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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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Assimilating seizure data from CA1 hippocampal OLM interneurons.Membrane potential measured (red) by whole cell recording from OLM interneurons during spontaneous seizures (A). In (B–D) we show membrane potential, , and  of the same cell respectively estimated (black) by using UKF. As shown in Figure S1, we also tracked the remaining variables for IN. Data provided by Jokubas Ziburkus. Panel (A) modified from [23] with permission American Physiological Society.
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pcbi-1000776-g007: Assimilating seizure data from CA1 hippocampal OLM interneurons.Membrane potential measured (red) by whole cell recording from OLM interneurons during spontaneous seizures (A). In (B–D) we show membrane potential, , and of the same cell respectively estimated (black) by using UKF. As shown in Figure S1, we also tracked the remaining variables for IN. Data provided by Jokubas Ziburkus. Panel (A) modified from [23] with permission American Physiological Society.

Mentions: Pyramidal cells and interneurons in the hippocampus reside in different layers with different cell densities. To investigate whether there exist significant differences in the microenvironment surrounding these two cell types we assimilated membrane potential data from OLM interneurons in the hippocampus and reconstructed and ion concentrations inside and outside the cells. As shown in Figure 7, both the baseline level and peak near the interneurons must be very high as compared to that seen for the pyramidal cells (cf. Figure 4B). This is an important prediction in light of the recently observed interplay between pyramidal cells and interneurons during in vitro seizures [23]; in these experiments pyramidal cells were silent when the interneurons were intensively firing. Following intense firing the interneurons entered a state of depolarization block simultaneously with the emergence of intense epileptiform firing in pyramidal cells. Such a novel pattern of interleaving neuronal activity is proposed to be a possible mechanism for the sudden drop in inhibition during seizures – it may be permissive of runaway excitatory activity. The mechanism leading to such interplay, specifically the reasons for differential firing patterns in pyramidal cells and interneurons are unknown. Our results here indicate the potential role of the neuronal microenvironment in producing such interplay. Our findings suggest that the buffering mechanism in the OLM layer is weaker as compared with the pyramidal layer, thus causing higher in the OLM layer. The higher surrounding the interneurons causes increased excitability of the cell by raising the reversal potential for currents (higher than the pyramidal cells, see equation 7). The higher reversal potential for currents causes the interneuron to spontaneously burst fire at higher frequency and eventually drives the interneuron to transition into depolarization block when firing is peaked. As the INs enter the depolarized state, the inhibitory synaptic input from the INs to the PCs drops substantially, releasing PCs to generate the intense excitatory activity of seizures (equation 8, Figure S3). The collapse of inhibition due to the entrance of INs into a depolarized state also helps explain the sudden decrease in inhibition at seizure onset in neocortex described by Trevelyan, et al. [36] as the loss of inhibitory veto. As shown in Figure S1, we also tracked the remaining variables for the INs.


Assimilating seizure dynamics.

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

Assimilating seizure data from CA1 hippocampal OLM interneurons.Membrane potential measured (red) by whole cell recording from OLM interneurons during spontaneous seizures (A). In (B–D) we show membrane potential, , and  of the same cell respectively estimated (black) by using UKF. As shown in Figure S1, we also tracked the remaining variables for IN. Data provided by Jokubas Ziburkus. Panel (A) modified from [23] with permission American Physiological Society.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1000776-g007: Assimilating seizure data from CA1 hippocampal OLM interneurons.Membrane potential measured (red) by whole cell recording from OLM interneurons during spontaneous seizures (A). In (B–D) we show membrane potential, , and of the same cell respectively estimated (black) by using UKF. As shown in Figure S1, we also tracked the remaining variables for IN. Data provided by Jokubas Ziburkus. Panel (A) modified from [23] with permission American Physiological Society.
Mentions: Pyramidal cells and interneurons in the hippocampus reside in different layers with different cell densities. To investigate whether there exist significant differences in the microenvironment surrounding these two cell types we assimilated membrane potential data from OLM interneurons in the hippocampus and reconstructed and ion concentrations inside and outside the cells. As shown in Figure 7, both the baseline level and peak near the interneurons must be very high as compared to that seen for the pyramidal cells (cf. Figure 4B). This is an important prediction in light of the recently observed interplay between pyramidal cells and interneurons during in vitro seizures [23]; in these experiments pyramidal cells were silent when the interneurons were intensively firing. Following intense firing the interneurons entered a state of depolarization block simultaneously with the emergence of intense epileptiform firing in pyramidal cells. Such a novel pattern of interleaving neuronal activity is proposed to be a possible mechanism for the sudden drop in inhibition during seizures – it may be permissive of runaway excitatory activity. The mechanism leading to such interplay, specifically the reasons for differential firing patterns in pyramidal cells and interneurons are unknown. Our results here indicate the potential role of the neuronal microenvironment in producing such interplay. Our findings suggest that the buffering mechanism in the OLM layer is weaker as compared with the pyramidal layer, thus causing higher in the OLM layer. The higher surrounding the interneurons causes increased excitability of the cell by raising the reversal potential for currents (higher than the pyramidal cells, see equation 7). The higher reversal potential for currents causes the interneuron to spontaneously burst fire at higher frequency and eventually drives the interneuron to transition into depolarization block when firing is peaked. As the INs enter the depolarized state, the inhibitory synaptic input from the INs to the PCs drops substantially, releasing PCs to generate the intense excitatory activity of seizures (equation 8, Figure S3). The collapse of inhibition due to the entrance of INs into a depolarized state also helps explain the sudden decrease in inhibition at seizure onset in neocortex described by Trevelyan, et al. [36] as the loss of inhibitory veto. As shown in Figure S1, we also tracked the remaining variables for the INs.

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