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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.

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.

We consider a mathematical model of mesoscopic human cortical ictal electrical activity. We compare the model results with ictal electrocortical data recorded from three human subjects and show how the two agree. We determine that, in the model system, seizures result from increased connectivity between excitatory and inhibitory cell populations, or from decreased connectivity within either excitatory or inhibitory cell populations. We compare the model results with the disinhibition and 4-AP models of epilepsy and suggest how the model may guide the development of new anticonvulsant therapies.