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Scent of a father: Paternal body odors boost interbrain synchrony.

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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] § MATERIALS AND METHODS › Connectivity analysis ↔ Interbrain connectivity- Clean.py, lines 108–179 · score 0.66 · 8–12 Hz, frequency bands, 4–7 Hz, wPLI, theta, connectivity
  2. [2] § RESULTS › Higher father-infant inter brain synchrony compared to stranger-infant ↔ Interbrain connectivity- Clean.py, lines 108–179 · score 0.50 · 8–12 Hz, frequency band, theta, alpha, connections

Paper

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The authors' code

Python · 179 lines · 6.9 KB · CC-BY-4.0 · 2 matches

  1. # -*- coding: utf-8 -*-
  2. """
  3. Created on Wed Jun 8 11:38:10 2022
  5. @author: yaara
  6. """
  8. import os
  9. import mne
  10. import pandas as pd
  11. import numpy as np
  12. from mne.connectivity import spectral_connectivity as sc
  14. # -----------------------------------------------------------------------------
  15. # USER CONFIGURATIONS (SCRIPT CONSTANTS)
  16. # PATH_DIR_SRC : Path to source files folder
  17. # PATH_DIR_DEST: Path to target output folder
  18. # PATH_DURATION_CSV: Path to .csv file containing a duration column, the
  19. # duration is the minimal time between the different paradigms for each
  20. # subject. The duration
  21. # SBJ_COL_NAME : Name of subject names column
  22. # DUR_COL_NAME : Name of number of epochs column
  23. # OFFSET_START : Margin of epochs to crop from the beginning of the data
  24. # OFFSET_END : Margin of epochs to crop from the end of the data (after
  25. # TAG : Paradigm tag
  26. # cropping the by the duration value)
  27. # -----------------------------------------------------------------------------
  28. PATH_DIR_SRC = " source folder"
  29. PATH_DIR_DEST = "output folder"
  30. PATH_DURATION_CSV = "times "
  34. SBJ_COL_NAME = "file"
  35. DUR_COL_NAME = "EPOCHS NUM"
  36. OFFSET_START = 4
  37. OFFSET_END = 4
  38. TAG = 'Condition'
  39. final_epochs_summary=[]
  41. # -----------------------------------------------------------------------------
  42. # USER ARGUMENTS VALIDATION
  43. # -----------------------------------------------------------------------------
  44. assert os.path.isdir(PATH_DIR_SRC), "invalid source folder"
  45. assert os.path.isdir(PATH_DIR_DEST), "invalid destination folder"
  46. assert os.path.isfile(PATH_DURATION_CSV), "invalid path to csv file"
  47. assert PATH_DURATION_CSV.endswith(".xlsx"), "invalid file type"
  48. excel_file = pd.read_excel(PATH_DURATION_CSV)
  49. assert SBJ_COL_NAME in excel_file, \
  50. "csv file does not contain mandatory subject name column"
  51. assert DUR_COL_NAME in excel_file, \
  52. "csv file does not contain mandatory duration column"
  53. assert isinstance(OFFSET_START, int), "offset should be an integer value"
  54. assert isinstance(OFFSET_END, int), "offset should be an integer value"
  57. # -----------------------------------------------------------------------------
  58. # HELPER FUNCTIONS
  59. # -----------------------------------------------------------------------------
  60. def min_cut_dict() -> dict:
  61. """
  62. dict with subject names as keys, and their respective duration as values
  63. :return: dict object
  64. """
  65. excel_file = pd.read_excel(PATH_DURATION_CSV)
  66. return dict(zip(excel_file[SBJ_COL_NAME], excel_file[DUR_COL_NAME]))
  69. def synch_drop_logs(subj_1, subj_2):
  70. """
  71. synchronize drop logs between the two subjects, modified in place
  72. :param subj_1: mne raw instance
  73. :param subj_2: mne raw instance
  74. :return:
  75. """
  76. log_1 = subj_1.drop_log
  77. log_2 = subj_2.drop_log
  78. clean_1 = [(a, b) for a, b in zip(log_1, log_2) if not a]
  79. ind_drop_1 = [i for ((a, b), i) in zip(clean_1, list(range(len(clean_1))))
  80. if not a and b]
  81. clean_2 = [(a, b) for a, b in zip(log_2, log_1) if not a]
  82. ind_drop_2 = [i for ((a, b), i) in zip(clean_2, list(range(len(clean_2))))
  83. if not a and b]
  84. subj_1.drop(ind_drop_1)
  85. subj_2.drop(ind_drop_2)
  88. def apply_offset(subj, start, end, duration=None):
  89. """
  90. drop 'start' number of epochs from the beginning of the data and 'end' from
  91. the end
  92. :param subj: mne raw instacne
  93. :param start: # of epochs to crop from the start
  94. :param end: # of epochs to crop from the end
  95. :param duration:
  96. :return:
  97. """
  98. log = subj.drop_log
  99. ind_drop_start = list(range(len([x for x in log[:start] if not x])))
  100. clean = [x for x in log[:int(duration-end)] if not x]
  101. clean_total = [x for x in log if not x]
  102. ind_drop_end = list(range(len(clean), len(clean_total)))
  103. subj.drop(ind_drop_start+ind_drop_end)
  107. # split files in source folder into pairs (by experiment)
  108. files_ls = os.listdir(PATH_DIR_SRC)
  109. files_ls = [file for file in files_ls if file.endswith('.fif')]
  110. files_ls.sort()
  111. files_ls = np.array(files_ls).reshape(int(len(files_ls)/2), 2)
  112. dic_duration = min_cut_dict()
  114. # loop over every subject pair
  115. for (subj_1, subj_2) in files_ls:
  116. # load and pre-process data
  117. os.chdir(PATH_DIR_SRC)
  118. raw_1 = mne.read_epochs(subj_1, preload=True)
  119. raw_2 = mne.read_epochs(subj_2, preload=True)
  120. raw_1.pick_types(eeg=True,
  121. exclude=['FCz', 'Fp1', 'Fp2', 'F7', 'F8', 'O1', 'O2', 'Oz',
  122. 'Cz', 'Pz', 'Fz'])
  123. raw_2.pick_types(eeg=True,
  124. exclude=['Fp1', 'Fp2', 'F7', 'F8','O1', 'O2', 'Oz', 'Cz',
  125. 'Pz', 'Fz'])
  127. apply_offset(raw_1, OFFSET_START, OFFSET_END, dic_duration[subj_1[0:4]])
  128. synch_drop_logs(raw_1, raw_2)
  129. drop_comb = zip(raw_1.drop_log, raw_2.drop_log)
  131. # Store the number of final epochs for each family
  132. family_number = subj_1[0:6] # Extract family number
  133. final_epochs_count = len(raw_2) # Number of epochs after processing
  134. final_epochs_summary.append({'Family': family_number, 'Final Epochs': final_epochs_count})
  138. # preparations for spectral connectivity
  139. data_1 = raw_1.get_data()
  140. data_2 = raw_2.get_data()
  141. data_combined = np.concatenate((data_1, data_2), axis=1)
  142. info = mne.create_info(
  143. ch_names=raw_1.info['ch_names']+raw_1.info['ch_names'],
  144. ch_types=np.repeat('eeg', 12), sfreq=raw_1.info['sfreq'])
  145. events = np.array([np.array([i, 0, i])
  146. for i in range(data_combined.shape[0])])
  147. events_id = dict(zip([str(x[0]) for x in events], [x[0] for x in events]))
  148. raw_combined = mne.EpochsArray(data_combined, info, events, -1,
  149. events_id)
  150. raw_combined.info['chs'] = raw_1.info['chs']+raw_2.info['chs']
  151. freq_bands = {'theta': (4, 7),
  152. 'alpha':(8,12)
  153. }
  154. fmin = np.array([f for f, _ in freq_bands.values()])
  155. fmax = np.array([f for _, f in freq_bands.values()])
  158. # spectral connectivity
  160. sc_data = sc(raw_combined, method='wpli', fmin=fmin, fmax=fmax,
  161. mode='multitaper', faverage=True, n_jobs=1, verbose=False)
  162. connB, freqsB, timesB, n_epochsB, n_tapersB = sc_data
  164. # export as .csv file
  165. os.chdir(PATH_DIR_DEST)
  166. file_name = subj_1[0:6]+TAG+'.csv'
  167. chnl_1 = np.array(list(range(12)) * 12)
  168. chnl_2 = chnl_1.copy()
  169. chnl_1.sort()
  170. pd.DataFrame(
  171. dict(channel_1=chnl_1, channel_2=chnl_2,
  172. **{key: np.reshape(connB[:, :, idx], 12 ** 2)
  173. for idx, key in enumerate(freq_bands.keys())})
  174. ).to_csv(file_name)
  175. # EXPORT FINAL EPOCHS TABLE
  176. # -------------------------------------------------------------------------
  177. final_epochs_df = pd.DataFrame(final_epochs_summary)
  178. final_epochs_df.to_csv(os.path.join(PATH_DIR_DEST, 'Epochs_Summary.csv'), index=False)
  179. print("Final epochs summary saved successfully!")

Interbrain connectivity- Clean.py, under CC-BY-4.0 · at the source

Overview

Authors: Yaara Endevelt-Shapira1,2, Linoy Schwartz1, Ruth Feldman1
ORCID iDs: Yaara Endevelt-Shapira, Linoy Schwartz, Ruth Feldman
  1. Center for Developmental Social Neuroscience, Reichman University, Herzliya, Israel
  2. Institute for Learning & Brain Sciences, University of Washington, Seattle, WA, United States
Institutions: Reichman University (Israel); University of Washington (United States); The Center for Developmental Social Neuroscience (Israel)
Journal: Science advances, volume 12, issue 29, article eaed6110
Dates: received 5 November 2025; accepted 10 June 2026; published online 15 July 2026; in print July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1126/sciadv.aed6110 · PMID 42455951 · PMCID PMC13371932 · OpenAlex W7168365020
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), developmental (subfield)
Methods: Spectral & time-frequency, Statistics, Preprocessing, Connectivity
MeSH: Brain*, Fathers*, Odorants*, Smell*, Electroencephalography, Female, Humans, Infant, Male (* major topic)
Topic: Olfactory and Sensory Function Studies (Sensory Systems, Neuroscience), according to OpenAlex
Funding: Bezos Family Foundation
Citations: not cited yet (Europe PMC); 77 references in the paper

Abstract

Olfactory cues are ancient signals and mammalian young utilize maternal body odors (BO) to form a bond to their habitat in the absence of the mother, strengthen associative learning, and grow a social brain. Whether infants respond to their father’s BO and how paternal BO impact their brain maturation is still unknown. Utilizing ecologically-valid paradigms with dual-electroencephalography recordings, we measured infant-father and infant-stranger interbrain synchrony during naturalistic interactions and assessed the effects of paternal BO. Infants show greater interbrain synchrony with father compared to a stranger male. Paternal BO increase infant-stranger interbrain synchrony and enhance the father-typical positive arousal, providing evidence that infants process their father’s odor and can invoke his presence in his absence. Fathers contribute to brain maturation by enhancing cross-hemisphere connectivity in alpha rhythm that sustains attention regulation during stimulating social interactions. Paternal olfactory cues can invoke infants’ associative learning and may push the development of more complex neural and behavioral competencies.

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.

Zenodo 18358991

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: Python (1)
Size: 2 files, 1 script
Software Heritage: not checked
Found in: “Data, code, and materials availability:”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: MNE-Python (1 file), NumPy (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)
1 file
At the source: doi.org/10.5281/zenodo.18358991

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

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;
  • 1 script, 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, code, and materials availability

All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials. This study did not generate new materials. Data supporting the findings of this study and the code for the inter-brain connectivity analysis are available on are deposited in https://doi.org/10.5281/zenodo.18358991.

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

Versions

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Version 2, 28 September 2026

  • Funding: added Bezos Family Foundation

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 9 MeSH terms, 67 references.

Cite

This paper

Endevelt-Shapira, Y., Schwartz, L., & Feldman, R. (2026). Scent of a father: Paternal body odors boost interbrain synchrony. Science advances, 12(29), eaed6110. https://doi.org/10.1126/sciadv.aed6110

BibTeX

@article{endeveltshapira2026scent,
author = {Endevelt-Shapira, Yaara and Schwartz, Linoy and Feldman, Ruth},
title = {{Scent of a father: Paternal body odors boost interbrain synchrony}},
journal = {Science advances},
year = {2026},
month = jul,
volume = {12},
number = {29},
pages = {eaed6110},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.aed6110},
url = {https://doi.org/10.1126/sciadv.aed6110},
pmid = {42455951},
pmcid = {PMC13371932}
}

RIS

TY - JOUR
AU - Endevelt-Shapira, Yaara
AU - Schwartz, Linoy
AU - Feldman, Ruth
TI - Scent of a father: Paternal body odors boost interbrain synchrony
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/07/15
VL - 12
IS - 29
SP - eaed6110
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.aed6110
UR - https://doi.org/10.1126/sciadv.aed6110
LA - en
ER -

CSL-JSON

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"given": "Yaara"
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"container-title-short": "Sci Adv",
"volume": "12",
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"page": "eaed6110",
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"PMCID": "PMC13371932",
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"publisher": "American Association for the Advancement of Science",
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