scholarly journals Machine Learning Based Classification of Deep Brain Stimulation Outcomes in a Rat Model of Binge Eating Using Ventral Striatal Oscillations

2018 ◽  
Vol 9 ◽  
Author(s):  
Wilder T. Doucette ◽  
Lucas Dwiel ◽  
Jared E. Boyce ◽  
Amanda A. Simon ◽  
Jibran Y. Khokhar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Brain Stimulation ◽  
Binge Eating ◽  
Brain Stimulation ◽  
Rat Model ◽  
Deep Brain

scholarly journals Machine learning based classification of deep brain stimulation outcomes in a rat model of binge eating using ventral striatal oscillations

2018 ◽  
Author(s):  
Wilder T. Doucette ◽  
Lucas Dwiel ◽  
Jared E. Boyce ◽  
Amanda A. Simon ◽  
Jibran Y. Khokhar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Brain Stimulation ◽  
Binge Eating ◽  
Treatment Outcomes ◽  
Brain Stimulation ◽  
Rat Model ◽  
Network Activity ◽  
Sprague Dawley ◽  
Underlying Network ◽  
Deep Brain

AbstractNeuromodulation-based interventions continue to be evaluated across an array of appetitive disorders but broader implementation of these approaches remains limited due to variable treatment outcomes. We hypothesize that individual variation in treatment outcomes may be linked to differences in the networks underlying these disorders. Here, Sprague-Dawley rats received deep brain stimulation separately within each nucleus accumbens (NAc) sub-region (core and shell) using a within-animal crossover design in a rat model of binge eating. Significant reductions in binge size were observed with stimulation of either target but with significant variation in effectiveness across individuals. When features of local field potentials (LFPs) recorded from the NAc were used as predictors of the pre-defined stimulation outcomes (response or non-response) from each rat using a machine-learning approach (lasso), stimulation outcomes could be predicted with greater accuracy than expected by chance (effect sizes: core = 1.13, shell = 1.05). Further, these LFP features could be used to identify the best stimulation target for each animal (core vs. shell) with an effect size = 0.96. These data suggest that individual differences in underlying network activity may contribute to the variable outcomes of circuit based interventions and that measures of network activity have the potential to individually guide the selection of an optimal stimulation target and improve overall treatment response rates.

Prelimbic Cortex Deep Brain Stimulation Reduces Binge Size in a Chronic Binge Eating Rat Model

2018 ◽  
Vol 96 (1) ◽  
pp. 33-39 ◽  
Author(s):  
Can Sarica ◽  
Mazhar Ozkan ◽  
Husniye Hacioglu Bay ◽  
Umit Sehirli ◽  
Filiz Onat ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Binge Eating ◽  
Brain Stimulation ◽  
Rat Model ◽  
Prelimbic Cortex ◽  
Deep Brain

Real-time machine learning classification of pallidal borders during deep brain stimulation surgery

2020 ◽  
Vol 17 (1) ◽  
pp. 016021 ◽  
Author(s):  
Dan Valsky ◽  
Kim T Blackwell ◽  
Idit Tamir ◽  
Renana Eitan ◽  
Hagai Bergman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Brain Stimulation ◽  
Real Time ◽  
Brain Stimulation ◽  
Time Machine ◽  
Machine Learning Classification ◽  
Deep Brain Stimulation Surgery ◽  
Deep Brain

scholarly journals Nucleus accumbens deep brain stimulation in a rat model of binge eating

2015 ◽  
Vol 5 (12) ◽  
pp. e695-e695 ◽  
Author(s):  
W T Doucette ◽  
J Y Khokhar ◽  
A I Green
Keyword(s):  
Deep Brain Stimulation ◽  
Nucleus Accumbens ◽  
Binge Eating ◽  
Brain Stimulation ◽  
Rat Model ◽  
Deep Brain

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.

scholarly journals Deep Brain Stimulation rescues memory and synaptic activity in a rat model of global ischemia

2019 ◽  
pp. 1222-18 ◽  
Author(s):  
Elise Gondard ◽  
Lucy Teves ◽  
Lihua Wang ◽  
Chris McKinnon ◽  
Clement Hamani ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Rat Model ◽  
Global Ischemia ◽  
Synaptic Activity ◽  
Deep Brain
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