Abstract
Current strategies for optimizing deep brain stimulation (DBS) therapy involve multiple postoperative visits. During each visit, stimulation parameters are adjusted until desired therapeutic effects are achieved and adverse effects are minimized. However, the efficacy of these therapeutic parameters may decline with time due at least in part to disease progression, interactions between the host environment and the electrode, and lead migration. As such, development of closed-loop control systems that can respond to changing neurochemical environments, tailoring DBS therapy to individual patients, is paramount for improving the therapeutic efficacy of DBS. Evidence obtained using electrophysiology and imaging techniques in both animals and humans suggests that DBS works by modulating neural network activity. Recently, animal studies have shown that stimulation-evoked changes in neurotransmitter release that mirror normal physiology are associated with the therapeutic benefits of DBS. Therefore, to fully understand the neurophysiology of DBS and optimize its efficacy, it may be necessary to look beyond conventional electrophysiological analyses and characterize the neurochemical effects of therapeutic and non-therapeutic stimulation. By combining electrochemical monitoring and mathematical modeling techniques, we can potentially replace the trial-and-error process used in clinical programming with deterministic approaches that help attain optimal and stable neurochemical profiles. In this manuscript, we summarize the current understanding of electrophysiological and electrochemical processing for control of neuromodulation therapies. Additionally, we describe a proof-of-principle closed-loop controller that characterizes DBS-evoked dopamine changes to adjust stimulation parameters in a rodent model of DBS. The work described herein represents the initial steps toward achieving a “smart” neuroprosthetic system for treatment of neurologic and psychiatric disorders.
Highlights
Neurologic and psychiatric disorders can be characterized by motor, behavioral, cognitive, affective, or perceptual traits that affect how individuals move, feel, think, and behave (Benabid et al, 2005; Nemeroff, 2007; Williams and Okun, 2013)
EXPERIMENTAL PARADIGM To quantify the dynamics of stimulation-evoked dopamine release, recording fast scan cyclic voltammetry (FSCV) carbon fiber microelectrode (CFM) and bipolar deep brain stimulation (DBS) macroelectrodes www.frontiersin.org were implanted into the striatum and medial forebrain bundle (MFB), respectively, in four anesthetized rats
Our preliminary results in four anesthetized rats suggest that mathematical models can be used to describe the relationships between stimulation-evoked extracellular dopamine responses and DBS parameters (R2 = 0.8). These results show that adjusting stimulation parameter intensity can mod
Summary
Neurologic and psychiatric disorders can be characterized by motor, behavioral, cognitive, affective, or perceptual traits that affect how individuals move, feel, think, and behave (Benabid et al, 2005; Nemeroff, 2007; Williams and Okun, 2013). LOCAL FIELD POTENTIALS Local field potential (LFP) analysis is an electrophysiological technique for detecting changes in brain activity that offers great potential for understanding the network effects of DBS (Tsang et al, 2012; Priori et al, 2013) This technique is capable of recording chronic electrical activity directly from single and multiple neural units using micro and macro electrodes implanted within the nucleus of interest (Bronte-Stewart et al, 2009; Giannicola et al, 2012). EXPERIMENTAL PARADIGM To quantify the dynamics of stimulation-evoked dopamine release, recording FSCV CFM and bipolar DBS macroelectrodes www.frontiersin.org were implanted into the striatum and MFB, respectively, in four anesthetized rats. To further understand the network effects of DBS and optimize the therapeutic efficacy of stimulation, it may be necessary to combine electrophysiological (e.g., LFP, ECoG) and neurochemical feedback signals
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