Practical design of closed-loop DBS algorithms using systematic in silico verification

Project Information

SilVerDBS

Grant agreement ID: 101030486

DOI

10.3030/101030486

Project closed

EC signature date 7 April 2021

Start date 1 January 2022

End date 2 April 2024

Funded under

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Total cost

€ 184 590,72

EU contribution

€ 184 590,72

Coordinated by

UNIVERSITY COLLEGE DUBLIN, NATIONAL UNIVERSITY OF IRELAND, DUBLIN
Ireland

Periodic Reporting for period 1 - SilVerDBS (Practical design of closed-loop DBS algorithms using systematic in silico verification)

Reporting period: 2022-01-01 to 2023-12-31

Summary of the context and overall objectives of the project

Deep brain stimulation (DBS) is a treatment for Parkinson’s disease (PD), and other neurological disorders. It uses electrodes implanted in the deep structures of the brain, involved in control of movements, and delivering electrical stimulation, generated by an implanted generator, to alleviate PD symptoms. While well-established and effective, DBS has several limitations. The clinical methods use fixed stimulation parameters, which do not adapt to the patient's needs in real time. This mismatch leads to decreased battery life, incomplete control of disease symptoms, and stimulation-induced side effects. Additionally, the process of parameter selection is long and manual, increasing the cost and lowering availability of this treatment.

Adaptive - or closed-loop - DBS (aDBS) is an extension of this method, using the measurement of the electrical activity of the brain to adjust the stimulation parameters in real time, responding to patient’s needs and changes in symptom severity. Several aDBS methods have been tested clinically with promising results but these studies are limited by low number of subjects and short duration of the experiment. This lack of data prevents us from drawing conclusions on what aDBS method offers the highest benefits while minimising the risks.

Computational modelling of deep brain stimulation allows for fast and reproducible testing of the proposed methods with no risk to the patients and additional possibility to investigate mechanisms of DBS action. While many computational studies of DBS have been published to date, they usually focus on a single method applied to a single model. Results from these studies are good indicators of the directions that the field can evolve towards, but they are limited by the assumptions present in the models. This calls for a systematic solution, enabling comparison of the efficacy and efficiency of the proposed methods, to ultimately make recommendations to the DBS device manufacturers, and the clinicians working in the field.

The aim of this project was to develop a systematic testing environment in which the proposed closed-loop algorithms can be tested against a range of computational models, to establish their relative strengths and weaknesses, and to use the results from this testing to propose novel aDBS algorithms.

As a result of this project, an adaptive DBS algorithm method has been implemented in a computational model of the brain structures implicated in Parkinson’s disease. The computational model has been developed in the direction of greater modularity, to further the goal of creating a systematic testing and verification environment. Additionally, conventional aDBS methods have been implemented in an embedded system, used in a study exploring effects of closed-loop DBS in rat model of Parkinson’s disease, creating a platform for future practical testing of the proposed methods, and proving that rats are a viable model organism for aDBS development.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

Throughout the project, significant advancements were made in the design and implementation of novel closed-loop DBS controllers. A comprehensive literature review was conducted, resulting in a review article (in preparation) that synthesises current aDBS approaches and identifies future research directions. This work guided the selection of adaptive control algorithms, with Iterative Feedback Tuning (IFT) being chosen for its real-time adaptation suitability. The IFT algorithm was then successfully integrated into the computational model for DBS, and preliminary results were presented at major conferences like Neural Engineering and the Society for Neuroscience, receiving valuable feedback from the scientific community.

The development of a systematic controller verification environment involved enriching the computational model of the cortico-basal ganglia loop with additional anatomical and physiological details and a configuration interface that facilitates and streamlines parameter exploration and controller testing. Ensuring the repeatability and reproducibility of results was a priority, with rigorous testing and introduction of parallel computing techniques to significantly reduce simulation times. To enhance portability and reproducibility, the model was packaged into a Docker container.

Hardware implementation efforts focused on closed-loop stimulation algorithms in freely-moving hemiparkinsonian rats, in collaboration with Dr. Judith Evers. This led to several conference presentations and a journal publication. Real-time signal processing capabilities were implemented on the MultiChannel Systems W2100 platform, allowing for precise modulation of stimulation parameters. The study design incorporated intensity-matched sham stimulation to rigorously evaluate the efficacy of closed-loop algorithms. A user-friendly graphical interface was developed to facilitate experimental setup and monitoring, and logistical challenges were addressed by gaining facility access and setting up remote monitoring.

Effective project management included independent goal setting, time management, and regular progress discussions with Prof. Lowery. Financial management involved requisitioning necessary equipment and handling conference registrations. The project also saw successful integration and collaboration within the department, including supervision of Master students and assistance to PhD students. Dissemination of research findings was achieved through presentations at various international conferences (EMBC 2022, NER 2023, SfN 2023) and a journal publication (Neuromodulation), with clear acknowledgment of EU funding. While initial outreach activities were not implemented due to remote work and personal leave, participation in the Kytos Seminar Series provided a platform for a popular science talk on closed-loop DBS. Through these concerted efforts, the project advanced the understanding and implementation of closed-loop DBS, establishing a strong foundation for future research in neuromodulation therapies.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The project’s advancement of the state of the art to-date involves laying foundations for the systematic verification of adaptive DBS controllers. Expanding the capabilities of computational models of basal ganglia, with focus on reproducibility and repeatability, as well as development of capacity to test efficacy and efficiency of the proposed methods in preclinical animal studies is an important step towards the final goal of this project. With additional publications in preparation, we expect to push the state of the art even further, ultimately being able to make recommendations on the applicability of a family of aDBS solutions, based on the patient’s individual needs, as well as the available stimulation/recording devices.

The potential impacts are substantial, promising more effective and personalised DBS therapies for Parkinson's disease. Socio-economically, the project could reduce healthcare costs by improving treatment efficacy and minimising the need for invasive procedures. The wider societal implications include enhanced quality of life for patients, reduced caregiver burden, and significant advancements in neuroengineering research, fostering interdisciplinary collaborations and driving future innovations in neuromodulation therapies.

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