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Assimilating seizure dynamics.

Ullah G, Schiff SJ - PLoS Comput. Biol. (2010)

Bottom Line: However, existing techniques are too limited to measure all but a small fraction of the physical variables and parameters of neuronal networks.We demonstrate that it is now possible to meaningfully estimate the dynamics of small neuronal networks using as few as a single measured variable.Data assimilation, the fusing of measurement with computational models, has significant potential to improve the way we observe and understand brain dynamics.

View Article: PubMed Central - PubMed

Affiliation: Center for Neural Engineering, Department of Engineering Science and Mechanics, The Pennsylvania State University, University Park, Pennsylvania, USA. ghanim@psu.edu

ABSTRACT
Observability of a dynamical system requires an understanding of its state-the collective values of its variables. However, existing techniques are too limited to measure all but a small fraction of the physical variables and parameters of neuronal networks. We constructed models of the biophysical properties of neuronal membrane, synaptic, and microenvironment dynamics, and incorporated them into a model-based predictor-controller framework from modern control theory. We demonstrate that it is now possible to meaningfully estimate the dynamics of small neuronal networks using as few as a single measured variable. Specifically, we assimilate noisy membrane potential measurements from individual hippocampal neurons to reconstruct the dynamics of networks of these cells, their extracellular microenvironment, and the activities of different neuronal types during seizures. We use reconstruction to account for unmeasured parts of the neuronal system, relating micro-domain metabolic processes to cellular excitability, and validate the reconstruction of cellular dynamical interactions against actual measurements. Data assimilation, the fusing of measurement with computational models, has significant potential to improve the way we observe and understand brain dynamics.

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Expanded view of third seizure in Figure 4 illustrating how  changes during a seizure.(A) membrane potential, , (B) extracellular potassium concentration, .
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pcbi-1000776-g005: Expanded view of third seizure in Figure 4 illustrating how changes during a seizure.(A) membrane potential, , (B) extracellular potassium concentration, .

Mentions: In Figure 5, we show an expanded view of a single cell response during a single seizure from Figure 4. Extracellular potassium concentration increases several fold above baseline values during seizures [31]. During a single seizure, starts rising from a baseline value of 3.0mM as the seizure begins and peaks at 7mM at the middle of the seizure (Figure 5). Interestingly the estimated by UKF matches very closely the measured seen in vitro studies [34].


Assimilating seizure dynamics.

Ullah G, Schiff SJ - PLoS Comput. Biol. (2010)

Expanded view of third seizure in Figure 4 illustrating how  changes during a seizure.(A) membrane potential, , (B) extracellular potassium concentration, .
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2865517&req=5

pcbi-1000776-g005: Expanded view of third seizure in Figure 4 illustrating how changes during a seizure.(A) membrane potential, , (B) extracellular potassium concentration, .
Mentions: In Figure 5, we show an expanded view of a single cell response during a single seizure from Figure 4. Extracellular potassium concentration increases several fold above baseline values during seizures [31]. During a single seizure, starts rising from a baseline value of 3.0mM as the seizure begins and peaks at 7mM at the middle of the seizure (Figure 5). Interestingly the estimated by UKF matches very closely the measured seen in vitro studies [34].

Bottom Line: However, existing techniques are too limited to measure all but a small fraction of the physical variables and parameters of neuronal networks.We demonstrate that it is now possible to meaningfully estimate the dynamics of small neuronal networks using as few as a single measured variable.Data assimilation, the fusing of measurement with computational models, has significant potential to improve the way we observe and understand brain dynamics.

View Article: PubMed Central - PubMed

Affiliation: Center for Neural Engineering, Department of Engineering Science and Mechanics, The Pennsylvania State University, University Park, Pennsylvania, USA. ghanim@psu.edu

ABSTRACT
Observability of a dynamical system requires an understanding of its state-the collective values of its variables. However, existing techniques are too limited to measure all but a small fraction of the physical variables and parameters of neuronal networks. We constructed models of the biophysical properties of neuronal membrane, synaptic, and microenvironment dynamics, and incorporated them into a model-based predictor-controller framework from modern control theory. We demonstrate that it is now possible to meaningfully estimate the dynamics of small neuronal networks using as few as a single measured variable. Specifically, we assimilate noisy membrane potential measurements from individual hippocampal neurons to reconstruct the dynamics of networks of these cells, their extracellular microenvironment, and the activities of different neuronal types during seizures. We use reconstruction to account for unmeasured parts of the neuronal system, relating micro-domain metabolic processes to cellular excitability, and validate the reconstruction of cellular dynamical interactions against actual measurements. Data assimilation, the fusing of measurement with computational models, has significant potential to improve the way we observe and understand brain dynamics.

Show MeSH
Related in: MedlinePlus