Sustained Clinical Benefit of Adaptive Deep Brain Stimulation in Parkinson's Disease Using Gamma Oscillations: A Case Report.

Background: Adaptive deep brain stimulation (aDBS) dynamically adjusts stimulation parameters according to patient needs. We recently showed that chronic aDBS utilizing invasive neural signals for feedback control is superior to conventional DBS (cDBS) during normal daily life in a 2-month trial. The stability of aDBS over longer periods remains unclear.

Chronic adaptive deep brain stimulation versus conventional stimulation in Parkinson's disease: a blinded randomized feasibility trial.

Deep brain stimulation (DBS) is a widely used therapy for Parkinson's disease (PD) but lacks dynamic responsiveness to changing clinical and neural states. Feedback control might improve therapeutic effectiveness, but the optimal control strategy and additional benefits of 'adaptive' neurostimulation are unclear. Here we present the results of a blinded randomized cross-over pilot trial aimed at determining the neural correlates of specific motor signs in individuals with PD and the feasibility of using these signals to drive adaptive DBS.

Personalized chronic adaptive deep brain stimulation outperforms conventional stimulation in Parkinson's disease.

Deep brain stimulation is a widely used therapy for Parkinson's disease (PD) but currently lacks dynamic responsiveness to changing clinical and neural states. Feedback control has the potential to improve therapeutic effectiveness, but optimal control strategy and additional benefits of "adaptive" neurostimulation are unclear. We implemented adaptive subthalamic nucleus stimulation, controlled by subthalamic or cortical signals, in three PD patients (five hemispheres) during normal daily life.

A multimodal deep learning system to distinguish late stages of AMD and to compare expert vs. AI ocular biomarkers.

Within the next 1.5 decades, 1 in 7 U.S.

Ballistocardiogram Artifact Reduction in Simultaneous EEG-fMRI Using Deep Learning.

Objective: The concurrent recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a technique that has received much attention due to its potential for combined high temporal and spatial resolution. However, the ballistocardiogram (BCG), a large-amplitude artifact caused by cardiac induced movement contaminates the EEG during EEG-fMRI recordings. Removal of BCG in software has generally made use of linear decompositions of the corrupted EEG.