Intrinsic organization of cortical networks predicts state anxiety: an functional near-infrared spectroscopy (fNIRS) study

Abstract

Although state anxiety has been characterized by hyper-responsive subcortical activity and its bottom-up connectivity with cortical regions, the role of cortical networks in state anxiety is not yet well understood. To this end, we decoded individual state anxiety by using a machine-learning approach based on resting-state functional connectivity (RSFC) with functional near-infrared spectroscopy (fNIRS). Our results showed that the RSFC among a set of cortical networks were highly predictive of state anxiety, rather than trait anxiety. Specifically, these networks included connectivity between cortical areas in the default mode network (DMN) and dorsal attention network (DAN), and connectivity within the DMN, which were negatively correlated with state anxiety; connectivity between cortical areas in the DMN and frontoparietal network (FPN), FPN and salience network (SN), FPN and DAN, DMN and SN, which were positively correlated with state anxiety. These findings suggest a predictive role of intrinsic cortical organization in the assessment of state anxiety. The work provides new insights into potential neural mechanisms of emotion states and implications for prognosis, diagnosis, and treatment of affective disorders.

A comparison of anxiety levels before and during the COVID-19 pandemic

“…One path forward is to use neuroimaging methods to confirm self-reported information. Functional near-infrared spectroscopy (fNIRS) is a non-invasive optical imaging technique that has been used to distinguish between neurotypical controls and individuals with a variety of mental health conditions, including major depression ( Husain et al, 2020 ), PTSD ( Gramlich et al, 2017 ), and anxiety ( Duan et al, 2020 ). The ability of fNIRS to detect changes in cortical oxy-hemoglobin during a task provides a more objective measure of neural activity between groups.…”

Connectome-Based Predictive Modeling of Individual Anxiety

“…The neural model from anxiety prediction measured by the BAI significantly predicted trait anxiety measured by the STAI-T, suggesting that the predictive network of individual anxiety in the current study is indeed significantly related to trait anxiety. It recently has been shown that state anxiety, rather than trait anxiety, could be predicted based on the cortical connectivity (Duan et al 2020), suggesting that distinctive connectivity patterns might be involved in prediction of state anxiety and trait anxiety. Although this does not exclude a potential involvement of transient anxious mood states, this result demonstrates that the BAI scores used in this study are predictive of anxiety as a trait.…”

Towards Understanding Human Functional Brain Development With Explainable Artificial Intelligence: Challenges and Perspectives

“…A fNIRS study investigating intrinsic FC of cortical networks to predict anxiety states using linear ridge regression (RR) models is done by Duan et al [48]. The resting state FC was calculated using Pearson correlation coefficient for 1035 edges between 46 nodes (fNIRS channels).…”