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Related Experiment Video

Updated: Dec 15, 2025

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
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Improved dynamic connection detection power in estimated dynamic functional connectivity considering multivariate

Somayeh Maleki Balajoo1, Davud Asemani1, Ali Khadem1

  • 1Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran.

Human Brain Mapping
|July 10, 2020
PubMed
Summary

The KELLER algorithm significantly improves dynamic functional connectivity (dFC) detection in resting-state fMRI data compared to sliding window correlation (SWC) methods. KELLER demonstrates higher statistical power and identifies key brain network connections missed by conventional approaches.

Keywords:
dynamic connection detectabilitydynamic functional connectivityhypothesis testingmultivariate dependenciesresting state functional magnetic resonance imagingsurrogate data

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biostatistics

Background:

  • Dynamic functional connectivity (dFC) estimation is crucial for understanding brain function.
  • Conventional sliding window correlation (SWC) methods have limitations in detecting dynamic connections due to equal weighting, suboptimal time scales, and bivariate analysis.
  • Existing methods struggle with accuracy and sensitivity in capturing transient brain network interactions.

Purpose of the Study:

  • To introduce and evaluate the kernel-reweighted logistic regression (KELLER) algorithm for estimating dFC from resting-state functional magnetic resonance imaging (rs-fMRI) data.
  • To compare the performance of KELLER against sliding window correlation (SWC) and tapered-SWC (T-SWC) using simulated and real rs-fMRI data.
  • To assess the ability of KELLER to detect statistically significant dynamic brain region pairs and identify network-specific connectivity patterns.

Main Methods:

  • Utilized the kernel-reweighted logistic regression (KELLER) algorithm, adapted from genetic studies, to estimate dFC by considering both spatial and temporal patterns.
  • Compared KELLER with SWC and T-SWC methods, varying window lengths for SWC/T-SWC, on simulated and real rs-fMRI datasets.
  • Employed hypothesis testing to evaluate the detection of dynamically connected brain region pairs and assessed similarity of identified patterns with known brain networks like the default mode network (DMN).

Main Results:

  • KELLER achieved a statistical power of 87.35% for detecting dynamic connections, significantly outperforming T-SWC (70.17%) and SWC (58.54%) in simulations.
  • On real rs-fMRI data, T-SWC with a 100s window showed the highest similarity (68% of subjects) to KELLER's estimated mean dynamic pattern.
  • KELLER successfully identified known dynamic connections within the default mode network (DMN), which were missed by SWC-based methods.

Conclusions:

  • The KELLER algorithm offers a superior approach for estimating dFC in rs-fMRI, overcoming key limitations of traditional SWC methods.
  • KELLER demonstrates enhanced sensitivity and statistical power in detecting dynamic functional brain connections.
  • This method holds promise for more accurate characterization of brain network dynamics and identifying clinically relevant connectivity alterations.