This research is part of a broader study on Autism Spectrum Disorder (ASD) detection, which consists of three main components: feature design, classification, and feature selection. The primary objective of this study is to investigate whether different graph inference methods and graph-based features can contribute to finding new biomarkers in the brains of individuals with ASD. To easily interpret and visualize the results, a graphical user interface (GUI) was developed. The feature design phase includes both classical statistical features and innovative graph-based features, derived through a variety of statistical and Graph Signal Processing (GSP) graph inference methods. The top three performing methods were: sample covariance with graphical lasso (59.3% accuracy, 64.2% AUC), rSpecT-adjacency (58.0% accuracy, 62.5% AUC), and rSpecT-laplacian (58.8% accuracy, 61.5% AUC). the connectivity patterns visualized using the developed tools revealed distinct differences between methods, offering promising insights for neuroscientific interpretation and potential biomarker discovery.