Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

An image analysis algorithm for dendritic spines.

Ingrid Y Y Koh1, W Brent Lindquist, Karen Zito

  • 1Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY 11794-3600, USA. ingrid@ams.sunysb.edu

Neural Computation
|May 22, 2002
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Synaptic Plasticity as a Function of the Temporal Derivative.

bioRxiv : the preprint server for biology·2026
Same author

Neuropixels Opto: combining high-resolution electrophysiology and optogenetics.

Nature methods·2026
Same author

Topographic structure and function of locus coeruleus norepinephrine neurons.

bioRxiv : the preprint server for biology·2026
Same author

Functional reorganization of motor cortex connectivity during learning.

bioRxiv : the preprint server for biology·2026
Same author

Glutamate indicators with increased sensitivity and tailored deactivation rates.

Nature methods·2025
Same author

Pediatric Intracranial Gunshot Wounds: Lessons From the Newark Experience.

The Journal of surgical research·2025
Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026
Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026
Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026
Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026
Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026
Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026
See all related articles

Automated analysis of neuronal dendritic spine morphology offers a faster, more objective method than manual measurements. This geometric approach accurately quantifies spine structure, improving neural network function studies.

Area of Science:

  • Neuroscience
  • Computational Biology
  • Biophysics

Background:

  • Neuronal dendrites and spines are crucial for neural network connectivity and function.
  • Existing manual methods for analyzing dendritic spine morphology are time-consuming and prone to bias.
  • Advances in imaging and computation necessitate improved data analysis techniques.

Purpose of the Study:

  • To develop an automated geometric approach for quantifying 3D dendritic spine structure.
  • To enable objective and efficient analysis of large datasets of neuronal morphology.

Main Methods:

  • A novel geometric algorithm was developed to detect and quantify dendritic spines from laser scanning microscopy image stacks.
  • Measurements included spine length, volume, density, and shape classification.

Related Experiment Videos

  • The algorithm was validated against manual analysis using static and time-lapse images of hippocampal pyramidal neurons.

    • Main Results:

    • Automated measurements of spine length and density showed strong agreement with manual analysis.
    • The algorithm demonstrated high performance, particularly for time-series data analysis.
    • Shape classification of dendritic spines was also successfully performed.

    Conclusions:

    • Automated analysis of dendritic spine morphology significantly enhances objectivity and efficiency.
    • This approach facilitates the study of neural network function through detailed morphological analysis.
    • The presented methods are generalizable to other aspects of neuronal morphology analysis.