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

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.

journal_name

J Neural Eng

authors

Eken A,Çolak B,Bal NB,Kuşman A,Kızılpınar SÇ,Akaslan DS,Baskak B

doi

10.1088/1741-2552/ab50b2

subject

Has Abstract

pub_date

2019-12-16 00:00:00

pages

016012

issue

1

eissn

1741-2560

issn

1741-2552

journal_volume

17

pub_type

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