How Does Deep Brain Stimulation Work? Present Understanding and Future Questions

Emory University, Atlanta, Georgia, United States
Journal of Clinical Neurophysiology (Impact Factor: 1.43). 01/2004; 21(1):40-50. DOI: 10.1097/00004691-200401000-00006
Source: PubMed

ABSTRACT High-frequency deep brain stimulation (DBS) of the thalamus or basal ganglia represents an effective clinical technique for the treatment of several medically refractory movement disorders (e.g., Parkinson's disease, essential tremor, and dystonia). In addition, new clinical applications of DBS for other neurologic and psychiatric disorders (e.g., epilepsy and obsessive-compulsive disorder) have been vaulted forward. Although DBS has been effective in the treatment of movement disorders and is rapidly being explored for the treatment of other neurologic disorders, the scientific understanding of its mechanisms of action remains unclear and continues to be debated in the scientific community. Optimization of DBS technology for present and future therapeutic applications will depend on identification of the therapeutic mechanism(s) of action. The goal of this review is to address the present knowledge of the effects of high frequency stimulation within the central nervous system and comment on the functional implications of this knowledge for uncovering the mechanism(s) of DBS. Four general hypotheses have been developed to explain the mechanism(s) of DBS: depolarization blockade, synaptic inhibition, synaptic depression, and stimulation-induced modulation of pathologic network activity. Using the results from microdialysis, neural recording, functional imaging, and neural modeling experiments, the authors address the main hypotheses and attempt to reconcile what have been considered conflicting results from different research modalities.

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    • "The cellular response of single neurons to extracellular electrical fields has been well characterized over short time scales (Smith and Grace, 1992; Benazzouz et al., 2000; Hashimoto et al., 2003; Maurice et al., 2003; Kita et al., 2005; Miocinovic et al., 2006). It is known that excitation of efferent axons or fibers of passage near the site of stimulation results in network changes in neurotransmission and electrical activity (Grill et al., 2004; McIntyre et al., 2004a,b; Johnson et al., 2008; McIntyre and Hahn, 2010; Shah et al., 2010). Furthermore, functional and metabolic imaging studies have shown that successful treatment of neurologic and psychiatric disorders is associated with metabolic normalization in proximal and distal regions of the brain (Mayberg et al., 2000, 2005; Mure et al., 2011). "
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    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.
    Frontiers in Neuroscience 06/2014; 8(8):169. DOI:10.3389/fnins.2014.00169 · 3.66 Impact Factor
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    • "For example, structured patterns of stimulation may be more effective for some neuropsychiatric disorders, while a noisier stimulus, similar to that used in electroconvulsive therapy, may be more appropriate for other disorders. In support of this postulate, the frequencies used in deep brain stimulation, even in the same region, vary with the disease being treated (McIntyre et al., 2004). Stimulation of the internal capsule and adjacent ventral striatum are effective for treating obsessive-compulsive disorder only at frequencies between 100 and 130 Hz (Greenberg et al., 2010). "
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    ABSTRACT: When exposed to rewarding stimuli, only some animals develop persistent craving. Others are resilient and do not. How the activity of neural populations relates to the development of persistent craving behavior is not fully understood. Previous computational studies suggest that synchrony helps a network embed certain patterns of activity, although the role of synchrony in reward-dependent learning has been less studied. Increased synchrony has been reported as a marker for both susceptibility and resilience to developing persistent craving. Here we use computational simulations to study the effect of reward salience on the ability of synchronous input to embed a new pattern of activity into a neural population. Our main finding is that weak stimulus-reward correlations can facilitate the short-term repetition of a pattern of neural activity, while blocking long-term embedding of that pattern. Interestingly, synchrony did not have this dual effect on all patterns, which suggests that synchrony is more effective at embedding some patterns of activity than others. Our results demonstrate that synchrony can have opposing effects in networks sensitive to the correlation structure of their inputs, in this case the correlation between stimulus and reward. This work contributes to an understanding of the interplay between synchrony and reward-dependent plasticity.
    Frontiers in Neural Circuits 04/2014; 8:44. DOI:10.3389/fncir.2014.00044 · 3.60 Impact Factor
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    • "According to the modeling study (McIntyre et al., 2004), subthreshold HFS suppressed intrinsic firings in the cell bodies, while suprathreshold HFS generated efferent outputs at the stimulus frequency in the axon without representative activation of the cell bodies. Thus, although stimulation may fail to activate cell bodies of GPi neurons due to strong GABAergic inhibition, it can still excite the efferent axons and provide inhibitory inputs to the thalamus at the stimulus frequency. "
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    ABSTRACT: Applying high-frequency stimulation (HFS) to deep brain structure, known as deep brain stimulation (DBS), has now been recognized an effective therapeutic option for a wide range of neurological and psychiatric disorders. DBS targeting the basal ganglia thalamo-cortical loop, especially the internal segment of the globus pallidus (GPi), subthalamic nucleus (STN) and thalamus, has been widely employed as a successful surgical therapy for movement disorders, such as Parkinson's disease, dystonia and tremor. However, the neurophysiological mechanism underling the action of DBS remains unclear and is still under debate: does DBS inhibit or excite local neuronal elements? In this review, we will examine this question and propose the alternative interpretation: DBS dissociates inputs and outputs, resulting in disruption of abnormal signal transmission.
    Frontiers in Systems Neuroscience 03/2014; 8:33. DOI:10.3389/fnsys.2014.00033
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