Comparing Analysis Methods in Functional Calcium Imaging of the Insect Brain
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2015 |
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| Erschienen: |
2015 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
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| Enthalten in: |
PloS one - 10(2015), 6 vom: 05., Seite e0129614 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Balkenius, Anna [VerfasserIn] |
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| Links: |
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| Themen: |
Calcium |
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| Anmerkungen: |
Date Completed 07.04.2016 Date Revised 13.11.2018 published: Electronic-eCollection Citation Status MEDLINE |
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| doi: |
10.1371/journal.pone.0129614 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM249687712 |
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| 520 | |a 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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