Cortical Source Analysis of Resting State EEG Data in Children with Attention Deficit Hyperactivity Disorder
Overview
Psychiatry
Authors
Affiliations
Objective: This study investigated age-dependent and subtype-related alterations in electroencephalography (EEG) power spectra and current source densities (CSD) in children with attention deficit and hyperactivity disorder (ADHD).
Methods: We performed spectral and cortical source (exact low-resolution electromagnetic tomography, eLORETA) analyses using resting state EEG recordings from 40 children (8-16 years) with combined and inattentive subtypes of ADHD and 41 age-matched healthy controls (HC). Group differences in EEG spectra and CSD were investigated at each scalp location, voxel and cortical region in delta, theta, alpha and beta bands. We also explored associations between topographic changes in EEG power and CSD and age.
Results: Compared to healthy controls, combined ADHD subtype was characterized with significantly increased diffuse theta/beta power ratios (TBR) with a widespread decrease in beta CSD. Inattentive ADHD subtype presented increased TBR in all brain regions except in posterior areas with a global increase in theta source power. In both ADHD and HC, older age groups showed significantly lower delta source power and TBR and higher alpha and beta source power than younger age groups. Compared to HC, ADHD was characterized with increases in theta fronto-central and temporal source power with increasing age.
Conclusions: Our results confirm that TBR can be used as a neurophysiological biomarker to differentiate ADHD from healthy children at both the source and sensor levels.
Significance: Our findings emphasize the importance of performing the source imaging analysis in order to better characterize age-related changes in resting-state EEG activity in ADHD and controls.
Enriquez-Geppert S, Krc J, van Dijk H, deBeus R, Eugene Arnold L, Arns M Appl Psychophysiol Biofeedback. 2024; .
PMID: 39674997 DOI: 10.1007/s10484-024-09675-w.
Enhanced ADHD classification through deep learning and dynamic resting state fMRI analysis.
Firouzi M, Kazemi K, Ahmadi M, Helfroush M, Aarabi A Sci Rep. 2024; 14(1):24473.
PMID: 39424632 PMC: 11489689. DOI: 10.1038/s41598-024-74282-y.
Zanus C, Miladinovic A, De Dea F, Skabar A, Stecca M, Ajcevic M Entropy (Basel). 2023; 25(9).
PMID: 37761543 PMC: 10530036. DOI: 10.3390/e25091244.
Can biomarkers be used to diagnose attention deficit hyperactivity disorder?.
Chen H, Yang Y, Odisho D, Wu S, Yi C, Oliver B Front Psychiatry. 2023; 14:1026616.
PMID: 36970271 PMC: 10030688. DOI: 10.3389/fpsyt.2023.1026616.
Aberrant brain dynamics and spectral power in children with ADHD and its subtypes.
Luo N, Luo X, Zheng S, Yao D, Zhao M, Cui Y Eur Child Adolesc Psychiatry. 2022; 32(11):2223-2234.
PMID: 35996018 PMC: 10576687. DOI: 10.1007/s00787-022-02068-6.