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Partial correlation-based functional connectivity analysis for functional near-infrared spectroscopy signals.

Ata Akın1

  • 1Acibadem University, Department of Medical Engineering, Atasehir, Istanbul, Turkey.

Journal of Biomedical Optics
|December 16, 2017
PubMed
Summary

This study introduces a novel partial correlation-based functional connectivity (PC-FC) method for functional near-infrared spectroscopy (fNIRS) data. The analysis revealed significant differences in global efficiency during a Stroop task, correlating with reaction times.

Keywords:
Stroop taskfunctional connectivityfunctional near-infrared spectroscopyglobal efficiencypartial correlation

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Biomedical Engineering

Background:

  • Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technique.
  • Analyzing functional connectivity in fNIRS data is crucial for understanding brain networks.
  • Existing methods may not fully capture complex network dynamics.

Purpose of the Study:

  • To propose a novel theoretical framework for functional connectivity analysis in fNIRS data.
  • To introduce a partial correlation-based functional connectivity (PC-FC) method.
  • To investigate brain network efficiency during cognitive tasks.

Main Methods:

  • Developed a PC-FC analysis by generating a common background signal from high-passed fNIRS data.
  • Applied the PC-FC method to fNIRS data collected during a Stroop task.
  • Computed global efficiency (GE) metrics for neutral, congruent, and incongruent stimuli.

Main Results:

  • Significant differences in global efficiency (GE) were observed across Stroop task conditions (p=0.0073).
  • A strong positive correlation (r=0.729, p=0.0259) was found between reaction time interference and GE interference.
  • The PC-FC analysis effectively captured network changes related to cognitive load.

Conclusions:

  • The proposed PC-FC analysis is a viable method for fNIRS data.
  • This approach reveals significant alterations in brain network efficiency during cognitive tasks.
  • PC-FC analysis shows promise for correlating brain network dynamics with behavioral measures.