Markov Model-Based Method to Analyse Time-Varying Networks in EEG Task-Related Data

The dynamic nature of functional brain networks is being increasingly recognized in cognitive neuroscience, and methods to analyse such time-varying networks in EEG/MEG data are required. In this work, we propose a pipeline to characterize time-varying networks in single-subject EEG task-related data and further, evaluate its validity on both simulated and experimental datasets. Pre-processing is done to remove channel-wise and trial-wise differences in activity. Functional networks are estimated from short non-overlapping time windows within each “trial,” using a sparse-MVAR (Multi-Variate Auto-Regressive) model. Functional “states” are then identified by partitioning the entire space of functional networks into a small number of groups/symbols via k-means clustering.The multi-trial sequence of symbols is then described by a Markov Model (MM). We show validity of this pipeline on realistic electrode-level simulated EEG data, by demonstrating its ability to discriminate “trials” from two experimental conditions in a range of scenarios. We then apply it to experimental data from two individuals using a Brain-Computer Interface (BCI) via a P300 oddball task. Using just the Markov Model parameters, we obtain statistically significant discrimination between target and non-target trials. The functional networks characterizing each ‘state’ were also highly similar between the two individuals. This work marks the first application of the Markov Model framework to infer time-varying networks from EEG/MEG data. Due to the pre-processing, results from the pipeline are orthogonal to those from conventional ERP averaging or a typical EEG microstate analysis. The results provide powerful proof-of-concept for a Markov model-based approach to analyzing the data, paving the way for its use to track rapid changes in interaction patterns as a task is being performed. MATLAB code for the entire pipeline has been made available.Peer reviewe

A study of anticipatory non-autonomous systems

Rhythms are manifested ubiquitously in dynamical biological processes. These fundamental processes which are necessary for the survival of living organisms include metabolism, breathing, heart beat, and, above all, the circadian rhythm coupled to the diurnal cycle. Thus, in mathematical biology, biological processes are often represented as linear or nonlinear oscillators. In the framework of nonlinear and dissipative systems (ie. the flow of energy, substances, or sensory information), they generate stable internal oscillations as a response to environmental input and, in turn, utilise such output as a means of coupling with the environment

Anticipation from sensation: using anticipating synchronisation to stabilise a system with inherent sensory delay

We present a novel way of using a dynamical model for predictive tracking control that can adapt to a wide range of delays without parameter update. This is achieved by incorporating the paradigm of anticipating synchronisation (AS), where a `slave' system predicts a `master' via delayed self-feedback. By treating the delayed output of the plant as one half of a `sensory' AS coupling, the plant and an internal dynamical model can be synchronised such that the plant consistently leads the target's motion. We use two simulated robotic systems with differing arrangements of the plant and internal model (`parallel' and `serial') to demonstrate that this form of control adapts to a wide range of delays without requiring the parameters of the controller to be changed

Embedded fuzzy logic controller for positive and negative pressure control in pneumatic soft robots

A key challenge in soft robotics is controlling the large deformation experienced as a result of high compliance nature of soft robots. In this work, a software control strategy for regulating the amount of internal positive and negative air pressure inside pneumatic soft robots is presented. Since the air pressure has a direct effect on the amount of deformation, the position of the robot is controlled. Pressure control was implemented with a fuzzy logic controller, which is described with its performance shown. The approach can be integrated into any specified soft robotic actuator requiring pneumatic actuation e.g. bending, triangular and muscle actuators

Deriving functional astrocytes from mouse embryonic stem cells with a fast and efficient protocol

A growing number of studies highlight the structural and functional diversity of astrocytes throughout the central nervous system. These cells are now seen as heterogeneous as neurons and are implicated in a number of neurological and psychiatric diseases. Efficient generation of diverse subtypes of astrocytes can be a useful tool in investigating synaptogenesis and patterns of activity in developing neural networks. In this study, we developed a protocol for the fast and efficient differentiation of astrocytes from mouse embryonic stem cells, as evidenced by the upregulation of genes related to astrocytic development (Gfap, Aldh1l1). Generated astrocytes exhibit phenotypic diversity, which is demonstrated by the variant expression of markers such as GFAP, ALDH1L1, AQP4 and S100β, amongst subgroups within the same cell population. In addition, astrocytes exhibited differential calcium transients upon stimulation with ATP. Our protocol will facilitate investigations, regarding the involvement of astrocytes in the structural and functional connectivity of neural networks

Fast and efficient differentiation of mouse embryonic stem cells into ATP-responsive astrocytes

Astrocytes are multifunctional cells in the CNS, involved in the regulation of neurovascular coupling, the modulation of electrolytes and the cycling of neurotransmitters at synapses. Induction of astrocytes from stem cells remains a largely underdeveloped area, as current protocols are time consuming, lack granularity in astrocytic subtype generation and often are not as efficient as neural induction methods. In this paper we present an efficient method to differentiate astrocytes from mouse embryonic stem cells. Our technique uses a cell suspension protocol to produce embryoid bodies (EBs) that are neurally inducted and seeded onto laminin coated surfaces. Plated EBs attach to the surface and release migrating cells to their surrounding environment, which are further inducted into the astrocytic lineage, through an optimized, heparin-based media. Characterization and functional assessment of the cells consists of immunofluorescent labelling for specific astrocytic proteins and sensitivity to ATP stimulation. Our experimental results show that even at the earliest stages of the protocol, cells are positive for astrocytic markers (GFAP, ALDH1L1, S100β, GLAST) with variant expression patterns and purinergic receptors (P2Y). Generated astrocytes also exhibit differential Ca2+ transients upon stimulation with ATP, which evolve over the differentiation period. Metabotropic purinoceptors P2Y1R are expressed and we offer preliminary evidence that metabotropic purinoceptors contribute to Ca2+ transients. Our protocol is simple, efficient and fast, facilitating its use in multiple investigations, particularly in vitro studies of engineered neural networks

Milliseconds matter: temporal order of visuo-tactile stimulation affects the ownership of a virtual hand

The sense of body ownership, that one’s body belongs to oneself, is a result of the integration of different sensory streams. This sense however is not error-free; in 1998 Botvinick and Cohen [3] showed the rubber hand illusion (RHI), an illusion that made a subject feel a rubber hand as their own. An important factor to induce the illusion is the timing of the applied visual and tactile stimulation to the rubber hand. Temporal delays greater than 500 ms eliminate the illusory ownership. This study investigates previously unexplored small delays between stimulation modalities and their effect for the perception of the RHI. Through a virtual reality setup of the RHI paradigm, it is shown that small delays can significantly alter the strength of the illusion. The order of the sensory modality presented plays a catalytic role to whether or not the inter-modal delay will have an effect on the illusion’s strength

A compact low-cost electronic hardware design for actuating soft robots

A low cost, compact embedded design approach for actuating soft robots is presented. The complete fabrication procedure and mode of operation was demonstrated, and the performance of the complete system was also demonstrated by building a microcontroller based hardware system which was used to actuate a soft robot for bending motion. The actuation system including the electronic circuit board and actuation components was embedded in a 3D-printed casing to ensure a compact approach for actuating soft robots. Results show the viability of the system in actuating and controlling siliconebased soft robots to achieve bending motions. Qualitative measurements of uniaxial tensile test, bending distance and pressure were obtained. This electronic design is easy to reproduce and integrate into any specified soft robotic device requiring pneumatic actuation

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%