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Related Concept Videos

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT01:25

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

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Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
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Related Experiment Video

Updated: Sep 10, 2025

TACI: An ImageJ Plugin for 3D Calcium Imaging Analysis
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Calciumnetexplorer: an R package for network analysis of calcium imaging data.

Simone Lenci1, Dirk Sieger2

  • 1Centre for Discovery Brain Sciences, University of Edinburgh, 49 Little France Crescent, Edinburgh, EH16 4SB, UK. s.lenci@ed.ac.uk.

BMC Bioinformatics
|August 26, 2025
PubMed
Summary
This summary is machine-generated.

CalciumNetExploreR (CNER) is a new R package that simplifies calcium imaging data analysis. It provides a user-friendly pipeline for exploring neuronal functional networks and their dynamic properties.

Keywords:
Calcium imagingComputational neuroscienceGraphNetworkR

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Data Science

Background:

  • Analyzing complex functional networks from calcium imaging data is challenging.
  • Requires multiple tools, custom scripts, and coding expertise.
  • Need for streamlined and standardized analysis of neuronal population time-series data.

Purpose of the Study:

  • Introduce CalciumNetExploreR (CNER), an R package.
  • Streamline and standardize the analysis of neuronal population time-series data.
  • Facilitate deeper insights into functional organization and dynamic properties of neuronal networks.

Main Methods:

  • CNER integrates normalization, binarization, and population activity visualization.
  • Includes network construction, degree distribution, and principal component analysis.
  • Evaluates power spectral density and event frequency calculations within a cohesive pipeline.

Main Results:

  • Enables efficient extraction and comparison of network metrics (clustering coefficients, global efficiency, community structures).
  • Simplifies examination of functional connectivity and network topology.
  • Provides means to characterize cellular functional networks and analogous structures.

Conclusions:

  • CNER is a user-friendly package for neuroscientists.
  • Allows incorporation of robust statistical and graphical analyses without extensive coding.
  • Reduces barriers in large-scale data analysis for deeper insights into neuronal networks.