The physics of pattern formation has made dramatic strides over the past 20 years. Nevertheless, the ability to apply this knowledge to neuronal systems has been limited by our knowledge of the dynamics of neuronal interactions, and our lack of a system parameter to control such patterns. Over the previous period of this R01, we have demonstrated that electric fields can serve as a feedback parameter to adaptively control patterns of neuronal activity. Furthermore, we have demonstrated that the interactions between individual neurons as they form patterns can be characterized in detail. This renewal proposal will seek to test the Hypothesis that Parametric control of neuronal patterns can be achieved with electric field feedback. Such feedback can serve as a basic tool to explore how neuronal ensembles dynamically form and change their patterns of activity, and serve as a means to interact with and selectively modify pathological neuronal dynamics.
The Specific Aims for this research plan are to develop intelligent interaction and control strategies for neuronal activity patterns using 1) In Vitro experiments to determine how the intracellular interactions between neurons determine the nature of the transition between patterns under the influence of feedback electric fields, 2) develop a compartmental computational model to directly simulate these experiments, 3) to apply insights gained from the compartmental model to design and test rational control strategies for seizures and theta rhythm in In Vitro and In Vivo experiments. The long term goals are 1) basic science: to understand neuronal pattern formation and control from a physical point of view. I hope to exploit this insight to create more rational modulation and control strategies to probe the dynamics of neuronal networks, and 2) medical: to lay a foundation for interfacing with and controlling pathological brain dynamics without requiring surgical destruction of parts of the brain.
| La Corte, Giorgio; Wei, Yina; Chernyy, Nick et al. (2014) Frequency dependence of behavioral modulation by hippocampal electrical stimulation. J Neurophysiol 111:470-80 |
| Ingram, Justin; Zhang, Chunfeng; Cressman, John R et al. (2014) Oxygen and seizure dynamics: I. Experiments. J Neurophysiol 112:205-12 |
| Ziburkus, Jokubas; Cressman, John R; Schiff, Steven J (2013) Seizures as imbalanced up states: excitatory and inhibitory conductances during seizure-like events. J Neurophysiol 109:1296-306 |
| Berzhanskaya, Julia; Chernyy, Nick; Gluckman, Bruce J et al. (2013) Modulation of hippocampal rhythms by subthreshold electric fields and network topology. J Comput Neurosci 34:369-89 |
| Ingram, Justin M; Zhang, Chunfeng; Xu, Jian et al. (2013) FRET excited ratiometric oxygen sensing in living tissue. J Neurosci Methods 214:45-51 |
| Ullah, Ghanim; Schiff, Steven J (2010) Assimilating seizure dynamics. PLoS Comput Biol 6:e1000776 |
| Riester, Markus; Stephan-Otto Attolini, Camille; Downey, Robert J et al. (2010) A differentiation-based phylogeny of cancer subtypes. PLoS Comput Biol 6:e1000777 |
| Chernyy, Nick; Schiff, Steven J; Gluckman, Bruce J (2009) Time dependence of stimulation/recording-artifact transfer function estimates for neural interface systems. Conf Proc IEEE Eng Med Biol Soc 2009:1380-3 |
| Ullah, Ghanim; Cressman Jr, John R; Barreto, Ernest et al. (2009) The influence of sodium and potassium dynamics on excitability, seizures, and the stability of persistent states. II. Network and glial dynamics. J Comput Neurosci 26:171-83 |
| Sunderam, Sridhar; Chernyy, Nick; Peixoto, Nathalia et al. (2009) Seizure entrainment with polarizing low-frequency electric fields in a chronic animal epilepsy model. J Neural Eng 6:046009 |
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