Brain connectivity patterns can predict the expression of autistic traits in typically developing children

Brain connectivity patterns can predict the expression of autistic traits in typically developing children

Brain connectivity patterns can predict the expression of autistic traits in typically developing children

A neuroimaging study published in the journal Biological Psychology found evidence that measures of brain connectivity can predict the expression of autistic traits in typically developing children. Children with a higher degree of autistic traits showed increased theta connectivity in right anterior brain regions.

When performing cognitive tasks, the brain must communicate between different regions of the brain. Neuroimaging research has revealed that people with autism spectrum disorders (ASD) show alterations in patterns of connectivity between brain areas, which may help explain the social and cognitive difficulties typically experienced by people with autism.

Although studies have identified neural differences that accompany ASD, it is unclear whether neural differences can predict autistic traits in the general population. Study author Aron T. Hill and his colleagues conducted an experiment to test whether differences in brain connectivity can predict the expression of autistic traits in typically developing children.

“We conducted this research because we wanted to explore potential relationships between brain activity and autistic social traits. Functional connectivity allows us to examine how various regions of the brain interact or communicate with each other,” explained Hill, a researcher at Deakin University.

“Previous research has indicated that connectivity may be impaired in people with autism, however, it was less clear whether there was an association between connectivity and autistic traits in typically developing individuals (i.e. say those without an autism diagnosis.) We wanted to see if connectivity, measured using electroencephalography (EEG) – a method that allows us to record electrical activity in the brain via electrodes placed above of the head – could predict the expression of autistic traits in typically developing children.This is an important step towards establishing brain-derived physiological measures of autistic traits.

The study participants were 127 typically developing children between the ages of 4 and 12 years old. The children had no diagnosis of any known neurological or neurodevelopmental disorder. During the study, EEG was used to record resting-state neural activity. The children also completed the Social Reactivity Scale 2nd Edition (SRS-2), which measured social deficits associated with ASDs.

The researchers analyzed functional connectivity – a measure of how brain regions interact with each other – in seven key brain areas. They then assessed whether any of these connectivity values ​​would predict autistic traits measured by the SRS. Connectivity in one of the key areas, the right anterior region, showed a significant positive association with total SRS-2 scores. Children with increased right anterior connectivity showed greater expression of autistic traits.

“The key take-home message from this study is that non-invasive recordings of neural communication (i.e. functional connectivity) in the brain may indeed provide a means of predicting autistic social traits in children with autism. typical development,” Hill told PsyPost. “In this study, we specifically found that increased connectivity on right frontal brain areas was associated with more pronounced expression of autistic traits.”

Hill and his team then assessed associations between connectivity in the right anterior region and each of the five SRS subscales. Although this revealed no significant effects, the results tended towards associations between right anterior connectivity and the ‘social motivation’ and ‘restricted interests and repetitive behaviors’ subscales.

The researchers say their findings are consistent with the findings of a 2021 study by Aykan and colleagues that also found an association between higher connectivity in the right anterior region and autistic traits, but in neurotypical adults. The current results extend these findings to show that this same association is found in neurotypical children.

“It was pleasing to see that our results seem to align well with recent similar findings reported in adults. Importantly, our work was able to extend the potential link between functional brain connectivity and autism traits to typically developing children,” said Hill.

According to the study authors, the results provide evidence that resting-state EEG can be used to measure correlations between functional connectivity and the expression of autistic social traits in typically developing children. “This suggests that theta connectivity may play an important role in social information processing,” the researchers wrote.

However, in addition to other limitations, the authors noted that it will be important for future work to test associations between brain connectivity and autistic traits using task-based paradigms in addition to state recordings. rest.

“A caveat is that in this study, we chose to focus on connectivity in only one specific frequency band taken from the EEG recording (the theta band),” Hill explained. “This was done because recent work in adults had previously reported an association between connectivity and autistic traits at this frequency, so we wanted to assess whether this occurs in children.”

“So we don’t yet know if this discovery extends to other brain frequencies. This would be an interesting topic for future work to address. In addition, our results come from a sample of children from infancy to middle childhood (4 to 12 years old). Exploring whether similar results can be obtained in a wider age range in children could be an interesting next step. ”

The study, “Right Anterior Theta Connectivity Predicts Autistic Social Traits in Typically Developing Children,” was authored by Aron T. Hill, Jodie Van Der Elst, Felicity J. Bigelow, Jarrad AG Lum, and Peter G. Enticott .

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