Sox1-deficient mice suffer from epilepsy associated with abnormal ventral forebrain development and olfactory cortex hyperexcitability

Mutations in several classes of embryonically-expressed transcription factor genes are associated with behavioral disorders and epilepsies. However, there is little known about how such genetic and neurodevelopmental defects lead to brain dysfunction. Here we present the characterization of an epilepsy syndrome caused by the absence of the transcription factor SOX1 in mice. In vivo electroencephalographic recordings from SOX1 mutants established a correlation between behavioral changes and cortical output that was consistent with a seizure origin in the limbic forebrain. In vitro intracellular recordings from three major forebrain regions, neocortex, hippocampus and olfactory (piriform) cortex (OC) showed that only the OC exhibits abnormal enhanced synaptic excitability and spontaneous epileptiform discharges. Furthermore, the hyperexcitability of the OC neurons was present in mutants prior to the onset of seizures but was completely absent from both the hippocampus and neocortex of the same animals. The local inhibitory GABAergic neurotransmission remained normal in the OC of SOX1-deficient brains, but there was a severe developmental deficit of OC postsynaptic target neurons, mainly GABAergic projection neurons within the olfactory tubercle and the nucleus accumbens shell. Our data show that SOX1 is essential for ventral telencephalic development and suggest that the neurodevelopmental defect disrupts local neuronal circuits leading to epilepsy in the SOX1-deficient mice

Investigation of the effects of the novel anticonvulsant compound carisbamate(RWJ-333369) on rat piriform cortical neurones in vitro

Background and purpose: Carisbamate is being developed for adjuvant treatment of partial onset epilepsy. Carisbamate produces anticonvulsant effects in primary generalized, complex partial and absence-type seizure models, and exhibits neuroprotective and antiepileptogenic properties in rodent epilepsy models. Phase IIb clinical trials of carisbamate demonstrated efficacy against partial onset seizures; however, its mechanisms of action remain unknown. Here, we report the effects of carisbamate on membrane properties, evoked and spontaneous synaptic transmission and induced epileptiform discharges in layer II-III neurones in piriform cortical brain slices. Experimental approach: Effects of carisbamate were investigated in rat piriform cortical neurones by using intracellular electrophysiological recordings. Key results: Carisbamate (50–400 mmol·L-1) reversibly decreased amplitude, duration and rise-time of evoked action potentials and inhibited repetitive firing, consistent with use-dependent Na+ channel block; 150–400 mmol·L-1 carisbamate reduced neuronal input resistance, without altering membrane potential. After microelectrode intracellular Cl- loading, carisbamate depolarized cells, an effect reversed by picrotoxin. Carisbamate (100–400 mmol·L-1) also selectively depressed lateral olfactory tract-afferent evoked excitatory synaptic transmission (opposed by picrotoxin), consistent with activation of a presynaptic Cl conductance. Lidocaine (40–320 mmol·L-1) mimicked carisbamate, implying similar modes of action. Carisbamate (300–600 mmol·L-1) had no effect on spontaneous GABAA miniature inhibitory postsynaptic currents and at lower concentrations (50–200 mmol·L-1) inhibited Mg2+-free or 4-aminopyridine-induced seizure-like discharges. Conclusions and implications: Carisbamate blocked evoked action potentials use-dependently, consistent with a primary action on Na+ channels and increased Cl- conductances presynaptically and, under certain conditions, postsynaptically to selectively depress excitatory neurotransmission in piriform cortical layer Ia-afferent terminals.

A Functional Magnetic Resonance Imaging Predictor of Treatment Response to Venlafaxine in Generalized Anxiety Disorder

Background: Functional magnetic resonance imaging (fMRI) holds promise as a noninvasive means of identifying neural responses that can be used to predict treatment response before beginning a drug trial. Imaging paradigms employing facial expressions as presented stimuli have been shown to activate the amygdala and anterior cingulate cortex (ACC). Here, we sought to determine whether pretreatment amygdala and rostral ACC (rACC) reactivity to facial expressions could predict treatment outcomes in patients with generalized anxiety disorder (GAD).Methods: Fifteen subjects (12 female subjects) with GAD participated in an open-label venlafaxine treatment trial. Functional magnetic resonance imaging responses to facial expressions of emotion collected before subjects began treatment were compared with changes in anxiety following 8 weeks of venlafaxine administration. In addition, the magnitude of fMRI responses of subjects with GAD were compared with that of 15 control subjects (12 female subjects) who did not have GAD and did not receive venlafaxine treatment.Results The magnitude of treatment response was predicted by greater pretreatment reactivity to fearful faces in rACC and lesser reactivity in the amygdala. These individual differences in pretreatment rACC and amygdala reactivity within the GAD group were observed despite the fact that 1) the overall magnitude of pretreatment rACC and amygdala reactivity did not differ between subjects with GAD and control subjects and 2) there was no main effect of treatment on rACC-amygdala reactivity in the GAD group.Conclusions: These findings show that this pattern of rACC-amygdala responsivity could prove useful as a predictor of venlafaxine treatment response in patients with GAD.

Linear and nonlinear generalized Fourier transforms

This article presents an overview of a transform method for solving linear and integrable nonlinear partial differential equations. This new transform method, proposed by Fokas, yields a generalization and unification of various fundamental mathematical techniques and, in particular, it yields an extension of the Fourier transform method.

Describing long-term trends in precipitation using generalized additive models

With the current concern over climate change, descriptions of how rainfall patterns are changing over time can be useful. Observations of daily rainfall data over the last few decades provide information on these trends. Generalized linear models are typically used to model patterns in the occurrence and intensity of rainfall. These models describe rainfall patterns for an average year but are more limited when describing long-term trends, particularly when these are potentially non-linear. Generalized additive models (GAMS) provide a framework for modelling non-linear relationships by fitting smooth functions to the data. This paper describes how GAMS can extend the flexibility of models to describe seasonal patterns and long-term trends in the occurrence and intensity of daily rainfall using data from Mauritius from 1962 to 2001. Smoothed estimates from the models provide useful graphical descriptions of changing rainfall patterns over the last 40 years at this location. GAMS are particularly helpful when exploring non-linear relationships in the data. Care is needed to ensure the choice of smooth functions is appropriate for the data and modelling objectives. (c) 2008 Elsevier B.V. All rights reserved.

A family study of co-morbidity between generalized social phobia and generalized anxiety disorder in a non-clinic sample

Background: High rates of co-morbidity between Generalized Social Phobia (GSP) and Generalized Anxiety Disorder (GAD) have been documented. The reason for this is unclear. Family studies are one means of clarifying the nature of co-morbidity between two disorders. Methods: Six models of co-morbidity between GSP and GAD were investigated in a family aggregation study of 403 first-degree relatives of non-clinical probands: 37 with GSP, 22 with GAD, 15 with co-morbid GSP/GAD, and 41 controls with no history of GSP or GAD. Psychiatric data were collected for probands and relatives. Mixed methods (direct and family history interviews) were utilised. Results: Primary contrasts (against controls) found an increased rate of pure GSP in the relatives of both GSP probands and co-morbid GSP/GAD probands, and found relatives of co-morbid GSP/GAD probands to have an increased rate of both pure GAD and comorbid GSP/GAD. Secondary contrasts found (i) increased GSP in the relatives of GSP only probands compared to the relatives of GAD only probands; and (ii) increased GAD in the relatives of co-morbid GSP/GAD probands compared to the relatives of GSP only probands. Limitations: The study did not directly interview all relatives, although the reliability of family history data was assessed. The study was based on an all-female proband sample. The implications of both these limitations are discussed. Conclusions: The results were most consistent with a co-morbidity model indicating independent familial transmission of GSP and GAD. This has clinical implications for the treatment of patients with both disorders. (C) 2006 Elsevier B.V. All fights reserved.

Identification of nonlinear systems using generalized kernel models

Nonlinear system identification is considered using a generalized kernel regression model. Unlike the standard kernel model, which employs a fixed common variance for all the kernel regressors, each kernel regressor in the generalized kernel model has an individually tuned diagonal covariance matrix that is determined by maximizing the correlation between the training data and the regressor using a repeated guided random search based on boosting optimization. An efficient construction algorithm based on orthogonal forward regression with leave-one-out (LOO) test statistic and local regularization (LR) is then used to select a parsimonious generalized kernel regression model from the resulting full regression matrix. The proposed modeling algorithm is fully automatic and the user is not required to specify any criterion to terminate the construction procedure. Experimental results involving two real data sets demonstrate the effectiveness of the proposed nonlinear system identification approach.