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

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

PmD479 is an Unutilized Gene for Powdery Mildew Resistance in Common Wheat.

Plant biotechnology journal·2026
Same author

A computational model to describe multi-regional brain architecture during neurodegeneration in Alzheimer's disease.

Scientific reports·2026
Same author

Energy-preserving shifted bipartite graph learning for unpaired large-scale multi-view clustering.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Advancing EEG-based assessment of consciousness and cognition in prolonged disorders of consciousness.

Communications medicine·2026
Same author

From Searching to Coping, How Chinese Patients With Breast Cancer Navigate Web-Based Health Information: Semistructured Interview Study.

Journal of medical Internet research·2026

Related Experiment Video

Updated: May 17, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

A least trimmed square regression method for second level FMRI effective connectivity analysis.

Xingfeng Li1, Damien Coyle, Liam Maguire

  • 1Intelligent Systems Research Centre, University of Ulster at Magee, Derry, UK. x.li@ulster.ac.uk

Neuroinformatics
|October 25, 2012
PubMed
Summary
This summary is machine-generated.

A new robust regression method (LTS) improves effective connectivity analysis by identifying and excluding outliers. This approach enhances accuracy in second-level analysis, especially when dealing with significant model variability.

More Related Videos

A Randomized, Sham-Controlled Trial of Cranial Electrical Stimulation for Fibromyalgia Pain and Physical Function, Using Brain Imaging Biomarkers
08:33

A Randomized, Sham-Controlled Trial of Cranial Electrical Stimulation for Fibromyalgia Pain and Physical Function, Using Brain Imaging Biomarkers

Published on: January 5, 2024

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

Related Experiment Videos

Last Updated: May 17, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

A Randomized, Sham-Controlled Trial of Cranial Electrical Stimulation for Fibromyalgia Pain and Physical Function, Using Brain Imaging Biomarkers
08:33

A Randomized, Sham-Controlled Trial of Cranial Electrical Stimulation for Fibromyalgia Pain and Physical Function, Using Brain Imaging Biomarkers

Published on: January 5, 2024

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

Area of Science:

  • Neuroscience
  • Statistics
  • Biomedical Engineering

Background:

  • Effective connectivity analysis is crucial for understanding brain network dynamics.
  • Conventional methods are sensitive to outliers and model variability from initial analyses.
  • Integrating data across runs or subjects requires robust statistical approaches.

Purpose of the Study:

  • To introduce a robust regression method (Least Trimmed Squares - LTS) for second/high-level effective connectivity analysis.
  • To address the limitations of non-robust methods in handling model variability and outliers.
  • To compare the performance of the LTS method against conventional approaches using simulated and real datasets.

Main Methods:

  • Least Trimmed Squares (LTS) robust regression was employed to identify and exclude outliers in model variability.
  • A bootstrap method was utilized for LTS estimation and outlier detection.
  • A mixed-effects model framework was used for combining second-level model coefficients after outlier exclusion.
  • The Newton-Raphson (NR) type Levenberg-Marquardt algorithm estimated the mixed model variance.

Main Results:

  • The LTS robust method demonstrated significant differences compared to non-robust methods in second-level effective connectivity analysis.
  • Non-robust methods were found to be highly susceptible to model variability originating from first-level analyses.
  • Real-data comparisons revealed significant discrepancies between conventional methods and the mixed-effects model when substantial model variability was present.

Conclusions:

  • The proposed LTS robust regression method offers improved accuracy for second/high-level effective connectivity analysis.
  • Conventional methods neglecting random effects are inadequate when significant model variability exists.
  • A mixed-effects model is recommended for second-level effective connectivity analysis to account for random effects and enhance reliability.