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Related Concept Videos

Muscle Stimulation Frequency01:22

Muscle Stimulation Frequency

The contraction strength of muscles is regulated by motor neurons, which modulate the frequency of action potentials dispatched to the motor units based on the body's requirements. This process of varying the muscle stimulation frequency allows muscles to contract with a force that is precisely tailored to the needs of the moment, whether lifting a feather or a heavy box.
Wave summation
At low firing rates, motor neurons induce individual twitch contractions in muscle fibers. These twitches...

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Related Experiment Video

Updated: Jun 21, 2026

Simultaneous Transcranial Alternating Current Stimulation and Functional Magnetic Resonance Imaging
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Frequency modulation increases the specificity of time-resolved connectivity: A resting-state fMRI study.

Ashkan Faghiri1, Kun Yang2, Andreia Faria3

  • 1Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA.

Network Neuroscience (Cambridge, Mass.)
|October 2, 2024
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Summary

A new method, single sideband modulation plus sliding window Pearson correlation (SSB+SWPC), improves analysis of time-resolved functional network connectivity (trFNC). This approach captures rapid brain network changes more effectively than traditional methods, revealing differences in psychosis patients.

Keywords:
First episode of psychosis (FEP)Frequency modulationIndependent component analysisResting-state fMRISingle sideband modulation (SSB)Sliding window Pearson correlation (SWPC)

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Area of Science:

  • Neuroscience
  • Network Science
  • Signal Processing

Background:

  • Time-resolved networks are crucial for analyzing dynamic brain functional connectivity.
  • Sliding window Pearson correlation (SWPC) is a common method but has limitations due to high-pass filtering, especially with short windows needed for rapid change detection.
  • SWPC's filtering can remove important low-frequency information when using short windows.

Purpose of the Study:

  • To introduce a novel approach, single sideband modulation plus SWPC (SSB+SWPC), for constructing time-resolved functional network connectivity (trFNC).
  • To enhance the ability to capture rapid changes in brain connectivity by utilizing shorter analysis windows.
  • To compare the performance of SSB+SWPC against traditional SWPC using simulated and real fMRI data.

Main Methods:

  • Development and application of a single sideband modulation (SSB) technique adapted from communication theory.
  • Construction of time-resolved functional network connectivity (trFNC) using the proposed SSB+SWPC method.
  • Validation using simulated data and resting-state functional magnetic resonance imaging (fMRI) data from individuals with first episode psychosis (FEP) and typical controls (TC).

Main Results:

  • SSB+SWPC demonstrated superior performance compared to standard SWPC in analyzing trFNC.
  • Individuals with FEP showed prolonged engagement in brain states characterized by weaker whole-brain connectivity.
  • SSB+SWPC uniquely identified that typical controls (TC) spent more time in states exhibiting negative connectivity between subcortical and cortical regions.

Conclusions:

  • The proposed SSB+SWPC method is more sensitive for capturing temporal variations in trFNC.
  • This enhanced sensitivity allows for more accurate analysis of rapid changes in brain network dynamics.
  • The findings highlight potential differences in dynamic functional connectivity patterns between individuals with FEP and typical controls.