SmaRT2P:用于生成和处理人口双光子钙成像的智能线记录轨迹的软件。

Q1 Computer Science
Monica Moroni, Marco Brondi, Tommaso Fellin, Stefano Panzeri
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引用次数: 0

摘要

双光子荧光钙成像可以以亚细胞的空间分辨率记录大神经群的活动,但它的典型特点是低信噪比(SNR),当大量神经元成像时,检测单个或几个动作电位的准确性较差。我们最近表明,在活体群体成像中,使用轨迹对感兴趣的区域进行最佳采样的智能线扫描方法增加了荧光信号的信噪比和单尖峰检测的准确性。然而,智能线扫描需要高度专业化的软件来设计记录轨迹,与采集硬件接口,并有效地处理采集到的数据。此外,智能线扫描需要优化策略来处理运动伪影和神经污染。在这里,我们开发并验证了SmaRT2P,这是一个开源的、用户友好的、易于界面的基于matlab的软件环境,用于在双光子钙成像实验中进行优化的智能线扫描。SmaRT2P旨在与流行的采集软件(例如ScanImage)接口,并实现新的策略来检测运动伪影,估计神经污染,并最大限度地减少它们对从神经元群体成像中提取的功能信号的影响。SmaRT2P以模块化的方式构建,允许处理管道的灵活性,在参数设置方面需要最少的用户干预。使用SmaRT2P进行智能线扫描有可能促进大型神经元群的功能研究,在检测单个和少数动作电位放电方面具有更高的信噪比和准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

SmaRT2P: a software for generating and processing smart line recording trajectories for population two-photon calcium imaging.

SmaRT2P: a software for generating and processing smart line recording trajectories for population two-photon calcium imaging.

SmaRT2P: a software for generating and processing smart line recording trajectories for population two-photon calcium imaging.

SmaRT2P: a software for generating and processing smart line recording trajectories for population two-photon calcium imaging.

Two-photon fluorescence calcium imaging allows recording the activity of large neural populations with subcellular spatial resolution, but it is typically characterized by low signal-to-noise ratio (SNR) and poor accuracy in detecting single or few action potentials when large number of neurons are imaged. We recently showed that implementing a smart line scanning approach using trajectories that optimally sample the regions of interest increases both the SNR fluorescence signals and the accuracy of single spike detection in population imaging in vivo. However, smart line scanning requires highly specialised software to design recording trajectories, interface with acquisition hardware, and efficiently process acquired data. Furthermore, smart line scanning needs optimized strategies to cope with movement artefacts and neuropil contamination. Here, we develop and validate SmaRT2P, an open-source, user-friendly and easy-to-interface Matlab-based software environment to perform optimized smart line scanning in two-photon calcium imaging experiments. SmaRT2P is designed to interface with popular acquisition software (e.g., ScanImage) and implements novel strategies to detect motion artefacts, estimate neuropil contamination, and minimize their impact on functional signals extracted from neuronal population imaging. SmaRT2P is structured in a modular way to allow flexibility in the processing pipeline, requiring minimal user intervention in parameter setting. The use of SmaRT2P for smart line scanning has the potential to facilitate the functional investigation of large neuronal populations with increased SNR and accuracy in detecting the discharge of single and few action potentials.

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来源期刊
Brain Informatics
Brain Informatics Computer Science-Computer Science Applications
CiteScore
9.50
自引率
0.00%
发文量
27
审稿时长
13 weeks
期刊介绍: Brain Informatics is an international, peer-reviewed, interdisciplinary open-access journal published under the brand SpringerOpen, which provides a unique platform for researchers and practitioners to disseminate original research on computational and informatics technologies related to brain. This journal addresses the computational, cognitive, physiological, biological, physical, ecological and social perspectives of brain informatics. It also welcomes emerging information technologies and advanced neuro-imaging technologies, such as big data analytics and interactive knowledge discovery related to various large-scale brain studies and their applications. This journal will publish high-quality original research papers, brief reports and critical reviews in all theoretical, technological, clinical and interdisciplinary studies that make up the field of brain informatics and its applications in brain-machine intelligence, brain-inspired intelligent systems, mental health and brain disorders, etc. The scope of papers includes the following five tracks: Track 1: Cognitive and Computational Foundations of Brain Science Track 2: Human Information Processing Systems Track 3: Brain Big Data Analytics, Curation and Management Track 4: Informatics Paradigms for Brain and Mental Health Research Track 5: Brain-Machine Intelligence and Brain-Inspired Computing
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