Differential effects of the CCKA receptor ligands PD-140, 548 and A-71623 on latent inhibition in the rat.

Latent inhibition (LI) is a behavioural paradigm in which repeated exposure to a stimulus without consequence inhibits the formation of any new associations with that stimulus. To the extent that LI reflects a process of learning to ignore irrelevant stimuli, disrupted LI has been suggested as an animal model for the attentional deficits observed in schizophrenia. The antipsychotic potential of cholecystokinin (CCK) stems from its colocalization with dopamine (DA) in the mesolimbic pathway, where it demonstrates both excitatory and inhibitory effects on dopaminergic activity. This may be explained by mediation through different receptor subtypes. A variety of hypotheses has emerged regarding the potential clinical application of subtype-selective CCK-based drugs. The present experiments examined the effects on LI of two selective CCKA ligands: PD-140,548 (a CCKA antagonist, Experiment 1: 0.001, 0.01, and 0.1 mg/kg) and A-71623 (a CCKA agonist, Experiment 2: 0.02, 0.05, and 0.1 mg/kg). In both experiments, the effects of haloperidol (0.1 mg/kg) were also investigated. Animals receiving 0.1 mg/kg of haloperidol or 0.001 or 0.1 mg/kg (but not 0.01 mg/kg) of PD-140,548 treated the preexposed stimulus as irrelevant after a low number of preexposures. In contrast, no facilitatory effect on LI was detectable at any of the A-71623 doses. The finding that A-71623 failed to enhance LI indicates that it is unlikely that this compound would have any antipsychotic effect within the clinical setting. Considering the facilitatory effect exerted by PD-140,548 on LI, it is probable that the inhibition of CCK activity might prove a more promising strategy for the pharmacological treatment of schizophrenia.

Effects of amisulpride, risperidone and chlorpromazine on auditory and visual latent inhibition, prepulse inhibition, executive function and eye movements in healthy volunteers.

In view of the evidence that cognitive deficits in schizophrenia are critically important for long-term outcome, it is essential to establish the effects that the various antipsychotic compounds have on cognition, particularly second-generation drugs. This parallel group, placebo-controlled study aimed to compare the effects in healthy volunteers (n = 128) of acute doses of the atypical antipsychotics amisulpride (300 mg) and risperidone (3 mg) to those of chlorpromazine (100 mg) on tests thought relevant to the schizophrenic process: auditory and visual latent inhibition, prepulse inhibition of the acoustic startle response, executive function and eye movements. The drugs tested were not found to affect auditory latent inhibition, prepulse inhibition or executive functioning as measured by the Cambridge Neuropsychological Test Battery and the FAS test of verbal fluency. However, risperidone disrupted and amisulpride showed a trend to disrupt visual latent inhibition. Although amisulpride did not affect eye movements, both risperidone and chlorpromazine decreased peak saccadic velocity and increased antisaccade error rates, which, in the risperidone group, correlated with drug-induced akathisia. It was concluded that single doses of these drugs appear to have little effect on cognition, but may affect eye movement parameters in accordance with the amount of sedation and akathisia they produce. The effect risperidone had on latent inhibition is likely to relate to its serotonergic properties. Furthermore, as the trend for disrupted visual latent inhibition following amisulpride was similar in nature to that which would be expected with amphetamine, it was concluded that its behaviour in this model is consistent with its preferential presynaptic dopamine antagonistic activity in low dose and its efficacy in the negative symptoms of schizophrenia.

An Infinite Latent Attribute Model for Network Data

Latent variable models for network data extract a summary of the relational structure underlying an observed network. The simplest possible models subdivide nodes of the network into clusters; the probability of a link between any two nodes then depends only on their cluster assignment. Currently available models can be classified by whether clusters are disjoint or are allowed to overlap. These models can explain a "flat" clustering structure. Hierarchical Bayesian models provide a natural approach to capture more complex dependencies. We propose a model in which objects are characterised by a latent feature vector. Each feature is itself partitioned into disjoint groups (subclusters), corresponding to a second layer of hierarchy. In experimental comparisons, the model achieves significantly improved predictive performance on social and biological link prediction tasks. The results indicate that models with a single layer hierarchy over-simplify real networks.

Supervised Gaussian Process Latent Variable Model for Dimensionality Reduction

The Gaussian process latent variable model (GP-LVM) has been identified to be an effective probabilistic approach for dimensionality reduction because it can obtain a low-dimensional manifold of a data set in an unsupervised fashion. Consequently, the GP-LVM is insufficient for supervised learning tasks (e. g., classification and regression) because it ignores the class label information for dimensionality reduction. In this paper, a supervised GP-LVM is developed for supervised learning tasks, and the maximum a posteriori algorithm is introduced to estimate positions of all samples in the latent variable space. We present experimental evidences suggesting that the supervised GP-LVM is able to use the class label information effectively, and thus, it outperforms the GP-LVM and the discriminative extension of the GP-LVM consistently. The comparison with some supervised classification methods, such as Gaussian process classification and support vector machines, is also given to illustrate the advantage of the proposed method.

The differential effects of chlorpromazine and haloperidol on latent inhibition in healthy volunteers.

Latent inhibition (LI) is a measure of reduced learning about a stimulus to which there has been prior exposure without any consequence. It therefore requires a comparison between a pre-exposed (PE) and a non-pre-exposed (NPE) condition. Since, in animals, LI is disrupted by amphetamines and enhanced by antipsychotics, LI disruption has been proposed as a measure of the characteristic attentional deficit in schizophrenia: the inability to ignore irrelevant stimuli. The findings in humans are, however, inconsistent. In particular, a recent investigation suggested that since haloperidol disrupted LI in healthy volunteers, and LI was normal in non-medicated patients with schizophrenia, the previous findings in schizophrenic patients were entirely due to the negative effects of their medication on LI (Williams et al., 1998). We conducted two studies of antipsychotic drug effects on auditory LI using a within-subject, parallel group design in healthy volunteers. In the first of these, single doses of haloperidol (1 mg. i.v.) were compared with paroxetine (20 mg p.o.) and placebo, and in the second, chlorpromazine (100 mg p.o.) was compared with lorazepam (2 mg. p.o.) and placebo. Eye movements, neuropsychological test performance (spatial working memory (SWM), Tower of London and intra/extra dimensional shift, from the CANTAB test battery) and visual analogue rating scales, were also included as other measures of attention and frontal lobe function. Haloperidol was associated with a non-significant reduction in LI scores, and dysphoria/akathisia (Barnes Akathisia Rating Scale) in three-quarters of the subjects. The LI finding may be explained by increased distractibility which was indicated by an increase in antisaccade directional errors in this group. In contrast, LI was significantly increased by chlorpromazine but not by an equally sedative dose of lorazepam (both drugs causing marked decreases in peak saccadic velocity). Paroxetine had no effect on LI, eye movements or CANTAB neuropsychological test performance. Haloperidol was associated with impaired SWM, which correlated with the degree of dysphoria/akathisia, but no other drug effects on CANTAB measures were detected. We conclude that the effect of antipsychotics on LI is both modality and pharmacologically dependent and that further research using a wider range of antipsychotic compounds is necessary to clarify the cognitive effects of these drugs, and to determine whether there are important differences between them.

Hui and Walter's latent-class model extended to estimate diagnostic test properties from surveillance data: a latent model for latent data

Diagnostic test sensitivity and specificity are probabilistic estimates with far reaching implications for disease control, management and genetic studies. In the absence of 'gold standard' tests, traditional Bayesian latent class models may be used to assess diagnostic test accuracies through the comparison of two or more tests performed on the same groups of individuals. The aim of this study was to extend such models to estimate diagnostic test parameters and true cohort-specific prevalence, using disease surveillance data. The traditional Hui-Walter latent class methodology was extended to allow for features seen in such data, including (i) unrecorded data (i.e. data for a second test available only on a subset of the sampled population) and (ii) cohort-specific sensitivities and specificities. The model was applied with and without the modelling of conditional dependence between tests. The utility of the extended model was demonstrated through application to bovine tuberculosis surveillance data from Northern and the Republic of Ireland. Simulation coupled with re-sampling techniques, demonstrated that the extended model has good predictive power to estimate the diagnostic parameters and true herd-level prevalence from surveillance data. Our methodology can aid in the interpretation of disease surveillance data, and the results can potentially refine disease control strategies.

Balanced excitation and inhibition: Model based analysis of local field potentials

We have developed a model of the local field potential (LFP) based on the conservation of charge, the independence principle of ionic flows and the classical Hodgkin–Huxley (HH) type intracellular model of synaptic activity. Insights were gained through the simulation of the HH intracellular model on the nonlinear relationship between the balance of synaptic conductances and that of post-synaptic currents. The latter is dependent not only on the former, but also on the temporal lag between the excitatory and inhibitory conductances, as well as the strength of the afferent signal. The proposed LFP model provides a method for decomposing the LFP recordings near the soma of layer IV pyramidal neurons in the barrel cortex of anaesthetised rats into two highly correlated components with opposite polarity. The temporal dynamics and the proportional balance of the two components are comparable to the excitatory and inhibitory post-synaptic currents computed from the HH model. This suggests that the two components of the LFP reflect the underlying excitatory and inhibitory post-synaptic currents of the local neural population. We further used the model to decompose a sequence of evoked LFP responses under repetitive electrical stimulation (5 Hz) of the whisker pad. We found that as neural responses adapted, the excitatory and inhibitory components also adapted proportionately, while the temporal lag between the onsets of the two components increased during frequency adaptation. Our results demonstrated that the balance between neural excitation and inhibition can be investigated using extracellular recordings. Extension of the model to incorporate multiple compartments should allow more quantitative interpretations of surface Electroencephalography (EEG) recordings into components reflecting the excitatory, inhibitory and passive ionic current flows generated by local neural populations.

A General Latent Class Model for Performance Evaluation of Diagnostic Tests in the Absence of a Gold Standard: An Application to Chagas Disease

We propose a new general Bayesian latent class model for evaluation of the performance of multiple diagnostic tests in situations in which no gold standard test exists based on a computationally intensive approach. The modeling represents an interesting and suitable alternative to models with complex structures that involve the general case of several conditionally independent diagnostic tests, covariates, and strata with different disease prevalences. The technique of stratifying the population according to different disease prevalence rates does not add further marked complexity to the modeling, but it makes the model more flexible and interpretable. To illustrate the general model proposed, we evaluate the performance of six diagnostic screening tests for Chagas disease considering some epidemiological variables. Serology at the time of donation (negative, positive, inconclusive) was considered as a factor of stratification in the model. The general model with stratification of the population performed better in comparison with its concurrents without stratification. The group formed by the testing laboratory Biomanguinhos FIOCRUZ-kit (c-ELISA and rec-ELISA) is the best option in the confirmation process by presenting false-negative rate of 0.0002% from the serial scheme. We are 100% sure that the donor is healthy when these two tests have negative results and he is chagasic when they have positive results.

A Mechanistic Latent Variable Model for Estimating Drug Concentrations in the Male Genital Tract

The purpose of this study is to develop statistical methodology to facilitate indirect estimation of the concentration of antiretroviral drugs and viral loads in the prostate gland and the seminal vesicle. The differences in antiretroviral drug concentrations in these organs may lead to suboptimal concentrations in one gland compared to the other. Suboptimal levels of the antiretroviral drugs will not be able to fully suppress the virus in that gland, lead to a source of sexually transmissible virus and increase the chance of selecting for drug resistant virus. This information may be useful selecting antiretroviral drug regimen that will achieve optimal concentrations in most of male genital tract glands. Using fractionally collected semen ejaculates, Lundquist (1949) measured levels of surrogate markers in each fraction that are uniquely produced by specific male accessory glands. To determine the original glandular concentrations of the surrogate markers, Lundquist solved a simultaneous series of linear equations. This method has several limitations. In particular, it does not yield a unique solution, it does not address measurement error, and it disregards inter-subject variability in the parameters. To cope with these limitations, we developed a mechanistic latent variable model based on the physiology of the male genital tract and surrogate markers. We employ a Bayesian approach and perform a sensitivity analysis with regard to the distributional assumptions on the random effects and priors. The model and Bayesian approach is validated on experimental data where the concentration of a drug should be (biologically) differentially distributed between the two glands. In this example, the Bayesian model-based conclusions are found to be robust to model specification and this hierarchical approach leads to more scientifically valid conclusions than the original methodology. In particular, unlike existing methods, the proposed model based approach was not affected by a common form of outliers.

Development of a Tumour Growth Inhibition Model to Elucidate the Effects of Ritonavir on Intratumoural Metabolism and Anti-tumour Effect of Docetaxel in a Mouse Model for Hereditary Breast Cancer

In a mouse tumour model for hereditary breast cancer, we previously explored the anti-cancer effects of docetaxel, ritonavir and the combination of both and studied the effect of ritonavir on the intratumoural concentration of docetaxel. The objective of the current study was to apply pharmacokinetic (PK)-pharmacodynamic (PD) modelling on this previous study to further elucidate and quantify the effects of docetaxel when co-administered with ritonavir. PK models of docetaxel and ritonavir in plasma and in tumour were developed. The effect of ritonavir on docetaxel concentration in the systemic circulation of Cyp3a knock-out mice and in the implanted tumour (with inherent Cyp3a expression) was studied, respectively. Subsequently, we designed a tumour growth inhibition model that included the inhibitory effects of both docetaxel and ritonavir. Ritonavir decreased docetaxel systemic clearance with 8% (relative standard error 0.4%) in the co-treated group compared to that in the docetaxel only-treated group. The docetaxel concentration in tumour tissues was significantly increased by ritonavir with mean area under the concentration-time curve 2.5-fold higher when combined with ritonavir. Observed tumour volume profiles in mice could be properly described by the PK/PD model. In the co-treated group, the enhanced anti-tumour effect was mainly due to increased docetaxel tumour concentration; however, we demonstrated a small but significant anti-tumour effect of ritonavir addition (p value <0.001). In conclusion, we showed that the increased anti-tumour effect observed when docetaxel is combined with ritonavir is mainly caused by enhanced docetaxel tumour concentration and to a minor extent by a direct anti-tumour effect of ritonavir.