Atypical Resting-State EEG Graph Metrics of Network Efficiency Across Development in Autism and Their Association with Social Cognition: Results from the LEAP Study.
EEG network efficiency gaps in autism are real but small, age-specific, and only weakly tied to social skills.
01Research in Context
What this study did
de Jonge et al. (2025) looked at resting-state EEG in autistic and neurotypical people aged 5-65. They asked if brain network efficiency changes with age and if those changes link to social skills.
The team used graph math to measure how fast and smooth alpha waves travel across the brain. They split the sample into kids, teens, and adults.
What they found
Teens with autism had lower long-range alpha efficiency. Adults with autism showed lower local clustering and small-worldness. These gaps were small and only showed up in the alpha band.
Social-cognition scores only linked to EEG in teens, and the link was weak. No strong brain-behavior payoff appeared for kids or adults.
How this fits with other research
Worsham et al. (2015) saw lower inter-hemispheric beta coherence in autistic boys during photic drive. E et al. used resting alpha, so the two EEG oddities sit in different bands and tasks rather than clash.
Anthony et al. (2020) found no EEG predictive-coding deficit in autistic youth, an apparent contradiction. The key is that J et al. measured MMN, while E et al. measured network efficiency—different EEG markers can yield different answers.
Lotfizadeh et al. (2020) used the same LEAP lifespan sample and also found weak neural-social links, this time with cardiac interoception. Together the studies show that subtle brain metrics keep giving us faint or null ties to real-world symptoms.
Why it matters
If you run EEG as an outcome, do not expect big social jumps—efficiency tweaks are tiny and age-bound. Track alpha-band metrics separately for teens and adults, and pair them with direct social probes rather than hoping for a strong proxy score.
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02At a glance
03Original abstract
Autism has been associated with differences in functional brain network organization. However, the exact nature of these differences across development compared to non-autistic individuals and their relationship to autism-related social cognition, remains unclear. This study first aimed to identify EEG resting-state network characteristics in autistic versus non-autistic children, adolescents, and adults. Second, we investigated associations with social cognition measures. Analyzing resting-state EEG data from the EU-AIMS Longitudinal European Autism Project, we compared network metrics (global efficiency, clustering coefficient, and small-worldness) between 344 autistic and non-autistic individuals within and across age groups in four frequency bands (delta, theta, alpha, and beta). If significant, we explored their relationships to measures of empathy (empathy quotient), complex emotion recognition [reading the mind in the eyes task (RMET)], and theory of mind (animated shapes task). Compared to their non-autistic peers, autistic adolescents showed lower alpha global efficiency, while autistic adults showed lower alpha clustering and small-worldness. No network differences were observed among children. In adolescents, higher long-range integration was tentatively associated with higher RMET scores; in those with high autistic traits, higher long-range integration related to fewer parent-reported empathic behaviors. No brain-behavior relationships were observed in adults. Our findings suggest subtle differences in network topology between autistic and non-autistic individuals, with less efficient long-range efficiency during adolescence, and less local and overall network efficiency in adulthood. Furthermore, long-range integration may play a role in complex emotion recognition and empathy difficulties associated with autism in adolescence.
Journal of autism and developmental disorders, 2025 · doi:10.1038/s41598-017-16440-z