PROJECT SUMMARY/ABSTRACT
Successful treatment of Parkinson's disease (PD) using deep brain stimulation (DBS) therapy requires an
optimal setting of stimulation parameters to correct brain function anomalies. The commonly employed DBS
1.0 electrodes have only four contact locations (with no stimulation directionality) that are used to electric
pulses to a target volume of the brain. DBS 1.0 electrodes require the optimization of four stimulation
parameters: signal frequency, voltage, pulse width, and contact location. In current standard-of-care
optimization protocol, the DBS parameters are adjusted (via trial and error) until the physician determines an
optimal set of parameters. This empirical optimization protocol requires numerous clinical visits (~6 weeks
interval) that substantially increases the time to optimization (TTO) per patient (~1 year), financial burden,
and ultimately limits the number of patients that can have access to DBS therapy. Even though there are more
effective electrodes, DBS 1.0 electrodes are mostly used by clinicians because their smaller parameter space
pose less difficulty during manual clinical optimization. However, DBS 1.0 electrodes cannot be directed to
stimulate a smaller volume of tissue, which can lead to extraneous stimulations that can reduce patient clinical
benefits and increase side effects. By contrast, the newer DBS electrodes (dubbed DBS 2.0) have a greater
number of contact locations and can be programmed to stimulate a smaller volume of tissue at multiple levels
and directions. Several published reports have shown that DBS 2.0 electrodes (compared to DBS 1.0) are more
energy-efficient and improve patient outcomes with lesser side-effects and a wider therapeutic window.
However, the expanded DBS 2.0 parameter space has made empirical programming of the electrodes difficult
as the TTO per patient is beyond acceptable clinical timeframes. This increased difficulty has hindered
adoption of DBS 2.0 electrodes by clinicians. To significantly shorten and simplify DBS 2.0 parameter
optimization—thus enabling its wider adoption for more precise therapy—a uniquely qualified multi-
disciplinary team of magnetic resonance imaging (MRI) physicists, artificial intelligence (AI) engineers, and
clinicians from GE Research and the University Health Network propose to: 1) develop a semi-automated
functional MRI (fMRI) and deep learning (DL)-based system for rapid optimization of DBS 2.0 parameters; 2)
demonstrate its clinical benefit in the treatment of PD patients using bilateral stimulation of the sub-thalamic
nucleus with DBS 2.0 electrodes in a pilot study. Success of this program will decrease the TTO per patient for
PD patients with DBS 2.0 implants to ~1 hour, and will improve patient throughput and outcomes in the
treatment of PD. The proposed fMRI-DL-based optimization method may also improve access by making it
possible for non-expert centers (without highly specialized clinicians) to carry out stimulation parameters
optimization in patients after the electrode insertion surgery have been completed in expert centers.
