Computational Evidence for a Competitive Thalamocortical Model of Spikes and Spindle Activity in Rolandic Epilepsy

Rolandic epilepsy (RE) is the most common idiopathic focal childhood epilepsy syndrome, characterized by sleep-activated epileptiform spikes and seizures and cognitive deficits in school age children. Recent evidence suggests that this disease may be caused by disruptions to the Rolandic thalamocortical circuit, resulting in both an abundance of epileptiform spikes and a paucity of sleep spindles in the Rolandic cortex during non-rapid eye movement sleep (NREM); electrographic features linked to seizures and cognitive symptoms, respectively. The neuronal mechanisms that support the competitive shared thalamocortical circuitry between pathological epileptiform spikes and physiological sleep spindles are not well-understood. In this study we introduce a computational thalamocortical model for the sleep-activated epileptiform spikes observed in RE. The cellular and neuronal circuits of this model incorporate recent experimental observations in RE, and replicate the electrophysiological features of RE. Using this model, we demonstrate that: (1) epileptiform spikes can be triggered and promoted by either a reduced NMDA current or h-type current; and (2) changes in inhibitory transmission in the thalamic reticular nucleus mediates an antagonistic dynamic between epileptiform spikes and spindles. This work provides the first computational model that both recapitulates electrophysiological features and provides a mechanistic explanation for the thalamocortical switch between the pathological and physiological electrophysiological rhythms observed during NREM sleep in this common epileptic encephalopathy

Chimeras in physics and biology : Synchronization and desynchronization of rhythms

Rhythmen prägen unser Leben auf vielfältige Weise, z. B. durch Herzschlag und Atmung, oszillierende Gehirnströme, Lebenszyklen und Jahreszeiten, Uhren und Metronome, pulsierende Laser, Übertragung von Datenpaketen, und vieles andere. Die Physik komplexer nichtlinearer Systeme hat Methoden entwickelt, wie periodische Schwingungen und deren Synchronisation in komplexen Netzwerken, die aus vielen Bestandteilen zusammengesetzt sind, beschrieben und analysiert werden können. Synchronisierte Oszillationen, aber auch völlig desynchronisierte, chaotische Oszillationen spielen eine große Rolle in vielen Netzwerken in Natur und Technik. Beispielsweise ist das synchronisierte Feuern aller Neuronen im Gehirn ein pathologischer Zustand, etwa bei Epilepsie oder Parkinson-Erkrankung, und sollte unterdrückt werden, wie auch synchrone mechanische Schwingungen von Brücken. Andererseits ist die Synchronisation erwünscht beim stabilen Betrieb von Stromnetzen oder bei der verschlüsselten Kommunikation mit chaotischen Signalen. In Netzwerken aus identischen Komponenten können sich überraschenderweise auch spontan Hybrid-Zustände („Schimären“) bilden, die aus räumlich koexistierenden synchronisierten und desynchronisierten Bereichen bestehen, welche scheinbar nicht zusammen passen. Diese könnten relevant sein bei der Auslösung oder Beendigung epileptischer Anfälle, oder beim halbseitigen Schlaf einer Gehirnhälfte, der bei bestimmten Zugvögeln oder Säugetieren auftritt, oder beim kaskadenartigen Zusammenbruch des Stromnetzes.Rhythms influence our life in various ways, e.g., through heart beat and respiration, oscillating brain currents, life cycles and seasons, clocks and metronomes, pulsating lasers, transmission of data packets, and many others. The physics of complex nonlinear systems has developed methods to describe and analyze periodic oscillations and their synchronization in complex networks, which are composed of many components. Synchronized oscillations as well as completely asynchronous chaotic oscillations play a major role in many networks in nature and technology. For instance, the synchronous firing of all neurons in the brain represents a pathological state, like in epilepsy or Parkinson’s disease, and should be suppressed, as well as the synchronous mechanical vibration of bridges. On the other hand, synchronization is desirable for the stable operation of power grids or in encrypted communication with chaotic signals. In networks composed of identical components, intriguing hybrid states (“chimeras”) may form spontaneously, which consist of spatially coexisting synchronized and desynchronized domains, i.e., seemingly incongruous parts. This might be of relevance in inducing and terminating epileptic seizures, or in unihemispheric sleep which is found in certain migratory birds and mammals, or in cascading failures of the power grid.DFG, 163436311, Kontrolle selbstorganisierender nichtlinearer Systeme: Theoretische Methoden und AnwendungskonzepteDFG, 308748074, DFG-RSF: Komplexe dynamische Netzwerke: Effekte von heterogenen, adaptiven und zeitverzögerten Kopplunge

Neural Field Models: A mathematical overview and unifying framework

Rhythmic electrical activity in the brain emerges from regular non-trivial interactions between millions of neurons. Neurons are intricate cellular structures that transmit excitatory (or inhibitory) signals to other neurons, often non-locally, depending on the graded input from other neurons. Often this requires extensive detail to model mathematically, which poses several issues in modelling large systems beyond clusters of neurons, such as the whole brain. Approaching large populations of neurons with interconnected constituent single-neuron models results in an accumulation of exponentially many complexities, rendering a realistic simulation that does not permit mathematical tractability and obfuscates the primary interactions required for emergent electrodynamical patterns in brain rhythms. A statistical mechanics approach with non-local interactions may circumvent these issues while maintaining mathematically tractability. Neural field theory is a population-level approach to modelling large sections of neural tissue based on these principles. Herein we provide a review of key stages of the history and development of neural field theory and contemporary uses of this branch of mathematical neuroscience. We elucidate a mathematical framework in which neural field models can be derived, highlighting the many significant inherited assumptions that exist in the current literature, so that their validity may be considered in light of further developments in both mathematical and experimental neuroscience.Comment: 55 pages, 10 figures, 2 table