Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/11727/4809
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Item Combining functional near-infrared spectroscopy and EEG measurements for the diagnosis of attention-deficit hyperactivity disorder(2020) Guven, Aysegul; Altinkaynak, Miray; Dolu, Nazan; Izzetoglu, Meltem; Pektas, Ferhat; Ozmen, Sevgi; Demirci, Esra; Batbat, Turgay; 0000-0002-3104-7587; AAG-4494-2019Recently multimodal neuroimaging which combines signals from different brain modalities has started to be considered as a potential to improve the accuracy of diagnosis. The current study aimed to explore a new method for discriminating attention-deficit hyperactivity disorder (ADHD) patients and control group by means of simultaneous measurement of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Twenty-three pre-medicated combined type ADHD children and 21 healthy children were included in the study. Nonlinear brain dynamics of subjects were obtained from EEG signal using Higuchi fractal dimensions and Lempel-Ziv complexity, latency and amplitude values of P3 wave obtained from auditory evoked potentials and frontal cortex hemodynamic responses calculated from fNIRS. Lower complexity values, prolonged P3 latency and reduced P3 amplitude values were found in ADHD children. fNIRS indicated that the control subjects exhibited higher right prefrontal activation than ADHD children. Features are analyzed, looking for the best classification accuracy and finally machine learning techniques, namely Support Vector Machines, Naive Bayes and Multilayer Perception Neural Network, are introduced for EEG signals alone and for combination of fNIRS and EEG signals. Naive Bayes provided the best classification with an accuracy rate of 79.54% and 93.18%, using EEG and EEG-fNIRS systems, respectively. Our findings demonstrate that utilization of information by combining features obtained from fNIRS and EEG improves the classification accuracy. As a conclusion, our method has indicated that EEG-fNIRS multimodal neuroimaging is a promising method for ADHD objective diagnosis.Item A heritable profile of six miRNAs in autistic patients and mouse models(2020) Dolu, Nazan; 0000-0002-3104-7587; 32514154; AAG-4494-2019Autism spectrum disorder (ASD) is a group of developmental pathologies that impair social communication and cause repetitive behaviors. The suggested roles of noncoding RNAs in pathology led us to perform a comparative analysis of the microRNAs expressed in the serum of human ASD patients. The analysis of a cohort of 45 children with ASD revealed that six microRNAs (miR-19a-3p, miR-361-5p, miR-3613-3p, miR-150-5p, miR-126-3p, and miR-499a-5p) were expressed at low to very low levels compared to those in healthy controls. A similar but less pronounced decrease was registered in the clinically unaffected parents of the sick children and in their siblings but never in any genetically unrelated control. Results consistent with these observations were obtained in the blood, hypothalamus and sperm of two of the established mouse models of ASD: valproic acid-treated animals and Cc2d1a(+/-) heterozygotes. In both instances, the same characteristic miRNA profile was evidenced in the affected individuals and inherited together with disease symptoms in the progeny of crosses with healthy animals. The consistent association of these genetic regulatory changes with the disease provides a starting point for evaluating the changes in the activity of the target genes and, thus, the underlying mechanism(s). From the applied societal and medical perspectives, once properly confirmed in large cohorts, these observations provide tools for the very early identification of affected children and progenitors.Item Diagnosis of Attention Deficit Hyperactivity Disorder with combined time and frequency features(2020) Altinkaynak, Miray; Dolu, Nazan; Guven, Aysegul; Pektas, Ferhat; Ozmen, Sevgi; Demirci, Esra; Izzetoglu, Meltem; 0000-0002-3104-7587; AAG-4494-2019The aim of this study was to build a machine learning model to discriminate Attention Deficit Hyperactivity Disorder (ADHD) patients and healthy controls using information from both time and frequency analysis of Event Related Potentials (ERP) obtained from Electroencephalography (EEG) signals while participants performed an auditory oddball task. The study included 23 unmedicated ADHD patients and 23 healthy controls. The EEG signal was analyzed in time domain by nonlinear brain dynamics and morphological features, and in time-frequency domain with wavelet coefficients. Selected features were applied to various machine learning techniques including; Multilayer Perceptron, Naive Bayes, Support Vector Machines, k-nearest neighbor, Adaptive Boosting, Logistic Regression and Random Forest to classify ADHD patients and healthy controls. Longer P300 latencies and smaller P300 amplitudes were observed in ADHD patients relative to controls. In fractal dimension calculation relative to the control group, the ADHD group demonstrated reduced complexity. In addition, certain wavelet coefficients provided significantly different values in both groups. Combining these extracted features, our results indicated that Multilayer Perceptron method provided the best classification with an accuracy rate of 91.3% and a high level of reliability of concurrence (Kappa = 0.82). The results showed that combining time and frequency domain features can be a useful and discriminative for diagnostic purposes in ADHD. The study presents a supporting diagnostic tool that uses EEG signal processing and machine learning algorithms. The findings would be helpful in the objective diagnosis of ADHD. (C) 2020 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.Item Effects of methylphenidate treatment in children with ADHD: a multimodal EEG/fNIRS approach(2019) Dolu, Nazan; Altinkaynak, Miray; Guven, Aysegul; Ozmen, Sevgi; Demirci, Esra; Izzetoglu, Meltem; Pektas, Ferhat; 0000-0002-3104-7587; AAG-4494-2019OBJECTIVE In this study we investigated the stimulant methylphenidate (MPH) effects in Attention deficit hyperactivity disorder (ADHD) from neuroimaging and neurophysiological perspective by simultaneous recording functional near infrared spectroscopy (fNIRS) and electroencephalography (EEG) during attention task. METHODS Using fNIRS we obtained frontal cortex hemodynamic responses and using event related potentials (ERP) we obtained amplitude values of P3 component of 18 children with ADHD and gender matched 18 healthy controls performing an oddball task. Same recordings were repeated 3 months after extended-release MPH (OROS-MPH) administration for ADHD group. Prefrontal cortex oxygenation and P3 amplitude were compared between control and pre-MPH ADHD groups and between Pre-MPH and post-MPH ADHD groups. RESULTS fNIRS indicated that the healthy controls exhibited higher right prefrontal activation than pre-MPH children with ADHD. Reduced P3 amplitude values were found in children with ADHD compared the control group. Reduced right prefrontal activation and P3 amplitude was normalized in ADHD group after MPH therapy. CONCLUSION Recently multimodal neuroimaging which combine signals from different brain modalities have started to be considered as a potential to improve the accuracy of diagnosis. The current study provides MPH effect assessment in children with ADHD using multimodal EEG/fNIRS system for the first time. This study suggests combination of neuroimaging and electrophysiological parameters is a promising approach to investigate MPH effect assessment in children with ADHD.Item Evaluation of divided attention using different stimulation models in event-related potentials(2019) Batbat, Turgay; Gueven, Aysegul; Dolu, Nazan; 0000-0002-3104-7587; 31352660; AAG-4494-2019Divided attention is defined as focusing on different tasks at once, and this is described as one of the biggest problems of today's society. Default examinations for understanding attention are questionnaires or physiological signals, like evoked potentials and electroencephalography. Physiological records were obtained using visual, auditory, and auditory-visual stimuli combinations with 48 participants-18-25-year-old university students-to find differences between sustained and divided attention. A Fourier-based filter was used to get a 0.01-30-Hz frequency band. Fractal dimensions, entropy values, power spectral densities, and Hjorth parameters from electroencephalography and P300 components from evoked potentials were calculated as features. To decrease the size of the feature set, some features, which yield less detail level for data, were eliminated. The visual and auditory stimuli in selective attention were compared with the divided attention state, and the best accuracy was found to be 88.89% on a support vector machine with linear kernel. As a result, it was seen that divided attention could be more difficult to determine from selective attention, but successful classification could be obtained with appropriate methods. Contrary to literature, the study deals with the infrastructure of attention types by working on a completely healthy and attention-high group.Item Prefrontal Brain Activation in Subtypes of Attention Deficit Hyperactivity Disorder: A Functional Near-Infrared Spectroscopy Study(2018) Dolu, Nazan; Altinkaynak, Miray; Guven, Aysegul; Izzetoglu, Meltem; Demirci, Esra; Ozmen, Sevgi; Pektas, Ferhat; 0000-0002-3104-7587; AAG-4494-2019According to clinical symptoms, attention deficit and hyperactivity disorder (ADHD) is categorized into three groups: the predominantly inattentive subtype (ADHD-I),the predominantly hyperactive-impulsive subtype (ADHD-HI), and the combined subtype (ADHD-C). Recent advances in neuroimaging have demonstrated new approaches for assessing the ADHD subtypes with underlying pathophysiology.This study aims to examine the hemodynamic response and reaction time (RT) in healthy children and the ADHD subtypes as measured by functional near-infrared spectroscopy (fNIRS) during an auditory oddball attention task. The sample was made up of 40 children divided into four groups: control group (n=14), ADHD-I group (n=9), ADHD-HI group (n=6), and ADHD-C group (n=11). The target responses were identified and were grand-averaged for each participant. Right prefrontal cortex hemodynamic responses and groups performances on RT were compared between subtypes and between controls and subtypes. Functional near-infrared spectroscopy indicated that while control subjects exhibited higher activation than all ADHD subtypes, the ADHD subtypes did not differ from one another. Relative to control subjects, a longer RT was observed in all ADHD subtypes. The ADHD-I group showed significantly longer RTs compared to the ADHD-HI and ADHD-C groups.This study can bring a new perspective to the continuing controversy about ADHD subtypes, and the findings may help in the evaluation of fNIRS, RT, and RT variability studies in ADHD.