Increased prefrontal cortex connectivity during cognitive challenge assessed by fNIRS imaging

Area of Science:

• Neuroscience
• Cognitive Science
• Biomedical Engineering

Background:

• Functional connectivity (FC) analysis is crucial for understanding brain function.
• The prefrontal cortex (PFC) plays a key role in cognitive processes.
• Non-invasive neuroimaging techniques are needed to study brain activity during cognitive tasks.

Purpose of the Study:

• To investigate changes in PFC functional connectivity (FC) during rest versus increased mental workload.
• To evaluate the effectiveness of functional near-infrared spectroscopy (fNIRS) combined with graph theory for assessing FC.
• To develop a pattern recognition test to induce cognitive challenges.

Main Methods:

• Utilized functional near-infrared spectroscopy (fNIRS) to measure brain activity.
• Applied graph theory to analyze functional connectivity (FC) from fNIRS data.
• Developed and implemented a pattern recognition task to simulate mental workload.
• Employed correlation-based signal improvement (CBSI) to refine fNIRS signal quality.

Main Results:

• Functional connectivity (FC) parameters in the prefrontal cortex (PFC) were significantly increased during the mental workload condition compared to rest.
• The developed pattern recognition test effectively elicited a strong response in the PFC.
• Correlation-based signal improvement (CBSI) helped to isolate neural signals from noise.

Conclusions:

• Cognitive challenges demonstrably enhance functional connectivity (FC) within the prefrontal cortex (PFC).
• The combination of fNIRS and graph theory is a promising approach for investigating brain function across different cognitive states.
• This methodology holds potential for future research in cognitive neuroscience and clinical applications.