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Dual-site theta stimulation modulates connectivity, but not sequence memory in older adults.

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 · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Materials and methods › EEG analysis › Connectivity analysis ↔ eeg_analysis/08_source_loc_outcome_measures.py, lines 1–28 · score 0.96 · debiased weighted phase, amplitude envelope correlation, amplitude coupling, parietal ROIs, inverse solution, phase coupling
  2. [2] § Materials and methods › Statistical analysis ↔ statistical_analysis/03_lmm_phase_angles.R, lines 1–84 · score 0.82 · model fit metrics, linear mixed model, phase angle, emmeans, lme4, sex
  3. [3] § Materials and methods › Statistical analysis ↔ statistical_analysis/02_lmm_connectivity.R, lines 1–82 · score 0.77 · model fit metrics, linear mixed model, emmeans, lme4, sex, post
  4. [4] § Materials and methods › EEG analysis › Source analysis ↔ eeg_analysis/08_source_loc_outcome_measures.py, lines 1–28 · score 0.77 · weighted minimum norm, source localization, pipeline, ROIs, cortical, frontal
  5. [5] § Materials and methods › EEG analysis › Preprocessing ↔ eeg_analysis/07_epoching_SD_rule.py, lines 45–101 · score 0.72 · epochs exceeding, rejection threshold, peak amplitude, channels
  6. [6] § Materials and methods › Electric field simulations ↔ eeg_analysis/08_source_loc_outcome_measures.py, lines 146–149 · score 0.64 · electric field simulations, MNI, peak, frontal, parietal, EEG
  7. [7] § Results › Increased functional connectivity between target sites through anti-phase and in-phase theta-tACS in older adults ↔ statistical_analysis/02_lmm_connectivity.R, lines 1–82 · score 0.63 · weighted phase lag, linear mixed model, wPLI, debiased, stim, post
  8. [8] § Materials and methods › EEG analysis › Preprocessing ↔ eeg_analysis/06_do_ica.py, the whole file · a weak match · score 0.62 · ocular artefact, components, rejected, EOG, threshold, Preprocessing
  9. [9] § Results › Increased functional connectivity between target sites through anti-phase and in-phase theta-tACS in older adults ↔ statistical_analysis/03_lmm_phase_angles.R, lines 1–84 · score 0.52 · phase angle shift, frequency bands, R2, marginal, sham, model
  10. [10] § Materials and methods › Transcranial alternating current stimulation ↔ eeg_analysis/09_TOM_prepare_data.py, lines 124–181 · score 0.52 · sham stimulation, ramp, bipolar, montages, baseline, positions

Paper

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The authors' code

Python · 310 lines · 12 KB · no license · 3 matches

  1. """
  2. 08_source_loc_outcome_measures.py
  4. Performs cortical source localization and computes functional connectivity
  5. measures between frontal and parietal regions of interest.
  7. This script:
  8. 1. Loads epoched EEG data and pre-computed forward solutions
  9. 2. Computes inverse solutions using weighted minimum-norm estimation
  10. 3. Extracts time courses from frontal and parietal ROIs
  11. 4. Calculates connectivity measures:
  12. - wPLI (debiased weighted phase lag index) for phase-coupling
  13. - AECc (orthogonalized amplitude envelope correlation) for amplitude-coupling
  14. - Phase angle (mean phase difference between ROIs)
  16. Part of the analysis pipeline for:
  17. "Dual-site theta stimulation modulates connectivity, but not sequence
  18. memory in older adults"
  19. """
  21. import mne
  22. from os.path import join
  23. import numpy as np
  24. import pandas as pd
  25. from mne.minimum_norm import make_inverse_operator, apply_inverse_epochs
  26. from mne_connectivity import spectral_connectivity_epochs
  27. from scipy.signal import hilbert
  28. from mne.filter import filter_data
  31. # =============================================================================
  32. # Helper functions
  33. # =============================================================================
  35. def mni_radius_label(stc, mni_coord, radius, subject, subjects_dir=None, name=None):
  36. """
  37. Create a label containing all vertices within a radius of an MNI coordinate.
  39. Parameters
  40. ----------
  41. stc : SourceEstimate
  42. Source estimate object (used to get vertex information).
  43. mni_coord : array-like
  44. MNI coordinates [x, y, z] of the ROI center.
  45. radius : float
  46. Radius in mm around the coordinate to include.
  47. subject : str
  48. FreeSurfer subject name.
  49. subjects_dir : str, optional
  50. Path to FreeSurfer subjects directory.
  51. name : str, optional
  52. Name for the label.
  54. Returns
  55. -------
  56. dict
  57. Dictionary with 'lh' key containing the Label object.
  58. """
  59. coords = mne.vertex_to_mni(stc.vertices[0], 0, subject, subjects_dir)
  60. dists = np.linalg.norm(mni_coord - coords, axis=-1)
  61. inds = np.where(dists < radius)
  62. lh_inds = inds[0]
  63. lh_label = mne.Label(vertices=stc.vertices[0][lh_inds], hemi="lh", name=name)
  64. return {"lh": lh_label}
  67. def compute_aecc_epochwise(epochs_data, sfreq):
  68. """
  69. Calculate Amplitude Envelope Correlation with orthogonalization (AECc).
  71. Uses symmetric orthogonalization to correct for signal leakage: the signal
  72. of one ROI is orthogonalized with respect to the other and vice versa,
  73. and the resulting correlations are averaged.
  75. Parameters
  76. ----------
  77. epochs_data : ndarray, shape (n_epochs, 2, n_times)
  78. Band-pass filtered time course data for two ROIs.
  79. sfreq : float
  80. Sampling frequency of the data.
  82. Returns
  83. -------
  84. float
  85. Mean AECc value across all epochs.
  87. References
  88. ----------
  89. Hipp et al. (2012). Large-scale cortical correlation structure of
  90. spontaneous oscillatory activity. Nature Neuroscience, 15(6), 884-890.
  91. doi:10.1038/nn.3101
  92. """
  93. n_epochs, n_channels, _ = epochs_data.shape
  94. if n_channels != 2:
  95. raise ValueError("This function is designed for exactly two channels.")
  97. aecc_per_epoch = []
  99. for epoch_idx in range(n_epochs):
  100. data_epoch = epochs_data[epoch_idx, :, :]
  102. # Signals for channel i (frontal) and j (parietal)
  103. s_i = data_epoch[0, :]
  104. s_j = data_epoch[1, :]
  106. # Symmetric orthogonalization
  107. s_j_orth = s_j - (np.dot(s_j, s_i) / np.dot(s_i, s_i)) * s_i
  108. s_i_orth = s_i - (np.dot(s_i, s_j) / np.dot(s_j, s_j)) * s_j
  110. # Calculate amplitude envelopes using the Hilbert transform
  111. env_i = np.abs(hilbert(s_i))
  112. env_j = np.abs(hilbert(s_j))
  113. env_j_orth = np.abs(hilbert(s_j_orth))
  114. env_i_orth = np.abs(hilbert(s_i_orth))
  116. # Correlate envelope with orthogonalized envelope of the other signal
  117. corr_ij = np.corrcoef(env_i, env_j_orth)[0, 1]
  118. corr_ji = np.corrcoef(env_j, env_i_orth)[0, 1]
  120. # Force negative correlations to zero (following Hipp et al., 2012)
  121. corr_ij = max(0.0, corr_ij)
  122. corr_ji = max(0.0, corr_ji)
  124. # Average the two directional correlations
  125. aecc_value = (corr_ij + corr_ji) / 2.0
  126. aecc_per_epoch.append(aecc_value)
  128. return np.mean(aecc_per_epoch)
  131. # =============================================================================
  132. # Configuration - Set these paths before running
  133. # =============================================================================
  134. out_dir = "" # Set to your data directory containing subject folders
  135. subjects_dir = "" # Set to your FreeSurfer subjects directory
  136. overwrite = True
  138. # Frequency bands of interest
  139. bands = {
  140. "theta": (4.0, 8.0),
  141. "alpha": (8.0, 12.0),
  142. "beta": (12.0, 30.0)
  143. }
  144. band_order = ["theta", "alpha", "beta"]
  146. # ROI coordinates (MNI) - derived from electric field simulation peak locations
  147. ROI_FRONTAL = [-48, 26, 7]
  148. ROI_PARIETAL = [-55, -48, 31]
  149. ROI_RADIUS = 10 # mm
  151. # Subject IDs
  152. subjs = ["MT-OG-503", "MT-OG-511", "MT-OG-512", "MT-OG-520",
  153. "MT-OG-532", "MT-OG-533", "MT-OG-534", "MT-OG-536",
  154. "MT-OG-537", "MT-OG-551", "MT-OG-560", "MT-OG-563",
  155. "MT-OG-566", "MT-OG-580", "MT-OG-586", "MT-OG-593",
  156. "MT-OG-600", "MT-OG-606", "MT-OG-609", "MT-OG-614",
  157. "MT-OG-616", "MT-OG-617", "MT-OG-620", "MT-OG-622",
  158. "MT-OG-633", "MT-OG-634", "MT-OG-638", "MT-OG-648",
  159. "MT-OG-649", "MT-OG-650", "MT-OG-656", "MT-OG-661",
  160. "MT-OG-663", "MT-OG-664", "MT-OG-672", "MT-OG-673"]
  162. preposts = ["pre", "post"]
  163. sessions = ["Session1", "Session2", "Session3"]
  165. # Minimum number of epochs required for reliable connectivity estimates
  166. MIN_EPOCHS = 30
  169. # =============================================================================
  170. # Initialize output DataFrames
  171. # =============================================================================
  172. connectivity = pd.DataFrame(columns=["ID", "session", "prepost", "frequency",
  173. "wpli_debiased", "aecc", "phase_angle"])
  176. # =============================================================================
  177. # Main processing loop
  178. # =============================================================================
  179. for subj in subjs:
  180. subj_dir = join(out_dir, subj[-3:])
  182. for sess in sessions:
  183. # Load source space and forward solution (computed once per session)
  184. src = mne.read_source_spaces(join(subj_dir, f"{subj}-src.fif"))
  185. ctx_fwd = mne.read_forward_solution(join(subj_dir, f"{subj}_{sess}_ctx-fwd.fif"))
  187. for pp in preposts:
  188. # Load epoched data
  189. epo = mne.read_epochs(join(subj_dir, f"{subj}_{pp}{sess[-1]}-6sec_epo.fif"),
  190. preload=True)
  192. # Skip if insufficient epochs for reliable estimates
  193. if len(epo) < MIN_EPOCHS:
  194. print(f"Skipping {subj} {sess} {pp}: only {len(epo)} epochs (< {MIN_EPOCHS})")
  195. con_ind = pd.DataFrame({
  196. "ID": [subj] * len(bands),
  197. "session": [sess[-1]] * len(bands),
  198. "prepost": [pp] * len(bands),
  199. "frequency": list(bands.keys()),
  200. "wpli_debiased": [np.nan] * len(bands),
  201. "aecc": [np.nan] * len(bands),
  202. "phase_angle": [np.nan] * len(bands)
  203. })
  204. connectivity = pd.concat([connectivity, con_ind])
  205. continue
  207. # Set average reference
  208. epo.set_eeg_reference(projection=True)
  210. # Create inverse operator
  211. cov = mne.make_ad_hoc_cov(epo.info)
  212. inverse_operator = make_inverse_operator(epo.info, ctx_fwd, cov,
  213. loose=0.0, depth=None)
  215. # Apply inverse solution
  216. method = "MNE"
  217. snr = 1.0
  218. lambda2 = 1.0 / snr**2
  219. stcs = apply_inverse_epochs(epo, inverse_operator, lambda2, method=method)
  221. # Compute mean source estimate for ROI definition
  222. mean_stc = sum(stcs) / len(stcs)
  224. # Define ROIs based on electric field peak coordinates
  225. dat_front = mni_radius_label(stc=mean_stc,
  226. mni_coord=ROI_FRONTAL,
  227. radius=ROI_RADIUS,
  228. subject=subj,
  229. subjects_dir=subjects_dir,
  230. name='frontal-lh')
  232. dat_par = mni_radius_label(stc=mean_stc,
  233. mni_coord=ROI_PARIETAL,
  234. radius=ROI_RADIUS,
  235. subject=subj,
  236. subjects_dir=subjects_dir,
  237. name='parietal-lh')
  239. # Extract time courses with PCA flip to avoid signal cancellation
  240. data = mne.extract_label_time_course(stcs=stcs,
  241. labels=[dat_front["lh"], dat_par["lh"]],
  242. src=src,
  243. mode='pca_flip',
  244. allow_empty=False,
  245. return_generator=False,
  246. mri_resolution=True)
  248. # -----------------------------------------------------------------
  249. # Compute connectivity measures
  250. # -----------------------------------------------------------------
  252. # Phase-based connectivity (wPLI) and coherency (for phase angle)
  253. con = spectral_connectivity_epochs(
  254. data,
  255. method=['wpli2_debiased', 'cohy'],
  256. mode="fourier",
  257. sfreq=epo.info["sfreq"],
  258. fmin=4,
  259. fmax=30,
  260. faverage=False,
  261. n_jobs=4,
  262. )
  264. freqs = np.array(con[0].freqs)
  266. # Reshape connectivity data: (n_nodes, n_nodes, n_freqs) -> (n_connections, n_freqs)
  267. wpli_data = con[0].get_data().reshape(-1, con[0].shape[-1])
  268. cohy_data = con[1].get_data().reshape(-1, con[1].shape[-1])
  270. # Connection index [1, 0] (parietal → frontal) = index 2 after reshaping
  271. con_index = 2
  273. # Compute AECc for each frequency band
  274. aecc_results = {}
  275. for band_name in band_order:
  276. fmin, fmax = bands[band_name]
  277. filtered_data = filter_data(data, sfreq=epo.info["sfreq"],
  278. l_freq=fmin, h_freq=fmax, verbose=False)
  279. aecc_results[band_name] = compute_aecc_epochwise(filtered_data,
  280. sfreq=epo.info["sfreq"])
  282. # Store results for each frequency band
  283. for band_name in band_order:
  284. fmin, fmax = bands[band_name]
  285. freq_idx = np.where((freqs >= fmin) & (freqs < fmax))[0]
  287. # Calculate mean phase angle from complex coherency
  288. complex_vals_in_band = cohy_data[con_index, freq_idx]
  289. mean_complex_val = np.mean(complex_vals_in_band)
  290. mean_phase_angle = np.angle(mean_complex_val)
  292. # Store results
  293. results = {
  294. "ID": subj,
  295. "session": sess[-1],
  296. "prepost": pp,
  297. "frequency": band_name,
  298. "wpli_debiased": wpli_data[con_index, freq_idx].mean(),
  299. "aecc": aecc_results[band_name],
  300. "phase_angle": mean_phase_angle
  301. }
  303. con_ind = pd.DataFrame([results])
  304. connectivity = pd.concat([connectivity, con_ind])
  307. # =============================================================================
  308. # Save results
  309. # =============================================================================
  310. connectivity.to_csv(join(out_dir, "connectivity_measures.csv"), index=False)

08_source_loc_outcome_measures.py, no license · at the source

Overview

Authors: Nina M Ehrhardt1, D Yorben Lodema2,3, Robert Fleischmann1, Ulrike Grittner4,5, Dayana Hayek1, Jevri Hanna1,6, Robert Malinowski1, Shu-Chen Li7,8, Axel Thielscher9,10, Agnes Flöel1,11, Daria Antonenko1
ORCID iDs: Robert Fleischmann, Ulrike Grittner, Shu-Chen Li, Axel Thielscher, Daria Antonenko
  1. Department of Neurology, Universitätsmedizin Greifswald, Greifswald 17475, Germany
  2. Department of Intensive Care Medicine and University Medical Center Utrecht Brain Center, University Medical Centre Utrecht, Utrecht, The Netherlands
  3. Department of Psychiatry and University Medical Center Utrecht Brain Center, University Medical Centre Utrecht, Utrecht, The Netherlands
  4. Berlin Institute of Health (BIH), Berlin 10178, Germany
  5. Institute of Biometry and Clinical Epidemiology, Charité – Universitätsmedizin Berlin, Berlin 10117, Germany
  6. University of Stuttgart, Stuttgart 70569, Germany
  7. Chair of Lifespan Developmental Neuroscience, Faculty of Psychology, Technische Universität Dresden, Dresden 01062, Germany
  8. Centre for Tactile Internet with Human-in-the-Loop, Technische Universität Dresden, Dresden 01062, Germany
  9. Section for Magnetic Resonance, Department of Health Technology, Technical University of Denmark, Lyngby 2800 Kgs, Denmark
  10. Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen 2650, Denmark
  11. German Centre for Neurodegenerative Diseases (DZNE) Standort Greifswald, Greifswald 17489, Germany
Institutions: Universitätsmedizin Greifswald (Germany); University Medical Center Utrecht (Netherlands); Berlin Institute of Health at Charité - Universitätsmedizin Berlin (Germany); Charité - Universitätsmedizin Berlin (Germany); University of Stuttgart (Germany); Centre for Tactile Internet with Human-in-the-Loop (Germany); Technische Universität Dresden (Germany); Amager Hospital (Denmark); Copenhagen University Hospital (Denmark); Technical University of Denmark (Denmark); German Center for Neurodegenerative Diseases (Germany)
Journal: Brain communications, volume 8, issue 3, article fcag153
Dates: received 5 December 2025; accepted 24 April 2026; published online 27 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/braincomms/fcag153 · PMID 42131138 · PMCID PMC13166891 · OpenAlex W7156694812
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, Connectivity, Statistics, fMRI & imaging, Physiology & signal measures
Keywords: transcranial alternating stimulation, large-scale brain oscillations, ageing, non-invasive brain stimulation, episodic memory
Topic: Transcranial Magnetic Stimulation Studies (Neurology, Neuroscience), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (FL 379/35-1, 5429/1, AN 1103/3-1, 426477764, 327654276, FL 379/24–1, 467143400, FL379/34‐1, 539593253, AN 1103/6-1, SFB1315/B03)
Citations: not cited yet (Europe PMC); 82 references in the paper

Abstract

Understanding and modulating memory functions in older adults continues to be a fundamental challenge for neuroscientific research. Given age-associated declines in long-range connectivity, network approaches targeting these connections are of particular interest. We investigated whether dual-site transcranial alternating current stimulation can modulate episodic (sequential) memory in cognitively healthy older adults (N = 44, aged 60–80 years). In a sham-controlled crossover design, participants received in-phase (0°) and anti-phase (180°) transcranial alternating current stimulation during a temporal order memory task, with a counterbalanced order of conditions. We computed source analysis-based weighted phase lag indices and corrected amplitude envelope correlation between left hemispheric fronto-parietal stimulation targets from resting-state electroencephalography to quantify modulation of functional connectivity, and conducted analyses of phase angles between these targets. No overall memory effects were observed in either active stimulation conditions, compared with sham. However, the results showed an interaction between memory modulation and age, indicating that the older the participants the higher the memory improvement in the anti-phase condition. Functional coupling increase was observed in both anti- and in-phase conditions as an elevated weighted phase lag indices theta change, compared with sham. No differences were observed for weighted phase lag indices in other frequency bands (alpha, beta) or for the corrected amplitude envelope correlation (theta, alpha, beta). Theta phase angle shifts were increased in the anti-phase compared with the sham condition. Further, in the anti-phase condition, the increase in theta connectivity was linked to age and memory improvement, indicating a potential mechanistic link between neurophysiological and cognitive outcomes. In sum, our findings suggest that dual-site anti-phase stimulation may increase functional connectivity in older adults with large interindividual variability in memory effects, warranting further investigation to optimize stimulation strategies.

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 10 matches between paragraphs and lines of code.

OSF u854p

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: Python (9), R (3)
Size: 14 files, 12 scripts
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: MNE-Python (8 files), NumPy (5 files), pandas (5 files), broom (3 files), easystats (3 files), emmeans (3 files), lme4 (3 files), lmerTest (3 files), tidyverse (3 files), MNE-Connectivity (1 file), SciPy (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
12 files
At the source: osf.io/u854p

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

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  • 1 repository 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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Data

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

Data availability

The data that support the findings of this study are available from the corresponding author (D.A.) upon reasonable request. The code used in this paper is available in via the Open Science Framework (OSF): https://osf.io/u854p.

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

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 5 keywords, 1 funder, 79 references.

Cite

This paper

Ehrhardt, N. M., Lodema, D. Y., Fleischmann, R., Grittner, U., Hayek, D., Hanna, J., Malinowski, R., Li, S.-C., Thielscher, A., Flöel, A., & Antonenko, D. (2026). Dual-site theta stimulation modulates connectivity, but not sequence memory in older adults. Brain communications, 8(3), fcag153. https://doi.org/10.1093/braincomms/fcag153

BibTeX

@article{ehrhardt2026dual,
author = {Ehrhardt, Nina M and Lodema, D Yorben and Fleischmann, Robert and Grittner, Ulrike and Hayek, Dayana and Hanna, Jevri and Malinowski, Robert and Li, Shu-Chen and Thielscher, Axel and Flöel, Agnes and Antonenko, Daria},
title = {{Dual-site theta stimulation modulates connectivity, but not sequence memory in older adults}},
journal = {Brain communications},
year = {2026},
month = apr,
volume = {8},
number = {3},
pages = {fcag153},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/braincomms/fcag153},
url = {https://doi.org/10.1093/braincomms/fcag153},
pmid = {42131138},
pmcid = {PMC13166891}
}

RIS

TY - JOUR
AU - Ehrhardt, Nina M
AU - Lodema, D Yorben
AU - Fleischmann, Robert
AU - Grittner, Ulrike
AU - Hayek, Dayana
AU - Hanna, Jevri
AU - Malinowski, Robert
AU - Li, Shu-Chen
AU - Thielscher, Axel
AU - Flöel, Agnes
AU - Antonenko, Daria
TI - Dual-site theta stimulation modulates connectivity, but not sequence memory in older adults
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/04/27
VL - 8
IS - 3
SP - fcag153
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/braincomms/fcag153
UR - https://doi.org/10.1093/braincomms/fcag153
LA - en
ER -

CSL-JSON

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27
]
]
}
}

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

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