Metabifurcation analysis of a mean field model of the cortex

Mean field models (MFMs) of cortical tissue incorporate salient, average features of neural masses in order to model activity at the population level, thereby linking microscopic physiology to macroscopic observations, e.g., with the electroencephalogram (EEG). One of the common aspects of MFM descriptions is the presence of a high-dimensional parameter space capturing neurobiological attributes deemed relevant to the brain dynamics of interest. We study the physiological parameter space of a MFM of electrocortical activity and discover robust correlations between physiological attributes of the model cortex and its dynamical features. These correlations are revealed by the study of bifurcation plots, which show that the model responses to changes in inhibition belong to two archetypal categories or “families”. After investigating and characterizing them in depth, we discuss their essential differences in terms of four important aspects: power responses with respect to the modeled action of anesthetics, reaction to exogenous stimuli such as thalamic input, and distributions of model parameters and oscillatory repertoires when inhibition is enhanced. Furthermore, while the complexity of sustained periodic orbits differs significantly between families, we are able to show how metamorphoses between the families can be brought about by exogenous stimuli. We here unveil links between measurable physiological attributes of the brain and dynamical patterns that are not accessible by linear methods. They instead emerge when the nonlinear structure of parameter space is partitioned according to bifurcation responses. We call this general method “metabifurcation analysis”. The partitioning cannot be achieved by the investigation of only a small number of parameter sets and is instead the result of an automated bifurcation analysis of a representative sample of 73,454 physiologically admissible parameter sets. Our approach generalizes straightforwardly and is well suited to probing the dynamics of other models with large and complex parameter spaces.

Brain computer interface control via functional connectivity dynamics

The dynamics of inter-regional communication within the brain during cognitive processing – referred to as functional connectivity – are investigated as a control feature for a brain computer interface. EMDPL is used to map phase synchronization levels between all channel pair combinations in the EEG. This results in complex networks of channel connectivity at all time–frequency locations. The mean clustering coefficient is then used as a descriptive feature encapsulating information about inter-channel connectivity. Hidden Markov models are applied to characterize and classify dynamics of the resulting complex networks. Highly accurate levels of classification are achieved when this technique is applied to classify EEG recorded during real and imagined single finger taps. These results are compared to traditional features used in the classification of a finger tap BCI demonstrating that functional connectivity dynamics provide additional information and improved BCI control accuracies.

How the brain blinks: towards a neurocognitive model of the attentional blink

When people monitor a visual stream of rapidly presented stimuli for two targets (T1 and T2), they often miss T2 if it falls into a time window of about half a second after T1 onset-the attentional blink (AB). We provide an overview of recent neuroscientific studies devoted to analyze the neural processes underlying the AB and their temporal dynamics. The available evidence points to an attentional network involving temporal, right-parietal and frontal cortex, and suggests that the components of this neural network interact by means of synchronization and stimulus-induced desynchronization in the beta frequency range. We set up a neurocognitive scenario describing how the AB might emerge and why it depends on the presence of masks and the other event(s) the targets are embedded in. The scenario supports the idea that the AB arises from "biased competition", with the top-down bias being generated by parietal-frontal interactions and the competition taking place between stimulus codes in temporal cortex.

A mesoscopic modelling approach to anaesthetic action on brain electrical activity

Relating the measurable, large scale, effects of anaesthetic agents to their molecular and cellular targets of action is necessary to better understand the principles by which they affect behavior, as well as enabling the design and evaluation of more effective agents and the better clinical monitoring of existing and future drugs. Volatile and intravenous general anaesthetic agents (GAs) are now known to exert their effects on a variety of protein targets, the most important of which seem to be the neuronal ion channels. It is hence unlikely that anaesthetic effect is the result of a unitary mechanism at the single cell level. However, by altering the behavior of ion channels GAs are believed to change the overall dynamics of distributed networks of neurons. This disruption of regular network activity can be hypothesized to cause the hypnotic and analgesic effects of GAs and may well present more stereotypical characteristics than its underlying microscopic causes. Nevertheless, there have been surprisingly few theories that have attempted to integrate, in a quantitative manner, the empirically well documented alterations in neuronal ion channel behavior with the corresponding macroscopic effects. Here we outline one such approach, and show that a range of well documented effects of anaesthetics on the electroencephalogram (EEG) may be putatively accounted for. In particular we parameterize, on the basis of detailed empirical data, the effects of halogenated volatile ethers (a clinically widely used class of general anaesthetic agent). The resulting model is able to provisionally account for a range of anaesthetically induced EEG phenomena that include EEG slowing, biphasic changes in EEG power, and the dose dependent appearance of anomalous ictal activity, as well as providing a basis for novel approaches to monitoring brain function in both health and disease.

A model of the dynamic relationship between blood flow and volume changes during brain activation

The temporal relationship between changes in cerebral blood flow (CBF) and cerebral blood volume (CBV) is important in the biophysical modeling and interpretation of the hemodynamic response to activation, particularly in the context of magnetic resonance imaging and the blood oxygen level-dependent signal. Grubb et al. (1974) measured the steady state relationship between changes in CBV and CBF after hypercapnic challenge. The relationship CBV proportional to CBFPhi has been used extensively in the literature. Two similar models, the Balloon (Buxton et al., 1998) and the Windkessel (Mandeville et al., 1999), have been proposed to describe the temporal dynamics of changes in CBV with respect to changes in CBF. In this study, a dynamic model extending the Windkessel model by incorporating delayed compliance is presented. The extended model is better able to capture the dynamics of CBV changes after changes in CBF, particularly in the return-to-baseline stages of the response.

Molecular targets underlying SUMO-mediated neuroprotection in brain ischemia

SUMOylation (small ubiquitin-like modifier conjugation) is an important post-translational modification which is becoming increasingly implicated in the altered protein dynamics associated with brain ischemia. The function of SUMOylation in cells undergoing ischemic stress and the identity of small ubiquitin-like modifier (SUMO) targets remain in most cases unknown. However, the emerging consensus is that SUMOylation of certain proteins might be part of an endogenous neuroprotective response. This review brings together the current understanding of the underlying mechanisms and downstream effects of SUMOylation in brain ischemia, including processes such as autophagy, mitophagy and oxidative stress. We focus on recent advances and controversies regarding key central nervous system proteins, including those associated with the nucleus, cytoplasm and plasma membrane, such as glucose transporters (GLUT1, GLUT4), excitatory amino acid transporter 2 glutamate transporters, K+ channels (K2P1, Kv1.5, Kv2.1), GluK2 kainate receptors, mGluR8 glutamate receptors and CB1 cannabinoid receptors, which are reported to be SUMO-modified. A discussion of the roles of these molecular targets for SUMOylation could play following an ischemic event, particularly with respect to their potential neuroprotective impact in brain ischemia, is proposed.

Novel single trial movement classification based on temporal dynamics of EEG

Various complex oscillatory processes are involved in the generation of the motor command. The temporal dynamics of these processes were studied for movement detection from single trial electroencephalogram (EEG). Autocorrelation analysis was performed on the EEG signals to find robust markers of movement detection. The evolution of the autocorrelation function was characterised via the relaxation time of the autocorrelation by exponential curve fitting. It was observed that the decay constant of the exponential curve increased during movement, indicating that the autocorrelation function decays slowly during motor execution. Significant differences were observed between movement and no moment tasks. Additionally, a linear discriminant analysis (LDA) classifier was used to identify movement trials with a peak accuracy of 74%.

Changes in music tempo entrain movement related brain activity

The neural mechanisms of music listening and appreciation are not yet completely understood. Based on the apparent relationship between the beats per minute (tempo) of music and the desire to move (for example feet tapping) induced while listening to that music it is hypothesised that musical tempo may evoke movement related activity in the brain. Participants are instructed to listen, without moving, to a large range of musical pieces spanning a range of styles and tempos during an electroencephalogram (EEG) experiment. Event-related desynchronisation (ERD) in the EEG is observed to correlate significantly with the variance of the tempo of the musical stimuli. This suggests that the dynamics of the beat of the music may induce movement related brain activity in the motor cortex. Furthermore, significant correlations are observed between EEG activity in the alpha band over the motor cortex and the bandpower of the music in the same frequency band over time. This relationship is observed to correlate with the strength of the ERD, suggesting entrainment of motor cortical activity relates to increased ERD strength