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Parkinson's Disease: Treatment

Parkinson's Disease: Treatment

Neurodegenerative disorders, such as Parkinson's Disease (PD), involve the gradual and irreversible destruction of neurons in particular brain areas. These disorders exhibit standard features like proteinopathies, selective vulnerability of some neurons, and an interaction of intrinsic properties, genetics, and environmental influences in neural injury.
Parkinson's Disease is primarily a result of the loss of dopaminergic neurons in the substantia nigra pars compacta. The cornerstone of...

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Updated: Sep 17, 2025

Controlling Parkinson's Disease With Adaptive Deep Brain Stimulation

Controlling Parkinson's Disease With Adaptive Deep Brain Stimulation

Published on: July 16, 2014

Movement-responsive deep brain stimulation for Parkinson's disease using a remotely optimized neural decoder.

Tanner C Dixon¹, Gabrielle Strandquist², Alicia Zeng³

¹Department of Neurology, University of California San Francisco, San Francisco, CA, USA.

Nature Biomedical Engineering

|June 27, 2025

Summary

This summary is machine-generated.

Adaptive deep brain stimulation (aDBS) improves Parkinson's symptoms by adjusting electrical signals in real-time. This movement-responsive approach enhances motor function and reduces side effects, offering a more personalized treatment.

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Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

Area of Science:

Neuroscience
Biomedical Engineering
Neurological Disorders

Background:

Deep brain stimulation (DBS) is a common treatment for advanced Parkinson's disease.
Conventional DBS (cDBS) uses fixed stimulation parameters, which may not address dynamic patient needs.
Adaptive DBS (aDBS) offers a promising alternative by adjusting stimulation based on real-time physiological or behavioral states.

Purpose of the Study:

To develop and evaluate a novel adaptive deep brain stimulation (aDBS) algorithm for Parkinson's disease.
To mitigate movement slowness by delivering stimulation increases during movement using decoded motor signals.
To assess the efficacy of movement-responsive aDBS compared to conventional DBS and a control condition.

Main Methods:

An aDBS algorithm was designed to increase stimulation during movement based on decoded brain signals.
The algorithm's performance was compared against an inverted control and conventional DBS (cDBS).
A machine learning pipeline was developed for remote optimization of aDBS parameters in a home setting.

Main Results:

The movement-responsive aDBS algorithm improved dominant hand movement speed and participant-reported therapeutic efficacy compared to the control.
Typing speed increased and dyskinesia decreased with aDBS compared to cDBS.
Proof of principle for remote, machine learning-assisted optimization of aDBS parameters was demonstrated.

Conclusions:

Movement-responsive aDBS shows potential as a therapeutic strategy for Parkinson's disease, specifically targeting motor symptoms like slowness.
This approach allows for dynamic alignment of therapy with patient-specific needs, potentially improving outcomes.
Machine learning-assisted programming can simplify the optimization of aDBS, facilitating its clinical translation and scalability.