A computational toolbox and step-by-step tutorial for the analysis of neuronal population dynamics in calcium imaging data

The development of new imaging and optogenetics techniques to study the dynamics of large neuronal circuits is generating datasets of unprecedented volume and complexity, demanding the development of appropriate analysis tools. We present a tutorial for the use of a comprehensive computational toolbox for the analysis of neuronal population activity imaging. It consists of tools for image pre-processing and segmentation, estimation of significant single-neuron single-trial signals, mapping event-related neuronal responses, detection of activity-correlated neuronal clusters, exploration of population dynamics, and analysis of clusters’ features against surrogate control datasets. They are integrated in a modular and versatile processing pipeline, adaptable to different needs. The clustering module is capable of detecting flexible, dynamically activated neuronal assemblies, consistent with the distributed population coding of the brain. We demonstrate the suitability of the toolbox for a variety of calcium imaging datasets, and provide a case study to explain its implementation.

[1]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[2]  Stefan R. Pulver,et al.  Ultra-sensitive fluorescent proteins for imaging neuronal activity , 2013, Nature.

[3]  M. A. Smith,et al.  Spatial and Temporal Scales of Neuronal Correlation in Primary Visual Cortex , 2008, The Journal of Neuroscience.

[4]  Takashi Kawashima,et al.  Mapping brain activity at scale with cluster computing , 2014, Nature Methods.

[5]  S. Easter,et al.  Development of the retinofugal projections in the embryonic and larval zebrafish (Brachydanio rerio) , 1994, The Journal of comparative neurology.

[6]  Michael J. Berry,et al.  Weak pairwise correlations imply strongly correlated network states in a neural population , 2005, Nature.

[7]  M. Orger,et al.  Whole-Brain Activity Maps Reveal Stereotyped, Distributed Networks for Visuomotor Behavior , 2014, Neuron.

[8]  Michel A. Picardo,et al.  GABAergic Hub Neurons Orchestrate Synchrony in Developing Hippocampal Networks , 2009, Science.

[9]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  M. Abeles,et al.  Detecting precise firing sequences in experimental data , 2001, Journal of Neuroscience Methods.

[11]  Mehdi Khamassi,et al.  Principal component analysis of ensemble recordings reveals cell assemblies at high temporal resolution , 2009, Journal of Computational Neuroscience.

[12]  Zengcai V. Guo,et al.  Flow of Cortical Activity Underlying a Tactile Decision in Mice , 2014, Neuron.

[13]  R. Yuste,et al.  Visual stimuli recruit intrinsically generated cortical ensembles , 2014, Proceedings of the National Academy of Sciences.

[14]  John G. Sled,et al.  Wanted dead or alive? The tradeoff between in-vivo versus ex-vivo MR brain imaging in the mouse , 2011, Front. Neuroinform..

[15]  Adriano B. L. Tort,et al.  Neuronal Assembly Detection and Cell Membership Specification by Principal Component Analysis , 2011, PloS one.

[16]  Mehdi Khamassi,et al.  Coherent Theta Oscillations and Reorganization of Spike Timing in the Hippocampal- Prefrontal Network upon Learning , 2010, Neuron.

[17]  Rainer W. Friedrich,et al.  Olfactory pattern classification by discrete neuronal network states , 2010, Nature.

[18]  K. Harris.  Neural signatures of cell assembly organization , 2005, Nature Reviews Neuroscience.

[19]  Germán Sumbre,et al.  Spontaneous Neuronal Network Dynamics Reveal Circuit’s Functional Adaptations for Behavior , 2015, Neuron.

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

[21]  Ethan K. Scott,et al.  Functional Profiles of Visual-, Auditory-, and Water Flow-Responsive Neurons in the Zebrafish Tectum , 2016, Current Biology.

[22]  K. Svoboda,et al.  A Cellular Resolution Map of Barrel Cortex Activity during Tactile Behavior , 2015, Neuron.

[23]  Spencer L. Smith,et al.  Parallel processing of visual space by neighboring neurons in mouse visual cortex , 2010, Nature Neuroscience.

[24]  W. Newsome,et al.  Context-dependent computation by recurrent dynamics in prefrontal cortex , 2013, Nature.

[25]  Alan Julian Izenman,et al.  Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning , 2008 .

[26]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[27]  G. Laurent,et al.  Transient Dynamics versus Fixed Points in Odor Representations by Locust Antennal Lobe Projection Neurons , 2005, Neuron.

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

[29]  Rafael Yuste,et al.  Fast nonnegative deconvolution for spike train inference from population calcium imaging. , 2009, Journal of neurophysiology.

[30]  S. Romano,et al.  Sustained Rhythmic Brain Activity Underlies Visual Motion Perception in Zebrafish , 2016, Cell reports.

[31]  M. Wilson,et al.  Temporally Structured Replay of Awake Hippocampal Ensemble Activity during Rapid Eye Movement Sleep , 2001, Neuron.

[32]  D. Tank,et al.  Functional Clustering of Neurons in Motor Cortex Determined by Cellular Resolution Imaging in Awake Behaving Mice , 2009, The Journal of Neuroscience.

[33]  Ifije E. Ohiorhenuan,et al.  Sparse coding and high-order correlations in fine-scale cortical networks , 2010, Nature.

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

[35]  G L Gerstein,et al.  Detecting spatiotemporal firing patterns among simultaneously recorded single neurons. , 1988, Journal of neurophysiology.

[36]  M. Khamassi,et al.  Replay of rule-learning related neural patterns in the prefrontal cortex during sleep , 2009, Nature Neuroscience.

[37]  Kevin L. Briggman,et al.  Multifunctional pattern-generating circuits. , 2008, Annual review of neuroscience.

[38]  S. Wang,et al.  Reliable Coding Emerges from Coactivation of Climbing Fibers in Microbands of Cerebellar Purkinje Neurons , 2009, The Journal of Neuroscience.

[39]  C. Niell,et al.  Functional Imaging Reveals Rapid Development of Visual Response Properties in the Zebrafish Tectum , 2005, Neuron.

[40]  G. Sumbre,et al.  A microfluidic device to study neuronal and motor responses to acute chemical stimuli in zebrafish , 2015, Scientific Reports.

[41]  Takashi R Sato,et al.  Characterization and adaptive optical correction of aberrations during in vivo imaging in the mouse cortex , 2011, Proceedings of the National Academy of Sciences.

[42]  Philipp J. Keller,et al.  Whole-brain functional imaging at cellular resolution using light-sheet microscopy , 2013, Nature Methods.

[43]  Sooyoung Chung,et al.  Functional imaging with cellular resolution reveals precise micro-architecture in visual cortex , 2005, Nature.

[44]  Takeharu Nagai,et al.  Quantitative Comparison of Genetically Encoded Ca2+ Indicators in Cortical Pyramidal Cells and Cerebellar Purkinje Cells , 2011, Front. Cell. Neurosci..

[45]  Byron M. Yu,et al.  Dimensionality reduction for large-scale neural recordings , 2014, Nature Neuroscience.

[46]  Klaus Nordhausen,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition by Trevor Hastie, Robert Tibshirani, Jerome Friedman , 2009 .

[47]  Byron M. Yu,et al.  Techniques for extracting single-trial activity patterns from large-scale neural recordings , 2007, Current Opinion in Neurobiology.

[48]  Tsai-Wen Chen,et al.  Comprehensive imaging of cortical networks , 2015, Current Opinion in Neurobiology.

[49]  Jonathon Shlens,et al.  The Structure of Multi-Neuron Firing Patterns in Primate Retina , 2006, The Journal of Neuroscience.

[50]  Nicholas J. Priebe,et al.  Local Integration Accounts for Weak Selectivity of Mouse Neocortical Parvalbumin Interneurons , 2015, Neuron.

[51]  O. Sporns,et al.  Identification and Classification of Hubs in Brain Networks , 2007, PloS one.

[52]  K. Deisseroth.  Circuit dynamics of adaptive and maladaptive behaviour , 2014, Nature.

[53]  E. Yaksi,et al.  Reconstruction of firing rate changes across neuronal populations by temporally deconvolved Ca2+ imaging , 2006, Nature Methods.

[54]  Damian J. Wallace,et al.  Chasing the cell assembly , 2010, Current Opinion in Neurobiology.

[55]  R. Segev,et al.  Sparse low-order interaction network underlies a highly correlated and learnable neural population code , 2011, Proceedings of the National Academy of Sciences.

[56]  S. Rumpel,et al.  Discrete Neocortical Dynamics Predict Behavioral Categorization of Sounds , 2012, Neuron.

[57]  Naoshige Uchida,et al.  Demixed principal component analysis of neural population data , 2014, eLife.

[58]  Wolf Singer,et al.  Detecting Multineuronal Temporal Patterns in Parallel Spike Trains , 2012, Front. Neuroinform..

[59]  Mark D Humphries,et al.  Spike-Train Communities: Finding Groups of Similar Spike Trains , 2011, The Journal of Neuroscience.

[60]  Germán Sumbre,et al.  Fast functional imaging of multiple brain regions in intact zebrafish larvae using Selective Plane Illumination Microscopy , 2013, BMC Neuroscience.

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

[62]  V. Jayaraman,et al.  Intensity versus Identity Coding in an Olfactory System , 2003, Neuron.

[63]  Benjamin F. Grewe,et al.  High-speed in vivo calcium imaging reveals neuronal network activity with near-millisecond precision , 2010, Nature Methods.

[64]  P. Dayan,et al.  A mathematical model explains saturating axon guidance responses to molecular gradients , 2016, eLife.

[65]  Christian Borgelt,et al.  Finding neural assemblies with frequent item set mining , 2013, Front. Neuroinform..

[66]  Ondrej Novak,et al.  Two-Photon Processor and SeNeCA: a freely available software package to process data from two-photon calcium imaging at speeds down to several milliseconds per frame. , 2013, Journal of neurophysiology.

[67]  P. O. White,et al.  PROMAX: A QUICK METHOD FOR ROTATION TO OBLIQUE SIMPLE STRUCTURE , 1964 .

[68]  Peter E. Latham,et al.  Pairwise Maximum Entropy Models for Studying Large Biological Systems: When They Can Work and When They Can't , 2008, PLoS Comput. Biol..

[69]  Tsai-Wen Chen,et al.  Cell type‐specific relationships between spiking and [Ca2+]i in neurons of the Xenopus tadpole olfactory bulb , 2007, The Journal of physiology.