Multi-patient learning increases accuracy for Subthalamic nucleus identification in deep brain stimulation

Establishing the exact position of basal ganglia is key in several brain surgeries, particularly in deep brain stimulation for patients suffering from Parkinson's disease. There have been recent attempts to introduce automatic systems with the ability to localize, with high accuracy, specific brain regions. These systems usually follow the classical supervised learning paradigm, in which training data from different patients are employed to construct a classifier that is patient-independent. In this paper, we show how by sharing information from different patients, it is possible to increase accuracy for targeting the Subthalamic Nucleus. We do this in the context of multi-task learning, where different but related tasks are used simultaneously to leverage the performance of a learning system. Results show that the multitask framework can outperform the traditional patient-independent scenario in two different real datasets.