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Inter-brain functional connectivity: Are we measuring the right thing?

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  1. [1] § Materials and methods ↔ Simulation Codes/Figure_2_PLV_COH_Panel_B_SNR.py, lines 57–78 · score 0.74 · aperiodic component, 0.005–0.5 Hz, amplitude modulated, white noise, 0.005 Hz, SNR
  2. [2] § Materials and methods ↔ Simulation Codes/Figure_2_AEC_Panel_B_SNR.py, lines 55–76 · score 0.73 · aperiodic component, 0.005–0.5 Hz, amplitude modulated, white noise, 0.005 Hz, SNR

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 251 lines · 7.4 KB · no license · 1 match

  1. #!/usr/bin/env python
  2. # coding: utf-8
  4. #%% ### Imports ###
  6. import numpy as np
  7. import matplotlib.pyplot as plt
  8. from mne_connectivity import spectral_connectivity_time
  9. from fooof import Bands
  10. from neurodsp.sim import sim_powerlaw, sim_oscillation
  11. from neurodsp.sim.utils import modulate_signal
  12. from neurodsp.utils import set_random_seed, create_times
  13. import pickle
  15. #%% ### Simulation Settings ###
  17. # PLV or COH (Choose connectivity method to compute) PLV=0, COH=1
  18. method_index = 0 # PLV=0, COH=1
  19. if method_index == 0:
  20. con_method = ('plv','PLV')
  21. else:
  22. con_method = ('coh', 'COH')
  24. # Other settings
  25. n_seconds = 30 # duration of simulation
  26. fs = 1000 # sampling freq
  27. exp_modulating_sig = 0.5 # exponent for modulating signal
  28. exp_ap_sig = 1.0 # exponent for aperiodic signal
  29. sim = 50 # 50number of simulations
  30. n_samples = n_seconds * fs
  31. times = create_times(n_seconds, fs)
  32. n_cycles = {3:'r', 5:'b', 7:'g', 10:'k'}
  34. # Frequency sweep (defines center frequencies to sweep across)
  35. freq_band = Bands({'alpha': [7, 13]})
  36. mid, low, high = 10, 6, 14
  37. step = 0.1
  38. cfs_P2 = np.arange(low, high+0.1, step)
  39. # print(cfs_P2)
  40. osc_power = [0.01, 0.05, 0.1, 0.15, 0.25, 0.5] # for SNR variations
  41. cf_P1 = [10] # Fixed frequency for P1
  43. # Note: osc_power to assess the effect of SNR variations was implemented based on the following resources:
  44. # https://github.com/OscillationMethods/OscillationMethods/blob/main/07-PowerConfounds.ipynb
  45. # https://pubmed.ncbi.nlm.nih.gov/34268825/
  47. #%% ### Signal Simulation ###
  49. set_random_seed(0)
  51. signals_P1 = {10: {osc: [] for osc in osc_power}} # nested by osc_power
  52. #{10:{0.01:[], 0.05:[], 0.1:[], 0.15:[], 0.25:[], 0.5:[]}}
  54. signals_P2 = {osc: [] for osc in osc_power} # nested by osc_power
  55. #{0.01:[],0.05:[],0.1:[],0.15:[],0.25:[],0.5:[]}
  57. for i in range(sim):
  58. mod = sim_powerlaw(n_seconds, fs, exponent=-exp_modulating_sig, f_range=[0.005, 0.5]) # modulating_signal
  59. ap1 = sim_powerlaw(n_seconds, fs, exponent=-exp_ap_sig) # aperiodic_comp1
  60. ap2 = sim_powerlaw(n_seconds, fs, exponent=-exp_ap_sig) # aperiodic_comp2
  61. wn1 = np.random.randn(n_samples) # white noise1
  62. wn2 = np.random.randn(n_samples) # white noise2
  64. # Create P1 signals
  65. for cf in cf_P1:
  66. for osc in osc_power:
  67. osc1 = sim_oscillation(n_seconds, fs, cf, variance=osc) # Pure oscillation P1, with different osc_power (for SNR)
  68. sig1 = modulate_signal(osc1, mod) + ap1 + (wn1 * 1e0) # Signal P1: Amplitude modulated signal + aperiodic component + white noise
  69. signals_P1[cf][osc].append(sig1)
  71. # Create P2 signals
  72. for osc in osc_power:
  73. sig2s = []
  74. for cf2 in cfs_P2:
  75. osc2 = sim_oscillation(n_seconds, fs, cf2, variance=osc) # Pure oscillation P2, with different osc_power (for SNR)
  76. sig2 = modulate_signal(osc2, mod) + ap2 + (wn2 * 1e0) # Signal P2: Amplitude modulated signal + aperiodic component + white noise
  77. sig2s.append(sig2)
  78. signals_P2[osc].append(sig2s)
  80. # Reorganize P2 signals by freq and power
  81. signals_P2_by_freq = {osc: [] for osc in osc_power}
  82. for osc in osc_power:
  83. for i in range(len(cfs_P2)):
  84. P2sig_freq_sim = []
  85. for j in range(sim):
  86. P2sig_freq_sim.append(signals_P2[osc][j][i])
  87. signals_P2_by_freq[osc].append(P2sig_freq_sim)
  88. signals_P2_by_freq[osc] = np.array(signals_P2_by_freq[osc])
  90. # Convert P1 to arrays
  91. for cf in cf_P1:
  92. for osc in osc_power:
  93. signals_P1[cf][osc] = np.array(signals_P1[cf][osc])
  95. #%% ### Connectivity Calculation ###
  97. # Connectivity function
  98. def conn_calc(signals_P2, signals_P1, sfreq, band, n_cycles=10, method='plv'):
  99. signals = np.stack((signals_P2, signals_P1), axis=1)
  100. freqs = np.arange(band[0], band[1], 0.1)
  101. con = spectral_connectivity_time(
  102. signals, freqs, method=method,
  103. indices=(np.array([0]), np.array([1])),
  104. sfreq=sfreq, padding=1, mode='cwt_morlet',
  105. n_cycles=n_cycles, average=True, verbose='error')#, n_jobs='auto')
  106. # Extract connectivity estimates and return average value
  107. con_estimate = con.get_data()
  108. return np.mean(con_estimate)
  110. # dictionary to store all results (conn_values[cf][band][cycle][freq, snr])
  111. conn_values = {
  112. cf: {
  113. 'alpha': {
  114. cycle: np.zeros((len(cfs_P2), len(osc_power)))
  115. for cycle in n_cycles
  116. }
  117. } for cf in cf_P1
  118. }
  120. # Compute Connectivity
  121. for cf in cf_P1:
  122. f_band = freq_band['alpha']
  123. for cycle in n_cycles:
  124. for p_idx, osc in enumerate(osc_power):
  125. for i in range(len(cfs_P2)):
  126. val = conn_calc(signals_P2_by_freq[osc][i], signals_P1[cf][osc], fs, f_band, n_cycles=cycle, method=con_method[0])
  127. conn_values[cf]['alpha'][cycle][i, p_idx] = val
  129. #%% ### Saving data ###
  131. for_plotting = {
  132. "conn_values": conn_values,
  133. "n_cycles": n_cycles,
  134. "cfs_P2": cfs_P2,
  135. "osc_power": osc_power,
  136. "con_method": con_method,
  137. "sim": sim
  138. }
  140. with open(f"{con_method[0]}_SNR.pkl", "wb") as pickle_file:
  141. pickle.dump(for_plotting, pickle_file)
  143. #%% ### Loading data ###
  145. # import pickle
  146. # method_index = 0 # PLV=0, COH=1
  147. # if method_index == 0:
  148. # con_method = ('plv','PLV')
  149. # else:
  150. # con_method = ('coh', 'COH')
  152. # with open(f"{con_method[0]}_SNR.pkl", "rb") as pickle_file:
  153. # loaded = pickle.load(pickle_file)
  155. # # Access individual variables
  156. # cfs_P2 = loaded["cfs_P2"]
  157. # conn_values = loaded["conn_values"]
  158. # # conf = loaded["conf"]
  159. # n_cycles = loaded["n_cycles"]
  161. #%% ### Plotting ###
  163. import matplotlib.pyplot as plt
  164. import seaborn as sns
  165. import numpy as np
  167. plt.rc('font', family='Helvetica')
  168. sns.set_style("white")
  170. # Plotting Parameters
  171. fig_width_cm = 4
  172. fig_height_cm = 4
  173. dpi = 1200
  174. fontsize = 6
  175. cf = 10
  176. band = 'alpha'
  177. cycle = 5
  179. # To set up ticks
  180. osc_power = [0.01, 0.05, 0.1, 0.15, 0.25, 0.5]
  181. labels_x = [f'{s:.2f}'.lstrip('0') if s < 1 else f'{s:.2f}' for s in osc_power]
  182. labels_x = labels_x[::-1] # flip the order, for plotting purposes
  184. freqs = np.linspace(6, 14, 81) # frequency sweep (reversed)
  185. desired_freqs = np.arange(6, 15, 2)
  186. ytick_pos = [np.argmin(np.abs(freqs - f)) + 1.5 for f in desired_freqs]
  188. # Data for plotting
  189. data = conn_values[cf][band][cycle] # shape (81, 6)
  190. data_T=data.T # to transpose the figure, to make it closer to the original
  191. data_T_flip = data.T[::-1, :]
  193. # Create figure
  194. fig_inches = (fig_width_cm / 2.54, fig_height_cm / 2.54)
  195. fig, ax = plt.subplots(figsize=fig_inches, dpi=dpi)
  197. # Heatmap
  198. sns.heatmap(
  199. data_T_flip,
  200. ax=ax,
  201. cmap='BuPu',
  202. cbar=False,
  203. vmin=0,
  204. vmax=1.0,
  205. linewidths=0.0,
  206. linecolor="white"
  207. )
  209. # Axis Labels and Ticks
  210. ax.set_yticks(np.arange(len(osc_power)) + 0.5)
  211. ax.set_yticklabels(labels_x, fontsize=fontsize)
  212. ax.set_ylabel('SNR', fontsize=fontsize)
  214. ax.set_xticks(ytick_pos)
  215. ax.set_xticklabels(desired_freqs, fontsize=fontsize)
  216. ax.set_xlabel("P2's Frequency (Hz)", fontsize=fontsize)
  218. ax.tick_params(
  219. axis='both',
  220. which='both',
  221. direction='out',
  222. length=3,
  223. width=0.5,
  224. color='black',
  225. bottom=True,
  226. top=False,
  227. left=True,
  228. right=False,
  229. labelsize=fontsize
  230. )
  232. # Rotate y-axis labels
  233. for label in ax.get_yticklabels():
  234. label.set_rotation(0)
  236. # Spines
  237. for spine in ax.spines.values():
  238. spine.set_linewidth(0.5)
  239. spine.set_visible(True)
  240. spine.set_edgecolor('black')
  242. # Title
  243. ax.set_title(f'SNR effect on {con_method[1]}',
  244. fontsize=fontsize, fontweight='bold')
  246. # Save figure
  247. plt.savefig(f'{con_method[0]}_Figure_2_SNR.pdf',
  248. format='pdf', dpi=dpi, bbox_inches='tight')
  249. plt.show()

Figure_2_PLV_COH_Panel_B_SNR.py, no license · at the source

Overview

Authors: Juan Camilo Avendano-Diaz1, Patrick Sothmann1, Riitta Hari1,2, Lauri Parkkonen1
ORCID iDs: Juan Camilo Avendano-Diaz, Riitta Hari, Lauri Parkkonen
  1. Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
  2. Department of Art and Media, Aalto University, Espoo, Finland
Institutions: Aalto University (Finland)
Journal: PloS one, volume 21, issue 7, article e0353371
Dates: received 3 December 2025; accepted 23 June 2026; published online 14 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pone.0353371 · PMID 42447111 · PMCID PMC13367725 · OpenAlex W4411990437
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.07.02.662718
Status: code verified
Categories: EEG (modality), MEG (modality), human (organism)
Methods: Spectral & time-frequency, Connectivity
MeSH: Brain*, Nerve Net*, Brain Mapping, Electroencephalography, Humans, Magnetoencephalography (* major topic)
Journal subjects: Biology and Life Sciences, Neuroscience, Brain Mapping, Magnetoencephalography, Research and Analysis Methods, Imaging Techniques, Neuroimaging, Bioassays and Physiological Analysis, Electrophysiological Techniques, Brain Electrophysiology, Electroencephalography, Physiology, Electrophysiology, Neurophysiology, Medicine and Health Sciences, Clinical Medicine, Clinical Neurophysiology, Engineering and Technology, Signal Processing, Signal to Noise Ratio, Cognitive Science, Physical Sciences, Mathematics, Probability Theory, Probability Distribution, Normal Distribution, White Noise, Respiratory Physiology
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Business Finland (Grant “DIGIMIND” 7981/31/2022); Norman Loveless Memorial Fund
Citations: not cited yet (Europe PMC); 45 references in the paper

Abstract

Hyperscanning—the simultaneous recording of brain activity from multiple individuals—and the study of inter-brain synchronization is gaining popularity in social neuroscience. MEG/EEG hyperscanning studies often estimate inter-brain functional connectivity using phase-based metrics applied to oscillatory brain signals, assuming matching peak frequencies between the individuals studied. However, in reality peak frequencies typically differ between subjects and even between brain regions. Using simulated MEG/EEG signals, we systematically assessed how inter-individual frequency differences affect commonly used connectivity measures. Phase-based metrics were highly sensitive to frequency differences across individuals, whereas amplitude envelope correlation remained comparatively stable under these conditions. Our results underscore the need for connectivity metrics specifically tailored for inter-brain analyses. These findings are relevant to a range of disciplines that are increasingly integrating hyperscanning into their methodological toolkits.

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.

OSF 4cd2r

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: Python (25)
Size: 25 files, 25 scripts
Software Heritage: not checked
Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (25 files), NumPy (25 files), MNE-Connectivity (23 files), NeuroDSP (23 files), specparam (formerly FOOOF) (23 files), MNE-Python (21 files), seaborn (2 files), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
25 files
At the source: doi.org/10.17605/OSF.IO/4CD2R

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;
  • 25 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);
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Data

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

Data Availability

All relevant data (simulation codes) are available in a permanent repository named “Inter-brain FC”: https://doi.org/10.17605/OSF.IO/4CD2R.

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

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Version 2, 28 September 2026

  • Authors: added Lauri Parkkonen (0000-0002-0130-0801); removed Lauri Parkkonen

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 6 MeSH terms, 2 funders, 42 references.

Cite

This paper

Avendano-Diaz, J. C., Sothmann, P., Hari, R., & Parkkonen, L. (2026). Inter-brain functional connectivity: Are we measuring the right thing? PloS one, 21(7), e0353371. https://doi.org/10.1371/journal.pone.0353371

BibTeX

@article{avendanodiaz2026inter,
author = {Avendano-Diaz, Juan Camilo and Sothmann, Patrick and Hari, Riitta and Parkkonen, Lauri},
title = {{Inter-brain functional connectivity: Are we measuring the right thing?}},
journal = {PloS one},
year = {2026},
month = jul,
volume = {21},
number = {7},
pages = {e0353371},
publisher = {PLOS},
issn = {1932-6203},
doi = {10.1371/journal.pone.0353371},
url = {https://doi.org/10.1371/journal.pone.0353371},
pmid = {42447111},
pmcid = {PMC13367725}
}

RIS

TY - JOUR
AU - Avendano-Diaz, Juan Camilo
AU - Sothmann, Patrick
AU - Hari, Riitta
AU - Parkkonen, Lauri
TI - Inter-brain functional connectivity: Are we measuring the right thing?
T2 - PloS one
J2 - PLoS One
PY - 2026
DA - 2026/07/14
VL - 21
IS - 7
SP - e0353371
SN - 1932-6203
PB - PLOS
DO - 10.1371/journal.pone.0353371
UR - https://doi.org/10.1371/journal.pone.0353371
LA - en
ER -

CSL-JSON

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