Towards assessing subcortical “deep brain” biomarkers of PTSD with functional near-infrared spectroscopy

Stephanie Balters, Marc R. Schlichting, Lara Foland-Ross, Sabrina Brigadoi, Jonas G. Miller, Mykel J. Kochenderfer, Amy S. Garrett, Allan L. Reiss

Research output: Contribution to journalArticlepeer-review


Assessment of brain function with functional near-infrared spectroscopy (fNIRS) is limited to the outer regions of the cortex. Previously, we demonstrated the feasibility of inferring activity in subcortical “deep brain” regions using cortical functional magnetic resonance imaging (fMRI) and fNIRS activity in healthy adults. Access to subcortical regions subserving emotion and arousal using affordable and portable fNIRS is likely to be transformative for clinical diagnostic and treatment planning. Here, we validate the feasibility of inferring activity in subcortical regions that are central to the pathophysiology of posttraumatic stress disorder (PTSD; i.e. amygdala and hippocampus) using cortical fMRI and simulated fNIRS activity in a sample of adolescents diagnosed with PTSD (N = 20, mean age = 15.3 ± 1.9 years) and age-matched healthy controls (N = 20, mean age = 14.5 ± 2.0 years) as they performed a facial expression task. We tested different prediction models, including linear regression, a multilayer perceptron neural network, and a k-nearest neighbors model. Inference of subcortical fMRI activity with cortical fMRI showed high prediction performance for the amygdala (r > 0.91) and hippocampus (r > 0.95) in both groups. Using fNIRS simulated data, relatively high prediction performance for deep brain regions was maintained in healthy controls (r > 0.79), as well as in youths with PTSD (r > 0.75). The linear regression and neural network models provided the best predictions.

Original languageEnglish (US)
Pages (from-to)3969-3984
Number of pages16
JournalCerebral Cortex
Issue number7
StatePublished - Apr 1 2023


  • PTSD
  • fMRI
  • fNIRS
  • machine learning

ASJC Scopus subject areas

  • Cellular and Molecular Neuroscience
  • Cognitive Neuroscience


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