Atherosclerosis and arteriosclerosis parameters in stroke patients associate with paraoxonase polymorphism and esterase activities.

BACKGROUND AND PURPOSE: Polymorphic paraoxonase (PON1) variants can variably prevent low- and high-density lipoprotein oxidation, but their role in provoking atherosclerosis remained unclear. We addressed this issue by profiling PON1 polymorphisms and enzymatic activities, and assessing atherosclerosis and cerebral arteriosclerosis severity in post-stroke patients. METHODS: Carotid artery intima-media-thickness (IMT), cerebral white matter lesions (WML), serum PON1 -108C/T, Q192R and L55M polymorphisms, and PON and acetylcholinesterase (AChE) enzyme activities were determined in 237 patients. RESULTS: Genetic variation at the PON1 locus showed a strong influence on PON1 activity in ischaemic stroke patients, but lacked direct influence on IMT. Stroke patients with PON1 QQ192 or MM55 genotypes demonstrated lower PON and arylesterase activities at both Day 1 and 12 months post-stroke than patients with either RQ/RR192 or LM/LL55 genotypes (P < 0.001). Furthermore, patients with carotid atherosclerosis and/or cerebral arteriosclerosis expressed as IMT, carotid plaques and WML had lower 12 months PON1 activity than patients without (P = 0.02, P = 0.027 and P = 0.001, respectively), and PON and AChE hydrolysis rates were more tightly correlated in patients carrying the PON1 192R compared with the 192QQ allele, in a gene dose-dependent manner (P < 0.001). CONCLUSION: Our findings show inverse PON1 activity-carotid atherosclerosis and -cerebral arteriosclerosis association in stroke patients: the lower the PON1 activity the more progressed is the atherosclerotic process and the weaker is the association with AChE activity. Extending previous PON1 genetic studies in stroke populations, our study emphasizes the PON1 activity as a potential anti-atherogenic element and proposes involvement of cholinesterase activities in its effects.

Trial to trial variability in either stimulus or action causes apparent correlation and synchrony in neuronal activity.

In this report we show that the observed inter-neuronal correlation reflects a superposition of correlations associated with the intrinsic correlation between neurons, and correlations associated with variability in the stimuli presented to, or the actions performed by, the subject. We argue that the effects of either stimulus or action variability on the observed correlation, though generally ignored, can be substantial. Specifically, we demonstrate how observed correlations are effected by trial to trial variability in either stimulus or action. In addition, assuming that all relevant stimuli and actions are known, we outline a method for eliminating their effects on the observed correlation. It is also shown that tuning of correlations to a stimulus or an action might be a direct consequence of variability in that stimulus or action, even in the absence of any modulation of direct inter-neuronal interaction. The effects of stimulus and action variability should therefore be carefully considered when designing and interpreting experiments involving multi-neuronal recordings.

Failure in identification of overlapping spikes from multiple neuron activity causes artificial correlations.

Recording of multiple neurons from a single electrode is common practice during extra-cellular recordings. Separation and sorting of spikes originating from the different neurons can be performed either on-line or off-line using multiple methods for pattern matching. However, all spike sorting techniques fail either fully or partially in identifying spikes from multiple neurons when they overlap due to occurrence within a short time interval. This failure, that we termed the 'shadowing effect', causes the well-known phenomenon of decreased cross-correlation at zero offset. However, the shadowing effect also causes other artifacts in the auto and cross-correlation of the recorded neurons. These artifacts are significant mainly in brain areas with high firing rate or increased firing synchrony leading to a high probability of spike overlap. Cross correlation of cells recorded from the same electrodes tends to reflect the autocorrelation functions of the two cells, even when there are no functional interactions between the cells. Therefore, the cross-correlation function tends to have a short-term (about the length of the refractory period) peak. A long-term (hundreds of milliseconds to a few seconds) trough in the cross-correlation can be seen in cells with bursting and pausing activities recorded from the same electrode. Even the autocorrelation functions of the recorded neurons feature firing properties of other neurons recorded from the same electrode. Examples of these effects are given from our recordings in the globus pallidus of behaving primates and from the literature. Results of simulations of independent simple model neurons exhibit the same properties as the recorded neurons. The effect is analyzed and can be estimated to enable better evaluation of the underlying firing patterns and the actual synchronization of neighboring neurons recorded by a single electrode.

The neuronal refractory period causes a short-term peak in the autocorrelation function.

Autocorrelation functions are a major tool for the understanding of single-cell firing patterns. Short-term peaks in autocorrelation functions have previously been interpreted as a tendency towards bursting activity or elevated probability to emit spikes in a short time-scale. These peaks can actually be a result of the firing of a neuron with a refractory period followed by a period of constant firing probability. Analytic studies and simulations of such neurons replicate the autocorrelation functions of real-world neurons. The relative size of the peak increases with the refractory period and with the firing rate of the cell. This phenomenon is therefore more notable in areas such as the globus pallidus and cerebellum and less clear in the cerebral cortex. We describe here a compensation factor that can be calculated from the neuron's hazard function. This factor can be removed from the original autocorrelation function to reveal the underlying firing pattern of the cell.

Toward a curse of dimensionality appropriate (CODA) asymptotic theory for semi-parametric models.

We argue, that due to the curse of dimensionality, there are major difficulties with any pure or smoothed likelihood-based method of inference in designed studies with randomly missing data when missingness depends on a high-dimensional vector of variables. We study in detail a semi-parametric superpopulation version of continuously stratified random sampling. We show that all estimators of the population mean that are uniformly consistent or that achieve an algebraic rate of convergence, no matter how slow, require the use of the selection (randomization) probabilities. We argue that, in contrast to likelihood methods which ignore these probabilities, inverse selection probability weighted estimators continue to perform well achieving uniform n 1/2-rates of convergence. We propose a curse of dimensionality appropriate (CODA) asymptotic theory for inference in non- and semi-parametric models in an attempt to formalize our arguments. We discuss whether our results constitute a fatal blow to the likelihood principle and study the attitude toward these that a committed subjective Bayesian would adopt. Finally, we apply our CODA theory to analyse the effect of the 'curse of dimensionality' in several interesting semi-parametric models, including a model for a two-armed randomized trial with randomization probabilities depending on a vector of continuous pretreatment covariates X. We provide substantive settings under which a subjective Bayesian would ignore the randomization probabilities in analysing the trial data. We then show that any statistician who ignores the randomization probabilities is unable to construct nominal 95 per cent confidence intervals for the true treatment effect that have both: (i) an expected length which goes to zero with increasing sample size; and (ii) a guaranteed expected actual coverage rate of at least 95 per cent over the ensemble of trials analysed by the statistician during his or her lifetime. However, we derive a new interval estimator, depending on the Randomization probabilities, that satisfies (i) and (ii).

Estimating linear functionals of a PET image.

The authors discuss the effect of having a finite number of detectors on the estimation of a bounded linear functional of a PET image. They argue that the functional should be estimated directly from the detector counts and give an estimate of the effect of the binning of the counts to order D(-2), where D is the number of detectors. The authors then suggest an estimator that includes a data determined bias correction reducing the bias to o(D(-2)).