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.

Clinical deep brain stimulation (DBS) systems can be programmed with thousands of different stimulation parameter combinations (e.g. electrode contact(s), voltage, pulse width, frequency). Our goal was to develop novel computational tools to characterize the effects of stimulation parameter adjustment for DBS. The volume of tissue activated (VTA) represents a metric used to estimate the spatial extent of DBS for a given parameter setting. Traditional methods for calculating the VTA rely on activation function (AF)-based approaches and tend to overestimate the neural response when stimulation is applied through multiple electrode contacts. Therefore, we created a new method for VTA calculation that relied on artificial neural networks (ANNs). The ANN-based predictor provides more accurate descriptions of the spatial spread of activation compared to AF-based approaches for monopolar stimulation. In addition, the ANN was able to accurately estimate the VTA in response to multi-contact electrode configurations. The ANN-based approach may represent a useful method for fast computation of the VTA in situations with limited computational resources, such as a clinical DBS programming application on a tablet computer.

Objective: The underlying hypothesis of our work is that specific clinical neuropsychiatric benefits can be achieved by selective activation of specific axonal pathways during deep brain stimulation (DBS). As such, the goal of this study was to develop a method for identifying axonal pathways whose activation is most likely necessary for achieving therapeutic benefits during DBS.Experimental design: Our approach combined clinical data, diffusion tensor tractography, and computer models of patient‐specific neurostimulation to identify particular axonal pathways activated by DBS and determine their correlations with individual clinical outcome measures. We used this method to evaluate a cohort of seven treatment‐resistant depression patients treated with DBS of the ventral anterior internal capsule and ventral striatum (VC/VS).Principal observations: Clinical responders exhibited five axonal pathways that were consistently activated by DBS. All five pathways coursed lateral and medial to the VS or dorsal and lateral to the nucleus accumbens; however, details of their specific trajectories differed. Similarly, one common pathway was identified across nonresponders.Conclusions: Our method and preliminary results provide important background for studies aiming to expand scientific characterization of neural circuitry associated with specific psychiatric outcomes from DBS. Furthermore, identification of pathways linked to therapeutic benefit provides opportunities to improve clinical selection of surgical targets and stimulation settings for DBS devices. Hum Brain Mapp, 2012.

Deep brain stimulation (DBS) has recently emerged as a potential treatment for medically intractable neuropsychiatric disorders. Pilot clinical studies with encouraging results have been performed with DBS of the ventral anterior internal capsule (VAIC) and subgenual cingulate white matter (Cg25WM) for the treatment of obsessive-compulsive disorder and depression. However, little is known about the underlying response of individual neurons, or the networks they are connected to, when DBS is applied to the VAIC or Cg25WM. This review summarizes current understanding of the response of axons to DBS, and discusses the general brain network architectures thought to underlie neuropsychiatric disorders. We also employ diffusion tensor imaging tractography to better understand the axonal trajectories surrounding DBS electrodes implanted in the VAIC or Cg25WM. Finally, we attempt to reconcile various data sets by presenting generalized hypotheses on potential therapeutic mechanisms of DBS for neuropsychiatric disease.

Functional magnetic resonance imaging (fMRI) is a powerful method for identifying in vivo network activation evoked by deep brain stimulation (DBS). Identify the global neural circuitry effect of subthalamic nucleus (STN) DBS in nonhuman primates (NHP). An in-house developed MR image-guided stereotactic targeting system delivered a mini-DBS stimulating electrode, and blood oxygenation level-dependent (BOLD) activation during STN DBS in healthy NHP was measured by combining fMRI with a normalized functional activation map and general linear modeling. STN DBS significantly increased BOLD activation in the sensorimotor cortex, supplementary motor area, caudate nucleus, pedunculopontine nucleus, cingulate, insular cortex, and cerebellum (FDR < 0.001). Our results demonstrate that STN DBS evokes neural network grouping within the motor network and the basal ganglia. Taken together, these data highlight the importance and specificity of neural circuitry activation patterns and functional connectivity.