Bayesian network structure and predictability of autistic traits

Giovanni Briganti*, Donald R. Williams, Joris Mulder, Paul Linkowski

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

The aim of this work is to explore the construct of autistic traits through the lens of network analysis with recently introduced Bayesian methods. A conditional dependence network structure was estimated from a data set composed of 649 university students that completed an autistic traits questionnaire. The connectedness of the network is also explored, as well as sex differences among female and male subjects in regard to network connectivity. The strongest connections in the network are found between items that measure similar autistic traits. Traits related to social skills are the most interconnected items in the network. Sex differences are found between female and male subjects. The Bayesian network analysis offers new insight on the connectivity of autistic traits as well as confirms several findings in the autism literature.
Original languageEnglish
Pages (from-to)344-357
JournalPsychological Reports
Volume125
Issue number1
DOIs
Publication statusPublished - 2022

Keywords

  • ASPERGER-SYNDROME
  • Autism
  • DISORDERS
  • FMRI
  • FUNCTIONING AUTISM
  • MEDIAL PREFRONTAL CORTEX
  • POPULATION
  • RELIABILITY
  • SPECTRUM QUOTIENT AQ
  • VERSION
  • network analysis
  • students

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