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Despite its wide use, DBS presents numerous challenges for both clinicians and engineers. One challenge is the design of novel, more efficient DBS therapies, which are hampered by the lack of complete understanding about the cellular mechanisms of therapeutic DBS. Another challenge is the existence of redundancy in clinical outcomes, that is, different DBS programs can result in similar clinical benefits but very little information (e.g., predictive models, longitudinal data, metrics, etc.) is available to select one program over another. Finally, there is high variability in patients\u2019 responses to DBS, which forces clinicians to carefully adjust the stimulation settings to each patient via lengthy programming sessions. Researchers in neural engineering and systems biology have been tackling these challenges over the past few years with the specific goal of developing novel DBS therapies, design methodologies, and computational tools that optimize the therapeutic effects of DBS in each patient. Furthermore, efforts are being made to automatically adapt the DBS treatment to the fluctuations of disease symptoms. A review of the quantitative approaches currently available for the treatment of Parkinson\u2019s disease is presented here with an emphasis on the contributions that systems theoretical approaches have provided to understand the global dynamics of complex neuronal circuits in the brain under DBS.<\/jats:p><jats:p>This article is categorized under:\n<jats:list list-type=\"simple\">\n<jats:list-item><jats:p>Translational, Genomic, and Systems Medicine &gt; Therapeutic Methods<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>Analytical and Computational Methods &gt; Computational Methods<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>Analytical and Computational Methods &gt; Dynamical Methods<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>Physiology &gt; Mammalian Physiology in Health and Disease<\/jats:p><\/jats:list-item>\n<\/jats:list>\n<\/jats:p>","DOI":"10.1002\/wsbm.1421","type":"journal-article","created":{"date-parts":[[2018,3,20]],"date-time":"2018-03-20T19:50:50Z","timestamp":1521575450000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Systems approaches to optimizing deep brain stimulation therapies in Parkinson\u2019s disease"],"prefix":"10.1002","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2133-9471","authenticated-orcid":false,"given":"Sabato","family":"Santaniello","sequence":"first","affiliation":[{"name":"Biomedical Engineering Department and CT Institute for the Brain and Cognitive Sciences University of Connecticut  Storrs Connecticut"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John T.","family":"Gale","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery Emory University School of Medicine  Atlanta 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