Role of Machine Learning for Classification of Movement Disorder and Deep Brain Stimulation Status

Author(s):  
Robert LeMoyne ◽  
Timothy Mastroianni ◽  
Donald Whiting ◽  
Nestor Tomycz
Author(s):  
Jeri Yvonne Williams ◽  
David G Standaert

Dystonia is a movement disorder characterized by sustained or intermittent muscle contractions. Classification of dystonia is based on age of onset, distribution of body parts affected, and underlying etiology. A large number of different genetic forms of dystonia have been discovered in recent years. Although these syndromes are important to recognize, the majority of dystonias encountered in clinical practice are of unknown cause. Therapy of dystonia includes medications, particularly anticholinergic drugs, use of botulinum toxins, and deep brain stimulation.


2018 ◽  
Vol 9 ◽  
Author(s):  
Wilder T. Doucette ◽  
Lucas Dwiel ◽  
Jared E. Boyce ◽  
Amanda A. Simon ◽  
Jibran Y. Khokhar ◽  
...  

2020 ◽  
Vol 17 (1) ◽  
pp. 016021 ◽  
Author(s):  
Dan Valsky ◽  
Kim T Blackwell ◽  
Idit Tamir ◽  
Renana Eitan ◽  
Hagai Bergman ◽  
...  

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Alexandre Boutet ◽  
Radhika Madhavan ◽  
Gavin J. B. Elias ◽  
Suresh E. Joel ◽  
Robert Gramer ◽  
...  

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.


Cephalalgia ◽  
2016 ◽  
Vol 36 (12) ◽  
pp. 1143-1148 ◽  
Author(s):  
Massimo Leone ◽  
Alberto Proietti Cecchini

Background: Deep brain stimulation of the posterior hypothalamic area was first introduced in 2000 to treat drug-refractory chronic cluster headache (CH). Findings: So far, hypothalamic stimulation has been employed in 79 patients suffering from various forms of intractable short-lasting unilateral headache forms, mainly trigeminal autonomic cephalalgias. The majority were (88.6%) chronic CH, including one patient who suffered from symptomatic chronic CH-like attacks; the remaining were short-lasting unilateral neuralgiform headache attacks with conjunctival injection and tearing (SUNCT), one had paroxysmal hemicranias and one symptomatic trigeminal neuralgia. Overall, after a mean follow up of 2.2 years, 69.6% (55) hypothalamic-stimulated patients showed a ≥50% improvement. Conclusions: These observations need confirmation in randomised, controlled trials. A key role of the posterior hypothalamic area in the pathophysiology of unilateral short-lasting headaches, possibly by regulating the duration rather than triggering the attacks, can be hypothesised. Because of its invasiveness, hypothalamic stimulation can be proposed only after other, less-invasive, neurostimulation procedures have been tried.


2016 ◽  
Vol 32 (4) ◽  
pp. 438-439 ◽  
Author(s):  
Inge A. Meijer ◽  
Joan Miravite ◽  
Brian H. Kopell ◽  
Naomi Lubarr

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