scholarly journals Automatic Localization of the Subthalamic Nucleus on Patient-Specific Clinical MRI by Incorporating 7T MRI and Machine Learning: Application in Deep Brain Stimulation

2018 ◽  
Author(s):  
Jinyoung Kim ◽  
Yuval Duchin ◽  
Reuben R. Shamir ◽  
Remi Patriat ◽  
Jerrold Vitek ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Brain Stimulation ◽  
Subthalamic Nucleus ◽  
Brain Stimulation ◽  
Machine Learning Algorithms ◽  
Patient Specific ◽  
Accurate Localization ◽  
Adjacent Structures ◽  
Clinical Mri ◽  
Deep Brain

ABSTRACTDeep Brain Stimulation (DBS) of the subthalamic nucleus (STN) has shown clinical potential for relieving the motor symptoms of advanced Parkinson’s disease. While accurate localization of the STN is critical for consistent across-patients effective DBS, clear visualization of the STN under standard clinical MR protocols is still challenging. Therefore, intraoperative microelectrode recordings (MER) are incorporated to accurately localize the STN. However, MER require significant neurosurgical expertise and lengthen the surgery time. Recent advances in 7T MR technology facilitate the ability to clearly visualize the STN. The vast majority of centers, however, still do not have 7T MRI systems, and fewer have the ability to collect and analyze the data. This work introduces an automatic STN localization framework based on standard clinical MRIs without additional cost in the current DBS planning protocol. Our approach benefits from a large database of 7T MRI and its clinical MRI pairs. We first model in the 7T database, using efficient machine learning algorithms, the spatial and geometric dependency between the STN and its adjacent structures (predictors). Given a standard clinical MRI, our method automatically computes the predictors and uses the learned information to predict the patient-specific STN. We validate our proposed method on clinical T2W MRI of 80 subjects, comparing with experts-segmented STNs from the corresponding 7T MRI pairs. The experimental results show that our framework provides more accurate and robust patient-specific STN localization than using state-of-the-art atlases. We also demonstrate the clinical feasibility of the proposed technique assessing the post-operative electrode active contact locations.

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.

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 Investigating Risk Factors and Predicting Complications in Deep Brain Stimulation Surgery with Machine Learning Algorithms

2020 ◽  
Vol 134 ◽  
pp. e325-e338 ◽  
Author(s):  
Farrokh Farrokhi ◽  
Quinlan D. Buchlak ◽  
Matt Sikora ◽  
Nazanin Esmaili ◽  
Maria Marsans ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Deep Brain Stimulation Surgery ◽  
Deep Brain

Automatic Segmentation of the Subthalamic Nucleus: A Viable Option to Support Planning and Visualization of Patient-Specific Targeting in Deep Brain Stimulation

2019 ◽  
Vol 17 (5) ◽  
pp. 497-502 ◽  
Author(s):  
Peter C Reinacher ◽  
Bálint Várkuti ◽  
Marie T Krüger ◽  
Tobias Piroth ◽  
Karl Egger ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Subthalamic Nucleus ◽  
Brain Stimulation ◽  
Automatic Segmentation ◽  
Rapid Onset ◽  
Patient Specific ◽  
Exit Point ◽  
Specific Targeting ◽  
Significant Difference ◽  
Deep Brain

Abstract BACKGROUND Automatic segmentation is gaining relevancy in image-based targeting of neural structures. OBJECTIVE To evaluate its feasibility, we retrospectively analyzed the concordance of magnetic resonance imaging (MRI)-based automatic segmentation of the subthalamic nucleus (STN) and intraoperative microelectrode recordings (MERs). METHODS Electrodes (n = 60) for deep brain stimulation were implanted in the STN of patients (n = 30; median age 57 yr) with Parkinson disease (n = 29) or rapid-onset dystonia parkinsonism (n = 1). Elements (Brainlab, Munich, Germany) were used to segment the STN, using 2 volumetric T1 (±contrast) and volumetric T2 images as input. The stereotactic computed tomography was coregistered with the imaging, and the original stereotactic coordinates were imported. MERs (0.5-1 mm steps) along the anterior, central, and lateral trajectories were used to determine differences between the image-segmented STN boundary and MER-based STN entry and exit. RESULTS Of 175 trajectories, 105 penetrated or touched (≤0.7 mm) the STN. The overall median deviation between the segmented STN boundary and electrophysiological recordings was 1.1 mm for MER-based STN entry and 2.0 mm for STN exit. Regarding the entry point of the STN, there was no statistically significant difference between MRI-based automatic segmentation and the electrophysiological trajectories analyzed with intraoperative MER. The exit point was significantly different between both methods in the central and lateral trajectories. CONCLUSION MRI-based automatic segmentation of the STN is a viable, patient-specific targeting approach that can be used alongside traditional targeting methods in deep brain stimulation to support preoperative planning and visualization of target structures and aid postoperative optimization of programming.

Selection of deep brain stimulation contacts using volume of tissue activated software following subthalamic nucleus stimulation

2020 ◽  
pp. 1-8
Author(s):  
Mathilde Devaluez ◽  
Melissa Tir ◽  
Pierre Krystkowiak ◽  
Mickael Aubignat ◽  
Michel Lefranc
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Subthalamic Nucleus ◽  
Clinical Data ◽  
Brain Stimulation ◽  
Therapeutic Window ◽  
Patient Specific ◽  
Subthalamic Nucleus Stimulation ◽  
Deep Brain

OBJECTIVEHigh-frequency deep brain stimulation (DBS) of the subthalamic nucleus (STN) is effective in the treatment of motor symptoms of Parkinson’s disease. Using a patient-specific lead and volume of tissue activated (VTA) software, it is possible to visualize contact positions in the context of the patient’s own anatomy. In this study, the authors’ aim was to demonstrate that VTA software can be used in clinical practice to help determine the clinical effectiveness of stimulation in patients with Parkinson’s disease undergoing DBS of the STN.METHODSBrain images of 26 patients undergoing STN DBS were analyzed using VTA software. Preoperative clinical and neuropsychological data were collected. Contacts were chosen by two experts in DBS blinded to the clinical data. A therapeutic window of amplitude was determined. These results were compared with the parameter settings for each patient. Data were obtained at 3 months and 1 year postsurgery.RESULTSIn 90.4% (95% CI 82%–98%) of the patients, the contacts identified by the VTA software were concordant with the clinically effective contacts or with an effective contact in contact-by-contact testing. The therapeutic window of amplitude selected virtually included 81.3% of the clinical amplitudes.CONCLUSIONSVTA software appears to present significant concordance with clinical data for selecting contacts and stimulation parameters that could help in postoperative follow-up and programming.

scholarly journals Sign-specific stimulation “hot” and “cold” spots in Parkinson’s disease validated with machine learning

2021 ◽  
Author(s):  
Alexandre Boutet ◽  
Jurgen Germann ◽  
Dave Gwun ◽  
Aaron Loh ◽  
Gavin J B Elias ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Subthalamic Nucleus ◽  
Brain Stimulation ◽  
Hot Spots ◽  
Out Of Sample ◽  
Cold Spots ◽  
Deep Brain

Abstract Deep brain stimulation of the subthalamic nucleus has become a standard therapy for Parkinson’s disease. Despite extensive experience, however, the precise target of optimal stimulation and the relationship between site of stimulation and alleviation of individual signs remains unclear. We examined whether machine learning could predict the benefits in specific parkinsonian signs when informed by precise locations of stimulation. We studied 275 Parkinson’s disease patients who underwent subthalamic nucleus deep brain stimulation between 2003 and 2018. We selected pre-deep brain stimulation and best available post-deep brain stimulation scores from motor items of the Unified Parkinson's Disease Rating Scale (UPDRS-III) to discern sign-specific changes attributable to deep brain stimulation. Volumes of tissue activated were computed and weighted by i) tremor, ii) rigidity, iii) bradykinesia, and iv) axial signs changes. Then, sign-specific sites of optimal (“hot spots”) and suboptimal efficacy (“cold spots”) were defined. These areas were subsequently validated using machine learning prediction of sign-specific outcomes with in-sample and out-of-sample data (n = 51 subthalamic nucleus deep brain stimulation patients from another institution). Tremor and rigidity hot spots were largely located outside and dorsolateral to the subthalamic nucleus whereas hot spots for bradykinesia and axial signs had larger overlap with the subthalamic nucleus. Using volume of tissue activated overlap with sign-specific hot and cold spots, support vector machine classified patients into quartiles of efficacy with ≥92% accuracy. The accuracy remained high (68–98%) when only considering volume of tissue activated overlap with hot spots but was markedly lower (41–72%) when only using cold spots. The model also performed poorly (44–48%) when using only stimulation voltage, irrespective of stimulation location. Out-of-sample validation accuracy was ≥96% when using volume of tissue activated overlap with the sign-specific hot and cold spots. In two independent datasets, distinct brain areas could predict sign-specific clinical changes in Parkinson’s disease patients with subthalamic nucleus deep brain stimulation. With future prospective validation, these findings could individualize stimulation delivery to optimize quality of life improvement.

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