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 Video

Updated: Dec 29, 2025

Functional Mapping with Simultaneous MEG and EEG
06:04

Functional Mapping with Simultaneous MEG and EEG

Published on: June 14, 2010

18.3K

A heuristic information cluster search approach for precise functional brain mapping.

Nima Asadi1, Yin Wang2, Ingrid Olson2,3

  • 1Department of Computer and Information Sciences, College of Science and Technology, Temple University, Philadelphia, Pennsylvania.

Human Brain Mapping
|February 9, 2020
PubMed
Summary

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

Determinants of human milk lactoferrin concentrations in rural Bangladesh.

PLOS global public health·2026
Same author

The impact of enhancing nutrition and antenatal infection treatment on birth outcomes in Amhara, Ethiopia: a pragmatic factorial, cluster-randomised clinical effectiveness study.

BMJ global health·2025
Same author

Social reward and nonsocial reward processing across the adult lifespan: An interim multi-echo fMRI and diffusion dataset.

Data in brief·2024
Same author

Perinatal inflammation, fetal growth restriction, and long-term neurodevelopmental impairment in Bangladesh.

Pediatric research·2024
Same author

Preliminary validation of the Virtual Kitchen Challenge as an objective and sensitive measure of everyday function associated with cerebrovascular disease.

Alzheimer's & dementia (Amsterdam, Netherlands)·2024
Same author

Lactoferrin intake from maternal milk during the neonatal hospitalization and early brain development among preterm infants.

Pediatric research·2024

A new data-driven algorithm precisely detects informative brain regions for cognitive condition research, improving upon the limitations of current neuroimaging methods like searchlight analysis.

Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Machine Learning

Background:

  • Accurately identifying brain regions associated with cognitive conditions is crucial in neuroimaging.
  • Multivariate pattern analysis (MVPA) methods, such as searchlight analysis, are popular but have limitations.
  • Existing methods can misidentify informative regions due to fixed sphere assignments, manual parameter tuning, and complexity.

Purpose of the Study:

  • To introduce a novel, fully data-driven algorithm for precise detection of informative brain clusters.
  • To address limitations of current neuroimaging analysis techniques, including searchlight and regularization-based approaches.
  • To enhance the speed and interpretability of brain region detection in cognitive studies.

Main Methods:

  • A fully data-driven maximum relevance minimum redundancy (MRMR) search algorithm was developed.
Keywords:
algorithm designdata miningfunctional magnetic resonance imagingneuroimaging

More Related Videos

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

1.4K
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

16.1K

Related Experiment Videos

Last Updated: Dec 29, 2025

Functional Mapping with Simultaneous MEG and EEG
06:04

Functional Mapping with Simultaneous MEG and EEG

Published on: June 14, 2010

18.3K
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

1.4K
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

16.1K
  • An efficient algorithmic implementation was proposed to accelerate the analysis.
  • The proposed algorithm was compared against the searchlight procedure and LASSO regularization using real and synthetic datasets.

    • Main Results:

    • The proposed MRMR algorithm demonstrated higher precision in detecting information value within brain regions.
    • The algorithm achieved superior map specificity compared to benchmark methods.
    • The data-driven approach effectively alleviated limitations associated with traditional methods.

    Conclusions:

    • The developed MRMR algorithm offers a more precise and specific method for identifying informative brain regions in neuroimaging.
    • This approach enhances the characterization of cognitive conditions by overcoming limitations of existing techniques.
    • The efficient implementation makes this a valuable tool for advancing cognitive neuroscience research.