Comparing White Matter Fiber Bundle Segmentation Methods for Autism Prediction

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects social communication and behavior. Early diagnosis is crucial to enhance the patient's quality of life through treatments and therapies. In this research, two white matter (WM) fiber bundle segmentation methods are analyzed and compared in terms of their performance and impact on the results obtained from the analyzes applied to a database comprising 37 adolescents, 19 subjects with autism and 18 controls. To achieve this, we conducted the segmentation of deep white matter tracts, and computed average diffusion-based indices for each tract, such as Apparent Diffusion Coefficient (ADC), Fractional Anisotropy (FA), and Generalized Fractional Anisotropy (GFA). We applied statistical tests to identify features with significant differences between groups based on the results of two segmentation methods. Significant differences in diffusion-based indices were found in certain cingulate, thalamic, corticospinal, and corpus callosum fascicles. Furthermore, we performed classification between patients and controls using each fascicle feature independently with the Support Vector Machine (SVM) and Decision Trees (DT) algorithms. Finally, we applied the classifiers to the most relevant features for each segmentation method. Overall, even with the limitations of our small database, we demonstrated that the segmentation algorithm has a high impact on WM tract-based analyzes and prediction, with the autocencoder-based algorithm showing better results than a distance-based method.