Abstract
Optical recording facilitates monitoring the activity of a large neural network at the cellular scale, but the analysis and interpretation of the collected data remain challenging. Here, we present a MATLAB-based toolbox, named NeuroCa, for the automated processing and quantitative analysis of large-scale calcium imaging data. Our tool includes several computational algorithms to extract the calcium spike trains of individual neurons from the calcium imaging data in an automatic fashion. Two algorithms were developed to decompose the imaging data into the activity of individual cells and subsequently detect calcium spikes from each neuronal signal. Applying our method to dense networks in dissociated cultures, we were able to obtain the calcium spike trains of [Formula: see text] neurons in a few minutes. Further analyses using these data permitted the quantification of neuronal responses to chemical stimuli as well as functional mapping of spatiotemporal patterns in neuronal firing within the spontaneous, synchronous activity of a large network. These results demonstrate that our method not only automates time-consuming, labor-intensive tasks in the analysis of neural data obtained using optical recording techniques but also provides a systematic way to visualize and quantify the collective dynamics of a network in terms of its cellular elements.
Highlights
The sophisticated functions of the brain are governed by the coordinated activity of multiple neurons.[1,2,3] deciphering the spatial and temporal patterns of neuronal activity in a large population is essential to understand the operating principles of neural circuits.[4]
Our approach was aimed at converting the two-dimensional images of optical neural data to the collection of neuronal calcium spike trains (“data processing”) and using these spike data for the various analyses of neural dynamics (“data analysis”)
Much higher spatial resolution was achieved in our method; for example, our circular Hough transform (CHT)-based method showed less than 5% of the error rate, which was two times less than the previous algorithm based on watershed transform (11%).[42]
Summary
The sophisticated functions of the brain are governed by the coordinated activity of multiple neurons.[1,2,3] deciphering the spatial and temporal patterns of neuronal activity in a large population is essential to understand the operating principles of neural circuits.[4] Optical recording using activitydependent fluorescence sensors, calcium indicators, is a powerful method due to its superior resolution in space by comparison with electrophysiological approaches.[5] Nowadays, it becomes feasible to simultaneously capture the activity of hundreds and thousands of individual cells from dissociated cultures,[6,7] tissue slices,[8,9,10] or the brain of living animals,[11,12,13] providing the glimpses of collective neural dynamics. The analysis of calcium imaging data requires spike sorting to isolate the signals of individual cells and quantitative representation of their activity patterns. Typical approaches have relied on the manual annotation of cells[19,20,21] and qualitative comparison of their fluorescent signals[13,22,23] despite the consumption of considerable time and human
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