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Comparing Analysis Methods in Functional Calcium Imaging of the Insect Brain.

Balkenius A, Johansson AJ, Balkenius C - PLoS ONE (2015)

Bottom Line: We investigate four different methods for background estimation in calcium imaging of the insect brain and evaluate their performance on six data sets consisting of data recorded from two sites in two species of moths.The calcium fluorescence decay curve outside the potential response is estimated using either a low-pass filter or constant, linear or polynomial regression, and is subsequently used to calculate the magnitude, latency and duration of the response.The magnitude and variance of the responses that are obtained by the different methods are compared, and, by computing the receiver operating characteristics of a classifier based on response magnitude, we evaluate the ability of each method to detect the stimulus type and conclude that a polynomial approximation of the background gives the overall best result.

View Article: PubMed Central - PubMed

Affiliation: Swedish University of Agricultural Sciences, Alnarp, Sweden.

ABSTRACT
We investigate four different methods for background estimation in calcium imaging of the insect brain and evaluate their performance on six data sets consisting of data recorded from two sites in two species of moths. The calcium fluorescence decay curve outside the potential response is estimated using either a low-pass filter or constant, linear or polynomial regression, and is subsequently used to calculate the magnitude, latency and duration of the response. The magnitude and variance of the responses that are obtained by the different methods are compared, and, by computing the receiver operating characteristics of a classifier based on response magnitude, we evaluate the ability of each method to detect the stimulus type and conclude that a polynomial approximation of the background gives the overall best result.

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Related in: MedlinePlus

Mean squared error (MSE) of the background estimation on artificial bleaching data with different amounts of noise.A noise level of 25 means that the sigma of the gaussian noise was set to 25 times the range of the bleaching process.
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pone.0129614.g008: Mean squared error (MSE) of the background estimation on artificial bleaching data with different amounts of noise.A noise level of 25 means that the sigma of the gaussian noise was set to 25 times the range of the bleaching process.

Mentions: When tested with different amounts of noise, the constant, linear and low-pass methods give different absolute errors, but show similar deterioration with more noise (Fig 8). However, the polynomial method behaves differently. It is more resistant to modest noise levels but has the worst performance with higher levels of noise.


Comparing Analysis Methods in Functional Calcium Imaging of the Insect Brain.

Balkenius A, Johansson AJ, Balkenius C - PLoS ONE (2015)

Mean squared error (MSE) of the background estimation on artificial bleaching data with different amounts of noise.A noise level of 25 means that the sigma of the gaussian noise was set to 25 times the range of the bleaching process.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4457531&req=5

pone.0129614.g008: Mean squared error (MSE) of the background estimation on artificial bleaching data with different amounts of noise.A noise level of 25 means that the sigma of the gaussian noise was set to 25 times the range of the bleaching process.
Mentions: When tested with different amounts of noise, the constant, linear and low-pass methods give different absolute errors, but show similar deterioration with more noise (Fig 8). However, the polynomial method behaves differently. It is more resistant to modest noise levels but has the worst performance with higher levels of noise.

Bottom Line: We investigate four different methods for background estimation in calcium imaging of the insect brain and evaluate their performance on six data sets consisting of data recorded from two sites in two species of moths.The calcium fluorescence decay curve outside the potential response is estimated using either a low-pass filter or constant, linear or polynomial regression, and is subsequently used to calculate the magnitude, latency and duration of the response.The magnitude and variance of the responses that are obtained by the different methods are compared, and, by computing the receiver operating characteristics of a classifier based on response magnitude, we evaluate the ability of each method to detect the stimulus type and conclude that a polynomial approximation of the background gives the overall best result.

View Article: PubMed Central - PubMed

Affiliation: Swedish University of Agricultural Sciences, Alnarp, Sweden.

ABSTRACT
We investigate four different methods for background estimation in calcium imaging of the insect brain and evaluate their performance on six data sets consisting of data recorded from two sites in two species of moths. The calcium fluorescence decay curve outside the potential response is estimated using either a low-pass filter or constant, linear or polynomial regression, and is subsequently used to calculate the magnitude, latency and duration of the response. The magnitude and variance of the responses that are obtained by the different methods are compared, and, by computing the receiver operating characteristics of a classifier based on response magnitude, we evaluate the ability of each method to detect the stimulus type and conclude that a polynomial approximation of the background gives the overall best result.

No MeSH data available.


Related in: MedlinePlus