Frontal Lobe Epilepsy Research Articles

The effect of facial expression intensity on emotion recognition and psychosocial performance in patients with frontal or temporal lobe epilepsy

PurposeNo studies have examined the relationship between the intensity of facial emotion expression and theory of mind (ToM) ability in people with epilepsy. This study aimed to explore facial emotion recognition in a group of patients with frontal (FLE) or temporal lobe epilepsy (TLE) and its relationship with the intensities of perceived facial emotion expressions, ToM, and social functioning. MethodsTwenty-six patients with FLE or TLE and 30 matched controls were included in the study. All participants completed the facial emotion recognition test, Faux Pas Recognition (FPR) test measuring advanced ToM, Symptom Checklist-90-Revised, Social and Occupational Functioning Scale for Epilepsy (SOFSE), and background neuropsychological tests. ResultsThe patient group was significantly worse than the control group in recognizing facial expressions of negative emotions, particularly for medium-intensity facial expression of fear. There was no significant difference between the groups in recognizing high-intensity fear facial expressions. The scores of FPR (overall and affective ToMs) in the patient group were significantly lower than those in the control group. Additionally, the facial emotion recognition was significantly associated with the total score of FPR, and the FPR total score remarkably correlated with the Communication subscale score of the SOFSE. ConclusionsPatients with FLE or TLE had impaired ability to recognize medium-intensity facial expressions of fear. Moreover, patients’ ToM deficit significantly correlated not only with their emotion recognition problem but also with their social-communicative competence. Nevertheless, we also found that increasing the intensity of expression can improve the accuracy of emotion recognition in patients with epilepsy. These findings may provide considerations for further longitudinal studies and interventions on the social difficulties of people with epilepsy.

The L1624Q Variant in SCN1A Causes Familial Epilepsy Through a Mixed Gain and Loss of Channel Function.

Variants of the SCN1A gene encoding the neuronal voltage-gated sodium channel NaV1.1 cause over 85% of all cases of Dravet syndrome, a severe and often pharmacoresistent epileptic encephalopathy with mostly infantile onset. But with the increased availability of genetic testing for patients with epilepsy, variants in SCN1A have now also been described in a range of other epilepsy phenotypes. The vast majority of these epilepsy-associated variants are de novo, and most are either nonsense variants that truncate the channel or missense variants that are presumed to cause loss of channel function. However, biophysical analysis has revealed a significant subset of missense mutations that result in increased excitability, further complicating approaches to precision pharmacotherapy for patients with SCN1A variants and epilepsy. We describe clinical and biophysical data of a familial SCN1A variant encoding the NaV1.1 L1624Q mutant. This substitution is located on the extracellular linker between S3 and S4 of Domain IV of NaV1.1 and is a rare case of a familial SCN1A variant causing an autosomal dominant frontal lobe epilepsy. We expressed wild-type (WT) and L1642Q channels in CHO cells. Using patch-clamp to characterize channel properties at several temperatures, we show that the L1624Q variant increases persistent current, accelerates fast inactivation onset and decreases current density. While SCN1A-associated epilepsy is typically considered a loss-of-function disease, our results put L1624Q into a growing set of mixed gain and loss-of-function variants in SCN1A responsible for epilepsy.

Decomposing MRI phenotypic heterogeneity in epilepsy: a step towards personalized classification.

In drug-resistant temporal lobe epilepsy, precise predictions of drug response, surgical outcome and cognitive dysfunction at an individual level remain challenging. A possible explanation may lie in the dominant 'one-size-fits-all' group-level analytical approaches that does not allow parsing interindividual variations along the disease spectrum. Conversely, analysing inter-patient heterogeneity is increasingly recognized as a step towards person-centred care. Here, we used unsupervised machine learning to estimate latent relations (or disease factors) from 3 T multimodal MRI features [cortical thickness, hippocampal volume, fluid-attenuated inversion recovery (FLAIR), T1/FLAIR, diffusion parameters] representing whole-brain patterns of structural pathology in 82 patients with temporal lobe epilepsy. We assessed the specificity of our approach against age- and sex-matched healthy individuals and a cohort of frontal lobe epilepsy patients with histologically verified focal cortical dysplasia. We identified four latent disease factors variably co-expressed within each patient and characterized by ipsilateral hippocampal microstructural alterations, loss of myelin and atrophy (Factor 1), bilateral paralimbic and hippocampal gliosis (Factor 2), bilateral neocortical atrophy (Factor 3) and bilateral white matter microstructural alterations (Factor 4). Bootstrap analysis and parameter variations supported high stability and robustness of these factors. Moreover, they were not expressed in healthy controls and only negligibly in disease controls, supporting specificity. Supervised classifiers trained on latent disease factors could predict patient-specific drug response in 76 ± 3% and postsurgical seizure outcome in 88 ± 2%, outperforming classifiers that did not operate on latent factor information. Latent factor models predicted inter-patient variability in cognitive dysfunction (verbal IQ: r = 0.40 ± 0.03; memory: r = 0.35 ± 0.03; sequential motor tapping: r = 0.36 ± 0.04), again outperforming baseline learners. Data-driven analysis of disease factors provides a novel appraisal of the continuum of interindividual variability, which is probably determined by multiple interacting pathological processes. Incorporating interindividual variability is likely to improve clinical prognostics.

The intellectual profile of pediatric patients with posterior cortex epilepsy

BackgroundCognitive functioning in epileptic syndromes has been widely explored in patients with temporal lobe epilepsy (TLE), but few studies have investigated the neuropsychological profile in posterior cortex epilepsy (PCE). In this study, we investigated the presurgical intellectual profile of children and adolescents with drug-resistant PCE. MethodsChildren and adolescents diagnosed with PCE (n = 25) participated in this study. The data were obtained from medical records, with assessments carried out between the years 2003 and 2019. To compare the intellectual profile, we also included patients diagnosed with frontal (n = 26) and temporal lobe epilepsy (n = 40). The Wechsler Intelligence Scales were used for the assessment of general intelligence. ResultsThere was an effect of the brain region on the Working Memory Index (p < 0.01), in which patients with TLE had significantly higher scores than groups with FLE (p < 0.01) and PCE (p < 0.05). We also demonstrated that patients with PCE tended to perform worse in the Processing Speed Index than patients with TLE (p = 0.055). The Full-Scale Intelligence Quotient, Verbal Comprehension, and Perceptual Reasoning indexes did not differ among the brain regions. ConclusionsChildren and adolescents with PCE demonstrated significant impairment in working memory and processing speed. The pattern of cognitive dysfunction in PCE was similar to that observed in FLE, which expands the evidence of the involvement of frontoparietal networks on cognitive proficiency.

Clinical features of automatisms and correlation with the seizure onset zones: A cluster analysis of 74 surgically-treated cases

ObjectivesTo identify semiologic features of automatisms correlating to different seizure onset zones (SOZ). MethodsIn total, 204 seizures from 74 patients with either oral or manual automatisms were assessed. Patients were divided into four groups depending on the SOZ into frontal, posterior, neocortical temporal, and mesial temporal cortex groups. A k-means analysis was applied on 11 semiologic features on a multi-criteria scale. Then, the resulting clinical patterns were correlated with the SOZs determined by presurgical anatomy-electroclinical data (25 cases with stereo-EEG). ResultsFour clinical patterns of automatisms with different accompanying symptoms were identified. The clinical features of clusters 1 and 4 were mostly found in temporal epilepsy whereas clusters 2 and 3 were more frequent in extratemporal epilepsy. Cluster 1 was significantly correlated with mesial temporal lobe epilepsy (p = .017) and was characterised by aura, postictal confusion, short automatisms delay. Cluster 3 included 1/3 patients with frontal lobe epilepsy and was characterised by emotionality. Cluster 4 was related to neocortical temporal lobe epilepsy and characterised by dystonia and short automatism delay (p = .011). ConclusionThe distinct semiologic patterns of automatisms may provide information which may allow clinicians to define the SOZs. These findings could improve diagnostic accuracy and surgical outcome.

Author Response: Clinical Reasoning: A 70-Year-Old Man With Right Arm and Leg Shaking.

We appreciate the comments from Drs. Freund and Tatum and agree that frontal lobe seizures are an important consideration.1 In small studies of patients with frontal lobe epilepsy, the ictal EEG was obscured by an artifact or seemed normal in only 1.5% of cases localized to the dorsolateral frontal cortex. However, up to 50% of cases were localized to the medial frontal cortex.2 Our patient had bilateral symptoms of increased muscle tone and an impaired ability to speak upon presentation, which persisted throughout the duration of continuous EEG.1 The EEG demonstrated generalized slowing without evidence of epileptiform abnormality. The patient gradually improved in the timeline expected after acute stroke, without intervention of antiepileptic treatment.1 For this reason and those outlined in our article, we believe stroke is the most likely cause of the patient's symptomology. The co-occurrence of acute stroke and seizure has previously been reported to be as high as 9%, with most cases representing nonconvulsive status epilepticus (NCSE), as opposed to convulsive seizures.3 Although it is unclear whether the co-occurrence of acute stroke and status epilepticus leads to higher mortality rates, there is evidence that this incidence leads to higher rates of functional disability.4 Therefore, we agree it is important to consider seizure in the differential diagnosis of a patient presenting with acute stroke, despite a negative surface EEG.

AI-Enabled Algorithm for Automatic Classification of Sleep Disorders Based on Single-Lead Electrocardiogram.

Healthy sleep is an essential physiological process for every individual to live a healthy life. Many sleep disorders both destroy the quality and decrease the duration of sleep. Thus, a convenient and accurate detection or classification method is important for screening and identifying sleep disorders. In this study, we proposed an AI-enabled algorithm for the automatic classification of sleep disorders based on a single-lead electrocardiogram (ECG). An AI-enabled algorithm—named a sleep disorder network (SDN)—was designed for automatic classification of four major sleep disorders, namely insomnia (INS), periodic leg movement (PLM), REM sleep behavior disorder (RBD), and nocturnal frontal-lobe epilepsy (NFE). The SDN was constructed using deep convolutional neural networks that can extract and analyze the complex and cyclic rhythm of sleep disorders that affect ECG patterns. The SDN consists of five layers, a 1D convolutional layer, and is optimized via dropout and batch normalization. The single-lead ECG signal was extracted from the 35 subjects with the control (CNT) and the four sleep disorder groups (seven subjects of each group) in the CAP Sleep Database. The ECG signal was pre-processed, segmented at 30 s intervals, and divided into the training, validation, and test sets consisting of 74,135, 18,534, and 23,168 segments, respectively. The constructed SDN was trained and evaluated using the CAP Sleep Database, which contains not only data on sleep disorders, but also data of the control group. The proposed SDN algorithm for the automatic classification of sleep disorders based on a single-lead ECG showed very high performances. We achieved F1 scores of 99.0%, 97.0%, 97.0%, 95.0%, and 98.0% for the CNT, INS, PLM, RBD, and NFE groups, respectively. We proposed an AI-enabled method for the automatic classification of sleep disorders based on a single-lead ECG signal. In addition, it represents the possibility of the sleep disorder classification using ECG only. The SDN can be a useful tool or an alternative screening method based on single-lead ECGs for sleep monitoring and screening.

P.095 The many clinical facets of pediatric occipital spikes and the predictive value of consistent EEG dipole

Background: Pediatric occipital epileptiform discharges (OEDS) occur in various clinical settings, including benign and symptomatic epilepsies. The study objective is to determine electro-clinical predictors for aetiology and prognosis in children with OEDs. Methods: 205 patients with OEDs were classified into seizure groups: symptomatic (n=98), idiopathic focal (IF) (n=57), idiopathic generalized (IG) (n=18), no-seizures (n=27) and febrile seizures (n=5). Results: The median age of seizure onset was 3 years (range: 0-19). There was more EEG background slowing (P&lt;0.05) in the symptomatic; photosensitivity (P&lt;0.0001) and GSW (P&lt;0.0001) in IG; and presence of consistent EEG spike dipole in IF group. The symptomatic had more DD (P&lt; 0.0001), autism (P &lt;0.019), and school difficulties (P&lt;0.001) than the IF and IG groups, but not different from the no-seizure group. Conclusions: OEDs with consistent dipole spike is predictive of IF epilepsy. In contrast to frontal and temporal lobe epilepsy, only 30% with symptomatic epilepsy had occipital-predominant neuro-imaging abnormalities. Notably, neuro-psychiatric co-morbidities were similar between the symptomatic and no-seizure group.

The search for statistical patterns of pathological activity in human EEG signals in focal epilepsy

Modern data science faces a lot of challenges, one of which is the search for diagnostic criteria for neurological diseases. New methods of statistical analysis are actively applied in the field of biophysics to solve this issue. In this paper we apply the Memory Functions Formalism to analyze electroencephalogram signal recordings in the sleeping state of 8 healthy subjects and 19 patients with nocturnal lobe epilepsy. We observe the considerable difference of statistical memory effects and fractal properties at the pathology in comparison with the control group. Furthermore, we reveal significant alterations in brain rhythms at power spectra of statistical memory functions for two groups of subjects. As a result, we show that the application of the statistical analysis methodology of bioelectrical brain cortex activity recordings, after appropriate verification, can be useful in the search for diagnostic criteria of nocturnal frontal lobe epilepsy.

A multimodal neuroimaging approach to non lesional frontal lobe epilepsy

Frontal Lobe Epilepsy (FLE) is a common form of epilepsy usually caused by structural lesions on frontal areas. Some FLE patients are defined “non-lesional” (nlFLE) because of the absence of clearly identifiable abnormalities on qualitative brain MRI. Our aim was to investigate nlFLE patients to identify possible microstructural abnormalities, through advanced neuroimaging techniques.

Structural and functional motor-network disruptions predict selective action-concept deficits: Evidence from frontal lobe epilepsy

Built on neurodegenerative lesions models, the disrupted motor grounding hypothesis (DMGH) posits that motor-system alterations selectively impair action comprehension. However, major doubts remain concerning the dissociability, neural signatures, and etiological generalizability of such deficits. Few studies have compared action-concept outcomes between disorders affecting and sparing motor circuitry, and none has examined their multimodal network predictors via data-driven approaches. Here, we first assessed action- and object-concept processing in patients with frontal lobe epilepsy (FLE), patients with posterior cortex epilepsy (PCE), and healthy controls. Then, we examined structural and functional network signatures via diffusion tensor imaging and resting-state connectivity measures. Finally, we used these measures to predict behavioral performance with an XGBoost machine learning regression algorithm. Relative to controls, FLE (but not PCE) patients exhibited selective action-concept deficits together with structural and functional abnormalities along motor networks. The XGBoost model reached a significantly large effect size only for action-concept outcomes in FLE, mainly predicted by structural (cortico-spinal tract, anterior thalamic radiation, uncinate fasciculus) and functional (M1-parietal/supramarginal connectivity) motor networks. These results extend the DMGH, suggesting that action-concept deficits are dissociable markers of frontal/motor (relative to posterior) disruptions, directly related to the structural and functional integrity of motor networks, and traceable beyond canonical movement disorders.

Seizure outcomes of large volume temporo-parieto-occipital and frontal surgery in children with drug-resistant epilepsy

ObjectiveIn this study, we investigate the seizure outcomes of temporo-parieto-occipital (TPO) and frontal disconnections or resections in children with drug-resistant epilepsy (DRE) in order to determine factors which may predict surgical results. MethodsChildren with DRE, who underwent either TPO or frontal disconnection or resection at Great Ormond Street Hospital for Children between 2000 and 2017, were identified from a prospectively collated operative database. Demographic data, age at surgery, type of surgery, scalp EEGs and operative histopathology were collected. Magnetic resonance imaging (MRI) was assessed to determine completeness of disconnection and presence of radiological lesion beyond the disconnection margins. Seizure outcome at 6, 12, and 24 months post-surgery was assessed using the Engel Scale (ES). Logistic regression was used to identify relationships between data variables and seizure outcome. Results46 children (males = 28, females = 18; age range 0.5–16.6 years) who underwent TPO (n = 32, including a re-do disconnection) or frontal disconnection or resection (n = 15) were identified. Patients in the TPO treatment group had more favourable seizure outcomes than those in the frontal treatment group (ES I–II in 56 %vs 47 % at 6 months, 52 % vs 46 % at 12 months). Presence of the lesion beyond disconnection boundaries and older age at the time of surgery were associated with poorer seizure outcome. Gender, surgery type, completeness of disconnection, scalp EEG findings and underlying pathology were not related to seizure outcome, but subgroup numbers were small. ConclusionsBoth TPO and frontal disconnection are effective treatments for selected children with posterior multi-lobar or diffuse frontal lobe epilepsy. Confinement of the MRI lesion within the disconnection margins and a younger age at surgery are associated with favourable seizure outcomes. Further studies are required to elucidate these findings.

Decreased fetal heart rate variability and maternal combined dose of sodium valproate and phenytoin.

A 32-year-old Japanese woman was admitted at 38 weeks' gestation with regular uterine contractions. She was diagnosed with frontal lobe epilepsy at the age of 17, and she had been treated with several anti-epileptic drugs (AEDs) since then but complied poorly due mainly to nausea and frequent seizure attacks.

Neuropsychological outcomes after frontal lobectomy to treat intractable epilepsy

ObjectiveFrontal lobectomy is often used as a surgical treatment for frontal lobe epilepsy, especially when a large epileptogenic zone in the frontal lobe is inferred from preoperative evaluation. The frontal lobe is important for cognitive functions such as executive functions and verbal fluency, but the neuropsychological outcome after a frontal or prefrontal lobectomy that includes both the dorsolateral prefrontal cortex and ventral prefrontal cortex has not been studied thoroughly. In the present study, we evaluated neuropsychological outcomes after patients with frontal lobe epilepsy received a frontal or prefrontal lobectomy. MethodsWe retrospectively reviewed the data of patients with frontal lobe epilepsy who underwent a frontal or prefrontal lobectomy that includes both the dorsolateral prefrontal cortex and ventral prefrontal cortex at 16 years or older from October 2004 to December 2014, with a minimum postoperative follow-up of 24 months. We analyzed and compared neuropsychological outcomes, including executive functions, verbal fluency, intelligence, and memory, before and after the operation. ResultsEighteen patients were 16 years or older and underwent pre- and postoperative (2 years after the operation) neuropsychological evaluations. Patients showed significant deterioration only on the Benton Visual Retention Test. Performance on tests of frontal lobe functions, such as executive function and verbal fluency, showed no significant deterioration. ConclusionsOverall cognitive performance, including functions widely thought to depend on the frontal lobe, is stable after a frontal or prefrontal lobectomy to treat frontal lobe epilepsy.

Arterial spin labeling for presurgical localization of refractory frontal lobe epilepsy in children

BackgroundEpilepsy is one of the most common chronic neurological diseases. Despite the great variety and prevalence of antiepileptic drug treatments, one-third of epilepsies remain drug resistant. The frontal lobe is extensive, and frontal lobe seizures are difficult to locate, which increases the difficulty of the preoperative localization of the epileptogenic zone.Case presentationTwo previously healthy girls with refractory frontal lobe epilepsy showed significant perfusion abnormalities in the right frontal lobe using the cerebral blood perfusion (CBF) quantitative analysis system. They became seizure-free after lesionectomy of the frontal lobe by ASL combined with electroencephalography (EEG) rapid localization. The histopathological diagnosis was focal cortical dysplasia (FCD) type IIa and IIb.ConclusionsThe positive outcome suggests that the combined use of ASL with EEG could be a beneficial option for the presurgical evaluation of pediatric epilepsy.

Peripheric smell regions in patients with temporal and frontal lobe epilepsies: An MRI evaluation

ObjectivesWe investigated peripheric smell regions of olfactory bulb (OB) volume and olfactory sulcus (OS) depth in temporal and frontal lobe epilepsy patients by cranial magnetic resonance imaging (MRI). MethodsIn this retrospective study, cranial MRI images of 150 adult patients were included. Group 1 was consisted of 50 adult patients with temporal lobe epilepsy (TLE). Group 2 was consisted of 50 adult patients with frontal lobe epilepsy (FLE). The control group (Group 3) was consisted of 50 healthy subjects without epilepsy. OB volume and OS depth were measured in all groups. ResultsOB volumes of the temporal and frontal epilepsy groups were significantly lower than those of the control group (padjusted < 0.0175). However, OS depths were not different between groups 1–3 (p > 0.05). In the temporal and frontal epilepsy groups, there were positive correlations between OB volumes; OS depths; left OB volume and bilateral OS depths p < 0.05). There were no significant correlations between OB volume and OS depth; and age and gender of the epilepsy group (p > 0.05). ConclusionWe concluded that temporal and frontal epilepsy maybe related to decrease in OB volume and may cause olfactory impairment. Olfactory deficit maybe related to central epileptic focus. Therefore, early diagnosis and appropriate treatment of epilepsy are important to prevent olfactory impairment.

Automated Characterization of Cyclic Alternating Pattern Using Wavelet-Based Features and Ensemble Learning Techniques with EEG Signals.

Sleep is highly essential for maintaining metabolism of the body and mental balance for increased productivity and concentration. Often, sleep is analyzed using macrostructure sleep stages which alone cannot provide information about the functional structure and stability of sleep. The cyclic alternating pattern (CAP) is a physiological recurring electroencephalogram (EEG) activity occurring in the brain during sleep and captures microstructure of the sleep and can be used to identify sleep instability. The CAP can also be associated with various sleep-related pathologies, and can be useful in identifying various sleep disorders. Conventionally, sleep is analyzed using polysomnogram (PSG) in various sleep laboratories by trained physicians and medical practitioners. However, PSG-based manual sleep analysis by trained medical practitioners is onerous, tedious and unfavourable for patients. Hence, a computerized, simple and patient convenient system is highly desirable for monitoring and analysis of sleep. In this study, we have proposed a system for automated identification of CAP phase-A and phase-B. To accomplish the task, we have utilized the openly accessible CAP sleep database. The study is performed using two single-channel EEG modalities and their combination. The model is developed using EEG signals of healthy subjects as well as patients suffering from six different sleep disorders namely nocturnal frontal lobe epilepsy (NFLE), sleep-disordered breathing (SDB), narcolepsy, periodic leg movement disorder (PLM), insomnia and rapid eye movement behavior disorder (RBD) subjects. An optimal orthogonal wavelet filter bank is used to perform the wavelet decomposition and subsequently, entropy and Hjorth parameters are extracted from the decomposed coefficients. The extracted features have been applied to different machine learning algorithms. The best performance is obtained using ensemble of bagged tress (EBagT) classifier. The proposed method has obtained the average classification accuracy of 84%, 83%, 81%, 78%, 77%, 76% and 72% for NFLE, healthy, SDB, narcolepsy, PLM, insomnia and RBD subjects, respectively in discriminating phases A and B using a balanced database. Our developed model yielded an average accuracy of 78% when all 77 subjects including healthy and sleep disordered patients are considered. Our proposed system can assist the sleep specialists in an automated and efficient analysis of sleep using sleep microstructure.

Brain structural differences in temporal lobe and frontal lobe epilepsy patients: A voxel-based morphometry and vertex-based surface analysis.

Exploration of the effect of chronic recurrent seizures in focal epilepsy on brain volumes has produced many conflicting reports. To determine differences in brain structure in temporal lobe epilepsy (TLE) and extratemporal epilepsy (using frontal lobe epilepsy (FLE) a surrogate) further, we performed a retrospective analysis of a large cohort of patients with seizure-onset zone proven by intracranial monitoring. A total of 120 TLE patients, 86 FLE patients, and 54 healthy controls were enrolled in this study. An analysis of variance of voxel-based morphometry (VBM) was used to seek morphometric brain differences among TLE patients, FLE patients, and healthy controls. Additionally, a vertex-based surface analysis was utilized to analyze the hippocampus and thalamus. Significant side-specific differences in hippocampal gray matter volume were present between the left TLE (LTLE), right TLE RTLE (RTLE), and control groups (p<0.05, family-wise error (FWE) corrected). Vertex analyses revealed significant volume reduction in inferior parts of the left hippocampus in the LTLE group and lateral parts of the right hippocampus in the RTLE group compared to controls (p<0.05, FWE corrected). Significant differences were also detected between the LTLE and control group in the bilateral medial and inferior thalamus (p<0.05, FWE corrected). FLE patients did not exhibit focal atrophy of gray matter across the brain. Our results highlight the variation in morphometric lateralized changes in the brain between different epilepsy onset zones, providing critical insight into the natural history of people with drug-resistant focal epilepsies.

Nicotine Patch Improved Autosomal Dominant Sleep-Related Hypermotor Epilepsy

Autosomal dominant sleep-related hypermotor epilepsy (ADSHE) is a genetic focal epilepsy associated with mutations in the nicotinic acetylcholine receptor. Mutations in two major nicotinic acetylcholine receptor subunit genes, CHRNA4 and CHRNB2, are commonly associated with ADSHE.1Saberi B. Tehrani B.S. Pore-forming M2 domains of CHRNA4 and CHRNB2 mutations in autosomal dominant nocturnal frontal lobe epilepsy (ADNFLE) phenotype.Am J Biomed Sci Res. 2019; 1: 222-223Crossref Google Scholar In up to one-third of patients with ADSHE, seizures are refractory to antiseizure medications. Here, we report complete seizure freedom with transdermal nicotine in two pediatric patients from the same family with medically refractory ADSHE. Both patients, who are siblings, presented with exclusively nocturnal focal seizures in childhood, at ages seven years (Patient 1) and 4.5 years (Patient 2). Elelctroencephalographies (EEGs) demonstrated focal electroclinical seizures with hypermotor features and interictal epileptiform discharges in sleep (Fig A and B). Genetic testing revealed a heterozygous missense mutation in CHRNA4 (c.851C>T P.Ser284Leu), which has been described as a common pathogenic gain-of-function mutation in ADSHE. Patient 2 was also diagnosed with an autism spectrum disorder. Both patients had a high nightly seizure burden with minimal improvement on numerous antiseizure medications. A trial of low-dose (7 mg/24 hour) nicotine patch was initiated at ages 16 years (Patient 1) and 14 years (Patient 2). Clinical seizures stopped in both the first night the patch was applied. Follow-up overnight EEGs confirmed seizure freedom and showed normalization or near-normalization of their interictal EEGs (Fig C). Both patients have experienced sustained seizure freedom for over 18 months with nicotine patch 7 mg/24 hour in addition to oxcarbazepine. Transdermal nicotine has been reported to provide marked seizure improvement in a subset of patients with ADSHE.2Fox J. Thodeson D.M. Dolce A.M. Nicotine: a targeted therapy for epilepsy due to nAChR gene variants.J Child Neurol. 2020; 36: 371-377Crossref PubMed Scopus (3) Google Scholar,3Lossius K. de Saint Martin A. Myren-Svelstad S. et al.Remarkable effect of transdermal nicotine in children with CHRNA4-related autosomal dominant sleep-related hypermotor epilepsy.Epilepsy Behav. 2020; 105: 106944Abstract Full Text Full Text PDF PubMed Scopus (11) Google Scholar The siblings reported here have the same CHRNA4 mutation as a 19-year-old patient who had considerable seizure improvement, although not complete resolution, with transdermal nicotine at a higher dose of 14 mg/24 hour.2Fox J. Thodeson D.M. Dolce A.M. Nicotine: a targeted therapy for epilepsy due to nAChR gene variants.J Child Neurol. 2020; 36: 371-377Crossref PubMed Scopus (3) Google Scholar Lossius et al. described striking improvement in seizures and neuropsychological test performance in three six- to 10-year-old children with a different CHRNA4 mutation (c.839C>T P.Ser280Phe) who were treated with 3.5 to 7 mg/24 hour nicotine.3Lossius K. de Saint Martin A. Myren-Svelstad S. et al.Remarkable effect of transdermal nicotine in children with CHRNA4-related autosomal dominant sleep-related hypermotor epilepsy.Epilepsy Behav. 2020; 105: 106944Abstract Full Text Full Text PDF PubMed Scopus (11) Google Scholar With different patient characteristics, age as well as specific mutation might contribute to the efficacy of nicotine patches in controlling seizure activity. Christensen et al. discussed how nicotine increases neuronal firing of cholinergic cells in younger animals, which in turn affects the action potential shape and afterhyperpolarization.4Christensen M.H. Ishibashi M. Nielsen M.L. Leonard C.S. Kohlmeier K.A. Age-related changes in nicotine response of cholinergic and non-cholinergic laterodorsal tegmental neurons: implications for the heightened adolescent susceptibility to nicotine addiction.Neuropharmacology. 2014; 85: 263-283Crossref PubMed Scopus (15) Google Scholar Nicotine has been found to cause age-dependent changes in gene expression in adolescent female rat brains.5Polesskaya O.O. Fryxell K.J. Merchant A.D. et al.Nicotine causes age-dependent changes in gene expression in the adolescent female rat brain.Neurotoxicol Teratol. 2007; 29: 126-140Crossref PubMed Scopus (33) Google Scholar In adolescence, GABAergic signaling in the prefrontal cortex, an important brain region associated with ADSHE, is still developing.6Caballero A. Tseng K.Y. GABAergic function as a limiting factor for prefrontal maturation during adolescence.Trends Neurosci. 2016; 39: 441-448Abstract Full Text Full Text PDF PubMed Scopus (74) Google Scholar Therefore, age might be an important factor when determining whether nicotine patch treatment may be beneficial for patients with ADSHE, with regard to both seizure improvement and neuropsychologic outcomes. A limitation of our report is that our patients are from the same family and therefore may possess unique characteristics that influenced their dramatic response to transdermal nicotine. This possibility might prevent generalization of our results to all patients with same mutation in CHRNA4. Nevertheless, the two siblings reported here add to the small number of pediatric reports documenting the successful use of nicotine patches in ADSHE. Future research is needed to establish whether the nicotine patch could become an adjunct or first-line treatment in a subset of pediatric patients with ADSHE. The authors wish to thank all the patients, family members, and staff members who participated in the study.

SY6.5. Temporal plus epilepsies

Temporal plus epilepsies are characterized by the epileptogenic foci outside the temporal lobe and a mix of temporal and extratemporal features. They have a wide spectrum of semiological presentation depending on the extent of network. They can arise from frontal, parietal, occipital lobes along with temporal lobe networks. We discuss in this review the semiology of different types. Lateralizing and localizing features are mentioned in the table below the review is not exhaustive and it is recommended to read large descriptive semiology’s in journals. Temporal plus epilepsies will have red flags for temporal lobe in that there will be extratemporal auras or signs. These would suggest wider networks more than temporal. So if a typical temporal lobe seizure has amnesia, arrest, lack of awareness, aphasia, etc. Presence of a visual aura would favor a temporal plus network. CL = Contralateral, IL = ipsilateral, NonLAT = Nonlateralizing, FLE = Frontal lobe epilepsy, PLE = Parietal lobe epilepsy, OLE = Occipital lobe epilepsy, SSMA = Supplementary sensorimotor area, IPSI = Ipsilateral.