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Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder, andbehavioral treatment interventions have shown promise for young children withASD. However, there is limited progress in understanding the effect of eachtype of treatment. In this project, we aim to detect structural changes in thebrain after treatment and select structural features associated with treatmentoutcomes. The difficulty in building large databases of patients who havereceived specific treatments and the high dimensionality of medical imageanalysis problems are the challenges in this work. To select predictivefeatures and build accurate models, we use the sure independence screening(SIS) method. SIS is a theoretically and empirically validated method forultra-high dimensional general linear models, and it achieves both predictiveaccuracy and correct feature selection by iterative feature selection. Comparedwith step-wise feature selection methods, SIS removes multiple features in eachiteration and is computationally efficient. Compared with other linear modelssuch as elastic-net regression, support vector regression (SVR) and partialleast squares regression (PSLR), SIS achieves higher accuracy. We validated thesuperior performance of SIS in various experiments: First, we extract brainstructural features from FreeSurfer, including cortical thickness, surfacearea, mean curvature and cortical volume. Next, we predict different measuresof treatment outcomes based on structural features. We show that SIS achievesthe highest correlation between prediction and measurements in all tasks.Furthermore, we report regions selected by SIS as biomarkers for ASD.

Prediction of treatment outcome for autism from structure of the brain based on sure independence screening