Prediction of Autism Treatment Response from Baseline fMRI using Random Forests and Tree Bagging
This addresses the challenge of selecting effective behavioral interventions for ASD children to avoid trial-and-error delays, though it is incremental as it applies existing machine learning techniques to a specific domain.
The study tackled the problem of predicting which children with autism spectrum disorders (ASD) will respond to Pivotal Response Treatment (PRT) by using baseline fMRI data, achieving the highest prediction accuracy among tested methods in a leave-one-out cross-validation with 19 participants.
Treating children with autism spectrum disorders (ASD) with behavioral interventions, such as Pivotal Response Treatment (PRT), has shown promise in recent studies. However, deciding which therapy is best for a given patient is largely by trial and error, and choosing an ineffective intervention results in loss of valuable treatment time. We propose predicting patient response to PRT from baseline task-based fMRI by the novel application of a random forest and tree bagging strategy. Our proposed learning pipeline uses random forest regression to determine candidate brain voxels that may be informative in predicting treatment response. The candidate voxels are then tested stepwise for inclusion in a bagged tree ensemble. After the predictive model is constructed, bias correction is performed to further increase prediction accuracy. Using data from 19 ASD children who underwent a 16 week trial of PRT and a leave-one-out cross-validation framework, the presented learning pipeline was tested against several standard methods and variations of the pipeline and resulted in the highest prediction accuracy.