Cortical source analysis of resting state EEG data in children with attention deficit hyperactivity disorder

A study on the relationship between non-epileptic fast (40 – 200 Hz) oscillations in scalp EEG and development in children

“…Functional maturation of the brain is well expressed by the developmental changes in electroencephalogram (EEG) background activity. Many groups have attempted to identify and characterize neurophysiological findings that are related to neurodevelopmental disorders such as autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD), exploring possible relationships between these disorders and various types of findings including EEG patterns, cortical source analysis, event-related potentials, functional MRI, and functional near-infrared spectroscopy [1][2][3][4][5][6][7]. Correlations have been also investigated between intelligence and certain EEG findings such as EEG power, phase, and coherence [8,9].…”

Disrupted Functional Rich-Club Organization of the Brain Networks in Children with Attention-Deficit/Hyperactivity Disorder, a Resting-State EEG Study

“…The EEG recordings included three one-minute eyes-closed EEG epochs interleaved with periods of one-minute eyes-open intervals to prevent participants from falling asleep. The EEG data were first re-referenced using an average montage and preprocessed using the processing pipeline shown in Figure 1 [ 5 ]. In brief, the data preprocessing included artifact rejection, bad or noisy channel interpolation, band-pass filtering within 0.5–30 Hz and segmentation into five-second epochs with 25% overlap to reduce the data loss due to windowing [ 5 ], all of which was performed using custom-written routines in MATLAB (MathWorks, Natick, MA, USA), EEGlab (v2019.0, [ 38 ]) and Fieldtrip toolbox (v2019) [ 39 ].…”

“…The EEG data were first re-referenced using an average montage and preprocessed using the processing pipeline shown in Figure 1 [ 5 ]. In brief, the data preprocessing included artifact rejection, bad or noisy channel interpolation, band-pass filtering within 0.5–30 Hz and segmentation into five-second epochs with 25% overlap to reduce the data loss due to windowing [ 5 ], all of which was performed using custom-written routines in MATLAB (MathWorks, Natick, MA, USA), EEGlab (v2019.0, [ 38 ]) and Fieldtrip toolbox (v2019) [ 39 ]. Artifactual EEG segments, including eye movement and blinking, muscular activation and movement artifacts, were visually identified by an EEG expert and excluded from further analysis.…”

“…Having a high temporal resolution (milliseconds), EEG can provide important information about the neural dynamics underlying functional network dysfunction in ADHD. Resting-state EEG studies have reported characteristic alterations in the spectral power of low- and high-frequency oscillations in ADHD children [ 5 , 6 ]. EEG source analysis has also shown age- and subtype-related modifications in cortical activity in ADHD children compared with typically developing individuals in different frequency bands [ 5 ].…”

“…In this study, we aimed to investigate alterations in the rich-club organization of the brain networks in the source space in children with the most common subtypes of ADHD (ADHD I and ADHD C ) in comparison with typically developing individuals (TD). We examined two hypotheses: (1) patient groups would show frequency-specific alterations in functional connectivity, network topologies and rich-club organization in comparison with TD individuals, and (2) alterations in the resting-state regional source activity reported in ADHD children [ 5 ] would be associated with atypical functional connectivity. To test these hypotheses, the EEG source analysis was first performed on resting-state, high-density EEG data by the exact Low Resolution Electric Tomography software (eLORETA) in different frequency bands.…”