There is a several decade history demonstrating that electrical polarization of neurons can modulate neuronal firing, and that such polarization can suppress (or excite) spiking activity and seizures. We have demonstrated seizure control using both open- and closed-loop stimulation strategies (J Neurophysiol, 76:4202-4205,1996; J Neurosci, 21:590-600, 2001). With past NIMH and CRCNS support (R01MH50006, 1R01EB014641) ? we discovered a unification in the computational biophysics of spikes, seizures, and spreading depression (J Neurosci, 34:11733-11743, 2014). These findings demonstrate that the repertoire of the dynamics of the neuronal membrane encompasses a broad range of dynamics ranging from normal to pathological, and that seizures and spreading depression are manifestations of the inherent properties of those membranes. Recently we achieved a major experimental verification of key predictions from the unification predictions in in vivo epilepsy. Most recently, we achieved the experimental goal of the most recent CRCNS project, ?Model-Based Control of Spreading Depression?, by demonstrating that neuronal polarization can suppress (or enhance), block, or prevent spreading depression, the physiological underpinning of migraine auras. Remarkably, this suppression requires the opposite polarity as that required to suppress spikes and seizures, and is fully consistent with the computational biophysical models of spreading depression. Further surprising findings from these experiments was that suppression of spreading depression does not appear to generate seizures, and vice versa, that when the brain is in seizure activity suppression does not generate spreading depression. The implications of the above is that in controlling brain dynamics from different states of the brain, that there can be state dependent control which is qualitatively very different from that required in other states. Furthermore, the control algorithms required to maintain a given steady state (e.g. normal spiking) may differ from that required to guide a system from a pathological state back into a steady state. We propose the hypothesis that there is an entirely new framework for feedback control of neuronal circuitry ? State Dependent Control. This is a model-based framework, wherein neuronal systems are sensed through electrical or optical sensors, and the data assimilated into a biophysical computational model of the possible states. Feedback control is then applied based upon the state, and the trajectory of the system through state space is continually observed. Working out state dependent control for brain activity has health implications for not only epilepsy and migraine, but more broadly in intensive care settings because of the harmful effects of spreading depression waves in traumatic brain injury, stroke, and subarachnoid hemorrhage.
We have discovered a unification of normal action potential spikes of a neuron, and the pathological states of seizures and spreading depression, which are different states of the repertoire of the neuron. We have found that we can electrically suppress seizures, and spreading depression, but consistent with neuronal biophysics, the electrical currents are the opposite polarity. We thus here propose a novel strategy of state dependent control of brain activity, which has potential health implications to improve the treatment of epilepsy, migraines, traumatic brain injury, stroke, and subarachnoid hemorrhage.
| Whalen, Andrew J; Xiao, Ying; Kadji, Herve et al. (2018) Control of Spreading Depression with Electrical Fields. Sci Rep 8:8769 |
| Whalen, Andrew J; Brennan, Sean N; Sauer, Timothy D et al. (2016) Effects Of Symmetry On The Structural Controllability Of Neural Networks: A Perspective. Proc Am Control Conf 2016:5785-5790 |
| Kuhlmann, Levin; Grayden, David B; Wendling, Fabrice et al. (2015) Role of multiple-scale modeling of epilepsy in seizure forecasting. J Clin Neurophysiol 32:220-6 |
| Whalen, Andrew J; Brennan, Sean N; Sauer, Timothy D et al. (2015) Observability and Controllability of Nonlinear Networks: The Role of Symmetry. Phys Rev X 5: |
| Ullah, Ghanim; Wei, Yina; Dahlem, Markus A et al. (2015) The Role of Cell Volume in the Dynamics of Seizure, Spreading Depression, and Anoxic Depolarization. PLoS Comput Biol 11:e1004414 |
| Wei, Yina; Ullah, Ghanim; Schiff, Steven J (2014) Unification of neuronal spikes, seizures, and spreading depression. J Neurosci 34:11733-43 |
| Wei, Yina; Ullah, Ghanim; Ingram, Justin et al. (2014) Oxygen and seizure dynamics: II. Computational modeling. J Neurophysiol 112:213-23 |
| Dahlem, Markus A; Rode, Sebastian; May, Arne et al. (2013) Towards dynamical network biomarkers in neuromodulation of episodic migraine. Transl Neurosci 4: |
| Jiruska, Premysl; de Curtis, Marco; Jefferys, John G R et al. (2013) Synchronization and desynchronization in epilepsy: controversies and hypotheses. J Physiol 591:787-97 |
| Whalen, Andrew J; Brennan, Sean N; Sauer, Timothy D et al. (2012) Observability of Neuronal Network Motifs. Proc Conf Inf Sci Syst 2012: |
