Movement-responsive deep brain stimulation for Parkinson’s Disease using a remotely optimized neural decoder

Deep brain stimulation (DBS) has garnered widespread use as an effective treatment for advanced Parkinson's Disease (PD). Conventional DBS (cDBS) provides electrical stimulation to the basal ganglia at fixed amplitude and frequency, yet patients’ therapeutic needs are often dynamic with residual symptom fluctuations or side-effects. Adaptive DBS (aDBS) is an emerging technology that modulates stimulation with respect to real-time clinical, physiological, or behavioral states, enabling therapy to dynamically align with patient-specific symptoms. Here, we report an aDBS algorithm intended to mitigate movement slowness by delivering targeted stimulation increases during movement using decoded motor signals from the brain. Our approach demonstrated improvements in dominant hand movement speeds and patient-reported therapeutic efficacy compared to an inverted control, as well as increased typing speed and reduced dyskinesia compared to cDBS. Furthermore, we demonstrate proof-of-principle of ..., Neural data sensing configuration and signal processing
All neural recordings were performed during monopolar stimulation. Bipolar recordings in the STN were collected using a “sandwich” configuration (electrodes on either side of the stimulating contact) for common-mode rejection of the stimulation artifact. Two channels of neural data were recorded along the ECoG array, using the first and second electrodes for one channel (postcentral gyrus) and the third and fourth electrodes for the other (precentral gyrus). Neural time-domain data were recorded at a sampling rate of 500Hz. Device-embedded signal processing consisted of short-time Fourier transforms (STFT) with an FFT size of 256 pt and an interval of 50 ms. 
Behavioral data streaming, signal processing, and synchronization
Detailed descriptions of the behavioral data collection platform can be found in Strandquist et al., 2023. Briefly, arm movement data were collected using Apple Watches (Apple, Inc) worn on each wrist and u..., , # Data from: Movement-responsive deep brain stimulation for Parkinson’s Disease using a remotely optimized neural decoder

[https://doi.org/10.5061/dryad.4xgxd25hw](https://doi.org/10.5061/dryad.4xgxd25hw)

This dataset contains all necessary data for recreating the analyses performed in the manuscript \"Movement-responsive deep brain stimulation for Parkinson's Disease using a remotely optimized neural decoder\"

## Description of the data and file structure

Study data are separated into folders corresponding to each figure. All data for producing the respective figure and associated analyses are contained in that folder. All data are contained in CSV files that may be read into python as dataframes using the *pandas* library or numpy arrays using the *numpy* library.

Below is a full list of the files with thorough descriptions of their contents:

1. fig2b_neural_left.csv, fig2b_neural_right.csv - example neural data corresponding to left and right brain hemispheres. Columns: `time` (t..., The study participant provided written informed consent prior to participating in the study, and consented to release of data in the dataset and information in the associated manuscript. Explicit description of the dataset contents was communicated during the consent process. The dataset does not contain any personal identifiers. Only the processed data directly necessary for reproducing the figures and statistical analyses of the associated manuscript are included.