OSCR

Social interactions between people of same and different generations shape longitudinal changes in interpersonal neural synchrony, loneliness, and social connection.

Code ↔ Paper

4 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 4 matches
  1. [1] § 5 Methods › 5.4 Measures › 5.4.2 fNIRS recordings. ↔ fNIRS Interbrain Coherence/fNIRS/3Dheadmodels/script_structuresensor1.m, lines 327–387 · score 0.78 · left IFG, left TPJ, right IFG, right TPJ, MNI, ROIs
  2. [2] § 5 Methods › 5.4 Measures › 5.4.2 fNIRS recordings. ↔ fNIRS Interbrain Coherence/fNIRS/3Dheadmodels/script_structuresensor1.m, lines 170–230 · score 0.68 · MNI space, FieldTrip, scan, fiducial, channel, optodes
  3. [3] § 5 Methods › 5.6 Data analysis › 5.6.1 Interbrain synchrony. ↔ Group Level Analysis/INS/2a-Frequentisttest.Rmd, lines 127–150 · score 0.67 · LTPJ RTPJ, 50 seconds, LIFG RTPJ, RIFG LTPJ, LIFG LTPJ, LIFG RIFG
  4. [4] § 5 Methods › 5.6 Data analysis › 5.6.1 Interbrain synchrony. ↔ fNIRS Interbrain Coherence/fNIRS/3Dheadmodels/script_structuresensor1.m, lines 232–284 · score 0.50 · optode positioning, location, Channel, S4, S2, Interbrain

Paper

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The authors' code

MATLAB · 421 lines · 16 KB · no license · 3 matches

  1. clear all;
  2. %% 0 Run these to set path & defaults
  3. addpath('/Users/rmoffat/Library/CloudStorage/OneDrive-ETHZurich/MATLAB/NIRS/fieldtrip-20240201')
  4. ft_defaults
  5. %% 1 Make 3D scan with the structure sensor
  6. % * place the cap on the subject's head and make sure that Cz is midway
  7. % between nasion-inion and LPA-RPA
  8. % * mark the fiducials (nasion, LPA, RPA, inion, Cz) with stickers
  9. % * make a 3D scan of the subject while walking around them with the
  10. % structure sensor (ask for help if necessary).n
  11. % * email to own email adress
  12. % * save the "Model" folder on a place you specify below in root_dir and
  13. % unzip
  15. %% 2 Specify where you saved the model folder
  16. sub_id= 'sub-309_session-5';
  17. root_dir='/Users/rmoffat/Library/CloudStorage/OneDrive-ETHZurich/SOBALAB/2023_projects/CortiVision/data/headmodels';
  18. obj_file=fullfile(root_dir, 'source', sub_id, 'model.obj');
  19. cd(fullfile(root_dir, 'processed', sub_id)) % or other folder where you want to save your data
  23. % 304_session-4 mesh is colourles
  27. % % you also need to specify your path to fieldtrip for a later step
  28. [ftver, ftpath] = ft_version;
  30. %% 3 Load the 3D-model
  32. head_surface = ft_read_headshape(obj_file);
  33. % needed if estimated unit is not m, but dm!
  34. % head_surface.pos = head_surface.pos * 10; % Convert from dm to m
  36. % Convert the units to mm
  37. head_surface = ft_convert_units(head_surface, 'mm');
  39. % Visualize the mesh surface
  40. figure('name', 'Mesh check');%title for clearity
  41. hold on
  42. ft_plot_mesh(head_surface);
  43. hold off
  46. %% 4 Specify the fiducials + optodes
  47. % Note that I also call Cz a fiducial, which is in fact not a fiducial, but
  48. % which we will use in a later stage to for coregistration.
  49. % Here I define the names of the transmitters and receivers. Adapt
  50. % according to own device.
  52. Sxnames=cell(16,1); %normal sources 1-8 and short sources are 13-16, total of 12 labels
  53. Dxnames=cell(10,1);
  54. idx=1;
  56. for i=1:16 %normal sources labels
  57. Sxnames{idx}=sprintf('S%d', i);
  58. idx=idx+1;
  59. end
  61. for i=1:10
  62. Dxnames{i}=sprintf('D%d', i);
  63. end
  66. cfg = [];
  67. cfg.channel={};
  68. cfg.channel{1} = 'Nz';
  69. cfg.channel{2} = 'LPA';
  70. cfg.channel{3} = 'RPA';
  71. cfg.channel{4}= 'Iz';
  72. cfg.channel{5} = 'Cz';
  73. cfg.channel(6:21)=Sxnames';
  74. cfg.channel(22:31)=Dxnames';
  75. cfg.method = 'headshape';
  76. opto = ft_electrodeplacement(cfg, head_surface);%2 figures appear: first figure for axes
  77. % (type 'n' in command window), second figure for electrode placement
  78. title ('axes check');
  80. save('opto.mat', 'opto')
  81. % Do you want to change the anatomical labels for the axes [Y, n]? --> n
  82. % Use "Rotate 3D" to rotate the 3D model.
  83. % Click/unclick "Colors" to toggle the colors on and off: best is to use the color
  84. % view for the fiducials, but the structure view for the optodes
  85. % Use the mouse to click on fiducials/optodes on the head and subsequently on the corresponding
  86. % label in the list to assign the markers. (Make sure you're not in "Rotate 3D" mode anymore!)
  87. % If an error was made: double click on the label to remove this marker
  88. % If ready --> press Q
  92. %% 5 Allign the axes of the coordinate system with the fiducial positions (ctf coordinates)
  93. % for the mesh
  94. load ('opto.mat')
  95. cfg = [];
  96. cfg.method = 'fiducial';
  97. cfg.coordsys = 'ctf';
  98. cfg.fiducial.nas = opto.elecpos(1,:); %position of Nz
  99. cfg.fiducial.lpa = opto.elecpos(2,:); %position of LPA
  100. cfg.fiducial.rpa = opto.elecpos(3,:); %position of RPA
  101. head_surface_ctf = ft_meshrealign(cfg, head_surface);
  103. %ft_plot_axes(head_surface_ctf)
  104. figure('name', 'axes check');
  105. ft_plot_mesh(head_surface_ctf, 'axes', true) %visualize the head surface,
  106. % including the axes of the coordinate system. You can check that the positive
  107. % and negative y-axes go through the preauricular points.
  109. % for the optodes
  110. fid.chanpos = [110 0 0; 0 90 0; 0 -90 0]; % CTF coordinates of the fiducials
  111. fid.elecpos = [110 0 0; 0 90 0; 0 -90 0]; % just like electrode positions
  112. fid.label = {'Nz','LPA','RPA'}; % same labels as in elec
  113. fid.unit = 'mm'; % same units as mri
  115. cfg = [];
  116. cfg.method = 'fiducial';
  117. cfg.coordsys = 'ctf';
  118. cfg.target = fid; % see above
  119. cfg.elec = opto;
  120. cfg.fiducial = {'Nz', 'LPA', 'RPA'}; % labels of fiducials in fid and in elec
  121. opto_ctf = ft_electroderealign(cfg);
  123. % Visualize the optodes on the alligned head surface
  124. % Notice that the colorview is not that clean as the the structure view
  125. figure('name','optodes on head');
  126. ft_plot_mesh(head_surface_ctf)
  127. ft_plot_sens(opto_ctf,'label', 'on', 'fontsize', 10, 'elecsize', 10, 'style', 'b')% I added optode labels, remove if in the way
  129. % to have the same visualization without the colors
  130. figure('Name','optodes on colourless head');
  131. ft_plot_mesh(removefields(head_surface_ctf, 'color'), 'tag', 'headshape', 'facecolor', 'skin', 'material', 'dull', 'edgecolor', 'none', 'facealpha', 1);
  132. lighting gouraud
  133. l = lightangle(0, 90); set(l, 'Color', [1 1 1]/2)
  134. l = lightangle( 0, 0); set(l, 'Color', [1 1 1]/3)
  135. l = lightangle( 90, 0); set(l, 'Color', [1 1 1]/3)
  136. l = lightangle(180, 0); set(l, 'Color', [1 1 1]/3)
  137. l = lightangle(270, 0); set(l, 'Color', [1 1 1]/3)
  138. alpha 0.9
  139. ft_plot_sens(opto_ctf, 'label', 'on', 'fontsize', 10,'elecsize', 10, 'style', 'b')
  141. save(['opto_ctf.mat'], 'opto_ctf')
  144. %% 6. Move optode inward
  146. load('opto_ctf.mat');
  147. cfg = [];
  148. cfg.method = 'moveinward';
  149. cfg.moveinward = 5; % determine distance to skin
  150. cfg.channel = 2:length(opto_ctf.label); % do not move the nasion inward
  151. cfg.keepchannel = true;
  152. cfg.elec = opto_ctf;
  153. opto_inw = ft_electroderealign(cfg);
  155. % visualize
  156. figure;
  157. ft_plot_mesh(removefields(head_surface_ctf, 'color'), 'tag', 'headshape', 'facecolor', 'skin', 'material', 'dull', 'edgecolor', 'none', 'facealpha', 1);
  158. lighting gouraud
  159. l = lightangle(0, 90); set(l, 'Color', [1 1 1]/2)
  160. l = lightangle( 0, 0); set(l, 'Color', [1 1 1]/3)
  161. l = lightangle( 90, 0); set(l, 'Color', [1 1 1]/3)
  162. l = lightangle(180, 0); set(l, 'Color', [1 1 1]/3)
  163. l = lightangle(270, 0); set(l, 'Color', [1 1 1]/3)
  164. alpha 0.7
  165. ft_plot_sens(opto_inw, 'elecsize', 10, 'style', 'b')
  167. %opto_inw = opto_ctf;
  168. save(['opto_inw.mat'], 'opto_inw')
  170. %% 7 Coregister optodes to MNI atlas
  171. % load("opto.mat")
  172. % load("opto_ctf.mat")
  173. % load("opto_inw.mat")
  176. % or event better: to an anatomical scan of your subject if you have one.
  177. % In that case, you will first need to segment the skin from your
  178. % anatomical MRI scan. For more information see: http://www.fieldtriptoolbox.org/tutorial/headmodel_eeg_bem/
  181. % load skin surface of standard atlas
  182. skin_template=fullfile(ftpath, 'template', 'headmodel', 'skin', 'standard_skin_14038.vol');
  183. skin=ft_read_headshape(skin_template);
  185. % visualization with optodes
  186. hold on; ft_plot_sens(opto_inw); %no skin mesh in this plot? 2d not 3d
  187. % you should notice that your optodes, which are represented in the ctf
  188. % coordinate system, are rotated with 90 degrees compared to the head model
  189. % which is represented in the MNI coordinate system. For more information
  190. % see: http://www.fieldtriptoolbox.org/faq/how_are_the_different_head_and_mri_coordinate_systems_defined/
  192. % We will use the MNI coordinates of the fiducials, which can be found in
  193. % the standard_1020.elc file of the fieldtrip template folder,
  194. % to guide the coregistration
  195. elec1020=ft_read_sens(fullfile(ftpath, 'template', 'electrode', 'standard_1020.elc'));
  196. % select rpa, lpa, nasion, inion and cz from elec1020
  197. cfg=[];
  198. cfg.method='moveinward'; % this is just a hack to select the right channels
  199. cfg.moveinward=10;
  200. cfg.channel={'Nz'; 'RPA'; 'LPA'; 'Iz'; 'Cz'};
  201. electarg=ft_electroderealign(cfg, elec1020);
  203. % There are two ways to coregister the optodes to the MNI space: (1)
  204. % interactively/manually or (2) automatically based on the fiducials we
  205. % specified here above
  207. % (1) interactively
  208. % So first a 90 degree turn to go from ctf to mni coordinate system
  209. cfg=[];
  210. cfg.method='interactive';
  211. cfg.headshape=skin;
  212. cfg.target=electarg; % you can use the fiducials to guide your coregistration
  213. opto_MNI=ft_electroderealign(cfg, opto_inw);
  214. % You might notice that this is quite time consuming and needs some
  215. % practice. Therefore, I prefer to do it automatically, but this is only
  216. % possible if you have enough fiducials/marker points.
  218. % (2) automatically
  219. % cfg=[];0
  220. % cfg.method='template';
  221. % cfg.warp= 'traditional';
  222. % cfg.channel={'LPA', 'RPA', 'Nz', 'Cz', 'Iz'};
  223. % cfg.target=electarg;
  224. % opto_MNI=ft_electroderealign(cfg, opto_inw);
  225. save('opto_MNI.mat', 'opto_MNI')
  227. % plot together
  228. figure;ft_plot_mesh(skin, 'edgecolor', 'none', 'facecolor', 'skin'); camlight
  229. hold on; ft_plot_sens(opto_MNI, 'elecsize', 20,'facecolor', 'k', 'label', 'label');
  230. hold on; ft_plot_sens(electarg, 'elecsize', 20, 'facecolor', 'b');
  232. %% 8 Calculate channel locations based on optode positions (in this case in CTF space)
  233. % load("opto_MNI.mat")
  234. % load("opto_ctf.mat")
  236. % use optode namings instead of eeg namings
  237. % define here the channels:
  239. channels = {'S1_D2', 'S2_D2', 'S2_D3', 'S2_D1','S3_D3', 'S3_D1', 'S4_D4', 'S4_D5', 'S5_D4', 'S5_D5','S6_D4', 'S7_D6', 'S7_D7','S8_D6', 'S8_D7', 'S8_D8','S9_D8','S10_D9', 'S10_D10','S11_D9', 'S11_D10','S12_D10', 'S13_D2', 'S14_D8', 'S15_D5', 'S16_D9'};
  240. % For these files the old montage needs to be used. Take care to follow the
  241. % old montage during the optode specification (%%4)
  242. % sub-103_session-1, sub-301_session-1,sub-101_session-1,
  243. % sub-201_session-1, sub-102_session-1, sub-202_session-1
  244. %
  245. % Old Montage
  246. % channels = {'S1_D2 ', 'S2_D2', 'S2_D3', 'S3_D3', 'S2_D1','S3_D1', 'S4_D5', 'S5_D5', 'S4_D4','S6_D4','S6_D5','S7_D6', 'S7_D7', 'S8_D7','S8_D6', 'S8_D8', 'S9_D8','S10_D9 ', 'S11_D9', 'S12_D9','S12_D10', 'S11_D10','S13_D1', 'S14_D6', 'S15_D5', 'S16_D9'};
  247. [dxnames, rem] = strtok(channels, {'_', ' '});
  248. [sxnames, rem] = strtok(rem, {'_', ' '});
  250. % change the naming
  251. opto_lay = opto_ctf;
  252. opto_lay.optopos=opto_ctf.chanpos;
  253. opto_lay.chantype=cell(length(opto_ctf.chantype),1);
  254. opto_lay.chantype(:) = {'nirs'};
  255. opto_lay.optolabel = opto_ctf.label;
  256. opto_lay.label=channels;
  257. opto_lay.tra=zeros(length(channels),length(opto_lay.optolabel));
  258. for i=1:length(channels)
  259. opto_lay.tra(i,:) = strcmp(dxnames{i},opto_lay.optolabel)+strcmp(sxnames{i}, opto_lay.optolabel);
  260. end
  261. opto_lay=rmfield(opto_lay, {'chanpos', 'chantype', 'chanunit', 'elecpos'});
  263. % calculate the channel positions
  264. opto_layout = ft_datatype_sens(opto_lay)
  266. % save
  267. save('opto_layout.mat', 'opto_layout')
  269. % calculate channel locations also in MNI space
  270. % change the naming
  271. opto_temp = opto_MNI;
  272. opto_temp.optopos=opto_MNI.chanpos;
  273. opto_temp.chantype=cell(length(opto_MNI.chantype),1);
  274. opto_temp.chantype(:) = {'nirs'};
  275. opto_temp.optolabel = opto_MNI.label;
  276. opto_temp.label=channels;
  277. opto_temp.tra=zeros(length(channels),length(opto_temp.optolabel));
  278. for i=1:length(channels)
  279. opto_temp.tra(i,:) = strcmp(dxnames{i},opto_temp.optolabel)+strcmp(sxnames{i}, opto_temp.optolabel);
  280. end
  281. opto_temp=rmfield(opto_temp, {'chanpos', 'chantype', 'chanunit', 'elecpos'});
  283. % calculate the channel positions
  284. chan_MNI = ft_datatype_sens(opto_temp);
  286. %% 9 Determine anatomical label for the channels (this is only possible in MNI space)
  287. % % documentati on: http://www.fieldtriptoolbox.org/faq/how_can_i_determine_the_anatomical_label_of_a_source/
  288. % % load atlas (AAL atlas), but you can also load another atlas, see: http://www.fieldtriptoolbox.org/template/atlas/
  289. % atlas = ft_read_atlas([ftpath filesep 'template/atlas/aal/ROI_MNI_V4.nii']);
  290. %
  291. % channels = {'S1_D2', 'S2_D2', 'S2_D3', 'S2_D1','S3_D3', 'S3_D1', 'S4_D4', 'S4_D5', 'S5_D4', 'S5_D5','S6_D4', 'S7_D6', 'S7_D7','S8_D6', 'S8_D7', 'S8_D8','S9_D8','S10_D9', 'S10_D10','S11_D9', 'S11_D10','S12_D10', 'S13_D2', 'S14_D8', 'S15_D5', 'S16_D9'};
  292. %
  293. % % Look up the corresponding anatomical label
  294. % cfg = [];
  295. % cfg.roi = chan_MNI.chanpos(match_str(chan_MNI.label,channels),:);
  296. % cfg.atlas = atlas;
  297. % cfg.output = 'multiple';
  298. % cfg.minqueryrange=1;
  299. % cfg.maxqueryrange=25; %if no label was found, increase the queryrange
  300. % labels = ft_volumelookup(cfg, atlas);
  301. %
  302. % % Select the anatomical label with the highest probability
  303. %
  304. % for i=1:length(channels)
  305. % [~, indx] = max(labels(i).count);
  306. % label{i}=char(labels(i).name(indx));
  307. % end
  308. %
  309. % % show results in table
  310. % table(channels', label', 'VariableNames', {'channel', 'label'})
  312. %% CSV table python
  314. labeling = char(chan_MNI.label);
  316. % f= "fidcuial";
  317. % s = "source";
  318. % d = "dectector";
  319. % types = repelem([f; s; d], [5 10 10]);
  320. %
  321. % pythontable = table(labeling,types,opto_MNI.elecpos(:,1),opto_MNI.elecpos(:,2),opto_MNI.elecpos(:,3), 'VariableNames',{'name','type', 'x','y','z'})
  322. pythontable = table(labeling,chan_MNI.chanpos(:,1),chan_MNI.chanpos(:,2),chan_MNI.chanpos(:,3), 'VariableNames',{'name', 'x','y','z'})
  325. writetable(pythontable, [sub_id, '.csv']); %change per participant
  327. %% compare ROI coordinates
  328. regions = {'left TPJ', 'right TPJ', 'left IFG', 'right IFG'};
  329. ROIs = char(regions);
  330. ROI_coordinates = [-56 -54 20; 56 -52 18; -48 22 8; 50 22 16];
  331. % ROI_tabel = table(ROIs, ROI_coordinates(:,1), ROI_coordinates(:,2), ROI_coordinates(:,3), 'VariableNames',{'ROI', 'x','y','z'});
  332. % Chan_coordinates = table(chan_MNI.chanpos(:,1),chan_MNI.chanpos(:,2),chan_MNI.chanpos(:,3), 'VariableNames',{ 'x','y','z'});
  335. P=chan_MNI.chanpos;
  337. l_tpj = ROI_coordinates(1,:);
  338. r_tpj = ROI_coordinates(2,:);
  339. l_ifg = ROI_coordinates(3,:);
  340. r_ifg = ROI_coordinates(4,:);
  342. l_tpj_distance = zeros(26,1);
  343. for i = 1:length(P)
  344. X = [l_tpj ; P(i,:)];
  345. d= pdist(X, 'euclidean');
  346. l_tpj_distance(i)=d;
  347. end
  349. r_tpj_distance = zeros(26,1);
  350. for i = 1:length(P)
  351. X = [r_tpj ; P(i,:)];
  352. d= pdist(X, 'euclidean');
  353. r_tpj_distance(i)=d;
  354. end
  356. l_ifg_distance = zeros(26,1);
  357. for i = 1:length(P)
  358. X = [l_ifg ; P(i,:)];
  359. d= pdist(X, 'euclidean');
  360. l_ifg_distance(i)=d;
  361. end
  363. r_ifg_distance = zeros(26,1);
  364. for i = 1:length(P)
  365. X = [r_ifg ; P(i,:)];
  366. d= pdist(X, 'euclidean');
  367. r_ifg_distance(i)=d;
  368. end
  370. alldistances = [l_tpj_distance, r_tpj_distance, l_ifg_distance, r_ifg_distance];
  371. distance = table(labeling,l_tpj_distance, r_tpj_distance, l_ifg_distance, r_ifg_distance)
  372. writetable(distance, [sub_id, '_distances.csv'])%change per participant
  374. nearest = [];
  375. nearest.chanlabels = chan_MNI.label;
  376. nearest.ROIS = regions;
  377. nearest.distance = alldistances;
  379. near= strings(4,2); %make a list of the closest channel per ROI stored in varaible 'near'
  380. channear = {};
  381. for i=1:4
  382. [min_value, index] = min(nearest.distance(:,i));
  383. near(i,1) = num2str(min_value);
  384. near(i,2) = nearest.chanlabels(1,index);
  385. end
  387. chanlabel = char(channear);
  389. %% distances between optode pairs
  390. %if you are doing this for participants who already had the entire script
  391. %run on them, just run section 2,8 and this section, uncomment load
  392. %opto_MNI and ctf at the top of section 8
  394. optode_distances = [];
  396. % Loop through each channel pair
  397. for i = 1:numel(channels)
  398. % Extract electrode labels for the current channel pair
  399. optodes = strsplit(channels{i}, '_');
  401. % Find the indices of electrodes in the data structure
  402. idx_optodes = cellfun(@(x) find(strcmp(chan_MNI.optolabel, x)), optodes);
  404. % Extract coordinates of electrodes in the channel pair
  405. coords = chan_MNI.optopos(idx_optodes, :);
  407. % Calculate distance between electrodes
  408. dist = pdist(coords);
  410. % Store channel distance
  411. optode_distances = [optode_distances; dist];
  412. end
  414. % Create a table with channel names and distances
  415. channeltable = channels';
  416. optode_dist = table(channeltable, optode_distances, 'VariableNames', {'Channel', 'Distance'});
  417. disp(optode_dist);
  418. writetable(optode_dist, [sub_id, '_optode_dist.csv'])%change per participant

script_structuresensor1.m, no license · at the source

Overview

Authors: Ryssa Moffat1, Guillaume Dumas2,3, Emily S. Cross1
ORCID iDs: Ryssa Moffat, Emily S. Cross
  1. Social Brain Sciences Lab, ETH Zurich, Zurich, Switzerland
  2. CHU Sainte-Justine Azrieli Research Center, Department of Psychiatry, University of Montreal, Montréal, Quebec, Canada
  3. Mila – Quebec Artificial Intelligence Institute, Montréal, Quebec, Canada
Institutions: ETH Zurich (Switzerland); Centre Hospitalier Universitaire Sainte-Justine (Canada); Mila - Quebec Artificial Intelligence Institute (Canada); CHU Sainte-Justine Research Center (Canada); Université de Montréal (Canada)
Journal: PLoS biology, volume 24, issue 8, article e3003899
Dates: received 29 January 2026; accepted 26 June 2026; published online 20 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pbio.3003899 · PMID 42623338 · PMCID PMC13492753 · OpenAlex W7203819231
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism)
Methods: Spectral & time-frequency, Connectivity, Statistics, fMRI & imaging
MeSH: Intergenerational Relations*, Interpersonal Relations*, Loneliness*, Social Interaction*, Adult, Female, Humans, Longitudinal Studies, Male, Middle Aged, Social Group, Young Adult (* major topic)
Journal subjects: People and Places, Population Groupings, Age Groups, Adults, Elderly, Biology and Life Sciences, Psychology, Collective Human Behavior, Interpersonal Relationships, Social Sciences, Psychological Attitudes, Neuroscience, Cognitive Science, Cognitive Psychology, Attention, Behavior, Verbal Behavior, Verbal Communication, Research and Analysis Methods, Bioassays and Physiological Analysis, Electrophysiological Techniques, Brain Electrophysiology, Electroencephalography, Physiology, Electrophysiology, Neurophysiology, Brain Mapping, Medicine and Health Sciences, Clinical Medicine, Clinical Neurophysiology, Imaging Techniques, Neuroimaging, Sociology, Social Systems
Topic: Action Observation and Synchronization (Social Psychology, Psychology), according to OpenAlex
Funding: Cortivision Pathfinder Program (CPP-2023/09/01); Institut de Valorisation des Données (CF00137433); Fonds de recherche du Québec (285289); Natural Sciences and Engineering Research Council of Canada (DGECR-2023-00089); Caran D’Ache (712517); Social Brain Sciences Lab, ETH Zurich
Citations: not cited yet (Europe PMC); 140 references in the paper

Abstract

Loneliness is globally acknowledged as a severe and burgeoning health risk, fueling interest in helping people of all ages form meaningful social connections. One promising approach consists of intergenerational social programs. While behavioral and qualitative evidence derived from such programs promise health and wellbeing benefits, the physiological consequences of repeated intergenerational encounters remain unknown. Insight into physiological changes will shed light on the mechanisms of social connection. We charted longitudinal changes in interpersonal neural synchrony (INS) in 31 intergenerational (older/younger adult) and 30 same-generation (younger adult) dyads across a six-session creative drawing program. At each session, dyads completed self-report measures, drew together and alone, and had their cortical activation recorded with fNIRS. In both groups, INS was greater while dyads drew together than alone. Across sessions, intergenerational dyads’ INS decreased and same-generation dyads’ INS increased. INS in RIFG~RTPJ and RIFG~RIFG were predictive of loneliness levels and feelings of social closeness, respectively. This exploratory longitudinal research reinforces the multi-faceted nature of INS dynamics as social connections are forged.

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

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OSF xcgp6

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State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: R (19), MATLAB (1)
Size: 51 files, 20 scripts
Software Heritage: not checked
Found in: “Data Availability”
Holds: 19 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggpubr (19 files), patchwork (19 files), tidyverse (19 files), brms (17 files), emmeans (17 files), data.table (2 files), ggplot2 (2 files), FieldTrip (1 file), Statistics and Machine Learning Toolbox (1 file)
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20 files
At the source: osf.io/xcgp6/

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Found in: the text, “5.5 Procedure”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source: osf.io/zms2v

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Data

Datasets cited

Data Availability

These data were collected after the submission of the following preregistration: https://osf.io/hz6tm. All of the data collected and analysed for this study, as well as all of our code, can be found in our repository entitled ‘Longitudinal perspectives on intergenerational inter-brain synchrony’ on OSF: https://osf.io/xcgp6/.

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Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 12 MeSH terms, 6 funders, 123 references.

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This paper

Moffat, R., Dumas, G., & Cross, E. S. (2026). Social interactions between people of same and different generations shape longitudinal changes in interpersonal neural synchrony, loneliness, and social connection. PLoS biology, 24(8), e3003899. https://doi.org/10.1371/journal.pbio.3003899

BibTeX

@article{moffat2026social,
author = {Moffat, Ryssa and Dumas, Guillaume and Cross, Emily S.},
title = {{Social interactions between people of same and different generations shape longitudinal changes in interpersonal neural synchrony, loneliness, and social connection}},
journal = {PLoS biology},
year = {2026},
month = aug,
volume = {24},
number = {8},
pages = {e3003899},
publisher = {PLOS},
issn = {1544-9173},
doi = {10.1371/journal.pbio.3003899},
url = {https://doi.org/10.1371/journal.pbio.3003899},
pmid = {42623338},
pmcid = {PMC13492753}
}

RIS

TY - JOUR
AU - Moffat, Ryssa
AU - Dumas, Guillaume
AU - Cross, Emily S.
TI - Social interactions between people of same and different generations shape longitudinal changes in interpersonal neural synchrony, loneliness, and social connection
T2 - PLoS biology
J2 - PLoS Biol
PY - 2026
DA - 2026/08/20
VL - 24
IS - 8
SP - e3003899
SN - 1544-9173
PB - PLOS
DO - 10.1371/journal.pbio.3003899
UR - https://doi.org/10.1371/journal.pbio.3003899
LA - en
ER -

CSL-JSON

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