EEG functional brain connectivity strengthens with age during attentional processing to faces in children

Abstract

Studying functional connectivity may generate clues to the maturational changes that occur in children, expressed by the dynamical organization of the functional network assessed by electroencephalographic recordings (EEG). In the present study, we compared the EEG functional connectivity pattern estimated by linear cross-correlations of the electrical brain activity of three groups of children (6, 8, and 10 years of age) while performing odd-ball tasks containing facial stimuli that are chosen considering their importance in socioemotional contexts in everyday life. On the first task, the children were asked to identify the sex of faces, on the second, the instruction was to identify the happy expressions of the faces. We estimated the stable correlation pattern (SCP) by the average cross-correlation matrix obtained separately for the resting state and the task conditions and quantified the similarity of these average matrices comparing the different conditions. The accuracy improved with higher age. Although the topology of the SCPs showed high similarity across all ages, the two older groups showed a higher correlation between regions associated with the attentional and face processing networks compared to the youngest group. Only in the youngest group, the similarity metric decreased during the sex condition. In general, correlation values strengthened with age and during task performance compared to rest. Our findings indicate that there is a spatially extended stable brain network organization in children like that reported in adults. Lower similarity scores between several regions in the youngest children might indicate a lesser ability to cope with tasks. The brain regions associated with the attention and face networks presented higher synchronization across regions with increasing age, modulated by task demands.

Enhancing autism spectrum disorder classification in children through the integration of traditional statistics and classical machine learning techniques in EEG analysis

“…The average age of the children was 3.92 ± 1.38 (mean ± std), with a range of 2.08–5.92 years. Although the age range of the children was more than 3 years, the EEG correlation pattern was found to be stable at younger ages (less than 6 years) and only started to diverge at 10 years 15 . All children were diagnosed with autism spectrum disorder (ASD) and were referred to the neurology department by pediatricians or child psychiatrists for diagnostic purposes (neurological and metabolic diagnosis according to ICD 10 16 ).…”

“…Our search for early indicators of ASD was based on EEG connectivity in resting-state conditions using clinical data. We selected EEG connectivity for the classification and statistical and ML inferences as the connectivity differences between typically developing children and those with ASD have been identified as one of the most commonly detected using traditional statistics 12 , 15 , 29 – 36 . Although EEG connectivity delivers promising results, due to a lack of correlations between different methods 12 , studies using different methods have produced somewhat inconsistent results regarding the nature of these differences or even show a lack of associations between EEG connectivity and ASD 37 .…”

“…PLV analyses showed increased connectivity on all significant connections in children with ASD as compared to the TD control group—contradictory to previous studies investigating the neural correlates of ASD. These disparities could stem from many factors, one of them being the sleep data used for analyses in our study, while many studies showing decreased connectivity in children with ASD used task data 15 , 33 , 39 , 41 . Other possible reasons for these disparities may include low sample size, where a few subjects skew the results, high group heterogeneity, a wide range of methodologies employed in experiments examining resting-state EEG abnormalities in ASD 9 , or a combination of all these factors.…”

Effects of sex on the attentional orientation bias toward emotional expressions in children: An eye-tracking study.

“…The stimuli were successfully employed in an emotional recognition study (Benitez-Lopez & Ramos-Loyo, 2022) with a sample of 29 typically developing children with a similar age range (8 to 10.11 years old) and the same race as those of the present study. In addition, the facial database has been used previously in different published studies in adult populations (Llamas-Alonso et al, 2020, 2022, Ramos-Loyo et al, 2022). We used adult stimuli rather than youth to prevent “own-age bias” (Anastasi & Rhodes, 2005; Kuefner et al, 2008; Lamont et al, 2005) as well as the reported poorer performance when children are categorizing other children’s faces by sex.…”

Dynamic networks of cortico-muscular interactions in sleep and neurodegenerative disorders

“…It has been recently found that pairs of brain waves interact through distinct coupling functions, and that physiological states (sleep/wake, sleep stages, rest/exercise, cognitive tasks) and conditions (age maturation) are uniquely characterized by an ensemble of coupling forms among brain waves and by specific network topology necessary to facilitate physiological functions ( Liu K. K. et al, 2015 ; Lin et al, 2020 ; Chen et al, 2022 ; Hu et al, 2022 ; Martinez-Gutierrez et al, 2022 ; Ramos-Loyo et al, 2022 ). Such functional networks among brain waves are expression of synchronization mechanisms integrating different neuronal networks ( Wu et al, 2022 ).…”