5 research outputs found

    Dynamics of collective multi-stability in models of multi-unit neuronal systems

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    In this study, we investigate the correspondence between dynamic patterns of behavior in two types of computational models of neuronal activity. The first model type is the realistic neuronal model; the second model type is the phenomenological or analytical model. In the simplest model set-up of two interconnected units, we define a parameter space for both types of systems where their behavior is similar. Next we expand the analytical model to two sets of 90 fully interconnected units with some overlap, which can display multi-stable behavior. This system can be in three classes of states: (i) a class consisting of a single resting state, where all units of a set are in steady state, (ii) a class consisting of multiple preserving states, where subsets of the units of a set participate in limit cycle, and (iii) a class consisting of a single saturated state, where all units of a set are recruited in a global limit cycle. In the third and final part of the work, we demonstrate that phase synchronization of units can be detected by a single output uni

    Dynamics of brain states and cortical excitability in paroxysmal neurological conditions

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    Epilepsy and migraine are neurological conditions that are characterised by periods of disruption of normal neuronal functioning. Aside from this paroxysmal feature, both conditions share genetic mutations and altered cortical excitability. People with epilepsy appear to be diagnosed with migraine more often than people without epilepsy and, likewise, people with migraine seem to be diagnosed with epilepsy more often than people without migraine. Changes in cortical excitability may help explain the pathophysiological link between both conditions, and could be a biomarker to monitor disease activity. In this thesis, the association between migraine and epilepsy and their relation to cortical excitability is further explored. A meta-analysis of previous population based studies provides epidemiological evidence for the co-occurrence of migraine and epilepsy. The combination of computer modelling with human electroencephalographic recordings offers insight into multi-stability of brain states in epilepsy. Results described in this thesis show that Transcranial Magnetic Stimulation can be used to measure cortical excitability, but that its use as a biomarker of disease activity in epilepsy is limited due to large interindividual variability. By combining Transcranial Magnetic Stimulation with electroencephalography, two novel variables that may contribute to cortical excitability are investigated: phase clustering, which possibly reflecting functional neuronal connectivity, and the non-linear residual of a stimulus-response curve, which may reflect brain state multi-stability. The results presented in this thesis suggest that the higher propensity to global synchronisation is not shared between epilepsy and migraine. These new variables have potential value to differentiate people with epilepsy, but not people with migraine, from normal controls

    Critical fluctuations and coupling of stochastic neural mass models

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