FASP: A machine learning approach to functional astrocyte phenotyping from time-lapse calcium imaging data

Yinxue Wang, Guilai Shi, David J. Miller, Yizhi Wang, Gerard Broussard, Yue Wang, Lin Tian, Guoqiang Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

We propose a machine learning approach to characterize the functional status of astrocytes, the most abundant cells in human brain, based on time-lapse Ca2+ imaging data. The interest in analyzing astrocyte Ca2+ dynamics is evoked by recent discoveries that astrocytes play proactive regulatory roles in neural information processing, and is enabled by recent technical advances in modern microscopy and ultrasensitive genetically encoded Ca2+ indicators. However, current analysis relies on eyeballing the time-lapse imaging data and manually drawing regions of interest, which not only limits the analysis throughput but also at risk to miss important information encoded in the big complex dynamic data. Thus, there is an increased demand to develop sophisticated tools to dissect Ca2+ signaling in astrocytes, which is challenging due to the complex nature of Ca2+ signaling and low signal to noise ratio. We develop Functional AStrocyte Phenotyping (FASP) to automatically detect functionally independent units (FIUs) and extract the corresponding characteristic curves in an integrated way. FASP is data-driven and probabilistically principled, flexibly accounts for complex patterns and accurately controls false discovery rates. We demonstrate the effectiveness of FASP on both synthetic and real data sets.

Original languageEnglish (US)
Title of host publication2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings
PublisherIEEE Computer Society
Pages351-354
Number of pages4
Volume2016-June
ISBN (Electronic)9781479923502
DOIs
StatePublished - Jun 15 2016
Event2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Prague, Czech Republic
Duration: Apr 13 2016Apr 16 2016

Other

Other2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016
CountryCzech Republic
CityPrague
Period4/13/164/16/16

Keywords

  • astrocyte calcium dynamics
  • functional phenotyping
  • machine learning
  • time-lapse calcium imaging

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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    Wang, Y., Shi, G., Miller, D. J., Wang, Y., Broussard, G., Wang, Y., Tian, L., & Yu, G. (2016). FASP: A machine learning approach to functional astrocyte phenotyping from time-lapse calcium imaging data. In 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings (Vol. 2016-June, pp. 351-354). [7493281] IEEE Computer Society. https://doi.org/10.1109/ISBI.2016.7493281