Oxytocin modulation of resting-state functional connectivity network topology in individuals with higher autistic traits

Abstract

Background: Altered connectivity patterns in socio-emotional brain networks are characteristic of individuals with autism spectrum disorder. Despite recent research on intranasal oxytocin's modulation effects of network topology in autism, its specific effects on the functional connectivity network topology remain underexplored.

Methods: To address this gap, we conducted an exploratory data-driven study employing a dimensional approach using data from a large cohort of 250 neurotypical adult male subjects with either high or low autistic traits and who had administered 24 IU of intranasal oxytocin or placebo in a randomized, controlled, double-blind design. Resting-state functional connectivity data were analyzed using network-based statistical methods and graph theoretical approaches.

Results: The findings from treatment × autistic trait group interactions revealed significantly different effects of oxytocin in local (cluster coefficient, efficiency, nodal path length, degree and betweenness centrality) but not global graph metrics in individuals with higher autistic traits compared to those with lower ones, across multiple brain regions. Changes across multiple measures were found in the motor, auditory/language, visual, default mode and socio-emotional processing networks, all of which are influenced in autism spectrum disorder.

Conclusion: Overall, findings from this dimensional approach demonstrate that oxytocin particularly targets widespread enhancement of local but not global neural network processing parameters in neurotypical individuals with higher autistic traits. This suggests that intranasal oxytocin may represent a therapeutic option for social, emotional and sensorimotor symptoms in individuals with autism spectrum disorder by modulating local integration within brain regions involved in their regulation.

Keywords

autistic traits / oxytocin / resting-state fMRI / graph theory / topology measures

Author contributions

Abraham Tonny Hagan (Formal Analysis, Investigation, Methodology, Validation, Visualization, Writing - original draft), Lei Xu (Data curation, Formal Analysis, Resources, Writing - review & editing), Juan Kou (Data curation, Funding acquisition, Investigation, Resources, Writing - review & editing), Yuan Hu (Data curation, Formal Analysis), Benjamin Klugah-Brown (Formal Analysis, Methodology, Writing - review & editing), Jialin Li (Data curation), Mercy Chepngetich Bore (Formal Analysis), Benjamin Becker (Writing - review & editing), Keith Maurice Kendrick (Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing - review & editing), and Xi Jiang (Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing - review & editing).

Conflict of interest

Keith Kendrick holds the position of Editor-in-Chief, and Benjamin Becker is a member of the editorial board of Psychoradiology. They were blinded from reviewing or making decisions on the manuscript. The authors declare no other conflict of interests.

Acknowledgements

This work was supported by Sichuan Province Key Research and Development Project [grant number–2023YFWZ0003–KMK], the National Natural Science Foundation of China [grant number–62276050–XJ, grant number–32100893–JK], and Sichuan Science and Technology Program [grant number–2024NSFSC0655–XJ].

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