Neurophysiological Marker of ADHD in Children

Study Overview
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Brief Summary
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This study investigated quantitative electroencephalography (QEEG) subtypes as auxiliary tools to assess Attention Deficit Hyperactivity Disorder (ADHD). Patient assessed using the Korean version of the Diagnostic Interview Schedule for Children Version IV and were assigned to one of three groups: ADHD, ADHD-Not Otherwise specified (NOS), and Neurotypical (NT). The investigators measure absolute and relative EEG power in 19 channels and conducted an auditory continuous performance test. The investigators analyzed QEEG according to the frequency range: delta (1-4 Hz), theta (4-8 Hz), slow alpha (8-10 Hz), fast alpha (10-13.5 Hz), and beta (13.5-30 Hz). The subjects were then grouped by Ward's method of cluster analysis using the squared Euclidian distance to measure dissimilarities.

Detailed Description
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Participants between 7 and 12 years of age diagnosed with ADHD according to the DSM-5 criteria were included in the study. The ADHD diagnosis was based on a Korean version of the Diagnostic Interview Schedule for Children Version IV (DISC-IV), which is a structured interview tool, and these diagnoses were confirmed by multiple child and adolescent psychiatrists. If participants did not meet the ADHD diagnostic criteria of DSM-IV and DISC-IV, they were assigned to the Neurotypical (NT) group. Based on the results of the DISC-IV test, participants were assigned to the ADHD or Non-Other Specified (NOS) group. Patients who met the diagnostic criteria of ADHD in DSM-IV, but whose score did not exceed six, and had a score of more than three in the attention deficit or hyperactivity/impactivity scale of DISC-IV were included in the NOS group. Children with a history of brain damage, neurological disorders, genetic disorders, substance dependence, epilepsy, or any other mental disorder were excluded from participation. Children with an IQ of 70 or lower according to the Korean-Wechsler Intelligence Scale for Children (Fourth Edition) or who were receiving drug treatment were also excluded from this study. The EEG recordings were performed using a SynAmps2 direct-current (DC) amplifier and a 10-20 layout 64-channel Quick-Cap electrode-placement system (Neuroscan Inc., NC, USA). The EEG data were digitally recorded from 19 gold cup electrodes placed according to the international 10-20 system. The impedances were maintained below 5 kΩ, and the sampling rate was 1000 Hz. The investigators use the linked mastoid reference and two additional bipolar electrodes to measure horizontal and vertical eye movements. During the recording, each participant laid in a dimly lit, electrically shielded, sound-attenuated room. Resting EEG recordings were recorded after three minutes with the participants' eyes closed.

Official Title
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Neurophysiological Marker of Attention Deficit Hyperactivity Disorder in Children

Conditions
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Attention Deficit Hyperactivity Disorder, Psychiatric Diagnosis, Diagnosis, Psychiatric

Intervention / Treatment
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* Diagnostic Test: electroencephalography absolute delta power
* Diagnostic Test: electroencephalography relative delta power
* Diagnostic Test: electroencephalography absolute theta power
* Diagnostic Test: electroencephalography relative theta power
* Diagnostic Test: electroencephalography absolute slow alpha power
* Diagnostic Test: electroencephalography relative slow alpha power
* Diagnostic Test: electroencephalography absolute fast alpha power
* Diagnostic Test: electroencephalography relative fast alpha power
* Diagnostic Test: electroencephalography absolute beta power
* Diagnostic Test: electroencephalography relative beta power
* Diagnostic Test: Korean ADHD rating scale
* Diagnostic Test: Korean Version of Diagnostic Interview Schedule for Children Version IV


Participation Criteria
=================
Eligibility Criteria
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Inclusion Criteria: Participants between 7 and 12 years of age diagnosed with ADHD according to the DSM-5 criteria were included in the study Exclusion Criteria: Children with a history of brain damage, neurological disorders, genetic disorders, substance dependence, epilepsy, or any other mental disorder were excluded from participation. Children with an IQ of 70 or lower according to the Korean-Wechsler Intelligence Scale for Children (Fourth Edition) or who were receiving drug treatment were also excluded from this study

Ages Eligible for Study
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Minimum Age: 7 Years
Maximum Age: 12 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| ADHD<br> | Diagnostic Test: electroencephalography absolute delta power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: electroencephalography relative delta power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: electroencephalography absolute theta power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: electroencephalography relative theta power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: electroencephalography absolute slow alpha power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: electroencephalography relative slow alpha power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: electroencephalography absolute fast alpha power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: electroencephalography relative fast alpha power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: electroencephalography absolute beta power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: electroencephalography relative beta power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: Korean ADHD rating scale<br>* The KARS is a standardized screening tool for ADHD in Korean children and rating scale completed by the parents.<br>Diagnostic Test: Korean Version of Diagnostic Interview Schedule for Children Version IV<br>* The DISC-IV is a structured diagnostic tool that was developed for use in epidemiological studies in children and adolescents<br>|
| NT NeuroTypical<br> | Diagnostic Test: electroencephalography absolute delta power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: electroencephalography relative delta power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: electroencephalography absolute theta power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: electroencephalography relative theta power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: electroencephalography absolute slow alpha power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: electroencephalography relative slow alpha power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: electroencephalography absolute fast alpha power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: electroencephalography relative fast alpha power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: electroencephalography absolute beta power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: electroencephalography relative beta power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: Korean ADHD rating scale<br>* The KARS is a standardized screening tool for ADHD in Korean children and rating scale completed by the parents.<br>Diagnostic Test: Korean Version of Diagnostic Interview Schedule for Children Version IV<br>* The DISC-IV is a structured diagnostic tool that was developed for use in epidemiological studies in children and adolescents<br>|
| ADHD NOS<br> | Diagnostic Test: electroencephalography absolute delta power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: electroencephalography relative delta power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: electroencephalography absolute theta power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: electroencephalography relative theta power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: electroencephalography absolute slow alpha power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: electroencephalography relative slow alpha power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: electroencephalography absolute fast alpha power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: electroencephalography relative fast alpha power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: electroencephalography absolute beta power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: electroencephalography relative beta power<br>* We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings<br>Diagnostic Test: Korean ADHD rating scale<br>* The KARS is a standardized screening tool for ADHD in Korean children and rating scale completed by the parents.<br>Diagnostic Test: Korean Version of Diagnostic Interview Schedule for Children Version IV<br>* The DISC-IV is a structured diagnostic tool that was developed for use in epidemiological studies in children and adolescents<br>|

What is the study measuring?
-----------------
Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| QEEG topographical plots of the results of the statistical comparisons to normative values (z-scores) using Neuroguide software | The investigators measures QEEG on first outpatient clinic and conduct topography for mapping | through study completion, an average of 1 year | 

 Terms related to the study
=================
Keywords Provided by Centre Hospitalier Valida
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ADHD, QEEG, Neurotypical