Age-Stratified Differences in Morphological Connectivity Patterns in
  ASD: An sMRI and Machine Learning Approach
Gokul Manoj Sandeep Singh Sengar Jac Fredo Agastinose Ronickom
Purpose: Age biases have been identified as an essential factor in the
diagnosis of ASD. The objective of this study was to compare the effect of
different age groups in classifying ASD using morphological features (MF) and
morphological connectivity features (MCF). Methods: The structural magnetic
resonance imaging (sMRI) data for the study was obtained from the two publicly
available databases, ABIDE-I and ABIDE-II. We considered three age groups, 6 to
11, 11 to 18, and 6 to 18, for our analysis. The sMRI data was pre-processed
using a standard pipeline and was then parcellated into 148 different regions
according to the Destrieux atlas. The area, thickness, volume, and mean
curvature information was then extracted for each region which was used to
create a total of 592 MF and 10,878 MCF for each subject. Significant features
were identified using a statistical t-test (p<0.05) which was then used to
train a random forest (RF) classifier. Results: The results of our study
suggested 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 MCF
with RF in the 6 to 11 age group performed better in the classification than
the other groups and produced an accuracy, F1 score, recall, and precision of
75.8%, 83.1%, 86%, and 80.4%, respectively. Conclusion: Our study thus
demonstrates that morphological connectivity and age-related diagnostic model
could be an effective approach to discriminating ASD.