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Autism Spectrum Disorder

Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.

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Related Experiment Video

Updated: Apr 16, 2026

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

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

Alessandra Retico, Michela Tosetti, Filippo Muratori

Functional Neurology

|March 13, 2015

Summary

This summary is machine-generated.

Machine learning (ML) classification enhances neuroimaging analysis for diagnosing psychiatric and neurodegenerative diseases. This review focuses on ML applications in structural MRI for autism spectrum disorders (ASDs), aiming to improve clinical translation.

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Area of Science:

Neuroimaging analysis
Machine learning applications
Psychiatric and neurodevelopmental disorders

Background:

Machine learning (ML) classification is increasingly used in neuroimaging.
ML offers potential for enhanced diagnostic power and predictive indexing in clinical neuroscience.
Applications span neurodegenerative diseases and psychiatric conditions, including autism spectrum disorders (ASDs).

Purpose of the Study:

To provide a comprehensive review of ML classification techniques applied to structural MRI in ASDs.
To highlight the potential of ML for extracting pathological descriptors and predictive indices in heterogeneous neurodevelopmental disorders.
To offer a perspective on future developments for clinical translation of ML methods in ASD research.

Main Methods:

Review of existing literature on machine learning classification.
Application of ML techniques to structural magnetic resonance imaging (sMRI) data.
Analysis of ML's role in identifying biomarkers for autism spectrum disorders.

Main Results:

ML classification is a growing tool in neuroimaging for diagnostics and prediction.
Structural MRI data analyzed with ML shows promise for understanding ASD heterogeneity.
Current research focuses on extracting predictive indices from brain data for single-subject assessment.

Conclusions:

ML classification techniques are valuable for analyzing neuroimaging data in psychiatric disorders like ASDs.
Further development is needed to translate ML findings from ASD research into clinical practice.
ML holds potential to improve diagnostic accuracy and treatment response prediction in autism spectrum disorders.