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At least 1,063 records · Page 59Linked to original sources
PubMed2005

Spline-based non-parametric regression for periodic functions and its application to directional tuning of neurons.

The activity of neurons in the brain often varies systematically with some quantitative feature of a stimulus or action. A well-known example is the tendency of the firing rates of neurons in the primary motor cortex to vary with the direction of a subject's arm or wrist movement. When this movement is constrained to vary in only two dimensions, the direction of movement may be characterized by an angle, and the neuronal firing rate can be written as a function of this angle. The firing rate function has traditionally been fit with a cosine, but recent evidence suggests that departures from cosine tuning occur frequently. We report here a new non-parametric regression method for fitting periodic functions and demonstrate its application to the fitting of neuronal data. The method is an extension of Bayesian adaptive regression splines (BARS) and applies both to normal and non-normal data, including Poisson data, which commonly arise in neuronal applications. We compare the new method to a periodic version of smoothing splines and some parametric alternatives and find the new method to be especially valuable when the smoothness of the periodic function varies unevenly across its domain.

Action Potentials↗
PubMed2001

Bayesian model selection: analysis of a survival model with a surviving fraction.

We describe a methodology for model comparison in a Bayesian framework as applied to survival with a surviving fraction. This is illustrated using a case study of a randomized and controlled clinical trial investigating time until recurrence of depression. Posterior distributions are simulated using Metropolis-within-Gibbs Markov chain methods. Models reflecting the effects of covariates on the log odds of being in the surviving fraction, the log of the hazard rate, as well as both and neither are compared. Bayes factors for comparing the models are obtained by using the bridge sampling method of calculating normalizing constants.

Algorithms↗
PubMed1996

Measurement of pulsatile flow using MRI and a Bayesian technique of probability analysis.

This work shows that complete spatial information of periodic pulsatile fluid flows can be rapidly obtained by Bayesian probability analysis of flow encoded magnetic resonance imaging data. These data were acquired as a set of two-dimensional images (complete two-dimensional sampling of k-space or reciprocal position space) but with a sparse (six point) and nonuniform sampling of q-space or reciprocal displacement space. This approach enables more precise calculation of fluid velocity to be achieved than by conventional two q-sample phase encoding of velocities, without the significant time disadvantage associated with the complete flow measurement required for Fourier velocity imaging. For experimental comparison with the Bayesian analysis applied to nonuniformly sampled q-space data, a Fourier velocity imaging technique was used with one-dimensional spatial encoding within a selected slice and a uniform sampling of q-space using 64 values of the pulsed gradients to encode fluid flow. Because the pulsatile flows were axially symmetric within the resolution of the experiment, the radial variation of fluid velocity, in the direction of the pulsed gradients, was reconstructed from one-dimensional spatial projections of the velocity by exploiting the central slice theorem. Data were analysed for internal consistency using linearised flow theories. The results show that nonuniform q-space sampling followed by Bayesian probability analysis is at least as accurate as the combined uniform q-space sampling with Fourier velocity imaging and projection reconstruction method. Both techniques give smaller errors than a two-point sampling of q-space (the conventional flow encoding experiment).

Bayes Theorem↗
PubMed2007

Bayesian estimation of intervention effect with pre- and post-misclassified binomial data.

We consider studies in which an enrolled subject tests positive on a fallible test. After an intervention, disease status is re-diagnosed with the same fallible instrument. Potential misclassification in the diagnostic test causes regression to the mean that biases inferences about the true intervention effect. The existing likelihood approach suffers in situations where either sensitivity or specificity is near 1. In such cases, common in many diagnostic tests, confidence interval coverage can often be below nominal for the likelihood approach. Another potential drawback of the maximum likelihood estimator (MLE) method is that it requires validation data to eliminate identification problems. We propose a Bayesian approach that offers improved performance in general, but substantially better performance than the MLE method in the realistic case of a highly accurate diagnostic test. We obtain this superior performance using no more information than that employed in the likelihood method. Our approach is also more flexible, doing without validation data if necessary, but accommodating multiple sources of information, if available, thereby systematically eliminating identification problems. We show via a simulation study that our Bayesian approach outperforms the MLE method, especially when the diagnostic test has high sensitivity, specificity, or both. We also consider a real data example for which the diagnostic test specificity is close to 1 (false positive probability close to 0).

Acoustic Impedance Tests↗
PubMed1994

Population Pharmacokinetics of Digoxin in Duchenne Muscular Dystrophy (DMD) Patients.

Routine clinical pharmacokinetic data collected from Duchenne muscular dystrophy (DMD) patients receiving digoxin have been analyzed to evaluate the role of patients' characteristics for estimating dosing regimens. The data were analyzed using NONMEM, a computer program designed for population pharmacokinetic analysis that allows pooling of data. The pharmacokinetic model of digoxin was described using a one-compartment steady-state model. The effect of factors on digoxin clearance was investigated. NONMEM estimates indicate that digoxin clearance was influenced by the variables of body weight and combination with diuretics. The interindividual variability in digoxin clearance was modeled with proportional error with an estimated coefficient of variation of 25%, and the intraindividual variability in digoxin concentration was modeled with equal error with an estimated standard deviation of 0.0828 ng ml(minus sign1). In order to determine whether the population parameters obtained in this study were accurate, we administered digoxin according to individual dosage regimens in four DMD patients, using these values obtained by the Bayesian method. As a result, we found that mean prediction error, which indicates the deviation of prediction accuracy for digoxin concentration in plasma, was small, as were mean absolute prediction error and root mean squared error, showing the accuracy of this prediction method. The dosing method based on clearance values obtained by NONMEM analysis allowed the prediction of the steady-state concentration as a function of maintenance dose with acceptable error for therapeutic drug monitoring.

Journal Article↗
PubMed2003

Unsupervised robust nonparametric estimation of the hemodynamic response function for any fMRI experiment.

This paper deals with the estimation of the blood oxygen level-dependent response to a stimulus, as measured in functional magnetic resonance imaging (fMRI) data. A precise estimation is essential for a better understanding of cerebral activations. The most recent works have used a nonparametric framework for this estimation, considering each brain region as a system characterized by its impulse response, the so-called hemodynamic response function (HRF). However, the use of these techniques has remained limited since they are not well-adapted to real fMRI data. Here, we develop a threefold extension to previous works. We consider asynchronous event-related paradigms, account for different trial types and integrate several fMRI sessions into the estimation. These generalizations are simultaneously addressed through a badly conditioned observation model. Bayesian formalism is used to model temporal prior information of the underlying physiological process of the brain hemodynamic response. By this way, the HRF estimate results from a tradeoff between information brought by the data and by our prior knowledge. This tradeoff is modeled with hyperparameters that are set to the maximum-likelihood estimate using an expectation conditional maximization algorithm. The proposed unsupervised approach is validated on both synthetic and real fMRI data, the latter originating from a speech perception experiment.

Algorithms↗
PubMed2006

Sound source localization in real sound fields based on empirical statistics of interaural parameters.

The role of temporal fluctuations and systematic variations of interaural parameters in localization of sound sources in spatially distributed, nonstationary noise conditions was investigated. For this, Bayesian estimation was applied to interaural parameters calculated with physiologically plausible time and frequency resolution. Probability density functions (PDFs) of the interaural level differences (ILDs) and phase differences (IPDs) were estimated by measuring histograms for a directional sound source perturbed by several types of interfering noise at signal-to-noise ratios (SNRs) between -5 and +30 dB. A moment analysis of the PDFs reveals that the expected values shift and the standard deviations increase considerably with decreasing SNR, and that the PDFs have non-Gaussian shape at medium SNRs. A d' analysis of the PDFs indicates that elevation discrimination is possible even at low SNRs in the median plane by integrating information across frequency. Absolute sound localization was simulated by a Bayesian maximum a posteriori (MAP) procedure. The simulation is based on frequency integration of broadly tuned "detectors." Confusion patterns of real and estimated sound source directions are similar to those of human listeners. The results indicate that robust processing strategies are needed to exploit interaural parameters successfully in noise conditions due to their strong temporal fluctuations.

Acoustic Stimulation↗
PubMedDate not supplied

A Bayesian approach for constructing genetic maps when markers are miscoded.

The advent of molecular markers has created opportunities for a better understanding of quantitative inheritance and for developing novel strategies for genetic improvement of agricultural species, using information on quantitative trait loci (QTL). A QTL analysis relies on accurate genetic marker maps. At present, most statistical methods used for map construction ignore the fact that molecular data may be read with error. Often, however, there is ambiguity about some marker genotypes. A Bayesian MCMC approach for inferences about a genetic marker map when random miscoding of genotypes occurs is presented, and simulated and real data sets are analyzed. The results suggest that unless there is strong reason to believe that genotypes are ascertained without error, the proposed approach provides more reliable inference on the genetic map.

Bayes Theorem↗
PubMed1985

Bayesian estimation and prediction of clearance in high-dose methotrexate infusions.

Much attention has been paid to the problem of estimating the pharmacokinetic parameters of individual patients in order to optimize dosage choices. Individual kinetics determined by test-dose bolus injection are a basis for predicting drug concentrations after high-dose methotrexate infusion and for computing appropriate dosages. Simplifications may be attempted, even allowing the test-dose to be omitted by using Bayesian estimation rather than likelihood estimation. To individualize pharmacokinetic parameters, Bayesian estimation combines information about population characteristics and those of individuals based on few measured plasma levels during high-dose infusion. Application of this procedure to methotrexate reveals interesting predictive performances and ability to handle variation due to intraindividual time variability without using test doses. The methodology promises to be more efficient in computing dosages in order to avoid toxic levels and will be less expensive in routine clinical use.

Aged↗
PubMed2006

Accurate and fast off and online fuzzy ARTMAP-based image classification with application to genetic abnormality diagnosis.

We propose and investigate the fuzzy ARTMAP neural network in off and online classification of fluorescence in situ hybridization image signals enabling clinical diagnosis of numerical genetic abnormalities. We evaluate the classification task (detecting a several abnormalities separately or simultaneously), classifier paradigm (monolithic or hierarchical), ordering strategy for the training patterns (averaging or voting), training mode (for one epoch, with validation or until completion) and model sensitivity to parameters. We find the fuzzy ARTMAP accurate in accomplishing both tasks requiring only very few training epochs. Also, selecting a training ordering by voting is more precise than if averaging over orderings. If trained for only one epoch, the fuzzy ARTMAP provides fast, yet stable and accurate learning as well as insensitivity to model complexity. Early stop of training using a validation set reduces the fuzzy ARTMAP complexity as for other machine learning models but cannot improve accuracy beyond that achieved when training is completed. Compared to other machine learning models, the fuzzy ARTMAP does not loose but gain accuracy when overtrained, although increasing its number of categories. Learned incrementally, the fuzzy ARTMAP reaches its ultimate accuracy very fast obtaining most of its data representation capability and accuracy by using only a few examples. Finally, the fuzzy ARTMAP accuracy for this domain is comparable with those of the multilayer perceptron and support vector machine and superior to those of the naive Bayesian and linear classifiers.

Artificial Intelligence↗
PubMed2007

Clustering of genes into regulons using integrated modeling-COGRIM.

We present a Bayesian hierarchical model and Gibbs Sampling implementation that integrates gene expression, ChIP binding, and transcription factor motif data in a principled and robust fashion. COGRIM was applied to both unicellular and mammalian organisms under different scenarios of available data. In these applications, we demonstrate the ability to predict gene-transcription factor interactions with reduced numbers of false-positive findings and to make predictions beyond what is obtained when single types of data are considered.

CCAAT-Enhancer-Binding Protein-beta↗
PubMed1997

Design, construction and evaluation of systems to predict risk in obstetrics.

We present a systematic, practical approach to developing risk prediction systems, suitable for use with large databases of medical information. An important part of this approach is a novel feature selection algorithm which uses the area under the receiver operating characteristic (ROC) curve to measure the expected discriminative power of different sets of predictor variables. We describe this algorithm and use it to select variables to predict risk of a specific adverse pregnancy outcome: failure to progress in labour. Neural network, logistic regression and hierarchical Bayesian risk prediction models are constructed, all of which achieve close to the limit of performance attainable on this prediction task. We show that better prediction performance requires more discriminative clinical information rather than improved modelling techniques. It is also shown that better diagnostic criteria in clinical records would greatly assist the development of systems to predict risk in pregnancy.

Algorithms↗
PubMed1995

A Bayesian framework for understanding texture segmentation in the primary visual cortex.

This paper presents a mathematical theory for understanding the computations involved in texture segmentation in the primary visual cortex. We propose that texture segmentation is a part of the early visual system's overall strategy to infer surfaces of objects in a visual scene. Based on this insight, we use the Bayesian inference paradigm to formulate the texture segmentation problem into a maximum a posteriori surface inference problem. The dynamical system for finding the optimal solution of this problem can be characterized by two concurrent and interactive processes: a gradual sharpening of the boundary signals and a simultaneous smoothing of the surface signals. The behavior of these dynamical processes was studied using both analytical and computational methods. We present some computational results and mathematical predictions. This theory suggests a novel framework for understanding the functional roles of the complex cells in the primary visual cortex.

Bayes Theorem↗
PubMed1997

Second-stage least squares versus penalized quasi-likelihood for fitting hierarchical models in epidemiologic analyses.

Hierarchical regression analysis holds much promise for epidemiologic analysis, but has as yet seen limited application because of lack of easily used software and the relatively lengthy run times of preferred fitting methods (such as true maximum likelihood and Bayesian approaches). This paper compares three relatively simple choices for estimation of the regression coefficients: maximum-likelihood first stage combined with a weighted-least-squares second stage (MLLS); joint iteratively reweighted least squares fitting of first and second stage (JILS); and empirically penalized quasi-likelihood (EPQL). These choices can be combined with various methods for estimating the second-stage variance; the two contrasted here are based on first and second-stage residuals. JILS and EPQL yielded indistinguishable results, and had small sample performance superior to MLLS. In larger samples there was little practical difference among the methods. Use of first-stage residuals to estimate the prior variance required considerably more computation than use of second-stage residuals, but produced no discernible improvement in regression coefficient estimates. All three methods performed well for estimation of first-stage parameters but were less satisfactory for estimation of second-stage parameters.

Bayes Theorem↗
PubMed2002

Microarray analysis of corneal fibroblast gene expression after interleukin-1 treatment.

PURPOSE: To identify changes in gene expression in human corneal fibroblasts after exposure to interleukin-1alpha. METHODS: RNA was isolated from cultured human corneal fibroblasts after treatment with interleukin-1alpha and subjected to DNA microarray analysis. Changes in gene expression were determined by comparison with untreated cells in three independent experiments after a Bayesian statistical analysis of variance. RESULTS: Changes in gene expression were reproducibly observed in 165 genes representing previously identified and novel chemokines, matrix molecules, membrane receptors, angiogenic mediators, and transcription factors that correlated with pathophysiological responses to inflammation. Dramatic increases in gene expression were observed with exodus-1 (CCL20), MMP-12, and RhoA. CONCLUSIONS: DNA microarray analysis of the corneal fibroblast response to interleukin-1alpha provides important insight into modeling changes in gene expression and suggests novel therapeutic targets for the control of corneal inflammation.

Cells, Cultured↗
PubMedDate not supplied

Hypothesis testing for the genetic background of quantitative traits.

The testing of Bayesian point null hypotheses on variance component models have resulted in a tough assigmment for which no clear and generally accepted method exists. In this work we present what we believe is a succeeding approach to such a task. It is based on a simple reparameterization of the model in terms of the total variance and the proportion of the additive genetic variance with respect to it, as well as on the explicit inclusion on the prior probability of a discrete component at origin. The reparameterization was used to bypass an arbitrariness related to the impropriety of uninformative priors onto unbounded variables while the discrete component was necessary to overcome the zero probability assigned to sets of null measure by the usual continuous variable models. The method was tested against computer simulations with appealing results.

Algorithms↗
PubMed2005

Inferring figure-ground using a recurrent integrate-and-fire neural circuit.

Several theories of early visual perception hypothesize neural circuits that are responsible for assigning ownership of an object's occluding contour to a region which represents the "figure." Previously, we have presented a Bayesian network model which integrates multiple cues and uses belief propagation to infer local figure-ground relationships along an object's occluding contour. In this paper, we use a linear integrate-and-fire model to demonstrate how such inference mechanisms could be carried out in a biologically realistic neural circuit. The circuit maps the membrane potentials of individual neurons to log probabilities and uses recurrent connections to represent transition probabilities. The network's "perception" of figure-ground is demonstrated for several examples, including perceptually ambiguous figures, and compared qualitatively and quantitatively with human psychophysics.

Action Potentials↗
PubMed2006

Detection of gene copy number changes in CGH microarrays using a spatially correlated mixture model.

MOTIVATION: Comparative genomic hybridization array experiments that investigate gene copy number changes present new challenges for statistical analysis and call for methods that incorporate spatial dependence between sequences along the chromosome. For this purpose, we propose a novel method called CGHmix. It is based on a spatially structured mixture model with three states corresponding to genomic sequences that are either unmodified, deleted or amplified. Inference is performed in a Bayesian framework. From the output, posterior probabilities of belonging to each of the three states are estimated for each genomic sequence and used to classify them. RESULTS: Using simulated data, CGHmix is validated and compared with both a conventional unstructured mixture model and with a recently proposed data mining method. We demonstrate the good performance of CGHmix for classifying copy number changes. In addition, the method provides a good estimate of the false discovery rate. We also present the analysis of a cancer related dataset. SUPPLEMENTARY INFORMATION: http://www.bgx.org.uk/papers.html

Algorithms↗