Investigation of Brain Functional Networks in Children Suffering from Attention Deficit Hyperactivity Disorder
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ORIGINAL PAPER
Investigation of Brain Functional Networks in Children Suffering from Attention Deficit Hyperactivity Disorder Hossein. Dini1 · Farnaz.Ghassemi1 · Mohammad. S. E. Sendi2 Received: 17 December 2019 / Accepted: 22 August 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract ADHD defects the recognition of facial emotions. This study assesses the neurophysiological differences between children with ADHD and matched healthy controls during a face emotional recognition task. The study also explores how brain connectivity is affected by ADHD. Electroencephalogram (EEG) signals were recorded from 64 scalp electrodes. Event-related phase coherence (ERPCOH) method was applied to pre-processed signals, and functional connectivity between any pair of electrodes was computed in different frequency bands. A logistic regression (LR) classifier with elastic net regularization (ENR) was trained to classify ADHD and HC participants using the functional connectivity of frequency bands as a potential biomarker. Subsequently, the brain network is constructed using graph-theoretic techniques, and graph indices such as clustering coefficient (C) and shortest path length (L) were calculated. Significant intra-hemispheric and the inter-hemispheric discrepancy between ADHD and healthy control (HC) groups in the beta band was observed. The graph features indicate that the clustering coefficient is significantly higher in the ADHD group than that in the HC group. At the same time, the shortest path length is significantly lower in the beta band. ADHD’s brain networks have a problem in transferring information among various neural regions, which can cause a deficiency in the processing of facial emotions. The beta band seems better to reflect the differences between ADHD and HC. The observed functional connectivity and graph differences could also be helpful in ADHD investigations. Keywords Attention deficit hyperactivity disorder (ADHD) · Facial emotion recognition · Functional connectivity · Graph theory · Logistic regression
Introduction Attention deficit hyperactivity disorder is a prevalent psychiatric disorder that begins at childhood and is sorted by inattention, impulsivity, and hyperactivity (Cortese 2012). In the past, ADHD was thought to be a childhood disorder that improves over time, but some studies showed that between 35 to 80% of children with ADHD experience problems in adolescence (Barkley et al. 2006). Also, several studies in the emotional perception field in children and adolescents Communicated by Micah M. Murray. * Farnaz.Ghassemi [email protected] 1
Department of Biomedical Engineering, Amirkabir University of Technology (TehranPolytechnic), Tehran, Iran
School of Electrical and Computer Engineering, Georgia Institute of Technology, 30308 Atlanta, USA
2
with ADHD imply failure in recognizing facial emotions (Collin et al. 2013). Since an impairment in recognizing the emotions of others causes an experience of human infertility in ADHD, exploring nonverbal sympt
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