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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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Identification of neuron-to-glia connections.(A) Connections from the 48 neurons to glial cells 1, 2, 3, 4, 5, and 6, all of which were identified using the t-statistics, , are shown in the top left, top right, middle left, middle right, bottom right and bottom left panels, respectively. Each ROI labeled by a green numeral indicates a glial cell for which the model's cross-validated likelihood deteriorated when the corresponding neuron-to-glia connection was removed. (B) Visualization of projection range to each of the six glial cells. The color of each ellipse corresponds to that of the “receiver” glial cell.
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pcbi-1003949-g005: Identification of neuron-to-glia connections.(A) Connections from the 48 neurons to glial cells 1, 2, 3, 4, 5, and 6, all of which were identified using the t-statistics, , are shown in the top left, top right, middle left, middle right, bottom right and bottom left panels, respectively. Each ROI labeled by a green numeral indicates a glial cell for which the model's cross-validated likelihood deteriorated when the corresponding neuron-to-glia connection was removed. (B) Visualization of projection range to each of the six glial cells. The color of each ellipse corresponds to that of the “receiver” glial cell.

Mentions: We also identified 89 neuron-to-glia connections out of 288 neuron-to-glia pairs with a similar t-statistic, (), where denotes the neuron-to-glia connection (Fig. 5A) (see ‘Functional connectivity analysis’ section in Methods). The average response function of the identified neuron-to-glia connections suggested small and inhibitory effects of neuronal activities on glial activities. The t-test () determined the temporal average of the response functions to be significantly negative. These results seemed to be inconsistent with those in experimental studies [8], [27], which have demonstrated excitatory neuron-to-glia connections. This inconsistency can be attributed to effects from other brain areas that were not considered in our study (e.g., the dentate gyrus), or to different experimental conditions. We need to emphasize that we observed spontaneous activities in our experiment while the preceding experiments mostly measured activities evoked by stimulation [19], [43] (also see Discussion).


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)

Identification of neuron-to-glia connections.(A) Connections from the 48 neurons to glial cells 1, 2, 3, 4, 5, and 6, all of which were identified using the t-statistics, , are shown in the top left, top right, middle left, middle right, bottom right and bottom left panels, respectively. Each ROI labeled by a green numeral indicates a glial cell for which the model's cross-validated likelihood deteriorated when the corresponding neuron-to-glia connection was removed. (B) Visualization of projection range to each of the six glial cells. The color of each ellipse corresponds to that of the “receiver” glial cell.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003949-g005: Identification of neuron-to-glia connections.(A) Connections from the 48 neurons to glial cells 1, 2, 3, 4, 5, and 6, all of which were identified using the t-statistics, , are shown in the top left, top right, middle left, middle right, bottom right and bottom left panels, respectively. Each ROI labeled by a green numeral indicates a glial cell for which the model's cross-validated likelihood deteriorated when the corresponding neuron-to-glia connection was removed. (B) Visualization of projection range to each of the six glial cells. The color of each ellipse corresponds to that of the “receiver” glial cell.
Mentions: We also identified 89 neuron-to-glia connections out of 288 neuron-to-glia pairs with a similar t-statistic, (), where denotes the neuron-to-glia connection (Fig. 5A) (see ‘Functional connectivity analysis’ section in Methods). The average response function of the identified neuron-to-glia connections suggested small and inhibitory effects of neuronal activities on glial activities. The t-test () determined the temporal average of the response functions to be significantly negative. These results seemed to be inconsistent with those in experimental studies [8], [27], which have demonstrated excitatory neuron-to-glia connections. This inconsistency can be attributed to effects from other brain areas that were not considered in our study (e.g., the dentate gyrus), or to different experimental conditions. We need to emphasize that we observed spontaneous activities in our experiment while the preceding experiments mostly measured activities evoked by stimulation [19], [43] (also see Discussion).

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