Excess health care use is significantly and persistently reduced following diagnosis of late-onset epilepsy.

OBJECTIVE: The incidence of late-onset epilepsy (LOE) is rising, and these patients may use an excess of health care resources. This study aimed to measure pre-/post-diagnostic health care use (HCU) for patients with LOE compared to controls. METHODS: This was an observational open cohort study covering years 1998-2019 using UK population-based linked primary care (Clinical Practice Research Datalink [CPRD]) and hospital (HES) electronic health records. The participants included patients with incident LOE enrolled in CPRD and 1:10 age-, sex-, and general practice-matched controls. The exposure was incident LOE (diagnosed at age ≥65) using a 5-year washout. The main outcome was all HCU (primary care [PC], accident and emergency [A&E], admitted patient and outpatient care) using inverse proportional weighting to PC use and HCU by setting. An interrupted time-series analysis was used to examine pre-/post-diagnostic HCU between patients with LOE and controls over 4 years either side of diagnosis/matching date. An adjusted mixed-effects negative binomial regression was used for post-diagnosis HCU interactions. RESULTS: Of 2 569 874 people ≥65 years of age, 1048 (4%) developed incident LOE. Mean weighted total HCU increased by 32 visits per patient-year (95% confidence interval [95% CI]: 13-50, p = .003) until LOE diagnosis, and then dropped by a mean of 60 visits per patient-year (95% CI: -81 to -40). There was an acute rise and fall over the 1-2 years immediately pre-/post-diagnosis. Incident HCU remained higher for LOE compared to controls post-diagnosis (adjusted incidence rate ratio: 1.72; 95% CI: 1.65-1.70; p < .001), including A&E, outpatient, and admitted care. SIGNIFICANCE: Health care use demonstrates an acute on chronic rise over the 4 years before diagnosis of LOE. To what extent the partial reversal of the acute pre-diagnosis rise, and the mediators of the accelerated increase compared to controls are attributed to epilepsy, comorbid and bidirectional disease states, or a combination of both warrants further exploration.

Association of comorbid-socioeconomic clusters with mortality in late onset epilepsy derived through unsupervised machine learning.

BACKGROUND AND OBJECTIVES: Late-onset epilepsy is a heterogenous entity associated with specific aetiologies and an elevated risk of premature mortality. Specific multimorbid-socioeconomic profiles and their unique prognostic trajectories have not been described. We sought to determine if specific clusters of late onset epilepsy exist, and whether they have unique hazards of premature mortality. METHODS: We performed a retrospective observational cohort study linking primary and hospital-based UK electronic health records with vital statistics data (covering years 1998-2019) to identify all cases of incident late onset epilepsy (from people aged ≥65) and 1:10 age, sex, and GP practice-matched controls. We applied hierarchical agglomerative clustering using common aetiologies identified at baseline to define multimorbid-socioeconomic profiles, compare hazards of early mortality, and tabulating causes of death stratified by cluster. RESULTS: From 1,032,129 people aged ≥65, we identified 1048 cases of late onset epilepsy who were matched to 10,259 controls. Median age at epilepsy diagnosis was 68 (interquartile range: 66-72) and 474 (45%) were female. The hazard of premature mortality related to late-onset epilepsy was higher than matched controls (hazard ratio [HR] 1.73; 95% confidence interval [95%CI] 1.51-1.99). Ten unique phenotypic clusters were identified, defined by 'healthy' males and females, ischaemic stroke, intracerebral haemorrhage (ICH), ICH and alcohol misuse, dementia and anxiety, anxiety, depression in males and females, and brain tumours. Cluster-specific hazards were often similar to that derived for late-onset epilepsy as a whole. Clusters that differed significantly from the base late-onset epilepsy hazard were 'dementia and anxiety' (HR 5.36; 95%CI 3.31-8.68), 'brain tumour' (HR 4.97; 95%CI 2.89-8.56), 'ICH and alcohol misuse' (HR 2.91; 95%CI 1.76-4.81), and 'ischaemic stroke' (HR 2.83; 95%CI 1.83-4.04). These cluster-specific risks were also elevated compared to those derived for tumours, dementia, ischaemic stroke, and ICH in the whole population. Seizure-related cause of death was uncommon and restricted to the ICH, ICH and alcohol misuse, and healthy female clusters. SIGNIFICANCE: Late-onset epilepsy is an amalgam of unique phenotypic clusters that can be quantitatively defined. Late-onset epilepsy and cluster-specific comorbid profiles have complex effects on premature mortality above and beyond the base rates attributed to epilepsy and cluster-defining comorbidities alone.

Machine learning using multimodal clinical, electroencephalographic, and magnetic resonance imaging data can predict incident depression in adults with epilepsy: A pilot study.

OBJECTIVE: This study was undertaken to develop a multimodal machine learning (ML) approach for predicting incident depression in adults with epilepsy. METHODS: We randomly selected 200 patients from the Calgary Comprehensive Epilepsy Program registry and linked their registry-based clinical data to their first-available clinical electroencephalogram (EEG) and magnetic resonance imaging (MRI) study. We excluded patients with a clinical or Neurological Disorders Depression Inventory for Epilepsy (NDDI-E)-based diagnosis of major depression at baseline. The NDDI-E was used to detect incident depression over a median of 2.4 years of follow-up (interquartile range [IQR] = 1.5-3.3 years). A ReliefF algorithm was applied to clinical as well as quantitative EEG and MRI parameters for feature selection. Six ML algorithms were trained and tested using stratified threefold cross-validation. Multiple metrics were used to assess model performances. RESULTS: Of 200 patients, 150 had EEG and MRI data of sufficient quality for ML, of whom 59 were excluded due to prevalent depression. Therefore, 91 patients (41 women) were included, with a median age of 29 (IQR = 22-44) years. A total of 42 features were selected by ReliefF, none of which was a quantitative MRI or EEG variable. All models had a sensitivity > 80%, and five of six had an F1 score ≥ .72. A multilayer perceptron model had the highest F1 score (median = .74, IQR = .71-.78) and sensitivity (84.3%). Median area under the receiver operating characteristic curve and normalized Matthews correlation coefficient were .70 (IQR = .64-.78) and .57 (IQR = .50-.65), respectively. SIGNIFICANCE: Multimodal ML using baseline features can predict incident depression in this population. Our pilot models demonstrated high accuracy for depression prediction. However, overall performance and calibration can be improved. This model has promise for identifying those at risk for incident depression during follow-up, although efforts to refine it in larger populations along with external validation are required.

Adult onset epilepsy is defined by phenotypic clusters with unique comorbidities and risks of death.

OBJECTIVE: This study was undertaken to identify clusters of adult onset epilepsy with distinct comorbidities and risks of early and late death. METHODS: This was a retrospective open cohort study that included all adults meeting a case definition for epilepsy after the Acceptable Mortality Recording date in the Health Improvement Network database for the years 2000-2012 inclusive. Unsupervised agglomerative hierarchical clustering was performed to identify unique clusters of patients based on their predicted risk of early (<4 years of epilepsy diagnosis) and late (≥4 years from diagnosis) mortality and patient-level clinical characteristics. RESULTS: We identified 10 499 presumed incident cases of epilepsy from 11 194 182 patients. Four phenotypic clusters were identified in the early and late risk periods. Early clusters include older adults with cardiovascular disease and a high risk of death (median predicted risk = 20%, interquartile range [IQR] = 9%-31%), a group with moderate risk of death and cancer (median predicted risk = 6%, IQR = 2%-15%), a group with psychiatric disease/substance use and few somatic comorbidities (median predicted risk = 5%, IQR = 2%-9%), and one with a younger age at onset and few comorbidities (median predicted risk = 4%, IQR = 1%-11%). There was minimal movement of individuals between clusters for those surviving the early risk period. Age- and sex-standardized 3-year mortality ratios were more than sixfold higher than the general population for every cluster, even those primarily comprised of healthy younger adults. SIGNIFICANCE: Adult onset epilepsy is marked by unique clusters of comorbid conditions and elevated risks of death that form discrete populations for targeted therapeutic interventions. These clusters remain relatively stable between the early and late mortality risk periods. Of particular interest are the clusters marked by young and otherwise healthy adults whose standardized mortality ratio is sixfold higher than general population despite few conventional risk factors for premature death.

Predicting postoperative epilepsy surgery satisfaction in adults using the 19-item Epilepsy Surgery Satisfaction Questionnaire and machine learning.

OBJECTIVE: The 19-item Epilepsy Surgery Satisfaction Questionnaire (ESSQ-19) is a validated and reliable post hoc means of assessing patient satisfaction with epilepsy surgery. Prediction models building on these data can be used to counsel patients. METHODS: The ESSQ-19 was derived and validated on 229 patients recruited from Canada and Sweden. We isolated 201 (88%) patients with complete clinical data for this analysis. These patients were adults (≥18 years old) who underwent epilepsy surgery 1 year or more prior to answering the questionnaire. We extracted each patient's ESSQ-19 score (scale is 0-100; 100 represents complete satisfaction) and relevant clinical variables that were standardized prior to the analysis. We used machine learning (linear kernel support vector regression [SVR]) to predict satisfaction and assessed performance using the R2 calculated following threefold cross-validation. Model parameters were ranked to infer the importance of each clinical variable to overall satisfaction with epilepsy surgery. RESULTS: Median age was 41 years (interquartile range [IQR] = 32-53), and 116 (57%) were female. Median ESSQ-19 global score was 68 (IQR = 59-75), and median time from surgery was 5.4 years (IQR = 2.0-8.9). Linear kernel SVR performed well following threefold cross-validation, with an R2 of .44 (95% confidence interval = .36-.52). Increasing satisfaction was associated with postoperative self-perceived quality of life, seizure freedom, and reductions in antiseizure medications. Self-perceived epilepsy disability, age, and increasing frequency of seizures that impair awareness were associated with reduced satisfaction. SIGNIFICANCE: Machine learning applied postoperatively to the ESSQ-19 can be used to predict surgical satisfaction. This algorithm, once externally validated, can be used in clinical settings by fixing immutable clinical characteristics and adjusting hypothesized postoperative variables, to counsel patients at an individual level on how satisfied they will be with differing surgical outcomes.

Primary care electronic medical records can be used to predict risk and identify potentially modifiable factors for early and late death in adult onset epilepsy.

OBJECTIVE: To use clinically informed machine learning to derive prediction models for early and late premature death in epilepsy. METHODS: This was a population-based primary care observational cohort study. All patients meeting a case definition for incident epilepsy in the Health Improvement Network database for inclusive years 2000-2012 were included. A modified Delphi process identified 30 potential risk factors. Outcome was early (within 4 years of epilepsy diagnosis) and late (4 years or more from diagnosis) mortality. We used regularized logistic regression, support vector machines, Gaussian naive Bayes, and random forest classifiers to predict outcomes. We assessed model calibration, discrimination, and generalizability using the Brier score, mean area under the receiver operating characteristic curve (AUC) derived from stratified fivefold cross-validation, plotted calibration curves, and extracted measures of association where possible. RESULTS: We identified 10 499 presumed incident cases from 11 194 182 patients. All models performed comparably well following stratified fivefold cross-validation, with AUCs ranging from 0.73 to 0.81 and from 0.71 to 0.79 for early and late death, respectively. In addition to comorbid disease, social habits (alcoholism odds ratio [OR] for early death = 1.54, 95% confidence interval [CI] = 1.12-2.11 and OR for late death = 2.62, 95% CI = 1.66-4.16) and treatment patterns (OR for early death when no antiseizure medication [ASM] was prescribed at baseline = 1.33, 95% CI = 1.07-1.64 and OR for late death after receipt of enzyme-inducing ASM at baseline = 1.32, 95% CI = 1.04-1.66) were significantly associated with increased risk of premature death. Baseline ASM polytherapy (OR = 0.55, 95% CI = 0.36-0.85) was associated with reduced risk of early death. SIGNIFICANCE: Clinically informed models using routine electronic medical records can be used to predict early and late mortality in epilepsy, with moderate to high accuracy and evidence of generalizability. Medical, social, and treatment-related risk factors, such as delayed ASM prescription and baseline prescription of enzyme-inducing ASMs, were important predictors.

Psychosocial profiles and their predictors in epilepsy using patient-reported outcomes and machine learning.

OBJECTIVE: To apply unsupervised machine learning to patient-reported outcomes to identify clusters of epilepsy patients exhibiting unique psychosocial characteristics. METHODS: Consecutive outpatients seen at the Calgary Comprehensive Epilepsy Program outpatient clinics with complete patient-reported outcome measures on quality of life, health state valuation, depression, and epilepsy severity and disability were studied. Data were acquired at each patient's first clinic visit. We used k-means++ to segregate the population into three unique clusters. We then used multinomial regression to determine factors that were statistically associated with patient assignment to each cluster. RESULTS: We identified 462 consecutive patients with complete patient-reported outcome measure (PROM) data. Post hoc analysis of each cluster revealed one reporting elevated measures of psychosocial health on all five PROMs ("high psychosocial health" cluster), one with intermediate measures ("intermediate" cluster), and one with poor overall measures of psychosocial health ("poor psychosocial health" cluster). Failing to achieve at least 1 year of seizure freedom (relative risk [RR] = 4.34, 95% confidence interval [CI] = 2.13-9.09) predicted placement in the "intermediate" cluster relative to the "high" cluster. In addition, failing to achieve seizure freedom, social determinants of health, including the need for partially or completely subsidized income support (RR = 6.10, 95% CI = 2.79-13.31, P < .001) and inability to drive (RR = 4.03, 95% CI = 1.6-10.00, P = .003), and a history of a psychiatric disorder (RR = 3.16, 95% CI = 1.46-6.85, P = .003) were associated with the "poor" cluster relative to the "high" cluster. SIGNIFICANCE: Seizure-related factors appear to drive placement in the "intermediate" cluster, with social determinants driving placement in the "poor" cluster, suggesting a threshold effect. Precision intervention based on cluster assignment, with an initial emphasis on improving social support and careful titration of medications for those reporting the worst psychosocial health, could help optimize health for patients with epilepsy.

Prediction Tools for Psychiatric Adverse Effects After Levetiracetam Prescription.

Importance: Levetiracetam is a commonly used antiepileptic drug, yet psychiatric adverse effects are common and may lead to treatment discontinuation. Objective: To derive prediction models to estimate the risk of psychiatric adverse effects from levetiracetam use. Design, Setting, and Participants: Retrospective open cohort study. All patients meeting the case definition for epilepsy after the Acceptable Mortality Reporting date in The Health Improvement Network (THIN) database based in the United Kingdom (inclusive January 1, 2000, to May 31, 2012) who received a first-ever prescription for levetiracetam were included. Of 11 194 182 patients registered in THIN, this study identified 7400 presumed incident cases (66.1 cases per 100 000 persons) over a maximum of 12 years' follow-up. The index date was when patients received their first prescription code for levetiracetam, and follow-up lasted 2 years or until an event, loss to follow-up, or censoring. The analyses were performed on April 22, 2018. Exposure: A presumed first-ever prescription for levetiracetam. Main Outcomes and Measures: The outcome of interest was a Read code for any psychiatric sign, symptom, or disorder as reached through consensus by 2 authors. This study used regression techniques to derive 2 prediction models, one for the overall population and one for those without a history of a psychiatric sign, symptom, or disorder during the study period. Results: Among 1173 patients with epilepsy receiving levetiracetam, the overall median age was 39 (interquartile range, 25-56) years, and 590 (50.3%) were female. A total of 14.1% (165 of 1173) experienced a psychiatric symptom or disorder within 2 years of index prescription. The odds of reporting a psychiatric symptom were significantly elevated for women (odds ratio [OR], 1.41; 95% CI, 0.99-2.01; P = .05) and those with a preexposure history of higher social deprivation (OR, 1.15; 95% CI, 1.01-1.31; P = .03), depression (OR, 2.20; 95% CI, 1.49-3.24; P < .001), anxiety (OR, 1.74; 95% CI, 1.11-2.72; P = .02), or recreational drug use (OR, 2.02; 95% CI, 1.20-3.37; P = .008). The model performed well after stratified k = 5-fold cross-validation (area under the curve [AUC], 0.68; 95% CI, 0.58-0.79). There was a gradient in risk, with probabilities increasing from 8% for 0 risk factors to 11% to 17% for 1, 17% to 31% for 2, 30% to 42% for 3, and 49% when all risk factors were present. For those free of a preexposure psychiatric code, a second model performed comparably well after k = 5-fold cross-validation (AUC, 0.72; 95% CI, 0.54-0.90). Specificity was maximized using threshold cutoffs of 0.10 (full model) and 0.14 (second model); a score below these thresholds indicates safety of prescription. Conclusions and Relevance: This study derived 2 simple models that predict the risk of a psychiatric adverse effect from levetiracetam. These algorithms can be used to guide prescription in clinical practice.

Prescription trends and psychiatric symptoms following first receipt of one of seven common antiepileptic drugs in general practice.

We sought to examine the risk of psychiatric symptoms associated with a first prescription for specific antiepileptic drugs (AEDs) used in monotherapy in a general cohort of patients with epilepsy. We used The Health Improvement Network database (comprising the years 2000-2012) to identify incident patients with epilepsy. The index date was that on which they met the case definition for epilepsy, and analyses only included patients who remained on monotherapy or received no AED therapy following diagnosis to avoid confounding by polytherapy. Psychiatric symptoms were defined using mental health clinical or treatment (medical or therapeutic) code. We analyzed the AED of interest as a time-varying covariate in multivariate Cox proportional hazard regression models controlling for confounding factors. We identified 9595 patients with incident epilepsy, 7400 of whom (77%) received a first-recorded AED prescription. Prescriptions for newer generation AEDs (lamotrigine and levetiracetam) steadily increased (constituting over 30% of all AED prescriptions by 2012) while valproate use significantly declined in females (~40% in 2002 to just over 20% by 2012). A total of 2190 patients were first exposed to carbamazepine (29.3%) and 222 to lamotrigine (3%), both of which were associated with a lower hazard of any coded psychiatric symptom or disorder in multivariate analyses (hazard ratio [HR]: 0.84, 95% confidence interval [95% CI]: 0.73-0.97; p = 0.02 and HR: 0.83, 95% CI: 0.70-0.99; p = 0.03, respectively, for carbamazepine and lamotrigine). Carbamazepine was also associated with a lower hazard for depression (HR: 0.81; 95% CI: 0.69-0.96; p = 0.013) and anxiety (HR: 0.77; 95% CI: 0.63-0.95; p = 0.013) in secondary analyses. This study provides evidence that carbamazepine and lamotrigine are associated with lower hazards for psychiatric symptoms following a diagnosis of epilepsy. These estimates can be used in clinical settings, and the precision should improve with more contemporary data that include larger proportions of newer generation AEDs.

Serotonin reuptake inhibitors and mortality in epilepsy: A linked primary-care cohort study.

OBJECTIVE: Preliminary evidence suggests that serotonin reuptake inhibitor (SRI) use may increase postictal respiratory drive and prevent death. We sought to determine whether SRIs are associated with improved all-cause and possible seizure-specific mortality in patients with epilepsy. METHODS: Patients with epilepsy and a random 10:1 sample without epilepsy were extracted from The ClinicAl research using LInked Bespoke studies and Electronic health Records (CALIBER) resource. The hazard ratio (HR) of all-cause and possible seizure-specific mortality, treating SRI use as a time-varying covariate, was determined using the date of a second SRI prescription as exposure and in discrete 6-month periods over the entire duration of follow-up. We used Cox regression and competing risk models with Firth correction to calculate the HR. We controlled for age, sex, depression, comorbidity, (Charlson comorbidity index) and socioeconomic status (Index of Multiple Deprivation). RESULTS: We identified 2,718,952 eligible patients in CALIBER, of whom 16,379 (0.60%) had epilepsy. Median age and follow-up were 44 (interquartile range [IQR] 29-61]) and 6.4 years (IQR 2.4-10.4 years), respectively, and 53% were female. A total of 2,178 patients (13%) had at least two SRI prescriptions. Hazard of all-cause mortality was significantly elevated following a second prescription for an SRI (HR 1.64 95% confidence interval [95% CI] 1.44-1.86; p < 0.001). The HR was similar in 163,778 age, sex, and general practitioner (GP) practice-matched controls without epilepsy. Exposure to an SRI was not associated with seizure-related death (HR 1.08, 95% CI 0.59-1.97; 0.796). SIGNIFICANCE: There is no evidence in this large population-based cohort that SRIs protect against all-cause mortality or seizure-specific mortality. Rather, SRI use was associated with increased mortality, irrespective of epilepsy, which is probably due to various factors associated with the use of antidepressants. Larger studies with systematically collected clinical data are needed to shed further light on these findings.

A T-type channel-calmodulin complex triggers αCaMKII activation.

Calmodulin (CaM) is an important signaling molecule that regulates a vast array of cellular functions by activating second messengers involved in cell function and plasticity. Low voltage-activated calcium channels of the Cav3 family have the important role of mediating low threshold calcium influx, but were not believed to interact with CaM. We find a constitutive association between CaM and the Cav3.1 channel at rest that is lost through an activity-dependent and Cav3.1 calcium-dependent CaM dissociation. Moreover, Cav3 calcium influx is sufficient to activate αCaMKII in the cytoplasm in a manner that depends on an intact Cav3.1 C-terminus needed to support the CaM interaction. Our findings thus establish that T-type channel calcium influx invokes a novel dynamic interaction between CaM and Cav3.1 channels to trigger a signaling cascade that leads to αCaMKII activation.

Assessing the role of IKCa channels in generating the sAHP of CA1 hippocampal pyramidal cells.

Our previous work reported that KCa3.1 (IKCa) channels are expressed in CA1 hippocampal pyramidal cells and contribute to the slow afterhyperpolarization that regulates spike accommodation in these cells. The current report presents data from single cell RT-PCR that further reveals mRNA in CA1 cells that corresponds to the sequence of an IKCa channel from transmembrane segments 5 through 6 including the pore region, revealing the established binding sites for 4 different IKCa channel blockers. A comparison of methods to internally apply the IKCa channel blocker TRAM-34 shows that including the drug in an electrode from the onset of an experiment is unviable given the speed of drug action upon gaining access for whole-cell recordings. Together the data firmly establish IKCa channel expression in CA1 neurons and clarify methodological requirements to obtain a block of IKCa channel activity through internal application of TRAM-34.

An investigation into the psychosocial effects of the postictal state.

OBJECTIVE: To determine whether postictal cognitive and behavioral impairment (PCBI) is independently associated with specific aspects of a patient's psychosocial health in those with epilepsy and nonepileptic events. METHODS: We used the University of Calgary's Comprehensive Epilepsy Clinic prospective cohort database to identify patients reporting PCBI. The cohort was stratified into those diagnosed with epilepsy or nonepileptic events at first clinic visit. Univariate comparisons and stepwise multiple logistic regression with backward elimination method were used to identify factors associated with PCBI for individuals with epilepsy and those with nonepileptic events. We then determined if PCBI was independently associated with depression and the use of social assistance when controlling for known risk factors. RESULTS: We identified 1,776 patients, of whom 1,510 (85%) had epilepsy and 235 had nonepileptic events (13%). PCBI was independently associated with depression in those with epilepsy (odds ratio [OR] 1.73; 95% confidence interval [CI] 1.06-2.83; p = 0.03) and with the need for social assistance in those with nonepileptic events (OR 4.81; 95% CI 2.02-11.42; p < 0.001). CONCLUSIONS: PCBI appears to be significantly associated with differing psychosocial outcomes depending on the patient's initial diagnosis. Although additional research is necessary to examine causality, our results suggest that depression and employment concerns appear to be particularly important factors for patients with PCBI and epilepsy and nonepileptic attacks, respectively.

Towards a clinically informed, data-driven definition of elderly onset epilepsy.

OBJECTIVE: Elderly onset epilepsy represents a distinct subpopulation that has received considerable attention due to the unique features of the disease in this age group. Research into this particular patient group has been limited by a lack of a standardized definition and understanding of the attributes associated with elderly onset epilepsy. METHODS: We used a prospective cohort database to examine differences in patients stratified according to age of onset. Linear support vector machine learning incorporating all significant variables was used to predict age of onset according to prespecified thresholds. Sensitivity and specificity were calculated and plotted in receiver-operating characteristic (ROC) space. Feature coefficients achieving an absolute value of 0.25 or greater were graphed by age of onset to define how they vary with time. RESULTS: We identified 2,449 patients, of whom 149 (6%) had an age of seizure onset of 65 or older. Fourteen clinical variables had an absolute predictive value of at least 0.25 at some point over the age of epilepsy-onset spectrum. Area under the curve in ROC space was maximized between ages of onset of 65 and 70. Features identified through machine learning were frequently threshold specific and were similar, but not identical, to those revealed through simple univariable and multivariable comparisons. SIGNIFICANCE: This study provides an empirical, clinically informed definition of "elderly onset epilepsy." If validated, an age threshold of 65-70 years can be used for future studies of elderly onset epilepsy and permits targeted interventions according to the patient's age of onset.

Determinants of rebound burst responses in rat cerebellar nuclear neurons to physiological stimuli.

KEY POINTS: Cerebellar Purkinje cells project GABAergic inhibitory input to neurons of the deep cerebellar nuclei (DCN) that generate a rebound increase in firing, but the specific patterns of input that might elicit a rebound response have not been established. We used recordings of Purkinje cell firing obtained during perioral whisker stimulation in vivo to create a physiological stimulus template to activate Purkinje cell afferents in vitro. DCN cell bursts were evoked by the stimulus pattern but not in relation to the perioral whisker stimulus, complex spikes or regular patterns within the Purkinje cell record. Reverse correlation revealed that bursts were triggered by an elevation-pause pattern of Purkinje cell firing, with pause duration a key factor in burst generation. Our data identify for the first time a physiological pattern of Purkinje cell input that can be encoded by the generation of rebound bursts in DCN cells. ABSTRACT: The end result of signal processing in cerebellar cortex is encoded in the output of Purkinje cells that project inhibitory input to deep cerebellar nuclear (DCN) neurons. DCN cells can respond to a period of inhibition in vitro with a rebound burst of firing, yet the optimal physiological pattern of Purkinje cell input that might evoke a rebound burst is unknown. The current study used spike trains recorded from rat Purkinje cells in response to perioral stimuli in vivo to create a physiological pattern to stimulate Purkinje cell axons in vitro. The perioral stimulus-evoked Purkinje cell firing pattern proved to be virtually ineffective in evoking a rebound burst despite the ability to reliably evoke rebounds using a traditional brief 100 Hz stimulus. Similarly, neither complex spike firing nor Purkinje cell patterns identified by CV2 analysis were reliably associated with rebound bursts. Reverse correlation revealed that the optimal Purkinje cell input to evoke a rebound burst was a sequential increase in mean firing rate of at least 30 Hz above baseline over 250 ms followed by a reduction of 40-60 Hz below baseline for up to 500 ms. The most important factor was the duration of a pause in Purkinje cell firing that allowed DCN cells to recover from a state of net inhibitory influence. These data indicate that physiological patterns of Purkinje cell firing can elicit rebound bursts in DCN cells in vitro, with pauses in Purkinje cell firing rate acting as a key stimulus for DCN cell rebound responses.

Correlates of disability related to seizures in persons with epilepsy.

OBJECTIVE: Seizure-related disability is an important contributor to health-related quality of life in persons with epilepsy. Yet, there is little information on patient-centered reports of seizure-related disability, as most studies focus on specific constructs of health-related disability, rather than epilepsy. We investigated how patients rate their own disability and how these ratings correlate with various clinical and sociodemographic characteristics. METHODS: In a prospective cohort of 250 adults with epilepsy consecutively enrolled in the Neurological Disease and Depression Study (NEEDs), we obtained a broad range of clinical and patient-reported measures, including patients' ratings of seizure-related disability and epilepsy severity using self-completed, single-item, 7-point response global assessment scales. Spearman's correlation, multiple linear regression, and mediation analyses were used to examine the association between seizure-related disability scores and clinical and demographic characteristics of persons with epilepsy. RESULTS: The mean age and duration of epilepsy was 39.8 and 16.7 years, respectively. About 29.5% of the patients reported their seizures as "not at all disabling," whereas 5.8% of the patients reported them as "extremely disabling." Age, seizure freedom at 1 year, anxiety, and epilepsy severity were identified as statistically significant predictors of disability scores. The indirect effects of age and seizure freedom, attributable to mediation through epilepsy severity, accounted for 25.0% and 30.3% of the total effects of these determinants on seizure-related disability, respectively. SIGNIFICANCE: Measuring seizure-related disability has heuristic value and it has important correlates and mediators that can be targeted for intervention in practice. Addressing modifiable factors associated with disability (e.g., seizure freedom and anxiety) could have a significant impact on decreasing the burden of disability in people with epilepsy.

Determinants of disease severity in adults with epilepsy: Results from the Neurological Diseases and Depression Study.

BACKGROUND: Epilepsy severity has been recognized as a significant predictor of health-related quality of life in patients with epilepsy. However, clinical markers of epilepsy severity such as seizure frequency often fail to capture important aspects of the disease. This study investigates the factors associated with patient-reported severity of epilepsy, assessed by the Global Assessment of Severity of Epilepsy (GASE) scale in adults with epilepsy. METHODS: Data from a cohort of 250 patients consecutively enrolled in the Neurological Diseases and Depression Study (NEEDS) were used to assess the determinants of epilepsy severity as measured by the GASE scale. Multiple linear regression analyses were used to examine the mediation effect of clinical and sociodemographic characteristics on patients' ratings on the GASE scale. RESULTS: The mean age of the study participants was 39.8 (SD=14.9) years, of which 44.4% were male. About 66.8% of the participants reported "not at all severe" or "a little severe" epilepsy, while 0.4% reported "extremely severe" epilepsy. One-year seizure freedom, number of antiseizure medications, medication side effects, depression, anxiety, and seizure-related disability were identified as significant determinants of patients' ratings of epilepsy severity. Seizure-related disability mediated the effects of 1-year seizure freedom, number of antiseizure medications, and medication side effects on epilepsy severity. CONCLUSION: Overall, patients with epilepsy who reported higher GASE scores were less likely to achieve 1-year seizure freedom and more likely to be on more antiseizure medications, experience more side effects from medication, endorse more depression and anxiety symptoms, and have increased self-reported seizure-related disability. The identified determinants of global, self-rated epilepsy severity can aid the design of appropriate interventions and support services for patients with severe epilepsy.

IKCa channels are a critical determinant of the slow AHP in CA1 pyramidal neurons.

Control over the frequency and pattern of neuronal spike discharge depends on Ca2+-gated K+ channels that reduce cell excitability by hyperpolarizing the membrane potential. The Ca2+-dependent slow afterhyperpolarization (sAHP) is one of the most prominent inhibitory responses in the brain, with sAHP amplitude linked to a host of circuit and behavioral functions, yet the channel that underlies the sAHP has defied identification for decades. Here, we show that intermediate-conductance Ca2+-dependent K+ (IKCa) channels underlie the sAHP generated by trains of synaptic input or postsynaptic stimuli in CA1 hippocampal pyramidal cells. These findings are significant in providing a molecular identity for the sAHP of central neurons that will identify pharmacological tools capable of potentially modifying the several behavioral or disease states associated with the sAHP.

The expression pattern of a Cav3-Kv4 complex differentially regulates spike output in cerebellar granule cells.

The cerebellum receives sensory information by mossy fiber input from a multitude of sources that require differential signal processing. A compartmentalization of function begins with the segregation of mossy fibers across 10 distinct lobules over the rostrocaudal axis, with tactile receptor afferents prevalent in anterior lobules and vestibular input in caudal lobules. However, it is unclear how these unique signals might be differentially processed at the circuit level across the cerebellum. As granule cells receive mossy fiber input, they represent a key stage at which postsynaptic mechanisms could influence signal processing. Granule cells express an A-type current mediated by Kv4 potassium channels that modify the latency and frequency of spike output. The current study examined the potential for a Cav3 calcium-Kv4 channel complex to regulate the response of granule cells to mossy fiber input in lobules 2 and 9 of the rat cerebellum. Similar A-type currents were recorded in both regions, but the Cav3 calcium current was expressed at a substantially higher density in lobule 9 cells, acting to increase A-type current availability through its influence on Kv4 voltage for inactivation. The difference in excitability imparted by Cav3-Kv4 interactions proves to allow lobule 2 granule cells to respond more effectively to tactile stimulus-like burst input and lobule 9 cells to slow shifts in input frequency characteristic of vestibular input. The expression pattern of Cav3 channels and its control of Kv4 availability thus provides a novel means of processing widely different forms of sensory input across cerebellar lobules.

Signal processing by T-type calcium channel interactions in the cerebellum.

T-type calcium channels of the Cav3 family are unique among voltage-gated calcium channels due to their low activation voltage, rapid inactivation, and small single channel conductance. These special properties allow Cav3 calcium channels to regulate neuronal processing in the subthreshold voltage range. Here, we review two different subthreshold ion channel interactions involving Cav3 channels and explore the ability of these interactions to expand the functional roles of Cav3 channels. In cerebellar Purkinje cells, Cav3 and intermediate conductance calcium-activated potassium (IKCa) channels form a novel complex which creates a low voltage-activated, transient outward current capable of suppressing temporal summation of excitatory postsynaptic potentials (EPSPs). In large diameter neurons of the deep cerebellar nuclei, Cav3-mediated calcium current (I T) and hyperpolarization-activated cation current (I H) are activated during trains of inhibitory postsynaptic potentials. These currents have distinct, and yet synergistic, roles in the subthreshold domain with I T generating a rebound burst and I H controlling first spike latency and rebound spike precision. However, by shortening the membrane time constant the membrane returns towards resting value at a faster rate, allowing I H to increase the efficacy of I T and increase the range of burst frequencies that can be generated. The net effect of Cav3 channels thus depends on the channels with which they are paired. When expressed in a complex with a KCa channel, Cav3 channels reduce excitability when processing excitatory inputs. If functionally coupled with an HCN channel, the depolarizing effect of Cav3 channels is accentuated, allowing for efficient inversion of inhibitory inputs to generate a rebound burst output. Therefore, signal processing relies not only on the activity of individual subtypes of channels but also on complex interactions between ion channels whether based on a physical complex or by indirect effects on membrane properties.