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Purpose: Age biases have been identified as an essential factor in thediagnosis of ASD. The objective of this study was to compare the effect ofdifferent age groups in classifying ASD using morphological features (MF) andmorphological connectivity features (MCF). Methods: The structural magneticresonance imaging (sMRI) data for the study was obtained from the two publiclyavailable databases, ABIDE-I and ABIDE-II. We considered three age groups, 6 to11, 11 to 18, and 6 to 18, for our analysis. The sMRI data was pre-processedusing a standard pipeline and was then parcellated into 148 different regionsaccording to the Destrieux atlas. The area, thickness, volume, and meancurvature information was then extracted for each region which was used tocreate a total of 592 MF and 10,878 MCF for each subject. Significant featureswere identified using a statistical t-test (p<0.05) which was then used totrain a random forest (RF) classifier. Results: The results of our studysuggested that the performance of the 6 to 11 age group was the highest,followed by the 6 to 18 and 11 to 18 ages in both MF and MCF. Overall, the MCFwith RF in the 6 to 11 age group performed better in the classification thanthe other groups and produced an accuracy, F1 score, recall, and precision of75.8%, 83.1%, 86%, and 80.4%, respectively. Conclusion: Our study thusdemonstrates that morphological connectivity and age-related diagnostic modelcould be an effective approach to discriminating ASD.

Age-Stratified Differences in Morphological Connectivity Patterns in ASD: An sMRI and Machine Learning Approach