Prefrontal control over motor cortex cycles at beta-frequency during movement inhibition

A fully adapted behavior requires maximum efficiency to inhibit processes in the motor domain [ 1 ]. Although a number of cortical and subcortical brain regions have been implicated, converging evidence suggests that activation of right inferior frontal gyrus (r-IFG) and right presupplementary motor area (r-preSMA) is crucial for successful response inhibition [ 2, 3 ]. However, it is still unknown how these prefrontal areas convey the necessary signal to the primary motor cortex (M1), the cortical site where the final motor plan eventually has to be inhibited or executed. On the basis of the widely accepted view that brain oscillations are fundamental for communication between neuronal network elements [ 4–6 ], one would predict that the transmission of these inhibitory signals within the prefrontal-central networks (i.e., r-IFG/M1 and/or r-preSMA/M1) is realized in rapid, periodic bursts coinciding with oscillatory brain activity at a distinct frequency. However, the dynamics of corticocortical effective connectivity has never been directly tested on such timescales. By using double-coil transcranial magnetic stimulation (TMS) and electroencephalography (EEG) [ 7, 8 ], we assessed instantaneous prefrontal-to-motor cortex connectivity in a Go/NoGo paradigm as a function of delay from (Go/NoGo) cue onset. In NoGo trials only, the effects of a conditioning prefrontal TMS pulse on motor cortex excitability cycled at beta frequency, coinciding with a frontocentral beta signature in EEG. This establishes, for the first time, a tight link between effective cortical connectivity and related cortical oscillatory activity, leading to the conclusion that endogenous (top-down) inhibitory motor signals are transmitted in beta bursts in large-scale cortical networks for inhibitory motor control

Dynamic mechanisms of neocortical focal seizure onset.

Published onlineJournal ArticleResearch Support, Non-U.S. Gov'tRecent experimental and clinical studies have provided diverse insight into the mechanisms of human focal seizure initiation and propagation. Often these findings exist at different scales of observation, and are not reconciled into a common understanding. Here we develop a new, multiscale mathematical model of cortical electric activity with realistic mesoscopic connectivity. Relating the model dynamics to experimental and clinical findings leads us to propose three classes of dynamical mechanisms for the onset of focal seizures in a unified framework. These three classes are: (i) globally induced focal seizures; (ii) globally supported focal seizures; (iii) locally induced focal seizures. Using model simulations we illustrate these onset mechanisms and show how the three classes can be distinguished. Specifically, we find that although all focal seizures typically appear to arise from localised tissue, the mechanisms of onset could be due to either localised processes or processes on a larger spatial scale. We conclude that although focal seizures might have different patient-specific aetiologies and electrographic signatures, our model suggests that dynamically they can still be classified in a clinically useful way. Additionally, this novel classification according to the dynamical mechanisms is able to resolve some of the previously conflicting experimental and clinical findings.This work was supported by the Doctoral Training Centre in Systems Biology (University of Manchester), the Biotechnology and Biological Sciences Research Council, and the Engineering and Physical Sciences Research Council. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Dynamic mechanisms of neocortical focal seizure onset.

Recent experimental and clinical studies have provided diverse insight into the mechanisms of human focal seizure initiation and propagation. Often these findings exist at different scales of observation, and are not reconciled into a common understanding. Here we develop a new, multiscale mathematical model of cortical electric activity with realistic mesoscopic connectivity. Relating the model dynamics to experimental and clinical findings leads us to propose three classes of dynamical mechanisms for the onset of focal seizures in a unified framework. These three classes are: (i) globally induced focal seizures; (ii) globally supported focal seizures; (iii) locally induced focal seizures. Using model simulations we illustrate these onset mechanisms and show how the three classes can be distinguished. Specifically, we find that although all focal seizures typically appear to arise from localised tissue, the mechanisms of onset could be due to either localised processes or processes on a larger spatial scale. We conclude that although focal seizures might have different patient-specific aetiologies and electrographic signatures, our model suggests that dynamically they can still be classified in a clinically useful way. Additionally, this novel classification according to the dynamical mechanisms is able to resolve some of the previously conflicting experimental and clinical findings

Noise and delays in adaptive interacting oscillatory systems

Rodriguez J. Noise and delays in adaptive interacting oscillatory systems. Bielefeld: Universitätsbibliothek; 2013.In this thesis, we explore the global behavior of complex systems composed of interacting local dynamical systems, each set on a vertex of a network which characterizes the mutual interactions. We consider heterogeneous arrangements, meaning that for each vertex the local dynamics can be different. To better match potential applications we allow mutual interactions to be time delayed and subject to noise sources affecting either the orbits of the local dynamics and/or the connectivity of the network. Within this very general dynamical context, we construct and focus on interactions enabling a certain level of adaptation between the local dynamical systems. By propagation of information via the coupling network, the local parameters are adaptively tuned and ultimately reach a set of consensual values. This is explicitly and analytically carried out for frequency- and radius-adapting HOPF oscillators. We then consider adapting the time scale and the shape of periodic signals. We also study how adaptive mechanisms can be implemented in heterogeneous networks formed by a couple of subnetworks, the first one with adaptive capability and the second one without. The first subnetwork defines interactions between phase oscillators with adaptive frequency capability, the other subnetwork connects damped vibrating systems without adaptation. Next, noise sources are introduced into the dynamics via stochastic switchings of the network connections. This extra time-dependence in the network opens the possibility for parametric resonance and destabilization of a consensual oscillatory state, found for purely static networks. Finally, we introduce external noise environments which corrupt the orbits of the local systems. For ''All-to-All'' network topology, we analytically derive the effects of Gaussian and non-Gaussian noise sources and unveil noise induced emergent oscillating patterns of the relevant order parameter that characterizes this dynamics. Although in this thesis the emphasis is made on deriving analytical results, we systematically supplement our findings with extensive numerical simulations. They not only corroborate and illustrate our theoretical assertions but provide additional insights where analytical results could not be found