Seizure outcome after switching antiepileptic drugs: A matched, prospective study.

OBJECTIVE: Outcomes after changing antiepileptic drugs (AEDs) have largely been studied in single cohort series. We recently reported the first study to examine this question in a controlled manner. Here we expand on these results by using a matched, prospective methodology applied to both uncontrolled and well-controlled patients taking any AED. METHODS: We reviewed all outpatient notes over a 9-month period and identified patients with focal epilepsy who were on monotherapy. We classified those who switched AEDs as case patients, with those remaining on the same drug serving as controls. We matched cases with controls for seizure status (seizure-free in the preceding 6 months or not), current AED, and number of failed AEDs. We subsequently assessed outcome 6 months later. RESULTS: Seizure-free patients who switched drug (n = 12) had a 16.7% rate of seizure recurrence at 6 months, compared to 2.8% among controls remaining on the same drug (n = 36, p = 0.11). There was a 37% remission rate among uncontrolled patients who switched drug compared to 55.6% among controls (n = 27 per group, p = 0.18). Uncontrolled patients who had previously tried more than one AED were somewhat less likely to enter remission (p = 0.057). Neither AED mechanism of action nor change in dosage impacted outcome. SIGNIFICANCE: Herein we provide further estimation of the modest risk (~14%) associated with switching AEDs in patients in remission compared to being maintained on the same regimen. Uncontrolled patients were no more likely to enter remission after a drug switch than they were after remaining on the same drug, suggesting that spontaneous changes in disease state, and not drug response, underlie remission in this population

Long-term effect of antiepileptic drug switch on serum lipids and C-reactive protein.

BACKGROUND: Prior studies have shown that switching patients from inducing antiepileptic drugs (AEDs) to lamotrigine, levetiracetam, or topiramate reduces serum lipids and C-reactive protein (CRP). These studies were all of short duration, and some drugs, such as zonisamide, have not been investigated. METHODS: We recruited 41 patients taking phenytoin or carbamazepine who were being switched to zonisamide, lamotrigine, or levetiracetam. We measured serum lipids and CRP before the switch, \u3e6weeks after, and \u3e6months after. An untreated control group (n=14) underwent similar measurement. We combined these data with those of our previous investigation (n=34 patients and 16 controls) of a very similar design. RESULTS: There were no differences in outcome measures between the two inducing AEDs nor among the three noninducing AEDs. Total cholesterol (TC), atherogenic lipids, and CRP were higher under inducer treatment than in controls. All measures were elevated under inducer treatment relative to noninducer treatment, including TC (24mg/dL higher, 95% CI: 17.5-29.9, p CONCLUSIONS: We demonstrate that switching from inducing to noninducing AEDs produces an enduring reduction in serum lipids and CRP. These results provide further evidence that inducing AEDs may be associated with elevated vascular disease risk. These are the first vascular risk marker data in patients taking zonisamide, which shows a profile similar to that of other noninducing AEDs

Conversion from enzyme-inducing antiepileptic drugs to topiramate: effects on lipids and C-reactive protein.

PURPOSE: We previously demonstrated that converting patients from the enzyme-inducers phenytoin or carbamazepine to the non-inducers levetiracetam or lamotrigine reduces serum lipids and C-reactive protein (CRP). We sought to determine if the same changes would occur when patients were switched to topiramate, which has shown some evidence of enzyme induction at high doses. We also examined the effects of drug switch on low-density lipoprotein (LDL) particle concentration. METHODS: We converted 13 patients from phenytoin or carbamazepine monotherapy to topiramate monotherapy (most at doses of 100-150 mg/day). Fasting lipids, including LDL particle concentration, and CRP were obtained before and ≥6 weeks after the switch. A group of normal subjects had the same serial serologic measurements to serve as controls. RESULTS: Conversion from inducers to topiramate resulted in a -35 mg/dL decline in total cholesterol (p=0.033), with significant decreases in all cholesterol fractions, triglycerides, and LDL particle concentration (p≤0.03 for all), as well as a decrease of over 50% in serum CRP (p CONCLUSIONS: Changes seen when inducer-treated patients are converted to TPM closely mimic those seen when inducer-treated patients are converted to lamotrigine or levetiracetam. These findings provide evidence that CYP450 induction elevates CRP and serum lipids, including LDL particles, and that these effects are reversible upon deinduction. Low-dose TPM appears not to induce the enzymes involved in cholesterol synthesis

Functional connectivity evidence of cortico-cortico inhibition in temporal lobe epilepsy.

Epileptic seizures can initiate a neural circuit and lead to aberrant neural communication with brain areas outside the epileptogenic region. We focus on interictal activity in focal temporal lobe epilepsy and evaluate functional connectivity (FC) differences that emerge as function of bilateral versus strictly unilateral epileptiform activity. We assess the strength of FC at rest between the ictal and non-ictal temporal lobes, in addition to whole brain connectivity with the ictal temporal lobe. Results revealed strong connectivity between the temporal lobes for both patient groups, but this did not vary as a function of unilateral versus bilateral interictal status. Both the left and right unilateral temporal lobe groups showed significant anti-correlated activity in regions outside the epileptogenic temporal lobe, primarily involving the contralateral (non-ictal/non-pathologic) hemisphere, with precuneus involvement prominent. The bilateral groups did not show this contralateral anti-correlated activity. This anti-correlated connectivity may represent a form of protective and adaptive inhibition, helping to constrain epileptiform activity to the pathologic temporal lobe. The absence of this activity in the bilateral groups may be indicative of flawed inhibitory mechanisms, helping to explain their more widespread epileptiform activity. Our data suggest that the location and build up of epilepsy networks in the brain are not truly random, and are not limited to the formation of strictly epileptogenic networks. Functional networks may develop to take advantage of the regulatory function of structures such as the precuneus to instantiate an anti-correlated network, generating protective cortico-cortico inhibition for the purpose of limiting seizure spread or epileptogenesis

Temporal lobe epilepsy and surgery selectively alter the dorsal, not the ventral, default-mode network.

The default-mode network (DMN) is a major resting-state network. It can be divided in two distinct networks: one is composed of dorsal and anterior regions [referred to as the dorsal DMN (dDMN)], while the other involves the more posterior regions [referred to as the ventral DMN (vDMN)]. To date, no studies have investigated the potentially distinct impact of temporal lobe epilepsy (TLE) on these networks. In this context, we explored the effect of TLE and anterior temporal lobectomy (ATL) on the dDMN and vDMN. We utilized two resting-state fMRI sessions from left, right TLE patients (pre-/post-surgery) and normal controls (sessions 1/2). Using independent component analysis, we identified the two networks. We then evaluated for differences in spatial extent for each network between the groups, and across the scanning sessions. The results revealed that, pre-surgery, the dDMN showed larger differences between the three groups than the vDMN, and more particularly between right and left TLE than between the TLE patients and controls. In terms of change post-surgery, in both TLE groups, the dDMN also demonstrated larger changes than the vDMN. For the vDMN, the only changes involved the resected temporal lobe for each ATL group. For the dDMN, the left ATL group showed post-surgical increases in several regions outside the ictal temporal lobe. In contrast, the right ATL group displayed a large reduction in the frontal cortex. The results highlight that the two DMNs are not impacted by TLE and ATL in an equivalent fashion. Importantly, the dDMN was the more affected, with right ATL having a more deleterious effects than left ATL. We are the first to highlight that the dDMN more strongly bears the negative impact of TLE than the vDMN, suggesting there is an interaction between the side of pathology and DM sub-network activity. Our findings have implications for understanding the impact TLE and subsequent ATL on the functions implemented by the distinct DMNs

Gray Matter Sampling Differences Between Subdural Electrodes and Stereoelectroencephalography Electrodes.

Objective: Stereoelectroencephalography (SEEG) has seen a recent increase in popularity in North America; however, concerns regarding the spatial sampling capabilities of SEEG remain. We aimed to quantify and compare the spatial sampling of subdural electrode (SDE) and SEEG implants. Methods: Patients with drug-resistant epilepsy who underwent invasive monitoring were included in this retrospective case-control study. Ten SEEG cases were compared with ten matched SDE cases based on clinical presentation and pre-implantation hypothesis. To quantify gray matter sampling, MR and CT images were coregistered and a 2.5mm radius sphere was superimposed over the center of each electrode contact. The estimated recording volume of gray matter was defined as the cortical voxels within these spherical models. Paired t-tests were performed to compare volumes and locations of SDE and SEEG recording. A Ripley\u27s K-function analysis was performed to quantify differences in spatial distributions. Results: The average recording volume of gray matter by each individual contact was similar between the two modalities. SEEG implants sampled an average of 20% more total gray matter, consisted of an average of 17% more electrode contacts, and had 77% more of their contacts covering gray matter within sulci. Insular coverage was only achieved with SEEG. SEEG implants generally consist of discrete areas of dense local coverage scattered across the brain; while SDE implants cover relatively contiguous areas with lower density recording. Significance: Average recording volumes per electrode contact are similar for SEEG and SDE, but SEEG may allow for greater overall volumes of recording as more electrodes can be routinely implanted. The primary difference lies in the location and distribution of gray matter than can be sampled. The selection between SEEG and SDE implantation depends on sampling needs of the invasive implant

AR2, a novel automatic muscle artifact reduction software method for ictal EEG interpretation: Validation and comparison of performance with commercially available software.

Objective: To develop a novel software method (AR2) for reducing muscle contamination of ictal scalp electroencephalogram (EEG), and validate this method on the basis of its performance in comparison to a commercially available software method (AR1) to accurately depict seizure-onset location. Methods: A blinded investigation used 23 EEG recordings of seizures from 8 patients. Each recording was uninterpretable with digital filtering because of muscle artifact and processed using AR1 and AR2 and reviewed by 26 EEG specialists. EEG readers assessed seizure-onset time, lateralization, and region, and specified confidence for each determination. The two methods were validated on the basis of the number of readers able to render assignments, confidence, the intra-class correlation (ICC), and agreement with other clinical findings. Results: Among the 23 seizures, two-thirds of the readers were able to delineate seizure-onset time in 10 of 23 using AR1, and 15 of 23 using AR2 (

Genome modeling system: A knowledge management platform for genomics

In this work, we present the Genome Modeling System (GMS), an analysis information management system capable of executing automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. The GMS also serves as a platform for bioinformatics development, allowing a large team to collaborate on data analysis, or an individual researcher to leverage the work of others effectively within its data management system. Rather than separating ad-hoc analysis from rigorous, reproducible pipelines, the GMS promotes systematic integration between the two. As a demonstration of the GMS, we performed an integrated analysis of whole genome, exome and transcriptome sequencing data from a breast cancer cell line (HCC1395) and matched lymphoblastoid line (HCC1395BL). These data are available for users to test the software, complete tutorials and develop novel GMS pipeline configurations. The GMS is available at https://github.com/genome/gms

Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (similar to 25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available