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Phase-tuned modulation during reward expectancy in human anterior insular cortex.

Code ↔ Paper

2 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 2 matches
  1. [1] § Methods › Analysis of phase shifts in PAC temporal patterns ↔ Precession_utils.py, lines 119–192 · score 0.65 · circular linear correlation, circular variable, correlation coefficient, phases
  2. [2] § Methods › Identification of RBPs and their pre-activation ↔ extractRSAvalues.m, lines 47–99 · score 0.57 · brain patterns, Fisher, overlap, transformed, Spearman, vector

Paper

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The authors' code

Python · 388 lines · 12 KB · CC-BY-4.0 · 1 match

  1. import numpy as np
  2. import os
  3. import math
  4. import scipy as sp
  5. import pandas as pd
  6. import pycircstat as pcs
  7. import pyfftw
  8. import multiprocessing
  9. import numba
  10. from scipy.signal import find_peaks
  13. # These are the core functions used to identify both spatial and non-spatial phase precession
  15. def corrcc(alpha1, alpha2, axis=None):
  16. """
  17. Circular correlation coefficient for two circular random variables.
  19. Parameters
  20. ----------
  21. alpha1: 1d array
  22. sample of angles in radians
  23. alpha2: 1d array
  24. sample of angles in radians
  25. axis: int
  26. correlation coefficient is computed along this dimension
  27. (default axis=None, across all dimensions)
  28. Returns
  29. ----------
  30. rho: float
  31. Circular-circular correlation coefficient
  32. pval: float
  33. Circular-circular correlation p-value
  36. References: [Jammalamadaka2001]_
  37. Original: https://github.com/circstat/pycircstat
  38. modified by: Salman Qasim, 11/12/2018
  40. """
  41. assert alpha1.shape == alpha2.shape, 'Input dimensions do not match.'
  43. n = len(alpha1)
  45. # center data on circular mean
  46. alpha1_centered, alpha2_centered = pcs.descriptive.center(alpha1, alpha2, axis=axis)
  48. num = np.sum(np.sin(alpha1_centered) * np.sin(alpha2_centered), axis=axis)
  49. den = np.sqrt(np.sum(np.sin(alpha1_centered) ** 2, axis=axis) *
  50. np.sum(np.sin(alpha2_centered) ** 2, axis=axis))
  51. # compute correlation coefficient from p. 176
  52. rho = num / den
  54. # Modification:
  55. # significance of this correlation coefficient can be tested using the fact that Z is approx. normal
  57. l20 = np.mean(np.sin(alpha1_centered) ** 2)
  58. l02 = np.mean(np.sin(alpha2_centered) ** 2)
  59. l22 = np.mean((np.sin(alpha1_centered) ** 2) * (np.sin(alpha2_centered) ** 2))
  60. z = np.sqrt((n * l20 * l02) / l22) * rho
  61. pval = 2 * (1 - sp.stats.norm.cdf(np.abs(z))) # two-sided test
  63. return rho, pval
  65. def corrcc_uniform(alpha1, alpha2, axis=None):
  66. """
  67. Circular correlation coefficient for two circular random variables.
  68. Use if at least one of our variables may be a uniform distribution
  70. Parameters
  71. ----------
  72. alpha1: 1d array
  73. sample of angles in radians
  74. alpha2: 1d array
  75. sample of angles in radians
  76. axis: int
  77. correlation coefficient is computed along this dimension
  78. (default axis=None, across all dimensions)
  79. Returns
  80. ----------
  81. rho: float
  82. Circular-circular correlation coefficient
  83. pval: float
  84. Circular-circular correlation p-value
  87. References: [Jammalamadaka2001]_
  89. Original: https://github.com/circstat/pycircstat
  90. https://github.com/HoniSanders/measure_phaseprec/blob/master/cl_corr.m
  92. modified by: Salman Qasim, 11/12/2018
  94. """
  95. assert alpha1.shape == alpha2.shape, 'Input dimensions do not match.'
  97. n = len(alpha1)
  99. # center data on circular mean
  100. alpha1_centered, alpha2_centered = pcs.descriptive.center(alpha1, alpha2, axis=axis)
  102. # One of the sample means is not well defined due to uniform distribution of data
  103. # so take the difference of the resultant vector length for the sum and difference of the alphas
  104. num = pcs.descriptive.resultant_vector_length(alpha1 - alpha2) - pcs.descriptive.resultant_vector_length(alpha1 + alpha2)
  105. den = 2 * np.sqrt(np.sum(np.sin(alpha1_centered) ** 2, axis=axis) *
  106. np.sum(np.sin(alpha2_centered) ** 2, axis=axis))
  107. rho = n * num / den
  108. # significance of this correlation coefficient can be tested using the fact that Z is approx. normal
  110. l20 = np.mean(np.sin(alpha1_centered) ** 2)
  111. l02 = np.mean(np.sin(alpha2_centered) ** 2)
  112. l22 = np.mean((np.sin(alpha1_centered) ** 2) * (np.sin(alpha2_centered) ** 2))
  113. z = np.sqrt((n * l20 * l02) / l22) * rho
  114. pval = 2 * (1 - sp.stats.norm.cdf(np.abs(z))) # two-sided test
  116. return rho, pval
  119. def spatial_phase_precession(spike_phases, spike_position, slope_bounds=[-3*np.pi, 3*np.pi]):
  120. """
  121. Compute the circular-linear correlation as in: https://pubmed.ncbi.nlm.nih.gov/22487609/
  123. Parameters
  124. ----------
  125. circ : 1d array
  126. Circular data in radians (i.e. spike phases)
  127. lin : 1d array
  128. Linear data (i.e. spike positions)
  129. slope_bounds: 1d array, or tuple
  130. Slope range has to be restricted for optimization
  131. Returns
  132. ----------
  133. rho: float
  134. Circular-linear correlation coefficient
  135. pval: float
  136. Circular-linear correlation p-value
  137. sl: float
  138. Circular-linear correlation slope
  139. offs: float
  140. Circular-linear correlation offset
  142. Notes
  143. -----
  144. This is different from the linear-circular correlation used in: https://science.sciencemag.org/content/340/6138/1342
  146. I've modified the pcs.descriptive.corrcc function above to compute a p-value in two different scenarios
  148. """
  150. # Get rid of all the nans in this data
  151. nan_index = np.logical_or(np.isnan(circ), np.isnan(lin))
  152. circ = circ[~nan_index]
  153. lin = lin[~nan_index]
  155. # Make sure there are still valid data
  156. if np.size(lin) == 0:
  157. return np.nan, np.nan, np.nan, np.nan
  160. def myfun1(p):
  161. return -np.sqrt(
  162. (np.sum(np.cos(circ - (p * lin))) / len(circ)) ** 2 + (np.sum(np.sin(circ - (p * lin))) / len(circ)) ** 2)
  164. # finding the optimal slope, note that we have to restrict the range of slopes
  166. sl = sp.optimize.fminbound(myfun1, slope_bounds[0] / (np.max(lin) - np.min(lin)), slope_bounds[1] / (
  167. np.max(lin) - np.min(lin)))
  169. # calculate offset
  170. offs = np.arctan2(np.sum(np.sin(circ - (sl * lin))), np.sum(np.cos(circ - (sl * lin))))
  172. # circular variable derived from the linearization
  173. linear_circ = np.mod(abs(sl) * lin, 2 * np.pi)
  175. # # marginal distributions:
  176. # p1, z1 = pcs.tests.rayleigh(circ)
  177. # p2, z2 = pcs.tests.rayleigh(linear_circ)
  179. # circular-linear correlation:
  180. if (p1 > 0.5) | (p2 > 0.5):
  181. # This means at least one of our variables may be a uniform distribution
  182. rho, pval = corrcc_uniform(circ, linear_circ)
  183. else:
  184. rho, pval = corrcc(circ, linear_circ)
  186. # Assign the correct sign to rho
  187. if sl < 0:
  188. rho = -np.abs(rho)
  189. else:
  190. rho = np.abs(rho)
  192. return rho, pval, sl, offs
  195. @numba.jit(nopython=True)
  196. def pcorrelate(t, u, bins):
  197. """
  198. From : https://github.com/OpenSMFS/pycorrelate
  200. Compute correlation of two arrays of discrete events (Point-process).
  202. The input arrays need to be values of a point process, such as
  203. photon arrival times or positions. The correlation is efficiently
  204. computed on an arbitrary array of lag-bins. As an example, bins can be
  205. uniformly spaced in log-space and span several orders of magnitudes.
  206. (you can use :func:`make_loglags` to creat log-spaced bins).
  207. This function implements the algorithm described in
  208. `(Laurence 2006) <https://doi.org/10.1364/OL.31.000829>`__.
  210. Arguments:
  211. t (array): first array of "points" to correlate. The array needs
  212. to be monothonically increasing.
  213. u (array): second array of "points" to correlate. The array needs
  214. to be monothonically increasing.
  215. bins (array): bin edges for lags where correlation is computed.
  216. normalize (bool): if True, normalize the correlation function
  217. as typically done in FCS using :func:`pnormalize`. If False,
  218. return the unnormalized correlation function.
  220. Returns:
  221. Array containing the correlation of `t` and `u`.
  222. The size is `len(bins) - 1`.
  223. """
  224. nbins = len(bins) - 1
  226. # Array of counts (histogram)
  227. counts = np.zeros(nbins, dtype=np.int64)
  229. # For each bins, imin is the index of first `u` >= of each left bin edge
  230. imin = np.zeros(nbins, dtype=np.int64)
  231. # For each bins, imax is the index of first `u` >= of each right bin edge
  232. imax = np.zeros(nbins, dtype=np.int64)
  234. # For each ti, perform binning of (u - ti) and accumulate counts in Y
  235. for ti in t:
  236. for k, (tau_min, tau_max) in enumerate(zip(bins[:-1], bins[1:])):
  237. if k == 0:
  238. j = imin[k]
  239. # We start by finding the index of the first `u` element
  240. # which is >= of the first bin edge `tau_min`
  241. while j < len(u):
  242. if u[j] - ti >= tau_min:
  243. break
  244. j += 1
  246. imin[k] = j
  247. if imax[k] > j:
  248. j = imax[k]
  249. while j < len(u):
  250. if u[j] - ti >= tau_max:
  251. break
  252. j += 1
  253. imax[k] = j
  254. # Now j is the index of the first `u` element >= of
  255. # the next bin left edge
  256. counts += imax - imin
  257. G = counts / np.diff(bins)
  258. return G
  260. def fast_acf(counts, width, bin_width, cut_peak=True):
  262. """
  263. Super fast ACF function relying on numba (above).
  265. Parameters
  266. ----------
  267. cut_peak : bool
  268. Whether or not the largest central peak should be replaced for subsequent fitting
  269. counts : 1d array
  270. Variable of interest (i.e. spike times or spike phases)
  271. width: float
  272. Time window for ACF
  273. bin_width: float
  274. Width of bins
  276. Returns
  277. ----------
  278. acf: 1d array
  279. Counts for ACF
  280. bins: 1d array
  281. Lag bins for ACF
  283. Notes
  284. -----
  285. """
  287. n_b = int(np.ceil(width / bin_width)) # Num. edges per side
  288. # Define the edges of the bins (including rightmost bin)
  289. bins = np.linspace(-width, width, 2 * n_b, endpoint=True)
  290. temp = pcorrelate(counts, counts, np.split(bins, 2)[1])
  291. acf = np.ones(bins.shape[0] - 1)
  292. acf[0:temp.shape[0]] = np.flip(temp)
  293. acf[temp.shape[0]] = temp[0]
  294. acf[temp.shape[0] + 1:] = temp
  296. if cut_peak:
  297. acf[np.nanargmax(acf)] = np.sort(acf)[-2]
  299. return acf, bins
  301. def acf_power(acf, norm=True):
  303. """
  304. Compute the power spectrum of the signal by computing the FFT of the autocorrelation.
  306. Parameters
  307. ----------
  308. acf: 1d array
  309. Counts for ACF
  311. norm: bool
  312. To normalize or not
  314. Returns
  315. ----------
  316. psd: 1d array
  317. Power spectrum
  319. Notes
  320. -----
  321. """
  323. # Take the FFT
  324. fft = pyfftw.interfaces.numpy_fft.fft(acf, threads=multiprocessing.cpu_count())
  326. # Compute the power from the real component squared
  327. pow = (np.abs(fft) ** 2)
  329. # Account for nyquist
  330. psd = pow[0:round(pow.shape[0] / 2)]
  332. # normalize
  333. if norm:
  334. psd = psd / np.trapz(psd)
  336. return psd
  339. def nonspatial_phase_precession(unwrapped_spike_phases, width=4 * 2 * np.pi, bin_width=np.pi/3, cut_peak=True, norm=True, psd_lims = [0.65, 1.55]):
  341. """
  342. Compute the nonspatial spike-LFP relationship modulation index.
  344. Parameters
  345. ----------
  346. unwrapped_spike_phases : 1d array
  347. Spike phases that have been linearly unwrapped
  348. width: float
  349. Time window for ACF in cycles (default = 4 cycles)
  350. bin_width: float
  351. Width of bins in radians (default = 60 degrees)
  352. cut_peak : bool
  353. Whether or not the largest central peak should be replaced for subsequent fitting
  354. norm: bool
  355. To normalize the ACF or not
  356. Returns
  357. ----------
  358. max_freq: float
  359. Relative spike-LFP frequency of PSD peak
  361. MI: float
  362. Modulation index of non-spatial phase relationship
  365. Notes
  366. -----
  367. """
  369. frequencies = (np.arange(2 * (width // bin_width) - 1)) * (2 * np.pi) / (
  370. 2 * width - bin_width)
  372. freqs_of_interest = np.intersect1d(np.where(frequencies < psd_lims[0]),
  373. np.where(frequencies> psd_lims[1]))
  375. acf, _ = fast_acf(unwrapped_spike_phases, width, bin_width, cut_peak=cut_peak)
  376. psd = acf_power(acf, norm=norm)
  377. all_peaks = find_peaks(PSD_norm[freqs_of_interest], None)[0] # FIND ALL LOCAL MAXIMA IN WINDOW
  379. # make sure there is a peak.... .
  380. if ~np.any(all_peaks):
  381. return np.nan, np.nan
  383. max_peak = np.max(PSD_norm[freqs_of_interest][all_peaks])
  384. max_idx = [all_peaks[np.argmax(PSD_norm[freqs_of_interest][all_peaks])]]
  385. max_freq = frequencies[freqs_of_interest][max_idx]
  386. MI = max_peak / np.trapz(PSD_norm[freqs_of_interest])
  388. return max_freq, MI

Precession_utils.py, under CC-BY-4.0 · at the source

Overview

Authors: Linglin Yang1,2, Katia Lehongre3, Xinfeng Yu4, Hongyi Ye2, Vincent Navarro5,6,7, Sen Cheng8, Nikolai Axmacher9,10, Shuang Wang2, Hui Zhang9
ORCID iDs: Linglin Yang, Katia Lehongre, Xinfeng Yu, Hongyi Ye, Sen Cheng, Nikolai Axmacher
  1. Department of Psychiatry, Second Affiliated Hospital, School of Medicine, Zhejiang University,Hangzhou, China
  2. Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University,Hangzhou, China
  3. CENIR - Centre de Neuro-Imagerie de Recherche, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière,Paris, France
  4. Department of Radiology, Second Affiliated Hospital, School of Medicine, Zhejiang University,Hangzhou, China
  5. Sorbonne Université, Inserm, CNRS, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière,Paris, France
  6. Epilepsy Unit, AP-HP, PitiéSalpêtrière Hospital,Paris, France
  7. Clinical Neurophysiology Department, AP-HP, Pitié-Salpêtrière Hospital,Paris, France
  8. Institute for Neural Computation, Faculty of Computer Science, Ruhr University Bochum,Bochum, Germany
  9. Department of Neuropsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum,Bochum, Germany
  10. State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University,Beijing, PR China
Institutions: Zhejiang University (China); Pitié-Salpêtrière Hospital (France); Ruhr University Bochum (Germany); Beijing Normal University (China)
Journal: Communications biology, volume 9, issue 1, article 1148
Dates: received 3 October 2025; accepted 28 May 2026; published online 16 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s42003-026-10427-1 · PMID 42304129 · PMCID PMC13521940 · OpenAlex W7164931953
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism)
Methods: Spectral & time-frequency, Preprocessing, Statistics, Connectivity, Machine learning
Keywords: Cognitive control, Reward
MeSH: Insular Cortex*, Reward*, Adult, Electroencephalography, Female, Humans, Male, Theta Rhythm, Young Adult (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (DFG) (ZH822/1-1, 316803389 - SFB 1280, projects F01); National Natural Science Foundation of China (National Science Foundation of China) (81901319, 82471926, 81971207, 82171437)
Citations: not cited yet (Europe PMC); 84 references in the paper

Abstract

Reward expectancy engages the anterior insular cortex (AIC) to coordinate cognitive allocation. Using intracranial electroencephalographic data from epilepsy patients navigating a virtual T-maze, we identified reward-specific brain patterns (RBPs) that were preactivated in the AIC prior to reward onset. This pre-activation was followed by phase-amplitude coupling (PAC) between theta oscillations and gamma activity, with coupling strength positively correlated with the pre-activation level across contacts. Furthermore, this PAC exhibited a phase-precession-like effect (PPLE), characterized by a progressive shift of peak gamma activity to earlier theta phases across successive navigation rounds. Participants exhibiting stronger PPLE in the AIC showed greater trial-by-trial improvements in reward-collection performance. Taken together, these findings reveal a precise phase-tuned timing mechanism in the AIC that supports reward-directed behavior. Specifically, oscillatory coordination initiated by representational pre-activation, is dynamically refined through PPLE across successive exposures to the same reward. This mechanism accelerates responses to impending rewards, thereby optimizing adaptive behavior.

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 2 matches between paragraphs and lines of code.

figshare 30413173

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: MATLAB (5), Python (3)
Size: 15 files, 8 scripts
Software Heritage: not checked
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (3 files), NumPy (3 files), SciPy (3 files), CircStat (1 file), Numba (1 file), pandas (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
9 files
At the source:

Code availability

The custom-written Matlab and Python code supporting the findings of this study will be made available at the Figshare repository84 https://doi.org/10.6084/m9.figshare.30413173.

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;
  • 8 scripts, each with its path and the digest of its content;
  • 2 matches 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 raw iEEG data are confidential medical records and cannot be publicly deposited due to ethical and privacy restrictions. All numerical data used to generate the data shown are available at the Figshare repository84 https://doi.org/10.6084/m9.figshare.30413173. Additional 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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 2 keywords, 9 MeSH terms, 2 funders, 83 references.

Cite

This paper

Yang, L., Lehongre, K., Yu, X., Ye, H., Navarro, V., Cheng, S., Axmacher, N., Wang, S., & Zhang, H. (2026). Phase-tuned modulation during reward expectancy in human anterior insular cortex. Communications biology, 9(1), 1148. https://doi.org/10.1038/s42003-026-10427-1

BibTeX

@article{yang2026phase,
author = {Yang, Linglin and Lehongre, Katia and Yu, Xinfeng and Ye, Hongyi and Navarro, Vincent and Cheng, Sen and Axmacher, Nikolai and Wang, Shuang and Zhang, Hui},
title = {{Phase-tuned modulation during reward expectancy in human anterior insular cortex}},
journal = {Communications biology},
year = {2026},
month = jun,
volume = {9},
number = {1},
pages = {1148},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-10427-1},
url = {https://doi.org/10.1038/s42003-026-10427-1},
pmid = {42304129},
pmcid = {PMC13521940}
}

RIS

TY - JOUR
AU - Yang, Linglin
AU - Lehongre, Katia
AU - Yu, Xinfeng
AU - Ye, Hongyi
AU - Navarro, Vincent
AU - Cheng, Sen
AU - Axmacher, Nikolai
AU - Wang, Shuang
AU - Zhang, Hui
TI - Phase-tuned modulation during reward expectancy in human anterior insular cortex
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/06/16
VL - 9
IS - 1
SP - 1148
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-10427-1
UR - https://doi.org/10.1038/s42003-026-10427-1
LA - en
ER -

CSL-JSON

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"family": "Yang",
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[9] doi:10.64898/2026.03.12.710517 [code]
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