AIMC Topic: Deep Brain Stimulation
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Robot-Assisted Deep Brain Stimulation: High Accuracy and Streamlined Workflow.
Operative neurosurgery (Hagerstown, Md.)
Jun 14, 2022
BACKGROUND: A number of stereotactic platforms are available for performing deep brain stimulation (DBS) lead implantation. Robot-assisted stereotaxy has emerged more recently demonstrating comparable accuracy and shorter operating room times compare...
Predicting Motor Responsiveness to Deep Brain Stimulation with Machine Learning.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Feb 21, 2022
Deep brain stimulation is a complex movement disorder intervention that requires highly invasive brain surgery. Clinicians struggle to predict how patients will respond to this treatment. To address this problem, we are working toward developing a cl...
PassFlow: a multimodal workflow for predicting deep brain stimulation outcomes.
International journal of computer assisted radiology and surgery
Jul 3, 2021
PURPOSE: Deep Brain Stimulation (DBS) is a proven therapy for Parkinson's Disease (PD), frequently resulting in an enhancement of motor function. Nonetheless, several undesirable side effects can occur after DBS, which can worsen the quality of life ...
Development and Application of Medicine-Engineering Integration in the Rehabilitation of Traumatic Brain Injury.
BioMed research international
Jun 12, 2021
The rapid progress of the combination of medicine and engineering provides better chances for the clinical treatment and healthcare engineering. Traumatic brain injury (TBI) and its related symptoms have become a major global health problem. At prese...
Predicting optimal deep brain stimulation parameters for Parkinson's disease using functional MRI and machine learning.
Nature communications
May 24, 2021
Commonly 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,...
Comparison of clinical outcomes and accuracy of electrode placement between robot-assisted and conventional deep brain stimulation of the subthalamic nucleus: a single-center study.
Acta neurochirurgica
Mar 2, 2021
BACKGROUND: Several surgical methods are used for deep brain stimulation (DBS) of the subthalamic nucleus (STN) in Parkinson's disease (PD). This study aimed to compare clinical outcomes and electrode placement accuracy after robot-assisted (RAS) ver...
Machine learning for automated EEG-based biomarkers of cognitive impairment during Deep Brain Stimulation screening in patients with Parkinson's Disease.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Feb 24, 2021
OBJECTIVE: A downside of Deep Brain Stimulation (DBS) for Parkinson's Disease (PD) is that cognitive function may deteriorate postoperatively. Electroencephalography (EEG) was explored as biomarker of cognition using a Machine Learning (ML) pipeline.
Whole-brain modelling of resting state fMRI differentiates ADHD subtypes and facilitates stratified neuro-stimulation therapy.
NeuroImage
Feb 10, 2021
Recent advances in non-linear computational and dynamical modelling have opened up the possibility to parametrize dynamic neural mechanisms that drive complex behavior. Importantly, building models of neuronal processes is of key importance to fully ...
Clinical outcome prediction from analysis of microelectrode recordings using deep learning in subthalamic deep brain stimulation for Parkinson`s disease.
PloS one
Jan 26, 2021
BACKGROUND: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for improving the motor symptoms of advanced Parkinson's disease (PD). Accurate positioning of the stimulation electrodes is necessary for better clin...
Reconstructing lost BOLD signal in individual participants using deep machine learning.
Nature communications
Oct 7, 2020
Signal loss in blood oxygen level-dependent (BOLD) functional neuroimaging is common and can lead to misinterpretation of findings. Here, we reconstructed compromised fMRI signal using deep machine learning. We trained a model to learn principles gov...
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