Literature DB >> 31761311

Atypical gamma functional connectivity pattern during light sleep in children with attention deficit hyperactivity disorder.

Riyo Ueda1, Hiroshige Takeichi2, Yoshimi Kaga2, Masayoshi Oguri3, Yoshihiko Saito4, Eiji Nakagawa4, Yoshihiro Maegaki5, Masumi Inagaki6.   

Abstract

INTRODUCTION: We examined functional connectivity analyses in electroencephalograms (EEGs) of patients with attention deficit hyperactivitydisorder (ADHD) and in those of typically developing children (TDC) to uncover neurobiological abnormalities.
METHODOLOGY: We enrolled 31 children with ADHD (mean age 11.1 years; 23 boys) and 17 sex-, age-, and intelligence-matched TDC to undergo 19-channel EEGs during light sleep. We estimated functional connectivity using the phase lag index (PLI) and coherence measurements that capture the synchronization of EEG signals and graphed metrics with GRETNA. We also performed continuous performance tests (CPTs) on the children and obtained answered questionnaires on ADHD and autism spectrum disorder.
RESULTS: The central-to-posterior gamma PLI was lower in children with ADHD than that in TDC. The other PLI frequency bands and all coherence frequency bands were not statistically different between both groups. Individuals with high hyperactivity scores on questionnaires and low reaction times (SDs) on CPT had low motor and occipital pairs of gamma PLIs. Graph metrics showed no differences between the groups.
CONCLUSIONS: The difference in averaged gamma PLI (especially with motor and occipital pairs) between groups was more suitable for diagnosis than the averaged coherence. Lower averaged gamma PLIs reflected more severe ADHD symptoms. A prospective study with more controlled conditions is warranted to determine if gamma-band PLI can be used as an auxiliary tool for ADHD diagnosis.
Copyright © 2019 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  ADHD; Coherence; Gamma frequency band; Light sleep; Phase lag index (PLI)

Mesh:

Year:  2019        PMID: 31761311     DOI: 10.1016/j.braindev.2019.11.001

Source DB:  PubMed          Journal:  Brain Dev        ISSN: 0387-7604            Impact factor:   1.961


  3 in total

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  3 in total

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