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A statistical method of identifying interactions in neuron-glia systems based on functional multicell Ca2+ imaging.

Nakae K, Ikegaya Y, Ishikawa T, Oba S, Urakubo H, Koyama M, Ishii S - PLoS Comput. Biol. (2014)

Bottom Line: The interactions in our interest included functional connectivity and response functions.We evaluated the cross-validated likelihood of GLMs that resulted from the addition or removal of connections to confirm the existence of specific neuron-to-glia or glia-to-neuron connections.We only accepted addition or removal when the modification improved the cross-validated likelihood.

View Article: PubMed Central - PubMed

Affiliation: Integrated Systems Biology Laboratory, Graduate School of Informatics, Kyoto University, Sakyo-ku, Kyoto, Japan.

ABSTRACT
Crosstalk between neurons and glia may constitute a significant part of information processing in the brain. We present a novel method of statistically identifying interactions in a neuron-glia network. We attempted to identify neuron-glia interactions from neuronal and glial activities via maximum-a-posteriori (MAP)-based parameter estimation by developing a generalized linear model (GLM) of a neuron-glia network. The interactions in our interest included functional connectivity and response functions. We evaluated the cross-validated likelihood of GLMs that resulted from the addition or removal of connections to confirm the existence of specific neuron-to-glia or glia-to-neuron connections. We only accepted addition or removal when the modification improved the cross-validated likelihood. We applied the method to a high-throughput, multicellular in vitro Ca2+ imaging dataset obtained from the CA3 region of a rat hippocampus, and then evaluated the reliability of connectivity estimates using a statistical test based on a surrogate method. Our findings based on the estimated connectivity were in good agreement with currently available physiological knowledge, suggesting our method can elucidate undiscovered functions of neuron-glia systems.

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Outline of functional connectivity analysis.(A) We statistically estimated the whole network of neurons and glial cells based on the neuronal and glial activities obtained from time-lapse Ca2+ imaging. A neuron–glia system consists of four types of possible connections (depicted by arrows): between neurons (blue), from glial cells to neurons (orange), from neurons to glial cells (green), and between glial cells (red). (B) Each specific connection in the neuron-glia network was identified by basically comparing the cross-validated likelihood between two network structures: (1) one with the connection and (2) the other without the connection.
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pcbi-1003949-g002: Outline of functional connectivity analysis.(A) We statistically estimated the whole network of neurons and glial cells based on the neuronal and glial activities obtained from time-lapse Ca2+ imaging. A neuron–glia system consists of four types of possible connections (depicted by arrows): between neurons (blue), from glial cells to neurons (orange), from neurons to glial cells (green), and between glial cells (red). (B) Each specific connection in the neuron-glia network was identified by basically comparing the cross-validated likelihood between two network structures: (1) one with the connection and (2) the other without the connection.

Mentions: We tried to identify the neuron–glia system based on this observation time series by estimating the parameters of our neuron–glia network model (Fig. 2A. See ‘Generative model and MAP estimation’ section of Methods). We developed a generalized linear model (GLM) of a neuron–glia network as a variation of previous GLMs used for neuronal networks [41]. We could efficiently and uniquely obtain maximum a posteriori (MAP) estimates of the parameters by assuming that the present activities of neurons and glial cells were independent conditional on their past. Using the MAP estimates, we could avoid ‘overfitting’, where the model estimates were disturbed by noise involved in the relatively short observation time series.


A statistical method of identifying interactions in neuron-glia systems based on functional multicell Ca2+ imaging.

Nakae K, Ikegaya Y, Ishikawa T, Oba S, Urakubo H, Koyama M, Ishii S - PLoS Comput. Biol. (2014)

Outline of functional connectivity analysis.(A) We statistically estimated the whole network of neurons and glial cells based on the neuronal and glial activities obtained from time-lapse Ca2+ imaging. A neuron–glia system consists of four types of possible connections (depicted by arrows): between neurons (blue), from glial cells to neurons (orange), from neurons to glial cells (green), and between glial cells (red). (B) Each specific connection in the neuron-glia network was identified by basically comparing the cross-validated likelihood between two network structures: (1) one with the connection and (2) the other without the connection.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4230777&req=5

pcbi-1003949-g002: Outline of functional connectivity analysis.(A) We statistically estimated the whole network of neurons and glial cells based on the neuronal and glial activities obtained from time-lapse Ca2+ imaging. A neuron–glia system consists of four types of possible connections (depicted by arrows): between neurons (blue), from glial cells to neurons (orange), from neurons to glial cells (green), and between glial cells (red). (B) Each specific connection in the neuron-glia network was identified by basically comparing the cross-validated likelihood between two network structures: (1) one with the connection and (2) the other without the connection.
Mentions: We tried to identify the neuron–glia system based on this observation time series by estimating the parameters of our neuron–glia network model (Fig. 2A. See ‘Generative model and MAP estimation’ section of Methods). We developed a generalized linear model (GLM) of a neuron–glia network as a variation of previous GLMs used for neuronal networks [41]. We could efficiently and uniquely obtain maximum a posteriori (MAP) estimates of the parameters by assuming that the present activities of neurons and glial cells were independent conditional on their past. Using the MAP estimates, we could avoid ‘overfitting’, where the model estimates were disturbed by noise involved in the relatively short observation time series.

Bottom Line: The interactions in our interest included functional connectivity and response functions.We evaluated the cross-validated likelihood of GLMs that resulted from the addition or removal of connections to confirm the existence of specific neuron-to-glia or glia-to-neuron connections.We only accepted addition or removal when the modification improved the cross-validated likelihood.

View Article: PubMed Central - PubMed

Affiliation: Integrated Systems Biology Laboratory, Graduate School of Informatics, Kyoto University, Sakyo-ku, Kyoto, Japan.

ABSTRACT
Crosstalk between neurons and glia may constitute a significant part of information processing in the brain. We present a novel method of statistically identifying interactions in a neuron-glia network. We attempted to identify neuron-glia interactions from neuronal and glial activities via maximum-a-posteriori (MAP)-based parameter estimation by developing a generalized linear model (GLM) of a neuron-glia network. The interactions in our interest included functional connectivity and response functions. We evaluated the cross-validated likelihood of GLMs that resulted from the addition or removal of connections to confirm the existence of specific neuron-to-glia or glia-to-neuron connections. We only accepted addition or removal when the modification improved the cross-validated likelihood. We applied the method to a high-throughput, multicellular in vitro Ca2+ imaging dataset obtained from the CA3 region of a rat hippocampus, and then evaluated the reliability of connectivity estimates using a statistical test based on a surrogate method. Our findings based on the estimated connectivity were in good agreement with currently available physiological knowledge, suggesting our method can elucidate undiscovered functions of neuron-glia systems.

Show MeSH
Related in: MedlinePlus