Neuroimaging-based methods for autism identification: A possible translational application?

Research output: Contribution to journalArticlepeer-review


Classification methods based on machine learning (ML) techniques are becoming widespread analysis tools in neuroimaging studies. They have the potential to enhance the diagnostic power of brain data, by assigning a predictive index, either of pathology or of treatment response, to the single subject’s acquisition. ML techniques are currently finding numerous applications in psychiatric illness, in addition to the widely studied neurodegenerative diseases. In this review we give a comprehensive account of the use of classification techniques applied to structural magnetic resonance images in autism spectrum disorders (ASDs). Understanding of these highly heterogeneous neurodevelopmental diseases could greatly benefit from additional descriptors of pathology and predictive indices extracted directly from brain data. A perspective is also provided on the future developments necessary to translate ML methods from the field of ASD research into the clinic.

Original languageEnglish
Pages (from-to)231-239
Number of pages9
JournalFunctional Neurology
Issue number4
Publication statusPublished - Oct 1 2014


  • Autism spectrum disorders
  • Brain alterations
  • Machine learning
  • Magnetic resonance imaging
  • Translational research

ASJC Scopus subject areas

  • Clinical Neurology
  • Neuroscience(all)


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