Frontiers in Neural Circuits (Jul 2020)

Automated Curation of CNMF-E-Extracted ROI Spatial Footprints and Calcium Traces Using Open-Source AutoML Tools

  • Lina M. Tran,
  • Lina M. Tran,
  • Lina M. Tran,
  • Andrew J. Mocle,
  • Andrew J. Mocle,
  • Adam I. Ramsaran,
  • Adam I. Ramsaran,
  • Alexander D. Jacob,
  • Alexander D. Jacob,
  • Paul W. Frankland,
  • Paul W. Frankland,
  • Paul W. Frankland,
  • Paul W. Frankland,
  • Paul W. Frankland,
  • Sheena A. Josselyn,
  • Sheena A. Josselyn,
  • Sheena A. Josselyn,
  • Sheena A. Josselyn,
  • Sheena A. Josselyn

DOI
https://doi.org/10.3389/fncir.2020.00042
Journal volume & issue
Vol. 14

Abstract

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In vivo 1-photon (1p) calcium imaging is an increasingly prevalent method in behavioral neuroscience. Numerous analysis pipelines have been developed to improve the reliability and scalability of pre-processing and ROI extraction for these large calcium imaging datasets. Despite these advancements in pre-processing methods, manual curation of the extracted spatial footprints and calcium traces of neurons remains important for quality control. Here, we propose an additional semi-automated curation step for sorting spatial footprints and calcium traces from putative neurons extracted using the popular constrained non-negative matrixfactorization for microendoscopic data (CNMF-E) algorithm. We used the automated machine learning (AutoML) tools TPOT and AutoSklearn to generate classifiers to curate the extracted ROIs trained on a subset of human-labeled data. AutoSklearn produced the best performing classifier, achieving an F1 score >92% on the ground truth test dataset. This automated approach is a useful strategy for filtering ROIs with relatively few labeled data points and can be easily added to pre-existing pipelines currently using CNMF-E for ROI extraction.

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