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    Utility Of Continuous Performance Test (MOXO-CPT) In Children With Pre-Dialysis Chronic Kidney Disease, Dialysis And Kidney Transplantation
    (2022) Buyukkaragoz, Bahar; Soysal Acar, A. Sebnem; Ekim, Mesiha; Bayrakci, Umut Selda; Bulbul, Mehmet; Caltik Yilmaz, Aysun; Bakkaloglu, Sevcan A.; 000829126000001
    Background Children with chronic kidney disease and on kidney replacement therapy may have neurocognitive and psychosocial disorders. Although kidney transplantation improves quality of life, psychological problems may exist in children who undergo kidney transplantation. Herein, we aimed to investigate attention-deficit hyperactivity disorder-like symptoms with MOXO-continuous performance test in children with pre-dialysis chronic kidney disease, dialysis and kidney transplantation. Methods The MOXO-continuous performance test measures four domains of attention-deficit hyperactivity disorder-like symptoms, including attention, timeliness, hyperactivity and impulsivity. Patients with at least three scores < - 1.5 standard deviations were considered as positive to MOXO-continuous performance test. Test scores of the pre-dialysis chronic kidney disease, dialysis (divided into peritoneal dialysis and hemodialysis subgroups) and kidney transplantation groups were compared. Correlations of test scores with the patient's clinical and laboratory characteristics and effects of hospitalizations and schooling were assessed. Results Seventy-two patients aged 13.3 +/- 3.4 years (23 with kidney transplantation, 23 on dialysis and 26 with pre-dialysis chronic kidney disease) were evaluated. Overall MOXO-continuous performance test positivity was 29%. No differences were detected between the three groups concerning total or z scores. Attention and timeliness z scores were significantly higher in females (p = 0.004 and p = 0 .008 , respectively). Age was positively correlated to attention and timeliness total scores (p = 0.000, r = 0.445 and p = 0.004, r = 0.243, respectively), and inversely correlated to hyperactivity total scores (p = 0.000, r = - 0.415). Conclusions Prevalence of attention-deficit hyperactivity disorder-like symptoms in the study population was much higher than that of pediatric attention-deficit hyperactivity disorder. We believe that the MOXO-continuous performance test is a valid supportive measure for evaluation of attention-deficit hyperactivity disorder diagnosis in children with various stages of chronic kidney disease or on kidney replacement therapy.
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    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-4494-2019
    Recently 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.