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Identifying maximal beta power from directional subthalamic local field potentials in Parkinson's disease.

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  1. [1] § Methods › Feature extraction ↔ notebooks/1_bssu_clean_FFT_FOOOF.ipynb, lines 64–177 · score 0.60 · monopolar power, power spectra, peak, active, Spectral, band

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The authors' code

Jupyter notebook · 519 lines · 15 KB · MIT · 1 match

  1. # %% [markdown]
  2. # # Removal of artifacts from time series and Fourier Transform to PSD
  4. # %%
  5. import os
  6. import sys
  7. import importlib
  8. from importlib import reload
  9. from dataclasses import dataclass, field, fields
  10. from itertools import compress
  11. import pandas as pd
  12. import numpy as np
  13. import matplotlib.pyplot as plt
  15. import ipywidgets as widgets
  16. from ipywidgets import interact, interact_manual
  17. from IPython.display import display
  18. import mplcursors
  19. #import mpld3
  20. from cycler import cycler
  22. import scipy
  23. import scipy.io as sio
  24. from scipy import signal
  25. from scipy.signal import spectrogram, hann, butter, filtfilt, freqz
  26. from scipy import stats
  28. import seaborn as sns
  29. import pingouin as pg
  30. import itertools
  31. from itertools import combinations
  32. from statannotations.Annotator import Annotator
  33. import statsmodels.api as sm
  34. import statsmodels.formula.api as smf
  35. from patsy import dmatrices
  36. import plotly.express as px
  38. from sklearn.linear_model import LogisticRegression
  39. from sklearn.model_selection import train_test_split, RepeatedStratifiedKFold, GridSearchCV
  40. from sklearn.metrics import classification_report
  41. from sklearn.decomposition import FastICA
  43. # import openpyxl
  44. # from openpyxl import Workbook, load_workbook
  45. # import xlrd
  46. import pickle
  47. import json
  48. import csv
  50. #mne
  51. import mne_bids
  52. import mne
  53. from mne.time_frequency import tfr_morlet
  54. from mne.stats import permutation_cluster_test
  55. from mne.preprocessing import ICA, create_ecg_epochs
  58. # TODO: add README file:
  59. # pip install pingouin
  60. # pip install statannotations
  61. # pip install fooof
  62. # pip install mpldatacursor
  64. # %%
  65. jennifer_user_path = os.getcwd()
  66. while jennifer_user_path[-14:] != 'jenniferbehnke':
  67. jennifer_user_path = os.path.dirname(jennifer_user_path)
  69. # directory to this Repository
  70. project_path = os.path.join(jennifer_user_path, 'code', 'Monopolar_power_estimation', 'monopolar_directional_beta')
  71. sys.path.append(project_path)
  73. os.chdir(project_path)
  75. import src.bssu.utils.find_folders as find_folders
  76. importlib.reload(find_folders)
  78. # import PyPerceive
  80. project_path = find_folders.chdir_repository("Py_Perceive")
  82. from PerceiveImport.classes import (
  83. main_class, modality_class, metadata_class,
  84. session_class, condition_class, task_class,
  85. contact_class, run_class
  86. )
  88. import PerceiveImport.methods.load_rawfile as load_rawfile
  89. import PerceiveImport.methods.find_folders as PyPerceive_find_folders
  90. import PerceiveImport.methods.metadata_helpers as metaHelpers
  92. # import meet
  94. project_path = find_folders.chdir_repository("meet")
  96. import meet as meet
  98. # import all functions from BetaSenSightLongterm
  99. project_path = find_folders.chdir_repository("BetaSenSightLongterm")
  101. # tfr, processing
  102. import src.monopolar_bssu.percept_lfp.artifact_cleaning as artifacts
  103. import src.monopolar_bssu.percept_lfp.FourierTransform_clean_data as FFT_clean
  104. import src.monopolar_bssu.percept_lfp.bssu_from_source_JSON as bssu_from_source_JSON
  105. import src.monopolar_bssu.percept_lfp.fooof_fit as fooof_fit
  107. # monopolar Referencing
  108. import src.bssu.monopolar.MonoRef_JLB as MonoRefJLB
  109. import src.bssu.monopolar.GroupMonopolarPSD as groupMonopol
  110. import src.bssu.monopolar.monoRef_weightPsdAverageByCoordinateDistance as MonoRefWeightedCoordinateDistance
  111. import src.bssu.monopolar.externalized_lfp as externalized
  112. import src.bssu.monopolar.monopol_method_comparison as monopol_comparison
  113. import src.bssu.monopolar.bssu_contacts_maximal_beta as bssu_contacts
  114. import src.bssu.monopolar.monoRef_Strelow as detec_strelow
  116. # Ranking Order
  117. import src.bssu.ranking.HighestRankedChannelPSD as highestRank
  118. import src.bssu.ranking.monopolPSDaverage_withinSubject as PSDaverageMonopol
  119. import src.bssu.ranking.BIPchannelGroups_ranks as BIP_ranks
  120. import src.bssu.ranking.Permutation_rankings as Permute_ranks
  123. # Clinical stimulation parameters
  124. import src.bssu.stimulation.activeStimulationContacts as activeStimContacts
  126. # utility functions
  127. import src.bssu.utils.loadResults as loadResults
  128. import src.bssu.utils.find_folders as find_folders
  129. import src.bssu.utils.writeGroupDataframes as writeGroupDF
  130. import src.bssu.utils.load_data_files as load_data
  131. import src.bssu.utils.monopol_comparison_helpers as mono_comp_helpers
  132. import src.bssu.utils.sub_session_dict as sub_session_dic
  134. # import Classes
  135. from src.bssu.classes import (metadataAnalysis_class, mainAnalysis_class, sessionAnalysis_class,
  136. channelAnalysis_class, featureAnalysis_class, frequencyBand_class)
  138. # import mni coordinates
  139. import src.bssu.mni.load_rotated_coordinates as load_mni
  142. importlib.reload(BSSuPsd)
  143. importlib.reload(MonoRefJLB)
  144. importlib.reload(loadResults)
  145. importlib.reload(highestRank)
  146. importlib.reload(groupMonopol)
  147. importlib.reload(PSDaverageMonopol)
  148. importlib.reload(FFpsd)
  149. importlib.reload(find_folders)
  150. importlib.reload(metadataAnalysis_class)
  151. importlib.reload(mainAnalysis_class)
  152. importlib.reload(sessionAnalysis_class)
  153. importlib.reload(channelAnalysis_class)
  154. importlib.reload(featureAnalysis_class)
  155. importlib.reload(frequencyBand_class)
  156. importlib.reload(PeakFrequency_psd)
  157. importlib.reload(power_spectra_plots)
  158. importlib.reload(BIP_channelGroups)
  159. importlib.reload(BIP_ranks)
  160. importlib.reload(activeStimContacts)
  161. importlib.reload(Permute_ranks)
  162. importlib.reload(BIP_perChannel)
  163. importlib.reload(load_mni)
  164. importlib.reload(writeGroupDF)
  165. importlib.reload(MonoRefWeightedCoordinateDistance)
  166. importlib.reload(load_data)
  167. importlib.reload(externalized)
  168. importlib.reload(monopol_comparison)
  169. importlib.reload(bssu_contacts)
  170. importlib.reload(mono_comp_helpers)
  171. importlib.reload(detec_strelow)
  172. importlib.reload(move_artifacts)
  173. importlib.reload(artifacts)
  174. importlib.reload(sub_session_dict)
  175. importlib.reload(FFT_clean)
  176. importlib.reload(bssu_from_source_JSON)
  177. importlib.reload(fooof_fit)
  179. # %%
  180. # load if you want to see complete Dataframes
  181. pd.set_option("display.max_rows", None)
  183. # %%
  184. # for interactive plots
  185. # %matplotlib ipympl
  187. # for static plots
  188. %matplotlib inline
  191. # %% [markdown]
  192. # ## 1. Run a script to clean ECG artifacts out of BSSU Channels
  194. # %% [markdown]
  195. # 1. Choose subjects and sessions that show ECG artifacts
  196. # 2. Visualize raw time series
  197. # 3. Fit ICA and visualize its components
  198. # 4. Pick which component contains the ECG artifact
  199. # 5. Clean the ECG artifact from the original data
  200. # 6. Plot the cleaned data and save
  202. # %%
  203. load_pyPerceive = artifacts.load_mne_object_pyPerceive(sub="059", session="fu12m", channel_group="SegmInterR")
  205. # %%
  206. load_pyPerceive.get_data()
  208. # %%
  209. load_json = bssu_from_source_JSON.load_json_data_if_perceive_error(sub="030", session="fu24m", condition="m0s0")
  211. # %%
  212. ecg_artifact_excel = artifacts.get_ecg_artifact_excel()
  214. # %% [markdown]
  215. # Run the cleaning script for each subject
  217. # %%
  218. clean_artifacts_one_sub_all_sessions = artifacts.ecg_cleaning("019")
  220. # %% [markdown]
  223. # %% [markdown]
  224. # Load 2D arrays of channel groups singularly
  226. # %%
  227. load_cleaned_pickle_file = FFT_clean.load_clean_data(sub="021", session="fu3m", channel_group="SegmInterR")
  228. load_cleaned_pickle_file
  230. # %%
  231. loadJSON = bssu_from_source_JSON.load_json_data_if_perceive_error(sub="030", session="fu24m", condition="m0s0")
  233. # %% [markdown]
  234. # ## 2. Plot the Power Spectra of the clean data
  236. # %%
  237. # plot the clean power spectra
  238. sub_list = ["017", "019", "021", "024", "025", "026", "028", "029", "030", "031", "032", "033", "036",
  239. "038", "040", "041", "045", "047", "048", "049", "050", "052", "055", "059", "060", "061", "062", "063", "065", "066"]
  242. filter_list = ["band-pass", "unfiltered"]
  244. for sub in sub_list:
  245. for filt in filter_list:
  246. plot_clean_power_spectra = FFT_clean.plot_power_spectrum(sub=sub, normalization="rawPsd", filter=filt)
  248. # %% [markdown]
  249. # ## 3. Write the JSON Files of the clean Power spectra in all normalization forms
  250. # - Important for running FOOOF afterwards
  252. # %%
  253. # write the cleaned data to a pickle file
  255. sub_list = ["017", "019", "021", "024", "025", "026", "028", "029", "030", "031", "032", "033", "036",
  256. "038", "040", "041", "045", "047", "048", "049", "050", "052", "055", "059", "060", "061", "062", "063", "065", "066"]
  258. for sub in sub_list:
  260. clean_data_file = FFT_clean.write_clean_json_files_with_psd(sub=sub)
  262. # %% [markdown]
  263. # Load the clean PSD file for each subject
  265. # %%
  266. clean_power_spectra = loadResults.load_sub_pickle_file(sub="017", filename="SPECTROGRAMPSD_clean")
  267. clean_power_spectra
  269. # %% [markdown]
  270. # ## 4. Run FOOOF for all clean power spectra
  272. # %%
  273. fooof_single_sub = fooof_fit.fooof_fit_single_cleaned(subject="019", fooof_version="v2")
  275. # %%
  276. fooof_all_subjects = fooof_fit.fooof_group_percept_clean(incl_sub= ["017", "019", "021", "024", "025", "026",
  277. "028", "029", "030", "031", "032", "033",
  278. "036", "038", "040", "041", "045", "047",
  279. "048", "049", "050", "052", "055", "059",
  280. "060", "061", "062", "063", "065", "066"], fooof_version="v2")
  282. # %% [markdown]
  283. # Read the group FOOOF file
  285. # %%
  286. loaded_fooof_result = loadResults.load_pickle_group_result(filename="fooof_group_data_percept", fooof_version="v2")
  287. loaded_fooof_result.head()
  289. # %% [markdown]
  290. # ## Plot raw time series from each subject hemisphere, session and channel group in M0S0
  292. # %%
  293. mne_object = artifacts.load_mne_object_pyPerceive(sub="033", session="fu12m", channel_group="SegmIntraL")
  295. # %%
  296. plot_time_series = artifacts.plot_ieeg_data(sub="024", session="fu18m", channel_group="SegmIntraL", ieeg_data=mne_object.get_data(), fig_title="raw_time_series")
  298. # %%
  299. plot_time_series = artifacts.plot_raw_time_series_before_MNE(sub="024", session="fu18m", channel_group="SegmIntraL")
  301. # %% [markdown]
  302. # Plot a group of raw time series...
  304. # %%
  305. time_series = artifacts.plot_raw_time_series(
  306. incl_sub=["024"],
  307. incl_session=["fu18m"],
  308. incl_condition=["m0s0"],
  309. )
  311. # %% [markdown]
  312. # FIT ICA
  313. # - n_components: if the artifact is strong, not many components needed
  314. # - include all EEG channels including those without ecg artifacts
  315. # - FastICA input must be 2D array: rows = time points, columns = channels
  317. # %%
  318. data = mne_object.get_data() # 2D shape (n_channels in rows, n_times in columns)
  320. # %%
  321. # Step 1: Initialize ICA
  322. # Function to fit data in shape (n_channels, n_times) to ICA
  323. # Output: tuple (ica_components, n_components)
  325. fit_ica = artifacts.fit_ica_on_channel_group(data)
  327. # %%
  328. ica_components = fit_ica[0]
  329. n_components = fit_ica[1]
  332. # %%
  333. # Step 2: Plot ICA components and visually inspect to find the component with the ECG artifact
  335. plot_components = artifacts.plot_ica_components(data)
  337. # %%
  338. # Step 3: Remove ECG artifact from data
  342. # %%
  343. # Step 3: Identify the component representing ECG artifact (replace 5 with the actual index)
  344. ecg_component_index = 5
  345. ecg_component = ica_components[:, ecg_component_index]
  347. # Step 3: Remove ECG artifact contribution from the initial EEG data
  349. # try different scaling of the ECG component
  350. scaled_ecg_component = 0.5 * ecg_component
  351. cleaned_ieeg_data = data.T - np.outer(scaled_ecg_component, ica.mixing_[:, ecg_component_index]) # both shape times x channels
  353. # Now, cleaned_ieeg_data contains the EEG data with the ECG artifact removed
  354. # Note: Depending on the scaling of the components, you might need to adjust the amplitude of the subtracted signal to achieve the desired artifact removal.
  355. # You may also need to experiment with the sign of the subtracted signal.
  356. # If the artifact is not completely removed, you can try multiplying the ECG component by a scaling factor before subtraction.
  359. # %%
  360. cleaned_ieeg_data.shape
  362. # %%
  363. # plot the cleaned channels
  365. cleaned_ieeg_data_transposed = cleaned_ieeg_data.T
  367. time_points = np.arange(cleaned_ieeg_data_transposed.shape[1])
  368. n_channels = cleaned_ieeg_data_transposed.shape[0]
  369. plt.figure(figsize=(100, 20))
  371. fig, axes = plt.subplots(n_channels, 1, figsize=(60, 2 * n_channels), sharex=True, sharey=True)
  373. for i in range(n_channels):
  374. axes[i].plot(time_points, cleaned_ieeg_data_transposed[i], label=f'Channel {i + 1}')
  375. axes[i].set_ylabel(f'Amplitude (Component {i + 1})')
  377. axes[n_channels - 1].set_xlabel('Time points')
  378. plt.tight_layout()
  379. plt.show()
  381. # %% [markdown]
  382. # Using MNE ICA: first re-create the raw MNE objects in PyPerceive and add the info chs kind: "dbs" or "ieeg"
  383. # - without a channel type ICA won't work
  385. # %%
  386. for chan in mne_object.info["chs"]:
  387. print(f"Channel Name: {chan['ch_name']}, Channel Type: {chan['kind']}")
  389. # %%
  390. mne_object.info["chs"]
  392. # %%
  393. #picks = mne.pick_types(mne_object.info, eeg=True, meg=False, stim=False, eog=False, exclude=[])
  394. all_picks = range(mne_object.info['nchan'])
  396. ica = ICA(n_components=20, random_state=97, max_iter="auto")
  397. ica.fit(mne_object, picks=all_picks)
  399. # %%
  400. ica.plot_components()
  401. plt.show()
  403. ica.exclude = [ecg_component_index] # Replace with the index of the ECG component
  404. cleaned_data = ica.apply(raw_data)
  407. plt.plot(cleaned_data[0]) # Replace 0 with the appropriate channel index
  408. plt.title('Cleaned Data without ECG Artifact')
  409. plt.show()
  412. # %%
  413. load_movement_artifact_table = loadResults.load_preprocessing_files(
  414. signal_filter="band-pass", table="movement_artifact_coord"
  415. )
  417. # %%
  418. table = load_movement_artifact_table
  419. table
  421. # %%
  422. clean_power_spectra_table = loadResults.load_preprocessing_files(
  423. table="cleaned_power_spectra"
  424. )
  426. clean_power_spectra_table
  428. # %%
  429. move_artifacts.plot_clean_power_spectra(signal_filter="band-pass")
  431. # %%
  432. hemisphere = "Right"
  434. # depending on hemisphere: define incl_contact
  435. incl_contact = {}
  436. if hemisphere == "Right":
  437. incl_contact["Right"] = ["RingR", "SegmIntraR", "SegmInterR"]
  439. elif hemisphere == "Left":
  440. incl_contact["Left"] = ["RingL", "SegmIntraL", "SegmInterL"]
  442. mainclass_sub = main_class.PerceiveData(
  443. sub = "017",
  444. incl_modalities= ["survey"],
  445. incl_session = ["fu3m"],
  446. incl_condition = ["m0s0"],
  447. incl_task = ["rest"],
  448. incl_contact=incl_contact[f"{hemisphere}"]
  449. )
  452. # %%
  453. for cont, contact in enumerate(incl_contact[f"{hemisphere}"]):
  455. time_series = getattr(mainclass_sub.survey, "fu3m")
  457. #time_series = getattr(mainclass_sub, "m0s0")
  458. #time_series = getattr(mainclass_sub.rest, contact)
  459. #time_series = time_series.run1.data
  461. # %%
  462. data = time_series.m0s0.rest.RingR.run1.data
  464. # %%
  465. channel_1=data.get_data()[0, :]
  467. # %%
  468. fig, ax = plt.subplots()
  469. lines = ax.plot(channel_1)
  470. ax.set_title("Mouse over a point")
  472. ax.plot(channel_1)
  474. x = np.arange(1, len(channel_1)+1)
  475. y = channel_1
  477. pos = []
  478. def onclick(event):
  479. pos.append([event.xdata,event.ydata])
  480. fig.canvas.mpl_connect('button_press_event', onclick)
  481. fig.show()
  483. # %%
  484. pos
  486. # %%
  487. time_series = move_artifacts.plot_raw_time_series(
  488. incl_sub=["024", "025"],
  489. incl_session=["fu3m", "fu12m"],
  490. incl_condition=["m0s0"],
  491. filter="band-pass"
  492. )
  494. # %%
  495. time_series["time_series"]
  497. # %%
  498. first = [x_list[0] for x_list in time_series["pos"]]
  499. second = [y_list[1] for y_list in time_series["pos"]]
  501. df = pd.DataFrame({"x": first, "y": second})
  502. df
  504. # %%
  507. # %% [markdown]
  508. # ## Plot Time Frequency Plots of all subject hemispheres
  510. # %%
  511. plot_time_frequency = TF.time_frequency(
  512. incl_sub=["017"],
  513. incl_session=["postop", "fu3m", "fu12m", "fu18m"],
  514. incl_condition=["m0s0"],
  515. filter_signal="band-pass"
  516. )
  518. # %%

1_bssu_clean_FFT_FOOOF.ipynb at commit 0a9fb48, under MIT · at the source

Overview

Authors: Jennifer K. Behnke1,2, Robert L. Peach3,4,5, Moritz Gerster6, Richard M. Köhler1, Jeroen G. V. Habets1,2, Johannes L. Busch1,2, Varvara Mathiopoulou1, Jonathan Kaplan1, Lucia K. Feldmann1, Juliette Vivien1,7, Chi Wang Ip3, Gerd-Helge Schneider8, Katharina Faust9, Patricia Krause1, Andrea A. Kühn1,2,10,11
ORCID iDs: Jennifer K. Behnke, Lucia K. Feldmann, Patricia Krause, Andrea A. Kühn
  1. Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité University Medicine,Berlin, Germany
  2. Berlin Institute of Health (BIH),Berlin, Germany
  3. Department of Neurology, University Hospital,Würzburg, Germany
  4. Department of Brain Sciences, Imperial College,London, UK
  5. UK Dementia Research Institute, Imperial College,London, UK
  6. Research Group Neural Interactions and Dynamics, Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences,Leipzig, Germany
  7. Humboldt-Universität zu Berlin, Berlin School of Mind and Brain,Berlin, Germany
  8. Department of Neurosurgery, Charité University Medicine,Berlin, Germany
  9. Department of Neurosurgery, University Hospital,Düsseldorf, Germany
  10. NeuroCure Clinical Research Centre, Charité University Medicine,Berlin, Germany
  11. German Center for Neurodegenerative Diseases (DZNE),Berlin, Germany
Institutions: Charité - Universitätsmedizin Berlin (Germany); Berlin Institute of Health at Charité - Universitätsmedizin Berlin (Germany); Universitätsklinikum Würzburg (Germany); Imperial College London (United Kingdom); UK Dementia Research Institute (United Kingdom); Max Planck Institute for Human Cognitive and Brain Sciences (Germany); Humboldt-Universität zu Berlin (Germany); Düsseldorf University Hospital (Germany); German Center for Neurodegenerative Diseases (Germany)
Journal: NPJ Parkinson's disease, volume 12, issue 1, article 114
Dates: received 13 August 2025; accepted 28 April 2026; published online 8 May 2026
Type: Brief report · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41531-026-01380-1 · PMID 42103747 · PMCID PMC13156298 · OpenAlex W4414146149
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.21203/rs.3.rs-7365542/v1
Status: code verified
Categories: extracellular electrophysiology (units, LFP) (modality), Parkinson's (population), clinical / translational (subfield)
Methods: Spectral & time-frequency, Preprocessing, Statistics, Connectivity
Keywords: Neurology, Neuroscience
Topic: Neurological disorders and treatments (Neurology, Medicine), according to OpenAlex
Funding: BIH-Charité Junior Clinician Scientist Program; Deutsche Forschungsgemeinschaft (DFG) (424778381); DAAD; Deutsche Forschungsgemeinschaft, 424778381; Interdisziplinäres Zentrum für Klinische Forschung, Universitätsklinikum Würzburg; Michael J. Fox Foundation for Parkinson’s Research; Germany´s Excellence Strategy - EXC-2049 Project Neurocure - BrainLab (390688087); Lundbeck Foundation (R336-2020-1035)
Citations: not cited yet (Europe PMC); 24 references in the paper

Abstract

Accurate subthalamic beta activity could guide deep brain stimulation programming in Parkinson’s disease, but bipolar recordings complicate contact selection. In 39 patients, we validated three methods to estimate pseudo-monopolar beta power. Distance-weighted methods (Euclidean, Strelow) agreed consistently with the externalized “ground-truth” beta distribution. Maximal beta power across 20s windows was more stable in ring than in directional channels. Beta-contacts from all methods aligned with clinically active stimulation contacts one year after surgery.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

JKBehnke/monopolar_directional_beta

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 0a9fb4836c4e5fef615901c8639800101eba371e, 22 October 2024
Languages: Python (28), Jupyter (8)
Size: 40 files, 36 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, 8 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (1 file), MNE-Python (1 file), MNE-BIDS (1 file), NumPy (1 file), pandas (1 file), Pingouin (1 file), Plotly (1 file), scikit-learn (1 file), SciPy (1 file), seaborn (1 file), statannotations (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
3 files
At the source: github.com/JKBehnke/monopolar_directional_beta

Code availability

The underlying code for this study is available in the repository ‘monopolar_directional_beta’ and can be accessed via this link https://github.com/JKBehnke/monopolar_directional_beta.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1 script, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 15 authors, 2 keywords, 8 funders, 24 references.

Cite

This paper

Behnke, J. K., Peach, R. L., Gerster, M., Köhler, R. M., Habets, J. G. V., Busch, J. L., Mathiopoulou, V., Kaplan, J., Feldmann, L. K., Vivien, J., Ip, C. W., Schneider, G.-H., Faust, K., Krause, P., & Kühn, A. A. (2026). Identifying maximal beta power from directional subthalamic local field potentials in Parkinson's disease. NPJ Parkinson's disease, 12(1), 114. https://doi.org/10.1038/s41531-026-01380-1

BibTeX

@article{behnke2026identifying,
author = {Behnke, Jennifer K. and Peach, Robert L. and Gerster, Moritz and Köhler, Richard M. and Habets, Jeroen G. V. and Busch, Johannes L. and Mathiopoulou, Varvara and Kaplan, Jonathan and Feldmann, Lucia K. and Vivien, Juliette and Ip, Chi Wang and Schneider, Gerd-Helge and Faust, Katharina and Krause, Patricia and Kühn, Andrea A.},
title = {{Identifying maximal beta power from directional subthalamic local field potentials in Parkinson's disease}},
journal = {NPJ Parkinson's disease},
year = {2026},
month = may,
volume = {12},
number = {1},
pages = {114},
publisher = {Nature Publishing Group},
issn = {2373-8057},
doi = {10.1038/s41531-026-01380-1},
url = {https://doi.org/10.1038/s41531-026-01380-1},
pmid = {42103747},
pmcid = {PMC13156298}
}

RIS

TY - JOUR
AU - Behnke, Jennifer K.
AU - Peach, Robert L.
AU - Gerster, Moritz
AU - Köhler, Richard M.
AU - Habets, Jeroen G. V.
AU - Busch, Johannes L.
AU - Mathiopoulou, Varvara
AU - Kaplan, Jonathan
AU - Feldmann, Lucia K.
AU - Vivien, Juliette
AU - Ip, Chi Wang
AU - Schneider, Gerd-Helge
AU - Faust, Katharina
AU - Krause, Patricia
AU - Kühn, Andrea A.
TI - Identifying maximal beta power from directional subthalamic local field potentials in Parkinson's disease
T2 - NPJ Parkinson's disease
J2 - NPJ Parkinsons Dis
PY - 2026
DA - 2026/05/08
VL - 12
IS - 1
SP - 114
SN - 2373-8057
PB - Nature Publishing Group
DO - 10.1038/s41531-026-01380-1
UR - https://doi.org/10.1038/s41531-026-01380-1
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41531-026-01380-1",
"type": "article-journal",
"title": "Identifying maximal beta power from directional subthalamic local field potentials in Parkinson's disease",
"container-title": "NPJ Parkinson's disease",
"author": [
{
"family": "Behnke",
"given": "Jennifer K."
},
{
"family": "Peach",
"given": "Robert L."
},
{
"family": "Gerster",
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},
{
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},
{
"family": "Habets",
"given": "Jeroen G. V."
},
{
"family": "Busch",
"given": "Johannes L."
},
{
"family": "Mathiopoulou",
"given": "Varvara"
},
{
"family": "Kaplan",
"given": "Jonathan"
},
{
"family": "Feldmann",
"given": "Lucia K."
},
{
"family": "Vivien",
"given": "Juliette"
},
{
"family": "Ip",
"given": "Chi Wang"
},
{
"family": "Schneider",
"given": "Gerd-Helge"
},
{
"family": "Faust",
"given": "Katharina"
},
{
"family": "Krause",
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},
{
"family": "Kühn",
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}
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"container-title-short": "NPJ Parkinsons Dis",
"volume": "12",
"issue": "1",
"page": "114",
"DOI": "10.1038/s41531-026-01380-1",
"PMID": "42103747",
"PMCID": "PMC13156298",
"ISSN": "2373-8057",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41531-026-01380-1",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
8
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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