Computer aided Planning and Navigation for Patient-Specific Deep Brain Stimulation

2013 ◽  
Author(s):  
Peter Gemmar ◽  
Florian Bernard ◽  
Andreas Husch ◽  
Pascal Martini ◽  
Klaus-Peter Koch ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Patient Specific ◽  
Computer Aided ◽  
Computer Aided Planning ◽  
Deep Brain

scholarly journals Electrode Location in a Microelectrode Recording-Based Model of the Subthalamic Nucleus Can Predict Motor Improvement After Deep Brain Stimulation for Parkinson’s Disease

2019 ◽  
Vol 9 (3) ◽  
pp. 51 ◽  
Author(s):  
Rens Verhagen ◽  
Lo Bour ◽  
Vincent Odekerken ◽  
Pepijn van den Munckhof ◽  
P. Schuurman ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Subthalamic Nucleus ◽  
Brain Stimulation ◽  
Anatomical Variation ◽  
Patient Specific ◽  
One Year ◽  
Motor Improvement ◽  
Deep Brain

Motor improvement after deep brain stimulation (DBS) in the subthalamic nucleus (STN) may vary substantially between Parkinson’s disease (PD) patients. Research into the relation between improvement and active contact location requires a correction for anatomical variation. We studied the relation between active contact location relative to the neurophysiological STN, estimated by the intraoperative microelectrode recordings (MER-based STN), and contralateral motor improvement after one year. A generic STN shape was transformed to fit onto the stereotactically defined MER sites. The location of 43 electrodes (26 patients), derived from MRI-fused CT images, was expressed relative to this patient-specific MER-based STN. Using regression analyses, the relation between contact location and motor improvement was studied. The regression model that predicts motor improvement based on levodopa effect alone was significantly improved by adding the one-year active contact coordinates (R2 change = 0.176, p = 0.014). In the combined prediction model (adjusted R2 = 0.389, p < 0.001), the largest contribution was made by the mediolateral location of the active contact (standardized beta = 0.490, p = 0.002). With the MER-based STN as a reference, we were able to find a significant relation between active contact location and motor improvement. MER-based STN modeling can be used to complement imaging-based STN models in the application of DBS.

scholarly journals Method for patient-specific finite element modeling and simulation of deep brain stimulation

2008 ◽  
Vol 47 (1) ◽  
pp. 21-28 ◽  
Author(s):  
Mattias Åström ◽  
Ludvic U. Zrinzo ◽  
Stephen Tisch ◽  
Elina Tripoliti ◽  
Marwan I. Hariz ◽  
...  
Keyword(s):  
Finite Element ◽  
Deep Brain Stimulation ◽  
Finite Element Modeling ◽  
Modeling And Simulation ◽  
Brain Stimulation ◽  
Patient Specific ◽  
Element Modeling ◽  
Deep Brain

scholarly journals Evaluation of Automatic Segmentation of Thalamic Nuclei through Clinical Effects Using Directional Deep Brain Stimulation Leads: A Technical Note

2020 ◽  
Vol 10 (9) ◽  
pp. 642
Author(s):  
Marie T. Krüger ◽  
Rebecca Kurtev-Rittstieg ◽  
Georg Kägi ◽  
Yashar Naseri ◽  
Stefan Hägele-Link ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Side Effects ◽  
Brain Stimulation ◽  
Technical Note ◽  
Patient Specific ◽  
Brain Anatomy ◽  
Clinical Effects ◽  
Thalamic Nuclei ◽  
Lead Placement ◽  
Deep Brain

Automatic anatomical segmentation of patients’ anatomical structures and modeling of the volume of tissue activated (VTA) can potentially facilitate trajectory planning and post-operative programming in deep brain stimulation (DBS). We demonstrate an approach to evaluate the accuracy of such software for the ventral intermediate nucleus (VIM) using directional leads. In an essential tremor patient with asymmetrical brain anatomy, lead placement was adjusted according to the suggested segmentation made by the software (Brainlab). Postoperatively, we used directionality to assess lead placement using side effect testing (internal capsule and sensory thalamus). Clinical effects were then compared to the patient-specific visualization and VTA simulation in the GUIDE™ XT software (Boston Scientific). The patient’s asymmetrical anatomy was correctly recognized by the software and matched the clinical results. VTA models matched best for dysarthria (6 out of 6 cases) and sensory hand side effects (5/6), but least for facial side effects (1/6). Best concordance was observed for the modeled current anterior and back spread of the VTA, worst for the current side spread. Automatic anatomical segmentation and VTA models can be valuable tools for DBS planning and programming. Directional DBS leads allow detailed postoperative assessment of the concordance of such image-based simulation and visualization with clinical effects.

scholarly journals Investigation into Deep Brain Stimulation Lead Designs: A Patient-Specific Simulation Study

2016 ◽  
Vol 6 (3) ◽  
pp. 39 ◽  
Author(s):  
Fabiola Alonso ◽  
Malcolm Latorre ◽  
Nathanael Göransson ◽  
Peter Zsigmond ◽  
Karin Wårdell
Keyword(s):  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Simulation Study ◽  
Patient Specific ◽  
Deep Brain

scholarly journals An Assessment of Current Brain Targets for Deep Brain Stimulation Surgery With Susceptibility-Weighted Imaging at 7 Tesla

Neurosurgery ◽  
2010 ◽  
Vol 67 (6) ◽  
pp. 1745-1756 ◽  
Author(s):  
Aviva Abosch ◽  
Essa Yacoub ◽  
Kamil Ugurbil ◽  
Noam Harel
Keyword(s):  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Image Resolution ◽  
7 Tesla ◽  
Patient Specific ◽  
Susceptibility Weighted Imaging ◽  
Target Area ◽  
Electrode Position ◽  
Microelectrode Recording ◽  
Deep Brain

Abstract BACKGROUND: Deep brain stimulation (DBS) surgery is used for treating movement disorders, including Parkinson disease, essential tremor, and dystonia. Successful DBS surgery is critically dependent on precise placement of DBS electrodes into target structures. Frequently, DBS surgery relies on normalized atlas-derived diagrams that are superimposed on patient brain magnetic resonance imaging (MRI) scans, followed by microelectrode recording and macrostimulation to refine the ultimate electrode position. Microelectrode recording carries a risk of hemorrhage and requires active patient participation during surgery. OBJECTIVE: To enhance anatomic imaging for DBS surgery using high-field MRI with the ultimate goal of improving the accuracy of anatomic target selection. METHODS: Using a 7-T MRI scanner combined with an array of acquisition schemes using multiple image contrasts, we obtained high-resolution images of human deep nuclei in healthy subjects. RESULTS: Superior image resolution and contrast obtained at 7 T in vivo using susceptibility-weighted imaging dramatically improved anatomic delineation of DBS targets and allowed the identification of internal architecture within these targets. A patient-specific, 3-dimensional model of each target area was generated on the basis of the acquired images. CONCLUSION: Technical developments in MRI at 7 T have yielded improved anatomic resolution of deep brain structures, thereby holding the promise of improving anatomic-based targeting for DBS surgery. Future study is needed to validate this technique in improving the accuracy of targeting in DBS surgery.

Magnetic resonance imaging techniques for visualization of the subthalamic nucleus

2011 ◽  
Vol 115 (5) ◽  
pp. 971-984 ◽  
Author(s):  
Ellen J. L. Brunenberg ◽  
Bram Platel ◽  
Paul A. M. Hofman ◽  
Bart M. ter Haar Romeny ◽  
Veerle Visser-Vandewalle
Keyword(s):  
Deep Brain Stimulation ◽  
Mr Imaging ◽  
Subthalamic Nucleus ◽  
Brain Stimulation ◽  
Imaging Techniques ◽  
Patient Specific ◽  
Anatomical Landmarks ◽  
Indirect Methods ◽  
Direct Targeting ◽  
Deep Brain

The authors reviewed 70 publications on MR imaging–based targeting techniques for identifying the subthalamic nucleus (STN) for deep brain stimulation in patients with Parkinson disease. Of these 70 publications, 33 presented quantitatively validated results. There is still no consensus on which targeting technique to use for surgery planning; methods vary greatly between centers. Some groups apply indirect methods involving anatomical landmarks, or atlases incorporating anatomical or functional data. Others perform direct visualization on MR imaging, using T2-weighted spin echo or inversion recovery protocols. The combined studies do not offer a straightforward conclusion on the best targeting protocol. Indirect methods are not patient specific, leading to varying results between cases. On the other hand, direct targeting on MR imaging suffers from lack of contrast within the subthalamic region, resulting in a poor delineation of the STN. These deficiencies result in a need for intraoperative adaptation of the original target based on test stimulation with or without microelectrode recording. It is expected that future advances in MR imaging technology will lead to improvements in direct targeting. The use of new MR imaging modalities such as diffusion MR imaging might even lead to the specific identification of the different functional parts of the STN, such as the dorsolateral sensorimotor part, the target for deep brain stimulation.

scholarly journals Patient-Specific Model-Based Investigation of Speech Intelligibility and Movement during Deep Brain Stimulation

2010 ◽  
Vol 88 (4) ◽  
pp. 224-233 ◽  
Author(s):  
Mattias Åström ◽  
Elina Tripoliti ◽  
Marwan I. Hariz ◽  
Ludvic U. Zrinzo ◽  
Irene Martinez-Torres ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Speech Intelligibility ◽  
Specific Model ◽  
Patient Specific ◽  
Model Based ◽  
Deep Brain

scholarly journals Biophysical basis of subthalamic local field potentials recorded from deep brain stimulation electrodes

2018 ◽  
Vol 120 (4) ◽  
pp. 1932-1944 ◽  
Author(s):  
Nicholas Maling ◽  
Scott F. Lempka ◽  
Zack Blumenfeld ◽  
Helen Bronte-Stewart ◽  
Cameron C. McIntyre
Keyword(s):  
Deep Brain Stimulation ◽  
Local Field ◽  
Brain Stimulation ◽  
Local Field Potentials ◽  
Patient Specific ◽  
Beta Activity ◽  
Field Potentials ◽  
Clinical Biomarkers ◽  
Scientific Platform ◽  
Deep Brain

Clinical deep brain stimulation (DBS) technology is evolving to enable chronic recording of local field potentials (LFPs) that represent electrophysiological biomarkers of the underlying disease state. However, little is known about the biophysical basis of LFPs, or how the patient’s unique brain anatomy and electrode placement impact the recordings. Therefore, we developed a patient-specific computational framework to analyze LFP recordings within a clinical DBS context. We selected a subject with Parkinson’s disease implanted with a Medtronic Activa PC+S DBS system and reconstructed their subthalamic nucleus (STN) and DBS electrode location using medical imaging data. The patient-specific STN volume was populated with 235,280 multicompartment STN neuron models, providing a neuron density consistent with histological measurements. Each neuron received time-varying synaptic inputs and generated transmembrane currents that gave rise to the LFP signal recorded at DBS electrode contacts residing in a finite element volume conductor model. We then used the model to study the role of synchronous beta-band inputs to the STN neurons on the recorded power spectrum. Three bipolar pairs of simultaneous clinical LFP recordings were used in combination with an optimization algorithm to customize the neural activity parameters in the model to the patient. The optimized model predicted a 2.4-mm radius of beta-synchronous neurons located in the dorsolateral STN. These theoretical results enable biophysical dissection of the LFP signal at the cellular level with direct comparison to the clinical recordings, and the model system provides a scientific platform to help guide the design of DBS technology focused on the use of subthalamic beta activity in closed-loop algorithms. NEW & NOTEWORTHY The analysis of deep brain stimulation of local field potential (LFP) data is rapidly expanding from scientific curiosity to the basis for clinical biomarkers capable of improving the therapeutic efficacy of stimulation. With this growing clinical importance comes a growing need to understand the underlying electrophysiological fundamentals of the signals and the factors contributing to their modulation. Our model reconstructs the clinical LFP from first principles and highlights the importance of patient-specific factors in dictating the signals recorded.

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