Indexing sensory plasticity: Evidence for distinct Predictive Coding and Hebbian learning mechanisms in the cerebral cortex.

The Roving Mismatch Negativity (MMN), and Visual LTP paradigms are widely used as independent measures of sensory plasticity. However, the paradigms are built upon fundamentally different (and seemingly opposing) models of perceptual learning; namely, Predictive Coding (MMN) and Hebbian plasticity (LTP). The aim of the current study was to compare the generative mechanisms of the MMN and visual LTP, therefore assessing whether Predictive Coding and Hebbian mechanisms co-occur in the brain. Forty participants were presented with both paradigms during EEG recording. Consistent with Predictive Coding and Hebbian predictions, Dynamic Causal Modelling revealed that the generation of the MMN modulates forward and backward connections in the underlying network, while visual LTP only modulates forward connections. These results suggest that both Predictive Coding and Hebbian mechanisms are utilized by the brain under different task demands. This therefore indicates that both tasks provide unique insight into plasticity mechanisms, which has important implications for future studies of aberrant plasticity in clinical populations.

Neurophysiologically-informed markers of individual variability and pharmacological manipulation of human cortical gamma.

The ability to quantify synaptic function at the level of cortical microcircuits from non-invasive data would be enormously useful in the study of neuronal processing in humans and the pathophysiology that attends many neuropsychiatric disorders. Here, we provide proof of principle that one can estimate inter-and intra-laminar interactions among specific neuronal populations using induced gamma responses in the visual cortex of human subjects - using dynamic causal modelling based upon the canonical microcircuit (CMC; a simplistic model of a cortical column). Using variability in induced (spectral) responses over a large cohort of normal subjects, we find that the predominant determinants of gamma responses rest on recurrent and intrinsic connections between superficial pyramidal cells and inhibitory interneurons. Furthermore, variations in beta responses were mediated by inter-subject differences in the intrinsic connections between deep pyramidal cells and inhibitory interneurons. Interestingly, we also show that increasing the self-inhibition of superficial pyramidal cells suppresses the amplitude of gamma activity, while increasing its peak frequency. This systematic and nonlinear relationship was only disclosed by modelling the causes of induced responses. Crucially, we were able to validate this form of neurophysiological phenotyping by showing a selective effect of the GABA re-uptake inhibitor tiagabine on the rate constants of inhibitory interneurons. Remarkably, we were able to recover the pharmacodynamics of this effect over the course of several hours on a per subject basis. These findings speak to the possibility of measuring population specific synaptic function - and its response to pharmacological intervention - to provide subject-specific biomarkers of mesoscopic neuronal processes using non-invasive data. Finally, our results demonstrate that, using the CMC as a proxy, the synaptic mechanisms that underlie the gain control of neuronal message passing within and between different levels of cortical hierarchies may now be amenable to quantitative study using non-invasive (MEG) procedures.

Computational neuroimaging strategies for single patient predictions.

Neuroimaging increasingly exploits machine learning techniques in an attempt to achieve clinically relevant single-subject predictions. An alternative to machine learning, which tries to establish predictive links between features of the observed data and clinical variables, is the deployment of computational models for inferring on the (patho)physiological and cognitive mechanisms that generate behavioural and neuroimaging responses. This paper discusses the rationale behind a computational approach to neuroimaging-based single-subject inference, focusing on its potential for characterising disease mechanisms in individual subjects and mapping these characterisations to clinical predictions. Following an overview of two main approaches - Bayesian model selection and generative embedding - which can link computational models to individual predictions, we review how these methods accommodate heterogeneity in psychiatric and neurological spectrum disorders, help avoid erroneous interpretations of neuroimaging data, and establish a link between a mechanistic, model-based approach and the statistical perspectives afforded by machine learning.

Dynamic causal modeling with neural fields.

The aim of this paper is twofold: first, to introduce a neural field model motivated by a well-known neural mass model; second, to show how one can estimate model parameters pertaining to spatial (anatomical) properties of neuronal sources based on EEG or LFP spectra using Bayesian inference. Specifically, we consider neural field models of cortical activity as generative models in the context of dynamic causal modeling (DCM). This paper considers the simplest case of a single cortical source modeled by the spatiotemporal dynamics of hidden neuronal states on a bounded cortical surface or manifold. We build this model using multiple layers, corresponding to cortical lamina in the real cortical manifold. These layers correspond to the populations considered in classical (Jansen and Rit) neural mass models. This allows us to formulate a neural field model that can be reduced to a neural mass model using appropriate constraints on its spatial parameters. In turn, this enables one to compare and contrast the predicted responses from equivalent neural field and mass models respectively. We pursue this using empirical LFP data from a single electrode to show that the parameters controlling the spatial dynamics of cortical activity can be recovered, using DCM, even in the absence of explicit spatial information in observed data.

DCM for complex-valued data: cross-spectra, coherence and phase-delays.

This note describes an extension of Bayesian model inversion procedures for the Dynamic Causal Modeling (DCM) of complex-valued data. Modeling complex data can be particularly useful in the analysis of multivariate ergodic (stationary) time-series. We illustrate this with a generalization of DCM for steady-state responses that models both the real and imaginary parts of sample cross-spectra. DCM allows one to infer underlying biophysical parameters generating data (like synaptic time constants, connection strengths and conduction delays). Because transfer functions and complex cross-spectra can be generated from these parameters, one can also describe the implicit system architecture in terms of conventional (linear systems) measures; like coherence, phase-delay or cross-correlation functions. Crucially, these measures can be derived in both sensor and source-space. In other words, one can examine the cross-correlation or phase-delay functions between hidden neuronal sources using non-invasive data and relate these functions to synaptic parameters and neuronal conduction delays. We illustrate these points using local field potential recordings from the subthalamic nucleus and globus pallidus, with a special focus on the relationship between conduction delays and the ensuing phase relationships and cross-correlation time lags between population activities.

Ten simple rules for dynamic causal modeling.

Dynamic causal modeling (DCM) is a generic Bayesian framework for inferring hidden neuronal states from measurements of brain activity. It provides posterior estimates of neurobiologically interpretable quantities such as the effective strength of synaptic connections among neuronal populations and their context-dependent modulation. DCM is increasingly used in the analysis of a wide range of neuroimaging and electrophysiological data. Given the relative complexity of DCM, compared to conventional analysis techniques, a good knowledge of its theoretical foundations is needed to avoid pitfalls in its application and interpretation of results. By providing good practice recommendations for DCM, in the form of ten simple rules, we hope that this article serves as a helpful tutorial for the growing community of DCM users.

Dynamic causal models of steady-state responses.

In this paper, we describe a dynamic causal model (DCM) of steady-state responses in electrophysiological data that are summarised in terms of their cross-spectral density. These spectral data-features are generated by a biologically plausible, neural-mass model of coupled electromagnetic sources; where each source comprises three sub-populations. Under linearity and stationarity assumptions, the model's biophysical parameters (e.g., post-synaptic receptor density and time constants) prescribe the cross-spectral density of responses measured directly (e.g., local field potentials) or indirectly through some lead-field (e.g., electroencephalographic and magnetoencephalographic data). Inversion of the ensuing DCM provides conditional probabilities on the synaptic parameters of intrinsic and extrinsic connections in the underlying neuronal network. This means we can make inferences about synaptic physiology, as well as changes induced by pharmacological or behavioural manipulations, using the cross-spectral density of invasive or non-invasive electrophysiological recordings. In this paper, we focus on the form of the model, its inversion and validation using synthetic and real data. We conclude with an illustrative application to multi-channel local field potential data acquired during a learning experiment in mice.

Bayesian estimation of synaptic physiology from the spectral responses of neural masses.

We describe a Bayesian inference scheme for quantifying the active physiology of neuronal ensembles using local field recordings of synaptic potentials. This entails the inversion of a generative neural mass model of steady-state spectral activity. The inversion uses Expectation Maximization (EM) to furnish the posterior probability of key synaptic parameters and the marginal likelihood of the model itself. The neural mass model embeds prior knowledge pertaining to both the anatomical [synaptic] circuitry and plausible trajectories of neuronal dynamics. This model comprises a population of excitatory pyramidal cells, under local interneuron inhibition and driving excitation from layer IV stellate cells. Under quasi-stationary assumptions, the model can predict the spectral profile of local field potentials (LFP). This means model parameters can be optimised given real electrophysiological observations. The validity of inferences about synaptic parameters is demonstrated using simulated data and experimental recordings from the medial prefrontal cortex of control and isolation-reared Wistar rats. Specifically, we examined the maximum a posteriori estimates of parameters describing synaptic function in the two groups and tested predictions derived from concomitant microdialysis measures. The modelling of the LFP recordings revealed (i) a sensitization of post-synaptic excitatory responses, particularly marked in pyramidal cells, in the medial prefrontal cortex of socially isolated rats and (ii) increased neuronal adaptation. These inferences were consistent with predictions derived from experimental microdialysis measures of extracellular glutamate levels.

Changes in effective connectivity of human superior parietal lobule under multisensory and unisensory stimulation.

Previous event-related potential (ERP) studies have identified the superior parietal lobule (SPL) as actively multisensory. This study compares effective, or contextually active, connections to this region under unisensory and multisensory conditions. Effective connectivity, the influence of one brain region over another, during unisensory visual, unisensory auditory and multisensory audiovisual stimulation was investigated. ERPs were recorded from subdural electrodes placed over the parietal lobe of three patients while they conducted a rapid reaction-time task. A generative model of interacting neuronal ensembles for ERPs was inverted in a scheme allowing investigation of the connections from and to the SPL, a multisensory processing area. Important features of the ensemble model include inhibitory and excitatory feedback connections to pyramidal cells and extrinsic input to the stellate cell pool, with extrinsic forward and backward connections delineated by laminar connection differences between ensembles. The framework embeds the SPL in a plausible connection of distinct neuronal ensembles mirroring the integrated brain regions involved in the response task. Bayesian model comparison was used to test competing feed-forward and feed-backward models of how the electrophysiological data were generated. Comparisons were performed between multisensory and unisensory data. Findings from three patients show differences in summed unisensory and multisensory ERPs that can be accounted for by a mediation of both forward and backward connections to the SPL. In particular, a negative gain in all forward and backward connections to the SPL from other regions was observed during the period of multisensory integration, while a positive gain was observed for forward projections that arise from the SPL.

Neurochemical and behavioural interactions between ibogaine and nicotine in the rat.

1. In vivo brain microdialysis has been employed to investigate the effects of ibogaine on nicotine-induced changes in dopamine overflow in the nucleus accumbens (NAc) of freely moving rats. The effects of the compound on locomotor responses to nicotine and behaviour in the elevated plus-maze were also examined. 2. No changes were observed in the dopamine overflow or the locomotor activity of the animals following the administration of ibogaine (40 mg kg-1, i.p.). However, ibogaine, administered 22 h earlier, significantly (P < 0.01) attenuated the increase in dopamine overflow but not the hyperlocomotion, evoked by nicotine. 3. In the elevated plus-maze test, significant reductions in the open:total runway entries in both saline-treated controls (P < 0.05) and nicotine-treated (P < 0.01) rats were obtained when the animals were tested 22 h after pretreatment with ibogaine (40 mg kg-1, i.p.). The total activity was significantly (P < 0.01) greater in the nicotine-treated rats but this response was not affected by ibogaine pretreatment. 4. Administration of ibogaine was associated with reductions in the tissue levels of 5-hydroxyindoleacetic acid (5-HIAA) in the NAc (P < 0.01) and striatum (P < 0.05) and an increase in the level of this metabolite in the medial prefrontal cortex (mPFC) (P < 0.01) while the levels of dopamine and 5-hydroxytryptamine (5-HT) in the mPFC were reduced (P < 0.05). The DOPAC/dopamine (P < 0.05) and 5-HIAA/5-HT (P < 0.01) ratios were significantly increased in the mPFC for at least 7 days after a single treatment with ibogaine. 5. Ibogaine attenuates the nicotine-induced increases in dopamine overflow in the NAc and may, therefore, inhibit the rewarding effects of this drug. However, the long lasting anxiogenesis induced by ibogaine warrant further investigation before its use could be recommended for smokers.

Effects of acute D-CPPene on mesoaccumbens dopamine responses to nicotine in the rat.

Acute administration of the NMDA receptor antagonist, D-CPPene (SDZ EAA 494; 3-(2-carboxypiperazin-4-yl)-1-propenyl-1-phosphonic acid; 2 mg/kg), abolished (P < 0.01) the sensitised mesoaccumbens dopamine response to nicotine (0.4 mg/kg) measured using in vivo microdialysis, but not the increased locomotor activity, observed in rats pretreated with nicotine prior to the test day. D-CPPene enhanced (P < 0.01) the mesoaccumbens dopamine response, but not the locomotor response, to acute nicotine given to drug-naive rats. The data suggest that sensitised mesoaccumbens dopamine responses to nicotine involve co-stimulation of NMDA receptors but that this effect is not closely related to sensitisation of the locomotor response to the drug.

Digoxin-specific Fab fragments impair renal function in the rat.

The effect on renal function, and the plasma and urinary disposition, of digoxin-specific antibody fragments (DSFab), were studied using the rat as an experimental model. After 24h, DSFab (2 mg kg-1, i.v.) caused decreases in urine volume and creatinine clearance of 34 and 33%, respectively, when measured in the same rats. However, only the creatinine clearance was significantly changed when compared with a separate saline-treated control group. Plasma and urinary creatinine concentrations were unaffected by DSFab treatment. Since creatinine clearance approximates to glomerular filtration rate (GFR), it appears that a dose of DSFab equivalent to about one-fifth of the usual clinical dose, causes a reduction in GFR of about one-third. In patients undergoing digitalis therapy, a degree of renal impairment is common and it is possible that this may be exacerbated by treatment with DSFab. DSFab had an elimination half-life of 178 min, an apparent volume of distribution (Vd) of 106 mL kg-1 and a plasma clearance of 0.42 mL kg-1 min-1. If it is assumed that the plasma volume of a rat is approximately 35 mL kg-1, the measured Vd suggests appreciable penetration of DSFab into the extracellular fluid at this dose. Seventy-two hours after injection, only 7.6% of the administered dose of DSFab was found in the urine.

A neural mass model of spectral responses in electrophysiology.

We present a neural mass model of steady-state membrane potentials measured with local field potentials or electroencephalography in the frequency domain. This model is an extended version of previous dynamic causal models for investigating event-related potentials in the time-domain. In this paper, we augment the previous formulation with parameters that mediate spike-rate adaptation and recurrent intrinsic inhibitory connections. We then use linear systems analysis to show how the model's spectral response changes with its neurophysiological parameters. We demonstrate that much of the interesting behaviour depends on the non-linearity which couples mean membrane potential to mean spiking rate. This non-linearity is analogous, at the population level, to the firing rate-input curves often used to characterize single-cell responses. This function depends on the model's gain and adaptation currents which, neurobiologically, are influenced by the activity of modulatory neurotransmitters. The key contribution of this paper is to show how neuromodulatory effects can be modelled by adding adaptation currents to a simple phenomenological model of EEG. Critically, we show that these effects are expressed in a systematic way in the spectral density of EEG recordings. Inversion of the model, given such non-invasive recordings, should allow one to quantify pharmacologically induced changes in adaptation currents. In short, this work establishes a forward or generative model of electrophysiological recordings for psychopharmacological studies.

Murray Valley encephalitis virus surveillance and control initiatives in Australia. National Arbovirus Advisory Committee of the Communicable Diseases Network Australia.

Mechanisms for monitoring Murray Valley encephalitis (MVE) virus activity include surveillance of human cases, surveillance for activity in sentinel animals, monitoring of mosquito vectors and monitoring of weather conditions. The monitoring of human cases is only one possible trigger for public health action and the additional surveillance systems are used in concert to signal the risk of human disease, often before the appearance of human cases. Mosquito vector surveillance includes mosquito trapping for speciation and enumeration of mosquitoes to monitor population sizes and relative composition. Virus isolation from mosquitoes can also be undertaken. Monitoring of weather conditions and vector surveillance determines whether there is a potential for MVE activity to occur. Virus isolation from trapped mosquitoes is necessary to define whether MVE is actually present, but is difficult to deliver in a timely fashion in some jurisdictions. Monitoring of sentinel animals indicates whether MVE transmission to vertebrates is actually occurring. Meteorological surveillance can assist in the prediction of potential MVE virus activity by signalling conditions that have been associated with outbreaks of Murray Valley encephalitis in humans in the past. Predictive models of MVE virus activity for south-eastern Australia have been developed, but due to the infrequency of outbreaks, are yet to be demonstrated as useful for the forecasting of major outbreaks. Surveillance mechanisms vary across the jurisdictions. Surveillance of human disease occurs in all States and Territories by reporting of cases to health authorities. Sentinel flocks of chickens are maintained in 4 jurisdictions (Western Australia, the Northern Territory, Victoria and New South Wales) with collaborations between Western Australia and the Northern Territory. Mosquito monitoring complements the surveillance of sentinel animals in these jurisdictions. In addition, other mosquito monitoring programs exist in other States (including South Australia and Queensland). Public health control measures may include advice to the general public and mosquito management programs to reduce the numbers of both mosquito larvae and adult vectors. Strategic plans for public health action in the event of MVE virus activity are currently developed or being developed in New South Wales, the Northern Territory, South Australia, Western Australia and Victoria. A southern tri-State agreement exists between health departments of New South Wales, Victoria and South Australia and the Commonwealth Department of Health and Aged Care. All partners have agreed to co-operate and provide assistance in predicting and combatting outbreaks of mosquito-borne disease in south-eastern Australia. The newly formed National Arbovirus Advisory Committee is a working party providing advice to the Communicable Diseases Network Australia on arbovirus surveillance and control. Recommendations for further enhancement of national surveillance for Murray Valley encephalitis are described.