CRCNS: Deep Neural Network Approaches for Closed-Loop Deep Brain Stimulation

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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. The proposed research builds upon the extensive and unique experiences with multi-day, extra-operative recording from DBS leads in patients at Charité Hospital and intraoperative ECoG and DBS recording from patients at the University of Pittsburgh, in order to develop computational methods to advance closed-loop, adaptive DBS (aDBS) strategies for movement disorders.