Biology subjects

Giovannucci, A.

Publications and source records attributed to Giovannucci, A..

3 recordsLinked to original sources

bioRxiv2018

CaImAn: An open source tool for scalable Calcium Imaging data Analysis

Advances in fluorescence microscopy enable monitoring larger brain areas in-vivo with finer time resolution. The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. Here we present CaImAn, an open-source library for calcium imaging data analysis. CaImAn provides automatic and scalable methods to address problems common to pre-processing, including motion correction, neural activity identification, and registration across different sessions of data collection. It does this while requiring minimal user intervention, with good performance on computers ranging from laptops to high-performance computing clusters. CaImAn is suitable for two-photon and one-photon imaging, and also enables real-time analysis on streaming data. To benchmark the performance of CaImAn we collected a corpus of ground truth annotations from multiple labelers on nine mouse two-photon datasets. We demonstrate that CaImAn achieves near-human performance in detecting locations of active neurons.

neuroscience↗

bioRxiv2017

OnACID: Online Analysis of Calcium Imaging Data in Real Time

Optical imaging methods using calcium indicators are critical for monitoring the activity of large neuronal populations in vivo. Imaging experiments typically generate a large amount of data that needs to be processed to extract the activity of the imaged neuronal sources. While deriving such processing algorithms is an active area of research, most existing methods require the processing of large amounts of data at a time, rendering them vulnerable to the volume of the recorded data, and preventing realtime experimental interrogation. Here we introduce OnACID, an Online framework for the Analysis of streaming Calcium Imaging Data, including i) motion artifact correction, ii) neuronal source extraction, and iii) activity denoising and deconvolution. Our approach combines and extends previous work on online dictionary learning and calcium imaging data analysis, to deliver an automated pipeline that can discover and track the activity of hundreds of cells in real time, thereby enabling new types of closed-loop experiments. We apply our algorithm on two large scale experimental datasets, benchmark its performance on manually annotated data, and show that it outperforms a popular offline approach.

neuroscience↗

bioRxiv2017

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

BackgroundMotion 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.\n\nNew 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.\n\nExisting 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.\n\nResultsNoRMCorre 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.\n\nConclusionsThe 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.

neuroscience↗