Department of Neurology, UC Davis School of Medicine

There are 719 publications in this collection, published between 1991 and 2023.

Epilepsy-Associated Dysfunction in the Voltage-Gated Neuronal Sodium Channel SCN1A

Lossin, Christoph

Mutations in SCN1A, the gene encoding the brain voltage-gated sodium channel subunit (NaV1.1) are associated with at least two forms of epilepsy, generalized epilepsy with febrile seizures plus (GEFS+) and severe myoclonic epilepsy of infancy (SMEI). We examined the functional properties of four GEFS+ alleles and one SMEI allele using whole-cell patch-clamp analysis of heterologously expressed recombinant human SCN1A. One previously reported GEFS+ mutation (I1656M) and an additional novel allele (R1657C), both affecting residues in a voltage-sensing S4 segment, exhibited a similar depolarizing shift in the voltage dependence of activation. Additionally, R1657C showed a 50% reduction in current density and accelerated recovery from slow inactivation. Unlike three other GEFS+ alleles that we recently characterized, neither R1657C nor I1656M gave rise to a persistent, noninactivating current. In contrast, two other GEFS+ mutations (A1685V and V1353L) and L986F, an SMEI-associated allele, exhibited complete loss of function. In conclusion, our data provide evidence for a wide spectrum of sodium channel dysfunction in familial epilepsy and demonstrate that both GEFS+ and SMEI can be associated with nonfunctional SCN1A alleles.

What Clinical Observations on the Epidemiology of Antiepileptic Drug Intractability Tell Us About the Mechanisms of Pharmacoresistance

Rogawski, Michael A.

In the past several years, there have been important advances in the clinical epidemiology of antiepileptic drug resistance, as reviewed by Mohanraj and Brodie. It would appear that by and large, intractability is independent of the choice of antiepileptic drug (AED). Many patients will become seizure free on the first agent tried, irrespective of which one their physician decides to pick. Nonresponders to the first drug are in a different category: it is likely that they will continue to have seizures no matter which medicine or combination of medicines is tried. This simple clinical observation puts important constraints on the possible biological mechanisms for pharmacoresistance. In this essay, I consider the implications of the new clinical research for studies on the neurobiological mechanisms of AED intractability.

New Molecular Targets for Antiepileptic Drugs: alpha2delta, SV2A and Kv7/KCNQ/M Potassium Channels

Rogawski, Michael A.

Many currently prescribed antiepileptic drugs (AEDs) act via voltage-gated sodium channels, through effects on -aminobutyric acid–mediated inhibition, or via voltage-gated calcium channels. Some newer AEDs do not act via these traditional mechanisms. The molecular targets for several of these nontraditional AEDs have been defined using cellular electrophysiology and molecular approaches. Here, we describe three of these targets: 2, auxiliary subunits of voltage-gated calcium channels through which the gabapentinoids gabapentin and pregabalin exert their anticonvulsant and analgesic actions; SV2A, a ubiquitous synaptic vesicle glycoprotein that may prepare vesicles for fusion and serves as the target for levetiracetam and its analog brivaracetam (which is currently in late-stage clinical development); and Kv7/KCNQ/M potassium channels that mediate the M-current, which acts a brake on repetitive firing and burst generation and serves as the target for the investigational AEDs retigabine and ICA-105665. Functionally, all of the new targets modulate neurotransmitter output at synapses, focusing attention on presynaptic terminals as critical sites of action for AEDs.

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Open Access Policy Deposits (716)

Episodic memory performance in a multi-ethnic longitudinal study of 13,037 elderly.

Age-related changes in memory are not uniform, even in the absence of dementia. Characterization of non-disease associated cognitive changes is crucial to gain a more complete understanding of brain aging. Episodic memory was investigated in 13,037 ethnically diverse elderly (ages 72 to 85 years) with two to 15 years of follow-up, and with known dementia status, age, sex, education, and APOE genotypes. Adjusted trajectories of episodic memory performance over time were estimated using Latent Class Mixed Models. Analysis was conducted using two samples at baseline evaluation: i) non-cognitively impaired individuals, and ii) all individuals regardless of dementia status. We calculated the age-specific annual incidence rates of dementia in the non-demented elderly (n = 10,220). Two major episodic memory trajectories were estimated: 1) Stable-consisting of individuals exhibiting a constant or improved memory function, and 2) Decliner-consisting of individuals whose memory function declined. The majority of the study participants maintain their memory performance over time. Compared to those with Stable trajectory, individuals characterized as Decliners were more likely to have non-white ethnic background, fewer years of education, a higher frequency of ε4 allele at APOE gene and five times more likely to develop dementia. The steepest decline in episodic memory was observed in Caribbean-Hispanics compared to non-Hispanic whites (p = 4.3 x 10(-15)). The highest incident rates of dementia were observed in the oldest age group, among those of Caribbean-Hispanics ancestry and among Decliners who exhibited rates five times higher than those with Stable trajectories (11 per 100 person-years versus 3 per 100 person-years. Age, education, ethnic background and APOE genotype influence the maintenance of episodic memory. Declining memory is one of the strongest predictors of incident dementia.

PARP1-mediated PARylation activity is essential for oligodendroglial differentiation and CNS myelination.

The function of poly(ADP-ribosyl) polymerase 1 (PARP1) in myelination and remyelination of the central nervous system (CNS) remains enigmatic. Here, we report that PARP1 is an intrinsic driver for oligodendroglial development and myelination. Genetic PARP1 depletion impairs the differentiation of oligodendrocyte progenitor cells (OPCs) into oligodendrocytes and impedes CNS myelination. Mechanistically, PARP1-mediated PARylation activity is not only necessary but also sufficient for OPC differentiation. At the molecular level, we identify the RNA-binding protein Myef2 as a PARylated target, which controls OPC differentiation through the PARylation-modulated derepression of myelin protein expression. Furthermore, PARP1's enzymatic activity is necessary for oligodendrocyte and myelin regeneration after demyelination. Together, our findings suggest that PARP1-mediated PARylation activity may be a potential therapeutic target for promoting OPC differentiation and remyelination in neurological disorders characterized by arrested OPC differentiation and remyelination failure such as multiple sclerosis.

Performance comparison of 10 different classification techniques in segmenting white matter hyperintensities in aging.

Introduction

White matter hyperintensities (WMHs) are areas of abnormal signal on magnetic resonance images (MRIs) that characterize various types of histopathological lesions. The load and location of WMHs are important clinical measures that may indicate the presence of small vessel disease in aging and Alzheimer's disease (AD) patients. Manually segmenting WMHs is time consuming and prone to inter-rater and intra-rater variabilities. Automated tools that can accurately and robustly detect these lesions can be used to measure the vascular burden in individuals with AD or the elderly population in general. Many WMH segmentation techniques use a classifier in combination with a set of intensity and location features to segment WMHs, however, the optimal choice of classifier is unknown.

Methods

We compare 10 different linear and nonlinear classification techniques to identify WMHs from MRI data. Each classifier is trained and optimized based on a set of features obtained from co-registered MR images containing spatial location and intensity information. We further assess the performance of the classifiers using different combinations of MRI contrast information. The performances of the different classifiers were compared on three heterogeneous multi-site datasets, including images acquired with different scanners and different scan-parameters. These included data from the ADC study from University of California Davis, the NACC database and the ADNI study. The classifiers (naïve Bayes, logistic regression, decision trees, random forests, support vector machines, k-nearest neighbors, bagging, and boosting) were evaluated using a variety of voxel-wise and volumetric similarity measures such as Dice Kappa similarity index (SI), Intra-Class Correlation (ICC), and sensitivity as well as computational burden and processing times. These investigations enable meaningful comparisons between the performances of different classifiers to determine the most suitable classifiers for segmentation of WMHs. In the spirit of open-source science, we also make available a fully automated tool for segmentation of WMHs with pre-trained classifiers for all these techniques.

Results

Random Forests yielded the best performance among all classifiers with mean Dice Kappa (SI) of 0.66±0.17 and ICC=0.99 for the ADC dataset (using T1w, T2w, PD, and FLAIR scans), SI=0.72±0.10, ICC=0.93 for the NACC dataset (using T1w and FLAIR scans), SI=0.66±0.23, ICC=0.94 for ADNI1 dataset (using T1w, T2w, and PD scans) and SI=0.72±0.19, ICC=0.96 for ADNI2/GO dataset (using T1w and FLAIR scans). Not using the T2w/PD information did not change the performance of the Random Forest classifier (SI=0.66±0.17, ICC=0.99). However, not using FLAIR information in the ADC dataset significantly decreased the Dice Kappa, but the volumetric correlation did not drastically change (SI=0.47±0.21, ICC=0.95).

Conclusion

Our investigations showed that with appropriate features, most off-the-shelf classifiers are able to accurately detect WMHs in presence of FLAIR scan information, while Random Forests had the best performance across all datasets. However, we observed that the performances of most linear classifiers and some nonlinear classifiers drastically decline in absence of FLAIR information, with Random Forest still retaining the best performance.

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