Evaluation of Machine Learning Algorithms for Classifying Deep Brain Stimulation Respective of ‘On’ and ‘Off’ Status

2019 ◽  
Author(s):  
Robert LeMoyne ◽  
Timothy Mastroianni ◽  
Cyrus McCandless ◽  
Donald Whiting ◽  
Nestor Tomycz
Keyword(s):  
Machine Learning ◽  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Deep Brain

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

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 Predicting optimal deep brain stimulation parameters for Parkinson’s disease using functional MRI and machine learning

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Alexandre Boutet ◽  
Radhika Madhavan ◽  
Gavin J. B. Elias ◽  
Suresh E. Joel ◽  
Robert Gramer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
A Priori ◽  
Brain Response ◽  
Brain Responses ◽  
Clinical Benefits ◽  
Deep Brain

AbstractCommonly used for Parkinson’s disease (PD), deep brain stimulation (DBS) produces marked clinical benefits when optimized. However, assessing the large number of possible stimulation settings (i.e., programming) requires numerous clinic visits. Here, we examine whether functional magnetic resonance imaging (fMRI) can be used to predict optimal stimulation settings for individual patients. We analyze 3 T fMRI data prospectively acquired as part of an observational trial in 67 PD patients using optimal and non-optimal stimulation settings. Clinically optimal stimulation produces a characteristic fMRI brain response pattern marked by preferential engagement of the motor circuit. Then, we build a machine learning model predicting optimal vs. non-optimal settings using the fMRI patterns of 39 PD patients with a priori clinically optimized DBS (88% accuracy). The model predicts optimal stimulation settings in unseen datasets: a priori clinically optimized and stimulation-naïve PD patients. We propose that fMRI brain responses to DBS stimulation in PD patients could represent an objective biomarker of clinical response. Upon further validation with additional studies, these findings may open the door to functional imaging-assisted DBS programming.

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