Citation

Balters S, Li R, Espil FM, Piccirilli A, Liu N, Gundran A, Carrion VG, Weems CF, Cohen JA, Reiss AL. J. Psychiatr. Res. 2021; 144: 494-502.

Copyright

(Copyright © 2021, Elsevier Publishing)

DOI

10.1016/j.jpsychires.2021.10.020

PMID

34768071

Abstract

Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technique with the potential to enable the assessment of posttraumatic stress disorder (PTSD) brain biomarkers in an affordable and portable manner. Consistent with biological models of PTSD, functional magnetic resonance imaging (fMRI) and fNIRS studies of adults with trauma exposure and PTSD symptoms suggest increased activation in the dorsolateral prefrontal cortex (dlPFC) and ventrolateral PFC (vlPFC) in response to negative emotion stimuli. We tested this theory with fNIRS assessment among youth exposed to traumatic stress and experiencing PTSD symptoms (PTSS). A portable fNIRS system collected hemodynamic responses from (N = 57) youth with PTSS when engaging in a classic emotion expression task that included fearful and neutral faces stimuli. The General Linear Model was applied to identify cortical activations associated with the facial stimuli. Subsequently, a prediction model was established via a Support Vector Regression to determine whether PTSS severity could be predicted based on fNIRS-derived cortical response measures and individual demographic information.

RESULTS were consistent with findings from adult fMRI and fNIRS studies of PTSS showing increased activation in the dlPFC and vlPFC in response to negative emotion stimuli. Subsequent prediction analysis revealed ten features (i.e., cortical responses from eight frontocortical fNIRS channels, age and sex) strongly correlated with PTSS severity (r = 0.65, p < .001). Our findings suggest the potential utility of fNIRS as a portable tool for the detection of putative PTSS brain biomarkers.

Language: en

Keywords

Youth; Emotion processing; Functional near-infrared spectroscopy; Posttraumatic stress symptoms; Predictive model; Symptom severity