Neuronal Correlates of Task Irrelevant Distractions Enhance the Detection of Attention Deficit/Hyperactivity Disorder

Abstract

Early diagnosis and treatment can reduce the symptoms of Attention Deficit/Hyperactivity Disorder (ADHD) in children, but medical diagnosis is usually delayed. Hence, it is important to increase the efficiency of early diagnosis. Previous studies used behavioral and neuronal data during GO/NOGO task to help detect ADHD and the accuracy differed considerably from 53% to 92%, depending on the employed methods and the number of electroencephalogram (EEG) channels. It remains unclear whether data from a few EEG channels can still lead to a good accuracy of detecting ADHD. Here, we hypothesize that introducing distractions into a VR-based GO/NOGO task can augment the detection of ADHD using 6-channel EEG because children with ADHD are easily distracted.Forty-nine ADHD children and 32 typically developing children were recruited. We use a clinically applicable system with EEG to record data. Statistical analysis and machine learning methods were employed to analyze the data. The behavioral results revealed significant differences in task performance when there are distractions. The presence of distractions leads to EEG changes in both groups, indicating immaturity in inhibitory control. Importantly, the distractions additionally enhanced the between-group differences in NOGO α and γ power, reflecting insufficient inhibition in different neural networks for distraction suppression in the ADHD group. Machine learning methods further confirmed that distractions enhance the detection of ADHD with an accuracy of 85.45%. In conclusion, this system can assist in fast screenings for ADHD and the findings of neuronal correlates of distractions can help design therapeutic strategies.

Exploring the possible relationship between skin microbiome and brain cognitive functions: a pilot EEG study

“…To further investigate the impact of bacterial growth on EEG signals, we applied several ML classifiers to separate datasets given the experimental manipulations. As EEG data are complex, many studies have confirmed that ML methods can be used to analyze EEG signals in a multivariate fashion for assisting in the diagnosis of brain disorders [50][51][52] . In this study, we employed ML methods to separate the datasets of each manipulation with an accuracy greater than 88%, confirming the alternating effects of experimental manipulations on brain signals, despite the paired t-test being unable to find a significant difference between glycerol and water.…”