New Frontiers for Deep Brain Stimulation: Directionality, Sensing Technologies, Remote Programming, Robotic Stereotactic Assistance, Asleep Procedures, and Connectomics

Abstract

Over the last few years, while expanding its clinical indications from movement disorders to epilepsy and psychiatry, the field of deep brain stimulation (DBS) has seen significant innovations. Hardware developments have introduced directional leads to stimulate specific brain targets and sensing electrodes to determine optimal settings via feedback from local field potentials. In addition, variable-frequency stimulation and asynchronous high-frequency pulse trains have introduced new programming paradigms to efficiently desynchronize pathological neural circuitry and regulate dysfunctional brain networks not responsive to conventional settings. Overall, these innovations have provided clinicians with more anatomically accurate programming and closed-looped feedback to identify optimal strategies for neuromodulation. Simultaneously, software developments have simplified programming algorithms, introduced platforms for DBS remote management via telemedicine, and tools for estimating the volume of tissue activated within and outside the DBS targets. Finally, the surgical accuracy has improved thanks to intraoperative magnetic resonance or computerized tomography guidance, network-based imaging for DBS planning and targeting, and robotic-assisted surgery for ultra-accurate, millimetric lead placement. These technological and imaging advances have collectively optimized DBS outcomes and allowed "asleep" DBS procedures. Still, the short- and long-term outcomes of different implantable devices, surgical techniques, and asleep vs. awake procedures remain to be clarified. This expert review summarizes and critically discusses these recent innovations and their potential impact on the DBS field.

Keywords: asleep; connectomics; deep brain stimulation; directionality; local field potential; robotic surgery; sensing; telemedicine.

Figure 1

DBS targets in Movement Disorders. (A) Subthalamic Nucleus (STN); (B) Ventral Intermediate Thalamic Nucleus (VIM); (C) Globus Pallidus Pars Interna (GPi).

Figure 2

DBS targets in Epilepsy. (A) Anterior Thalamic Nucleus (ANT); (B) Centromedian Thalamic Nucleus (CM); (C) Hippocampus.

Figure 3

DBS targets in Psychiatry. (A) Ventral Capsule/Ventral Striatum (VC/VS) and Anterior Limb of the Internal Capsule (ALIC); (B) subcallosal cingulate gyrus including Brodmann area 25 (SGC25); (C) Subthalamic Nucleus (STN).

Figure 4

Direct visualization of DBS targets and lead location. Surgical Information Sciences (SIS), provides a patient-specific model of DBS targets using the patient's own enhanced clinical MR image. (A,B) shows alpha maps visualizing the STN and GPi, respectively (red arrows); (C,D) show 3D reconstructions of the intended targets registered with the DBS lead location in respect to the anatomical structures (STN & GPi, respectively).

Figure 5

Abbott's NeuroSphere™ Virtual Clinic. The software platform enables the remote DBS clinician to not only investigate the patient's IPG and run system checks remotely but also to make stimulation therapy changes over the internet.

Figure 6

Local field potential recordings (LFPs). The figure shows the recording from n = 2 subthalamic nuclei in a patient with Parkinson disease. The raw LFP signal of a 10-s recording is presented in the 2 top traces. The frequency spectra are displayed in the 2 bottom graphs. LFPs are recorded and filtered with 8th poles low pass filter, corner frequency at 40 Hz, for stimulus artifact suppression (AlphaDBSvext, Newronika, Italy). The device streams the signal via radiofrequency to the receiver connected to the computer. Beta bursts activities are highlighted in yellow with low and high beta peeks in purple.

Figure 7

Medtronic's Percept™. Examples of use cases of local field potentials from Percept PC™ programming interfaces using exemplary representative data. (A) shows a signal of interest such as average beta power trended over long periods to show fluctuations and times when a correlated symptom might present: “Symptomatic,” periods where a symptom might be absent: “Asymptomatic,” and periods where a patient may be over-treated and at risk of side effects: “Over-Treated,” and (B) shows use of signal viewed in real time to understand how it responds to amplitude titration during in clinic programming, in this example evoking a suppressive effect subsequent to a stimulation increase.

Figure 8

Robotic Surgery assisted by Intraoperative Imaging. The utility of combining intraoperative robotic technology with intraoperative imaging allows for real time evaluation of accuracy of lead placement regarding the preoperative planned trajectory. This allows for adjustment of the cannula trajectory prior to MER recordings or lead placement. (A) Renishaw Neuromate robot. (B) Medtronic O-arm with mock set-up for acquiring intraoperative imaging. (C,D) Intraoperative O-arm image fused with preoperative SWI MRI showing a 1.08 mm lateral deviation from the planned trajectory.