Stochastic attractor models of visual working memory

This paper investigates models of working memory in which memory traces evolve according to stochastic attractor dynamics. These models have previously been shown to account for response-biases that are manifest across multiple trials of a visual working memory task. Here we adapt this approach by making the stable fixed points correspond to the multiple items to be remembered within a single-trial, in accordance with standard dynamical perspectives of memory, and find evidence that this multi-item model can provide a better account of behavioural data from continuous-report tasks. Additionally, the multi-item model proposes a simple mechanism by which swap-errors arise: memory traces diffuse away from their initial state and are captured by the attractors of other items. Swap-error curves reveal the evolution of this process as a continuous function of time throughout the maintenance interval and can be inferred from experimental data. Consistent with previous findings, we find that empirical memory performance is not well characterised by a purely-diffusive process but rather by a stochastic process that also embodies error-correcting dynamics.

The emergence of task-relevant representations in a nonlinear decision-making task

This paper describes the relationship between performance in a decision-making task and the emergence of task-relevant representations. Participants learnt two tasks in which the appropriate response depended on multiple relevant stimuli and the underlying stimulus-outcome associations were governed by a latent feature that participants could discover. We divided participants into good and bad performers based on their overall classification rate and computed behavioural accuracy for each feature value. We found that participants with better performance had a better representation of the latent feature space. We then used representation similarity analysis on Electroencephalographic (EEG) data to identify when these representations emerge. We were able to decode task-relevant representations in a time window emerging 700 ms after stimulus presentation, but only for participants with good task performance. Our findings suggest that, in order to make good decisions, it is necessary to create and extract a low-dimensional representation of the task at hand.

Reconstructing anatomy from electro-physiological data

Here we show how it is possible to make estimates of brain structure based on MEG data. We do this by reconstructing functional estimates onto distorted cortical manifolds parameterised in terms of their spherical harmonics. We demonstrate that both empirical and simulated MEG data give rise to consistent and plausible anatomical estimates. Importantly, the estimation of structure from MEG data can be quantified in terms of millimetres from the true brain structure. We show, for simulated data, that the functional assumptions which are closer to the functional ground-truth give rise to anatomical estimates that are closer to the true anatomy.

Reading front to back: MEG evidence for early feedback effects during word recognition

Magnetoencephalography studies in humans have shown word-selective activity in the left inferior frontal gyrus (IFG) approximately 130 ms after word presentation ( Pammer et al. 2004; Cornelissen et al. 2009; Wheat et al. 2010). The role of this early frontal response is currently not known. We tested the hypothesis that the IFG provides top-down constraints on word recognition using dynamic causal modeling of magnetoencephalography data collected, while subjects viewed written words and false font stimuli. Subject-specific dipoles in left and right occipital, ventral occipitotemporal and frontal cortices were identified using Variational Bayesian Equivalent Current Dipole source reconstruction. A connectivity analysis tested how words and false font stimuli differentially modulated activity between these regions within the first 300 ms after stimulus presentation. We found that left inferior frontal activity showed stronger sensitivity to words than false font and a stronger feedback connection onto the left ventral occipitotemporal cortex (vOT) in the first 200 ms. Subsequently, the effect of words relative to false font was observed on feedforward connections from left occipital to ventral occipitotemporal and frontal regions. These findings demonstrate that left inferior frontal activity modulates vOT in the early stages of word processing and provides a mechanistic account of top-down effects during word recognition.

Bayesian model selection maps for group studies

This technical note describes the construction of posterior probability maps (PPMs) for Bayesian model selection (BMS) at the group level. This technique allows neuroimagers to make inferences about regionally specific effects using imaging data from a group of subjects. These effects are characterised using Bayesian model comparisons that are analogous to the F-tests used in statistical parametric mapping, with the advantage that the models to be compared do not need to be nested. Additionally, an arbitrary number of models can be compared together. This note describes the integration of the Bayesian mapping approach with a random effects analysis model for BMS using group data. We illustrate the method using fMRI data from a group of subjects performing a target detection task.

Estimating the transfer function from neuronal activity to BOLD using simultaneous EEG-fMRI

Previous studies using combined electrical and hemodynamic measurements of brain activity, such as EEG and (BOLD) fMRI, have yielded discrepant results regarding the relationship between neuronal activity and the associated BOLD response. In particular, some studies suggest that this link, or transfer function, depends on the frequency content of neuronal activity, while others suggest that total neuronal power accounts for the changes in BOLD. Here we explored this dependency by comparing different frequency-dependent and -independent transfer functions, using simultaneous EEG-fMRI. Our results suggest that changes in BOLD are indeed associated with changes in the spectral profile of neuronal activity and that these changes do not arise from one specific spectral band. Instead they result from the dynamics of the various frequency components together, in particular, from the relative power between high and low frequencies. Understanding the nature of the link between neuronal activity and BOLD plays a crucial role in improving the interpretability of BOLD images as well as on the design of more robust and realistic models for the integration of EEG and fMRI.

Graph-partitioned spatial priors for functional magnetic resonance images

Spatial models of functional magnetic resonance imaging (fMRI) data allow one to estimate the spatial smoothness of general linear model (GLM) parameters and eschew pre-process smoothing of data entailed by conventional mass-univariate analyses. Recently diffusion-based spatial priors [Harrison, L.M., Penny, W., Daunizeau, J., and Friston, K.J. (2008). Diffusion-based spatial priors for functional magnetic resonance images. NeuroImage.] were proposed, which provide a way to formulate an adaptive spatial basis, where the diffusion kernel of a weighted graph-Laplacian (WGL) is used as the prior covariance matrix over GLM parameters. An advantage of these is that they can be used to relax the assumption of isotropy and stationarity implicit in smoothing data with a fixed Gaussian kernel. The limitation of diffusion-based models is purely computational, due to the large number of voxels in a brain volume. One solution is to partition a brain volume into slices, using a spatial model for each slice. This reduces computational burden by approximating the full WGL with a block diagonal form, where each block can be analysed separately. While fMRI data are collected in slices, the functional structures exhibiting spatial coherence and continuity are generally three-dimensional, calling for a more informed partition. We address this using the graph-Laplacian to divide a brain volume into sub-graphs, whose shape can be arbitrary. Their shape depends crucially on edge weights of the graph, which can be based on the Euclidean distance between voxels (isotropic) or on GLM parameters (anisotropic) encoding functional responses. The result is an approximation the full WGL that retains its 3D form and also has potential for parallelism. We applied the method to high-resolution (1 mm(3)) fMRI data and compared models where a volume was divided into either slices or graph-partitions. Models were optimized using Expectation-Maximization and the approximate log-evidence computed to compare these different ways to partition a spatial prior. The high-resolution fMRI data presented here had greatest evidence for the graph partitioned anisotropic model, which was best able to preserve fine functional detail.

Diffusion-based spatial priors for functional magnetic resonance images

We recently outlined a Bayesian scheme for analyzing fMRI data using diffusion-based spatial priors [Harrison, L.M., Penny, W., Ashburner, J., Trujillo-Barreto, N., Friston, K.J., 2007. Diffusion-based spatial priors for imaging. NeuroImage 38, 677–695]. The current paper continues this theme, applying it to a single-subject functional magnetic resonance imaging (fMRI) study of the auditory system. We show that spatial priors on functional activations, based on diffusion, can be formulated in terms of the eigenmodes of a graph Laplacian. This allows one to discard eigenmodes with small eigenvalues, to provide a computationally efficient scheme. Furthermore, this formulation shows that diffusion-based priors are a generalization of conventional Laplacian priors [Penny, W.D., Trujillo-Barreto, N.J., Friston, K.J., 2005. Bayesian fMRI time series analysis with spatial priors. NeuroImage 24, 350–362]. Finally, we show how diffusion-based priors are a special case of Gaussian process models that can be inverted using classical covariance component estimation techniques like restricted maximum likelihood [Patterson, H.D., Thompson, R., 1974. Maximum likelihood estimation of components of variance. Paper presented at: 8th International Biometrics Conference (Constanta, Romania)]. The convention in SPM is to smooth data with a fixed isotropic Gaussian kernel before inverting a mass-univariate statistical model. This entails the strong assumption that data are generated smoothly throughout the brain. However, there is no way to determine if this assumption is supported by the data, because data are smoothed before statistical modeling. In contrast, if a spatial prior is used, smoothness is estimated given non-smoothed data. Explicit spatial priors enable formal model comparison of different prior assumptions, e.g., that data are generated from a stationary (i.e., fixed throughout the brain) or non-stationary spatial process. Indeed, for the auditory data we provide strong evidence for a non-stationary process, which concurs with a qualitative comparison of predicted activations at the boundary of functionally selective regions.

Diffusion-based spatial priors for imaging

We describe a Bayesian scheme to analyze images, which uses spatial priors encoded by a diffusion kernel, based on a weighted graph Laplacian. This provides a general framework to formulate a spatial model, whose parameters can be optimized. The application we have in mind is a spatiotemporal model for imaging data. We illustrate the method on a random effects analysis of fMRI contrast images from multiple subjects; this simplifies exposition of the model and enables a clear description of its salient features. Typically, imaging data are smoothed using a fixed Gaussian kernel as a pre-processing step before applying a mass-univariate statistical model (e.g., a general linear model) to provide images of parameter estimates. An alternative is to include smoothness in a multivariate statistical model (Penny, W.D., Trujillo-Barreto, N.J., Friston, K.J., 2005. Bayesian fMRI time series analysis with spatial priors. Neuroimage 24, 350–362). The advantage of the latter is that each parameter field is smoothed automatically, according to a measure of uncertainty, given the data. In this work, we investigate the use of diffusion kernels to encode spatial correlations among parameter estimates. Nonlinear diffusion has a long history in image processing; in particular, flows that depend on local image geometry (Romeny, B.M.T., 1994. Geometry-driven Diffusion in Computer Vision. Kluwer Academic Publishers) can be used as adaptive filters. This can furnish a non-stationary smoothing process that preserves features, which would otherwise be lost with a fixed Gaussian kernel. We describe a Bayesian framework that incorporates non-stationary, adaptive smoothing into a generative model to extract spatial features in parameter estimates. Critically, this means adaptive smoothing becomes an integral part of estimation and inference. We illustrate the method using synthetic and real fMRI data.

Bilinear dynamical systems

In this paper, we propose the use of bilinear dynamical systems (BDS)s for model-based deconvolution of fMRI time-series. The importance of this work lies in being able to deconvolve haemodynamic time-series, in an informed way, to disclose the underlying neuronal activity. Being able to estimate neuronal responses in a particular brain region is fundamental for many models of functional integration and connectivity in the brain. BDSs comprise a stochastic bilinear neurodynamical model specified in discrete time, and a set of linear convolution kernels for the haemodynamics. We derive an expectation-maximization (EM) algorithm for parameter estimation, in which fMRI time-series are deconvolved in an E-step and model parameters are updated in an M-Step. We report preliminary results that focus on the assumed stochastic nature of the neurodynamic model and compare the method to Wiener deconvolution.

Dynamic causal modelling

In this paper we present an approach to the identification of nonlinear input-state-output systems. By using a bilinear approximation to the dynamics of interactions among states, the parameters of the implicit causal model reduce to three sets. These comprise (1) parameters that mediate the influence of extrinsic inputs on the states, (2) parameters that mediate intrinsic coupling among the states, and (3) [bilinear] parameters that allow the inputs to modulate that coupling. Identification proceeds in a Bayesian framework given known, deterministic inputs and the observed responses of the system. We developed this approach for the analysis of effective connectivity using experimentally designed inputs and fMRI responses. In this context, the coupling parameters correspond to effective connectivity and the bilinear parameters reflect the changes in connectivity induced by inputs. The ensuing framework allows one to characterise fMRI experiments, conceptually, as an experimental manipulation of integration among brain regions (by contextual or trial-free inputs, like time or attentional set) that is revealed using evoked responses (to perturbations or trial-bound inputs, like stimuli). As with previous analyses of effective connectivity, the focus is on experimentally induced changes in coupling (cf., psychophysiologic interactions). However, unlike previous approaches in neuroimaging, the causal model ascribes responses to designed deterministic inputs, as opposed to treating inputs as unknown and stochastic.

Dynamic causal modelling

In this paper we present an approach to the identification of nonlinear input-state-output systems. By using a bilinear approximation to the dynamics of interactions among states, the parameters of the implicit causal model reduce to three sets. These comprise (1) parameters that mediate the influence of extrinsic inputs on the states, (2) parameters that mediate intrinsic coupling among the states, and (3) [bilinear] parameters that allow the inputs to modulate that coupling. Identification proceeds in a Bayesian framework given known, deterministic inputs and the observed responses of the system. We developed this approach for the analysis of effective connectivity using experimentally designed inputs and fMRI responses. In this context, the coupling parameters correspond to effective connectivity and the bilinear parameters reflect the changes in connectivity induced by inputs. The ensuing framework allows one to characterise fMRI experiments, conceptually, as an experimental manipulation of integration among brain regions (by contextual or trial-free inputs, like time or attentional set) that is revealed using evoked responses (to perturbations or trial-bound inputs, like stimuli). As with previous analyses of effective connectivity, the focus is on experimentally induced changes in coupling (cf., psychophysiologic interactions). However, unlike previous approaches in neuroimaging, the causal model ascribes responses to designed deterministic inputs, as opposed to treating inputs as unknown and stochastic.

Posterior probability maps and SPMs

This technical note describes the construction of posterior probability maps that enable conditional or Bayesian inferences about regionally specific effects in neuroimaging. Posterior probability maps are images of the probability or confidence that an activation exceeds some specified threshold, given the data. Posterior probability maps (PPMs) represent a complementary alternative to statistical parametric maps (SPMs) that are used to make classical inferences. However, a key problem in Bayesian inference is the specification of appropriate priors. This problem can be finessed using empirical Bayes in which prior variances are estimated from the data, under some simple assumptions about their form. Empirical Bayes requires a hierarchical observation model, in which higher levels can be regarded as providing prior constraints on lower levels. In neuroimaging, observations of the same effect over voxels provide a natural, two-level hierarchy that enables an empirical Bayesian approach. In this note we present a brief motivation and the operational details of a simple empirical Bayesian method for computing posterior probability maps. We then compare Bayesian and classical inference through the equivalent PPMs and SPMs testing for the same effect in the same data.

Classical and Bayesian inference in neuroimaging: theory

This paper reviews hierarchical observation models, used in functional neuroimaging, in a Bayesian light. It emphasizes the common ground shared by classical and Bayesian methods to show that conventional analyses of neuroimaging data can be usefully extended within an empirical Bayesian framework. In particular we formulate the procedures used in conventional data analysis in terms of hierarchical linear models and establish a connection between classical inference and parametric empirical Bayes (PEB) through covariance component estimation. This estimation is based on an expectation maximization or EM algorithm. The key point is that hierarchical models not only provide for appropriate inference at the highest level but that one can revisit lower levels suitably equipped to make Bayesian inferences. Bayesian inferences eschew many of the difficulties encountered with classical inference and characterize brain responses in a way that is more directly predicated on what one is interested in. The motivation for Bayesian approaches is reviewed and the theoretical background is presented in a way that relates to conventional methods, in particular restricted maximum likelihood (ReML). This paper is a technical and theoretical prelude to subsequent papers that deal with applications of the theory to a range of important issues in neuroimaging. These issues include; (i) Estimating nonsphericity or variance components in fMRI time-series that can arise from serial correlations within subject, or are induced by multisubject (i.e., hierarchical) studies. (ii) Spatiotemporal Bayesian models for imaging data, in which voxels-specific effects are constrained by responses in other voxels. (iii) Bayesian estimation of nonlinear models of hemodynamic responses and (iv) principled ways of mixing structural and functional priors in EEG source reconstruction. Although diverse, all these estimation problems are accommodated by the PEB framework described in this paper.

Myocardial bradykinin production during coronary balloon angioplasty in humans

BACKGROUND

Recent studies have implicated the peptide bradykinin as a potential trigger of ischaemic preconditioning, the phenomenon whereby a brief episode of myocardial ischaemia induces an increased tolerance to subsequent more prolonged ischaemia. Brief myocardial ischaemia occurring during percutaneous transluminal coronary balloon angioplasty in humans is reported to be capable of inducing preconditioning.

DESIGN

We studied the intracardiac production of bradykinin in eight patients (seven men, mean age 53.5 years) undergoing elective percutaneous transluminal coronary angioplasty for a single left anterior descending coronary artery stenosis. Paired blood samples were obtained from the coronary sinus and the proximal aorta at baseline, immediately before balloon deflation after a 2-min inflation, and at 1, 3 and 5 min post deflation. Bradykinin levels were measured by radioimmunoassay.

RESULTS

There was no significant change either in aortic or coronary sinus bradykinin levels at any time point.

CONCLUSIONS

Intracardiac production of bradykinin is unlikely to be a trigger for ischaemic preconditioning after brief myocardial ischaemia in humans.

Temporal and spatial complexity measures for electroencephalogram based brain-computer interfacing

There has been much interest recently in the concept of using information from the motor cortex region of the brain, recorded using non-invasive scalp electrodes, to construct a crude interface with a computer. It is known that movements of the limbs, for example, are accompanied by desynchronisations and synchronisations within the scalp-recorded electroencephalogram (EEG). These event-related desynchronisations and synchronisations (ERD and ERS), however, appear to be present when volition to move a limb occurs, even when actual movement of the limb does not in fact take place. The determination and classification of the ERD/S offers many exciting possibilities for the control of peripheral devices via computer analysis. To date most effort has concentrated on the analysis of the changes in absolute frequency content of signals recorded from the motor cortex. The authors present results which tackle the issues of both the interpretation of changes in signals with time and across channels with simple methods which monitor the temporal and spatial 'complexity' of the data. Results are shown on synthetic and real data sets.

Neural networks in clinical medicine

Neural networks are parallel, distributed, adaptive information-processing systems that develop their functionality in response to exposure to information. This paper is a tutorial for researchers intending to use neural nets for medical decision-making applications. It includes detailed discussion of the issues particularly relevant to medical data as well as wider issues relevant to any neural net application. The article is restricted to back-propagation learning in multilayer perceptrons, as this is the neural net model most widely used in medical applications.

Magnesium deficiency in critically ill patients

A study was performed to assess the value of estimation of intracellular magnesium in peripheral blood cells (red and mononuclear blood cells) in critically ill patients as an index of tissue magnesium content. A magnesium loading test was used to diagnose magnesium depletion in 16 critically ill patients. Patients were divided into magnesium depleted and non-depleted groups according to their response to the loading test. Pre-infusion plasma and intracellular (blood cell) magnesium levels were measured. There were no significant difference between the magnesium depleted (mean plasma magnesium 0.81 mmol.l-1, red blood cell magnesium 2.34 mmol.l-1, mononuclear blood cell magnesium 25.16 mmol.kg-1 dry weight) and non-depleted groups (mean plasma magnesium 0.90 mmol.l-1, red blood cell magnesium 2.18 mmol.l-1, mononuclear blood cell magnesium 18.1 mmol.kg-1 dry weight). We conclude that the diagnosis of magnesium depletion cannot be excluded in the face of normal plasma, red blood cell or mononuclear blood cell concentrations of magnesium.

Endothelium and atherosclerosis

PURPOSE

To review the effect of damaged endothelium on the development of atherosclerotic disease.

BACKGROUND

Atherosclerotic cardiovascular disease is a complex problem involving lipid deposition, blood pressure, rheologic forces, carbohydrate tolerance and thrombogenic factors. The loss of functional, if not structural, integrity of the vascular endothelium is closely related to the initiation of atherosclerosis. The endothelium contributes to local vascular regulation. Normal endothelial cells are thrombo-resistant; they prevent leukocyte adhesion and control vascular tone by converting angiotensin I into angiotensin II, inactivating bradykinin, norepinephrine, serotonin and ADP, and by secreting vasodilator substances, such as prostacyclin and endothelium-derived relaxing factor (EDRF), and contracting factors such as endothelin. Endothelin is a potent vasoconstrictor peptide that increases intracellular calcium, causing a rapid and transient increase in c-fos and c-myc messenger (m)RNA levels and DNA synthesis in rat vascular smooth muscle cells. In isolated vessel segments, altered endothelial vasoreactivity is usually demonstrated following mechanical trauma to the endothelium.

METHODS AND RESULTS

We investigated the function of normal and damaged endothelium in pigs, following superficial balloon injury, which produces a significant alteration in endothelium-dependent coronary vasoreactivity. The anesthetized pigs were given an intracoronary (left anterior descending artery) infusion of acetylcholine. The balloon injury caused a local transient spasm while the distal uninjured vessel did not change. When acetylcholine was given before the balloon injury, the diameter of the left anterior descending artery did not change, even after preconstriction in vivo with prostaglandin (PG) F2 alpha, but acetylcholine given after the balloon angioplasty caused dose-dependent vasoconstriction. It is known that vasoconstriction in arteries can reduce blood flow and increase arterial wall-shear forces, which increase platelet deposition in injured arteries and may precipitate rupture of atherosclerotic plaques.

CONCLUSION

The intact endothelium is one of the greatest sources of protection from arterial thrombosis, atherosclerosis and vasoconstriction.

Cardiac postjunctional supersensitivity to beta-agonists after chronic chemical sympathectomy with 6-hydroxydopamine

The sensitivity to sympathomimetic amines of isolated atria removed from sham-injected and 6-hydroxydopamine-treated (6-OHDA) guinea-pigs was examined in the presence of an extraneuronal uptake blocker and an alpha-adrenoceptor antagonist. Three weeks of pretreatment with 6-OHDA resulted in leftwards shifts of the dose-response curves for the positive chronotropic and inotropic responses of right and left atria to isoprenaline. The responses to the partial agonist salbutamol were also potentiated after 6-OHDA pretreatment, revealed as an increase in the maximum response relative to isoprenaline. The supersensitivity was post-synaptic in origin and independent of changes in disposition or metabolism, since it was observed with agonists immune to neuronal uptake and O-methylation, and in the presence of extraneuronal uptake inhibition by metanephrine. It was also specific for the beta-adrenoceptor, no supersensitivity to histamine being found. In the right atria, the supersensitivity was partially masked by an opposing depressant effect after 6-OHDA pretreatment which was observed with histamine. Dissociation constants (KA) for the left atrial inotropic responses to orciprenaline were determined by use of the antagonist Ro 03-7894. Atria from 6-OHDA-pretreated animals were supersensitive to orciprenaline, but the KA value did not differ from that after sham injection. It could therefore be concluded that the increase in sensitivity was not due to an increase in affinity for the beta-adrenoceptor.

Failure of thromboxane synthetase inhibition to improve exercise tolerance in patients with stable angina pectoris

We report the results of a double-blind study of dazoxiben in which treatment was continued for 3 weeks in patients with angiographically proven coronary artery disease who were receiving no other antiplatelet drugs.

The influence of suction catheter tip design on tracheobronchial trauma and fluid aspiration efficiency

The suctioning efficiency and trauma-producing characteristics of five commercially available tracheobronchial suction catheters (Pharmaseal Tri-Flo, NCC Gentle-Flo, Argyle Aero-Flo, Argyle Dual Side-Hole, and Pharmaseal Whistle-Tip) were experimentally evaluated in anesthetized healthy dogs. The tendency of catheters to invaginate or "grab" tracheobronchial mucosa was observed with a bronchofiberscope during suctioning. Mucosal grabbing was seldom seen even at high (greater than 300 torr) vacuum levels with the cateter tip in the trachea. All catheters were observed to invaginate mucosa in lobar and segmental bronchi, with the frequency of grabbing being a function of airway anatomy, airway size, catheter orientation, tip design, and vacuum level. Catheters with multiple side-holes appeared to invaginate mucosa less frequently than the single side-hole catheter. Repeated suctioning of anesthetized healthy dogs followed by tracheobronchial excision, gross observation, and histologic examination of mucosal tissue biopsies demonstrated significant differences in the frequency and severity of lesions caused by the tracheobronchial suction procedure. All catheters were observed to damage airway lining, the damage related to multiple side-hole catheters appearing to be associated entirely with the act of cateter insertion and not with the application of vacuum. Only the Whistle-Tip design produced measurable damage beyond that related to catheter insertion. The average tip-suctioning effectiveness for each catheter, determined in vitro by aspirating a thin, uniform layer of simulated mucus, was found to be significantly higher for the Tri-Flo and Whistle-Tip catheters than the others, the Aero-Flo being least effective. Preliminary attempts to demonstrate this difference in suctioning effectiveness by comparing the performance of the catheters which displayed the highest and lowest tip suction effectiveness in a standardized clinical suctioning procedure revealed no significant difference in the percentage of mucus removed by either catheter. Additional studies should clarify this apparent contradiction.