Eken, AykutÇolak, BurçinBal, Neşe BurcuKuşman, AdnanKızılpınar, Selma ÇilemAkasla, Damla SayarBaskak, Bora2020-03-172019Eken A, Çolak B, Bal NB, Kuşman A, Kızılpınar SÇ, Akaslan DS, Baskak B. Hyperparameter-tuned prediction of somatic symptom disorder using functional near-infrared spectroscopy-based dynamic functional connectivity. J Neural Eng. 2019 Dec 16;17(1):016012. DOI: 10.1088/1741-2552/ab50b21741-2552http://hdl.handle.net/10230/43925Objective. 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.application/pdfengThis is the Accepted Manuscript version of an article accepted for publication in Journal of neural engineering. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.1088/1741-2552/ab50b2.info:eu-repo/semantics/articlehttp://dx.doi.org/10.1088/1741-2552/ab50b2Dynamic functional connectivityfNIRSHyperparameter optimizationMachine learningSomatic symptom disorderinfo:eu-repo/semantics/openAccess