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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 image preprocessing.(A) The rectangle indicates the target circuit of our analysis, a part of the hippocampal CA3 region of a rat, whose area was 18494. (B) The average Ca2+ fluorescence image over the whole observation period of 10 min. (C) Neuronal ROIs were defined as the regions exhibiting sufficiently large temporal variance within the Ca2+ imaging data (blue numerals. For more details on the detection procedure, see Methods). (D) Neuronal spikes in each ROI were detected as signal peaks (red points) with substantially high intensities in comparison to the standard deviation within the baseline. The baseline was estimated with an iterative procedure (see Methods). The blue line indicates the signal profile after baseline correction that includes detrending. (E) A spike profile for the ROIs from which we selected 48 ROIs that showed high frequencies of spikes. (F) We selected small and bright cell-like regions as glial ROIs (for more details, see Methods) in parallel with the detection of neuronal ROIs. (G) We took the time series as the average signal intensity within the ROI region for each glial ROI. (H) We obtained the activity time series of six glial ROIs after linear detrending and smoothing.
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pcbi-1003949-g001: Outline of image preprocessing.(A) The rectangle indicates the target circuit of our analysis, a part of the hippocampal CA3 region of a rat, whose area was 18494. (B) The average Ca2+ fluorescence image over the whole observation period of 10 min. (C) Neuronal ROIs were defined as the regions exhibiting sufficiently large temporal variance within the Ca2+ imaging data (blue numerals. For more details on the detection procedure, see Methods). (D) Neuronal spikes in each ROI were detected as signal peaks (red points) with substantially high intensities in comparison to the standard deviation within the baseline. The baseline was estimated with an iterative procedure (see Methods). The blue line indicates the signal profile after baseline correction that includes detrending. (E) A spike profile for the ROIs from which we selected 48 ROIs that showed high frequencies of spikes. (F) We selected small and bright cell-like regions as glial ROIs (for more details, see Methods) in parallel with the detection of neuronal ROIs. (G) We took the time series as the average signal intensity within the ROI region for each glial ROI. (H) We obtained the activity time series of six glial ROIs after linear detrending and smoothing.

Mentions: We developed a statistical method to identify the functional connectivity and response functions of neuron–glia networks in situ, which may reflect the dynamics of ionic receptors on neurons and glial cells. We applied it to a Ca2+ imaging dataset of an in vitro brain slice (see ‘ In vitro Ca2+ imaging’ section in Methods), by using the Ca2+ signal (concentration) as an indicator of neuronal as well as glial activities. We conducted high-resolution (18494 pixels) and high-speed Ca2+ imaging (100 Hz) from a CA3 region (184 94) of a rat's hippocampal slice to prepare the dataset by using Nipkow-type spinning-disk microscopy [40]. We observed spontaneous Ca2+ activities of neurons and glial cells within the 10 min of a fluorescence image series. An image preprocess applied to the image series extracted binary activities of 48 neurons and graded activities of six glial cells (Figs. 1E and 1H). The spike frequency of the 48 neurons was 0.03–1 Hz. The activity dataset thus consisted of the observation time series of 48 neurons and six glial cells.


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 image preprocessing.(A) The rectangle indicates the target circuit of our analysis, a part of the hippocampal CA3 region of a rat, whose area was 18494. (B) The average Ca2+ fluorescence image over the whole observation period of 10 min. (C) Neuronal ROIs were defined as the regions exhibiting sufficiently large temporal variance within the Ca2+ imaging data (blue numerals. For more details on the detection procedure, see Methods). (D) Neuronal spikes in each ROI were detected as signal peaks (red points) with substantially high intensities in comparison to the standard deviation within the baseline. The baseline was estimated with an iterative procedure (see Methods). The blue line indicates the signal profile after baseline correction that includes detrending. (E) A spike profile for the ROIs from which we selected 48 ROIs that showed high frequencies of spikes. (F) We selected small and bright cell-like regions as glial ROIs (for more details, see Methods) in parallel with the detection of neuronal ROIs. (G) We took the time series as the average signal intensity within the ROI region for each glial ROI. (H) We obtained the activity time series of six glial ROIs after linear detrending and smoothing.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003949-g001: Outline of image preprocessing.(A) The rectangle indicates the target circuit of our analysis, a part of the hippocampal CA3 region of a rat, whose area was 18494. (B) The average Ca2+ fluorescence image over the whole observation period of 10 min. (C) Neuronal ROIs were defined as the regions exhibiting sufficiently large temporal variance within the Ca2+ imaging data (blue numerals. For more details on the detection procedure, see Methods). (D) Neuronal spikes in each ROI were detected as signal peaks (red points) with substantially high intensities in comparison to the standard deviation within the baseline. The baseline was estimated with an iterative procedure (see Methods). The blue line indicates the signal profile after baseline correction that includes detrending. (E) A spike profile for the ROIs from which we selected 48 ROIs that showed high frequencies of spikes. (F) We selected small and bright cell-like regions as glial ROIs (for more details, see Methods) in parallel with the detection of neuronal ROIs. (G) We took the time series as the average signal intensity within the ROI region for each glial ROI. (H) We obtained the activity time series of six glial ROIs after linear detrending and smoothing.
Mentions: We developed a statistical method to identify the functional connectivity and response functions of neuron–glia networks in situ, which may reflect the dynamics of ionic receptors on neurons and glial cells. We applied it to a Ca2+ imaging dataset of an in vitro brain slice (see ‘ In vitro Ca2+ imaging’ section in Methods), by using the Ca2+ signal (concentration) as an indicator of neuronal as well as glial activities. We conducted high-resolution (18494 pixels) and high-speed Ca2+ imaging (100 Hz) from a CA3 region (184 94) of a rat's hippocampal slice to prepare the dataset by using Nipkow-type spinning-disk microscopy [40]. We observed spontaneous Ca2+ activities of neurons and glial cells within the 10 min of a fluorescence image series. An image preprocess applied to the image series extracted binary activities of 48 neurons and graded activities of six glial cells (Figs. 1E and 1H). The spike frequency of the 48 neurons was 0.03–1 Hz. The activity dataset thus consisted of the observation time series of 48 neurons and six glial cells.

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