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.

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

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

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

scholarly journals Deep brain stimulation of the subthalamic nucleus reestablishes neuronal information transmission in the 6-OHDA rat model of parkinsonism

2014 ◽  
Vol 111 (10) ◽  
pp. 1949-1959 ◽  
Author(s):  
Alan D. Dorval ◽  
Warren M. Grill
Keyword(s):  
Deep Brain Stimulation ◽  
Basal Ganglia ◽  
Information Transmission ◽  
Brain Stimulation ◽  
Rat Model ◽  
Information Transfer ◽  
Firing Pattern ◽  
Signal Propagation ◽  
Neuronal Firing ◽  
Deep Brain

Pathophysiological activity of basal ganglia neurons accompanies the motor symptoms of Parkinson's disease. High-frequency (>90 Hz) deep brain stimulation (DBS) reduces parkinsonian symptoms, but the mechanisms remain unclear. We hypothesize that parkinsonism-associated electrophysiological changes constitute an increase in neuronal firing pattern disorder and a concomitant decrease in information transmission through the ventral basal ganglia, and that effective DBS alleviates symptoms by decreasing neuronal disorder while simultaneously increasing information transfer through the same regions. We tested these hypotheses in the freely behaving, 6-hydroxydopamine-lesioned rat model of hemiparkinsonism. Following the onset of parkinsonism, mean neuronal firing rates were unchanged, despite a significant increase in firing pattern disorder (i.e., neuronal entropy), in both the globus pallidus and substantia nigra pars reticulata. This increase in neuronal entropy was reversed by symptom-alleviating DBS. Whereas increases in signal entropy are most commonly indicative of similar increases in information transmission, directed information through both regions was substantially reduced (>70%) following the onset of parkinsonism. Again, this decrease in information transmission was partially reversed by DBS. Together, these results suggest that the parkinsonian basal ganglia are rife with entropic activity and incapable of functional information transmission. Furthermore, they indicate that symptom-alleviating DBS works by lowering the entropic noise floor, enabling more information-rich signal propagation. In this view, the symptoms of parkinsonism may be more a default mode, normally overridden by healthy basal ganglia information. When that information is abolished by parkinsonian pathophysiology, hypokinetic symptoms emerge.

Amygdala Deep Brain Stimulation Is Superior to Paroxetine Treatment in a Rat Model of Posttraumatic Stress Disorder

2013 ◽  
Vol 6 (6) ◽  
pp. 837-844 ◽  
Author(s):  
David A. Stidd ◽  
Kimberly Vogelsang ◽  
Scott E. Krahl ◽  
Jean-Philippe Langevin ◽  
Jean-Marc Fellous
Keyword(s):  
Posttraumatic Stress Disorder ◽  
Deep Brain Stimulation ◽  
Posttraumatic Stress ◽  
Brain Stimulation ◽  
Rat Model ◽  
Stress Disorder ◽  
Paroxetine Treatment ◽  
Deep Brain

Severe seizures as a side effect of deep brain stimulation in the dorsal peduncular cortex in a rat model of depression

2019 ◽  
Vol 92 ◽  
pp. 269-275
Author(s):  
Milaine Roet ◽  
Sylvana Pol ◽  
Frédéric L.W.V.J. Schaper ◽  
Govert Hoogland ◽  
Ali Jahanshahi ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Rat Model ◽  
Side Effect ◽  
Deep Brain
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