Dandiset 000026: Subthalamic Nucleus Local Field Potentials (LFP) and Beta-Band Dynamics in Parkinson’s Patients
Study Design & Technical Specifications
Sample Size / Cohort
34 Patients
Subject / Study Model
CNS Experimental / Clinical Model
Analytical Modalities
Primary Abstract & Repository Description
Source: DANDI ArchiveEditorial Critique, Methodological Value & Reusability Guide
1. Scientific Context & Premise
This resource from DANDI Archive ("Dandiset 000026: Subthalamic Nucleus Local Field Potentials (LFP) and Beta-Band Dynamics in Parkinson’s Patients") addresses critical scientific questions within Parkinson's Disease (PD). Research in this domain currently faces major translational bottlenecks, including quantifying prodromal alpha-synuclein pathology, dopaminergic nigrostriatal circuit vulnerability, and non-motor progression heterogeneity. By focusing on key thematic elements (DBS, Local Field Potential, Beta Oscillations, Subthalamic Nucleus, NWB), this work contributes empirical evidence toward understanding how molecular, cellular, or systems-level alterations relate to core disease pathways such as α-synuclein seed amplification assays (SAA), LRRK2/GBA1 variants, DAT-SPECT, and digital motor kinematics. Integrating these findings into the broader open science ecosystem helps reduce fragmentation across independent laboratories and accelerates hypothesis verification.
2. Study Architecture & Findings
Structured as an open scientific dataset, this repository leverages Electrophysiology (LFP), Kinematics across CNS Experimental / Clinical Model (34 Patients). Its primary architectural value lies in providing standardized, machine-readable primary records via DANDI Archive under CC-BY 4.0. Unlike isolated summary tables in static publications, access to underlying Electrophysiology (LFP), Kinematics distributions enables independent inspection of signal-to-noise ratios, preprocessing parameter sensitivity, and cross-modality feature extraction.
3. Secondary Analysis & Reusability
Researchers and computational neuroscientists can reuse this resource across multiple secondary workflows: (1) Cross-Cohort Harmonization & Meta-Analysis — combining these Electrophysiology (LFP), Kinematics records with complementary repositories (such as OpenNeuro, DANDI, Agora, or Europe PMC) to increase statistical power; (2) Computational Benchmarking & Machine Learning — utilizing the documented features (DBS, Local Field Potential, Beta Oscillations, Subthalamic Nucleus, NWB) as training, validation, or out-of-distribution test sets for predictive biomarker discovery in Parkinson's Disease (PD); and (3) Experimental Protocol Alignment — referencing the study design (CNS Experimental / Clinical Model, 34 Patients) to calibrate sample size calculations (power analysis) and endpoint selection for upcoming preclinical or clinical studies.
4. Methodological Caveats & Governance
While curated from an authoritative scientific source, secondary analysts should account for potential cohort selection biases and site-specific batch effects. Specifically, when interpreting Electrophysiology (LFP), Kinematics results in Parkinson's Disease (PD), researchers must control for confounding variables (such as age, sex, post-mortem interval, scanner/instrument drift, or medication state) and verify compliance with open-access attribution requirements (CC-BY 4.0).
Data Access, Reproducibility & Analysis Starter Guide
NWB (Neurodata Without Borders) v2.x
Quick Data Access & Structured Export
Open the primary repository directly or download structured metadata (JSON / CSV) in one click.
This cellular neurophysiology resource utilizes the Neurodata Without Borders (NWB) standard, encapsulating intracellular/extracellular voltage traces, spike times, stimulus waveforms, and optical imaging ROI fluorescence inside self-describing HDF5/Zarr containers.
Programmatic Data Retrieval (Python Starter Code & Recommended SDKs)
Python Quickstart: Stream & Analyze NWB Electrophysiology Data
# 1. Install DANDI & PyNWB toolkit
# pip install dandi pynwb remfile h5py
from dandi.dandiapi import DandiAPIClient
import pynwb
import remfile
import h5py
# Connect to DANDI Archive and locate Dandiset 000026
with DandiAPIClient() as client:
dandiset = client.get_dandiset("000026", "draft")
assets = list(dandiset.get_assets())
nwb_asset = next(a for a in assets if a.path.endswith(".nwb"))
s3_url = nwb_asset.get_content_url(follow_redirects=1, strip_query=True)
# Stream NWB metadata & spike/voltage series directly over HTTP
byte_stream = remfile.File(s3_url)
with h5py.File(byte_stream, "r") as h5_file:
with pynwb.NWBHDF5IO(file=h5_file, load_namespaces=True) as io:
nwbfile = io.read()
print("Session Description:", nwbfile.session_description)
print("Acquisition Series:", list(nwbfile.acquisition.keys()))Recommended Open-Source Software & SDKs
Standardized 4-Step Workflow & Governance Checklist
Standardized Acquisition & Preprocessing Workflow
1. Stream or Download Dandiset via DANDI CLI / fsspec
Inspect NWB file hierarchies remotely via HTTP range requests (remfile/fsspec) before downloading multi-gigabyte recordings.
2. Extract Acquisition & Stimulus TimeSeries via PyNWB
Load ElectricalSeries or PatchClampSeries objects and align timestamps against behavioral or optogenetic stimulus epochs.
3. Electrophysiological Feature Quantification (IPFX / SpikeInterface)
Compute resting membrane potential, input resistance, action potential half-width, and firing rate adaptation.
4. Cross-Modal Transcriptomic / Morphological Alignment
For Patch-seq or multimodal dandi sets, link cell specimen IDs with transcriptomic cell-type taxonomies.
Reproducibility & Governance Checklist
- Cite both the DANDI Archive Dandiset DOI and original study authors in secondary publications.
- Verify sampling rate (Hz) and conversion/gain factors on ElectricalSeries before spectral analysis.
- Pin pynwb and hdmf package versions to guarantee deterministic HDF5 schema deserialization.
Tags & Modalities
Bibliometrics, Peer-Review Status & Citation Export
Cross-referenced via Semantic Scholar, Europe PMC & bioRxiv
1. Citation Metrics & Context Breakdown
Citation Context Breakdown
2. Peer-Review Validation & Revision History
Verified Publication / Open Dataset Record
Metadata and license integrity verified against primary repository records.
3. Standardized Academic Citation & BibTeX Export
Starr PA et al. (2026). Dandiset 000026: Subthalamic Nucleus Local Field Potentials (LFP) and Beta-Band Dynamics in Parkinson’s Patients. DANDI Archive. https://doi.org/10.48324/dandi.000026/0.201021.1832
@misc{pa2026dand,
title = {Dandiset 000026: Subthalamic Nucleus Local Field Potentials (LFP) and Beta-Band Dynamics in Parkinson’s Patients},
author = {Starr PA and de Hemptinne C and Movement Disorders Neuroengineering Lab},
year = {2026},
journal = {DANDI Archive},
howpublished = {\url{https://doi.org/10.48324/dandi.000026/0.201021.1832}},
doi = {10.48324/dandi.000026/0.201021.1832},
note = {CNS Open Data: parkinson}
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Academic Research & Medical Information Disclaimer
This page provides structured scientific metadata, methodological critiques, and open-data reusability guidelines intended strictly for neuroscience researchers and data scientists. It does not constitute medical advice, clinical diagnosis, or treatment recommendations.
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