Limits...
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.

Show MeSH

Related in: MedlinePlus

Identification of glia-to-neuron connections.(A) Connections from glial cells 1, 2, 3, 4, 5, and 6 to the 48 neurons, 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 an orange numeral indicates the neuron that gave the better cross-validated likelihood if the network structure included the corresponding glia-to-neuron connection. (B) Visualization of projection range of each glial cell. (Left) Projection ranges of the six glial cells are visualized. The color of each ellipse corresponds to that of the “sender” glial cell. (Right) Projection ranges of four glial cells out of the six to enable better visibility.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4230777&req=5

pcbi-1003949-g004: Identification of glia-to-neuron connections.(A) Connections from glial cells 1, 2, 3, 4, 5, and 6 to the 48 neurons, 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 an orange numeral indicates the neuron that gave the better cross-validated likelihood if the network structure included the corresponding glia-to-neuron connection. (B) Visualization of projection range of each glial cell. (Left) Projection ranges of the six glial cells are visualized. The color of each ellipse corresponds to that of the “sender” glial cell. (Right) Projection ranges of four glial cells out of the six to enable better visibility.

Mentions: We determined the existence of a connection () from the j-th glial cell to the i-th neuron using a newly designed t-statistic, , which determined whether the increase in the cross-validated likelihood resulting from the addition of the new connection was significant or not (see ‘Functional connectivity analysis’ section in Methods). We found that 24% of the glia-to-neuron pairs increased the cross-validated likelihood, and the remaining 76% decreased the cross-validated likelihood (Fig. S3). We also found that only 17 out of 288 possible glia-to-neuron connections could significantly increase the cross-validated likelihood () by performing the statistical test based on . This suggested sparsity in glia-to-neuron connections (Fig. 4A). When we compared the activities of a neuron–glia pair that was identified as connected (e.g., neuron 6 and glial cell 2) with another pair that was identified as not connected (e.g., neuron 6 and glial cell 1), the correlation between the neuronal firing rate and glial activity was higher for the connected pair () than that for the non-connected pair () (Fig. S4).


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 glia-to-neuron connections.(A) Connections from glial cells 1, 2, 3, 4, 5, and 6 to the 48 neurons, 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 an orange numeral indicates the neuron that gave the better cross-validated likelihood if the network structure included the corresponding glia-to-neuron connection. (B) Visualization of projection range of each glial cell. (Left) Projection ranges of the six glial cells are visualized. The color of each ellipse corresponds to that of the “sender” glial cell. (Right) Projection ranges of four glial cells out of the six to enable better visibility.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003949-g004: Identification of glia-to-neuron connections.(A) Connections from glial cells 1, 2, 3, 4, 5, and 6 to the 48 neurons, 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 an orange numeral indicates the neuron that gave the better cross-validated likelihood if the network structure included the corresponding glia-to-neuron connection. (B) Visualization of projection range of each glial cell. (Left) Projection ranges of the six glial cells are visualized. The color of each ellipse corresponds to that of the “sender” glial cell. (Right) Projection ranges of four glial cells out of the six to enable better visibility.
Mentions: We determined the existence of a connection () from the j-th glial cell to the i-th neuron using a newly designed t-statistic, , which determined whether the increase in the cross-validated likelihood resulting from the addition of the new connection was significant or not (see ‘Functional connectivity analysis’ section in Methods). We found that 24% of the glia-to-neuron pairs increased the cross-validated likelihood, and the remaining 76% decreased the cross-validated likelihood (Fig. S3). We also found that only 17 out of 288 possible glia-to-neuron connections could significantly increase the cross-validated likelihood () by performing the statistical test based on . This suggested sparsity in glia-to-neuron connections (Fig. 4A). When we compared the activities of a neuron–glia pair that was identified as connected (e.g., neuron 6 and glial cell 2) with another pair that was identified as not connected (e.g., neuron 6 and glial cell 1), the correlation between the neuronal firing rate and glial activity was higher for the connected pair () than that for the non-connected pair () (Fig. S4).

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