This present study aims to investigate neural mechanisms underlying ADHD compared to healthy children through the analysis of the complexity and the variability of the EEG brain signal using multiscale entropy (MSE), EEG signal standard deviation (SDs), as well as the mean, standard deviation (SDp) and coefficient of variation (CV) of absolute spectral power (PSD). For this purpose, a sample of children diagnosed with attention-deficit/hyperactivity disorder (ADHD) between 6 and 17 years old were selected based on the number of trials and diagnostic agreement, 32 for the open-eyes (OE) experimental condition and 25 children for the close-eyes (CE) experimental condition. Healthy control subjects were age- and gender-matched with the ADHD group. The MSE and SDs of resting-state EEG activity were calculated on 34 time scales using a coarse-grained procedure. In addition, the PSD was averaged in delta, theta, alpha, and beta frequency bands, and its mean, SDp, and CV were calculated. The results show that the MSE changes with age during development, increases as the number of scales increases and has a higher amplitude in controls than in ADHD. The absolute PSD results show CV differences between subjects in low and beta frequency bands, with higher variability values in the ADHD group. All these results suggest an increased EEG variability and reduced complexity in ADHD compared to controls.

Intensity-dependent amplitude changes (IDAP) have been extensively studied using event-related potentials (ERPs) and have been linked to several psychiatric disorders. This study aims to explore the application of functional near-infrared spectroscopy (fNIRS) in IDAP paradigms, which related to ERPs could indicate the existence of neurovascular coupling. Thirty-three and thirty-one subjects participated in two experiments, respectively. The first experiment consisted of the presentation of three-tone intensities (77.9 dB, 84.5 dB, and 89.5 dB) lasting 500 ms, each type randomly presented 54 times, while the second experiment consisted of the presentation of five-tone intensities (70.9 dB, 77.9 dB, 84.5 dB, 89.5 dB, and 94.5 dB) in trains of 8 tones lasting 70 ms each tone, the trains were presented 20 times. EEG was used to measure ERP components: N1, P2, and N1–P2 peak-to-peak amplitude. fNIRS allowed the analysis of the hemodynamic activity in the auditory, visual, and prefrontal cortices. The results showed an increase in N1, P2, and N1–P2 peak-to-peak amplitude with auditory intensity. Similarly, oxyhemoglobin and deoxyhemoglobin concentrations showed amplitude increases and decreases, respectively, with auditory intensity in the auditory and prefrontal cortices. Spearman correlation analysis showed a relationship between the left auditory cortex with N1 amplitude, and the right dorsolateral cortex with P2 amplitude, specifically for deoxyhemoglobin concentrations. These findings suggest that there is a brain response to auditory intensity changes that can be obtained by EEG and fNIRS, supporting the neurovascular coupling process. Overall, this study enhances our understanding of fNIRS application in auditory paradigms and highlights its potential as a complementary technique to ERPs.

This study analyses the spontaneous electroencephalogram (EEG) brain activity of 14 children diagnosed with Autism Spectrum Disorder (ASD) compared to 18 children with normal development, aged 5–11 years. (i) Power Spectral Density (PSD), (ii) variability across trials (coefficient of variation: CV), and (iii) complexity (multiscale entropy: MSE) of the brain signal analysis were computed on the resting state EEG. PSD (0.5–45 Hz) and CV were averaged over different frequency bands (low-delta, delta, theta, alpha, low-beta, high-beta and gamma). MSE were calculated with a coarse-grained procedure on 67 time scales and divided into fine, medium and coarse scales. In addition, significant neurophysiological variables were correlated with behavioral performance data (Kaufman Brief Intelligence Test (KBIT) and Autism Spectrum Quotient (AQ)). Results show increased PSD fast frequency bands (high-beta and gamma), higher variability (CV) and lower complexity (MSE) in children with ASD when compared to typically developed children. These results suggest a more variable, less complex and, probably, less adaptive neural networks with less capacity to generate optimal responses in ASD children.

The present report analyzed the time-frequency changes in Event-Related Spectral perturbations (ERSP) in a sample of ADHD children and adolescents compared to a normodevelopment (ND) sample. A delayed match-to-sample (DMTS) test of working memory (WM) was presented to a group of ADHD subjects (N = 29) and compared with ND group (N = 34) with ages between 6 and 17 years old. Time-frequency decomposition was computed through wavelets. ADHD subjects presented higher Reaction Time (RT), Standard Deviation of RT (Std of RT), and a reduced percentage of correct responses. The results showed a complex pattern of oscillatory bursts during the encoding, maintenance, and recognition phases with similar dynamics in both groups. ADHD children presented a reduced Event-Related Synchronization (ERS) in the Theta range during the encoding phase, and also a reduced Alpha ERS during the late period of the maintenance phase. S1 Early theta ERS was positively correlated with Std of RT. Behavioral data, early Theta, and late Alpha ERS classified correctly above 70 % of ADHD and ND subjects when a linear discriminant analysis was applied. The reduced encoding and maintenance impaired brain dynamics of ADHD subjects would justify the poorer performance of this group of subjects.