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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

The genetic landscape of human functional brain connectivity.

Nature communications·2026
Same author

Rare-variant aggregation highlights disease-linked genes associated with brain volume variation.

American journal of human genetics·2026
Same author

Investigating the methodological foundation of lesion network mapping.

Nature neuroscience·2026
Same author

Network-based analysis of differential white matter connectivity in major depressive disorder with and without comorbid anxiety.

Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology·2026
Same author

Structural connectomic signatures of childhood maltreatment across affective and psychotic disorders.

European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology·2025
Same author

The emergence of genetic variants linked to brain and cognitive traits in human evolution.

Cerebral cortex (New York, N.Y. : 1991)·2025

Related Experiment Video

Updated: May 15, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Estimating false positives and negatives in brain networks.

Marcel A de Reus1, Martijn P van den Heuvel

  • 1Department of Psychiatry, Rudolf Magnus Institute, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands. m.a.dereus-4@umcutrecht.nl

Neuroimage
|January 9, 2013
PubMed
Summary

Choosing the right group threshold is crucial for accurate human connectome reconstruction. A threshold between 30-90%, ideally around 60%, balances false positives and negatives in structural brain networks.

More Related Videos

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Related Experiment Videos

Last Updated: May 15, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Area of Science:

  • Neuroscience
  • Medical Imaging
  • Computational Biology

Background:

  • The human brain is a complex network (human connectome) reconstructed using diffusion tensor imaging.
  • In vivo, noninvasive reconstruction can be affected by false positive and false negative connections.
  • A 'group threshold' combines networks from multiple subjects, influencing connection accuracy but often chosen intuitively.

Purpose of the Study:

  • To develop a model for estimating the influence of group threshold choice on false positives and negatives.
  • To provide data-driven recommendations for optimal group threshold selection in human connectome reconstruction.

Main Methods:

  • Modeling the impact of group threshold on false positive and negative connections.
  • Analysis of how varying group thresholds affects the resulting structural brain network.

Main Results:

  • Group thresholds significantly impact the presence of false positives and negatives in brain network reconstructions.
  • Recommended group thresholds for optimal reconstruction lie between 30% and 90%.
  • A group threshold of approximately 60% offers a favorable balance between minimizing false positives and false negatives.

Conclusions:

  • The choice of group threshold is a critical parameter in human connectome analysis.
  • A group threshold between 30% and 90% is suggested, with 60% being a particularly suitable value.
  • This model aids in optimizing structural brain network reconstruction and subsequent analyses.