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Systems approaches to optimizing deep brain stimulation therapies in Parkinson’s disease

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Over the last 30 years, deep brain stimulation (DBS) has been used to treat chronic neurological diseases like dystonia, obsessive–compulsive disorders, essential tremor, Parkinson’s disease, and more recently, dementias, depression, cognitive disorders, and epilepsy. 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’ 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’s 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. This article is categorized under: Translational, Genomic, and Systems Medicine > Therapeutic Methods Analytical and Computational Methods > Computational Methods Analytical and Computational Methods > Dynamical Methods Physiology > Mammalian Physiology in Health and Disease
(a–b) Classic model of the basal ganglia in healthy (a) and parkinsonian (b) conditions. Cortex provides excitatory glutamatergic projections to the putamen (a part of striatum), which sends GABAergic inhibitory projections to the GPi and the SNr through two pathways: the “direct pathway” (putamen‐GPi/SNr) and the “indirect pathway” (putamen‐GPe‐STN‐GPi/SNr). Dopamine from the SNc facilitates striatal neurons involved in the direct pathway and inhibits those along the indirect pathway. In Parkinson’s disease (b), dopamine depletion causes hyper‐activity along the indirect pathway and hypo‐activity along the direct pathway. Green, red, and black arrows denote excitatory (i.e., glutamatergic), inhibitory (i.e., GABAergic), and dopaminergic projections, respectively. Thick and thin‐dashed arrows indicate hyper‐ and hypo‐activity, respectively. Abbreviations: VL, ventrolateral; GPe (GPi), external (internal) globus pallidus; STN, subthalamic nucleus; SNr (SNc), substantial nigra pars reticulata (pars compacta)
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Schematic of the chronic deep brain stimulation implant and devices for the treatment of Parkinson’s disease. An electrode is surgically implanted either in the subthalamic nucleus (STN) or the internal globus pallidus (GPi) and connected to an implanted pulse generator through subcutaneous wires. The pulse generator is programmed to deliver charge‐balanced, voltage‐controlled electric pulses. Typical duration of the anodic part of each pulse is 60–90 μs and pulse amplitude is 2–3 V. (Reprinted with permission from Hickey and Stacy () under the Creative Commons Attribution License (CC BY))
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Electrical equivalent circuit model of neuron with sodium (Na) and potassium (K) ion‐channels and leakage current (L)
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Example of integration of presurgical imaging and multicompartment modeling to estimate the volume of activated tissue in a Parkinson’s disease (PD) patient. Solution proposed by McIntyre, Chaturvedi, Shamir, and Lempka (). (a–b) Stereotactic coordinate system relative to the patient imaging data (a) and atlas representations of anatomical nuclei. Yellow and green volumes are the thalamus and subthalamic nucleus (STN), respectively. The blue line represents the planned surgical trajectory of the DBS electrode. (c–d) Stereotactic location (c) and final placement (d) of the deep brain stimulation (DBS) electrode, respectively. Yellow, green, and red dots indicate thalamic cells, STN cells, and substantia nigra pars reticulata (SNr) cells, respectively. Purple cylinders represent the electrode contacts. (e) Volume of tissue activated during therapeutic DBS (red volume). (Reprinted with permission from McIntyre et al. (). Copyright 2015 Elsevier Inc.)
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Schematic of point‐process model with a generalized linear structure and dependency on past spiking history and an exogenous sensory stimulus
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Example of phase response curve (PRC) estimated from a population of STN neuron models. (a) Spike density preceding (solid blue) and following (green) a single deep brain stimulation (DBS) pulse. Each spike density is fit separately with a 34 Hz sine wave (dashed lines). (b) A plot of the population‐average phase change vs. the phase at the DBS pulse onset. Gray dots indicate the intensity of neural oscillations in the band 13–30 Hz at the time of each DBS pulse. (Reprinted with permission from Holt and Netoff (). Copyright 2014 Springer Nature)
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Physiology > Mammalian Physiology in Health and Disease
Analytical and Computational Methods > Dynamical Methods
Analytical and Computational Methods > Computational Methods
Translational, Genomic, and Systems Medicine > Therapeutic Methods

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