysis was performed between the altered imaging findings and neuropsychiatry assessments. Finally, the potential role of structural-functional abnormalities in the diagnosis of epilepsy was further explored by using mediation analysis. Compared with healthy controls (n = 28), the area of reduced structural volume was concentrated in the bilateral cerebellum, right thalamus, and right middle cingulate cortex, with frontal, temporal, and occipital lobes also affected in early-onset focal epilepsy (n = 26), while late-onset patients (n = 31) displayed cerebellar, thalamic, and cingulate atrophy. Furthermore, correlation analyses suggest an association between structural abnormalities and cognitive assessments. Dysfunctional connectivity in the cerebellum, cingulate cortex, and frontal gyrus partially mediates the relationship between structural abnormalities and the diagnosis of early-onset focal epilepsy. This study identified structural and functional abnormalities in early-onset and late-onset focal epilepsy and explored characters in cognitive performance. Structural-functional coupling may play a potential role in the diagnosis of epilepsy.

Widespread slow oscillations support interictal epileptiform discharge networks in focal epilepsy.

Interictal epileptiform discharges (IEDs) often co-occur across spatially-separated cortical regions, forming IED networks. However, the factors prompting IED propagation remain unelucidated. We hypothesized that slow oscillations (SOs) might facilitate IED propagation. Here, the amplitude and phase synchronization of SOs preceding propagating and non-propagating IEDs were compared in 22 patients with focal epilepsy undergoing intracranial electroencephalography (EEG) evaluation. Intracranial channels were categorized into the irritative zone (IZ) and normal zone (NOZ) regarding the presence of IEDs. During wakefulness, we found that pre-IED SOs within the IZ exhibited higher amplitudes for propagating IEDs than non-propagating IEDs (delta band: p = 0.001, theta band: p < 0.001). This increase in SOs was also concurrently observed in the NOZ (delta band: p = 0.04). Similarly, the inter-channel phase synchronization of SOs prior to propagating IEDs was higher than those preceding non-propagating IEDs in the IZ (delta band: p = 0.04). Through sliding window analysis, we observed that SOs preceding propagating IEDs progressively increased in amplitude and phase synchronization, while those preceding non-propagating IEDs remained relatively stable. Significant differences in amplitude occurred approximately 1150 ms before IEDs. During non-rapid eye movement (NREM) sleep, SOs on scalp recordings also showed higher amplitudes before intracranial propagating IEDs than before non-propagating IEDs (delta band: p = 0.006). Furthermore, the analysis of IED density around sleep SOs revealed that only high-amplitude sleep SOs demonstrated correlation with IED propagation. Overall, our study highlights that transient but widely distributed SOs are associated with IED propagation as well as generation in focal epilepsy during sleep and wakefulness, providing new insight into the EEG substrate supporting IED networks.

Sub-genic intolerance, ClinVar, and the epilepsies: A whole-exome sequencing study of 29,165 individuals.

Both mild and severe epilepsies are influenced by variants in the same genes, yet an explanation for the resulting phenotypic variation is unknown. As part of the ongoing Epi25 Collaboration, we performed a whole-exome sequencing analysis of 13,487 epilepsy-affected individuals and 15,678 control individuals. While prior Epi25 studies focused on gene-based collapsing analyses, we asked how the pattern of variation within genes differs by epilepsy type. Specifically, we compared the genetic architectures of severe developmental and epileptic encephalopathies (DEEs) and two generally less severe epilepsies, genetic generalized epilepsy and non-acquired focal epilepsy (NAFE). Our gene-based rare variant collapsing analysis used geographic ancestry-based clustering that included broader ancestries than previously possible and revealed novel associations. Using the missense intolerance ratio (MTR), we found that variants in DEE-affected individuals are in significantly more intolerant genic sub-regions than those in NAFE-affected individuals. Only previously reported pathogenic variants absent in available genomic datasets showed a significant burden in epilepsy-affected individuals compared with control individuals, and the ultra-rare pathogenic variants associated with DEE were located in more intolerant genic sub-regions than variants associated with non-DEE epilepsies. MTR filtering improved the yield of ultra-rare pathogenic variants in affected individuals compared with control individuals. Finally, analysis of variants in genes without a disease association revealed a significant burden of loss-of-function variants in the genes most intolerant to such variation, indicating additional epilepsy-risk genes yet to be discovered. Taken together, our study suggests that genic and sub-genic intolerance are critical characteristics for interpreting the effects of variation in genes that influence epilepsy.

Dynamic evolution of frontal-temporal network connectivity in temporal lobe epilepsy: A magnetoencephalography study.

Temporal lobe epilepsy (TLE) frequently involves an intricate, extensive epileptic frontal-temporal network. This study aimed to investigate the interactions between temporal and frontal regions and the dynamic patterns of the frontal-temporal network in TLE patients with different disease durations. The magnetoencephalography data of 36 postoperative seizure-free patients with long-term follow-up of at least 1 year, and 21 age- and sex-matched healthy subjects were included in this study. Patients were initially divided into LONG-TERM (n = 18, DURATION >10 years) and SHORT-TERM (n = 18, DURATION ≤10 years) groups based on 10-year disease duration. For reliability, supplementary analyses were conducted with alternative cutoffs, creating three groups: 0 < DURATION ≤7 years (n = 11), 7 < DURATION ≤14 years (n = 11), and DURATION >14 years (n = 14). This study examined the intraregional phase-amplitude coupling (PAC) between theta phase and alpha amplitude across the whole brain. The interregional directed phase transfer entropy (dPTE) between frontal and temporal regions in the alpha and theta bands, and the interregional cross-frequency directionality (CFD) between temporal and frontal regions from the theta phase to the alpha amplitude were further computed and compared among groups. Partial correlation analysis was conducted to investigate correlations between intraregional PAC, interregional dPTE connectivity, interregional CFD, and disease duration. Whole-brain intraregional PAC analyses revealed enhanced theta phase-alpha amplitude coupling within the ipsilateral temporal and frontal regions in TLE patients, and the ipsilateral temporal PAC was positively correlated with disease duration (r = 0.38, p <.05). Interregional dPTE analyses demonstrated a gradual increase in frontal-to-temporal connectivity within the alpha band, while the direction of theta-band connectivity reversed from frontal-to-temporal to temporal-to-frontal as the disease duration increased. Interregional CFD analyses revealed that the inhibitory effect of frontal regions on temporal regions gradually increased with prolonged disease duration (r = -0.36, p <.05). This study clarified the intrinsic reciprocal connectivity between temporal and frontal regions with TLE duration. We propose a dynamically reorganized triple-stage network that transitions from balanced networks to constrained networks and further develops into imbalanced networks as the disease duration increases.

EANM practice guidelines for an appropriate use of PET and SPECT for patients with epilepsy.

Epilepsy is one of the most frequent neurological conditions with an estimated prevalence of more than 50 million people worldwide and an annual incidence of two million. Although pharmacotherapy with anti-seizure medication (ASM) is the treatment of choice, ~30% of patients with epilepsy do not respond to ASM and become drug resistant. Focal epilepsy is the most frequent form of epilepsy. In patients with drug-resistant focal epilepsy, epilepsy surgery is a treatment option depending on the localisation of the seizure focus for seizure relief or seizure freedom with consecutive improvement in quality of life. Beside examinations such as scalp video/electroencephalography (EEG) telemetry, structural, and functional magnetic resonance imaging (MRI), which are primary standard tools for the diagnostic work-up and therapy management of epilepsy patients, molecular neuroimaging using different radiopharmaceuticals with single-photon emission computed tomography (SPECT) and positron emission tomography (PET) influences and impacts on therapy decisions. To date, there are no literature-based praxis recommendations for the use of Nuclear Medicine (NM) imaging procedures in epilepsy. The aims of these guidelines are to assist in understanding the role and challenges of radiotracer imaging for epilepsy; to provide practical information for performing different molecular imaging procedures for epilepsy; and to provide an algorithm for selecting the most appropriate imaging procedures in specific clinical situations based on current literature. These guidelines are written and authorized by the European Association of Nuclear Medicine (EANM) to promote optimal epilepsy imaging, especially in the presurgical setting in children, adolescents, and adults with focal epilepsy. They will assist NM healthcare professionals and also specialists such as Neurologists, Neurophysiologists, Neurosurgeons, Psychiatrists, Psychologists, and others involved in epilepsy management in the detection and interpretation of epileptic seizure onset zone (SOZ) for further treatment decision. The information provided should be applied according to local laws and regulations as well as the availability of various radiopharmaceuticals and imaging modalities.

Focal electroclinical features in generalized tonic-clonic seizures: Decision flowchart for a diagnostic challenge.

OBJECTIVE: Bilateral tonic-clonic seizures with focal semiology or focal interictal electroencephalography (EEG) can occur in both focal and generalized epilepsy types, leading to diagnostic errors and inappropriate therapy. We investigated the prevalence and prognostic values of focal features in patients with idiopathic generalized epilepsy (IGE), and we propose a decision flowchart to distinguish between focal and generalized epilepsy in patients with bilateral tonic-clonic seizures and focal EEG or semiology. METHODS: We retrospectively analyzed video-EEG recordings of 101 bilateral tonic-clonic seizures from 60 patients (18 with IGE, 42 with focal epilepsy). Diagnosis and therapeutic response were extracted after ≥1-year follow-up. The decision flowchart was based on previous observations and assessed concordance between interictal and ictal EEG. RESULTS: Focal semiology in IGE was observed in 75% of seizures and 77.8% of patients, most often corresponding to forced head version (66.7%). In patients with multiple seizures, direction of head version was consistent across seizures. Focal interictal epileptiform discharges (IEDs) were observed in 61.1% of patients with IGE, whereas focal ictal EEG onset only occurred in 13% of seizures and 16.7% of patients. However, later during the seizures, a reproducible pattern of 7-Hz lateralized ictal rhythm was observed in 56% of seizures, associated with contralateral head version. We did not find correlation between presence of focal features and therapeutic response in IGE patients. Our decision flowchart distinguished between focal and generalized epilepsy in patients with bilateral tonic-clonic seizures and focal features with an accuracy of 96.6%. SIGNIFICANCE: Focal semiology associated with bilateral tonic-clonic seizures and focal IEDs are common features in patients with IGE, but focal ictal EEG onset is rare. None of these focal findings appears to influence therapeutic response. By assessing the concordance between interictal and ictal EEG findings, one can accurately distinguish between focal and generalized epilepsies.

The role of copy number variants in the genetic architecture of common familial epilepsies.

OBJECTIVE: Copy number variants (CNVs) contribute to genetic risk and genetic etiology of both rare and common epilepsies. Whereas many studies have explored the role of CNVs in sporadic or severe cases, fewer have been done in familial generalized and focal epilepsies. METHODS: We analyzed exome sequence data from 267 multiplex families and 859 first-degree relative pairs with a diagnosis of genetic generalized epilepsies or nonacquired focal epilepsies to predict CNVs. Validation and segregation studies were performed using an orthogonal method when possible. RESULTS: We identified CNVs likely to contribute to epilepsy risk or etiology in the probands of 43 of 1116 (3.9%) families, including known recurrent CNVs (16p13.11 deletion, 15q13.3 deletion, 15q11.2 deletion, 16p11.2 duplication, 1q21.1 duplication, and 5-Mb duplication of 15q11q13). We also identified CNVs affecting monogenic epilepsy genes, including four families with CNVs disrupting the DEPDC5 gene, and a de novo deletion of HNRNPU in one affected individual from a multiplex family. Several large CNVs (>500 kb) of uncertain clinical significance were identified, including a deletion in 18q, a large duplication encompassing the SCN1A gene, and a 15q13.3 duplication (BP4-BP5). SIGNIFICANCE: The overall CNV landscape in common familial epilepsies is similar to that of sporadic epilepsies, with large recurrent deletions at 15q11, 15q13, and 16p13 contributing in 2.5%-3% of families. CNVs that interrupt known epilepsy genes and rare, large CNVs were also identified. Multiple etiologies were found in a subset of families, emphasizing the importance of genetic testing for multiple affected family members. Rare CNVs found in a single proband remain difficult to interpret and require larger cohorts to confirm their potential role in disease. Overall, our work indicates that CNVs contribute to the complex genetic architecture of familial generalized and focal epilepsies, supporting the role for clinical testing in affected individuals.

Specific consistency score for rational selection of epilepsy resection surgery candidates.

OBJECTIVE: Degree of indication for epilepsy surgery is determined by taking multiple factors into account. This study aimed to investigate the usefulness of the Specific Consistency Score (SCS), a proposed score for focal epilepsy to rate the indication for epilepsy focal resection. METHODS: This retrospective cohort study included patients considered for resective epilepsy surgery in Kyoto University Hospital from 2011 to 2022. Plausible epileptic focus was tentatively defined. Cardinal findings were scored based on specificity and consistency with the estimated laterality and lobe. The total points represented SCS. The association between SCS and the following clinical parameters was assessed by univariate and multivariate analysis: (1) probability of undergoing resective epilepsy surgery, (2) good postoperative seizure outcome (Engel I and II or Engel I only), and (3) lobar concordance between the noninvasively estimated focus and intracranial electroencephalographic (EEG) recordings. RESULTS: A total of 131 patients were evaluated. Univariate analysis revealed higher SCS in the (1) epilepsy surgery group (8.4 [95% confidence interval (CI) = 7.8-8.9] vs. 4.9 [95% CI = 4.3-5.5] points; p < .001), (2) good postoperative seizure outcome group (Engel I and II; 8.7 [95% CI = 8.2-9.3] vs. 6.4 [95% CI = 4.5-8.3] points; p = .008), and (3) patients whose focus defined by intracranial EEG matched the noninvasively estimated focus (8.3 [95% CI = 7.3-9.2] vs. 5.4 [95% CI = 3.5-7.3] points; p = .004). Multivariate analysis revealed areas under the curve of .843, .825, and .881 for Parameters 1, 2, and 3, respectively. SIGNIFICANCE: SCS provides a reliable index of good indication for resective epilepsy surgery and can be easily available in many institutions not necessarily specializing in epilepsy.

Network signatures define consciousness state during focal seizures.

OBJECTIVE: Epilepsy is a common neurological disorder affecting 1% of the global population. Loss of consciousness in focal impaired awareness seizures (FIASs) and focal-to-bilateral tonic-clonic seizures (FBTCSs) can be devastating, but the mechanisms are not well understood. Although ictal activity and interictal connectivity changes have been noted, the network states of focal aware seizures (FASs), FIASs, and FBTCSs have not been thoroughly evaluated with network measures ictally. METHODS: We obtained electrographic data from 74 patients with stereoelectroencephalography (SEEG). Sliding window band power, functional connectivity, and segregation were computed on preictal, ictal, and postictal data. Five-minute epochs of wake, rapid eye movement sleep, and deep sleep were also extracted. Connectivity of subcortical arousal structures was analyzed in a cohort of patients with both SEEG and functional magnetic resonance imaging (fMRI). Given that custom neuromodulation of seizures is predicated on detection of seizure type, a convolutional neural network was used to classify seizure types. RESULTS: We found that in the frontoparietal association cortex, an area associated with consciousness, both consciousness-impairing seizures (FIASs and FBTCSs) and deep sleep had increases in slow wave delta (1-4 Hz) band power. However, when network measures were employed, we found that only FIASs and deep sleep exhibited an increase in delta segregation and a decrease in gamma segregation. Furthermore, we found that only patients with FIASs had reduced subcortical-to-neocortical functional connectivity with fMRI versus controls. Finally, our deep learning network demonstrated an area under the curve of .75 for detecting consciousness-impairing seizures. SIGNIFICANCE: This study provides novel insights into ictal network measures in FASs, FIASs, and FBTCSs. Importantly, although both FIASs and FBTCSs result in loss of consciousness, our results suggest that ictal network changes in FIASs uniquely resemble those that occur during deep sleep. Our results may inform novel neuromodulation strategies for preservation of consciousness in epilepsy.

SCN8A self-limited infantile epilepsy: Does epilepsy resolve?

SCN8A variants cause a spectrum of epilepsy phenotypes ranging from self-limited infantile epilepsy (SeLIE) to developmental and epileptic encephalopathy. SeLIE is an infantile onset focal epilepsy, occurring in developmentally normal infants, which often resolves by 3 years. Our aim was to ascertain when epilepsy resolves in SCN8A-SeLIE. We identified unpublished individuals with SCN8A-SeLIE and performed detailed phenotyping. Literature was searched for published SCN8A-SeLIE cases. Nine unpublished individuals from four families were identified (age at study = 3.5-66 years). Six had their last seizure after 3 years (range = 4-21 years); although drug-responsive and despite multiple weaning attempts (1-5), five of six remain on antiseizure medications (carbamazepine, n = 3; lamotrigine, n = 2). We identified 29 published individuals with SCN8A-SeLIE who had data on seizure progression. Of the 22 individuals aged at least 10 years, reported here or in the literature, nine of 22 (41%) had seizure offset prior to 3 years, five of 22 (23%) had seizure offset between 3 and 10 years, and eight of 22 (36%) had seizures after 10 years. Our data highlight that more than half of individuals with SCN8A-SeLIE continue to have seizures into late childhood. In contrast to SeLIE due to other etiologies, many individuals have a more persistent, albeit drug-responsive, form of epilepsy.

Why did my seizures start now? Influences of lesion connectivity and genetic etiology on age at seizure onset in focal epilepsy.

OBJECTIVE: Patients with focal, lesional epilepsy present with seizures at variable ages. Larger lesion size and overlap with sensorimotor or default mode network (DMN) have been associated with younger age at seizure onset in cohorts with mixed types of focal cortical dysplasia (FCD). Here, we studied determinants of age at seizure onset in patients with bottom-of-sulcus dysplasia (BOSD), a discrete type of FCD with highly localized epileptogenicity. METHODS: Eighty-four patients (77% operated) with BOSD were studied. Demographic, histopathologic, and genetic findings were recorded. BOSD volume and anatomical, primary versus association, rostral versus caudal, and functional network locations were determined. Normative functional connectivity analyses were performed using each BOSD as a region of interest in resting-state functional magnetic resonance imaging data of healthy children. Variables were correlated with age at seizure onset. RESULTS: Median age at seizure onset was 5.4 (interquartile range = 2-7.9) years. Of 50 tested patients, 22 had somatic and nine had germline pathogenic mammalian target of rapamycin (mTOR) pathway variants. Younger age at seizure onset was associated with greater BOSD volume (p = .002), presence of a germline pathogenic variant (p = .04), DMN overlap (p = .04), and increased functional connectivity with the DMN (p < .05, false discovery rate corrected). Location within sensorimotor cortex and networks was not associated with younger age at seizure onset in our relatively small but homogenous cohort. SIGNIFICANCE: Greater lesion size, pathogenic mTOR pathway germline variants, and DMN connectivity are associated with younger age at seizure onset in small FCD. Our findings strengthen the suggested role of DMN connectivity in the onset of FCD-related focal epilepsy and reveal novel contributions of genetic etiology.

Modification of brain conductivity in human focal epilepsy: A model-based estimation from stereoelectroencephalography.

OBJECTIVE: We have developed a novel method for estimating brain tissue electrical conductivity using low-intensity pulse stereoelectroencephalography (SEEG) stimulation coupled with biophysical modeling. We evaluated the hypothesis that brain conductivity is correlated with the degree of epileptogenicity in patients with drug-resistant focal epilepsy. METHODS: We used bipolar low-intensity biphasic pulse stimulation (.2 mA) followed by a postprocessing pipeline for estimating brain conductivity. This processing is based on biophysical modeling of the electrical potential induced in brain tissue between the stimulated contacts in response to pulse stimulation. We estimated the degree of epileptogenicity using a semi-automatic method quantifying the dynamic of fast discharge at seizure onset: the epileptogenicity index (EI). We also investigated how the location of stimulation within specific anatomical brain regions or within lesional tissue impacts brain conductivity. RESULTS: We performed 1034 stimulations of 511 bipolar channels in 16 patients. We found that brain conductivity was lower in the epileptogenic zone (EZ; unpaired median difference = .064, p < .001) and inversely correlated with the epileptogenic index value (p < .001, Spearman rho = -.32). Conductivity values were also influenced by anatomical site, location within lesion, and delay between SEEG electrode implantation and stimulation, and had significant interpatient variability. Mixed model multivariate analysis showed that conductivity is significantly associated with EI (F = 13.45, p < .001), anatomical regions (F = 5.586, p < .001), delay since implantation (F = 14.71, p = .003), and age at SEEG (F = 6.591, p = .027), but not with the type of lesion (F = .372, p = .773) or the delay since last seizure (F = 1.592, p = .235). SIGNIFICANCE: We provide a novel model-based method for estimating brain conductivity from SEEG low-intensity pulse stimulations. The brain tissue conductivity is lower in EZ as compared to non-EZ. Conductivity also varies significantly across anatomical brain regions. Involved pathophysiological processes may include changes in the extracellular space (especially volume or tortuosity) in epileptic tissue.

Predictors of drug-resistant epilepsy in childhood epilepsy syndromes: A subgroup analysis from a prospective cohort study.

OBJECTIVE: Previous studies assessing factors associated with drug-resistant epilepsy (DRE) were constrained by their amalgamation of all epilepsy syndromes in their analyses and the absence of uniform criteria for defining DRE. Our objective was to identify predictors of DRE among the four primary childhood epilepsy syndrome groups within a cohort of children with new onset seizures, using the International League Against Epilepsy (ILAE) definition of DRE and the recent classification of epilepsies. METHODS: This is a prospective study of 676 children with new onset seizures initiated on antiseizure medication. Patients were monitored for the occurrence of DRE according to the ILAE criteria and were categorized into one of four epilepsy groups: self-limited focal epilepsies (SeLFEs), genetic generalized epilepsies (GGEs), developmental epileptic encephalopathies (DEEs), and focal epilepsies. Cox regression analysis was performed to identify predictors of DRE within each epilepsy group. RESULTS: Overall, 29.3% of children were classified as having DRE, with the highest incidence observed among children diagnosed with DEEs (77.7%), followed by focal epilepsies (31.5%). Across the entire cohort, predictors of DRE included the presence of an epileptogenic lesion, a higher pretreatment number of seizures, experiencing multiple seizure types, presence and severity of intellectual and developmental delay, myoclonus, and younger age at epilepsy onset. Within the GGEs, only a younger age at seizure onset and experiencing multiple seizure types predicted DRE. Among focal epilepsies, predictors of DRE included the presence of an epileptogenic lesion, experiencing multiple seizure types, and having a greater number of pretreatment seizures. Within the DEEs, predictors of DRE were the occurrence of tonic seizures. Predictors of DRE within SeLFEs could not be identified. SIGNIFICANCE: This study indicates that different epilepsy syndromes are associated with distinct predictors of drug resistance. Anticipation of drug resistance within various groups is feasible using accessible clinical variables throughout the disease course.

WONOEP 2022: Neurotechnology for the diagnosis of epilepsy.

Epilepsy's myriad causes and clinical presentations ensure that accurate diagnoses and targeted treatments remain a challenge. Advanced neurotechnologies are needed to better characterize individual patients across multiple modalities and analytical techniques. At the XVIth Workshop on Neurobiology of Epilepsy: Early Onset Epilepsies: Neurobiology and Novel Therapeutic Strategies (WONOEP 2022), the session on "advanced tools" highlighted a range of approaches, from molecular phenotyping of genetic epilepsy models and resected tissue samples to imaging-guided localization of epileptogenic tissue for surgical resection of focal malformations. These tools integrate cutting edge research, clinical data acquisition, and advanced computational methods to leverage the rich information contained within increasingly large datasets. A number of common challenges and opportunities emerged, including the need for multidisciplinary collaboration, multimodal integration, potential ethical challenges, and the multistage path to clinical translation. Despite these challenges, advanced epilepsy neurotechnologies offer the potential to improve our understanding of the underlying causes of epilepsy and our capacity to provide patient-specific treatment.

Epilepsy syndromes in cerebral palsy: varied, evolving and mostly self-limited.

Seizures occur in approximately one-third of children with cerebral palsy. This study aimed to determine epilepsy syndromes in children with seizures and cerebral palsy due to vascular injury, anticipating that this would inform treatment and prognosis. We studied a population-based cohort of children with cerebral palsy due to prenatal or perinatal vascular injuries, born 1999-2006. Each child's MRI was reviewed to characterize patterns of grey and white matter injury. Children with syndromic or likely genetic causes of cerebral palsy were excluded, given their inherent association with epilepsy and our aim to study a homogeneous cohort of classical cerebral palsy. Chart review, parent interview and EEGs were used to determine epilepsy syndromes and seizure outcomes. Of 256 children, 93 (36%) had one or more febrile or afebrile seizures beyond the neonatal period and 87 (34%) had epilepsy. Children with seizures were more likely to have had neonatal seizures, have spastic quadriplegic cerebral palsy and function within Gross Motor Function Classification System level IV or V. Fifty-six (60%) children with seizures had electroclinical features of a self-limited focal epilepsy of childhood; we diagnosed these children with a self-limited focal epilepsy-variant given the current International League Against Epilepsy classification precludes a diagnosis of self-limited focal epilepsy in children with a brain lesion. Other epilepsy syndromes were focal epilepsy-not otherwise specified in 28, infantile spasms syndrome in 11, Lennox-Gastaut syndrome in three, genetic generalized epilepsies in two and febrile seizures in nine. No epilepsy syndrome could be assigned in seven children with no EEG. Twenty-one changed syndrome classification during childhood. Self-limited focal epilepsy-variant usually manifested with a mix of autonomic and brachio-facial motor features, and occipital and/or centro-temporal spikes on EEG. Of those with self-limited focal epilepsy-variant, 42/56 (75%) had not had a seizure for >2 years. Favourable seizure outcomes were also seen in some children with infantile spasms syndrome and focal epilepsy-not otherwise specified. Of the 93 children with seizures, at last follow-up (mean age 15 years), 61/91 (67%) had not had a seizure in >2 years. Children with cerebral palsy and seizures can be assigned specific epilepsy syndrome diagnoses typically reserved for normally developing children, those syndromes commonly being age-dependent and self-limited. Compared to typically developing children with epilepsy, self-limited focal epilepsy-variant occurs much more commonly in children with cerebral palsy and epilepsy. These findings have important implications for treatment and prognosis of epilepsy in cerebral palsy, and research into pathogenesis of self-limited focal epilepsy.

EEG in focal and generalized epilepsies: Pearls and perils.

Correctly diagnosing and classifying seizures and epilepsies is vital to ensure a tailored approach to patients with epilepsy. The ILAE seizure classification consists of two main groups: focal and generalized. Establishing if a seizure is focal or generalized is essential to classify the epilepsy type and the epilepsy syndrome, providing more personalized treatment and counseling about prognosis. EEG is one of the most essential tools for this classification process and further localization of the epileptogenic focus. However, some EEG findings are misleading and may postpone the correct diagnosis and proper treatment. Knowing the most common EEG pitfalls in focal and generalized epilepsies is valuable for clinical practice, avoiding misinterpretations. Some atypical features can be challenging in focal epilepsies, such as secondary bilateral synchrony, focal epileptiform activity induced by hyperventilation and photic stimulation, and non-focal slowing. On the other hand, more than 60 % of persons with idiopathic generalized epilepsies have at least one type of atypical abnormality. In this manuscript, we describe and illustrate some of the most common EEG findings that can make even experienced epileptologists question not only where the epileptogenic focus is but also if the patient has focal or generalized epilepsy. This review summarizes the perils and provide some pearls to assist EEG readers.

Construction and validation of an algorithm to separate focal and generalised epilepsy using clinical variables: A comparison of machine learning approaches.

PURPOSE: Epilepsy type, whether focal or generalised, is important in deciding anti-seizure medication (ASM). In resource-limited settings, investigations are usually not available, so a clinical separation is required. We used a naïve Bayes approach to devise an algorithm to do this, and compared its accuracy with algorithms devised by five other machine learning methods. METHODS: We used data on 28 clinical variables from 503 patients attending an epilepsy clinic in India with defined epilepsy type, as determined by an epileptologist with access to clinical, imaging, and EEG data. We adopted a machine learning approach to select the most relevant variables based on mutual information, to train the model on part of the data, and then to evaluate it on the remaining data (testing set). We used a naïve Bayes approach and compared the results in the testing set with those obtained by several other machine learning algorithms by measuring sensitivity, specificity, accuracy, area under the curve, and Cohen's kappa. RESULTS: The six machine learning methods produced broadly similar results. The best naïve Bayes algorithm contained eleven variables, and its accuracy was 92.2% in determining epilepsy type (sensitivity 92.0%, specificity 92.7%). An algorithm incorporating the best eight of these variables was only slightly less accurate - 91.0% (sensitivity 89.6%, and specificity 95.1%) - and easier for clinicians to use. CONCLUSION: A clinical algorithm with eight variables is effective and accurate at separating focal from generalised epilepsy. It should be useful in resource-limited settings, by epilepsy-inexperienced doctors, to help determine epilepsy type and therefore optimal ASMs for individual patients, without the need for EEG or neuroimaging.

Psychiatric complications following SEEG-guided radiofrequency thermocoagulations in patients with drug-resistant epilepsy.

SEEG-guided radiofrequency thermocoagulation (RF-TC) in the epileptogenic regions is a therapeutic option for patients with drug-resistant focal epilepsy who may have or not indication for epilepsy surgery. The most common adverse events of RF-TC are seizures, headaches, somatic pain, and sensory-motor deficits. If RF-TC could lead to psychiatric complications is unknown. In the present study, seven out of 164 patients (4.2 %) experienced psychiatric decompensation with or without memory deterioration after RF-TC of bilateral or unilateral amygdala and hippocampus. The appearance of symptoms was either acute, subacute, or chronic and the symptoms were either transient or lasted for several months. Common features among these patients were female sex, mesial temporal epilepsy, and a pre-existing history of psychological distress and memory dysfunction. Our study highlights the possibility of neuropsychiatric deterioration in specific patients following SEEG-guided RF-TC, despite its rarity.

Interictal head-turning sign in patients with idiopathic generalized epilepsy during initial medical interview: A matched multicenter study.

BACKGROUND: Utilizing interictal manifestations for the diagnosis of epilepsy is challenging. We investigated whether an interictal "head-turning sign," typically indicative of dependence on others observed in Alzheimer's disease, can act as a behavioral marker of idiopathic generalized epilepsy. METHODS: This multicenter study examined 579 consecutive patients, with a mean age of 36.8 ± 20.4 years, who did not have an intellectual disability and had their first outpatient visit for epilepsy evaluation between 2019 and 2023. Patients were categorized into IGE, non-IGE epilepsy, non-epileptic, and psychiatric conditions based on their ultimate diagnostic outcomes to identify difference of the occurrence of the head-turning sign among them. Additionally, we extracted data from patients under the age of 40, specifically adolescents and young adults (AYA). Then we used propensity score matching to confirm the reproducibility of observed differences and to identify associated factors within the AYA age group. RESULTS: The occurrence of the head-turning sign was significantly more prevalent in the IGE group compared to the non-IGE group (20.4 % vs. 2.2 %; P<0.0001) and non-epileptic group (20.4 % vs. 8.3 %; P=0.033). Following the matching, the head-turning sign was still evident in IGE relative to non-IGE patients (14.6 % vs. 4.5; P=0.004), yielding a 94 % specificity for IGE. IGE diagnosis (P<0.0001), myoclonic seizure (P<0.0001), being visited by a parent (P=0.017), and comorbidity with headache (P=0.021) were significantly associated with the head-turning sign. Multivariate analysis revealed that IGE (odds ratio: OR=2.80, P=0.028), attending with a parent (OR=2.92, P=0.029), and comorbidity with headache (OR=4.06, P=0.016) were independently associated with the head-turning sign. CONCLUSIONS: We confirmed a substantial association between the interictal "head-turning sign" and IGE. This unique sign may reflect a tendency towards dependence on others in IGE, and may serve as a promising diagnostic auxiliary marker for identifying IGE in the AYA age group.