scholarly journals AN EXTENSION OF THE FRI FRAMEWORK FOR CALCIUM TRANSIENT DETECTION

2015 ◽  
Author(s):  
Stephanie Reynolds ◽  
Caroline S Copeland ◽  
Simon R Schultz ◽  
Pier Luigi Dragotti
Keyword(s):  
Calcium Imaging ◽  
Real Data ◽  
Calcium Transient ◽  
High Temporal Resolution ◽  
Imaging Data ◽  
Spike Detection ◽  
Model Order ◽  
Two Photon ◽  
Calcium Indicators ◽  
Finite Rate Of Innovation

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.

scholarly journals Spike Detection Using FRI Methods and Protein Calcium Sensors: Performance Analysis and Comparisons

2015 ◽  
Author(s):  
Stephanie Reynolds ◽  
Jon Onativia ◽  
Caroline S Copeland ◽  
Simon R Schultz ◽  
Pier Luigi Dragotti
Keyword(s):  
Calcium Imaging ◽  
High Performance ◽  
Surrogate Data ◽  
Synthetic Dyes ◽  
Imaging Data ◽  
Spike Detection ◽  
Calcium Sensors ◽  
New Family ◽  
Calcium Indicator ◽  
Finite Rate Of Innovation

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 Crame ́r-Rao lower bound on the uncertainty of the position of a detected spike in calcium imaging data for each calcium indicator.

scholarly journals ABLE: an Activity-Based Level Set Segmentation Algorithm for Two-Photon Calcium Imaging Data

2017 ◽  
Author(s):  
Stephanie Reynolds ◽  
Therese Abrahamsson ◽  
Renaud Schuck ◽  
P. Jesper Sjöström ◽  
Simon R. Schultz ◽  
...  
Keyword(s):  
Level Set ◽  
Calcium Imaging ◽  
Cell Shape ◽  
Active Contours ◽  
Real Data ◽  
Imaging Data ◽  
Similar Time ◽  
Two Photon

AbstractWe 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 ex-terior, 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 achieves a 67.5% success rate.Significance statementTwo-photon calcium imaging enables the study of brain activity during learning and behaviour at single-cell resolution. To decode neuronal spiking activity from the data, algorithms are first required to detect the location of cells in the video. It is still common for scientists to perform this task manually, as the heterogeneity in cell shape and frequency of cellular overlap impede automatic segmentation algorithms. We developed a versatile algorithm based on a popular image segmentation approach (the Level Set Method) and demonstrated its capability to overcome these challenges. We include no assumptions on cell shape or stereotypical temporal activity. This lends our framework the flexibility to be applied to new datasets with minimal adjustment.

scholarly journals Community-based benchmarking improves spike rate inference from two-photon calcium imaging data

2018 ◽  
Vol 14 (5) ◽  
pp. e1006157 ◽  
Author(s):  
Philipp Berens ◽  
Jeremy Freeman ◽  
Thomas Deneux ◽  
Nikolay Chenkov ◽  
Thomas McColgan ◽  
...  
Keyword(s):  
Calcium Imaging ◽  
Spike Rate ◽  
Imaging Data ◽  
Community Based ◽  
Two Photon

In Vivo Calcium Imaging of Neural Network Function

Physiology ◽  
2007 ◽  
Vol 22 (6) ◽  
pp. 358-365 ◽  
Author(s):  
Werner Göbel ◽  
Fritjof Helmchen
Keyword(s):  
Calcium Imaging ◽  
Activity Patterns ◽  
Network Activity ◽  
Network Function ◽  
Two Photon ◽  
Calcium Indicators ◽  
Recent Developments ◽  
In Vivo Calcium Imaging ◽  
Function Network

Spatiotemporal activity patterns in local neural networks are fundamental to brain function. Network activity can now be measured in vivo using two-photon imaging of cell populations that are labeled with fluorescent calcium indicators. In this review, we discuss basic aspects of in vivo calcium imaging and highlight recent developments that will help to uncover operating principles of neural circuits.

scholarly journals CosMIC: A Consistent Metric for Spike Inference from Calcium Imaging

2017 ◽  
Author(s):  
Stephanie Reynolds ◽  
Therese Abrahamsson ◽  
P. Jesper Sjöström ◽  
Simon R. Schultz ◽  
Pier Luigi Dragotti
Keyword(s):  
Spike Train ◽  
Success Rate ◽  
Calcium Imaging ◽  
Ground Truth ◽  
Spike Trains ◽  
Imaging Data ◽  
Temporal Precision ◽  
Pulse Trains ◽  
Two Photon ◽  
Average Size

AbstractIn recent years, the development of algorithms to detect neuronal spiking activity from two-photon calcium imaging data has received much attention. Meanwhile, few researchers have examined the metrics used to assess the similarity of detected spike trains with the ground truth. We highlight the limitations of the two most commonly used metrics, the spike train correlation and success rate, and propose an alternative, which we refer to as CosMIC. Rather than operating on the true and estimated spike trains directly, the proposed metric assesses the similarity of the pulse trains obtained from convolution of the spike trains with a smoothing pulse. The pulse width, which is derived from the statistics of the imaging data, reflects the temporal tolerance of the metric. The final metric score is the size of the commonalities of the pulse trains as a fraction of their average size. Viewed through the lens of set theory, CosMIC resembles a continuous Sørensen-Dice coefficient — an index commonly used to assess the similarity of discrete, presence/absence data. We demonstrate the ability of the proposed metric to discriminate the precision and recall of spike train estimates. Unlike the spike train correlation, which appears to reward overestimation, the proposed metric score is maximised when the correct number of spikes have been detected. Furthermore, we show that CosMIC is more sensitive to the temporal precision of estimates than the success rate.

scholarly journals NoRMCorre: An online algorithm for piecewise rigid motion correction of calcium imaging data

2017 ◽  
Author(s):  
Eftychios A. Pnevmatikakis ◽  
Andrea Giovannucci
Keyword(s):  
Calcium Imaging ◽  
Motion Correction ◽  
Rigid Motion ◽  
Motion Artifacts ◽  
Streaming Data ◽  
Field Of View ◽  
Large Field ◽  
Motion Field ◽  
Imaging Data ◽  
Two Photon

AbstractBackgroundMotion correction is a challenging pre-processing problem that arises early in the analysis pipeline of calcium imaging data sequences. The motion artifacts in two-photon microscopy recordings can be non-rigid, arising from the finite time of raster scanning and non-uniform deformations of the brain medium.New methodWe introduce an algorithm for fast Non-Rigid Motion Correction (NoRMCorre) based on template matching. NoRMCorre operates by splitting the field of view into overlapping spatial patches that are registered at a sub-pixel resolution for rigid translation against a continuously updated template. The estimated alignments are subsequently up-sampled to create a smooth motion field for each frame that can efficiently approximate non-rigid motion in a piecewise-rigid manner.Existing methodsExisting approaches either do not scale well in terms of computational performance or are targeted to motion artifacts arising from low speed scanning, whereas modern datasets with large field of view are more prone to non-rigid brain deformation issues.ResultsNoRMCorre can be run in an online mode resulting in comparable to or even faster than real time motion registration on streaming data. We evaluate the performance of the proposed method with simple yet intuitive metrics and compare against other non-rigid registration methods on two-photon calcium imaging datasets. Open source Matlab and Python code is also made available.ConclusionsThe proposed method and code provide valuable support to the community for solving large scale image registration problems in calcium imaging, especially when non-rigid deformations are present in the acquired data.

scholarly journals Automatic sorting system for large calcium imaging data

2017 ◽  
Author(s):  
Takashi Takekawa ◽  
Hirotaka Asai ◽  
Noriaki Ohkawa ◽  
Masanori Nomoto ◽  
Reiko Okubo-Suzuki ◽  
...  
Keyword(s):  
Calcium Imaging ◽  
Cell Activity ◽  
Estimation Method ◽  
Matrix Decomposition ◽  
Real Data ◽  
Imaging Data ◽  
Initial State ◽  
Sorting System ◽  
The Stability ◽  
Activity Estimation

SUMMARYIt has become possible to observe neural activity in freely moving animals via calcium imaging using a microscope, which could not be observed previously. However, it remains difficult to extract the dynamics of nerve cells from the recorded imaging data. In this study, we greatly improved the stability, and robustness of the cell activity estimation method via non-negative matrix decomposition with shrinkage estimation of the baseline. In addition, by improving the initial state of the iterative algorithm using a newly proposed method to extract the shape of the cell via image processing, a solution could be obtained with a small number of iterations. These methods were applied to artificial and real data, and their effectiveness was confirmed.

scholarly journals ABLE: An Activity-Based Level Set Segmentation Algorithm for Two-Photon Calcium Imaging Data

eNeuro ◽  
2017 ◽  
Vol 4 (5) ◽  
pp. ENEURO.0012-17.2017 ◽  
Author(s):  
Stephanie Reynolds ◽  
Therese Abrahamsson ◽  
Renaud Schuck ◽  
P. Jesper Sjöström ◽  
Simon R. Schultz ◽  
...  
Keyword(s):  
Level Set ◽  
Calcium Imaging ◽  
Segmentation Algorithm ◽  
Imaging Data ◽  
Two Photon ◽  
Level Set Segmentation

scholarly journals Simultaneous Two-Photon Voltage or Calcium Imaging and Multi-Channel Local Field Potential Recordings in Barrel Cortex of Awake and Anesthetized Mice

2021 ◽  
Vol 15 ◽  
Author(s):  
Claudia Cecchetto ◽  
Stefano Vassanelli ◽  
Bernd Kuhn
Keyword(s):  
Local Field ◽  
Calcium Imaging ◽  
Local Field Potential ◽  
Barrel Cortex ◽  
Field Potential ◽  
Imaging Data ◽  
Spatiotemporal Resolution ◽  
Two Photon ◽  
Cranial Window ◽  
Sensory Evoked

Neuronal population activity, both spontaneous and sensory-evoked, generates propagating waves in cortex. However, high spatiotemporal-resolution mapping of these waves is difficult as calcium imaging, the work horse of current imaging, does not reveal subthreshold activity. Here, we present a platform combining voltage or calcium two-photon imaging with multi-channel local field potential (LFP) recordings in different layers of the barrel cortex from anesthetized and awake head-restrained mice. A chronic cranial window with access port allows injecting a viral vector expressing GCaMP6f or the voltage-sensitive dye (VSD) ANNINE-6plus, as well as entering the brain with a multi-channel neural probe. We present both average spontaneous activity and average evoked signals in response to multi-whisker air-puff stimulations. Time domain analysis shows the dependence of the evoked responses on the cortical layer and on the state of the animal, here separated into anesthetized, awake but resting, and running. The simultaneous data acquisition allows to compare the average membrane depolarization measured with ANNINE-6plus with the amplitude and shape of the LFP recordings. The calcium imaging data connects these data sets to the large existing database of this important second messenger. Interestingly, in the calcium imaging data, we found a few cells which showed a decrease in calcium concentration in response to vibrissa stimulation in awake mice. This system offers a multimodal technique to study the spatiotemporal dynamics of neuronal signals through a 3D architecture in vivo. It will provide novel insights on sensory coding, closing the gap between electrical and optical recordings.

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