Automated Video Detection of Epileptic Convulsion Slowing as a Precursor for Post-Seizure Neuronal Collapse

Paroxysmal Cerebral Disorder

Phase clustering in transcranial magnetic stimulation-evoked EEG responses in genetic generalized epilepsy and migraine

Paroxysmal Cerebral Disorder

Dynamics of epileptic phenomena determined from statistics of ictal transitions

Contains fulltext : 54678.pdf (publisher's version ) (Closed access)In this paper, we investigate the dynamical scenarios of transitions between normal and paroxysmal state in epilepsy. We assume that some epileptic neural network are bistable i.e., they feature two operational states, ictal and interictal that co-exist. The transitions between these two states may occur according to a Poisson process, a random walk process or as a result of deterministic time-dependent mechanisms. We analyze data from animal models of absence epilepsy, human epilepsies and in vitro models. The distributions of durations of ictal and interictal epochs are fitted with a gamma distribution. On the basis of qualitative features of the fits, we identify the dynamical processes that may have generated the underlying data. The analysis showed that the following hold. 1) The dynamics of ictal epochs differ from those of interictal states. 2) Seizure initiation can be accounted for by a random walk process while seizure termination is often mediated by deterministic mechanisms. 3) In certain cases, the transitions between ictal and interictal states can be modeled by a Poisson process operating in a bistable network. These results imply that exact prediction of seizure occurrence is not possible but termination of an ictal state by appropriate counter stimulation might be feasible.9 p

Spinon and η -spinon correlation functions of the Hubbard chain

We calculate real-space static correlation functions of spin and charge degrees of freedom of the one-dimensional Hubbard model that are described by operators related to singly occupied sites with spin up or spin down (spinons) and unoccupied or doubly occupied sites ( η -spinons). The spatial decay of their correlation functions is determined using density matrix renormalization group results. The nature and spatial extent of the correlations between two sites on the Hubbard chain is studied using the eigenstates and eigenvalues of the two-site reduced density matrix. The results show that the spinon-spinon correlation functions decay algebraically and the η -spinon correlation functions decay exponentially, both in the half- filling and metallic phases. The results provide evidence that these degrees of freedom are organized in boundstates in the interacting system.Portuguese FCT both in the frame- work of the Strategic Project PEST-C/FIS/UI607/2011 and under SFRH/BSAB/1177 /201

Spectral and non-linear analysis of thalamocortical neural mass model oscillatory dynamics

The chapter is organised in two parts: In the first part, the focus is on a combined power spectral and non-linear behavioural analysis of a neural mass model of the thalamocortical circuitry. The objective is to study the effectiveness of such ‘multi-modal’ analytical techniques in model-based studies investigating the neural correlates of abnormal brain oscillations in Alzheimer’s disease (AD). The power spectral analysis presented here is a study of the ‘slowing’ (decreasing dominant frequency of oscillation) within the alpha frequency band (8 – 13 Hz), a hallmark of Electroencephalogram (EEG) dynamics in AD. Analysis of the nonlinear dynamical behaviour focuses on the bifurcating property of the model. The results show that the alpha rhythmic content is maximal at close proximity to the bifurcation point — an observation made possible by the ‘multi-modal’ approach adopted herein. Furthermore, a slowing in alpha rhythm is observed for increasing inhibitory connectivity — a consistent feature of our research into neuropathological oscillations associated with AD. In the second part, we have presented power spectral analysis on a model that implements multiple feed-forward and feed-back connectivities in the thalamo-cortico-thalamic circuitry, and is thus more advanced in terms of biological plausibility. This study looks at the effects of synaptic connectivity variation on the power spectra within the delta (1 – 3 Hz), theta (4 – 7 Hz), alpha (8 – 13 Hz) and beta (14 – 30 Hz) bands. An overall slowing of EEG with decreasing synaptic connectivity is observed, indicated by a decrease of power within alpha and beta bands and increase in power within the theta and delta bands. Thus, the model behaviour conforms to longitudinal studies in AD indicating an overall slowing of EEG