PMID: 25764253Mar 13, 2015Paper

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

Functional Neurology
Alessandra ReticoS Calderoni

Abstract

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.

Related Concepts

Related Feeds

Autism

Autism spectrum disorder is associated with challenges with social skills, repetitive behaviors, and often accompanied by sensory sensitivities and medical issues. Here is the latest research on autism.

Related Papers

Autism : the International Journal of Research and Practice
Dermot Bowler
Autism : the International Journal of Research and Practice
Sven Bölte
Autism Research : Official Journal of the International Society for Autism Research
Marjorie SolomonCameron S Carter
© 2021 Meta ULC. All rights reserved