Developmental change in the resting state electroencephalogram: Insights into cognition and the brain

Modeling development of frontal electroencephalogram (EEG) asymmetry: Sex differences and links with temperament

Asymmetric patterns of frontal brain electrical activity reflect approach and avoidance tendencies, with stability of relative right activation associated with withdrawal emotions/motivation and left hemisphere activation linked with approach and positive affect. However, considerable shifts in approach/avoidance-related lateralization have been reported for children not targeted because of extreme temperament. In this study, dynamic effects of frontal electroencephalogram (EEG) power within and across hemispheres were examined throughout early childhood. Specifically, EEG indicators at 5, 10, 24, 36, 48, and 72 months-of-age (n = 410) were analyzed via a hybrid of difference score and panel design models, with baseline measures and subsequent time-to-time differences modeled as potentially influencing all subsequent amounts of time-to-time change (i.e., predictively saturated). Infant sex was considered as a moderator of dynamic developmental effects, with temperament attributes measured at 5 months examined as predictors of EEG hemisphere development. Overall, change in left and right frontal EEG power predicted declining subsequent change in the same hemisphere, with effects on the opposing neurobehavioral system enhancing later growth. Infant sex moderated the pattern of within and across-hemisphere effects, wherein for girls more prominent left hemisphere influences on the right hemisphere EEG changes were noted and right hemisphere effects were more salient for boys. Largely similar patterns of temperament prediction were observed for the left and the right EEG power changes, with limited sex differences in links between temperament and growth parameters. Results were interpreted in the context of comparable analyses using parietal power values, which provided evidence for unique frontal effects.

14 challenges and their solutions for conducting social neuroscience and longitudinal EEG research with infants

The use of electroencephalography (EEG) to study infant brain development is a growing trend. In addition to classical longitudinal designs that study the development of neural, cognitive and behavioural functions, new areas of EEG application are emerging, such as novel social neuroscience paradigms using dual infant-adult EEG recordings. However, most of the experimental designs, analysis methods, as well as EEG hardware were originally developed for single-person adult research. When applied to study infant development, adult-based solutions often pose unique problems that may go unrecognised. Here, we identify 14 challenges that infant EEG researchers may encounter when designing new experiments, collecting data, and conducting data analysis. Challenges related to the experimental design are: (1) small sample size and data attrition, and (2) varying arousal in younger infants. Challenges related to data acquisition are: (3) determining the optimal location for reference and ground electrodes, (4) control of impedance when testing with the high-density sponge electrode nets, (5) poor fit of standard EEG caps to the varying infant head shapes, and (6) ensuring a high degree of temporal synchronisation between amplifiers and recording devices during dual-EEG acquisition. Challenges related to the analysis of longitudinal and social neuroscience datasets are: (7) developmental changes in head anatomy, (8) prevalence and diversity of infant myogenic artefacts, (9) a lack of stereotypical topography of eye movements needed for the ICA-based data cleaning, (10) and relatively high inter-individual variability of EEG responses in younger cohorts. Additional challenges for the analysis of dual EEG data are: (11) developmental shifts in canonical EEG rhythms and difficulties in differentiating true inter-personal synchrony from spurious synchrony due to (12) common intrinsic properties of the signal and (13) shared external perturbation. Finally, (14) there is a lack of test-retest reliability studies of infant EEG. We describe each of these challenges and suggest possible solutions. While we focus specifically on the social neuroscience and longitudinal research, many of the issues we raise are relevant for all fields of infant EEG research.

Hyperconnectivity during screen-based stories listening is associated with lower narrative comprehension in preschool children exposed to screens vs dialogic reading: An EEG study

OBJECTIVES

Dialogic reading (DR) is a shared storybook reading intervention previously shown to have a positive effect on both literacy and general language skills. The aim of this study was to examine the effect of DR compared to screen-based intervention on electrophysiological markers supporting narrative comprehension using EEG.

METHODS

Thirty-two typically developing preschoolers, ages 4 to 6 years, were assigned to one of two intervention groups: Dialogic Reading Group (DRG, n = 16) or Screen Story Group (SSG, n = 16). We examined the effect of intervention type using behavioral assessment and a narrative comprehension task with EEG.

RESULTS

The DRG showed improved vocabulary and decreased functional connectivity during the stories-listening task, whereas the SSG group showed no changes in vocabulary or connectivity. Significantly decreased network strength and transitivity and increased network efficiency were observed in the DRG following intervention. Greater network strength and transitivity at follow-up were correlated with increased vocabulary.

CONCLUSIONS

The results suggest the beneficial effect of DR in preschool-age children on vocabulary and EEG-bands related to attention in the ventral stream during narrative comprehension. Decreased functional connectivity may serve as a marker for language gains following reading intervention.

SIGNIFICANCE

DR intervention for preschool-age children may reduce interfering connections related to attention, which is related to better narrative comprehension.

Delayed cortical maturation at the centrotemporal brain regions in patients with benign childhood epilepsy with centrotemporal spikes (BCECTS)

Benign childhood epilepsy with centrotemporal spikes (BCECTS) is an epilepsy syndrome commonly found in child and adolescent. Although the prognosis is mostly favorable as long as the seizure is well controlled. However, they are often suffering from the cognitive and behavioral problems which might be the consequences of the initial insults. It is still not clear whether the initial epileptiform discharges has long term impact on the resting-state brain activities at later ages. This study investigated the resting-state brain activities in BCECTS patients with clinical seizure remission stage (n = 16; 11 males) and compared with the non-epileptic, age-matched control subjects. Quantitative electroencephalography (qEEG) revealed a significantly higher absolute power of the theta and alpha waves in BCECTS patients with clinical seizure remission as compared with the non-epileptic control subjects. Interestingly, the differences were observed mainly over the centrotemporal electrodes which are the common sites of the initial epileptiform discharges. The differences were more significant in patients with bilateral epileptiform discharges than those with the unilateral epileptic activities. Typically, the brain wave power continuously decreases with increasing ages. Therefore, higher absolute powers of the brain waves indicate more delayed in cortical maturation compared with the non-epileptic control group. These findings indicated that BCECTS patients have delay cortical maturation at the centrotemporal brain regions even at the clinical seizure remission phase.

Study of driving skill level discrimination based on human physiological signal characteristics

The primary purpose of the study is to distinguish the differences in driving skill between novice and experienced drivers from the viewpoint of human cognitive behavior. Firstly, EEG (electroencephalogram) signals were collected using EEG acquisition equipment called Neuroscan. The δ sub-band and EOG (electrooculogram) signals were extracted from the EEG. Furthermore, the eye movement rate and the sample entropy (SampEn) values of δ sub-bands were calculated. Finally, the heart rate variability (HRV) characteristics, calculated using the SampEn algorithm, were used to analyze driving skill. The final result showed that human physiological signals (EEG, EOG and ECG (electrocardiogram)) could effectively distinguish different driving skills.