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William S. Gibson, Aaron E. Rusheen, Yoonbae Oh, Myung-Ho In, Krzysztof R. Gorny, Joel P. Felmlee, Bryan T. Klassen, Sung Jun Jung, Hoon-Ki Min, Kendall H. Lee, and Hang Joon Jo


Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an established neurosurgical treatment for the motor symptoms of Parkinson’s disease (PD). While often highly effective, DBS does not always yield optimal therapeutic outcomes, and stimulation-induced adverse effects, including paresthesia, muscle contractions, and nausea/lightheadedness, commonly occur and can limit the efficacy of stimulation. Currently, objective metrics do not exist for monitoring neural changes associated with stimulation-induced therapeutic and adverse effects.


In the present study, the authors combined intraoperative functional MRI (fMRI) with STN DBS in 20 patients with PD to test the hypothesis that stimulation-induced blood oxygen level–dependent signals contained predictive information concerning the therapeutic and adverse effects of stimulation.


As expected, DBS resulted in blood oxygen level–dependent activation in myriad motor regions, including the primary motor cortex, caudate, putamen, thalamus, midbrain, and cerebellum. Across the patients, DBS-induced improvements in contralateral Unified Parkinson’s Disease Rating Scale tremor subscores correlated with activation of thalamic, brainstem, and cerebellar regions. In addition, improvements in rigidity and bradykinesia subscores correlated with activation of the primary motor cortex. Finally, activation of specific sensorimotor-related subregions correlated with the presence of DBS-induced adverse effects, including paresthesia and nausea (cerebellar cortex, sensorimotor cortex) and unwanted muscle contractions (caudate and putamen).


These results suggest that DBS-induced activation patterns revealed by fMRI contain predictive information with respect to the therapeutic and adverse effects of DBS. The use of fMRI in combination with DBS therefore may hold translational potential to guide and improve clinical stimulator optimization in patients.

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J. Blair Price, Aaron E. Rusheen, Abhijeet S. Barath, Juan M. Rojas Cabrera, Hojin Shin, Su-Youne Chang, Christopher J. Kimble, Kevin E. Bennet, Charles D. Blaha, Kendall H. Lee, and Yoonbae Oh

The development of closed-loop deep brain stimulation (DBS) systems represents a significant opportunity for innovation in the clinical application of neurostimulation therapies. Despite the highly dynamic nature of neurological diseases, open-loop DBS applications are incapable of modifying parameters in real time to react to fluctuations in disease states. Thus, current practice for the designation of stimulation parameters, such as duration, amplitude, and pulse frequency, is an algorithmic process. Ideal stimulation parameters are highly individualized and must reflect both the specific disease presentation and the unique pathophysiology presented by the individual. Stimulation parameters currently require a lengthy trial-and-error process to achieve the maximal therapeutic effect and can only be modified during clinical visits. The major impediment to the development of automated, adaptive closed-loop systems involves the selection of highly specific disease-related biomarkers to provide feedback for the stimulation platform. This review explores the disease relevance of neurochemical and electrophysiological biomarkers for the development of closed-loop neurostimulation technologies. Electrophysiological biomarkers, such as local field potentials, have been used to monitor disease states. Real-time measurement of neurochemical substances may be similarly useful for disease characterization. Thus, the introduction of measurable neurochemical analytes has significantly expanded biomarker options for feedback-sensitive neuromodulation systems. The potential use of biomarker monitoring to advance neurostimulation approaches for treatment of Parkinson’s disease, essential tremor, epilepsy, Tourette syndrome, obsessive-compulsive disorder, chronic pain, and depression is examined. Further, challenges and advances in the development of closed-loop neurostimulation technology are reviewed, as well as opportunities for next-generation closed-loop platforms.