Recent studies showed that fine motor control dysfunction was observed in fibromyalgia (FM) syndrome as well as allodynia. However, brain signatures of this association still remain unclear. In this study, finger tapping task (FTT) and median nerve stimulation (MNS) were applied to both hands of 15 FM patients and healthy controls (HC) to understand this relationship. Hemodynamic activity was measured simultaneously using functional near-infrared spectroscopy (fNIRS). Experiments were analyzed separately by using 2x2 repeated measures ANOVA. Results for the FTT experiment revealed that HC showed higher activity than FM patients in bilateral superior parietal gyrus (SPG), left supramarginal gyrus (SMG) and right somatosensory cortex (SI). Furthermore, right-hand FTT resulted in higher activity than left-hand FTT in left SPG, left SI and right motor cortex (MI). In the MNS experiment, FM patients showed higher activity than HC in bilateral SPG, right SMG, right SI and right middle frontal gyrus (MFG). Negative correlation was observed in left SPG between FTT and MNS activities. Besides, MNS activity in left SPG was negatively correlated with left-hand pain threshold.This study revealed that left SPG might be an important indicator to associate fine motor loss and allodynia in FM.

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 persistent SSD patients and 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 & SUBc) and brush stimulation. We estimated 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 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 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.

Among several features used for clinical binary classification, behavioral performance, questionnaire scores, test results and physical exam reports can be counted. Attempts to include neuroimaging findings to support clinical diagnosis are scarce due to difficulties in collecting such data, as well as problems in integration of neuroimaging findings with other features. The binary classification method proposed here aims to merge small samples from multiple sites so that a large cohort which better describes the features of the disease can be built. We implemented a simple and robust framework for detection of fibromyalgia, using likelihood during decision level fusion. This framework supports sharing of classifier applications across clinical sites and arrives at a decision by fusing results from multiple classifiers. If there are missing opinions from some classifiers due to inability to collect their input features, such degradation in information is tolerated. We implemented this method using fNIRS data collected from fibromyalgia patients across three different tasks. Functional connectivity maps are derived from these tasks as features. In addition, self-reported clinical features are also used. Five classifiers are trained using kNN, LDA and SVM. Fusion of classification opinions from multiple classifiers based on log likelihood outperformed other fusion methods reported in the literature for detection of FM. When 2, 3, 4 and 5 classifers are fused, sensitivity and specificity figures of 100% could be obtained based on the choice of the classifier set.

Using functional near-infrared spectroscopy (fNIRS), modulation of hemodynamic responses by transcutaneous electrical nerve stimulation (TENS) during delivery of nociceptive stimulation was investigated in fibromyalgia (FM) patients and healthy controls for both hands. Two experiments were conducted: (1) median nerve stimulation with TENS and (2) painful stimulation using electronic von Frey filaments with TENS/placebo TENS. Mean [Formula: see text] brain activity was compared across groups and conditions using factorial ANOVA. Dominant (right) hand stimulation indicated significant interactions between group and condition in both hemispheres. results revealed that FM patients showed an increased activation in "pain + TENS" condition compared to the "pain + placebo TENS" condition while the brain activity patterns for these conditions in controls were reversed. Left-hand stimulation resulted in similar TENS effects (reduced activation for "pain + TENS" than "pain + placebo TENS") in both groups. TENS effects in FM patients might be manipulated by the stimulation side. While the nondominant hand was responsive to TENS treatment, the dominant hand was not. These results indicate that stimulation side might be an effective factor in FM treatment by using TENS. Future studies are needed to clarify the underlying mechanism for these findings.