Online analysis of microendoscopic 1-photon calcium imaging data streams

Abstract

In-vivo calcium imaging through microendoscopic lenses enables imaging of neuronal populations deep within the brains of freely moving animals. Previously, a constrained matrix factorization approach (CNMF-E) has been suggested to extract single-neuronal activity from microendoscopic data. However, this approach relies on offline batch processing of the entire video data and is demanding both in terms of computing and memory requirements. These drawbacks prevent its applicability to the analysis of large datasets and closed-loop experimental settings. Here we address both issues by introducing two different online algorithms for extracting neuronal activity from streaming microendoscopic data. Our first algorithm presents an online adaptation of the CNMF-E algorithm, which dramatically reduces its memory and computation requirements. Our second algorithm proposes a convolution-based background model for microendoscopic data that enables even faster (real time) processing on GPU hardware. Our approach is modular and can be combined with existing online motion artifact correction and activity deconvolution methods to provide a highly scalable pipeline for microendoscopic data analysis. We apply our algorithms on two previously published typical experimental datasets and show that they yield similar high-quality results as the popular offline approach, but outperform it with regard to computing time and memory requirements. Author summaryCalcium imaging methods enable researchers to measure the activity of genetically-targeted large-scale neuronal subpopulations. Whereas previous methods required the specimen to be stable, e.g. anesthetized or head-fixed, new brain imaging techniques using microendoscopic lenses and miniaturized microscopes have enabled deep brain imaging in freely moving mice.However, the very large background fluctuations, the inevitable movements and distortions of imaging field, and the extensive spatial overlaps of fluorescent signals complicate the goal of efficiently extracting accurate estimates of neural activity from the observed video data. Further, current activity extraction methods are computationally expensive due to the complex background model and are typically applied to imaging data after the experiment is complete. Moreover, in some scenarios it is necessary to perform experiments in real-time and closed-loop -analyzing data on-the-fly to guide the next experimental steps or to control feedback -, and this calls for new methods for accurate real-time processing. Here we address both issues by adapting a popular extraction method to operate online and extend it to utilize GPU hardware 1/24 that enables real time processing. Our algorithms yield similar high-quality results as the original offline approach, but outperform it with regard to computing time and memory requirements. Our results enable faster and scalable analysis, and open the door to new closed-loop experiments in deep brain areas and on freely-moving preparations.

Functional Ultrasound Imaging of the Human Spinal Cord

“…These algorithms are used to separate tissue motion signal from blood signal to generate relative fUSI pD signal intensity images 77 . To address potential physiological and motion artifacts unique to human spinal cord imaging, we adopt rigid motion correction techniques 78 that have successfully been used in fUSI 35 and other neuroimaging studies [79][80][81] . This was combined with in-house developed breathing, high frequency smoothing filtering and image stabilization approaches, to ensure robustness and reliability of our data processing and analysis results.…”

Open-Source Software for Real-time Calcium Imaging and Synchronized Neuron Firing Detection

“…Most batch CNMF algorithms are computationally intensive, but Friedrich et al developed an efficient algorithm that can run in real-time [22], [23] using sparse non-negative deconvolution [24], [10]. CaImAn uses this algorithm, but its latest version could only process already-recorded video data.…”

FIOLA: An accelerated pipeline for Fluorescence Imaging OnLine Analysis

“…1aa n db ) : i )c o r r e c tf o rm o t i o na r t i f a c t s ;i i )i d e n t i f yt h e approximate spatial location of neurons; iii) optimize spatial footprints to extract and separate fluorescence signals from potentially overlapping cells; iv) estimate the neural activity from fluorescence traces based on the biophysical properties of the expressed calcium/voltage indicator; and v) extract subthreshold activity for voltage signals. In the past years, a variety of algorithms [22,23,24,25,26]a n dp i p e l i n e s [ 27,28,29] have proposed online versions of such preprocessing steps, offering a variety of trade-offs between accuracy in signal extraction and computational performance, but never achieving both (see Discussion for more details). Indeed, real-time or high data-throughput scenarios still present unsolved challenges.…”