Personalized chronic adaptive deep brain stimulation outperforms conventional stimulation in Parkinson's disease
Oehrn, C. R.; Cernera, S.; Hammer, L. H.; Shcherbakova, M.; Yao, J.; Hahn, A.; Wang, S.; Ostrem, J. L.; Little, S.; Starr, P. A.
Show abstract
1.Deep brain stimulation is a widely used therapy for Parkinsons 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. We identified neurophysiological biomarkers of residual motor fluctuations using data-driven analyses of field potentials over a wide frequency range and varying stimulation amplitudes. Narrowband gamma oscillations (65-70 Hz) at either site emerged as the best control signal for sensing during stimulation. A blinded, randomized trial demonstrated improved motor symptoms and quality of life compared to clinically optimized standard stimulation. Our approach highlights the promise of personalized adaptive neurostimulation based on data-driven selection of control signals and may be applied to other neurological disorders.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Neuroimaging-AI Endophenotypes of Brain Diseases in the General Population: Towards a Dimensional System of Vulnerability 92%
- Antihypertensive effect of brain-targeted mechanical intervention with passive head motion 92%
- Biomimetic multi-channel microstimulation of somatosensory cortex conveys high resolution force feedback for bionic hands 91%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.