OSCR

Neurons of the human subthalamic nucleus engage with local delta frequency processes during action cancellation.

Code ↔ Paper

39 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 39 matches · 6 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Electrophysiological data acquisition and preprocessing of LFP/EEG data ↔ EEG_LFP_preprocess/preprocess_PD.m, lines 1–90 · score 0.94 · 45–55 Hz, CleanLine, notch filter, pop eegfiltnew, behavioral events, EEGLAB
  2. [2] § Methods › Single- and multiunit activity analysis ↔ unit_LFP_coupling/PC_cell_level.m, lines 1–117 · score 0.91 · Hilbert transformed, band pass filtered, vector length, Preferred phases, spiking activity, phase coupling
  3. [3] § Methods › Stop signal reaction time task paradigm and behavioral analysis ↔ behavioral_analysis/SSDp05_compare.m, lines 1–87 · score 0.91 · SSRT task, DBS stimulator, pd02, pd04, pd07, pd08
  4. [4] § Methods › Electrophysiological data acquisition and preprocessing of LFP/EEG data ↔ EEG_LFP_preprocess/preprocess_PD.m, lines 1–90 · score 0.89 · FastICA, EEGLab plugin, Independent Component, artifact, subtracted, densities
  5. [5] § Methods › Single- and multiunit activity analysis ↔ CellBase_R2013a/Functions/analysis_functions/raster_and_PSTH/psth_stats.m, lines 1–55 · score 0.88 · local extreme, ultimate psth, Mann Whitney, smaller window, baseline window, firing rates
  6. [6] § Methods › Statistics ↔ other/generate_clus_distr_TF.m, lines 1–115 · score 0.87 · frequency band separately, FDR correction, random epochs, wavelet coherence, cluster distribution, sum
  7. [7] § Methods › Single- and multiunit activity analysis ↔ unit_analysis/responsesorter_PD.m, lines 1–115 · score 0.85 · 1.5–3 s, ultimate psth, CellBase, baseline window, behavioral events, firing rates
  8. [8] § Methods › Statistics ↔ behavioral_analysis/RT_perf_compare.m, lines 1–67 · score 0.84 · post hoc Tukey, Kruskal Wallis, Mann Whitney, Kramer, Behavioral, patient
  9. [9] § Methods › Single- and multiunit activity analysis ↔ unit_analysis/L_ratio_ID_distrib.m, lines 1–89 · score 0.83 · refractory period, Isolation Distance, ratio threshold, MCLust, iv, violations
  10. [10] § Methods › Stop signal reaction time task paradigm and behavioral analysis ↔ behavioral_analysis/SSRTime_PD.m, lines 1–115 · score 0.79 · generalized linear regression, SSDp0.5, Median RT, fitting, model, probability
  11. [11] § Results › Stop signal reaction time task performed by patients with Parkinson’s disease ↔ behavioral_analysis/SSRTime_PD.m, lines 1–115 · score 0.76 · generalized linear regression, SSDs corresponding, stop signal delay, SSDp0.5, fitted, model
  12. [12] § Methods › Single- and multiunit activity analysis ↔ CellBase_R2013a/Functions/analysis_functions/spike_clusters/LRatio2.m, lines 1–99 · score 0.76 · waveform energy, Isolation Distance, WavePC1, multiunits, quality, amplitude
  13. [13] § Methods › Single- and multiunit activity analysis ↔ unit_LFP_coupling/PC_groups_f.m, lines 1–122 · score 0.72 · population phase histogram, phase distribution, frequency band, dominant, vector, MRL
  14. [14] § Methods › Statistics ↔ EEG_LFP_time_freq/TFpower_map_RT.m, lines 1–83 · score 0.71 · power coefficients, FDR correction, frequency band, wavelet, permutation, maps
  15. [15] § Methods › Data analysis of LFP/EEG data ↔ EEG_LFP_wav_coherence/wcoh_onebyone.m, lines 1–64 · score 0.66 · squared wavelet coherence, MSWC maps, wcoherence, channel, EEG, LFP
  16. [16] § Results › Bursting STN neurons are less responsive to go signals, less predictive of inhibitory performance and a subset of them strongly lock to delta ↔ unit_LFP_coupling/PC_bursting.m, lines 1–74 · score 0.65 · delta coupling strength, phase coupling strength, bursting units, homogeneity, Watson, PC
  17. [17] § Methods › Electrophysiological data acquisition and preprocessing of LFP/EEG data ↔ CellBase_R2013a/Functions/data_processing_functions/MakeTrialEvents2_gonogo.m, lines 1–97 · score 0.65 · TTL pulses, matched timestamps, broken, synchronized, stimuli, behavioral
  18. [18] § Methods › Data analysis of LFP/EEG data ↔ EEG_LFP_wav_coherence/EEG_LFP_Wcoh_PD.m, the whole file · a weak match · score 0.65 · squared wavelet coherence, MSWC maps, wcoherence, EEG, LFP
  19. [19] § Results › STN neurons were phase coupled to local delta activity, especially before unsuccessful stop attempts ↔ unit_LFP_coupling/PC_bursting.m, lines 1–74 · score 0.65 · delta coupled units, coupling strength, Mann Whitney, Phase coupling, stop signals, median
  20. [20] § Results › Frontal and STN delta power increased during SSRT performance ↔ EEG_LFP_wav_coherence/PD_EEG_LFP_wav_coherence_MAIN.m, the whole file · a weak match · score 0.64 · squared wavelet coherence, correlation maps, Event triggered, ETA, MSWC, intraoperative
  21. [21] § Methods › Statistics ↔ EEG_LFP_time_freq/PD_eeg_stats.m, lines 1–60 · score 0.64 · Fieldtrip toolbox, frequency maps, permutation, power, delta
  22. [22] § Methods › Stop signal reaction time task paradigm and behavioral analysis ↔ task_code/Frame2TTL.m, lines 1–61 · score 0.64 · Sanworks LLC, TTL, Arduino, sensor, connected, Matlab
  23. [23] § Methods › Stop signal reaction time task paradigm and behavioral analysis ↔ behavioral_analysis/PD_nosyncTE.m, lines 1–145 · score 0.60 · button pressing, SSRT task, incorrect, STOP signal, delays, PD
  24. [24] § Methods › Stop signal reaction time task paradigm and behavioral analysis ↔ behavioral_analysis/PD_nosyncTE.m, lines 1–145 · score 0.60 · button presses, Arduino, sensor, TTL, synchronization, Matlab
  25. [25] § Results › Frontal and STN delta power increased during SSRT performance ↔ EEG_LFP_time_freq/TFpower_map_RT.m, lines 1–83 · score 0.60 · subject correlation maps, wavelet power, Pearson, 1–4 Hz, contours, permutation
  26. [26] § Methods › Single- and multiunit activity analysis ↔ unit_analysis/unit_bursting.m, lines 1–69 · score 0.60 · empirical threshold, bursting units, AC, autocorrelograms, median
  27. [27] § Results › STN activity at the recording site predicts the direction of RT change after DBS surgery ↔ behavioral_analysis/updrs_behav_corr.m, lines 1–79 · score 0.60 · disease duration, UPDRS scores, LEDD, onset, motor, RT
  28. [28] § Results › Bursting STN neurons are less responsive to go signals, less predictive of inhibitory performance and a subset of them strongly lock to delta ↔ unit_analysis/unit_bursting.m, lines 1–69 · score 0.58 · median BI, UPDRS scores, bursting unit, AC, autocorrelogram, Correlation
  29. [29] § Results › STN neurons were phase coupled to local delta activity, especially before unsuccessful stop attempts ↔ unit_LFP_coupling/PC_groups_f.m, lines 1–122 · score 0.57 · rose diagram, population phase histogram, coupled, MRL, stop signals, LFP
  30. [30] § Methods › Data analysis of LFP/EEG data ↔ unit_LFP_coupling/PC_cell_level.m, lines 1–117 · score 0.55 · spike phase coupling, Dominant frequency, LFP, stop signals, channels, band
  31. [31] § Methods › Data analysis of LFP/EEG data ↔ unit_LFP_coupling/get_phas.m, the whole file · a weak match · score 0.54 · spike phase coupling, Dominant frequency, LFP, stop signals, channels, band
  32. [32] § Results › Frontal and STN delta power increased during SSRT performance ↔ EEG_LFP_time_freq/ERSP_plot_stat.m, lines 1–80 · score 0.54 · frequency windows, event related, spectral, topo, cue, maps
  33. [33] § Results › STN neurons were phase coupled to local delta activity, especially before unsuccessful stop attempts ↔ behavioral_analysis/RT_comp_cuepair_trialtypes.m, lines 1–115 · score 0.54 · high conflict trials, low conflict, sequence, 1–2, RT, patients
  34. [34] § Results › STN neurons respond to behaviorally relevant events during SSRT ↔ unit_analysis/unit_subregions.m, lines 1–78 · score 0.54 · STN subregions, behaviorally responsive, predictive units, microelectrode, motor, event
  35. [35] § Methods › Clinical evaluation ↔ behavioral_analysis/updrs_behav_corr.m, lines 1–79 · score 0.52 · UPDRS scores, DBS stimulation, medication, Motor, Clinical, patients
  36. [36] § Methods › Patient selection ↔ EEG_LFP_wav_coherence/EEG_LFP_Wcoh_compare_partitions.m, lines 1–77 · score 0.51 · Semmelweis University, Neurointervention, Neurosurgery, Clinical, Budapest, Hungary
  37. [37] § Methods › Clinical evaluation ↔ behavioral_analysis/PD_ssrt_behav_MAIN.m, the whole file · a weak match · score 0.50 · UPDRS scores, DBS stimulation, parkinsonian, implantation, patients
  38. [38] § Methods › Patient selection ↔ other/bootstatFDR_clustercorr.m, the whole file · a weak match · score 0.50 · Semmelweis University, Neurointervention, Neurosurgery, Budapest, Hungary
  39. [39] § Results › Bursting STN neurons are less responsive to go signals, less predictive of inhibitory performance and a subset of them strongly lock to delta ↔ behavioral_analysis/PD_ssrt_behav_MAIN.m, the whole file · a weak match · score 0.50 · UPDRS scores, DBS stimulator, SSDp05, implanted, correlation, RT

Paper

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The authors' code

MATLAB · 689 lines · 23 KB · CC0-1.0 · 2 matches

  1. function preprocess_PD(sess2analyse,epoch_win,EventTypes,SubEventTypes,preproc,baseline_win)
  2. %PREPROCESS_PD preprocesses EEG/LFP data for further analysis
  3. % PREPROCESS_PD(...) preprocesses raw, continuos EEG/LFP data, included in SESS2ANALYSE,
  4. % saves filtered and cleaned data in EEGLAB (.set) format.
  5. % This function requires the following toolboxes:
  6. % EEGLAB (Delorme A & Makeig S, 2004, 10.1016/j.jneumeth.2003.10.009)
  7. % + FileIO, ICLabel, Fieltrip-lite, firfilt, CleanLine plugins
  8. % DBSFILT (by Guillaume Lio,2012, https://github.com/guillaumelio/DBSFILT)
  9. % CSD (by Jürgen Kayser, 2009, doi:10.1016/j.clinph.2005.08.034)
  10. % FastICA (https://research.ics.aalto.fi/ica/fastica/)
  11. %
  12. % Preprocessing steps:
  13. % 1. Load raw data, import channel data, convert into EEGLAB dataset, save
  14. % raw data is .set format.
  15. % 2. Clean DBS artifacts (only if 'stimon' condition):
  16. % DBSFILT GUI (by Guillaume Lio,2012, https://github.com/guillaumelio/DBSFILT)
  17. % Applies frequency-domain Hampel filtering to clean DBS induced
  18. % artifacts.
  19. % 3. Import behavioral data into EEG dataset (according to TrialEvents.mat struct
  20. % synchronized with EEG previously)
  21. % 4. Resample: 250 Hz
  22. % 5. Filter: Lowpass filter with 100 Hz cutoff freq.
  23. % CleanLine filter (by Tim Mullen, 2011, https://github.com/sccn/cleanline)
  24. % Removes 50 Hz linenoise (uses sliding window to adaptively estimate sine wave amplitude to subtract)
  25. % If not sufficiently effective (has to be approved manually),
  26. % 45 - 55 Hz notch filter is applied (pop_eegfiltnew, EEGLAB plugin).
  27. % Two types of highpass filter (resulting in two datasets):
  28. % - 0.5 Hz cutoff freq - for data used for further analysis
  29. % - 2 Hz cutoff freq - for ICA
  30. % Filtered datasets are saved in patient's result directory
  31. % (curr_resdir in sess2analyse struct)
  32. % 6. Data epoching (only if preproc = 'epochs' !): data is epoched into trials,
  33. % according to behavioral events (EVENTTYPES, SUBEVENTTYPES)
  34. % and time window defined by EPOCH_WIN. Trial indeces are also saved
  35. % separately in Evinxx.mat file.
  36. % 7. Baseline subtraction: baseline (BASELINE_WIN) is subtracted from each epoch, each channel.
  37. % 8. Artifact rejection: - bad data portions/trials are removed manually from continuous/epoched EEG (pop_eegplot),
  38. % -- 'continu' preprocessing (PREPROC): boundaries (index & duration of rejected data) are inserted to EEG.event field
  39. % new EEG.event structure is saved in events_with_boundaries.mat file
  40. % -- 'epochs' preprocessing (PREPROC): indeces of rejected trials are saved into rejected_epochs.mat file
  41. % - (bad channels are removed and interpolated)
  42. % - Independent Component Analysis (FastICA, https://research.ics.aalto.fi/ica/fastica/)
  43. % is performed following bad data/trial rejection
  44. % using 2Hz highpass filtered data
  45. % - Resulting components have to be reviewd visually.
  46. % Then components corresponding to artifacts related to
  47. % blinks/facial movements/bad channels are selected.
  48. % Selected components to reject are also saved separately
  49. % to 'gcompreject_continu.mat'
  50. % - Selected components are subtracted from the 0.5 Hz
  51. % filtered data.
  52. % 9. Save final data structure: EEG_continu.set/EEG_2plot.set is used for further analysis.
  53. % Channel data also saved into a separate file:
  54. % [rectype '_EEG_chanlocs.mat']
  55. % 10. Current Source Density transormation is applied to postoperative EEG data:
  56. % CSD toolbox by Jürgen Kayser, 2009 (doi:10.1016/j.clinph.2005.08.034)
  57. % 11. Converts final postop EEG data to bipolar montage and saves data of F4-F3 derivation.
  58. %
  59. % Required inputs:
  60. % SESS2ANALYSE struct containing all necessary information (name of patient, side
  61. % of experiment, tag of condition, session folder path) of
  62. % session data that need to be analysed (see getdata2analyse)
  63. %
  64. % EPOCH_WIN 1x2 vector, time window relative to event timestamp in sec, for data epoching (ex: [-2 2])
  65. %
  66. % EVENTTYPES 1xN cell array of event labels, ex: {'StimulusOn','StopSignal','KeyPress1','Feedback'};
  67. %
  68. % SUBEVENTTYPES Nx2 cell array of partition ("subevent") labels, each row
  69. % corresponds to an event label, columns to partitions
  70. % {'CueStim','StopStim';'FailedStopTrial','SuccesfulStopTrial';'CueResponse','StopResponse';'Correct','Error';};
  71. %
  72. % PREPROC 'continu'| 'epochs' - preprocess and save eeg in coninuous/
  73. % epoched form
  74. %
  75. % BASELINE_WIN 1x2 vector, time window relative to event timestamp in sec, for baseline correction
  76. %
  77. % See also:
  79. % Johanna Petra Szabó, 10.2024
  80. % Lendulet Laboratory of Systems Neuroscience
  81. % Institute of Experimental Medicine, Budapest, Hungary
  82. % [email hidden]
  84. %}
  86. global rootdir ALLEEG CURRENTSET EEG ALLCOM
  89. rectime = sess2analyse(1).rectime;
  90. for snr = 1:length(sess2analyse)
  91. %%
  92. % Preallocate eeglab variables
  93. EEG =[]; ALLEEG = []; ALLCOM = {}; CURRENTSET = 0;
  96. curr_resdir = sess2analyse(snr).folder;
  97. side = sess2analyse(snr).side;
  98. condition = sess2analyse(snr).tag;
  99. patnm = sess2analyse(snr).patient;
  100. currsess = sess2analyse(snr).sessfolder;
  102. rectype = sess2analyse(snr).rectype;
  103. fprintf('%s %s %s...\n',patnm, side, condition);
  107. % Load raw data + filter/ Load filtered data
  109. [EEG_filt1 EEG_filt2 iste] = load_filter_data(currsess,patnm,side,condition,...
  110. curr_resdir,EventTypes,SubEventTypes,rectype, rectime);
  113. if iste==0 % if is TrialEvents.mat (if synchronization was successful)
  114. continue
  115. end
  117. if strcmp(preproc,'epochs')
  118. % Prepare epochs
  119. [EEG_filt1,EEG_filt2] = prep_epochs(EEG_filt1, EEG_filt2, epoch_win);
  120. eeg_setnm = 'EEG_2plot.set';
  122. %
  123. % elseif strcmp(preproc,'continu')
  124. % eeg_setnm = 'EEG_continu.set';
  125. end
  128. % Reject bad data + apply ICA
  130. if exist(fullfile(curr_resdir,eeg_setnm))~=2
  131. [EEG] = reject_bad_ica(EEG_filt1,EEG_filt2, curr_resdir);
  134. if strcmp(preproc,'epochs')
  135. % Save event indeces
  136. save_evinxx(EEG,EventTypes,SubEventTypes,curr_resdir,true);
  139. % Subtract baseline
  140. EEG = substr_bas(EEG,baseline_win);
  141. end
  145. % Save EEG 2 plot
  146. setnm = [curr_resdir filesep eeg_setnm];
  147. pop_saveset(EEG,setnm);
  148. chanlocs = EEG.chanlocs;
  149. save(fullfile(rootdir,[rectype '_EEG_chanlocs.mat']),'chanlocs')
  150. end
  153. end
  155. %% CSD transformation
  156. if strcmp(rectime,'postop')
  157. EEG_CSD_ft(sess2analyse)
  158. end
  162. %% Re-reference 2 bipolar montage (F4-F3) to match intraop recording
  163. if strcmp(rectime,'postop')
  164. reref2bipol(sess2analyse)
  165. end
  169. end
  171. %--------------------------------------------------------------------------
  172. function EEG = load_raw_data(currsess,rectype,rectime,patnm,side,condition,curr_resdir)
  174. global ALLEEG CURRENTSET EEG
  176. switch rectype
  177. case 'EEG'
  179. switch rectime
  180. case 'postop'
  181. EEG = load_postopeeg(currsess,side,condition,curr_resdir);
  183. case 'intraop'
  184. EEG = load_intraopeeg(currsess,patnm,side);
  185. end
  187. case 'LFP'
  188. EEG = load_intraoplfp(currsess,patnm,side);
  189. end
  191. EEG.eegtype = [rectype '_' rectime];
  192. [ALLEEG, EEG, CURRENTSET] = eeg_store( ALLEEG, EEG, 0 );
  193. end
  196. %-------------------------------------------------------------------------
  197. function EEG = load_postopeeg(currsess,side,tag,curr_resdir)
  198. dbstop if error
  201. EEG_ep1 = []; EEG_ep2 = []; iste = 1;
  203. all_eegfile = dir([currsess filesep '*.eeg']);
  204. currfile = find_filetag(all_eegfile,currsess,tag);
  206. % Import data
  207. %-------------
  208. dbfilt = 0;
  209. if ~isempty(dir([curr_resdir filesep 'EEG_raw*' '.set']))
  211. try
  212. % EEG = pop_loadset([curr_resdir filesep 'EEG_raw_TemporalFiltered_DBSfiltered.set']);
  213. EEG = pop_loadset([currsess filesep tag '_' side filesep 'EEG_raw_TemporalFiltered_DBSfiltered.set']);
  214. fprintf('Loading DBS filtered data...\n');
  215. dbfilt = 1;
  218. catch
  219. % EEG = pop_loadset([curr_resdir filesep 'EEG_raw.set']);
  220. EEG = pop_loadset([currsess filesep tag '_' side filesep 'EEG_raw.set']);
  221. fprintf('Loading raw data...\n');;
  222. end
  224. else
  225. % Load .eeg file
  226. EEG = pop_fileio([currsess filesep currfile]);
  228. % Channel locations
  229. %--------------------
  230. EEG = pop_chanedit(EEG);
  232. % Bad channel rejection
  233. % fig = figure;
  234. % pop_spectopo(EEG,1,[1,size(EEG.data,2)],'EEG','freqrange',[2 50],'title','Reject bad channel');
  235. %
  236. % rejch = input('Press 1 for bad channel rejection, press any key otherwise.\n');
  237. %
  238. % if rejch==1
  239. % ch2rem = input('Nr. of channels to remove (for ex: [1,5,13])\n');
  240. ch2rem = [17,22,41,46];
  241. EEG = pop_select(EEG,'nochannel',ch2rem);
  242. % EEG = pop_interp(EEG,ch2rem, 'spherical'); % interpolate
  243. % end
  244. % close(fig)
  246. % Save file in eeglab dataset format
  247. setnm = [curr_resdir filesep 'EEG_raw.set'];
  248. pop_saveset(EEG,setnm);
  249. end
  251. if strcmp(tag,'stimon') && dbfilt==0
  252. diary on
  253. db = DBSFILT;
  254. keyboard
  255. diary off
  256. close(db)
  257. EEG = []; EEG = pop_loadset([curr_resdir filesep 'EEG_raw_TemporalFiltered_DBSfiltered.set']);
  258. end
  259. end
  263. %--------------------------------------------------------------------------
  264. function EEG = load_intraopeeg(currsess,patnm,side);
  266. global rootdir
  267. load(fullfile(rootdir,'sessioninfos_EEG_intraop.mat'));
  268. patrow = find(strcmp(sessioninfos,patnm));
  270. try
  271. eegfile = dir(fullfile(currsess,['*EMG*' sessioninfos{patrow,3} '*.mat']));
  272. eegdat = load(fullfile(eegfile.folder,eegfile.name));
  273. catch
  274. fprintf('No EMG mat file %s\n',currsess);
  275. EEG = [];
  276. return
  277. end
  279. close(gcf)
  280. EEG = pop_importdata('dataformat','array','nbchan',1,'data',eegdat.Data',...
  281. 'setname', [patnm '_' side '_frontaleeg' ] ,'srate',eegdat.SampFreq,...
  282. 'subject',patnm,'pnts',length(eegdat.t),'xmin',0);
  283. % EEG.chanlocs.labels = sessioninfos{patrow,4};
  284. % EEG.ref = sessioninfos{patrow,5};
  285. EEG.chanlocs.labels = sessioninfos{patrow,5};
  286. EEG.ref = sessioninfos{patrow,4};
  287. end
  290. %--------------------------------------------------------------------------
  291. function [EEG_filt1 EEG_filt2 iste] = load_filter_data(currsess,patnm,side,condition,curr_resdir,EventTypes,SubEventTypes,rectype,rectime)
  295. EEG_ep1 = []; EEG_ep2 = []; iste = 1;
  296. cd(curr_resdir)
  298. if isempty(dir([curr_resdir filesep 'EEG_*_2HP.set'])) && isempty(dir([curr_resdir filesep 'EEG_*_05HP.set']))
  300. %Load raw data
  301. %---------------
  303. EEG = load_raw_data(currsess,rectype,rectime,patnm,side,condition,curr_resdir);
  304. global ALLEEG CURRENTSET EEG
  307. % Label events
  308. %--------------
  309. try
  310. EEG = behav_events(EEG,EventTypes,SubEventTypes,currsess,condition);
  311. iste = 1;
  312. catch
  313. fprintf('No TE file\n');
  314. iste = 0; EEG_filt1 = []; EEG_filt2 = [];
  315. return
  316. end
  318. % Downsample
  319. %-------------
  320. new_sr = 250;
  321. EEG = pop_resample(EEG,new_sr);
  322. EEG.srate = new_sr;
  324. % Filter
  325. %------------
  327. % Lowpass filter for stimoff
  328. if strcmp(condition,'stimoff')
  329. hicutoff = 100; % higher edge of passband: 100 Hz
  330. [EEG, com, b] = pop_eegfiltnew(EEG,[],hicutoff);
  331. end
  333. % Linenoise removal
  334. %%
  335. [EEG Lnfilt] = rem_line_noise(EEG);
  336. %%
  338. % High-pass filter: dataset 1 for analyses, dataset 2 for ICA
  340. locutoff = 0.5; % lower edge of passband: 0.5 Hz
  341. [EEG_filt1, com, b] = pop_eegfiltnew(EEG,locutoff,[]);
  344. locutoff = 2; % lower edge of passband: 2 Hz (ICA might be biased by low freqs)
  345. [EEG_filt2, com, b] = pop_eegfiltnew(EEG,locutoff,[]);
  349. % Reject data chunk if there is long-lasting noise
  351. % [EEG_filt1,EEG_filt2] = rej_badtrials(EEG_filt1,EEG_filt2,curr_resdir);
  354. % Save
  355. setnm = [curr_resdir filesep 'EEG_filt' Lnfilt '_05HP.set'];
  356. pop_saveset(EEG_filt1,setnm);
  358. setnm = [curr_resdir filesep 'EEG_filt' Lnfilt '_2HP.set'];
  359. pop_saveset(EEG_filt2,setnm);
  362. else
  364. eegf2= dir([curr_resdir filesep 'EEG_*_2HP.set']);
  365. eegf05= dir([curr_resdir filesep 'EEG_*_05HP.set']);
  366. EEG_filt1 = pop_loadset(eegf05(1).name);
  367. EEG_filt2 = pop_loadset(eegf2(1).name);
  368. iste = 1;
  369. end
  370. end
  372. %--------------------------------------------------------------------------
  373. function reref2bipol(sess2analyse)
  374. %REREF2BIPOL Re-reference data to bipolar montage (only F4-F3 derivation)
  375. % REREF2BIPOL(sess2analyse) Saves re-referenced EEG data as EEG_2plot_bipol.set
  377. % Required input: sess2analyse: structure containing data to analyse (see getdata2analyse)
  378. for snr = 1:length(sess2analyse)
  379. curr_resdir = sess2analyse(snr).folder;
  381. try
  382. EEG = pop_loadset(fullfile(curr_resdir,'EEG_2plot.set'));
  383. catch
  384. fprintf('NO EEG %s\n',curr_resdir);
  385. continue;
  386. end
  387. EEG = pop_select(EEG,'channel',{'F4','F3'});
  388. EEGbip = pop_reref(EEG,{'F3'});
  389. EEGbip.chanlocs(1).labels = 'F4-F3';
  391. pop_saveset(EEGbip,fullfile(curr_resdir,'EEG_2plot_bipol.set'))
  393. EEG = [];
  395. end
  396. end
  399. %--------------------------------------------------------------------------
  400. function EEG_CSD_ft(s2a)
  401. %EEG_CSD_FT Applies CSD tranformation on EEG data
  402. % EEG_CSD_ft(s2a) Applies CSD tranformation on EEG data defined
  403. % in S2A (see getdata2analyse), using CSD toolbox by Jürgen Kayser.
  404. % Kayser, J., Tenke, C.E. (2006a). doi:10.1016/j.clinph.2005.08.034
  405. % Kayser, J. (2009).Current source density (CSD) interpolation using spherical splines -
  406. % CSD Toolbox (Version 1.1) [http://psychophysiology.cpmc.columbia.edu/Software/CSDtoolbox].
  407. % New York State Psychiatric Institute: Division of Cognitive Neuroscience.
  408. %
  409. % Transformed data is saved as EEG_2plot_CSD.set in the patient's result
  410. % directory (sess2analyse.curr_resdir).
  411. %
  412. % Input parameter:
  413. % S2A struct with details of data to analyse (see getdata2analyse.m)
  415. for snr = 1:length(s2a)
  417. try
  418. EEG = pop_loadset(fullfile(s2a(snr).folder,'EEG_2plot.set'));
  419. catch
  420. fprintf('No EEG\n');
  421. continue
  422. % pause
  423. end
  425. EEG = pop_currentdensity(EEG, 'method','spline');
  427. try
  428. pop_saveset(EEG,fullfile(s2a(snr).folder,'EEG_2plot_CSD.set'))
  429. catch
  430. pause
  431. end
  432. end
  433. end
  435. %--------------------------------------------------------------------------
  436. function [EEG_ep1, EEG_ep2] = rej_badtrials(EEG_ep1,EEG_ep2,curr_resdir)
  438. global ALLEEG CURRENTSET EEG
  440. % Reject bad trials/data
  441. %-------------------------
  443. EEG = EEG_ep2;
  445. if EEG.trials~=1
  446. if exist([curr_resdir filesep 'rejected_epochs.mat'])==2; ifrej = 1; else; ifrej = 0; end;
  447. % else
  448. % if exist([curr_resdir filesep 'events_with_boundaries.mat'])==2; ifrej = 1; else; ifrej = 0; end;
  449. end
  452. if ifrej==0
  453. pop_eegplot(EEG,1,1,1,[],'srate',EEG.srate,'spacing',75,...
  454. 'eloc_file',EEG.chanlocs, 'winlength',30,'dispchans',32,'events',EEG.event,...
  455. 'plottitle', 'Reject bad trials/data');
  458. fig1 = gcf;
  459. input('Select trials/data to reject, if ready press REJECT button, than any write any character to command window.\n');
  460. end
  463. % if EEG.trials~=1
  466. if ifrej==0
  467. allep = 1:size(EEG_ep1.epoch,2);
  468. rejected_eps = allep(~ismember(allep,[EEG.epoch.index]));
  469. save([curr_resdir filesep 'rejected_epochs.mat'],'rejected_eps');
  470. elseif ifrej==1
  471. load([curr_resdir filesep 'rejected_epochs.mat']);
  472. EEG = pop_rejepoch(EEG,rejected_eps);
  473. end
  475. EEG_ep1 = pop_rejepoch(EEG_ep1,rejected_eps);
  479. % else
  480. % if ifrej==0
  481. % EEG_ep1.event = EEG.event;
  482. % events_with_boundaries = EEG.event;
  483. % save(fullfile(curr_resdir, 'events_with_boundaries.mat'),'events_with_boundaries')
  484. % elseif ifrej==1
  485. % load([curr_resdir filesep 'events_with_boundaries.mat'])
  486. % EEG_ep1.event = events_with_boundaries;
  487. % EEG.event = events_with_boundaries;
  488. % end
  489. %
  490. % end
  491. EEG_ep2 = EEG;
  492. end
  495. %--------------------------------------------------------------------------
  496. function [EEG_ep1] = reject_bad_ica(EEG_ep1,EEG_ep2, curr_resdir)
  498. % Reject bad trials manually
  499. % Perform independent component analysis on high-pass filtered EEG data
  500. % (EEG_ep2), removes manually selected ICA components from original (not
  501. % high-pass filtered) data (EEG_ep1).
  502. % curr_resdir: data folder to save new EEG data structure with ICA
  503. % components (EEG_ICA.set) + save selected components (gcompreject.mat)
  506. global ALLCOM ALLEEG CURRENTSET EEG
  508. % data for ICA analysis (it has to be assigned to EEG variable for the eeglab to properly execute ica related functions/GUIs)
  510. %% Reject bad trials
  511. if ~contains(curr_resdir,'LFP')
  513. [EEG_ep1, EEG_ep2] = rej_badtrials(EEG_ep1,EEG_ep2,curr_resdir);
  515. end
  518. %% ICA
  519. if ~contains(curr_resdir,'LFP') && length(EEG_ep1.chanlocs)>1
  521. if EEG.trials~=1
  522. ica_setnm = [curr_resdir filesep 'EEG_ICA.set'];
  523. crej_nm= [curr_resdir filesep 'gcompreject.mat'];
  524. else
  525. ica_setnm = [curr_resdir filesep 'EEG_ICA_continu.set'];
  526. crej_nm= [curr_resdir filesep 'gcompreject_continu.mat'];
  527. end
  529. if exist(ica_setnm)==2; ifica = 1; else; ifica = 0; end;
  531. if ifica==0
  533. % get rank of data
  535. % curr_rank = rank(reshape(EEG_ep2.data,[size(EEG_ep2.data,1),size(EEG_ep2.data,2)*size(EEG_ep2.data,3)]));
  537. EEG = EEG_ep2;
  538. EEG_ICA = pop_runica(EEG, 'icatype', 'fastica');
  539. pop_saveset(EEG_ICA,ica_setnm);
  541. else
  542. EEG_ICA = pop_loadset(ica_setnm);
  543. end
  544. EEG = EEG_ICA; % it has to be assigned to EEG variable for the eeglab to properly execute ica related functions/GUIs
  547. % Label components to reject
  548. if exist(crej_nm)~=2
  549. try
  550. pop_eegplot( EEG, 0, 1, 1,[],'dispchans',20);
  551. EEG= pop_selectcomps(EEG, 1:20 );
  552. catch
  553. fprintf('ICA gone wild.\n')
  554. close(gcf); close(gcf);
  555. %continue
  556. end
  557. input('Select ICs to reject, if ready, press any key.\n');
  558. end
  559. %% Remove selected ICA components from original EEG
  560. EEG_ep1 = applyica(EEG,EEG_ep1,crej_nm);
  562. end
  563. end
  567. %--------------------------------------------------------------------------
  568. function EEG_ep1 = applyica(EEG,EEG_ep1,crej_nm)
  570. if exist(crej_nm)~=2
  571. gcompreject = EEG.reject.gcompreject;
  572. save(crej_nm,'gcompreject');
  573. else
  574. load(crej_nm);
  575. end
  577. % Apply ICA for "minimally" filtered data (dataset 1)
  578. EEG_ep1.reject = EEG.reject; EEG_ep1.icawinv = EEG.icawinv;
  579. EEG_ep1.icasphere = EEG.icasphere; EEG_ep1.icaweights = EEG.icaweights; EEG_ep1.icachansind = EEG.icachansind;
  580. EEG_ep1.reject.gcompreject = gcompreject;
  582. EEG_ep1 = pop_subcomp(EEG_ep1,[],1,0);
  583. end
  586. %--------------------------------------------------------------------------
  587. function [EEG, Lnfilt] = rem_line_noise(EEG)
  588. % Removes power line noise (50 Hz) from EEG data (eeglab structure)
  589. % First tries CleanLine, if noise has not been removed sufficiently (has to
  590. % be checked visually on the appeared PSD), notch filter is applied (45-55
  591. % Hz notch filter). Label of applied filter is stored in Lnfilt variable.
  593. % Is there any line noise?
  594. [fig1, fig2] = check_eegdata(EEG);
  595. inp0 = input('Linenoise? If no linenoise, press 0, otherwise any key.\n');
  596. % inp0 = 1;
  597. %
  598. close(fig1); close(fig2);
  601. if inp0==0
  602. Lnfilt = 'NoLN';
  604. else
  605. % CLEANLINE FILTER
  607. EEG = pop_cleanline(EEG, 'bandwidth',2,'chanlist',[1:EEG.nbchan] ,...
  608. 'computepower',1,'linefreqs',50,'newversion',0,...
  609. 'normSpectrum',0,'p',0.01,'pad',2,'plotfigures',0,'scanforlines',0,...
  610. 'sigtype','Channels','taperbandwidth',2,'tau',100,'verb',1,'winsize',4,'winstep',4);
  611. Lnfilt = 'CLN';
  613. [fig1, fig2] = check_eegdata(EEG, strcat('CleanLine filtered data'));
  615. inp = input('Linenoise removed? If yes, press 1, otherwise any key\n');
  616. % inp = 0;
  618. if inp~=1
  619. close(fig1,fig2)
  620. % notch filter to remove 50 Hz linenoise
  621. EEG = pop_eegfiltnew(EEG, 'locutoff',45,'hicutoff',55,'revfilt',1,'plotfreqz',1);
  622. Lnfilt = 'notch';
  623. [fig1, fig2] = check_eegdata(EEG,'');
  624. end
  626. close(fig1,fig2);
  629. end
  630. end
  632. %--------------------------------------------------------------------------
  633. function [fig1, fig2] = check_eegdata(EEG, figtitle)
  634. % Plots power spectrum
  636. narginchk(1,2)
  637. if nargin<2
  638. figtitle = '';
  639. end
  641. % plot eeg time series
  643. pop_eegplot(EEG,1,1,1,[],'srate',EEG.srate,'spacing',75,...
  644. 'eloc_file',EEG.chanlocs, 'winlength',5,'dispchans',32,'events',EEG.event,...
  645. 'plottitle', figtitle);
  646. fig1 = gcf;
  648. % plot power spectrum
  650. fig2 = figure;
  651. pop_spectopo(EEG,1,[1,size(EEG.data,2)],'EEG','freqrange',[1 100],'title',figtitle);
  652. end
  655. %--------------------------------------------------------------------------
  656. function EEG_ep = substr_bas(EEG_ep,baseline_win)
  657. % Subtract baseline from each channel and epoch
  659. % EEG_ep: epoched EEG data (eeglab format)
  660. % baseline_win: baseline window relative to event timetamps in seconds (ex: [-2 -1])
  661. %-----------------------------------------------
  662. bas_fr = abs(baseline_win(1,1)) + abs(baseline_win(1,2))*EEG_ep.srate;
  663. EEG_ep.data = rmbase(EEG_ep.data,[],[1:bas_fr]); % dataset 1
  664. end
  669. %--------------------------------------------------------------------------
  670. function fnm = find_filetag(allfiles,session,tag)
  673. if (strcmp(session(end), 'l')||contains(session, 'left')) &&strcmp(tag, 'stimoff') %if left side - find the right marker file and behavior
  674. strmk = {'01' 'stim_off' 'stimoff' 'off'}; %stim off
  675. elseif (strcmp(session(end), 'l')||contains(session, 'left'))&&strcmp(tag, 'stimon')
  676. strmk = {'03' 'stim_on' 'stimon' 'on'}; % stim on
  677. elseif (strcmp(session(end), 'r')||contains(session, 'right'))&&strcmp(tag, 'stimoff') % right side
  678. strmk = {'02' 'stim_off' 'stimoff' 'off'}; %stim off
  679. elseif (strcmp(session(end), 'r')||contains(session, 'right'))&&strcmp(tag, 'stimon') % right side
  680. strmk = {'04' 'stim_on' 'stimon' 'on'}; % stim on
  681. end
  683. for i = 1:length(allfiles)
  684. current_file = allfiles(i).name;
  685. if any(cellfun(@(x) contains(current_file,x), strmk))
  686. fnm = current_file;
  687. end
  688. end
  689. end

preprocess_PD.m at commit 8a7f480, under CC0-1.0 · at the source

Overview

Authors: Johanna Petra Szabó1,2,3, Panna Hegedüs1,3,4, Tamás Laszlovszky1,3, László Halász5, Gabriella Miklós3,5, Bálint Király1,6, György Perczel5, Virág Bokodi2,7, Lászlo Entz5,8,9, István Ulbert5,10,11, Gertrúd Tamás12, Dániel Fabó13,14, Loránd Erőss5, Balázs Hangya1,6,15
ORCID iDs: Bálint Király, István Ulbert, Gertrúd Tamás, Dániel Fabó, Balázs Hangya
15 affiliations
  1. Laboratory of Systems Neuroscience, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
  2. Epilepsy Center, Institute of Neurosurgery and Neurointervention, Semmelweis University, Budapest, Hungary
  3. János Szentágothai Neurosciences Program, Semmelweis University School of PhD Studies, Budapest, Hungary
  4. Department of Pathology, Forensic and Insurance Medicine, Semmelweis University, Budapest, Hungary
  5. Department of Functional Neurosurgery, Institute of Neurosurgery and Neurointervention, Semmelweis University, Budapest, Hungary
  6. Division of Neurophysiology, Center for Brain Research, Medical University of Vienna, Vienna, Austria
  7. Roska Tamás Doctoral School of Sciences and Technologies, Péter Pázmány Catholic University, Budapest, Hungary
  8. Endomin Center, Clinic Hirslanden Zürich, Zürich, Switzerland
  9. MIND Clinic, Budapest, Hungary
  10. Institute of Cognitive Neuroscience and Psychology, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary
  11. Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
  12. Department of Neurology, Semmelweis University, Budapest, Hungary
  13. Department of Voice, Speech and Swallowing, Semmelweis University, Budapest, Hungary
  14. Department of Neurology, University of Szeged, Szeged, Hungary
  15. Subcortical Modulation Research Group, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
Institutions: Semmelweis University (Hungary); HUN-REN Institute of Experimental Medicine (Hungary); Medical University of Vienna (Austria); Pázmány Péter Catholic University (Hungary); Klinik Hirslanden (Switzerland); GastroZentrum Hirslanden Zürich (Switzerland); Institute of Cognitive Neuroscience and Psychology (Hungary); HUN-REN Research Centre for Natural Sciences (Hungary); University of Szeged (Hungary); Hungarian Research Network (Hungary)
Journal: Nature communications, volume 17, issue 1, article 5536
Dates: received 14 November 2024; accepted 23 March 2026; published online 21 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-71502-z · PMID 42014723 · PMCID PMC13287684 · OpenAlex W4404946208
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2024.12.02.24318298
Status: code verified
Categories: extracellular electrophysiology (units, LFP) (modality), human (organism), Parkinson's (population)
Methods: Spectral & time-frequency, Preprocessing, Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity, fMRI & imaging, Single-unit activity, calcium imaging, Physiology & signal measures
Keywords: Sensorimotor processing, Neuroscience
MeSH: Delta Rhythm*, Neurons*, Parkinson Disease*, Subthalamic Nucleus*, Action Potentials, Aged, Deep Brain Stimulation, Female, Humans, Local Field Potential Measurement, Male, Middle Aged, Reaction Time (* major topic)
Topic: Neurological disorders and treatments (Neurology, Medicine), according to OpenAlex
Funding: European Research Council (101123104)
Citations: cited by 1 paper (Europe PMC); 111 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

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adredish/MClust-Spike-Sorting-Toolbox

License: GPL-3.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 1bc8401d1bc869acc73e18c1dd110b401ef3984b, 21 August 2022
Languages: MATLAB (180), C (9), C/C++ (5), C++ (3)
Size: 208 files, 197 scripts
Software Heritage: not archived
Found in: the text, “Single- and multiunit activity analysis”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (4 files), Open Ephys analysis tools (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
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hangyabalazs/CellBase

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Commit: d8ca9b7fa7828c6503de42b5035c83dcacf14413, 24 February 2021
Languages: MATLAB (338), Java (1)
Size: 624 files, 339 scripts
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Found in: the text, “Single- and multiunit activity analysis”
Holds: README
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Tools: Statistics and Machine Learning Toolbox (56 files), Signal Processing Toolbox (7 files), export_fig (4 files), Image Processing Toolbox (1 file), Open Ephys analysis tools (1 file)
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hangyabalazs/human-STN-delta

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Commit: 8a7f480cb9c894a7a2f3fafcf90800f641f3dab2, 31 October 2025
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Size: 120 files, 117 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (34 files), EEGLAB (11 files), Psychtoolbox (6 files), Signal Processing Toolbox (5 files), Image Processing Toolbox (2 files), Wavelet Toolbox (2 files)
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At the source: github.com/hangyabalazs/human-STN-delta

Zenodo 18679923

License: CC0-1.0
State: the link answers, verified on 29 September 2026
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Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (34 files), EEGLAB (11 files), Psychtoolbox (6 files), Signal Processing Toolbox (5 files), Image Processing Toolbox (2 files), Wavelet Toolbox (2 files)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
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119 files
At the source: zenodo.org/records/18679923

kiralyb/human-STN-delta

License: CC0-1.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 8a7f480cb9c894a7a2f3fafcf90800f641f3dab2, 31 October 2025
Languages: MATLAB (117)
Size: 120 files, 117 scripts
Software Heritage: not archived
Found in: the references
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (34 files), EEGLAB (11 files), Psychtoolbox (6 files), Signal Processing Toolbox (5 files), Image Processing Toolbox (2 files), Wavelet Toolbox (2 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
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119 files
At the source: github.com/kiralyb/human-STN-delta

Code availability statement

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Read it in the paper: doi.org/10.1038/s41467-026-71502-z.

Tracing map

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What the map holds:

  • 5 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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  • 39 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

Datasets cited

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

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Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 14 authors, 2 keywords, 13 MeSH terms, 1 funder, 106 references.

Cite

This paper

Szabó, J. P., Hegedüs, P., Laszlovszky, T., Halász, L., Miklós, G., Király, B., Perczel, G., Bokodi, V., Entz, L., Ulbert, I., Tamás, G., Fabó, D., Erőss, L., & Hangya, B. (2026). Neurons of the human subthalamic nucleus engage with local delta frequency processes during action cancellation. Nature communications, 17(1), 5536. https://doi.org/10.1038/s41467-026-71502-z

BibTeX

@article{szabo2026neurons,
author = {Szabó, Johanna Petra and Hegedüs, Panna and Laszlovszky, Tamás and Halász, László and Miklós, Gabriella and Király, Bálint and Perczel, György and Bokodi, Virág and Entz, Lászlo and Ulbert, István and Tamás, Gertrúd and Fabó, Dániel and Erőss, Loránd and Hangya, Balázs},
title = {{Neurons of the human subthalamic nucleus engage with local delta frequency processes during action cancellation}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {5536},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71502-z},
url = {https://doi.org/10.1038/s41467-026-71502-z},
pmid = {42014723},
pmcid = {PMC13287684}
}

RIS

TY - JOUR
AU - Szabó, Johanna Petra
AU - Hegedüs, Panna
AU - Laszlovszky, Tamás
AU - Halász, László
AU - Miklós, Gabriella
AU - Király, Bálint
AU - Perczel, György
AU - Bokodi, Virág
AU - Entz, Lászlo
AU - Ulbert, István
AU - Tamás, Gertrúd
AU - Fabó, Dániel
AU - Erőss, Loránd
AU - Hangya, Balázs
TI - Neurons of the human subthalamic nucleus engage with local delta frequency processes during action cancellation
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/21
VL - 17
IS - 1
SP - 5536
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71502-z
UR - https://doi.org/10.1038/s41467-026-71502-z
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-71502-z",
"type": "article-journal",
"title": "Neurons of the human subthalamic nucleus engage with local delta frequency processes during action cancellation",
"container-title": "Nature communications",
"author": [
{
"family": "Szabó",
"given": "Johanna Petra"
},
{
"family": "Hegedüs",
"given": "Panna"
},
{
"family": "Laszlovszky",
"given": "Tamás"
},
{
"family": "Halász",
"given": "László"
},
{
"family": "Miklós",
"given": "Gabriella"
},
{
"family": "Király",
"given": "Bálint"
},
{
"family": "Perczel",
"given": "György"
},
{
"family": "Bokodi",
"given": "Virág"
},
{
"family": "Entz",
"given": "Lászlo"
},
{
"family": "Ulbert",
"given": "István"
},
{
"family": "Tamás",
"given": "Gertrúd"
},
{
"family": "Fabó",
"given": "Dániel"
},
{
"family": "Erőss",
"given": "Loránd"
},
{
"family": "Hangya",
"given": "Balázs"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "5536",
"DOI": "10.1038/s41467-026-71502-z",
"PMID": "42014723",
"PMCID": "PMC13287684",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-71502-z",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
21
]
]
}
}

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The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1371/journal.pbio.3003635 [code]
A cortico-subthalamic circuit rapidly engages and releases inhibition of specific movements depending on the environmental context.
Journal: PLoS biology
In common: EEGLAB, Image Processing Toolbox, Signal Processing Toolbox, 1 other tool, 9 references
[2] doi:10.7554/elife.109622 [code]
Distinct involvements of the subthalamic nucleus subpopulations in reward-biased decision-making in monkeys.
Journal: eLife
In common: Statistics and Machine Learning Toolbox, 10 references
[3] doi:10.1038/s41598-026-49900-6 [code]
Global neural oscillations underlie performance variability and attentional state fluctuations in humans.
Journal: Scientific reports
In common: Wavelet Toolbox, EEGLAB, Image Processing Toolbox, 2 other tools, 1 reference
[4] doi:10.1111/psyp.70271 [code]
Disentangling Respiratory Phase-Dependent and Phase-Independent Components of Anticipatory Cardiac Deceleration.
Journal: Psychophysiology
In common: Psychtoolbox, EEGLAB, Image Processing Toolbox, 2 other tools, 2 references
[5] doi:10.1038/s41593-026-02357-2 [code]
Experience reorganizes content-specific memory traces in macaques.
Journal: Nature neuroscience
In common: export_fig, EEGLAB, Image Processing Toolbox, 2 other tools, 1 reference
[6] doi:10.1016/j.celrep.2026.117646 [code]
Medial entorhinal-hippocampal desynchronization parallels the emergence of memory impairment in a mouse model of Alzheimer's disease pathology.
Journal: Cell reports
In common: export_fig, EEGLAB, Image Processing Toolbox, 2 other tools, 1 reference
[7] doi:10.1002/hbm.70602 [code]
Neuroimaging Correlates of Post-Stroke Pain After Ischemic Stroke: Secondary Analysis of the INSPiRE-TMS Trial.
Journal: Human brain mapping
In common: export_fig, Psychtoolbox, Image Processing Toolbox, 2 other tools, 1 reference
[8] doi:10.1038/s41586-026-10631-3 [code]
A prognostic human brain network for diffuse midline glioma.
Journal: Nature
In common: export_fig, Psychtoolbox, Image Processing Toolbox, 2 other tools, 1 reference
[9] doi:10.1111/ene.70678 [code]
Who Falls After a Stroke? Evidence From a Prospective Stroke Cohort.
Journal: European journal of neurology
In common: export_fig, Psychtoolbox, Image Processing Toolbox, 2 other tools, 1 reference
[10] doi:10.1038/s41467-026-75347-4 [code]
Sleep reveals dynamics integrating and segregating movement and stimulus representations in V1.
Journal: Nature communications
In common: export_fig, EEGLAB, Image Processing Toolbox, 2 other tools, 1 reference

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