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Distinct beta burst motifs exhibit opposing error relationships during motor adaptation

Code ↔ Paper

10 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 10 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Beta power and burst extraction ↔ python/burst_detection.py, lines 179–261 · score 0.95 · DC offset, Hilbert transform, instantaneous phase, detected peak, phase minimum, burst detection
  2. [2] § Methods › Beta power and burst extraction ↔ matlab/extract_bursts_single_trial.m, lines 118–188 · score 0.92 · DC offset, Hilbert transform, instantaneous phase, phase minimum, peak amplitude, deflection
  3. [3] § Methods › Beta power and burst extraction ↔ python/burst_detection.py, lines 264–330 · score 0.86 · aperiodic spectrum, event related, burst detection, detected bursts, burst waveforms, ERF
  4. [4] § Methods › Beta power and burst extraction ↔ matlab/extract_bursts.m, the whole file · a weak match · score 0.83 · aperiodic spectrum, event related, detected bursts, burst waveforms, ERF, regressed
  5. [5] § Methods › Beta power and burst extraction ↔ python/burst_detection.py, lines 131–177 · score 0.73 · noise floor, peak amplitude, peak frequency, iteration, Gaussian, subtracted
  6. [6] § Methods › Beta power and burst extraction ↔ matlab/extract_bursts_single_trial.m, lines 43–116 · score 0.73 · noise floor, peak amplitude, peak frequency, Gaussian, subtracted, band
  7. [7] § Methods › Statistics › Behavior ↔ 02_behav_stats.R, the whole file · a weak match · score 0.63 · emmeans, lme4, adaptation blocks, Pairwise, variable, zero
  8. [8] § Methods › Statistics › Burst features, burst count and PC-quartile time series ↔ xx_bursts_lmms.ipynb, lines 89–147 · score 0.54 · random intercepts, Separate models, variable, fit, error, implicit
  9. [9] § Results › Waveform-specific burst dynamics predict trial-by-trial error ↔ xx_bursts_lmms.ipynb, lines 625–767 · score 0.53 · Linear mixed, sliding window, beta burst, variable, fitted, models
  10. [10] § Methods › MEG ↔ xx_beta_power_lmms.ipynb, lines 179–231 · score 0.50 · MEG signal, removal, tracking, match, filtered

Paper

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The authors' code

Python · 330 lines · 13 KB · no license · 3 matches

  1. import copy
  2. import math
  3. import numpy as np
  4. from mne.filter import filter_data
  5. from scipy.signal import hilbert, argrelextrema
  6. from scipy.stats import linregress
  9. def gaus2d(x=0, y=0, mx=0, my=0, sx=1, sy=1):
  10. """
  11. Two-dimensional gaussian function
  12. :param x: x grid
  13. :param y: y grid
  14. :param mx: mean in x dimension
  15. :param my: mean in y dimension
  16. :param sx: standard deviation in x dimension
  17. :param sy: standard deviation in y dimension
  18. :return: Two-dimensional Gaussian distribution
  19. """
  20. return np.exp(-((x - mx) ** 2. / (2. * sx ** 2.) + (y - my) ** 2. / (2. * sy ** 2.)))
  23. def overlap(a, b):
  24. """
  25. Find if two ranges overlap
  26. :param a: first range [low, high]
  27. :param b: second range [low, high]
  28. :return: True if ranges overlap, false otherwise
  29. """
  30. return a[0] <= b[0] <= a[1] or b[0] <= a[0] <= b[1]
  33. def fwhm_burst_norm(tf, peak):
  34. """
  35. Find two-dimensional FWHM
  36. :param tf: TF spectrum
  37. :param peak: peak of activity [freq, time]
  38. :return: right, left, up, down limits for FWM
  39. """
  40. right_loc = np.nan
  41. # Find right limit (values to right of peak less than half value at peak)
  42. cand = np.where(tf[peak[0], peak[1]:] <= tf[peak] / 2)[0]
  43. # If any found, take the first one
  44. if len(cand):
  45. right_loc = cand[0]
  47. up_loc = np.nan
  48. # Find up limit (values above peak less than half value at peak)
  49. cand = np.where(tf[peak[0]:, peak[1]] <= tf[peak] / 2)[0]
  50. # If any found, take the first one
  51. if len(cand):
  52. up_loc = cand[0]
  54. left_loc = np.nan
  55. # Find left limit (values below peak less than half value at peak)
  56. cand = np.where(tf[peak[0], :peak[1]] <= tf[peak] / 2)[0]
  57. # If any found, take the last one
  58. if len(cand):
  59. left_loc = peak[1] - cand[-1]
  61. down_loc = np.nan
  62. # Find down limit (values below peak less than half value at peak)
  63. cand = np.where(tf[:peak[0], peak[1]] <= tf[peak] / 2)[0]
  64. # If any found, take the last one
  65. if len(cand):
  66. down_loc = peak[0] - cand[-1]
  68. # Set arms equal if only one found
  69. if down_loc is np.nan:
  70. down_loc = up_loc
  71. if up_loc is np.nan:
  72. up_loc = down_loc
  73. if left_loc is np.nan:
  74. left_loc = right_loc
  75. if right_loc is np.nan:
  76. right_loc = left_loc
  78. # Use the minimum arm in each direction (forces Gaussian to be symmetric in each dimension)
  79. horiz = np.nanmin([left_loc, right_loc])
  80. vert = np.nanmin([up_loc, down_loc])
  81. right_loc = horiz
  82. left_loc = horiz
  83. up_loc = vert
  84. down_loc = vert
  85. return right_loc, left_loc, up_loc, down_loc
  88. def extract_bursts_single_trial(raw_trial, tf, times, search_freqs, band_lims, aperiodic_spectrum, sfreq, w_size=.26):
  89. """
  90. Extract bursts from epoched data
  91. :param raw_trial: raw data for trial (time)
  92. :param tf: time-frequency decomposition for trial (freq x time)
  93. :param times: time steps
  94. :param search_freqs: frequency limits to search within for bursts (should be wider than band_lims)
  95. :param band_lims: keep bursts whose peak frequency falls within these limits
  96. :param aperiodic_spectrum: aperiodic spectrum
  97. :param sfreq: sampling rate
  98. :param w_size: window size to extract burst waveforms
  99. :return: disctionary with waveform, peak frequency, relative peak amplitude, absolute peak amplitude, peak
  100. time, peak adjustment, FWHM in frequency, FWHM in time, and polarity for each detected burst
  101. """
  102. bursts = {
  103. 'waveform': [],
  104. 'peak_freq': [],
  105. 'peak_amp_iter': [],
  106. 'peak_amp_base': [],
  107. 'peak_time': [],
  108. 'peak_adjustment': [],
  109. 'fwhm_freq': [],
  110. 'fwhm_time': [],
  111. 'polarity': [],
  112. 'waveform_times': []
  113. }
  115. # Grid for computing 2D Gaussians
  116. x_idx, y_idx = np.meshgrid(range(len(times)), range(len(search_freqs)))
  118. # Window size in points
  119. wlen = int(w_size * sfreq)
  120. half_wlen = int(wlen * .5)
  122. # Subtract 1/f
  123. trial_tf = tf - aperiodic_spectrum
  124. trial_tf[trial_tf < 0] = 0
  126. # Skip trial if no peaks above aperiodic
  127. if (trial_tf == 0).all():
  128. print("All values equal 0 after aperiodic subtraction")
  129. return bursts
  131. # TF for iterating
  132. trial_tf_iter = copy.copy(trial_tf)
  134. while True:
  135. # Compute noise floor
  136. thresh = 2 * np.std(trial_tf_iter)
  138. # Find peak
  139. [peak_freq_idx, peak_time_idx] = np.unravel_index(np.argmax(trial_tf_iter), trial_tf.shape)
  140. peak_freq = search_freqs[peak_freq_idx]
  141. peak_amp_iter = trial_tf_iter[peak_freq_idx, peak_time_idx]
  142. peak_amp_base = trial_tf[peak_freq_idx, peak_time_idx]
  143. # Stop if no peak above threshold
  144. if peak_amp_iter < thresh:
  145. break
  147. # Fit 2D Gaussian and subtract from TF
  148. rloc, lloc, uloc, dloc = fwhm_burst_norm(trial_tf_iter, (peak_freq_idx, peak_time_idx))
  150. # Detect degenerate Gaussian (limits not found)
  151. vert_isnan = any(np.isnan([uloc, dloc]))
  152. horiz_isnan = any(np.isnan([rloc, lloc]))
  153. if vert_isnan:
  154. v_sh = int((search_freqs.shape[0] - peak_freq_idx) / 2)
  155. if v_sh <= 0:
  156. v_sh = 1
  157. uloc = v_sh
  158. dloc = v_sh
  159. elif horiz_isnan:
  160. h_sh = int((times.shape[0] - peak_time_idx) / 2)
  161. if h_sh <= 0:
  162. h_sh = 1
  163. rloc = h_sh
  164. lloc = h_sh
  165. hv_isnan = any([vert_isnan, horiz_isnan])
  167. # Compute FWHM and convert to SD
  168. fwhm_f_idx = uloc + dloc
  169. fwhm_f = (search_freqs[1] - search_freqs[0]) * fwhm_f_idx
  170. fwhm_t_idx = lloc + rloc
  171. fwhm_t = (times[1] - times[0]) * fwhm_t_idx
  172. sigma_t = fwhm_t_idx / 2.355
  173. sigma_f = fwhm_f_idx / 2.355
  174. # Fitted Gaussian
  175. z = peak_amp_iter * gaus2d(x_idx, y_idx, mx=peak_time_idx, my=peak_freq_idx, sx=sigma_t, sy=sigma_f)
  176. # Subtract fitted Gaussian for next iteration
  177. new_trial_tf_iter = trial_tf_iter - z
  179. # If detected peak is within band limits and not degenerate
  180. if all([peak_freq >= band_lims[0], peak_freq <= band_lims[1], not hv_isnan]):
  181. # Bandpass filter within frequency range of burst
  182. freq_range = [
  183. np.max([0, peak_freq_idx - dloc]),
  184. np.min([len(search_freqs) - 1, peak_freq_idx + uloc])
  185. ]
  186. filtered = filter_data(raw_trial.reshape(1, -1), sfreq, search_freqs[freq_range[0]], search_freqs[freq_range[1]],
  187. verbose=False)
  189. # Hilbert transform
  190. analytic_signal = hilbert(filtered)
  191. # Get phase
  192. instantaneous_phase = np.unwrap(np.angle(analytic_signal)) % math.pi
  194. # Find local phase minima with negative deflection closest to TF peak
  195. # If no minimum is found, the error is caught and no burst is added
  196. min_phase_pts = argrelextrema(instantaneous_phase.T, np.less)[0]
  197. new_peak_time_idx = peak_time_idx
  198. try:
  199. new_peak_time_idx = min_phase_pts[np.argmin(np.abs(peak_time_idx - min_phase_pts))]
  200. adjustment = (new_peak_time_idx - peak_time_idx) * 1 / sfreq
  201. except:
  202. adjustment = 1
  204. # Keep if adjustment less than 30ms
  205. if np.abs(adjustment) < .03:
  207. # If burst won't be cutoff
  208. if new_peak_time_idx >= half_wlen and new_peak_time_idx + half_wlen <= len(times):
  209. peak_time = times[new_peak_time_idx]
  211. overlapped = False
  212. # Check for overlap
  213. for b_idx in range(len(bursts['peak_time'])):
  214. o_t = bursts['peak_time'][b_idx]
  215. o_fwhm_t = bursts['fwhm_time'][b_idx]
  216. if overlap([peak_time - .5 * fwhm_t, peak_time + .5 * fwhm_t],
  217. [o_t - .5 * o_fwhm_t, o_t + .5 * o_fwhm_t]):
  218. overlapped = True
  219. break
  221. if not overlapped:
  222. # Get burst
  223. burst = raw_trial[new_peak_time_idx - half_wlen:new_peak_time_idx + half_wlen]
  224. # Remove DC offset
  225. burst = burst - np.mean(burst)
  226. bursts['waveform_times'] = times[new_peak_time_idx - half_wlen:new_peak_time_idx + half_wlen] - \
  227. times[new_peak_time_idx]
  229. # Flip if positive deflection
  230. peak_dists = np.abs(argrelextrema(filtered.T, np.greater)[0] - new_peak_time_idx)
  231. trough_dists = np.abs(argrelextrema(filtered.T, np.less)[0] - new_peak_time_idx)
  233. polarity = 0
  234. if len(trough_dists) == 0 or (
  235. len(peak_dists) > 0 and np.min(peak_dists) < np.min(trough_dists)):
  236. burst *= -1.0
  237. polarity = 1
  239. bursts['waveform'].append(burst)
  240. bursts['peak_freq'].append(peak_freq)
  241. bursts['peak_amp_iter'].append(peak_amp_iter)
  242. bursts['peak_amp_base'].append(peak_amp_base)
  243. bursts['peak_time'].append(peak_time)
  244. bursts['peak_adjustment'].append(adjustment)
  245. bursts['fwhm_freq'].append(fwhm_f)
  246. bursts['fwhm_time'].append(fwhm_t)
  247. bursts['polarity'].append(polarity)
  249. trial_tf_iter = new_trial_tf_iter
  251. bursts['waveform'] = np.array(bursts['waveform'])
  252. bursts['peak_freq'] = np.array(bursts['peak_freq'])
  253. bursts['peak_amp_iter'] = np.array(bursts['peak_amp_iter'])
  254. bursts['peak_amp_base'] = np.array(bursts['peak_amp_base'])
  255. bursts['peak_time'] = np.array(bursts['peak_time'])
  256. bursts['peak_adjustment'] = np.array(bursts['peak_adjustment'])
  257. bursts['fwhm_freq'] = np.array(bursts['fwhm_freq'])
  258. bursts['fwhm_time'] = np.array(bursts['fwhm_time'])
  259. bursts['polarity'] = np.array(bursts['polarity'])
  261. return bursts
  264. def extract_bursts(raw_trials, tf, times, search_freqs, band_lims, aperiodic_spectrum, sfreq, w_size=.26):
  265. """
  266. Extract bursts from epoched data
  267. :param raw_trials: raw data for each trial (trial x time)
  268. :param tf: time-frequency decomposition for each trial (trial x freq x time)
  269. :param times: time steps
  270. :param search_freqs: frequency limits to search within for bursts (should be wider than band_lims)
  271. :param band_lims: keep bursts whose peak frequency falls within these limits
  272. :param aperiodic_spectrum: aperiodic spectrum
  273. :param sfreq: sampling rate
  274. :param w_size: window size to extract burst waveforms
  275. :return: disctionary with trial, waveform, peak frequency, relative peak amplitude, absolute peak amplitude, peak
  276. time, peak adjustment, FWHM in frequency, FWHM in time, and polarity for each detected burst
  277. """
  278. bursts = {
  279. 'trial': [],
  280. 'waveform': [],
  281. 'peak_freq': [],
  282. 'peak_amp_iter': [],
  283. 'peak_amp_base': [],
  284. 'peak_time': [],
  285. 'peak_adjustment': [],
  286. 'fwhm_freq': [],
  287. 'fwhm_time': [],
  288. 'polarity': [],
  289. 'waveform_times': []
  290. }
  292. # Compute event-related signal
  293. erf = np.mean(raw_trials, axis=0)
  295. # Iterate through trials
  296. for t_idx, tr_tf in enumerate(tf):
  298. # Regress out ERF
  299. slope, intercept, r, p, se = linregress(erf, raw_trials[t_idx, :])
  300. raw_trial = raw_trials[t_idx, :] - (intercept + slope * erf)
  302. trial_bursts=extract_bursts_single_trial(raw_trial, tr_tf, times, search_freqs, band_lims, aperiodic_spectrum,
  303. sfreq, w_size=w_size)
  305. n_trial_bursts=len(trial_bursts['peak_time'])
  306. bursts['trial'].extend([int(t_idx) for i in range(n_trial_bursts)])
  307. bursts['waveform'].extend(trial_bursts['waveform'])
  308. bursts['peak_freq'].extend(trial_bursts['peak_freq'])
  309. bursts['peak_amp_iter'].extend(trial_bursts['peak_amp_iter'])
  310. bursts['peak_amp_base'].extend(trial_bursts['peak_amp_base'])
  311. bursts['peak_time'].extend(trial_bursts['peak_time'])
  312. bursts['peak_adjustment'].extend(trial_bursts['peak_adjustment'])
  313. bursts['fwhm_freq'].extend(trial_bursts['fwhm_freq'])
  314. bursts['fwhm_time'].extend(trial_bursts['fwhm_time'])
  315. bursts['polarity'].extend(trial_bursts['polarity'])
  316. if len(trial_bursts['waveform_times']):
  317. bursts['waveform_times'] = trial_bursts['waveform_times']
  319. bursts['trial'] = np.array(bursts['trial'])
  320. bursts['waveform'] = np.array(bursts['waveform'])
  321. bursts['peak_freq'] = np.array(bursts['peak_freq'])
  322. bursts['peak_amp_iter'] = np.array(bursts['peak_amp_iter'])
  323. bursts['peak_amp_base'] = np.array(bursts['peak_amp_base'])
  324. bursts['peak_time'] = np.array(bursts['peak_time'])
  325. bursts['peak_adjustment'] = np.array(bursts['peak_adjustment'])
  326. bursts['fwhm_freq'] = np.array(bursts['fwhm_freq'])
  327. bursts['fwhm_time'] = np.array(bursts['fwhm_time'])
  328. bursts['polarity'] = np.array(bursts['polarity'])
  330. return bursts

burst_detection.py at commit 91fe424, no license · at the source

Overview

Authors: Quentin Moreau1, Maciej J. Szul1,2,3, Sébastien Daligaut4, Denis Schwartz4, James J. Bonaiuto1,2
ORCID iDs: Quentin Moreau, Maciej J. Szul, Denis Schwartz, James J. Bonaiuto
  1. Marc Jeannerod Institute of Cognitive Sciences, ISC, CNRS UMR 5229, Lyon, France
  2. Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France
  3. Department of Psychiatry and Psychotherapy, Faculty of Medicine, University of Tübingen, Tübingen, Germany
  4. MEG Departement, CERMEP-Imagerie du Vivant, 59 Bd Pinel, 69677, Bron, France
Institutions: Institut des Sciences Cognitives Marc Jeannerod (France); Centre National de la Recherche Scientifique (France); Lyon 1 Université (France); Université de Lyon (France); University of Tübingen (Germany); Centre d'Exploration et de Recherche Médicale par Emission de Positons (France)
Journal: bioRxiv (preprint)
Dates: published online 6 March 2026
Type: Preprint
License: CC BY-NC-ND
Identifiers: DOI 10.64898/2026.03.06.710026 · OpenAlex W7134131317
Open access: green, a free copy (OpenAlex)
Status: code verified
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, fMRI & imaging, Physiology & signal measures
Topic: Motor Control and Adaptation (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: European Research Council (864550)
Citations: not cited yet (Europe PMC); 57 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

Its files are read in the Code ↔ Paper reader above, with 10 matches between paragraphs and lines of code.

danclab/burst_detection

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 91fe42487dc701a1f60f7b6ef821c97b86ce506e, 8 February 2024
Languages: MATLAB (5), Python (2), Jupyter (2)
Size: 11 files, 9 scripts
Software Heritage: archived
Found in: the text, “Beta power and burst extraction”
Holds: README, 2 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (4 files), Matplotlib (2 files), MNE-Python (2 files), FieldTrip (1 file), Signal Processing Toolbox (1 file), Statistics and Machine Learning Toolbox (1 file), pandas (1 file), scikit-learn (1 file), SciPy (1 file), specparam (formerly FOOOF) (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
10 files
At the source: github.com/danclab/burst_detection

danclab/explicit_implicit_bursts

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: c234164e733830a0d576a62d136da1bd0a77480e, 14 September 2026
Languages: Jupyter (11), R (7), Python (1)
Size: 96 files, 19 scripts
Software Heritage: not archived
Found in: “Data and materials availability”
Holds: 11 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (12 files), pandas (12 files), Matplotlib (11 files), seaborn (10 files), emmeans (6 files), lme4 (6 files), SciPy (6 files), car (5 files), MNE-Python (4 files), tidyverse (4 files), lmerTest (3 files), statsmodels (3 files), scikit-learn (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
19 files
At the source: github.com/danclab/explicit_implicit_bursts

The paper's code and data availability statement is in the Data section.

Tracing map

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What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 28 scripts, each with its path and the digest of its content;
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

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Read it in the paper: doi.org/10.64898/2026.03.06.710026.

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Recorded: type, journal, dates, 5 authors, 1 funder, 52 references.

Cite

This paper

Moreau, Q., Szul, M. J., Daligaut, S., Schwartz, D., & Bonaiuto, J. J. (2026). Distinct beta burst motifs exhibit opposing error relationships during motor adaptation. bioRxiv (preprint). https://doi.org/10.64898/2026.03.06.710026

BibTeX

@article{moreau2026distinct,
author = {Moreau, Quentin and Szul, Maciej J. and Daligaut, Sébastien and Schwartz, Denis and Bonaiuto, James J.},
title = {{Distinct beta burst motifs exhibit opposing error relationships during motor adaptation}},
journal = {bioRxiv (preprint)},
year = {2026},
month = mar,
publisher = {bioRxiv},
issn = {2692-8205},
doi = {10.64898/2026.03.06.710026},
url = {https://doi.org/10.64898/2026.03.06.710026}
}

RIS

TY - JOUR
AU - Moreau, Quentin
AU - Szul, Maciej J.
AU - Daligaut, Sébastien
AU - Schwartz, Denis
AU - Bonaiuto, James J.
TI - Distinct beta burst motifs exhibit opposing error relationships during motor adaptation
T2 - bioRxiv (preprint)
J2 - bioRxiv
PY - 2026
DA - 2026/03/06
SN - 2692-8205
PB - bioRxiv
DO - 10.64898/2026.03.06.710026
UR - https://doi.org/10.64898/2026.03.06.710026
ER -

CSL-JSON

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"title": "Distinct beta burst motifs exhibit opposing error relationships during motor adaptation",
"container-title": "bioRxiv (preprint)",
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"family": "Moreau",
"given": "Quentin"
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{
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"given": "Sébastien"
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{
"family": "Schwartz",
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},
{
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"container-title-short": "bioRxiv",
"DOI": "10.64898/2026.03.06.710026",
"ISSN": "2692-8205",
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"issued": {
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2026,
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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:10.1162/imag.a.1040 [code]
Novel 4 He-OPMs support waveform-specific beta burst analysis comparable to SQUID-MEG
Journal: n/a
In common: specparam (formerly FOOOF), FieldTrip, MNE-Python, 7 other tools, 7 references, author Denis Schwartz
[2] doi:10.1162/imag.a.1226 [code]
A neuroscientist's guide to neural burst detection.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: specparam (formerly FOOOF), FieldTrip, MNE-Python, 8 other tools, 8 references
[3] doi:10.1162/imag.a.1169 [code]
Diazepam alters the shape of alpha oscillations recorded from human cortex using EEG.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: specparam (formerly FOOOF), car, emmeans, 7 other tools, 4 references
[4] doi:10.1016/j.nicl.2026.104012 [code]
Structural-functional multilayer brain network properties and outcome of combined repetitive transcranial magnetic stimulation and psychotherapy for obsessive-compulsive disorder.
Journal: NeuroImage. Clinical
In common: specparam (formerly FOOOF), car, lmerTest, 11 other tools
[5] doi:10.1093/cercor/bhag113 [code]
Long-term reliability and stability of parameterized resting state EEG: evidence from a five-year follow-up.
Journal: Cerebral cortex (New York, N.Y. : 1991)
In common: specparam (formerly FOOOF), emmeans, MNE-Python, 9 other tools, 2 references
[6] doi:10.7554/elife.107088 [code]
Development of auditory and spontaneous movement responses to music over the first postnatal year.
Journal: eLife
In common: car, FieldTrip, emmeans, 11 other tools
[7] doi:10.1016/j.isci.2026.115375 [code]
Bursts of regional cortical inhibition during smartphone use.
Journal: iScience
In common: Signal Processing Toolbox, Statistics and Machine Learning Toolbox, scikit-learn, 3 other tools, 7 references
[8] doi:10.1093/nc/niag029 [code]
A data-driven approach to identifying and evaluating connectivity-based neural correlates of conscious visual perception.
Journal: Neuroscience of consciousness
In common: car, emmeans, MNE-Python, 10 other tools, 1 reference
[9] doi:10.34133/csbj.0042 [code]
Using Steady-State Visual Evoked Potentials to Characterize Wide-Ranging Retinopathy Linked to &lt;i&gt;CRB1&lt;/i&gt;: Implications for Clinical Trials.
Journal: Computational and structural biotechnology journal
In common: car, emmeans, MNE-Python, 10 other tools
[10] doi:10.1162/imag.a.1321 [code]
Phase similarity between similar objects indicates representational merging across retrieval training but not sleep.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: car, emmeans, MNE-Python, 9 other tools, 1 reference

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