EEG Global Coherence in Scholar ADHD Children during Visual Object Processing

Abstract

Among neurodevelopmental disorders, attention deficit hyperactivity disorder (ADHD) is the main cause of school failure in children. Notably, visuospatial dysfunction has also been emphasized as a leading cause of low cognitive performance in children with ADHD. Consequently, the present study aimed to identify ADHD-related changes in electroencephalography (EEG) characteristics, associated with visual object processing in school-aged children. We performed Multichannel EEG recordings in 16-year-old children undergoing Navon’s visual object processing paradigm. We mapped global coherence during the processing of local and global visual stimuli that were consistent, inconsistent, or neutral. We found that Children with ADHD showed significant differences in global weighted coherence during the processing of local and global inconsistent visual stimuli and longer response times in comparison to the control group. Delta and theta EEG bands highlighted important features for classification in both groups. Thus, we advocate EEG coherence and low-frequency EEG spectral power as prospective markers of visual processing deficit in ADHD. Our results have implications for the development of diagnostic interventions in ADHD and provide a deeper understanding of the factors leading to low performance in school-aged children.

Aggressiveness in Italian Children with ADHD: MAOA Gene Polymorphism Involvement

“…It is reasonable that these alterations have repercussions on brain activity, which can be registered via electroencephalography in areas like the frontal lobe. Different studies have already demonstrated deficits in neuropsychological mechanisms like spatial organization and behavioral modulation in subjects with ADHD [58]. So, it would be interesting to compare genetic findings to neurophysiological data to obtain a progressively more complete profile of ADHD necessary for a tailored therapy according to the rules of precision medicine.…”

ADHD-AID: Aiding Tool for Detecting Children’s Attention Deficit Hyperactivity Disorder via EEG-Based Multi-Resolution Analysis and Feature Selection

“…These features were then reduced using a genetic algorithm, and then the chosen features were used to learn an ANN. Elsewhere, the study [ 27 ] explored the relationship between ADHD and visuospatial problems, which involve challenges in processing visual information. This study examined 16-year-old adolescents with ADHD and analyzed their brain activity using EEG during a visual processing task, comparing it to a control group.…”

“…EEG data can monitor alterations in brain function caused by ADHD [ 25 , 26 ]. However, complex-level structures in the complex records generated by the brains of humans are challenging to identify [ 27 ]. With the help of machine learning (ML), detecting these complicated patterns is achievable.…”