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Background/aim: Attention deficit hyperactivity disorder (ADHD), one of the most common neurodevelopmental disorders in childhood, is diagnosed clinically by assessing the symptoms of inattention, hyperactivity, and impulsivity. Also, there are limited objective assessment tools to support the diagnosis. Thus, in this study, a new electrooculography (EOG) based on visual stimulus tracking to support the diagnosis of ADHD was proposed. Materials and methods: Reference stimulus one-to-one tracking numbers (RSOT) and colour game detection (CGD) were applied to 53 medication-free children with ADHD and 36 healthy controls (HCs). Also, the test was applied six months after the treatment to children with ADHD. Parameters obtained during the visual stimulus tracking test were analyzed and Higuchi fractal dimension (HFD) and Hjorth parameters were calculated for all EOG records. Results: The average test success rate was higher in HCs than in children with ADHD. Based on machine learning algorithms, the proposed system can distinguish drug-free ADHD patients from HCs with an 89.13% classification performance and also distinguish drug-free children from treated children with an 80.47% classification performance. Conclusion: The findings showed that the proposed system could be helpful to support the diagnosis of ADHD and the follow-up of the treatment. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Epilepsy is one of the most common chronic neurological disorders. It is known that epileptic children have more psychiatric diseases than normal population and children with other chronic diseases. The present study was aimed to identify the psychopathology of children with epilepsy, their life quality and the psychopathology of their parents. METHODS: 48 children with epilepsy, 48 children for control group and their parents were included in this study. We used Ankara Development Screening Inventory (ADSI), Brief Infant Toddler Social Emotional Assessment (BITSEA), Early Childhood Inventory-Parent Scale (ECI-4), Pediatric Quality of Life Inventory (PedsQL) and Symptoms Checklist-90 (SCL-90) for assessments. RESULTS: There was no difference between the epilepsy and the control groups in BITSEA scores for 1-3 years. In the ECI-4 assessment for 4-6 years, Attention Deficit Hyperactivity Disorder (ADHD), general anxiety disorder (GAD), social phobia (SP), posttraumatic stress disorder (PTSD), dysthymic disorders and elimination disorder (ED) were found higher in the children with epilepsy. In the mothers of the children with epilepsy, psychiatric symptoms were found higher than the controls. There was no difference in quality of life scores between the children with epilepsy and the control groups. DISCUSSION AND CONCLUSION: In the present study, no psychopathology was detected in the children with epilepsy 1-3 years of age, while frequency of psychopathology was increased in those 4-6 years of age. Also, it was found that the frequency of psychiatric symptoms was increased in the mothers of the children with epilepsy. [ABSTRACT FROM AUTHOR]

This study evaluates the associations among the symptoms of anxiety, depression, and disruptive behavioral disorders (DBD) in the context of their relationships with reactive-proactive aggression and anxiety sensitivity in children with attention-deficit/hyperactivity disorder (ADHD). The sample consisted of 342 treatment-naive children with ADHD. The severity of ADHD and DBD symptoms were assessed via parent- and teacher-rated inventories. Anxiety sensitivity, reactive-proactive aggression and severity of anxiety and depression symptoms of children were evaluated by self-report inventories. According to structural equation modeling, depression and anxiety scores had a relation with the DBD scores through reactive-proactive aggression. Results also showed a negative relation of the total scores of anxiety sensitivity on DBD scores, while conduct disorder scores had a positive relation with anxiety scores. This study suggests that examining the relations of reactive-proactive aggression and anxiety sensitivity with internalizing and externalizing disorders could be useful for understanding the link among these disorders in ADHD. [ABSTRACT FROM AUTHOR]

The corrected Acknowledgement section should read as follows: B Acknowledgements b This study was supported by the TUBITAK under Project Number 114S470. Correction to: Neural Computing and Applications (2020) 32:8367-8380 https://doi.org/10.1007/... Unfortunately, the funding number of the article was erroneously published in the acknowledgement section of the article. [Extracted from the article]