Age- and Severity-Specific Deep Learning Models for Autism Spectrum Disorder Classification Using Functional Connectivity Measures

Autism spectrum disorder (ASD) is characterized by divergent etiological factors, comorbidities, severity levels, genetic influences, and functional connectivity (FC) patterns in the brain. In the literature, ASD classification based on age and severity using fMRI data is extremely limited. This study explores the impact of age, symptom severity, and brain FC patterns on the diagnosis of ASD using deep neural networks (DNNs). The ability to classify ASD by age and severity using fMRI data is extremely limited. This study explores the impact of age, symptom severity, and brain FC patterns on the diagnosis of ASD using deep neural networks (DNNs). The FC measures were extracted using Pearson's correlation coefficient (PCC), fractal connectivity (FrC), and nonfractal connectivity (NFrC) from the ABIDE I and II databases. We studied three age groups (6 to 11, 11 to 18, and 6 to 18 years) and two severity groups (ADOS score ≤ 11 and ADOS score > 11). The FC matrices are constructed from blood oxygen level-dependent (BOLD) time series signals, and the heat maps are used to generate features for the convolutional neural network (CNN), MobileNetV2, and DenseNet201 models. The MobileNetV2 classifier achieved 76.25% accuracy, 77.09% sensitivity, and 79.77% precision in the age group of 6 to 11 years using NFrC feature maps compared to other DNNs. According to ADOS total scores above 11, DenseNet201 demonstrated superior performance with 83.45% accuracy, 87.3% sensitivity, and 79.13% precision. Connectivity measured by NFrC consistently outperformed Frc measures. Various combinations of connectivity measures and classifiers consistently showed promising results for the age group of 6–11 years and the severity group with an ADOS score of more than 11. ASD's inherent heterogeneity can be addressed effectively by developing diagnostic models tailored to age and severity.