Combining deep and hand-crafted MRI features for identifying sex-specific differences in autism spectrum disorder versus normal controls

Autism spectrum disorder (ASD) is a collection of neuro-developmental disorders with many symptoms, most prominently social impairment. It is known that there is a significant prevalence (4:1) of ASD in males compared to females. This suggests that there will likely be distinct neuro-anatomical structures across males and females that contribute to the distinct disease etiologies across the two sexes. Hence, in this work, we seek to develop "sexspecific" machine learning models that attempt to capture neuroanatomical differences in brain morphometry across ASD versus normal controls using structural MRI scans. Specifically, we train two different machine-learning models (one for male and one for female cohorts) consisting of "hand-crafted" morphometric features such as shape and surface area of brain parcellations from Tlw MRI, with "deep" features learned from a Dense Convolutional Network (DenseNet). Our methodology consists of first computing morphometric hand-crafted features (i.e. volume and surface features of different brain parcellations) from the training cohort obtained from the ABIDE-II dataset consisting of 210 males and 98 females from structural Tlw MRI scans. We then employ feed-forward feature selection within a linear discriminant analysis classifier trained separately for the male and female cohorts, to distinguish ASD from normal controls. Additionally, we train a DenseNet model with 4 dense blocks (with 2 layers each) to extract deep features from Tlw MRI scans to distinguish ASD versus controls, separately for males and females. The deep features allow for capturing complementary data-driven feature differences in brain morphometry specific to male and female ASD cohort (versus normal controls). Finally, we combine the top morphometric and DenseNet features obtained from the training model and test them on the ABIDE I dataset for the male (n=85) and female (n=19) cohort separately, to distinguish ASD from normal controls. Our results demonstrated training and testing accuracies of 78% and 79% using hand-crafted features alone, 68% and 57% using DenseNet features alone, and 87% and 79% respectively using integrated hand-crafted and deep features for the male cohort. For the female cohort, we obtained accuracies of 81% and 84% with hand-crafted features alone, 71% and 62% for DenseNet features alone, and 81% and 84% with integrated hand-crafted and deep features, on training and testing sets respectively. With further optimization of deep features along with inclusion of a large multi-site cohort, our presented sex-specific ML approach could allow for improved diagnosis of ASD from controls, across males and females, using structural MRI scans.