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Detection of Emotional Sensitivity Using fNIRS Based Dynamic Functional Connectivity.

Tong Boon Tang, Jie Sheng Chong, Masashi Kiguchi

IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society

|May 10, 2021

Summary

Dynamic functional connectivity (FC) measured by functional Near-Infrared Spectroscopy (fNIRS) reveals new biomarkers for emotional sensitivity in nursing students. These fNIRS-based dynamic FC states show higher accuracy than heart rate variability in distinguishing emotional sensitivity.

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Area of Science:

Neuroscience
Biomarkers
Machine Learning

Background:

Emotional sensitivity is a critical factor in healthcare professions, influencing decision-making and patient care.
Existing biomarkers for emotional sensitivity, such as heart rate variability, have limitations in accuracy and specificity.
Dynamic functional connectivity (FC) offers a promising avenue for identifying novel biomarkers of emotional and cognitive states.

Purpose of the Study:

To develop and validate an analytical framework for identifying dynamic task-based FC features as biomarkers of emotional sensitivity.
To compare the efficacy of fNIRS-based dynamic FC biomarkers against heart rate variability in detecting emotional sensitivity.
To differentiate emotional sensitivity levels between nursing students and registered nurses using dynamic FC states.

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Main Methods:

Functional Near-Infrared Spectroscopy (fNIRS) was employed to measure dynamic FC using a sliding window correlation (SWC) analysis.
K-means clustering identified four recurring connectivity states, characterized by graph theory and semi-metric analysis.
A Random Forest (RF) classifier was utilized to detect emotional sensitivity based on extracted dynamic FC features (occurrence probability and state transition).

Main Results:

Registered nurses exhibited a dominant task-relevant connectivity state, while nursing students showed two states with higher task-irrelevant connectivity.
Nursing students demonstrated greater susceptibility to emotional stimuli compared to registered nurses.
The derived dynamic FC features achieved higher accuracy (81.65%) than heart rate variability (71.03%) in discriminating emotional sensitivity.

Conclusions:

Dynamic FC states derived from fNIRS are stable and effective biomarkers for emotional sensitivity.
The state distribution of dynamic FC can differentiate emotional sensitivity between nursing students and registered nurses.
These fNIRS-based biomarkers hold potential for developing targeted professional training programs for emotional sensitivity.