Objective: Somatic symptom disorder (SSD) is a reflection of medically unexplained physical symptoms that lead to distress and impairment in social and occupational functioning. SSD is phenomenologically diagnosed and its neurobiology remains unsolved.
Approach: In this study, we performed hyper-parameter optimized classification to distinguish 19 persistent SSD patients and 21 healthy controls by utilizing functional near-infrared spectroscopy via performing two painful stimulation experiments, individual pain threshold (IND) and constant sub-threshold (SUB) that include conditions with different levels of pain (INDc and SUBc) and brush stimulation. We estimated a dynamic functional connectivity time series by using sliding window correlation method and extracted features from these time series for these conditions and different cortical regions.
Main results: Our results showed that we found highest specificity (85%) with highest accuracy (82%) and 81% sensitivity using an SVM classifier by utilizing connections between right superior temporal-left angular gyri, right middle frontal (MFG)-left supramarginal gyri and right middle temporal-left middle frontal gyri from the INDc condition.
Significance: Our results suggest that fNIRS may distinguish subjects with SSD from healthy controls by applying pain in levels of individual pain-threshold and bilateral MFG, left inferior parietal and right temporal gyrus might be robust biomarkers to be considered for SSD neurobiology.