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Using multi-compartment ensemble modeling as an investigative tool of spatially distributed biophysical balances: application to hippocampal oriens-lacunosum/moleculare (O-LM) cells.

Sekulić V, Lawrence JJ, Skinner FK - PLoS ONE (2014)

Bottom Line: Models were quantified and ranked based on minimal error compared to a dataset of O-LM cell electrophysiological properties.Co-regulatory balances between conductances were revealed, two of which were dependent on the presence of dendritic Ih.These findings inform future experiments that differentiate between somatic and dendritic Ih, thereby continuing a cycle between model and experiment.

View Article: PubMed Central - PubMed

Affiliation: Toronto Western Research Institute, University Health Network, Toronto, Ontario, Canada; Department of Physiology, University of Toronto, Toronto, Ontario, Canada.

ABSTRACT
Multi-compartmental models of neurons provide insight into the complex, integrative properties of dendrites. Because it is not feasible to experimentally determine the exact density and kinetics of each channel type in every neuronal compartment, an essential goal in developing models is to help characterize these properties. To address biological variability inherent in a given neuronal type, there has been a shift away from using hand-tuned models towards using ensembles or populations of models. In collectively capturing a neuron's output, ensemble modeling approaches uncover important conductance balances that control neuronal dynamics. However, conductances are never entirely known for a given neuron class in terms of its types, densities, kinetics and distributions. Thus, any multi-compartment model will always be incomplete. In this work, our main goal is to use ensemble modeling as an investigative tool of a neuron's biophysical balances, where the cycling between experiment and model is a design criterion from the start. We consider oriens-lacunosum/moleculare (O-LM) interneurons, a prominent interneuron subtype that plays an essential gating role of information flow in hippocampus. O-LM cells express the hyperpolarization-activated current (Ih). Although dendritic Ih could have a major influence on the integrative properties of O-LM cells, the compartmental distribution of Ih on O-LM dendrites is not known. Using a high-performance computing cluster, we generated a database of models that included those with or without dendritic Ih. A range of conductance values for nine different conductance types were used, and different morphologies explored. Models were quantified and ranked based on minimal error compared to a dataset of O-LM cell electrophysiological properties. Co-regulatory balances between conductances were revealed, two of which were dependent on the presence of dendritic Ih. These findings inform future experiments that differentiate between somatic and dendritic Ih, thereby continuing a cycle between model and experiment.

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Ranking of O-LM models against experimental data.(A) The ranking of models against O-LM cell experimental recordings shows a gradual decrease of the goodness-of-fit of a given model as the rank of the model becomes poorer. Hyperpolarizing and depolarizing voltage responses of two representative models, a highly-ranked one (B, red arrow in A) and poorly-ranked one (C, black arrow in A) are shown for comparison purposes. Examples of experimental voltage traces are shown in (D, E).
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pone-0106567-g002: Ranking of O-LM models against experimental data.(A) The ranking of models against O-LM cell experimental recordings shows a gradual decrease of the goodness-of-fit of a given model as the rank of the model becomes poorer. Hyperpolarizing and depolarizing voltage responses of two representative models, a highly-ranked one (B, red arrow in A) and poorly-ranked one (C, black arrow in A) are shown for comparison purposes. Examples of experimental voltage traces are shown in (D, E).

Mentions: We next processed raw voltage trace data from the total set of models simulated, and extracted quantitative metrics from these traces so as to enable direct comparisons between model and physiological O-LM cell output (Fig. 1, Step 2(i)). The goal of this step was to develop a principled way to determine which model outputs best corresponded to O-LM cell voltage outputs. Toward this goal, we used PANDORA, a MATLAB toolbox designed for the statistical analysis of model and experimental voltage trace data [30]. We imported both the model and O-LM cell experimental data into PANDORA and performed a ranking function on the model outputs, where each model was assigned a distance (an error measure) with respect to the entire experimental dataset (see Methods). As described in detail in the Methods, this measure is a quantifiable distance metric that is a statistical measure of all of the model's electrophysiological features compared with experimental ones and provides an objective test of its goodness-of-fit to the experimental data. We used equal weighing of the features to avoid the unintentional introduction of bias that would result by considering one or multiple features to be more important than others. We thought this reasonable to do at this time since it is currently unknown which features are critical for O-LM cell function in network and behavioural contexts. It is also important to note that this distance metric takes into account the biological variability as the various features are weighted by their standard deviation. The models were sorted according to their distance from the experimental dataset such that highly-ranked models had low distance values, and poorly-ranked models had high distance values (Fig. 2A). Voltage responses to ±90 pA current steps illustrated that a highly-ranked (Fig. 2A, red arrow) O-LM cell model (Fig. 2B) better represented O-LM cell properties than a poorly-ranked (Fig. 2A, black arrow) O-LM cell model (Fig. 2C), as confirmed by comparing several features, since the distance measure takes all features into consideration (see Methods). Examples can be viewed in Fig. 2 where highly-ranked models (Fig. 2B) better represented the empirical set of physiological O-LM cell recordings (Fig. 2D, E), as compared to lower-ranked ones (Fig. 2C). Thus, highly-ranked models seemed to capture important intrinsic properties of O-LM cells which the poorly-ranked models did not.


Using multi-compartment ensemble modeling as an investigative tool of spatially distributed biophysical balances: application to hippocampal oriens-lacunosum/moleculare (O-LM) cells.

Sekulić V, Lawrence JJ, Skinner FK - PLoS ONE (2014)

Ranking of O-LM models against experimental data.(A) The ranking of models against O-LM cell experimental recordings shows a gradual decrease of the goodness-of-fit of a given model as the rank of the model becomes poorer. Hyperpolarizing and depolarizing voltage responses of two representative models, a highly-ranked one (B, red arrow in A) and poorly-ranked one (C, black arrow in A) are shown for comparison purposes. Examples of experimental voltage traces are shown in (D, E).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4215854&req=5

pone-0106567-g002: Ranking of O-LM models against experimental data.(A) The ranking of models against O-LM cell experimental recordings shows a gradual decrease of the goodness-of-fit of a given model as the rank of the model becomes poorer. Hyperpolarizing and depolarizing voltage responses of two representative models, a highly-ranked one (B, red arrow in A) and poorly-ranked one (C, black arrow in A) are shown for comparison purposes. Examples of experimental voltage traces are shown in (D, E).
Mentions: We next processed raw voltage trace data from the total set of models simulated, and extracted quantitative metrics from these traces so as to enable direct comparisons between model and physiological O-LM cell output (Fig. 1, Step 2(i)). The goal of this step was to develop a principled way to determine which model outputs best corresponded to O-LM cell voltage outputs. Toward this goal, we used PANDORA, a MATLAB toolbox designed for the statistical analysis of model and experimental voltage trace data [30]. We imported both the model and O-LM cell experimental data into PANDORA and performed a ranking function on the model outputs, where each model was assigned a distance (an error measure) with respect to the entire experimental dataset (see Methods). As described in detail in the Methods, this measure is a quantifiable distance metric that is a statistical measure of all of the model's electrophysiological features compared with experimental ones and provides an objective test of its goodness-of-fit to the experimental data. We used equal weighing of the features to avoid the unintentional introduction of bias that would result by considering one or multiple features to be more important than others. We thought this reasonable to do at this time since it is currently unknown which features are critical for O-LM cell function in network and behavioural contexts. It is also important to note that this distance metric takes into account the biological variability as the various features are weighted by their standard deviation. The models were sorted according to their distance from the experimental dataset such that highly-ranked models had low distance values, and poorly-ranked models had high distance values (Fig. 2A). Voltage responses to ±90 pA current steps illustrated that a highly-ranked (Fig. 2A, red arrow) O-LM cell model (Fig. 2B) better represented O-LM cell properties than a poorly-ranked (Fig. 2A, black arrow) O-LM cell model (Fig. 2C), as confirmed by comparing several features, since the distance measure takes all features into consideration (see Methods). Examples can be viewed in Fig. 2 where highly-ranked models (Fig. 2B) better represented the empirical set of physiological O-LM cell recordings (Fig. 2D, E), as compared to lower-ranked ones (Fig. 2C). Thus, highly-ranked models seemed to capture important intrinsic properties of O-LM cells which the poorly-ranked models did not.

Bottom Line: Models were quantified and ranked based on minimal error compared to a dataset of O-LM cell electrophysiological properties.Co-regulatory balances between conductances were revealed, two of which were dependent on the presence of dendritic Ih.These findings inform future experiments that differentiate between somatic and dendritic Ih, thereby continuing a cycle between model and experiment.

View Article: PubMed Central - PubMed

Affiliation: Toronto Western Research Institute, University Health Network, Toronto, Ontario, Canada; Department of Physiology, University of Toronto, Toronto, Ontario, Canada.

ABSTRACT
Multi-compartmental models of neurons provide insight into the complex, integrative properties of dendrites. Because it is not feasible to experimentally determine the exact density and kinetics of each channel type in every neuronal compartment, an essential goal in developing models is to help characterize these properties. To address biological variability inherent in a given neuronal type, there has been a shift away from using hand-tuned models towards using ensembles or populations of models. In collectively capturing a neuron's output, ensemble modeling approaches uncover important conductance balances that control neuronal dynamics. However, conductances are never entirely known for a given neuron class in terms of its types, densities, kinetics and distributions. Thus, any multi-compartment model will always be incomplete. In this work, our main goal is to use ensemble modeling as an investigative tool of a neuron's biophysical balances, where the cycling between experiment and model is a design criterion from the start. We consider oriens-lacunosum/moleculare (O-LM) interneurons, a prominent interneuron subtype that plays an essential gating role of information flow in hippocampus. O-LM cells express the hyperpolarization-activated current (Ih). Although dendritic Ih could have a major influence on the integrative properties of O-LM cells, the compartmental distribution of Ih on O-LM dendrites is not known. Using a high-performance computing cluster, we generated a database of models that included those with or without dendritic Ih. A range of conductance values for nine different conductance types were used, and different morphologies explored. Models were quantified and ranked based on minimal error compared to a dataset of O-LM cell electrophysiological properties. Co-regulatory balances between conductances were revealed, two of which were dependent on the presence of dendritic Ih. These findings inform future experiments that differentiate between somatic and dendritic Ih, thereby continuing a cycle between model and experiment.

Show MeSH