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A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

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Brain imaging-based machine learning in autism spectrum disorder: methods and applications.

Ming Xu¹, Vince Calhoun², Rongtao Jiang³

¹Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China 100190; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China 100049.

Journal of Neuroscience Methods

|June 26, 2021

Summary

Machine learning (ML) aids in diagnosing autism spectrum disorder (ASD) using brain imaging. Developing better datasets and understanding heterogeneity are key for improved ML-driven ASD classification.

Keywords:

Autism spectrum disorder (ASD)Classification Machine learning Prediction dMRI fMRI sMRI

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Area of Science:

Neuroscience
Medical Imaging
Artificial Intelligence

Background:

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition diagnosed behaviorally.
Current diagnostic methods lack objective biomarkers due to elusive pathogenesis.
Non-invasive brain imaging, like MRI, offers objective brain measurements.

Purpose of the Study:

To review advancements in machine learning (ML) for classifying individuals with and without ASD using neuroimaging data.
To analyze current trends, methodologies, and challenges in ML-based ASD diagnostic tools.
To provide recommendations for future research directions.

Main Methods:

Comprehensive literature review of neuroimaging-based ASD classification studies.
Analysis of publication trends and common machine learning pipelines.
Detailed discussion of representative studies, including imaging modalities, ML methods, and sample sizes.

Main Results:

Machine learning approaches are increasingly used for ASD classification with neuroimaging.
Diverse imaging modalities and ML algorithms are employed, with varying success based on study design.
Significant challenges remain in identifying robust biomarkers and handling heterogeneity.

Conclusions:

Accurate ASD diagnosis using ML and neuroimaging is challenging due to biological heterogeneity.
Establishing larger, comprehensive datasets is crucial for improving diagnostic performance.
Continued development of ML methods and a focus on individual differences will advance ASD diagnosis.