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Öğe Association of Fine Motor Loss and Allodynia in Fibromyalgia: An fNIRS Study(Routledge Journals, Taylor & Francis Ltd, 2018) Eken, Aykut; Gökçay, Didem; Yılmaz, Cemre; Baskak, Bora; Baltacı, Ayşegül; Kara, MuratRecent 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.Öğe Binary Classification Using Neural and Clinical Features: An Application in Fibromyalgia With Likelihood-Based Decision Level Fusion(Ieee-Inst Electrical Electronics Engineers Inc, 2019) Gökçay, Didem; Eken, Aykut; Baltacı, SerdarAmong 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 functional near infrared spectroscopy (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 k nearest neighborhood (kNN), linear discriminant analysis (LDA), and support vector machine (SVM). Fusion of classification opinions from multiple classifiers based on likelihood ratios outperformed individual classifier performances. When 2, 3, 4, and 5 different classifiers are fused, sensitivity, and specificity figures of 100% could be obtained based on the choice of the classifier set.Öğe Differential efficiency of transcutaneous electrical nerve stimulation in dominant versus nondominant hands in fibromyalgia: placebo-controlled functional near-infrared spectroscopy study(Spie-Soc Photo-Optical Instrumentation Engineers, 2018) Eken, Aykut; Kara, Murat; Baskak, Bora; Baltacı, Ayşegül; Gökçay, DidemUsing 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 Delta HbO(2) 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. Post hoc 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. (c) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE).Öğe Sigara Kullanma Durumunun Çoklu Fizyolojik Ölçümler Ve Makine Öğrenmesi Teknikleri Kullanılarak Tahmini(2021) Eken, Aykut; Çalışkan, Şevket; Çivilibal, Soner; Tosun, Pınar DenizSigara kullanımı toplumlarda gerek sağlık gerek ekonomik açıdan ciddi kayıplara sebep olmaktadır.Kullanım seviyesinin ölçümünde bir altın standart bulunmamasına rağmen, Fagerstörm NikotinBağımlılık Testi (Fagerstörm Test for Nicotine Dependency – FTND) ve HONC (Hooked on NicotineChecklist) gibi geleneksel testler ve çeşitli nörogörüntüleme yaklaşımları kişinin sigara içmedurumunun seviyesi hakkında bir bilgi vermektedir. Bu çalışmada, objektif bir veri olan fizyolojikparametrelerin subjektif bir veri olan bağımlılık testlerinin yerine kullanım seviye tespitinde yeni biryaklaşım olarak kullanılabileceğini göstermek amaçlanmıştır. Bu amaçla çeşitli seviyelerdeki sigarakullanıcılarından fizyolojik sinyaller (elektrokardiyogram (EKG), Solunum ve Fotopletismografi)toplanmıştır. Bu sinyallerden elde edilen çeşitli öz niteliklerden makine öğrenmesi yaklaşımlarıkullanılarak katılımcılar düşük seviye veya yüksek seviye olarak tahmin edilmeye çalışılmıştır.Çalışma için önceden FTND bağımlılık testine giren değişik kullanım seviyelerinde 95 katılımcı alınıpbu kişilerden sırasıyla 50 saniyelik EKG, solunum ve fotopletismografi sinyalleri alınmıştır. Öznitelikçıkarımından sonra, parametre optimizasyonu ve sınıflandırma içeren 10 kat içiçe çapraz geçerlilikgerçekleştirilmiştir. Yapılan sınıflandırma sonucunda destek vektör makinesi kullanılarak %93,diskriminant analizi kullanılarak ise %91 doğruluk başarımı elde edilmiştir. Bu sonuçlar, yukarıdabelirtilen fizyolojik parametrelerin makine öğrenmesi algoritmaları aracılığı ile sigara kullanımdurumunun tespitinde kullanılabileceğini göstermektedir.Öğe The Utility of fNIRS Signals versus Self-Report for Classification of Fibromyalgia Syndrome(Ieee, 2017) Eken, Aykut; Gökçay, Didem; Baskak, Bora; Baltacı, Ayşegül; Kara, MuratFibromyalgia (FM) is a widespread painful disease that has a 2-8% prevalence. Its diagnosis is generally performed by American College of Rheumatology (ACR) criteria. However, these criteria are subjective and their reliability is controversial. In this study, painful stimulation and Transcutaneous Electrical Nerve Stimulation (TENS) were applied to both hands of healthy controls and FM patients and hemodynamic responses was measured by using Functional Near Infrared Spectroscopy (fNIRS). Features extracted from hemodynamic responses and self-report data were used with 4 different classifiers and 14 different parameters. In conclusion, classification performed by objective data collected from fNIRS signals (95%) gave higher accuracy than classification performed by subjective self-report data (83%). This study showed that painful stimulation and TENS application can be used to diagnose Fibromyalgia disease by using fNIRS.
