deep_brain_stimulation

Deep brain stimulation (DBS)

Deep brain stimulation (DBS): Neurosurgical procedure that uses electrical stimulation through surgically implanted electrodes to produce neuromodulation of electrical signals for the purpose of symptom improvement. For many indications, DBS has supplanted ablative procedures in the brain.


Deep brain stimulation (DBS) is a neurosurgical procedure introduced in 1987, involving the implantation of a medical device called a neurostimulator (sometimes referred to as a 'brain pacemaker'), which sends electrical impulses, through implanted electrodes.

The system consists of a lead that is implanted into a specific deep brain target. The lead is connected to an implantable pulse generator (IPG), which is the power source of the system. The lead and the IPG are connected by an extension wire that is tunneled under the skin between both of them. This system is used to chronically stimulate the deep brain target by delivering a high-frequency current to this target.

Deep brain stimulation of different targets has been shown to drastically improve symptoms of a variety of neurological conditions. However, the occurrence of disabling side effects may limit the ability to deliver adequate amounts of current necessary to reach the maximal benefit. Computed models have suggested that reduction in electrode size and the ability to provide directional lead stimulation could increase the efficacy of such therapies 1).


Deep brain stimulation surgery, create an opportunity to conduct cognitive or behavioral experiments during the acquisition of invasive neurophysiology. Optimal design and implementation of intraoperative behavioral experiments require consideration of stimulus presentation, time and surgical constraints. Tekriwal et al., describe the use of a modular, inexpensive system that implements a decision-making paradigm, designed to overcome challenges associated with the operative environment.

They created an auditory, two-alternative forced choice (2AFC) task for intraoperative use. Behavioral responses were acquired using an Arduino based single-hand held joystick controller equipped with a 3-axis accelerometer, and two button presses, capable of sampling at 2 kHz. We include designs for all task relevant code, 3D printed components, and Arduino pin-out diagram.

They demonstrated feasibility both in and out of the operating room with behavioral results represented by three healthy control subjects and two Parkinson's disease subjects undergoing deep brain stimulator implantation. Psychometric assessment of performance indicated that the subjects could detect, interpret and respond accurately to the task stimuli using the joystick controller. We also demonstrate, using intraoperative neurophysiology recorded during the task, that the behavioral system described here allows us to examine neural correlates of human behavior.

COMPARISON WITH EXISTING METHODS: For low cost and minimal effort, any clinical neural recording system can be adapted for intraoperative behavioral testing with our experimental setup.

CONCLUSION: Our system will enable clinicians and basic scientists to conduct intraoperative awake and behaving electrophysiologic studies in humans 2).

see Deep Brain Stimulation Targets.

Research has demonstrated that multi-target DBS shows some benefits over single target DBS.

Scelzo et al. report a retrospective case series of women, followed in two DBS centers, who became pregnant and went on to give birth to a child while suffering from disabling MD or psychiatric diseases [Parkinson's disease, dystonia, Tourette's syndrome (TS), Obsessive Compulsive Disorder (OCD)] treated by DBS. Clinical status, complications and management before, during, and after pregnancy are reported. Two illustrative cases are described in greater detail.

DBS improved motor and behavioral disorders in all patients and allowed reduction in, or even total interruption of disease-specific medication during pregnancy. With the exception of the spontaneous early abortion of one fetus in a twin pregnancy, all pregnancies were uneventful in terms of obstetric and pediatric management. DBS parameters were adjusted in five patients in order to limit clinical worsening during pregnancy. Implanted material limited breast-feeding in one patient because of local pain at submammal stimulator site and led to local discomfort related to stretching of the cable with increasing belly size in another patient whose stimulator was implanted in the abdominal wall.

Not only is it safe for young women with MD, TS and OCD who have a DBS-System implanted to become pregnant and give birth to a baby but DBS seems to be the key to becoming pregnant, having children, and thus greatly improves quality of life 3).

Recent developments in the postoperative evaluation of deep brain stimulation surgery on the group level warrant the detection of achieved electrode positions based on postoperative imaging. Computed tomography (CT) is a frequently used imaging modality, but because of its idiosyncrasies (high spatial accuracy at low soft tissue resolution), it has not been sufficient for the parallel determination of electrode position and details of the surrounding brain anatomy (nuclei). The common solution is rigid fusion of CT images and magnetic resonance (MR) images, which have much better soft tissue contrast and allow accurate normalization into template spaces. Here, we explored a deep-learning approach to directly relate positions (usually the lead position) in postoperative CT images to the native anatomy of the midbrain and group space.

Materials and methods: Deep learning is used to create derived tissue contrasts (white matter, gray matter, cerebrospinal fluid, brainstem nuclei) based on the CT image; that is, a convolution neural network (CNN) takes solely the raw CT image as input and outputs several tissue probability maps. The ground truth is based on coregistrations with MR contrasts. The tissue probability maps are then used to either rigidly coregister or normalize the CT image in a deformable way to group space. The CNN was trained in 220 patients and tested in a set of 80 patients.

Results: Rigorous validation of such an approach is difficult because of the lack of ground truth. We examined the agreements between the classical and proposed approaches and considered the spread of implantation locations across a group of identically implanted subjects, which serves as an indicator of the accuracy of the lead localization procedure. The proposed procedure agrees well with current magnetic resonance imaging-based techniques, and the spread is comparable or even lower.

Postoperative CT imaging alone is sufficient for accurate localization of the midbrain nuclei and normalization to the group space. In the context of group analysis, it seems sufficient to have a single postoperative CT image of good quality for inclusion. The proposed approach will allow researchers and clinicians to include cases that were not previously suitable for analysis 4).

Harmsen et al. assessed the state of DBS-related research by analyzing the DBS literature as well as active studies sponsored by the National Institutes of Health (NIH) or German Research Foundation (Deutsche Forschungsgemeinschaft [DFG]).In total, 8,974 publications, 172 active NIH-funded projects, and 34 active DFG projects were identified. Records spanned 52 different disorders across 31 distinct brain targets and showed a shift toward studies examining conditions other than movement disorders. Most published works involved human research (80.6% of published studies), of which 10.2% were identified as clinical trials. Increasingly, studies focused on imaging or electrophysiological changes associated with DBS (69.8% NIH-active and 70.6% DFG-active vs. 25.8% published) or developing new stimulation techniques and adaptive technologies (37.8% NIH-active and 17.6% DFG-active vs. 6.5% published).

This overview in 2022 of past and present DBS-related studies provides insight into the status of DBS research and what we can anticipate in the future concerning new indications, improved/novel target selection and stimulation paradigms, closed-loop technology, and a better understanding of the mechanisms of action of DBS 5).

see Deep Brain Stimulation case series

A 79-year-old woman with a history of coarse tremors effectively managed with deep brain stimulation presented with multiple intracranial metastases from a newly diagnosed lung cancer and was referred for whole-brain radiation therapy. She was treated with a German helmet technique to a total dose of 30 Gy in 10 fractions using 6 MV photons via opposed lateral fields with the neurostimulator turned off prior to delivery of each fraction. The patient tolerated the treatment well with no acute complications and no apparent change in the functionality of her neurostimulator device or effect on her underlying neuromuscular disorder. This represents the first reported case of the safe delivery of whole-brain radiation therapy in a patient with an implanted neurostimulator device. In cases such as this, neurosurgeons and radiation oncologists should have discussions with patients about the risks of brain injury, device malfunction or failure of the device, and plans for rigorous testing of the device before and after radiation therapy 6).


1)
Pollo C, Kaelin-Lang A, Oertel MF, Stieglitz L, Taub E, Fuhr P, Lozano AM, Raabe A, Schüpbach M. Directional deep brain stimulation: an intraoperative double-blind pilot study. Brain. 2014 Jul;137(Pt 7):2015-26. doi: 10.1093/brain/awu102. Epub 2014 May 19. PubMed PMID: 24844728.
2)
Tekriwal A, Felsen G, Thompson JA. Modular auditory decision-making behavioral task designed for intraoperative use in humans. J Neurosci Methods. 2018 May 7. pii: S0165-0270(18)30134-1. doi: 10.1016/j.jneumeth.2018.05.004. [Epub ahead of print] PubMed PMID: 29746889.
3)
Scelzo E, Mehrkens JH, Bötzel K, Krack P, Mendes A, Chabardès S, Polosan M, Seigneuret E, Moro E, Fraix V. Deep Brain Stimulation during Pregnancy and Delivery: Experience from a Series of “DBS Babies”. Front Neurol. 2015 Sep 1;6:191. doi: 10.3389/fneur.2015.00191. eCollection 2015. PubMed PMID: 26388833.
4)
Reisert M, Sajonz BEA, Brugger TS, Reinacher PC, Russe MF, Kellner E, Skibbe H, Coenen VA. Where Position Matters-Deep-Learning-Driven Normalization and Coregistration of Computed Tomography in the Postoperative Analysis of Deep Brain Stimulation. Neuromodulation. 2022 Nov 21:S1094-7159(22)01330-7. doi: 10.1016/j.neurom.2022.10.042. Epub ahead of print. PMID: 36424266.
5)
Harmsen IE, Wolff Fernandes F, Krauss JK, Lozano AM. Where Are We with Deep Brain Stimulation? A Review of Scientific Publications and Ongoing Research. Stereotact Funct Neurosurg. 2022 Feb 1:1-14. doi: 10.1159/000521372. Epub ahead of print. PMID: 35104819.
6)
Kotecha R, Berriochoa CA, Murphy ES, Machado AG, Chao ST, Suh JH, Stephans KL. Report of whole-brain radiation therapy in a patient with an implanted deep brain stimulator: important neurosurgical considerations and radiotherapy practice principles. J Neurosurg. 2016 Apr;124(4):966-70. doi: 10.3171/2015.2.JNS142951. Epub 2015 Aug 28. PubMed PMID: 26315009.
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