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Remote clinical learning (RCL) may result in learner disengagement. The factors that influence medical student motivation during RCL remain poorly understood. The authors aimed to explore factors that affect medical student motivation during RCL and determine potential strategies to optimize student motivation during RCL.In December 2020, the authors conducted semistructured interviews with third- and fourth-year medical students at the University of California, San Francisco, who had experienced RCL. The authors coded transcripts and conducted an inductive thematic analysis using self-determination theory (SDT), which describes autonomy, competence, and relatedness as essential for motivation, as a sensitizing framework.Twelve students were interviewed. Four themes were identified and aligned with SDT: balancing flexibility and structure (autonomy), selecting appropriate resources (competence), setting reasonable expectations (competence), and building and maintaining community (relatedness). Students described a sense of tension between desiring flexibility and appreciating structure and accountability during RCL; a preference for high-yield, curated resources presented in an organized format during RCL; instances in which the remote curriculum fell short of their expectations or professional goals or in which they felt they had missed out on key clinical learning; and support sought from peers, mentors, and instructors during RCL, as well as the contribution of remote learning technology to a sense of community.The authors propose 4 guiding principles to address implementation of remote clinical curricula: provide students with choice within the bounds of a well-defined curriculum, curate and organize learning materials carefully and intentionally, orient students to the goals and objectives of the curriculum and discuss students' expectations for professional development, and incorporate structured opportunities for remote mentorship and peer-peer interaction and optimize these opportunities using technology. Educators can draw on the themes, guiding principles, and potential strategies identified to promote and maintain learner motivation during RCL.

PURPOSE Up to half of the children undergoing epilepsy surgery will continue to have seizures (szs) despite a cortical resection or ablation. Functional connectivity has shown promise in better identifying the epileptogenic zone. We hypothesized that cortical areas showing high information outflow during interictal epileptiform discharges are part of the epileptogenic zone. METHODS We identified 22 children with focal epilepsy who had undergone stereo electroencephalography, surgical resection or ablation, and had ≥1 year of postsurgical follow-up. The mean phase slope index, a directed measure of functional connectivity, was calculated for each electrode contact during interictal epileptiform discharges. The positive predictive value and negative predictive value for a sz-free outcome were calculated based on whether high information outflow brain regions were resected. RESULTS Resection of high outflow (z-score ≥ 1) and very high outflow (z-score ≥ 2) electrode contacts was associated with higher sz freedom (high outflow: χ2 statistic = 59.1; P < 0.001; very high outflow: χ2 statistic = 31.3; P < 0.001). The positive predictive value and negative predictive value for sz freedom based on resection at the electrode level increased at higher z-score thresholds with a peak positive predictive value of 0.86 and a peak negative predictive value of 0.9. CONCLUSIONS Better identification of the epileptogenic zone has the potential to improve epilepsy surgery outcomes. If the surgical plan can be modified to include these very high outflow areas, more children might achieve sz freedom. Conversely, if deficits from resecting these areas are unacceptable, ineffective surgeries could be avoided and alternative therapies offered.

Alzheimer’s disease (AD) is characterized by accumulation of amyloid-βand misfolded tau proteins causing synaptic dysfunction and progressive neurodegeneration and cognitive decline. Altered neural oscillations have been consistently demonstrated in AD. However, the trajectories of abnormal neural oscillations in AD progression and their relation to neurodegeneration and cognitive decline are unknown. We deployed robust event-based sequencing models (EBM) to investigate trajectories of long-range and local neural synchrony across AD stages, estimated from resting-state magnetoencephalography. Synchrony increases in delta-theta and decreases in alpha and beta bands showed progressive changes along the EBM stages of AD. Decreases in alpha and beta synchrony preceded both neurodegeneration and cognitive decline, indicating that frequency-specific neuronal synchrony abnormalities are early manifestations of AD pathophysiology. Long-range synchrony effects were larger than local synchrony, indicating greater sensitivity of connectivity metrics involving multiple brain regions. These results demonstrate the evolution of functional neuronal deficits along the sequence of AD progression.

Investigation of associated risk factors of valproic acid (VPA)–induced tremor helped in increasing tolerance and optimizing treatment scheme individually. To determine the risk factors of VPA-induced tremor, with particular attention on identifying tremor-susceptible gene mutations. Epileptic patients taking VPA were divided into a tremor and a non-tremor groups. A mutation of rs9652490 in the leucine-rich repeat and immunoglobulin domain-containing Nogo-receptor-interacting protein 1 (LINGO-1) gene was determined by Sanger sequencing. Cerebellar atrophy was assessed, and various cerebellar dimensions were measured on magnetic resonance imaging (MRI) scans. One hundred and eighty-one of 200 subjects were included. Multivariate regression analysis indicated several VPA-induced tremor-related factors: females (OR = 2.718, p = 0.014), family history of tremor (OR = 7.595, p = 0.003), treatment duration (> 24 months; OR = 3.294, p = 0.002), and daily dosage (> 1,000 mg/d; OR = 19.801, p = 0.008) of VPA. Chi-square tests revealed that treatment with VPA magnesium-ER (p = 0.030) and carbamazepine combination (p = 0.040) reduced the incidence of tremor. One hundred and seventy-six gene sequencing and 86 MRI results excluded any significant difference between the two groups in the mutation of rs9652490 within LINGO-1, the ratio of cerebellar atrophy or the cerebellar-dimension values (p > 0.05). However, mutation of rs9652490 within LINGO-1 was correlated with increased cerebellar atrophy (p = 0.001), reduced cerebellar hemisphere thickness (p = 0.025), and right cerebellar hemisphere longitudinal diameter (p = 0.047). Our cohort indicated risk (female, positive family history of tremor, daily dosage > 1000 mg and treatment duration > 24 months of VPA) and protective factors (VPA magnesium-ER and combination with CBZ) of VPA-induced tremor. Mutation of rs9652490 within LINGO-1 correlated with cerebellar atrophy, neither was correlated with VPA-induced tremor.

Background Epileptic seizures can be difficult to distinguish from other etiologies that cause cerebral hypoxia, especially cardiac diseases. Long QT syndrome (LQTS), especially LQTS type 2 (LQT2), frequently masquerades as seizures because of the transient cerebral hypoxia caused by ventricular arrhythmia. The high rate of sudden death in LQTS highlights the importance of accurate and early diagnosis; correct diagnosis of LQTS also prevents inappropriate treatment with anti-epileptic drugs (AEDs). Case presentation We report a case of congenital LQT2 with potassium voltage-gated channel subfamily H member 2 gene (KCNH2) mutation misdiagnosed as refractory epilepsy and treated with various AEDs for 22 years. The possibility of cardiac arrhythmia was suspected after the patient presented to the emergency room and the electrocardiograph (ECG) monitor showed paroxysmal ventricular tachycardia during attacks. Atypical seizure like attacks with prodromal uncomfortable chest sensation and palpitation, triggered by auditory stimulation, and typical ventricular tachycardia monitored by ECG raised suspicion for LQT2, which was confirmed by exome sequencing and epileptic seizure was ruled out by 24-h EEG monitoring. Although the patient rejected implantation of an implantable cardioverter defibrillator, β blocker was given and the syncope only attacked 1–2 per year when there was an incentive during the 5 years follow up. Conclusions Our case illustrates how long LQTS can masquerade convincingly as epilepsy and can be treated wrongly with AEDs, putting the patient at high risk of sudden cardiac death. Careful ECG evaluation is recommend for both patients with first seizure and those with refractory epilepsy.

PCDH19 is a nonclustered protocadherin molecule involved in axon bundling, synapse function, and transcriptional coregulation. Pathogenic variants in PCDH19 cause infantile-onset epilepsy known as PCDH19-clustering epilepsy or PCDH19-CE. Recent advances in DNA-sequencing technologies have led to a significant increase in the number of reported PCDH19-CE variants, many of uncertain significance. We aimed to determine the best approaches for assessing the disease relevance of missense variants in PCDH19. The application of the American College of Medical Genetics and Association for Molecular Pathology (ACMG-AMP) guidelines was only 50% accurate. Using a training set of 322 known benign or pathogenic missense variants, we identified MutPred2, MutationAssessor, and GPP as the best performing in silico tools. We generated a protein structural model of the extracellular domain and assessed 24 missense variants. We also assessed 24 variants using an in vitro reporter assay. A combination of these tools was 93% accurate in assessing known pathogenic and benign PCDH19 variants. We increased the accuracy of the ACMG-AMP classification of 45 PCDH19 variants from 50% to 94%, using these tools. In summary, we have developed a robust toolbox for the assessment of PCDH19 variant pathogenicity to improve the accuracy of PCDH19-CE variant classification. Refereed/Peer-reviewed

Responsive neurostimulation is a promising treatment for drug-resistant focal epilepsy; however, clinical outcomes are highly variable across individuals. The therapeutic mechanism of responsive neurostimulation likely involves modulatory effects on brain networks; however, with no known biomarkers that predict clinical response, patient selection remains empiric. This study aimed to determine whether functional brain connectivity measured non-invasively prior to device implantation predicts clinical response to responsive neurostimulation therapy. Resting-state magnetoencephalography was obtained in 31 participants with subsequent responsive neurostimulation device implantation between 15 August 2014 and 1 October 2020. Functional connectivity was computed across multiple spatial scales (global, hemispheric, and lobar) using pre-implantation magnetoencephalography and normalized to maps of healthy controls. Normalized functional connectivity was investigated as a predictor of clinical response, defined as percent change in self-reported seizure frequency in the most recent year of clinic visits relative to pre-responsive neurostimulation baseline. Area under the receiver operating characteristic curve quantified the performance of functional connectivity in predicting responders (≥50% reduction in seizure frequency) and non-responders (

Unfamiliarity with the indications for and benefits of magnetoencephalography (MEG) persists, even in the epilepsy community, and hinders its acceptance to clinical practice, despite the evidence. The wide treatment gap for patients with drug-resistant epilepsy and immense underutilization of epilepsy surgery had similar effects. Thus, educating referring physicians (epileptologists, neurologists, and neurosurgeons) both about the value of epilepsy surgery and about the potential benefits of MEG can achieve synergy and greatly improve the process of selecting surgical candidates. As a practical step toward a comprehensive educational process to benefit potential MEG users, current MEG referrers, and newcomers to MEG, the authors have elected to provide an illustrated guide to 10 everyday situations where MEG can help in the evaluation of people with drug-resistant epilepsy. They are as follows: (1) lacking or imprecise hypothesis regarding a seizure onset; (2) negative MRI with a mesial temporal onset suspected; (3) multiple lesions on MRI; (4) large lesion on MRI; (5) diagnostic or therapeutic reoperation; (6) ambiguous EEG findings suggestive of "bilateral" or "generalized" pattern; (7) intrasylvian onset suspected; (8) interhemispheric onset suspected; (9) insular onset suspected; and (10) negative (i.e., spikeless) EEG. Only their practical implementation and furtherance of personal and collective education will lead to the potentially impactful synergy of the two-MEG and epilepsy surgery. Thus, while fulfilling our mission as physicians, we must not forget that ignoring the wealth of evidence about the vast underutilization of epilepsy surgery - and about the usefulness and value of MEG in selecting surgical candidates - is far from benign neglect.

Repeated application of foundational science (FS) during medical reasoning results in encapsulation of knowledge needed to develop clinical expertise. Despite proven benefit of educating learners using a FS framework to anchor clinical decision making, how FS is integrated on clinical rotations has not been well characterized. This study examines how and when FS discussion occurs on internal medicine teaching rounds. We performed a convergent mixed method study. Six internal medicine teams at a quaternary hospital were observed during rounds and team members interviewed. Transcripts were analyzed using thematic analysis. Descriptive statistics provided a summary of the observations. Our study revealed that rounds used a teacher-centered model where FS knowledge was transmitted as pearls external to the clinical context. FS content arose primarily when the patient was complex. Barriers preventing FS discussion were lack of time and perceived lack of personal FS knowledge. Our study describes scenarios that commonly elicit discussion of FS on inpatient medicine rounds highlighting a ‘transmission’ model of FS knowledge. We suggest a learner-centered model that engages students in the practice of integrating FS into clinical reasoning.

Since the first demonstrations of network hyperexcitability in scientific models of Alzheimer’s disease, a growing body of clinical studies have identified subclinical epileptiform activity and associated cognitive decline in patients with Alzheimer’s disease. An obvious problem presented in these studies is lack of sensitive measures to detect and quantify network hyperexcitability in human subjects. In this study we examined whether altered neuronal synchrony can be a surrogate marker to quantify network hyperexcitability in patients with Alzheimer’s disease. Using magnetoencephalography (MEG) at rest, we studied 30 Alzheimer’s disease patients without subclinical epileptiform activity, 20 Alzheimer’s disease patients with subclinical epileptiform activity and 35 age-matched controls. Presence of subclinical epileptiform activity was assessed in patients with Alzheimer’s disease by long-term video-EEG and a 1-h resting MEG with simultaneous EEG. Using the resting-state source-space reconstructed MEG signal, in patients and controls we computed the global imaginary coherence in alpha (8–12 Hz) and delta–theta (2–8 Hz) oscillatory frequencies. We found that Alzheimer’s disease patients with subclinical epileptiform activity have greater reductions in alpha imaginary coherence and greater enhancements in delta–theta imaginary coherence than Alzheimer’s disease patients without subclinical epileptiform activity, and that these changes can distinguish between Alzheimer’s disease patients with subclinical epileptiform activity and Alzheimer’s disease patients without subclinical epileptiform activity with high accuracy. Finally, a principal component regression analysis showed that the variance of frequency-specific neuronal synchrony predicts longitudinal changes in Mini-Mental State Examination in patients and controls. Our results demonstrate that quantitative neurophysiological measures are sensitive biomarkers of network hyperexcitability and can be used to improve diagnosis and to select appropriate patients for the right therapy in the next-generation clinical trials. The current results provide an integrative framework for investigating network hyperexcitability and network dysfunction together with cognitive and clinical correlates in patients with Alzheimer’s disease.

Background/Introduction: Widespread network disruption has been hypothesized to be an important predictor of outcomes in patients with refractory temporal lobe epilepsy (TLE). Most studies e...

OBJECTIVE: Classification of epilepsy into types and subtypes is important for both clinical care and research into underlying disease mechanisms. A quantitative, data-driven approach may augment traditional electroclinical classification and shed new light on existing classification frameworks. METHODS: We used latent class analysis, a statistical method that assigns subjects into groups called latent classes based on phenotypic elements, to classify individuals with common familial epilepsies from the Epi4K Multiplex Families study. Phenotypic elements included seizure types, seizure symptoms, and other elements of the medical history. We compared class assignments to traditional electroclinical classifications and assessed familial aggregation of latent classes. RESULTS: A total of 1120 subjects with epilepsy were assigned to five latent classes. Classes 1 and 2 contained subjects with generalized epilepsy, largely reflecting the distinction between absence epilepsies and younger onset (class 1) versus myoclonic epilepsies and older onset (class 2). Classes 3 and 4 contained subjects with focal epilepsies, and in contrast to classes 1 and 2, these did not adhere as closely to clinically defined focal epilepsy subtypes. Class 5 contained nearly all subjects with febrile seizures plus or unknown epilepsy type, as well as a few subjects with generalized epilepsy and a few with focal epilepsy. Family concordance of latent classes was similar to or greater than concordance of clinically defined epilepsy types. SIGNIFICANCE: Quantitative classification of epilepsy has the potential to augment traditional electroclinical classification by (1) combining some syndromes into a single class, (2) splitting some syndromes into different classes, (3) helping to classify subjects who could not be classified clinically, and (4) defining the boundaries of clinically defined classifications. This approach can guide future research, including molecular genetic studies, by identifying homogeneous sets of individuals that may share underlying disease mechanisms.

Predictive coding (PC) has been suggested as one of the main mechanisms used by brains to interact with complex environments. PC theories posit top-down prediction signals, which are compared with actual outcomes, yielding in turn prediction-error signals, which are used, bottom-up, to modify the ensuing predictions. However, disentangling prediction from prediction-error signals has been challenging. Critically, while many studies found indirect evidence for predictive coding in the form of prediction-error signals, direct evidence for the prediction signal is mostly lacking. Here we provide clear evidence, obtained from intracranial cortical recordings in human surgical patients, that the human lateral prefrontal cortex generates prediction signals while anticipating an event. Patients listened to task-irrelevant sequences of repetitive tones including infrequent predictable or unpredictable pitch deviants. The amplitude of high frequency broadband (HFB) neural activity was decreased prior to the onset of expected relative to unexpected deviants in the frontal cortex only, and its amplitude was sensitive to the increasing likelihood of deviants following longer trains of standards in the unpredictable condition. Single trial HFB amplitudes predicted deviations and correlated with post-stimulus response to deviations. These results provide direct evidence for frontal cortex prediction signals independent of prediction-error signals.

Magnetoencephalography (MEG) is a robust method for non-invasive functional brain mapping of sensory cortices due to its exceptional spatial and temporal resolution. The clinical standard for MEG source localization of functional landmarks from sensory evoked responses is the equivalent current dipole (ECD) localization algorithm, known to be sensitive to initialization, noise, and manual choice of the number of dipoles. Recently many automated and robust algorithms have been developed, including the Champagne algorithm, an empirical Bayesian algorithm, with powerful abilities for MEG source reconstruction and time course estimation (Wipf et al. 2010; Owen et al. 2012). Here, we evaluate automated Champagne performance in a clinical population of tumor patients where there was minimal failure in localizing sensory evoked responses using the clinical standard, ECD localization algorithm. MEG data of auditory evoked potentials and somatosensory evoked potentials from 21 brain tumor patients were analyzed using Champagne, and these results were compared with equivalent current dipole (ECD) fit. Across both somatosensory and auditory evoked field localization, we found there was a strong agreement between Champagne and ECD localizations in all cases. Given resolution of 8mm voxel size, peak source localizations from Champagne were below 10mm of ECD peak source localization. The Champagne algorithm provides a robust and automated alternative to manual ECD fits for clinical localization of sensory evoked potentials and can contribute to improved clinical MEG data processing workflows.

Epileptic seizures can be difficult to distinguish from other etiologies that cause cerebral hypoxia, especially cardiac diseases. Long QT syndrome (LQTS), especially LQTS type 2 (LQT2), frequently masquerades as seizures because of the transient hypoxia caused by ventricular arrhythmia. Early and correct diagnosis of LQTS effectively prevents inappropriate treatment with anti-epileptic drugs (AEDs) and sudden death. We report a case of congenital LQT2 with potassium voltage-gated channel subfamily H member 2 gene (KCNH2) mutation misdiagnosed as refractory epilepsy and treated with various AEDs for 22 years. The possibility of cardiac arrhythmia was suspected after the electrocardiograph (ECG) monitor in emergency room showed paroxysmal ventricular tachycardia during attacks. Atypical seizure like attacks with prodromal uncomfortable chest sensation and palpitation, triggered by auditory stimulation, and typical ventricular tachycardia monitored by ECG raised suspicion for LQT2, which was confirmed by exome sequencing. Although the patient rejected implantation of an implantable cardioverter defibrillator, β-blocker was given and the syncope only attacked 1-2 per year when there was an incentive. Our case illustrates how long LQTS can masquerade convincingly as epilepsy and be treated wrongly with AEDs, putting the patient at high risk of sudden death. Careful ECG evaluation is recommend for both patients with first seizure and those with refractory epilepsy.

Because hyper-excitability has been shown to be a shared pathophysiological mechanism, we used the latest and largest genome-wide studies in amyotrophic lateral sclerosis (n = 36,052) and epilepsy (n = 38,349) to determine genetic overlap between these conditions. First, we showed no significant genetic correlation, also when binned on minor allele frequency. Second, we confirmed the absence of polygenic overlap using genomic risk score analysis. Finally, we did not identify pleiotropic variants in meta-analyses of the 2 diseases. Our findings indicate that amyotrophic lateral sclerosis and epilepsy do not share common genetic risk, showing that hyper-excitability in both disorders has distinct origins. ispartof: NEUROBIOLOGY OF AGING vol:92 ispartof: location:United States status: published

Functional connectivity is providing new insights into the network nature of epilepsy with growing clinical applications. Our objective was to validate a novel magnetoencephalography-based method to non-invasively measure the epileptic network. We retrospectively identified pediatric and adult patients with refractory focal epilepsy who underwent pre-surgical magnetoencephalography with subsequent intracranial electrographic monitoring. Magnetoencephalography tracings were visually reviewed, and interictal epileptiform discharges ("spikes") were individually marked. We then evaluated differences in whole-brain connectivity during brief epochs preceding the spikes and during the spikes using the Network-Based Statistic to test differences at the network level. In six patients with statistically-significant network differences, we observed substantial overlap between the spike-associated networks and electrographically active areas identified during intracranial monitoring (the spike-associated network was 78% and 83% sensitive for intracranial electroencephalography-defined regions in the irritative and seizure onset zones, respectively). These findings support the neurobiological validity of the spike-associated network method. Assessment of spike-associated networks has the potential to improve surgical planning in epilepsy surgery patients by identifying components of the epileptic network prior to implantation.

Background Leucine-rich glioma-inactivated 1 (LGI1) encephalitis is clinically heterogeneous, especially at presentation, and though it is sometimes found in association with tumor, this is by no means the rule. The aim of this study is to report a case of teratoma-associated LGI1 encephalitis, to summarize a retrospective series of LGI1 encephalitis and compare them. Methods Clinical data for 10 people with LGI1 encephalitis were collected. Patients were divided into those with and without associated teratoma, and compared for clinical characteristics. Microscopic pathological examination and immunohistochemical (IHC) assay of the LGI1 antibody were performed on teratoma tissue obtained by laparoscopic oophorocystectomy. Results The LGl1 patient with teratoma was similar to the non-teratoma (NT) group in many ways: age at onset (average 47.3 in NT group); percent presenting with rapidly progressive dementia (RPD) (67% of NT group) and psychiatric symptoms (33%); hyponatremia (78%); normal cerebrospinal fluid results except for positive LGI1 antibody (78%); bilateral hippocampal hyperintensity on magnetic resonance imaging (MRI) (44%); diffuse slow waves on electroencephalography (EEG) (33%); good response to immunotherapy (67%); and mild residual cognitive deficit (22%). Her chronic anxiety and presentation with status epilepticus were the biggest differences compared with the NT group. Teratoma pathology was characterized by mostly thyroid tissue and IHC assay confirmed positive LGI1 expression. Conclusion In our series, LGI1 encephalitis included common clinical features: RPD, faciobrachial dystonic seizures, behavioral disorders, hyponatremia, T2-MRI hyperintensity of hippocampus and residual cognitive deficit. We observed some differences in our case with teratoma, but a larger accumulation of cases is needed to improve our knowledge base.

Introduction Sleep is a highly stereotyped phenomenon that is ubiquitous across species. Although behaviorally appearing as a homogeneous process, sleep has been recognized as cortically heterogenous and locally dynamic. PET/fMRI studies have provided key insights into regional activation and deactivation with sleep onset, but they lack the high temporal resolution and electrophysiology for understanding neural interactions. Using simultaneous electrocorticography (EEG) and magnetoencephalography (MEG) imaging, we systematically characterize whole-brain neural oscillations and identify frequency specific, cortically-based patterns associated with sleep onset. Methods In this study, 14 healthy subjects underwent simultaneous EEG and MEG imaging. Sleep states were determined by scalp EEG. Eight 15s artifact-free epochs, e.g. 120s sensor time series, were selected to represent each behavioral state: N1, N2 and wake. Atlas-based source reconstruction was performed using adaptive beamforming methods. Functional connectivity measures were computed using imaginary coherence and across regions of interests (ROIs, segmentation of 210 cortical regions with Brainnetome Atlas) in multiple frequency bands, including delta (1-4Hz), theta (4-8Hz), alpha (8-12Hz), sigma (12-15Hz), beta (15-30Hz), and gamma (30-50Hz). Directional phase transfer entropy (PTE) was also evaluated to determine the direction of information flow with transition to sleep. Results We show that the transition to sleep is encoded in a spatially and temporally specific dynamic pattern of whole-brain functional connectivity. With sleep onset, there is increased functional connectivity diffusely within the delta frequency, while spatially specific profiles in other frequency bands, e.g. increased fronto-temporal connectivity in the alpha frequency band and fronto-occipital connectivity in the theta band. In addition, rather than a decoupling of anterior-posterior regions with transition to sleep, there is a spectral shift to delta frequencies observed in the synchrony and information flow of neural activity. Conclusion Sleep onset is cortically heterogeneous, composed of spatially and temporally specific patterns of whole-brain functional connectivity, which may play an essential role in the transition to sleep. Support (if any) Research reported in this publication was supported by the National Center for Advancing Translational Sciences of the NIH under Award Number (5TL1TR001871-05 to JMF). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.

Importance Network hyperexcitability may contribute to cognitive dysfunction in patients with Alzheimer disease (AD). Objective To determine the ability of the antiseizure drug levetiracetam to improve cognition in persons with AD. Design, Setting, and Participants The Levetiracetam for Alzheimer’s Disease–Associated Network Hyperexcitability (LEV-AD) study was a phase 2a randomized double-blinded placebo-controlled crossover clinical trial of 34 adults with AD that was conducted at the University of California, San Francisco, and the University of Minnesota, Twin Cities, between October 16, 2014, and July 21, 2020. Participants were adults 80 years and younger who had a Mini-Mental State Examination score of 18 points or higher and/or a Clinical Dementia Rating score of less than 2 points. Screening included overnight video electroencephalography and a 1-hour resting magnetoencephalography examination. Interventions Group A received placebo twice daily for 4 weeks followed by a 4-week washout period, then oral levetiracetam, 125 mg, twice daily for 4 weeks. Group B received treatment using the reverse sequence. Main Outcomes and Measures The primary outcome was the ability of levetiracetam treatment to improve executive function (measured by the National Institutes of Health Executive Abilities: Measures and Instruments for Neurobehavioral Evaluation and Research [NIH-EXAMINER] composite score). Secondary outcomes were cognition (measured by the Stroop Color and Word Test [Stroop] interference naming subscale and the Alzheimer’s Disease Assessment Scale–Cognitive Subscale) and disability. Exploratory outcomes included performance on a virtual route learning test and scores on cognitive and functional tests among participants with epileptiform activity. Results Of 54 adults assessed for eligibility, 11 did not meet study criteria, and 9 declined to participate. A total of 34 adults (21 women [61.8%]; mean [SD] age, 62.3 [7.7] years) with AD were enrolled and randomized (17 participants to group A and 17 participants to group B). Thirteen participants (38.2%) were categorized as having epileptiform activity. In total, 28 participants (82.4%) completed the study, 10 of whom (35.7%) had epileptiform activity. Overall, treatment with levetiracetam did not change NIH-EXAMINER composite scores (mean difference vs placebo, 0.07 points; 95% CI, −0.18 to 0.32 points;P = .55) or secondary measures. However, among participants with epileptiform activity, levetiracetam treatment improved performance on the Stroop interference naming subscale (net improvement vs placebo, 7.4 points; 95% CI, 0.2-14.7 points;P= .046) and the virtual route learning test (t= 2.36; Cohenf2= 0.11;P= .02). There were no treatment discontinuations because of adverse events. Conclusions and Relevance In this randomized clinical trial, levetiracetam was well tolerated and, although it did not improve the primary outcome, in prespecified analysis, levetiracetam improved performance on spatial memory and executive function tasks in patients with AD and epileptiform activity. These exploratory findings warrant further assessment of antiseizure approaches in AD. Trial Registration ClinicalTrials.gov Identifier:NCT02002819

Author(s): Shiota, Michelle N; Simpson, Michaela L; Kirsch, Heidi E; Levenson, Robert W | Abstract: ObjectiveConsiderable research indicates that individuals with dementia have deficits in the ability to recognize emotion in other people. The present study examined ability to detect emotional qualities of objects.MethodFifty-two patients with frontotemporal dementia (FTD), 20 patients with Alzheimer's disease (AD), 18 patients awaiting surgery for intractable epilepsy, and 159 healthy controls completed a newly developed test of ability to recognize emotional qualities of art (music and paintings), and pleasantness in simple sensory stimuli (tactile, olfactory, auditory), and to make aesthetic judgments (geometric shapes, room decor). A subset of participants also completed a test of ability to recognize emotions in other people.ResultsPatients with FTD showed a marked deficit in ability to recognize the emotions conveyed in art, compared with both healthy individuals and patients with AD (relative to controls, deficits in patients with AD only approached significance). This deficit remained robust after controlling for FTD patients' ability to recognize pleasantness in simple sensory stimuli, make aesthetic judgments, identify odors, and identify emotions in other people. Neither FTD nor AD patients showed deficits in recognizing pleasant sensory stimuli or making aesthetic judgments. Exploratory analysis of patients with epilepsy revealed no deficits in any of these domains.ConclusionPatients with FTD (but not AD) showed a significant, specific deficit in ability to interpret emotional messages in art, echoing FTD-related deficits in recognizing emotions in other people. This finding adds to our understanding of the impact these diseases have on the lives of patients and their caregivers. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

The epilepsies affect around 65 million people worldwide and have a substantial missing heritability component. We report a genome-wide mega-analysis involving 15,212 individuals with epilepsy and 29,677 controls, which reveals 16 genome-wide significant loci, of which 11 are novel. Using various prioritization criteria, we pinpoint the 21 most likely epilepsy genes at these loci, with the majority in genetic generalized epilepsies. These genes have diverse biological functions, including coding for ion-channel subunits, transcription factors and a vitamin-B6 metabolism enzyme. Converging evidence shows that the common variants associated with epilepsy play a role in epigenetic regulation of gene expression in the brain. The results show an enrichment for monogenic epilepsy genes as well as known targets of antiepileptic drugs. Using SNP-based heritability analyses we disentangle both the unique and overlapping genetic basis to seven different epilepsy subtypes. Together, these findings provide leads for epilepsy therapies based on underlying pathophysiology., SCOPUS: ar.j, info:eu-repo/semantics/published

Electrophysiology and neuroimaging provide conflicting evidence for the neural contributions to target detection. Scalp electroencephalography (EEG) studies localize the P3b event-related potential component mainly to parietal cortex, whereas neuroimaging studies report activations in both frontal and parietal cortices. We addressed this discrepancy by examining the sources that generate the target-detection process using electrocorticography (ECoG). We recorded ECoG activity from cortex in 14 patients undergoing epilepsy monitoring, as they performed an auditory or visual target-detection task. We examined target-related responses in 2 domains: high frequency band (HFB) activity and the P3b. Across tasks, we observed a greater proportion of electrodes that showed target-specific HFB power relative to P3b over frontal cortex, but their proportions over parietal cortex were comparable. Notably, there was minimal overlap in the electrodes that showed target-specific HFB and P3b activity. These results revealed that the target-detection process is characterized by at least 2 different neural markers with distinct cortical distributions. Our findings suggest that separate neural mechanisms are driving the differential patterns of activity observed in scalp EEG and neuroimaging studies, with the P3b reflecting EEG findings and HFB activity reflecting neuroimaging findings, highlighting the notion that target detection is not a unitary phenomenon.

OBJECTIVE Seizures are more frequent in patients with Alzheimer's disease (AD) and can hasten cognitive decline. However, the incidence of subclinical epileptiform activity in AD and its consequences are unknown. Motivated by results from animal studies, we hypothesized higher than expected rates of subclinical epileptiform activity in AD with deleterious effects on cognition. METHODS We prospectively enrolled 33 patients (mean age 62 years) who met criteria for AD, but had no history of seizures, and 19 age-matched, cognitively normal controls. Subclinical epileptiform activity was assessed, blinded to diagnosis, by overnight long-term video-electroencephalography and a one-hour resting magnetoencephalography exam with simultaneous EEG. Patients also had comprehensive clinical and cognitive evaluations, assessed longitudinally over an average period of 3.3 years. RESULTS Subclinical epileptiform activity was detected in 42.4% of AD patients and 10.5% of controls (p = 0.02). At the time of monitoring, AD patients with epileptiform activity did not differ clinically from those without such activity. However, patients with subclinical epileptiform activity showed faster declines in global cognition, determined by the Mini-Mental State Examination (3.9 points/year in patients with epileptiform activity vs. 1.6 points/year in patients without, p = 0.006), and in executive function (p = 0.01). INTERPRETATION Extended monitoring detects subclinical epileptiform activity in a substantial proportion of patients with AD. Patients with this indicator of network hyperexcitability are at risk for accelerated cognitive decline and might benefit from antiepileptic therapies. These data call for more sensitive and comprehensive neurophysiological assessments in AD patient evaluations and impending clinical trials. This article is protected by copyright. All rights reserved.

Magnetoencephalography (MEG) data is subject to many sources of environmental noise, and interference rejection is a necessary step in the processing of MEG data. Large amplitude interference caused by sources near the brain have been common in clinical settings and are difficult to reject. Artifact from vagal nerve stimulators (VNS) is a prototypical example. In this study, we describe a novel MEG interference rejection algorithm called dual signal subspace projection (DSSP), and evaluate its performance in clinical MEG data from people with epilepsy and implanted VNS. The performance of DSSP was evaluated in a retrospective cohort study of patients with epilepsy and VNS who had MEG scans for source localization of interictal epileptiform discharges. DSSP was applied to the MEG data and compared with benchmark for performance. We evaluated the clinical impact of interference rejection based on human expert detection and estimation of the location and time-course of interictal spikes, using an empirical Bayesian source reconstruction algorithm (Champagne). Clinical recordings, after DSSP processing, became more readable and a greater number of interictal epileptic spikes could be clearly identified. Source localization results of interictal spikes also significantly improved from those achieved before DSSP processing, including meaningful estimates of activity time courses. Therefore, DSSP is a valuable novel interference rejection algorithm that can be successfully deployed for the removal of strong artifacts and interferences in MEG.

This scientific commentary refers to ‘Correlating magnetoencephalography to stereo-electroencephalography in patients undergoing epilepsy surgery’, by Murakami et al. (doi:10.1093/brain/aww215) . The potential of magnetoencephalography (MEG)—the ability to measure cerebral activity via the brain’s magnetic fields—was recognized in the early decades after Hans Berger developed the first electroencephalography (EEG) device (Berger, 1929). The technical progress necessary to measure the weak cerebral magnetic fields, however, was not achieved until the 1960s. In 1968, using a single induction-coil magnetometer made with two million turns of copper wire, the magnetic field created by the posterior ‘alpha’ rhythm described by Berger decades earlier was finally recorded (Cohen, 1968). The next 20 years saw significant technical improvements in MEG; however, practical clinical applications of the technique were slow to materialize. In this issue of Brain , Murakami and co-workers provide evidence to support the role of MEG in the presurgical evaluation of patients with epilepsy (Murakami et al. , 2016). Epileptogenic source localization using MEG was first described in 1987 (Rose et al. , 1987) in a study comparing EEG, MEG, and electrocorticography (ECoG) localization of epileptogenic tissue. The MEG 3D equivalent current dipoles were calculated and found to be well correlated with the discharges identified on ECoG and via depth electrode placement. Magnetic source imaging (MSI), the combination of MEG and anatomical imaging modalities, began to show particular promise in the 1990s with the development of dual magnetometer and subsequent whole-head sensor array systems, allowing more efficient and detailed bi-hemispheric recordings. An initial report of …

This scientific commentary refers to ‘Interictal epileptiform activity outside the seizure onset zone impacts cognition’, by Ung et al. (doi:10.1093/brain/awx143).

Summary Background Despite progress in understanding the genetics of rare epilepsies, the more common epilepsies have proven less amenable to traditional gene-discovery analyses. We aimed to assess the contribution of ultra-rare genetic variation to common epilepsies. Methods We did a case-control sequencing study with exome sequence data from unrelated individuals clinically evaluated for one of the two most common epilepsy syndromes: familial genetic generalised epilepsy, or familial or sporadic non-acquired focal epilepsy. Individuals of any age were recruited between Nov 26, 2007, and Aug 2, 2013, through the multicentre Epilepsy Phenome/Genome Project and Epi4K collaborations, and samples were sequenced at the Institute for Genomic Medicine (New York, USA) between Feb 6, 2013, and Aug 18, 2015. To identify epilepsy risk signals, we tested all protein-coding genes for an excess of ultra-rare genetic variation among the cases, compared with control samples with no known epilepsy or epilepsy comorbidity sequenced through unrelated studies. Findings We separately compared the sequence data from 640 individuals with familial genetic generalised epilepsy and 525 individuals with familial non-acquired focal epilepsy to the same group of 3877 controls, and found significantly higher rates of ultra-rare deleterious variation in genes established as causative for dominant epilepsy disorders (familial genetic generalised epilepsy: odd ratio [OR] 2·3, 95% CI 1·7–3·2, p=9·1 × 10 −8 ; familial non-acquired focal epilepsy 3·6, 2·7–4·9, p=1·1 × 10 −17 ). Comparison of an additional cohort of 662 individuals with sporadic non-acquired focal epilepsy to controls did not identify study-wide significant signals. For the individuals with familial non-acquired focal epilepsy, we found that five known epilepsy genes ranked as the top five genes enriched for ultra-rare deleterious variation. After accounting for the control carrier rate, we estimate that these five genes contribute to the risk of epilepsy in approximately 8% of individuals with familial non-acquired focal epilepsy. Our analyses showed that no individual gene was significantly associated with familial genetic generalised epilepsy; however, known epilepsy genes had lower p values relative to the rest of the protein-coding genes (p=5·8 × 10 −8 ) that were lower than expected from a random sampling of genes. Interpretation We identified excess ultra-rare variation in known epilepsy genes, which establishes a clear connection between the genetics of common and rare, severe epilepsies, and shows that the variants responsible for epilepsy risk are exceptionally rare in the general population. Our results suggest that the emerging paradigm of targeting of treatments to the genetic cause in rare devastating epilepsies might also extend to a proportion of common epilepsies. These findings might allow clinicians to broadly explain the cause of these syndromes to patients, and lay the foundation for possible precision treatments in the future. Funding National Institute of Neurological Disorders and Stroke (NINDS), and Epilepsy Research UK.

Emerging evidence indicates that epileptic encephalopathies are genetically highly heterogeneous, underscoring the need for large cohorts of well-characterized individuals to further define the genetic landscape. Through a collaboration between two consortia (EuroEPINOMICS and Epi4K/EPGP), we analyzed exome-sequencing data of 356 trios with the "classical" epileptic encephalopathies, infantile spasms and Lennox Gastaut syndrome, including 264 trios previously analyzed by the Epi4K/EPGP consortium. In this expanded cohort, we find 429 de nova mutations, including de novo mutations in DNM1 in five individuals and de nova mutations in GABBR2, FASN, and RYR3 in two individuals each. Unlike previous studies, this cohort is sufficiently large to show a significant excess of de nova mutations in epileptic encephalopathy probands compared to the general population using a likelihood analysis (p = 8.2 x 10(-4)), supporting a prominent role for de novo mutations in epileptic encephalopathies. We bring statistical evidence that mutations in DNM1 cause epileptic encephalopathy, find suggestive evidence for a role of three additional genes, and show that at least 12% of analyzed individuals have an identifiable causal de novo mutation. Strikingly, 75% of mutations in these probands are predicted to disrupt a protein involved in regulating synaptic transmission, and there is a significant enrichment of de nova mutations in genes in this pathway in the entire cohort as well. These findings emphasize an important role for synaptic dysregulation in epileptic encephalopathies, above and beyond that caused by ion channel dysfunction.

Gene identification in epilepsy has mainly been limited to large families segregating genes of major effect and de novo mutations in epileptic encephalopathies. Many families that present with common non-acquired focal epilepsies and genetic generalized epilepsies remain unexplained. We assembled a cohort of 'genetically enriched' common epilepsies by collecting and phenotyping families containing multiple individuals with unprovoked seizures. We aimed to determine if specific clinical epilepsy features aggregate within families, and whether this segregation of phenotypes may constitute distinct 'familial syndromes' that could inform genomic analyses. Families with three or more individuals with unprovoked seizures were studied across multiple international centres. Affected individuals were phenotyped and classified according to specific electroclinical syndromes. Families were categorized based on syndromic groupings of affected family members, examined for pedigree structure and phenotypic patterns and, where possible, assigned specific familial epilepsy syndromes. A total of 303 families were assembled and analysed, comprising 1120 affected phenotyped individuals. Of the 303 families, 117 exclusively segregated generalized epilepsy, 62 focal epilepsy, and 22 were classified as genetic epilepsy with febrile seizures plus. Over one-third (102 families) were observed to have mixed epilepsy phenotypes: 78 had both generalized and focal epilepsy features within the same individual (n = 39), or within first or second degree relatives (n = 39). Among the genetic generalized epilepsy families, absence epilepsies were found to cluster within families independently of juvenile myoclonic epilepsy, and significantly more females were affected than males. Of the 62 familial focal epilepsy families, two previously undescribed familial focal syndrome patterns were evident: 15 families had posterior quadrant epilepsies, including seven with occipito-temporal localization and seven with temporo-parietal foci, and four families displayed familial focal epilepsy of childhood with multiple affected siblings that was suggestive of recessive inheritance. The findings suggest (i) specific patterns of syndromic familial aggregation occur, including newly recognized forms of familial focal epilepsy; (ii) although syndrome-specificity usually occurs in multiplex families, the one-third of families with features of both focal and generalized epilepsy is suggestive of shared genetic determinants; and (iii) patterns of features observed across families including pedigree structure, sex, and age of onset may hold clues for future gene identification. Such detailed phenotypic information will be invaluable in the conditioning and interpretation of forthcoming sequencing data to understand the genetic architecture and inter-relationships of the common epilepsy syndromes.

Objective. Large-amplitude artifacts from vagus nerve stimulator (VNS) implants for refractory epilepsy affect magnetoencephalography (MEG) recordings and are difficult to reject, resulting in unusable data from this important population of patients who are frequently evaluated for surgical treatment of epilepsy. Here we compare the performance of two artifact removal algorithms for MEG data: dual signal subspace projection (DSSP) and temporally extended signal space separation (tSSS). Approach. Each algorithm’s performance was first evaluated in simulations. We then tested the performance of each algorithm on resting-state MEG data from patients with VNS implants. We also examined how each algorithm improved source localization of somatosensory evoked fields in patients with VNS implants. Main results. DSSP and tSSS algorithms have a similar ability to reject interference in both simulated and real MEG data if the origin location for tSSS is appropriately set. If the origin set for tSSS is inappropriate, the signal after tSSS can be distorted due to a mismatch between the internal region and the actual source space. Both DSSP and tSSS are able to remove large-amplitude artifacts from outside the brain. DSSP might be a better choice than tSSS when the choice of origin location for tSSS is difficult. Significance. Both DSSP and tSSS algorithms can recover distorted MEG recordings from people with intractable epilepsy and VNS implants, improving epileptic spike identification and source localization of both functional activity and epileptiform activity.

This work presents a probabilistic method for mapping human sleep electroencephalogram (EEG) signals onto a state space based on a biologically plausible mathematical model of the cortex. From a noninvasive EEG signal, this method produces physiologically meaningful pathways of the cortical state over a night of sleep. We propose ways in which these pathways offer insights into sleep-related conditions, functions, and complex pathologies. To address explicitly the noisiness of the EEG signal and the stochastic nature of the mathematical model, we use a probabilistic Bayesian framework to map each EEG epoch to a distribution of likelihoods over all model sleep states. We show that the mapping produced from human data robustly separates rapid eye movement sleep (REM) from slow wave sleep (SWS). A Hidden Markov Model (HMM) is incorporated to improve the path results using the prior knowledge that cortical physiology has temporal continuity.

We present a model for the use of open loop optogenetic control to inhibit epileptiform activity in a meso scale model of the human cortex. The meso scale cortical model first developed by Liley et al. (2001) is extended to two dimensions and the nature of the seizure waves is studied. We adapt to the meso scale a 4 state functional model of Channelrhodopsin-2 (ChR2) ion channels. The effects of pulsed and constant illumination on the conductance of these ion channels is presented. The inhibitory cell population is targeted for the application of open loop control. Seizure waves are successfully suppressed and the inherent properties of the optogenetic channels ensures charge balance in the cortex, protecting it from damage.

Predictive coding theories posit that neural networks learn statistical regularities in the environment for comparison with actual outcomes, signaling a prediction error (PE) when sensory deviation occurs. PE studies in audition have capitalized on low-frequency event-related potentials (LF-ERPs), such as the mismatch negativity. However, local cortical activity is well-indexed by higher-frequency bands [high-γ band (Hγ): 80-150 Hz]. We compared patterns of human Hγ and LF-ERPs in deviance detection using electrocorticographic recordings from subdural electrodes over frontal and temporal cortices. Patients listened to trains of task-irrelevant tones in two conditions differing in the predictability of a deviation from repetitive background stimuli (fully predictable vs. unpredictable deviants). We found deviance-related responses in both frequency bands over lateral temporal and inferior frontal cortex, with an earlier latency for Hγ than for LF-ERPs. Critically, frontal Hγ activity but not LF-ERPs discriminated between fully predictable and unpredictable changes, with frontal cortex sensitive to unpredictable events. The results highlight the role of frontal cortex and Hγ activity in deviance detection and PE generation.

ObjectiveAsymmetric large-amplitude slow activity is sometimes observed on interictal electroencephalography (EEG) in epilepsy. However, few studies have examined slowing during magnetoencephalography (MEG) recordings, which are performed primarily to localize interictal spikes. Also, no prior investigations have compared the sensitivity of MEG to scalp EEG in detecting slow rhythms.MethodsWe performed a retrospective cohort study of focal epilepsy patients who received MEG followed by surgical resection at our institution. We examined MEG, simultaneous EEG, and long-term EEG recordings for prominent asymmetric slow activity (delta-range, 1-4 Hz), and evaluated post-operative seizure outcomes.ResultsWe studied 132 patients with ≥ 1 year post-operative follow-up (mean, 3.6 years). Mean age was 27 (range, 3-68) years, and 55% of patients were male. Asymmetric large-amplitude slow wave activity was observed on interictal MEG in 21 of 132 (16%) patients. Interictal slowing lateralized to the hemisphere of resection in all but one (95%) patient. Among the 21 patients with interictal MEG slowing, 11 (52%) individuals had similarly lateralized EEG slowing, 7 patients had no EEG slowing, and 3 had bilateral symmetric EEG slowing. Meanwhile, none of the 111 patients without lateralized MEG slowing had asymmetric EEG slowing, suggesting significantly higher sensitivity of MEG versus EEG in detecting asymmetric slowing (χ(2)=63.4, p

Optogenetics provides a method of neuron stimulation that has high spatial, temporal, and cell-type specificity. Here we present a model of optogenetic feedback control that targets the inhibitory population, which expresses light-sensitive channelrhodopsin-2 channels, in a mean-field model of undifferentiated cortex that is driven to seizures. The inhibitory population is illuminated with an intensity that is a function of electrode measurements obtained via the cortical model. We test the efficacy of this control method on seizurelike activity observed in two parameter spaces of the cortical model that most closely correspond to seizures observed in patients. We also compare the effect of closed-loop and open-loop control on seizurelike activity using a less-complicated ordinary differential equation model of the undifferentiated cortex in parameter space. Seizurelike activity is successfully suppressed in both parameter planes using optimal illumination intensities less likely to have adverse effects on cortical tissue.

Most neuropsychological research using food as a reward uses single-bid auctions. We wished to determine whether focal brain lesions would affect the ability and motivation to win snack food items in a computerized auction allowing multiple bids. This allowed us to assess participants' abilities under more complex conditions. We enrolled 154 male penetrating traumatic brain injury (pTBI) veterans, mean age 58, from the Vietnam Head Injury Study registry, and 53 male uninjured veterans, mean age 59. We used voxel-based lesion symptom mapping (VLSM) to identify effects of brain lesions on the ability to win items and on participants' answers to statements regarding their level of motivation and evaluation of how well they performed. Number of items won was not significantly associated with any lesions; however, lesions in gustatory cortex (GC) affected motivation and self-evaluation. Our findings provide further evidence of the primary GC's role in motivation for food and drink.

Objective: The goal of the current study was to examine the dynamics of language lateralization using magnetoencephalographic (MEG) imaging, to determine the sensitivity and specificity of MEG imaging, and to determine whether MEG imaging can become a viable alternative to the intracarotid amobarbital procedure (IAP), the current gold standard for preoperative language lateralization in neurosurgical candidates. Methods: MEG was recorded during an auditory verb generation task and imaging analysis of oscillatory activity was initially performed in 21 subjects with epilepsy, brain tumor, or arteriovenous malformation who had undergone IAP and MEG. Time windows and brain regions of interest that best discriminated between IAP-determined left or right dominance for language were identified. Parameters derived in the retrospective analysis were applied to a prospective cohort of 14 patients and healthy controls. Results: Power decreases in the beta frequency band were consistently observed following auditory stimulation in inferior frontal, superior temporal, and parietal cortices; similar power decreases were also seen in inferior frontal cortex prior to and during overt verb generation. Language lateralization was clearly observed to be a dynamic process that is bilateral for several hundred milliseconds during periods of auditory perception and overt speech production. Correlation with the IAP was seen in 13 of 14 (93%) prospective patients, with the test demonstrating a sensitivity of 100% and specificity of 92%. Interpretation: Our results demonstrate excellent correlation between MEG imaging findings and the IAP for language lateralization, and provide new insights into the spatiotemporal dynamics of cortical speech processing. ANN NEUROL 2012

Selective processing of task-relevant stimuli is critical for goal-directed behavior. We used electrocorticography to assess the spatio-temporal dynamics of cortical activation during a simple phonological target detection task, in which subjects press a button when a prespecified target syllable sound is heard. Simultaneous surface potential recordings during this task revealed a highly ordered temporal progression of high gamma (HG, 70–200 Hz) activity across the lateral hemisphere in less than 1 sec. The sequence demonstrated concurrent regional sensory processing of speech syllables in the posterior superior temporal gyrus (STG) and speech motor cortex, and then transitioned to sequential task-dependent processing from prefrontal cortex (PFC), to the final motor response in the hand sensorimotor cortex. STG activation was modestly enhanced for target over nontarget sounds, supporting a selective gain mechanism in early sensory processing, whereas PFC was entirely selective to targets, supporting its role in guiding response behavior. These results reveal that target detection is not a single cognitive event, but rather a process of progressive target selectivity that involves large-scale rapid parallel and serial processing in sensory, cognitive, and motor structures to support goal-directed human behavior.

Here we show that a mathematical model of the human sleep cycle can be used to obtain a detailed description of electroencephalogram (EEG) sleep stages, and we discuss how this analysis may aid in the prediction and prevention of seizures during sleep. The association between EEG data and the cortical model is found via locally linear embedding (LLE), a method of dimensionality reduction. We first show that LLE can distinguish between traditional sleep stages when applied to EEG data. It reliably separates REM and non-REM sleep and maps the EEG data to a low-dimensional output space where the sleep state changes smoothly over time. We also incorporate the concept of strongly connected components and use this as a method of automatic outlier rejection for EEG data. Then, by using LLE on a hybrid data set containing both sleep EEG and signals generated from the mesoscale cortical model, we quantify the relationship between the data and the mathematical model. This enables us to take any sample of sleep EEG data and associate it with a position among the continuous range of sleep states provided by the model; we can thus infer a trajectory of states as the subject sleeps. Lastly, we show that this method gives consistent results for various subjects over a full night of sleep and can be done in real time.

Objective To evaluate the ability of MEG to detect medial temporal spikes in patients with known medial temporal lobe epilepsy (MTLE) and to use magnetic source imaging (MSI) with equivalent current dipoles to examine localization and orientation of spikes and their relation to surgical outcome. Methods We prospectively obtained MSI on a total of 25 patients previously diagnosed with intractable MTLE. MEG was recorded with a 275 channel whole-head system with simultaneous 21-channel scalp EEG during inpatient admission one day prior to surgical resection. The patients’ surgical outcomes were classified based on one-year follow-up after surgery. Results Nineteen of the 22 patients (86.4%) had interictal spikes during the EEG and MEG recordings. Thirteen of 19 patients (68.4%) demonstrated unilateral temporal dipoles ipsilateral to the site of surgery. Among these patients, five (38.5%) patients had horizontal dipoles, one (7.7%) patient had vertical dipoles, and seven (53.8%) patients had both horizontal and vertical dipoles. Sixty percent of patients with non-localizing ictal scalp EEG had well-localized spikes on MSI ipsilateral to the side of surgery and 66.7% of patients with non-localizing MRI had well-localized spikes on MSI ipsilateral to the side of surgery. Concordance between MSI localization and the side of lobectomy was not associated with a likelihood of an excellent postsurgical outcome. Conclusions MSI can detect medial temporal spikes. It may provide important localizing information in patients with MTLE, especially when MRI and/or ictal scalp EEG are not localizing. Significance This study demonstrates that MSI has a good ability to detect interictal spikes from mesial temporal structures.

Object Routine scalp electroencephalography (EEG) cannot always distinguish whether generalized epileptiform discharges are the result of primary bilateral synchrony or secondary bilateral synchrony (SBS) from a focal origin; this is an important distinction because the latter may be amenable to resection. Whole-head magnetoencephalography (MEG) has superior spatial resolution compared with traditional EEG, and can potentially elucidate seizure foci in challenging epilepsy cases in which patients are undergoing evaluation for surgery. Methods Sixteen patients with medically intractable epilepsy in whom SBS was suspected were referred for magnetic source (MS) imaging. All patients had bilateral, synchronous, widespread, and most often generalized spike-wave discharges on scalp EEG studies, plus some other clinical (for example, seizure semiology) or MR imaging feature (for example, focal lesion) suggesting focal onset and hence possible surgical candidacy. The MS imaging modality is the combination of whole-head MEG and parametric reconstruction of corresponding electrical brain sources. An MEG and simultaneous EEG studies were recorded with a 275-channel whole-head system. Single-equivalent current dipoles were estimated from the MEG data, and dipole locations and orientations were superimposed on patients' MR images. Results The MS imaging studies revealed focal dipole clusters in 12 (75%) of the 16 patients, of which a single dipole cluster was identified in 7 patients (44%). Patient age, seizure type, duration of disease, video-EEG telemetry, and MR imaging results were analyzed to determine factors predictive of having clusters revealed on MS imaging. Of these factors, only focal MR imaging anatomical abnormalities were associated with dipole clusters (chi-square test, p = 0.03). Selective resections (including the dipole cluster) in 7 (87%) of 8 patients resulted in seizure-free or rare seizure outcomes (Engel Classes I and II). Conclusions Magnetic source imaging may provide noninvasive anatomical and neurophysiological confirmation of localization in patients in whom there is a suspicion of SBS (based on clinical or MR imaging data), especially in those with an anatomical lesion. Identification of a focal seizure origin has significant implications for both resective and nonresective treatment of intractable epilepsy.

Epilepsy creates significant morbidity, disability, and loss of productivity worldwide. Although unpredictable seizures define epilepsy, the cognitive and emotional difficulties encountered by people with epilepsy may have an even greater impact on everyday function. Epilepsy is associated with lower quality of life (QOL); while this is partially attributable to ongoing seizures, QOL is independently affected by comorbid affective disorders like depression and anxiety. These can accompany all types of epilepsy, but may be especially noteworthy in partial epilepsy syndromes with foci in the frontal or temporal lobes. There is growing evidence that epileptogenic lesions disrupt cortical networks that carry out emotional processing. In addition to epilepsy itself, treatment strategies also affect cognitive functioning and mood. Antiepileptic drugs, vagal nerve stimulation, and surgical resection have all been associated with changes in mood, emotional processing, and social cognition. Overall, the relationship between epilepsy and emotion is vastly complex; when assessing emotional difficulties in a person with epilepsy, we must consider the psychosocial impact of the disease on the patient, the underlying pathophysiology of the disease, and the effects of ongoing treatments.

The true incidence of seizures caused by general anesthetic drugs is unknown. Abnormal movements are common during induction of anesthesia, but they may not be indicative of true seizures. Conversely, epileptiform electrocortical activity is commonly induced by enflurane, etomidate, sevoflurane and, to a lesser extent, propofol, but it rarely progresses to generalized tonic-clonic seizures. Even "nonconvulsant" anesthetic drugs occasionally cause seizures in subjects with preexisting epilepsy. These seizures most commonly occur during induction or emergence from anesthesia, when the anesthetic drug concentration is relatively low. There is no unifying neural mechanism of anesthetic drug-related seizurogenesis. However, there is a growing body of experimental work suggesting that seizures are not caused simply by "too much excitation," but rather by excitation applied to a mass of neurons which are primed to react to the excitation by going into an oscillatory seizure state. Increased gamma-amino-butyric acid (GABA)ergic inhibition can sensitize the cortex so that only a small amount of excitation is required to cause seizures. This has been postulated to occur 1) at the network level by increasing the propensity for reverberation (e.g., by prolongation of the "inhibitory lag"), or 2) via different effects on subpopulations of interneurons ("inhibiting-the-inhibitors") or 3) at the synaptic level by changing the chloride reversal potential ("excitatory GABA"). On the basis of applied neuropharmacology, prevention of anesthetic-drug related seizures would include 1) avoiding sevoflurane and etomidate, 2) considering prophylaxis with adjunctive benzodiazepines (alpha-subunit GABA(A) agonists), or drugs that impair calcium entry into neurons, and 3) using electroencephalogram monitoring to detect early signs of cortical instability and epileptiform activity. Seizures may falsely elevate electroencephalogram indices of depth of anesthesia.