Techniques to identify and temporally correlate calcium transients between multiple regions of interest in vertebrate neural circuits.

Calcium imaging is commonly used to record dynamic changes in excitability from axons or cell bodies in the nervous system of vertebrates. These recordings often reveal discrete calcium transients that have variable amplitudes, durations, and rates of rise and decay, all of which can arise from an unstable or "noisy" baseline. This often leads to considerable ambiguity about how to discriminate and quantify calcium transients. We describe an analytical methodology that objectively identifies multiple calcium transients from multiple recording sites and quantifies the degree of temporal synchrony between each event. The methodology consists of multiple steps. The first step involves baselining, to either preserve the underlying shape of calcium transients or remove unwanted frequency components and transform the peaks of calcium transients into more easily detectable patterns. The second step is the application of at least one of two different spike detection algorithms, one based on a gradient estimate and the other on template matching. The third step is the quantification of synchrony between pairs of recordings using at least one of two time lag correlation measures. The fourth step is the identification of statistically significant coincident firing patterns. This allows discrimination of neuronal firing patterns between different sites that appear to occur simultaneously and that statistically could not be attributed to chance. The analytical methods we have demonstrated can be applied not only to calcium imaging but also to many other physiological recordings, where discrimination and temporal correlation of biological signals from multiple sites is required, particularly when arising from unstable baselines, with variable signal-to-noise ratios.NEW & NOTEWORTHY Dynamic imaging of intracellular calcium is commonly used to record changes in excitability in central and peripheral neurons. We describe a novel analytical methodology that objectively discriminates calcium transients from low signal-to-noise recordings from multiple sites and quantifies the degree of temporal synchrony between events. These new methods can be applied not only to calcium imaging but also to many other physiological recordings where discrimination and temporal correlation of biological signals from multiple sites is required.

[1]  Louisa J Steinberg,et al.  Emergence of band-pass filtering through adaptive spiking in the owl's cochlear nucleus. , 2014, Journal of neurophysiology.

[2]  D. Perkel,et al.  Simultaneously Recorded Trains of Action Potentials: Analysis and Functional Interpretation , 1969, Science.

[3]  H. Kaiser The Application of Electronic Computers to Factor Analysis , 1960 .

[4]  M. Salganicoff,et al.  Unsupervised waveform classification for multi-neuron recordings: a real-time, software-based system. I. Algorithms and implementation , 1988, Journal of Neuroscience Methods.

[5]  Shanti S. Gupta,et al.  On the distribution of the studentized maximum of equally correlated normal random variables , 1985 .

[6]  Rodrigo Quian Quiroga,et al.  Nonlinear multivariate analysis of neurophysiological signals , 2005, Progress in Neurobiology.

[7]  Sonja Grün,et al.  Analysis of Parallel Spike Trains , 2010 .

[8]  R. Kass,et al.  Multiple neural spike train data analysis: state-of-the-art and future challenges , 2004, Nature Neuroscience.

[9]  M S Lewicki,et al.  A review of methods for spike sorting: the detection and classification of neural action potentials. , 1998, Network.

[10]  Long-Term Recordings Improve the Detection of Weak Excitatory–Excitatory Connections in Rat Prefrontal Cortex , 2014, The Journal of Neuroscience.

[11]  Nick J. Spencer,et al.  Imaging stretch-activated firing of spinal afferent nerve endings in mouse colon , 2013, Front. Neurosci..

[12]  R. Quian Quiroga,et al.  Unsupervised Spike Detection and Sorting with Wavelets and Superparamagnetic Clustering , 2004, Neural Computation.

[13]  Trupti M. Kodinariya,et al.  Review on determining number of Cluster in K-Means Clustering , 2013 .

[14]  Håkan Johansson,et al.  Modern Techniques in Neuroscience Research , 1999, Springer Berlin Heidelberg.

[15]  R. Cattell The Scree Test For The Number Of Factors. , 1966, Multivariate behavioral research.