Deep Neural Network Approaches for Closed-Loop Deep Brain Stimulation
Deep Neural Network Approaches for Closed-Loop Deep Brain Stimulation
In this research study the researchers want to learn more about brain activity related to
 speech perception and production in patients with Parkinson's Disease who are undergoing deep
 brain stimulation (DBS).
Deep brain stimulation (DBS) is the gold-standard treatment for patients with medication
 resistant motor complications of Parkinson's disease (PD) and provides the only opportunity
 to record and stimulate in the human basal ganglia. Most recently, the concurrent use of
 research electrocorticography (ECoG) during DBS surgery, including pioneering work from
 Pittsburgh, has further enabled basic neuroscience investigation of human
 cortical-subcortical network dynamics. The discovery that aberrant synchronization of
 rhythmic neuronal activity recorded in PD patients is suppressed by DBS has advanced the
 concept that measures associated with pathological activity may be used as biomarkers to
 control the delivery of DBS therapy. Pilot studies of aDBS in PD have reported promising
 clinical results from triggering DBS stimulation when the signal recorded from the DBS
 electrode showed a high level of oscillatory power in the beta frequency range (13 - 35 Hz).
 That approach, however, has important limitations. Most importantly, beta power recorded from
 the DBS lead is suppressed by movement including PD tremor, its detection is highly dependent
 on lead location and the recording montage needed to record during stimulation is
 incompatible with directional current steering, a recent innovation employing segmented
 stimulation contacts. The inherent complexity of the increased parameter space through DBS
 innovations also overwhelms standard programming techniques. Finally, use of additional
 biomarker signals (e.g., recorded from cortex) is likely to improve the ability to adaptively
 control DBS for disorders marked by complex multidimensional symptomatologies such as PD. The
 current proposal will establish methods for overcoming these limitations by developing
 techniques for multi-feature classification from ECoG recordings, using advanced machine
 learning algorithms.
Inclusion Criteria:

 1. Subjects scheduled for DBS implantation, as determined by the clinical
 multidisciplinary movement disorders board with definitive diagnosis of Parkinson's
 disease

 2. Subjects able to provide informed consent and comply with task instructions.

 3. Subjects 18-85 years old

 Exclusion Criteria:

 1. Non-English-speaking subjects