Hyperparameter-tuned prediction of somatic symptom disorder using functional near-infrared spectroscopy-based dynamic functional connectivity

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• dc.contributor.author Eken, Aykut
• dc.contributor.author Çolak, Burçin
• dc.contributor.author Bal, Neşe Burcu
• dc.contributor.author Kuşman, Adnan
• dc.contributor.author Kızılpınar, Selma Çilem
• dc.contributor.author Akasla, Damla Sayar
• dc.contributor.author Baskak, Bora
• dc.date.accessioned 2020-03-17T16:54:08Z
• dc.date.issued 2019
• dc.description.abstract 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.en
• dc.description.sponsorship We also acknowledge the support, in part, of the Fogarty International Center, United States/ NIH Grant (No: D43TW005807 - Bora Baskak, MD) during the preparation of this manuscript.
• dc.format.mimetype application/pdf
• dc.identifier.citation Eken 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/ab50b2
• dc.identifier.doi http://dx.doi.org/10.1088/1741-2552/ab50b2
• dc.identifier.issn 1741-2552
• dc.identifier.uri http://hdl.handle.net/10230/43925
• dc.language.iso eng
• dc.publisher IOP Publishing Ltd.
• dc.relation.ispartof Journal of neural engineering. 2019 Dec 16;17(1):016012
• dc.rights This 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.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri https://creativecommons.org/licences/by-nc-nd/3.0
• dc.subject.keyword Dynamic functional connectivityen
• dc.subject.keyword fNIRSen
• dc.subject.keyword Hyperparameter optimizationen
• dc.subject.keyword Machine learningen
• dc.subject.keyword Somatic symptom disorderen
• dc.title Hyperparameter-tuned prediction of somatic symptom disorder using functional near-infrared spectroscopy-based dynamic functional connectivityen
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/acceptedVersion