We present an algorithm for detecting the location of cells from two-photon calcium imaging data. In our framework, multiple coupled active contours evolve, guided by a model-based cost function, to identify cell boundaries. An active contour seeks to partition a local region into two subregions, a cell interior and exterior, in which all pixels have maximally “similar” time courses. This simple, local model allows contours to be evolved predominantly independently. When contours are sufficiently close, their evolution is coupled, in a manner that permits overlap. We illustrate the ability of the proposed method to demix overlapping cells on real data. The proposed framework is flexible, incorporating no prior information regarding a cell’s morphology or stereotypical temporal activity, which enables the detection of cells with diverse properties. We demonstrate algorithm performance on a challenging mouse in vitro dataset, containing synchronously spiking cells, and a manually labelled mouse in vivo dataset, on which ABLE (the proposed method) achieves a 67.5% success rate.

Two-photon calcium imaging of the brain allows the spatiotemporal activity of neuronal networks to be monitored at cellular resolution. In order to analyse this activity it must first be possible to detect, with high temporal resolution, spikes from the time series corresponding to single neurons. Previous work has shown that finite rate of innovation (FRI) theory can be used to reconstruct spike trains from noisy calcium imaging data. In this paper we extend the FRI framework for spike detection from calcium imaging data to encompass data generated by a larger class of calcium indicators, including the genetically encoded indicator GCaMP6s. Furthermore, we implement least squares model-order estimation and perform a noise reduction procedure ('pre-whitening') in order to increase the robustness of the algorithm. We demonstrate high spike detection performance on real data generated by GCaMP6s, detecting 90% of electrophysiologically-validated spikes.

Abstract-Fast and accurate detection of action potentials from neurophysiological data is key to the study of information processing in the nervous system. Previous work has shown that finite rate of innovation (FRI) theory can be used to successfully reconstruct spike trains from noisy calcium imaging data. This is due to the fact that calcium imaging data can be modeled as streams of decaying exponentials which are a subclass of FRI signals. Recent progress in the development of genetically encoded calcium indicators (GECIs) has produced protein calcium sensors that exceed the sensitivity of the synthetic dyes traditionally used in calcium imaging experiments. In this paper, we compare the suitability for spike detection of the kinetics of a new family of GECIs (the GCaMP6 family) with the synthetic dye Oregon Green BAPTA-1. We demonstrate the high performance of the FRI algorithm on surrogate data for each calcium indicator and we calculate the Cramér-Rao lower bound on the uncertainty of the position of a detected spike in calcium imaging data for each calcium indicator.

Multifocal two-photon microscopy (MTPM) increases imaging speed over single-focus scanning by parallelizing fluorescence excitation. The imaged fluorescence’s susceptibility to crosstalk, however, severely degrades contrast in scattering tissue. Here we present a source-localized MTPM scheme optimized for high speed functional fluorescence imaging in scattering mammalian brain tissue. A rastered line array of beamlets excites fluorescence imaged with a complementary metal-oxide-semiconductor (CMOS) camera. We mitigate scattering-induced crosstalk by temporally oversampling the rastered image, generating grouped images with structured illumination, and applying Richardson-Lucy deconvolution to reassign scattered photons. Single images are then retrieved with a maximum intensity projection through the deconvolved image groups. This method increased image contrast at depths up to 112 μm in scattering brain tissue and reduced functional crosstalk between pixels during neuronal calcium imaging. Source-localization did not affect signal-to-noise ratio (SNR) in densely labeled tissue under our experimental conditions. SNR decreased at low frame rates in sparsely labeled tissue, with no effect at frame rates above 50 Hz. Our non-descanned source-localized MTPM system enables high SNR, 100 Hz capture of fluorescence transients in scattering brain, increasing the scope of MTPM to faster and smaller functional signals.

Fast and accurate detection of action potentials from neurophysiological data is key to the study of information processing in the nervous system. Previous work has shown that finite rate of innovation (FRI) theory can be used to successfully reconstruct spike trains from noisy calcium imaging data. This is due to the fact that calcium imaging data can be modeled as streams of decaying exponentials which are a subclass of FRI signals. Recent progress in the development of genetically encoded calcium indicators (GECIs) has produced protein calcium sensors that exceed the sensitivity of the synthetic dyes traditionally used in calcium imaging experiments. In this paper, we compare the suitability for spike detection of the kinetics of a new family of GECIs (the GCaMP6 family) with the synthetic dye Oregon Green BAPTA-1. We demonstrate the high performance of the FRI algorithm on surrogate data for each calcium indicator and we calculate the Cramér-Rao lower bound on the uncertainty of the position of a detected spike in calcium imaging data for each calcium indicator.