  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.
