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W D Penny

Publications and source records attributed to W D Penny.

8 recordsLinked to original sources
PubMed2004

Comparing dynamic causal models.

This article describes the use of Bayes factors for comparing dynamic causal models (DCMs). DCMs are used to make inferences about effective connectivity from functional magnetic resonance imaging (fMRI) data. These inferences, however, are contingent upon assumptions about model structure, that is, the connectivity pattern between the regions included in the model. Given the current lack of detailed knowledge on anatomical connectivity in the human brain, there are often considerable degrees of freedom when defining the connectional structure of DCMs. In addition, many plausible scientific hypotheses may exist about which connections are changed by experimental manipulation, and a formal procedure for directly comparing these competing hypotheses is highly desirable. In this article, we show how Bayes factors can be used to guide choices about model structure, both concerning the intrinsic connectivity pattern and the contextual modulation of individual connections. The combined use of Bayes factors and DCM thus allows one to evaluate competing scientific theories about the architecture of large-scale neural networks and the neuronal interactions that mediate perception and cognition.

Attention↗
PubMed2004

Modelling functional integration: a comparison of structural equation and dynamic causal models.

The brain appears to adhere to two fundamental principles of functional organisation, functional integration and functional specialisation, where the integration within and among specialised areas is mediated by effective connectivity. In this paper, we review two different approaches to modelling effective connectivity from fMRI data, structural equation models (SEMs) and dynamic causal models (DCMs). In common to both approaches are model comparison frameworks in which inferences can be made about effective connectivity per se and about how that connectivity can be changed by perceptual or cognitive set. Underlying the two approaches, however, are two very different generative models. In DCM, a distinction is made between the 'neuronal level' and the 'hemodynamic level'. Experimental inputs cause changes in effective connectivity expressed at the level of neurodynamics, which in turn cause changes in the observed hemodynamics. In SEM, changes in effective connectivity lead directly to changes in the covariance structure of the observed hemodynamics. Because changes in effective connectivity in the brain occur at a neuronal level DCM is the preferred model for fMRI data. This review focuses on the underlying assumptions and limitations of each model and demonstrates their application to data from a study of attention to visual motion.

Algorithms↗
PubMed2003

Multivariate autoregressive modeling of fMRI time series.

We propose the use of multivariate autoregressive (MAR) models of functional magnetic resonance imaging time series to make inferences about functional integration within the human brain. The method is demonstrated with synthetic and real data showing how such models are able to characterize interregional dependence. We extend linear MAR models to accommodate nonlinear interactions to model top-down modulatory processes with bilinear terms. MAR models are time series models and thereby model temporal order within measured brain activity. A further benefit of the MAR approach is that connectivity maps may contain loops, yet exact inference can proceed within a linear framework. Model order selection and parameter estimation are implemented by using Bayesian methods.

Algorithms↗
PubMed2000

Real-time brain-computer interfacing: a preliminary study using Bayesian learning.

Preliminary results from real-time 'brain-computer interface' experiments are presented. The analysis is based on autoregressive modelling of a single EEG channel coupled with classification and temporal smoothing under a Bayesian paradigm. It is shown that uncertainty in decisions is taken into account under such a formalism and that this may be used to reject uncertain samples, thus dramatically improving system performance. Using the strictest rejection method, a classification performance of 86.5 +/- 6.9% is achieved over a set of seven subjects in two-way cursor movement experiments.

Bayes Theorem↗
PubMed2000

EEG-based communication: a pattern recognition approach.

We present an overview of our research into brain-computer interfacing (BCI). This comprises an offline study of the effect of motor imagery on EEG and an online study that uses pattern classifiers incorporating parameter uncertainty and temporal information to discriminate between different cognitive tasks in real-time.

Communication Devices for People with Disabilities↗
PubMed1999

Dynamic models for nonstationary signal segmentation.

This paper investigates Hidden Markov Models (HMMs) in which the observations are generated from an autoregressive (AR) model. The overall model performs nonstationary spectral analysis and automatically segments a time series into discrete dynamic regimes. Because learning in HMMs is sensitive to initial conditions, we initialize the HMM model with parameters derived from a cluster analysis of Kalman filter coefficients. An important aspect of the Kalman filter implementation is that the state noise is estimated on-line. This allows for an initial estimation of AR parameters for each of the different dynamic regimes. These estimates are then fine-tuned with the HMM model. The method is demonstrated on a number of synthetic problems and on electroencephalogram data.

Algorithms↗
PubMed1997

Neural network modeling of the level of observation decision in an acute psychiatric ward.

Patients in an acute psychiatric ward need to be observed with varying levels of closeness. We report a series of experiments in which neural networks were trained to model this "level of observation" decision. One hundred eighty-seven such clinical decisions were used to train and test the networks which were evaluated by a multitrial v-fold cross-validation procedure. One neural network modeling approach was to break down the decision process into four subproblems, each of which was solved by a perceptron unit. This resulted in a hierarchical perceptron network having a structure that was equivalent to a sparsely connected two-layer perceptron. Neural network approaches were compared with nearest neighbor, linear regression, and naive Bayes classifiers. The hierarchical and sparse neural networks were the most accurate classifiers. This shows that the decision process is nonlinear, that neural nets can be more accurate than other statistical approaches, and that hierarchical decomposition is a useful methodology for neural network design.

Acute Disease↗