Meta-data Study in Autism Spectrum Disorder Classification Based on Structural MRI
This work addresses the challenge of inconsistent neuroimaging data quality for ASD diagnosis, but it is incremental as it focuses on analyzing existing meta-data impacts without introducing new methods.
The study investigated how meta-data conditions affect autism spectrum disorder classification accuracy using structural MRI from 20 sites, finding that varying data quality can lead to predictive accuracies worse than random guessing in some cases.
Accurate diagnosis of autism spectrum disorder (ASD) based on neuroimaging data has significant implications, as extracting useful information from neuroimaging data for ASD detection is challenging. Even though machine learning techniques have been leveraged to improve the information extraction from neuroimaging data, the varying data quality caused by different meta-data conditions (i.e., data collection strategies) limits the effective information that can be extracted, thus leading to data-dependent predictive accuracies in ASD detection, which can be worse than random guess in some cases. In this work, we systematically investigate the impact of three kinds of meta-data on the predictive accuracy of classifying ASD based on structural MRI collected from 20 different sites, where meta-data conditions vary.