MIN1PIPE: A Miniscope 1-Photon-Based Calcium Imaging Signal Extraction Pipeline.

In vivo calcium imaging using a 1-photon-based miniscope and a microendoscopic lens enables studies of neural activities in freely behaving animals. However, the high and fluctuating background, the inevitable movements and distortions of imaging field, and the extensive spatial overlaps of fluorescent signals emitted from imaged neurons inherent in this 1-photon imaging method present major challenges for extracting neuronal signals reliably and automatically from the raw imaging data. Here, we develop a unifying algorithm called the miniscope 1-photon imaging pipeline (MIN1PIPE), which contains several stand-alone modules and can handle a wide range of imaging conditions and qualities with minimal parameter tuning and automatically and accurately isolate spatially localized neural signals. We have quantitatively compared MIN1PIPE with other existing partial methods using both synthetic and real datasets obtained from different animal models and show that MIN1PIPE has superior efficiency and precision in analyzing noisy miniscope calcium imaging data.

[1]  A. Gamal,et al.  Miniaturized integration of a fluorescence microscope , 2011, Nature Methods.

[2]  Pablo E. Jercog,et al.  Neural ensemble dynamics underlying a long-term associative memory , 2017, Nature.

[3]  Mark J. Schnitzer,et al.  Direct Imaging of Hippocampal Epileptiform Calcium Motifs Following Kainic Acid Administration in Freely Behaving Mice , 2016, Front. Neurosci..

[4]  Lacey J. Kitch,et al.  Long-term dynamics of CA1 hippocampal place codes , 2013, Nature Neuroscience.

[5]  Timothy J. Gardner,et al.  Mesoscopic Patterns of Neural Activity Support Songbird Cortical Sequences , 2015, PLoS biology.

[6]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[7]  Attila Losonczy,et al.  Septo-hippocampal GABAergic signaling across multiple modalities in awake mice , 2013, Nature Neuroscience.

[8]  H. Sebastian Seung,et al.  Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks , 2016, NIPS.

[9]  Laurie D. Burns,et al.  High-speed, miniaturized fluorescence microscopy in freely moving mice , 2008, Nature Methods.

[10]  J. Betley,et al.  Neurons for hunger and thirst transmit a negative-valence teaching signal , 2015, Nature.

[11]  L. Paninski,et al.  The Spatiotemporal Organization of the Striatum Encodes Action Space , 2017, Neuron.

[12]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[13]  Mati Joshua,et al.  Emergence of Context-Dependent Variability across a Basal Ganglia Network , 2014, Neuron.

[14]  David Pfau,et al.  Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data , 2016, Neuron.

[15]  David S. Greenberg,et al.  Automated correction of fast motion artifacts for two-photon imaging of awake animals , 2009, Journal of Neuroscience Methods.

[16]  Jakob H. Macke,et al.  Fast amortized inference of neural activity from calcium imaging data with variational autoencoders , 2017, NIPS.

[17]  Takashi Kitamura,et al.  Engrams and circuits crucial for systems consolidation of a memory , 2017, Science.

[18]  Attila Losonczy,et al.  SIMA: Python software for analysis of dynamic fluorescence imaging data , 2014, Front. Neuroinform..

[19]  Karl Deisseroth,et al.  Visualizing Hypothalamic Network Dynamics for Appetitive and Consummatory Behaviors , 2015, Cell.

[20]  Yang Dan,et al.  Cell-Type-Specific Activity in Prefrontal Cortex during Goal-Directed Behavior , 2015, Neuron.

[21]  Rafael Yuste,et al.  moco: Fast Motion Correction for Calcium Imaging , 2015, Front. Neuroinform..

[22]  J. Serra,et al.  An overview of morphological filtering , 1992 .

[23]  Misha Elena Kilmer,et al.  Iterative Parameter-Choice and Multigrid Methods for Anisotropic Diffusion Denoising , 2011, SIAM J. Sci. Comput..

[24]  E. Pnevmatikakis,et al.  NoRMCorre: An online algorithm for piecewise rigid motion correction of calcium imaging data , 2017, Journal of Neuroscience Methods.

[25]  R. Mooney,et al.  Identification of a motor to auditory pathway important for vocal learning , 2017, Nature Neuroscience.

[26]  Michael Unser,et al.  A pyramid approach to subpixel registration based on intensity , 1998, IEEE Trans. Image Process..

[27]  Richard Mooney,et al.  Sequential Learning From Multiple Tutors and Serial Retuning of Auditory Neurons in a Brain Area Important to Birdsong Learning , 2004 .

[28]  Yang Dan,et al.  Circuit-based interrogation of sleep control , 2016, Nature.

[29]  Chen Sun,et al.  Distinct Neural Circuits for the Formation and Retrieval of Episodic Memories , 2017, Cell.

[30]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[31]  William Lovell,et al.  Ventral CA1 neurons store social memory , 2016 .

[32]  Frédéric E Theunissen.  Use it or lose it. Focus on: "sequential learning from multiple tutors and serial returning of auditory neurons in a brain area important to birdsong learning". , 2004, Journal of neurophysiology.

[33]  Benjamin F. Grewe,et al.  Distinct Hippocampal Pathways Mediate Dissociable Roles of Context in Memory Retrieval , 2016, Cell.

[34]  Maria Petrou,et al.  On the choice of the parameters for anisotropic diffusion in image processing , 2013, Pattern Recognit..

[35]  Amiram Grinvald,et al.  Independent component analysis of high-resolution imaging data identifies distinct functional domains , 2007, NeuroImage.

[36]  Mario Dipoppa,et al.  Suite2p: beyond 10,000 neurons with standard two-photon microscopy , 2016, bioRxiv.

[37]  Yang Dan,et al.  Calcium imaging of sleep–wake related neuronal activity in the dorsal pons , 2016, Nature Communications.

[38]  Liam Paninski,et al.  Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data , 2016, eLife.

[39]  Y. Dan,et al.  Delay Activity of Specific Prefrontal Interneuron Subtypes Modulates Memory-Guided Behavior , 2017, Nature Neuroscience.

[40]  Alon Rubin,et al.  Tracking the Same Neurons across Multiple Days in Ca2+ Imaging Data , 2017, Cell reports.

[41]  K. Conzelmann,et al.  Central amygdala circuits modulate food consumption through a positive valence mechanism , 2017, bioRxiv.

[42]  Alcino J. Silva,et al.  A shared neural ensemble links distinct contextual memories encoded close in time , 2016, Nature.

[43]  D. Tank,et al.  Imaging Large-Scale Neural Activity with Cellular Resolution in Awake, Mobile Mice , 2007, Neuron.

[44]  Matthew R Bailey,et al.  Decreasing Striatopallidal Pathway Function Enhances Motivation by Energizing the Initiation of Goal-Directed Action , 2016, The Journal of Neuroscience.

[45]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[46]  Adam M. Packer,et al.  Extracting regions of interest from biological images with convolutional sparse block coding , 2013, NIPS.

[47]  Michale S Fee,et al.  Singing-related neural activity distinguishes four classes of putative striatal neurons in the songbird basal ganglia. , 2010, Journal of neurophysiology.

[48]  Leo Grady,et al.  Spectral Log-Demons: Diffeomorphic Image Registration with Very Large Deformations , 2013, International Journal of Computer Vision.

[49]  Allison J Doupe,et al.  Song selectivity in the pallial-basal ganglia song circuit of zebra finches raised without tutor song exposure. , 2007, Journal of neurophysiology.

[50]  Tom Vercauteren,et al.  Diffeomorphic demons: Efficient non-parametric image registration , 2009, NeuroImage.

[51]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[52]  Toru Aonishi,et al.  Detecting cells using non-negative matrix factorization on calcium imaging data , 2014, Neural Networks.

[53]  Mark J. Schnitzer,et al.  Automated Analysis of Cellular Signals from Large-Scale Calcium Imaging Data , 2009, Neuron.