Mechanisms underlying decision-making as revealed by deep brain stimulation in patients with Parkinson's disease

Herz, D; Brown, P

Dataset

Mechanisms underlying decision-making as revealed by deep brain stimulation in patients with Parkinson's disease

Documentation:: The data was collected between 2016 and 2017 at the John Radcliffe Hospital (Oxford) and National Hospital for Neurology and Neurosurgery (London) in 7 patients with Parkinson's disease who had undergone deep brain stimulation surgery. Participants performed a moving dots task. They were either instructed to respond as quickly as possible (Speed condition) or as accurately as possible (Accuracy condition) and the moving dots had 50 % coherence (High Coherence condition) or 8 % coherence (Low Coherence condition). The task was performed three times. With continuous deep brain stimulation (cDBS), adaptive deep brain stimulation (aDBS) and off stimulation (off DBS). Behavioral data was acquired using PsychoPy and is saved as .mat-files (Matlab). Trial-averaged singe subject data is saved in SingleSubjectBehaviour.mat. This contains text files (Info_OFF and Info_DBS) explaining the data structure. Single trial, single subject data is saved as SingleTrialData_All.mat. This contains behavioural and neurophysiological single trial data for the three conditions (off DBS, cDBS, aDBS) separately. It also contains a text file explaining the data structure (Info). WindowAnalysis.mat contains behavioural data from timing-specific stimulation during aDBS for the effect of Instruction (Instr) and Coherence (Coh) for data aligned to the cue and the response. It also contains a text file explaining the data structure (Info). For drift diffusion modelling with HDDM (hierarchical drift diffusion modelling, Wiecki et al. Front Neuroinf 2013) the single trial data is also saved as cvs-files for the three conditions (SingleTrialData_offcDBS.csv for testing the effects of stimulation during cDBS (vs offDBS) and SingleTrialData_aDBS.csv for testing the effects of cue-locked stimulation during aDBS). HDDM requires installation of python (see http://ski.clps.brown.edu/hddm_docs/ for more details). All models used in the study are specified in HDDM_scripts.py. Local field potentials from the subthalamic nucleus were recorded using a custom-built device (see Little et al., 2013, Annals of Neurology for more details) and preprocessed in matlab (see related article: Herz et al., “Mechanisms underlying decision-making as revealed by deep brain stimulation in patients with Parkinson’s disease” for details). Neurophysiological data off DBS for Speed vs. Accuracy trials and Low vs. High Coherence trials are saved under Beta_LFP_off.mat. Neurophysiological data for the three DBS conditions for Low vs. High Coherence are saved under Beta_LowCoherenceDBS.mat. Both files include a text explaining the data structure (Info) and contain trial-averaged data. For relevant neurophysiological single trials data, see SingleTrialData_All.mat.

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APA Style

Herz, D., & Brown, P. (2017). Mechanisms underlying decision-making as revealed by deep brain stimulation in patients with Parkinson's disease. University of Oxford.

MLA Style

Herz, D., and P. Brown. Mechanisms Underlying Decision-Making as Revealed by Deep Brain Stimulation in Patients with Parkinson's Disease. University of Oxford, 2017.

Chicago Style

Herz, D, and P Brown. 2017. “Mechanisms Underlying Decision-Making as Revealed by Deep Brain Stimulation in Patients with Parkinson's Disease.” University of Oxford.
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