BACKGROUND OF THE INVENTION

Embodiments of the invention relate generally to a system and method for setting deep brain stimulation parameters, and more particularly to a system and method for predicting optimal deep brain stimulation parameters using brain network analytics.

Deep brain stimulation (DBS) is used for treating disabling neurological symptoms and psychiatric disorders. The procedure uses a neurostimulator to deliver electrical stimulation to the brain by way of surgically implanted electrodes. Depending on the condition being treated, the electrodes can be used to target certain cells within the brain or can be targeted toward areas of the brain that control movement or regulate abnormal impulses. For example, in this later case, the electrical stimulation can be used to disrupt abnormal nerve signals that cause tremor and/or other neurological symptoms. Over the past 20 years, more than 100,000 Parkinson's disease, essential tremor, dystonia and obsessive-compulsive disorder patients have seen significant symptom relief due to DBS treatment. Evidence now accumulates indicating that patients with chronic pain, post-traumatic stress disorder, and obesity may also benefit from DBS treatments.

Despite the long history of DBS, its underlying principles and mechanisms are still not clear. In particular, the understanding of how the brain responds to different DBS excitation parameters, such as electrode choice, frequency, current/voltage and pulse width is limited. While movement disorders such as Parkinson's disease provide immediate clinical feedback, most other disorders treated with DBS provide no real time feedback mechanism to let a clinician decide whether DBS has had its intended effect or whether the stimulation parameters are optimal for each individual patient. The immediate clinical feedback seen in Parkinson's disease patients is useful, but it is still unclear whether optimal settings (i.e., ceiling) has been achieved. Accordingly, the selection of optimal DBS excitation parameters that may best mitigate symptoms in a patient is largely based on trial and error. Specifically, existing methods for selection of optimal DBS excitation parameters are iterative processes that often requires numerous clinic visits by the patient to test the large number of possible stimulation parameters, with the patient being monitored over time to see how the patient evolves and determine if symptoms improve. This monitoring extends over a signification period of time, often 3-6 months or longer, resulting in a lengthy optimization period.

More recently, automated systems for parameter optimization have been proposed that use structural and functional MRI imaging to provide real time feedback to let a clinician decide whether DBS has had its intended effect or whether the stimulation parameters are optimal for each individual patient. That is, fMRI is used to provide a quick and efficient feedback mechanism by highlighting areas of brain activity related to DBS stimulation and allowing optimization of DBS stimulation parameters in close to real time. However, existing automated systems utilize an initial guess as to optimal set of parameter values that can result in feedback methods that converge to suboptimal local minima, thereby resulting in potential inaccuracies in the parameter optimization. Additionally, the existing automated systems can only perform the parameter optimization after DBS electrode implantation, using post-surgery structural and functional scans.

It would therefore be desirable to have a system and method that provides a more comprehensive solution for optimizing DBS excitation parameters. It would also be desirable for such optimization to not be limited to post-operative scans, but to be performed using imaging data pre-surgically, without DBS electrode implantation.

BRIEF DESCRIPTION OF THE INVENTION

In accordance with one aspect of the invention, a system for predicting optimal DBS parameters for a patient includes a DBS system controlled to send each of a plurality of DBS signals to one or more electrodes surgically implanted within one or more regions of a patient brain, wherein in sending the plurality of DBS signals the DBS system is controlled to perform a sweep of one or more DBS parameters, with each of the plurality of DBS signals comprising a different combination of DBS parameters. The system also includes a brain response acquisition system synchronized with the DBS system and controlled to collect brain response data resulting from each of the plurality of DBS signals. The system further includes a prediction system operably connected to the brain response acquisition system, the prediction system comprising a processor programmed to extract statistical metrics of brain response in the patient brain from the brain response data, access a DBS functional atlas comprising brain response maps derived from DBS treatment at optimal DBS parameter settings for a plurality of diseases, and predict optimal DBS parameters for the patient based on the statistical metrics of brain response and the DBS functional atlas. The DBS parameters swept by the DBS system and the optimal DBS parameters predicted by the prediction system comprise one or more of signal frequency and signal pulse width.

In accordance with another aspect of the invention, a system for predicting optimal DBS parameters applied by one or more DBS electrodes for treatment of a patient includes a brain response acquisition system controlled to collect functional brain data from the patient in a resting state and a prediction system operably connected to the brain response acquisition system. The prediction system includes a processor programmed to estimate a functional connectome of the patient from the functional brain data, access a functional brain atlas comprising brain response maps for one or more disease-specific regions-of-interest (ROIs) or voxels, extract disease-specific graph theoretic metrics for one or more ROIs in the functional connectome using the functional brain atlas, and predict optimal DBS parameters for the patient using the disease-specific graph theoretic metrics, the predicted optimal DBS parameters comprising one or more of signal frequency and signal pulse width.

In accordance with yet another aspect of the invention, a method for optimizing parameters of a DBS pulse signal applied by a DBS electrode for treatment of a patient includes inputting functional brain data into a predictor system, the functional brain data acquired responsive to a sweeping across one or more DBS parameters of a multi-dimensional parameter space of DBS parameters. The method also includes extracting statistical metrics of brain response from the functional brain data for one or more ROIs or voxels of the brain via the predictor system, accessing a DBS functional atlas comprising brain response maps derived from DBS treatment at optimal DBS parameter settings for a plurality of diseases via the predictor system, and predicting optimal DBS parameters for the patient based on the statistical metrics of brain response and the DBS functional atlas via the predictor system. Predicting the one or more optimal DBS parameters comprises predicting one or more of a signal frequency and a signal pulse width.

Various other features and advantages will be made apparent from the following detailed description and the drawings.

