Diagnostic classification of autism spectrum disorder using sMRI improves with the morphological distance-related features compared to morphological features
التفاصيل البيبلوغرافية
العنوان:
Diagnostic classification of autism spectrum disorder using sMRI improves with the morphological distance-related features compared to morphological features
In this study, we analyzed the performance of the morphological features (MF) and morphological distance-related features (MDRF) in the classification of autism spectrum disorder (ASD) and typical development (TD). Initially, we pre-processed the structural magnetic resonance images (sMRI) of ASD and TD from seven sites publicly available in the autism brain imaging data exchange (ABIDE-I and ABIDE-II) database using the standard pipeline. Further, sMRI images were parcellated into different regions using the Destrieux atlas. Moreover, MF (surface area) and MDRF were calculated from each region. We tested the performance of the MF and MDRF on each site by feeding them to classifiers such as random forest (RF), support vector machines (SVM), and multi-layer perceptron (MLP). Our results suggest that the MDRF were able to classify the ASD and TD better than the MF. Furthermore, the RF gives a single-site average classification accuracy of 91.78% and 95.27% using MF and MDRF respectively. We achieved the average classification accuracy of 69.08% and 82.91% between the sites using MF and MDRF respectively. Our results suggest that the frontal lobe and right hemisphere contribute more MDRF to the machine learning model. Furthermore, many features were found within the frontal lobe (15 distance features) and frontal-parietal (11 distance features) lobes of the top features in the USM site. The results suggest that the MDRF can be used as a valuable feature metric to classify ASD-like neurodevelopmental disorders.