ABSTRACT
Living in high-expressed emotion (EE) environments, characterized by critical, hostile, or over-involved family attitudes, has been linked to increased relapse rates among individuals with schizophrenia (SZ). In our previous work (Wang et al., 2023), we conducted the first feasibility study of using functional near-infrared spectroscopy (fNIRS) with our developed EE stimuli to examine cortical hemodynamics in SZ. To better understand the neural mechanisms underlying EE environmental factors in SZ, we extended our investigation by employing functional connectivity (FC) analysis with a graph theory approach to fNIRS signals. Relative to healthy controls (N=40), individuals with SZ (N=37) exhibited altered connectivity across the medial prefrontal cortex (mPFC), left ventrolateral prefrontal cortex (vlPFC), and left superior temporal gyrus (STG) while exposed to EE environments. Notably, while individuals with SZ were exposed to high-EE environments, (i) reduced connectivity was observed in these brain regions and (ii) the left vlPFC-STG coupling was found to be associated with the negative symptom severity. Taken together, our FC findings suggest individuals with SZ experience a more extensive disruption in neural functioning and coordination, particularly indicating an increased susceptibility to high-EE environments. This further supports the potential utility of integrating fNIRS with the created EE stimuli for assessing EE environmental influences, paving the way for more targeted therapeutic interventions.
ABSTRACT
In this study, we proposed an analytical framework to identify dynamic task-based functional connectivity (FC) features as new biomarkers of emotional sensitivity in nursing students, by using a combination of unsupervised and supervised machine learning techniques. The dynamic FC was measured by functional Near-Infrared Spectroscopy (fNIRS), and computed using a sliding window correlation (SWC) analysis. A k -means clustering technique was applied to derive four recurring connectivity states. The states were characterized by both graph theory and semi-metric analysis. Occurrence probability and state transition were extracted as dynamic FC network features, and a Random Forest (RF) classifier was implemented to detect emotional sensitivity. The proposed method was trialled on 39 nursing students and 19 registered nurses during decision-making, where we assumed registered nurses have developed strategies to cope with emotional sensitivity. Emotional stimuli were selected from International Affective Digitized Sound System (IADS) database. Experiment results showed that registered nurses demonstrated single dominant connectivity state of task-relevance, while nursing students displayed in two states and had higher level of task-irrelevant state connectivity. The results also showed that students were more susceptive to emotional stimuli, and the derived dynamic FC features provided a stronger discriminating power than heart rate variability (accuracy of 81.65% vs 71.03%) as biomarkers of emotional sensitivity. This work forms the first study to demonstrate the stability of fNIRS based dynamic FC states as a biomarker. In conclusion, the results support that the state distribution of dynamic FC could help reveal the differentiating factors between the nursing students and registered nurses during decision making, and it is anticipated that the biomarkers might be used as indicators when developing professional training related to emotional sensitivity.
Subject(s)
Brain , Spectroscopy, Near-Infrared , Humans , Magnetic Resonance ImagingABSTRACT
This study aims to investigate the generalizability of the semi-metric analysis of the functional connectivity (FC) for functional near-infrared spectroscopy (fNIRS) by applying it to detect the dichotomy in differential FC under affective and neutral emotional states in nursing students and registered nurses during decision making. The proposed method employs wavelet transform coherence to construct FC networks and explores semi-metric analysis to extract network redundancy features, which has not been considered in conventional fNIRS-based FC analyses. The trials of the proposed method were performed on 19 nursing students and 19 registered nurses via a decision-making task under different emotional states induced by affective and neutral emotional stimuli. The cognitive activities were recorded using fNIRS, and the emotional stimuli were adopted from the International Affective Digitized Sound System (IADS). The induction of emotional effects was validated by heart rate variability (HRV) analysis. The experimental results by the proposed method showed significant difference (FDR-adjusted p = 0.004) in the nursing students' cognitive FC network under the two different emotional conditions, and the semi-metric percentage (SMP) of the right prefrontal cortex (PFC) was found to be significantly higher than the left PFC (FDR-adjusted p = 0.036). The benchmark method (a typical weighted graph theory analysis) gave no significant results. In essence, the results support that the semi-metric analysis can be generalized and extended to fNIRS-based functional connectivity estimation.
