Prefrontal functional near-infrared spectroscopy (fNIRS) as a tool for studying emotional regulation
Poster No:
2426
Submission Type:
Abstract Submission
Authors:
Jihyun Cha1, Taehoon Kim1, Junggu Choi2, JongKwan Choi1, Sanghoon Han2
Institutions:
1OBELAB, Seoul, Korea, Republic of, 2Yonsei University, Seoul, Korea, Republic of
First Author:
Co-Author(s):
Introduction:
Daily stress affects individuals differently, with varying impacts on mental and physical health. Traditional stress biomarkers like salivary cortisol and ECG provide limited insights into the interaction between immediate stress responses and chronic stress levels. Individual traits and coping strategies further complicate the equation. Recent studies using fNIRS show prefrontal deactivation as a potential immediate biomarker for stress [1, 2], but its universality remains unclear; it is uncertain whether the deactivation simply reflects a reduced cognitive response to the mental task given stress. This study explores prefrontal responses to stress without explicit tasks, employing fNIRS alongside traditional biomarkers and questionnaires to validate the feasibility of the fNIRS as an assessor of acute or prolonged stress.
Methods:
A total of 78 participants (65 in the test and 13 in the control group) were recruited. The experiment consisted of three phases. First, the participants engaged in a resting-state fNIRS scanning (baseline phase). Second, during the Stress-induction phase, as the main "task," the test group watched a video clip of repulsive content (Fear Factor episode), whereas the control group watched neutral-to-positive content (cat video). Except for the content of the clips, all other aspects were identical across groups. Lastly, in the post-stress recovery phase, the participants underwent another resting-state scanning followed by their ratings on the video clips they watched, regarding the degree of stress change the clips caused on a scale of 1 to 7. In between these phases, salivary cortisol samples were collected.
During the experiment, participants' prefrontal regions were measured using a NIRSIT LITE Adult (OBELAB, Inc., Rep. of Korea), a portable 15-channel continuous wave fNIRS system roughly covering BA 10.
For analysis, the collected fNIRS signals were first converted to the delta optical density, on which a motion artifact correction algorithm (the Temporal Derivative Distribution Repair; TDDR) was applied[3]. The signals were then converted to relative oxygenated hemoglobin concentration (∆HbO) changes via the modified Beer-Lambert law [4]. A General Linear Model (GLM) with an autoregressive, iteratively reweighted least-squares method (AR-IRLS) [5] was fitted to extract the beta amplitude during the baseline, stress-induction, and post-stress phase. To reduce the individual differences in the baseline activation, the baseline beta weights were subtracted from the following two phases. Thus, here, we mainly focus on the period when the stress was induced by the video content and the period when the participants engaged in the resting state following the stressful event.
During the experiment, participants' prefrontal regions were measured using a NIRSIT LITE Adult (OBELAB, Inc., Rep. of Korea), a portable 15-channel continuous wave fNIRS system roughly covering BA 10.
For analysis, the collected fNIRS signals were first converted to the delta optical density, on which a motion artifact correction algorithm (the Temporal Derivative Distribution Repair; TDDR) was applied[3]. The signals were then converted to relative oxygenated hemoglobin concentration (∆HbO) changes via the modified Beer-Lambert law [4]. A General Linear Model (GLM) with an autoregressive, iteratively reweighted least-squares method (AR-IRLS) [5] was fitted to extract the beta amplitude during the baseline, stress-induction, and post-stress phase. To reduce the individual differences in the baseline activation, the baseline beta weights were subtracted from the following two phases. Thus, here, we mainly focus on the period when the stress was induced by the video content and the period when the participants engaged in the resting state following the stressful event.
·Stress-induction task procedure.
Results:
Subjective stress ratings revealed an increase in the test group (+1.44) and a decrease in controls (-0.92), indicating successful stress induction (p < . 001). ECG features showed marginal group x phase interactions, with elevated heart rate and LF/HF ratio during post-stress in the test group. No significant cortisol level changes were observed after baseline correction. fNIRS data indicated a significant group x phase interaction in the left frontopolar cortex, showing lower activation during stress induction and elevated activation during post-stress. The post-stress activation correlated with subjective stress ratings of video content. Subsequent correlation analyses revealed that activation during the post-stress phase was related to Self-Compassion Scale and Coping Inventory for Stressful Situations.
·Baseline corrected prefrontal activation during stress-induction and post-stress phase.
Conclusions:
Overall, prefrontal activation in response to stress reflects individuals' coping strategies rather than a direct physiological stress response. Unlike EGC signals, the frontal fNIRS signal may indicate rumination or emotional regulation post-stress.
Traditional methods may struggle to detect stress in individuals effectively suppressing immediate responses, highlighting fNIRS's potential in uncovering hidden repercussions.
Traditional methods may struggle to detect stress in individuals effectively suppressing immediate responses, highlighting fNIRS's potential in uncovering hidden repercussions.
Emotion, Motivation and Social Neuroscience:
Emotion and Motivation Other 2
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Novel Imaging Acquisition Methods:
NIRS 1
Keywords:
Emotions
Near Infra-Red Spectroscopy (NIRS)
Saliva
Other - Stress
1|2Indicates the priority used for review
Provide references using author date format
Al-Shargie, F.(2017). Assessment of mental stress effects on prefrontal cortical activities using canonical correlation analysis: an fNIRS-EEG study. Biomedical Optics Express, 8(5), 2583. [2]
Fishburn, F. A. (2019). Temporal Derivative Distribution Repair (TDDR): A motion correction method for fNIRS. NeuroImage, 184(June 2018), 171–179. [3]
Delpy, D. T. (1988). Estimation of optical pathlength through tissue from direct time of flight measurement. Physics in Medicine and Biology, 33(12), 1433–1442. [4]
Barker, J. W.(2013). Autoregressive model based algorithm for correcting motion and serially correlated errors in fNIRS. Biomedical Optics Express, 4(8), 1366. [5]