Impaired midfrontal‑motor theta phase synchronization characterizes maladaptive motivational behavior in people with obsessive‑compulsive disorder.

Code ↔ Paper

12 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 12 matches · 1 of them tie a paragraph to a whole file without given lines: a weak match, whose lines are not tinted

[1] § Materials and methods › Time–frequency decomposition ↔ code/ISPS/TF_singletrial_grouplevel.m, lines 310 to 330 · score 0.79 · 400–650 ms, 424–800 ms, theta band, 424 ms, 4–8 Hz, 400 ms · Report a mismatch
[2] § Materials and methods › Statistical analyses ↔ code/ISPS/TF_singletrial_grouplevel.m, lines 310 to 330 · score 0.78 · 400–650 ms, 424–800 ms, theta band, 424 ms, bilateral, 400 ms · Report a mismatch
[3] § Materials and methods › Individual regulatory orientation ↔ code/Regulatory orientation/relation.m, lines 338 to 474 · score 0.71 · partial Spearman, residual, BIS, covariates, BAI, BDI · Report a mismatch
[4] § Results › Heterogeneous regulatory orientations of midfrontal‑motor coupling and their clinical relevance in OCD ↔ code/Regulatory orientation/Φreg plot.m, lines 239 to 253 · score 0.63 · white triangles, regulatory orientation, ISPS motor, instrumental action, contours, axis · Report a mismatch
[5] § Results › Compared with healthy controls, people with OCD showed heightened Pavlovian bias and reduced learning rates ↔ code/computational_modelling/plot/M5_hcvsocd/DrawCombined_AllInOne2.m, lines 397 to 470 · score 0.58 · instrumental learning bias, feedback sensitivity, Go bias, Pavlovian bias, HDI, post · Report a mismatch
[6] § Materials and methods › Time–frequency decomposition ↔ code/EEGprocessing/TF (group-level).m, lines 754 to 813 · score 0.58 · Post hoc, electrodes contributed, clusters, congruent, theta · Report a mismatch
[7] § Results › Midfrontal theta in OCD supports conflict detection and modulation of motivational biases ↔ code/ISPS/TF_singletrial_grouplevel.m, lines 20 to 40 · score 0.55 · 400–650 ms, 4–8 Hz, band, 400 ms, electroencephalography, windows · Report a mismatch
[8] § Results › Heterogeneous regulatory orientations of midfrontal‑motor coupling and their clinical relevance in OCD ↔ code/Regulatory orientation/Φreg plot.m, lines 91 to 114 · score 0.53 · regulatory orientation, lPFC, instrumental action, Pavlovian bias, beta, motor · Report a mismatch
[9] § Materials and methods › EEG preprocessing ↔ code/EEGprocessing/eegpreprocess.m, lines 179 to 203 · score 0.52 · EEGLAB, PCA, runica, epoched, cue · Report a mismatch
[10] § Materials and methods › EEG preprocessing ↔ code/ISPS/ISPS (subject-level).m, the whole file · a weak match · score 0.52 · EEGLAB, phase synchrony, spline, Laplacian, preprocessing, baseline · Report a mismatch
[11] § Results › Compared with healthy controls, people with OCD showed heightened Pavlovian bias and reduced learning rates ↔ code/computational_modelling/modelrun/models4stan.R, lines 810 to 870 · score 0.51 · feedback sensitivity, Go bias, Pavlovian bias, M3a, M1, likelihood · Report a mismatch
[12] § Materials and methods › Computational models ↔ code/computational_modelling/modelrun/models4stan.R, lines 810 to 870 · score 0.51 · feedback sensitivity, Go bias, M3a, M1, cue, Pavlovian · Report a mismatch

Paper

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The authors' code

MATLAB · 718 lines · 27 KB · MIT · 3 matches

%%%%%%%%%%%%%%%%%%%%% Vectorize all trials at once %%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% =========================================================================
% Trial-level midfrontal theta POWER + Inter-channel Phase Coherence (ICPC)
% Adapted from Swart et al. (2017, DCCN, Nijmegen)
% For OCD dataset (EEGLAB → FieldTrip) [vectorized version: ICPC computed for all trials at once]
% =========================================================================
clear; clc; close all;
dbstop if error
%% ====== Paths and dependencies ======
data_dir = 'H:\MT_all(afterqujizhi)\cueoriandbu\error\HC';
fileList = dir(fullfile(data_dir, '*.set'));
nSub = numel(fileList);
addpath('D:\MATLAB\toolbox\eeglab_current\eeglab2022.0_old');
addpath('D:\MATLAB\toolbox\fieldtrip-20211209\fieldtrip-20211209');
ft_defaults;
%% ====== Analysis parameters ======
freqRange = [4 8]; % theta band
timeWin = [0.40 0.65]; % time window of interest (keep 0.40–0.65 s as requested)
% ---- Electrode group definitions ----
seedChans = {'E6','E129'}; % Midfrontal (FCz, Cz)
pfcChans = {'E23','E24','E26','E27','E2','E3','E123','E124'}; % bilateral PFC
lMotorCh = {'E36','E42','E41','E47'}; % left motor area
rMotorCh = {'E93','E98','E103','E104'}; % right motor area
% ---- Group weights (unchanged) ----
seedweights = [2.9194, 3.4082]; % Midfrontal weights
tweights_pfc = [0.7948 2.4114 1.2002 4.2642 3.9349 0.3577 1.0100 -1.0324];
tweights_motor_L = [-1.0139 0.6538 0.8044 0.2330];
tweights_motor_R = [1.2122 1.0770 0.5535 2.1191];
% Normalize weights (seed & t-weights) for vectorized use
sw = seedweights ./ sum(seedweights);
tw_pfc = tweights_pfc ./ sum(tweights_pfc);
tw_L = tweights_motor_L ./ sum(tweights_motor_L);
tw_R = tweights_motor_R ./ sum(tweights_motor_R);
%% ====== Initialize output matrices ======
trial2use = cell(nSub,1);
MFpower = cell(nSub,1);
ICPC_pfc = cell(nSub,1);
ICPC_motor_L = cell(nSub,1);
ICPC_motor_R = cell(nSub,1);
%% ====== Main loop ======
for s = 1:nSub
fprintf('\n=== Processing %s (%d/%d) ===\n', fileList(s).name, s, nSub);
% --- Step 1: load and convert ---
EEG = pop_loadset('filename', fileList(s).name, 'filepath', data_dir);
ft_data = eeglab2fieldtrip(EEG, 'preprocessing');
% --- Step 2: Laplacian (CSD) ---
cfg = [];
cfg.method = 'spline';
cfg.elec = ft_data.elec;
scd = ft_scalpcurrentdensity(cfg, ft_data);
nTrial = numel(scd.trial);
trial2use{s} = ones(1,nTrial); % Update here if trial exclusion is needed
%% ===============================================================
% ① Midfrontal θ Power (trial-wise) —— keep original implementation
% ===============================================================
cfg = [];
cfg.channel = seedChans;
cfg.bpfilter = 'yes';
cfg.bpfreq = freqRange;
filtered = ft_preprocessing(cfg, scd);
% Hilbert amplitude → square to get power
cfg = [];
cfg.hilbert = 'abs';
hilbertData = ft_preprocessing(cfg, filtered);
for tr = 1:nTrial
hilbertData.trial{tr} = hilbertData.trial{tr}.^2;
end
% Concatenate + weighted average + zscore
allP = [hilbertData.trial{:}]; % chan × (time*trial)
weights4power = repmat(seedweights',1,size(allP,2));
st_power = zscore(mean(weights4power .* allP,1));
nTime = size(hilbertData.trial{1},2);
st_power = reshape(st_power,[nTime,nTrial]);
% Extract mean within the time window
iTime = dsearchn(hilbertData.time{1}',timeWin(1)) : ...
dsearchn(hilbertData.time{1}',timeWin(2));
MFpower{s} = squeeze(mean(st_power(iTime,:),1)); % 1×nTrial
%% ===============================================================
% ② Midfrontal–Lateral PFC ICPC (trial-wise) —— vectorized implementation
% ===============================================================
cfg = [];
cfg.channel = [seedChans pfcChans];
cfg.bpfilter = 'yes'; cfg.bpfreq = freqRange;
filtered = ft_preprocessing(cfg, scd);
cfg = []; cfg.hilbert = 'angle';
hilbertdata = ft_preprocessing(cfg, filtered);
% Assemble as [nChan × nTime × nTrial]
nChan = numel(hilbertdata.label);
nTime = size(hilbertdata.trial{1},2);
nTrial = numel(hilbertdata.trial);
ang = reshape([hilbertdata.trial{:}], nChan, nTime, nTrial);
% Time-window indices (same as θ power)
iTime = dsearchn(hilbertdata.time{1}',timeWin(1)) : ...
dsearchn(hilbertdata.time{1}',timeWin(2));
% Compute all targets (PFC) and all trials at once:
% diff1/2: [nTarget × |iTime| × nTrial]
diff1_all = ang(1, iTime, :) - ang(3:end, iTime, :);
diff2_all = ang(2, iTime, :) - ang(3:end, iTime, :);
% PLV (mean across time) → [nTarget × 1 × nTrial]
plv1 = abs(mean(exp(1i*diff1_all), 2));
plv2 = abs(mean(exp(1i*diff2_all), 2));
% Weighted combination of the two seeds → [nTarget × nTrial]
plv_pfc = squeeze(sw(1).*plv1 + sw(2).*plv2); % after squeeze: [nTarget × nTrial]
% Linear combination with t-weights (signed) across targets → [1 × nTrial]
ICPC_pfc_vals = (tw_pfc * plv_pfc); % 1×nTrial
ICPC_pfc{s} = ICPC_pfc_vals;
%% ===============================================================
% ③ Midfrontal–Motor ICPC (Left / Right) —— vectorized implementation
% ===============================================================
cfg = [];
cfg.channel = [seedChans lMotorCh rMotorCh];
cfg.bpfilter = 'yes'; cfg.bpfreq = freqRange;
filtered = ft_preprocessing(cfg, scd);
cfg = []; cfg.hilbert = 'angle';
hilbertdata = ft_preprocessing(cfg, filtered);
% Assemble as [nChan × nTime × nTrial]
nChan = numel(hilbertdata.label);
nTime = size(hilbertdata.trial{1},2);
nTrial = numel(hilbertdata.trial);
ang = reshape([hilbertdata.trial{:}], nChan, nTime, nTrial);
% Time-window indices
iTime = dsearchn(hilbertdata.time{1}',timeWin(1)) : ...
dsearchn(hilbertdata.time{1}',timeWin(2));
% ---- Left motor (target indices 3:6) ----
diff1_L = ang(1, iTime, :) - ang(3:6, iTime, :); % [4 × |iTime| × nTrial]
diff2_L = ang(2, iTime, :) - ang(3:6, iTime, :);
plv1_L = abs(mean(exp(1i*diff1_L), 2)); % [4 × 1 × nTrial]
plv2_L = abs(mean(exp(1i*diff2_L), 2));
plv_L = squeeze(sw(1).*plv1_L + sw(2).*plv2_L); % [4 × nTrial]
Lvals = (tw_L * plv_L); % [1 × nTrial]
ICPC_motor_L{s} = Lvals;
% ---- Right motor (target indices 7:10) ----
diff1_R = ang(1, iTime, :) - ang(7:10, iTime, :); % [4 × |iTime| × nTrial]
diff2_R = ang(2, iTime, :) - ang(7:10, iTime, :);
plv1_R = abs(mean(exp(1i*diff1_R), 2));
plv2_R = abs(mean(exp(1i*diff2_R), 2));
plv_R = squeeze(sw(1).*plv1_R + sw(2).*plv2_R); % [4 × nTrial]
Rvals = (tw_R * plv_R); % [1 × nTrial]
ICPC_motor_R{s} = Rvals;
end
%% ====== Save results ======
save(fullfile(data_dir,'TrialLevel_MFpower_ICPC.mat'), ...
'MFpower','ICPC_pfc','ICPC_motor_L','ICPC_motor_R','trial2use');
fprintf('Saved trial-level θ-power and ICPC results (ICPC was computed with vectorization).\n');
%% Export the full CSV data
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% ================== Configuration ==================
data_dir = 'H:\MT_all(afterqujizhi)\cueoriandbu\error\HC';
mat_file = fullfile(data_dir, 'TrialLevel_MFpower_ICPC.mat');
out_csv = fullfile(data_dir, 'TrialLevel_MFpower_ICPC.csv');
%% ================== Load data ==================
S = load(mat_file, 'MFpower','ICPC_pfc','ICPC_motor_L','ICPC_motor_R','trial2use');
MFpower = S.MFpower;
ICPC_pfc = S.ICPC_pfc;
ICPC_motor_L = S.ICPC_motor_L;
ICPC_motor_R = S.ICPC_motor_R;
trial2use = S.trial2use;
% File order consistent with the original computation (.set order in the folder)
fileList = dir(fullfile(data_dir, '*.set'));
nSub = numel(fileList);
% Basic consistency check (optional)
if nSub ~= numel(MFpower)
warning('fileList count (%d) does not match MFpower subject count (%d). The smaller one will be used.', nSub, numel(MFpower));
end
nSub_use = min([nSub, numel(MFpower), numel(ICPC_pfc), numel(ICPC_motor_L), numel(ICPC_motor_R), numel(trial2use)]);
%% ================== Count total rows and preallocate ==================
rowsPerSub = zeros(nSub_use,1);
for s = 1:nSub_use
if ~isempty(MFpower{s})
rowsPerSub(s) = numel(MFpower{s});
else
rowsPerSub(s) = 0;
end
end
N = sum(rowsPerSub);
sub_col = strings(N,1);
trial_col = zeros(N,1);
MFpower_col = nan(N,1);
ICPCpfc_col = nan(N,1);
l_ICPCmotor_col = nan(N,1);
r_ICPCmotor_col = nan(N,1);
trial2use_col = nan(N,1);
%% ================== Expand to long table ==================
ptr = 1;
for s = 1:nSub_use
% Get the four measures and trial2use for this subject (all are 1×nTrial)
mp = MFpower{s};
pf = ICPC_pfc{s};
lmo = ICPC_motor_L{s};
rmo = ICPC_motor_R{s};
tuse = trial2use{s};
if isempty(mp)
continue; % Skip empty subject
end
nTrial = numel(mp);
% sub: first four characters of each filename (without extension)
fname = fileList(s).name; % e.g. 'OCD01.set' or 'x130.set'
base = erase(string(fname), '.set'); % remove extension
if strlength(base) >= 4
subID = extractBetween(base, 1, 4);
else
subID = base; % use original if length is < 4
end
% Write index range
idx = ptr:(ptr + nTrial - 1);
sub_col(idx) = repmat(subID, nTrial, 1);
trial_col(idx) = (1:nTrial).'; % trial index starts from 1
MFpower_col(idx) = mp(:);
ICPCpfc_col(idx) = pf(:);
l_ICPCmotor_col(idx) = lmo(:);
r_ICPCmotor_col(idx) = rmo(:);
% trial2use may be 1×nTrial or empty; set to 0 if empty
if ~isempty(tuse)
trial2use_col(idx) = tuse(:);
else
trial2use_col(idx) = 0;
end
ptr = ptr + nTrial;
end
%% ================== Build table and write CSV ==================
T = table( ...
sub_col, trial_col, ...
MFpower_col, ICPCpfc_col, l_ICPCmotor_col, r_ICPCmotor_col, trial2use_col, ...
'VariableNames', {'sub','trial','MFpower','ICPCpfc','l_ICPCmotor','r_ICPCmotor','trial2use'} ...
);
writetable(T, out_csv);
fprintf('CSV written: %s\nTotal rows = %d (sub×trial long table)\n', out_csv, height(T));
%% OCD
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%% Vectorize all trials at once %%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% =========================================================================
% Trial-level midfrontal theta POWER + Inter-channel Phase Coherence (ICPC)
% Adapted from Swart et al. (2017, DCCN, Nijmegen)
% For OCD dataset (EEGLAB → FieldTrip) [vectorized version: ICPC computed for all trials at once]
% =========================================================================
clear; clc; close all;
dbstop if error
%% ====== Paths and dependencies ======
data_dir = 'H:\MT_all(afterqujizhi)\cueoriandbu\error\OCD';
fileList = dir(fullfile(data_dir, '*.set'));
nSub = numel(fileList);
addpath('D:\MATLAB\toolbox\eeglab_current\eeglab2022.0_old');
addpath('D:\MATLAB\toolbox\fieldtrip-20211209\fieldtrip-20211209');
ft_defaults;
%% ====== Analysis parameters ======
freqRange = [4 8]; % theta band
timeWin = [0.424 0.8]; % time window of interest (keep 0.40–0.65 s as requested)
% ---- Electrode group definitions ----
seedChans = {'E6','E129'}; % Midfrontal (FCz, Cz)
pfcChans = {'E23','E24','E26','E27','E2','E3','E123','E124'}; % bilateral PFC
lMotorCh = {'E36','E42','E41','E47'}; % left motor area
rMotorCh = {'E93','E98','E103','E104'}; % right motor area
% ---- Group weights (unchanged) ----
seedweights = [3.4548, 4.4553]; % Midfrontal weights
tweights_pfc = [0.4255 2.1589 -0.2652 1.4101 0.8377 2.5389 1.6312 2.8189];
tweights_motor_L = [-1.1191 0.0210 1.1295 0.1666];
tweights_motor_R = [1.4087 0.7652 0.1929 0.0753];
% Normalize weights (seed & t-weights) for vectorized use
sw = seedweights ./ sum(seedweights);
tw_pfc = tweights_pfc ./ sum(tweights_pfc);
tw_L = tweights_motor_L ./ sum(tweights_motor_L);
tw_R = tweights_motor_R ./ sum(tweights_motor_R);
%% ====== Initialize output matrices ======
trial2use = cell(nSub,1);
MFpower = cell(nSub,1);
ICPC_pfc = cell(nSub,1);
ICPC_motor_L = cell(nSub,1);
ICPC_motor_R = cell(nSub,1);
%% ====== Main loop ======
for s = 1:nSub
fprintf('\n=== Processing %s (%d/%d) ===\n', fileList(s).name, s, nSub);
% --- Step 1: load and convert ---
EEG = pop_loadset('filename', fileList(s).name, 'filepath', data_dir);
ft_data = eeglab2fieldtrip(EEG, 'preprocessing');
% --- Step 2: Laplacian (CSD) ---
cfg = [];
cfg.method = 'spline';
cfg.elec = ft_data.elec;
scd = ft_scalpcurrentdensity(cfg, ft_data);
nTrial = numel(scd.trial);
trial2use{s} = ones(1,nTrial); % Update here if trial exclusion is needed
%% ===============================================================
% ① Midfrontal θ Power (trial-wise) —— keep original implementation
% ===============================================================
cfg = [];
cfg.channel = seedChans;
cfg.bpfilter = 'yes';
cfg.bpfreq = freqRange;
filtered = ft_preprocessing(cfg, scd);
% Hilbert amplitude → square to get power
cfg = [];
cfg.hilbert = 'abs';
hilbertData = ft_preprocessing(cfg, filtered);
for tr = 1:nTrial
hilbertData.trial{tr} = hilbertData.trial{tr}.^2;
end
% Concatenate + weighted average + zscore
allP = [hilbertData.trial{:}]; % chan × (time*trial)
weights4power = repmat(seedweights',1,size(allP,2));
st_power = zscore(mean(weights4power .* allP,1));
nTime = size(hilbertData.trial{1},2);
st_power = reshape(st_power,[nTime,nTrial]);
% Extract mean within the time window
iTime = dsearchn(hilbertData.time{1}',timeWin(1)) : ...
dsearchn(hilbertData.time{1}',timeWin(2));
MFpower{s} = squeeze(mean(st_power(iTime,:),1)); % 1×nTrial
%% ===============================================================
% ② Midfrontal–Lateral PFC ICPC (trial-wise) —— vectorized implementation
% ===============================================================
cfg = [];
cfg.channel = [seedChans pfcChans];
cfg.bpfilter = 'yes'; cfg.bpfreq = freqRange;
filtered = ft_preprocessing(cfg, scd);
cfg = []; cfg.hilbert = 'angle';
hilbertdata = ft_preprocessing(cfg, filtered);
% Assemble as [nChan × nTime × nTrial]
nChan = numel(hilbertdata.label);
nTime = size(hilbertdata.trial{1},2);
nTrial = numel(hilbertdata.trial);
ang = reshape([hilbertdata.trial{:}], nChan, nTime, nTrial);
% Time-window indices (same as θ power)
iTime = dsearchn(hilbertdata.time{1}',timeWin(1)) : ...
dsearchn(hilbertdata.time{1}',timeWin(2));
% Compute all targets (PFC) and all trials at once:
% diff1/2: [nTarget × |iTime| × nTrial]
diff1_all = ang(1, iTime, :) - ang(3:end, iTime, :);
diff2_all = ang(2, iTime, :) - ang(3:end, iTime, :);
% PLV (mean across time) → [nTarget × 1 × nTrial]
plv1 = abs(mean(exp(1i*diff1_all), 2));
plv2 = abs(mean(exp(1i*diff2_all), 2));
% Weighted combination of the two seeds → [nTarget × nTrial]
plv_pfc = squeeze(sw(1).*plv1 + sw(2).*plv2); % after squeeze: [nTarget × nTrial]
% Linear combination with t-weights (signed) across targets → [1 × nTrial]
ICPC_pfc_vals = (tw_pfc * plv_pfc); % 1×nTrial
ICPC_pfc{s} = ICPC_pfc_vals;
%% ===============================================================
% ③ Midfrontal–Motor ICPC (Left / Right) —— vectorized implementation
% ===============================================================
cfg = [];
cfg.channel = [seedChans lMotorCh rMotorCh];
cfg.bpfilter = 'yes'; cfg.bpfreq = freqRange;
filtered = ft_preprocessing(cfg, scd);
cfg = []; cfg.hilbert = 'angle';
hilbertdata = ft_preprocessing(cfg, filtered);
% Assemble as [nChan × nTime × nTrial]
nChan = numel(hilbertdata.label);
nTime = size(hilbertdata.trial{1},2);
nTrial = numel(hilbertdata.trial);
ang = reshape([hilbertdata.trial{:}], nChan, nTime, nTrial);
% Time-window indices
iTime = dsearchn(hilbertdata.time{1}',timeWin(1)) : ...
dsearchn(hilbertdata.time{1}',timeWin(2));
% ---- Left motor (target indices 3:6) ----
diff1_L = ang(1, iTime, :) - ang(3:6, iTime, :); % [4 × |iTime| × nTrial]
diff2_L = ang(2, iTime, :) - ang(3:6, iTime, :);
plv1_L = abs(mean(exp(1i*diff1_L), 2)); % [4 × 1 × nTrial]
plv2_L = abs(mean(exp(1i*diff2_L), 2));
plv_L = squeeze(sw(1).*plv1_L + sw(2).*plv2_L); % [4 × nTrial]
Lvals = (tw_L * plv_L); % [1 × nTrial]
ICPC_motor_L{s} = Lvals;
% ---- Right motor (target indices 7:10) ----
diff1_R = ang(1, iTime, :) - ang(7:10, iTime, :); % [4 × |iTime| × nTrial]
diff2_R = ang(2, iTime, :) - ang(7:10, iTime, :);
plv1_R = abs(mean(exp(1i*diff1_R), 2));
plv2_R = abs(mean(exp(1i*diff2_R), 2));
plv_R = squeeze(sw(1).*plv1_R + sw(2).*plv2_R); % [4 × nTrial]
Rvals = (tw_R * plv_R); % [1 × nTrial]
ICPC_motor_R{s} = Rvals;
end
%% ====== Save results ======
save(fullfile(data_dir,'TrialLevel_MFpower_ICPC.mat'), ...
'MFpower','ICPC_pfc','ICPC_motor_L','ICPC_motor_R','trial2use');
fprintf('Saved trial-level θ-power and ICPC results (ICPC was computed with vectorization).\n');
clc; clear;
%% Export the full CSV data
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% ================== Configuration ==================
data_dir = 'H:\MT_all(afterqujizhi)\cueoriandbu\error\OCD';
mat_file = fullfile(data_dir, 'TrialLevel_MFpower_ICPC.mat');
out_csv = fullfile(data_dir, 'TrialLevel_MFpower_ICPC.csv');
%% ================== Load data ==================
S = load(mat_file, 'MFpower','ICPC_pfc','ICPC_motor_L','ICPC_motor_R','trial2use');
MFpower = S.MFpower;
ICPC_pfc = S.ICPC_pfc;
ICPC_motor_L = S.ICPC_motor_L;
ICPC_motor_R = S.ICPC_motor_R;
trial2use = S.trial2use;
% File order consistent with the original computation (.set order in the folder)
fileList = dir(fullfile(data_dir, '*.set'));
nSub = numel(fileList);
% Basic consistency check (optional)
if nSub ~= numel(MFpower)
warning('fileList count (%d) does not match MFpower subject count (%d). The smaller one will be used.', nSub, numel(MFpower));
end
nSub_use = min([nSub, numel(MFpower), numel(ICPC_pfc), numel(ICPC_motor_L), numel(ICPC_motor_R), numel(trial2use)]);
%% ================== Count total rows and preallocate ==================
rowsPerSub = zeros(nSub_use,1);
for s = 1:nSub_use
if ~isempty(MFpower{s})
rowsPerSub(s) = numel(MFpower{s});
else
rowsPerSub(s) = 0;
end
end
N = sum(rowsPerSub);
sub_col = strings(N,1);
trial_col = zeros(N,1);
MFpower_col = nan(N,1);
ICPCpfc_col = nan(N,1);
l_ICPCmotor_col = nan(N,1);
r_ICPCmotor_col = nan(N,1);
trial2use_col = nan(N,1);
%% ================== Expand to long table ==================
ptr = 1;
for s = 1:nSub_use
% Get the four measures and trial2use for this subject (all are 1×nTrial)
mp = MFpower{s};
pf = ICPC_pfc{s};
lmo = ICPC_motor_L{s};
rmo = ICPC_motor_R{s};
tuse = trial2use{s};
if isempty(mp)
continue; % Skip empty subject
end
nTrial = numel(mp);
% sub: first four characters of each filename (without extension)
fname = fileList(s).name; % e.g. 'OCD01.set' or 'x130.set'
base = erase(string(fname), '.set'); % remove extension
if strlength(base) >= 5
subID = extractBetween(base, 1, 5);
else
subID = base; % use original if length is < 4
end
% Write index range
idx = ptr:(ptr + nTrial - 1);
sub_col(idx) = repmat(subID, nTrial, 1);
trial_col(idx) = (1:nTrial).'; % trial index starts from 1
MFpower_col(idx) = mp(:);
ICPCpfc_col(idx) = pf(:);
l_ICPCmotor_col(idx) = lmo(:);
r_ICPCmotor_col(idx) = rmo(:);
% trial2use may be 1×nTrial or empty; set to 0 if empty
if ~isempty(tuse)
trial2use_col(idx) = tuse(:);
else
trial2use_col(idx) = 0;
end
ptr = ptr + nTrial;
end
%% ================== Build table and write CSV ==================
T = table( ...
sub_col, trial_col, ...
MFpower_col, ICPCpfc_col, l_ICPCmotor_col, r_ICPCmotor_col, trial2use_col, ...
'VariableNames', {'sub','trial','MFpower','ICPCpfc','l_ICPCmotor','r_ICPCmotor','trial2use'} ...
);
writetable(T, out_csv);
fprintf('CSV written: %s\nTotal rows = %d (sub×trial long table)\n', out_csv, height(T));
clc; clear;
%% Generate CSV and MAT data for model fitting
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% ================== Configuration ==================
xlsx_file = 'H:\MT_all(afterqujizhi)\PIT_model.xlsx';
in_sheet = 'model';
out_csv = 'H:\MT_all(afterqujizhi)\TrialLevel_MFpower_ICPC.csv';
out_mat = 'H:\MT_all(afterqujizhi)\TrialLevel_MFpower_ICPC.mat';
TARGET_N = 73; % Target output is 73×1 cell
%% ================== Load and basic check ==================
T = readtable(xlsx_file, 'Sheet', in_sheet);
% Required columns (case-insensitive)
needCols = {'sub','group','trial','stimulus','MFpower','ICPCpfc', ...
'l_ICPCmotor','r_ICPCmotor','IVleft','IVright','trial2use'};
% Helper function for case-insensitive column access
getVar = @(tbl,name) tbl.(tbl.Properties.VariableNames{ ...
find(strcmpi(tbl.Properties.VariableNames,name),1)});
% Verify that all columns exist
for k = 1:numel(needCols)
if ~any(strcmpi(T.Properties.VariableNames, needCols{k}))
error('Column "%s" was not found (sheet %s).', needCols{k}, in_sheet);
end
end
%% ===== Helper: safely convert any column to double; unparseable values (empty/non-numeric) -> NaN =====
numify = @(v) ( ...
(isnumeric(v) .* double(v)) + ...
(~isnumeric(v) .* str2double(string(v))) );
% Note: for strings/categorical/cells, use string(...) then str2double(...);
% blanks/unparseable values become NaN
%% ===== Clean the four measure columns: empty/missing -> NaN (used for both CSV and MAT) =====
MFpower_all = numify(getVar(T,'MFpower'));
ICPCpfc_all = numify(getVar(T,'ICPCpfc'));
lICPC_all = numify(getVar(T,'l_ICPCmotor'));
rICPC_all = numify(getVar(T,'r_ICPCmotor'));
%% ================== 1) Export CSV (in the specified column order) ==================
Tout = table();
for k = 1:numel(needCols)
Tout.(needCols{k}) = getVar(T, needCols{k});
end
% Overwrite the original four columns with cleaned versions so empty values are written as NaN in CSV
Tout.MFpower = MFpower_all;
Tout.ICPCpfc = ICPCpfc_all;
Tout.l_ICPCmotor = lICPC_all;
Tout.r_ICPCmotor = rICPC_all;
writetable(Tout, out_csv);
fprintf('CSV exported: %s (%d rows × %d columns)\n', out_csv, height(Tout), width(Tout));
%% ================== 2) Build 73×1 cell structure and save MAT ==================
% —— Get and convert the other columns ——
sub_raw = getVar(T, 'sub');
group_raw = getVar(T, 'group');
trial_all = numify(getVar(T, 'trial'));
stim_all = numify(getVar(T, 'stimulus'));
IVleft_all = numify(getVar(T, 'IVleft'));
IVright_all = numify(getVar(T, 'IVright'));
trial2use_all = numify(getVar(T, 'trial2use'));
% —— Convert sub to string for grouping; also generate a numeric sub label for each row ——
sub_str = string(sub_raw);
sub_num_all = nan(height(T),1);
tryDigits = regexp(sub_str, '(\d+)', 'match', 'once'); % extract digits, e.g. 'HC30'->'30'
hasDigits = ~cellfun(@isempty, tryDigits);
sub_num_all(hasDigits) = str2double(string(tryDigits(hasDigits)));
% For rows where digits cannot be extracted, try direct numeric conversion
isnanMask = isnan(sub_num_all);
sub_num_all(isnanMask) = str2double(sub_str(isnanMask));
% —— Map group to numeric: HC->0, OCD->1; otherwise try numeric conversion ——
group_str = upper(strtrim(string(group_raw)));
group_num_all = nan(height(T),1);
group_num_all(group_str=="HC") = 0;
group_num_all(group_str=="OCD") = 1;
mask_nan = isnan(group_num_all);
group_num_all(mask_nan) = str2double(group_str(mask_nan));
% —— Group by sub_str (stable order) ——
[subs_unique, ~, G] = unique(sub_str, 'stable'); % G: group index for each row
nSub_actual = numel(subs_unique);
% Preallocate cell: first by actual subject count, then align to 73
fields = {'sub','group','trial','stimulus','MFpower','ICPCpfc', ...
'l_ICPCmotor','r_ICPCmotor','IVleft','IVright','trial2use'};
S = struct();
for f = 1:numel(fields)
S.(fields{f}) = cell(nSub_actual,1);
end
% —— Fill each cell by subject (1×trial row) ——
for i = 1:nSub_actual
idx = (G == i);
% Sort by trial to keep consistent order
[trial_i, order] = sort(trial_all(idx));
pick = find(idx);
pick = pick(order);
% sub: use the numeric sub label; if all NaN, fall back to group index i
sub_num_i = sub_num_all(pick);
if all(isnan(sub_num_i))
sub_num_i = repmat(double(i), numel(pick), 1);
end
% group: numeric value (HC=0/OCD=1/otherwise try numeric conversion/still NaN -> NaN)
group_num_i = group_num_all(pick);
% Write into structure (convert to 1×N row); all fields are double row vectors
S.sub{i} = sub_num_i(:).';
S.group{i} = group_num_i(:).';
S.trial{i} = trial_i(:).';
S.stimulus{i} = stim_all(pick).';
S.MFpower{i} = MFpower_all(pick).';
S.ICPCpfc{i} = ICPCpfc_all(pick).';
S.l_ICPCmotor{i} = lICPC_all(pick).';
S.r_ICPCmotor{i} = rICPC_all(pick).';
S.IVleft{i} = IVleft_all(pick).';
S.IVright{i} = IVright_all(pick).';
S.trial2use{i} = trial2use_all(pick).';
end
% —— Align to 73×1 cell: pad with empty cells if fewer, keep all if more ——
if nSub_actual < TARGET_N
padN = TARGET_N - nSub_actual;
for f = 1:numel(fields)
S.(fields{f}) = [S.(fields{f}); repmat({[]}, padN, 1)];
end
fprintf('Actual subject count %d < %d, padded with empty cells to %d×1.\n', nSub_actual, TARGET_N, TARGET_N);
elseif nSub_actual > TARGET_N
fprintf('Actual subject count %d > %d, saving the actual subject count without truncation.\n', nSub_actual, TARGET_N);
end
% —— Save MAT ——
save(out_mat, '-struct', 'S');
fprintf('MAT saved: %s\nFields: %s\nSize: %d×1 cell (if >73, actual subject count is kept)\n', ...
out_mat, strjoin(fields, ', '), max(TARGET_N, nSub_actual));

TF_singletrial_grouplevel.m at commit 3eae95c, under MIT · at the source

Overview

Authors: Yu Pang¹, Dongsheng Zhou², Ziwen Peng¹, Wanting Liu¹, Ruojie Huang³, Carol A Seger^1,4, Qi Chen³

ORCID iDs: Yu Pang, Qi Chen

Institutions: South China Normal University (China); Ningbo University (China); Ningbo Kangning Hospital (China); Shenzhen University (China); Colorado State University (United States)

Journal: PLoS biology, volume 24, issue 9, article e3003979

Dates: received 25 March 2026; accepted 17 August 2026; published online 1 September 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1371/journal.pbio.3003979 · PMID 42678993 · PMCID PMC13568527 · OpenAlex W7204938623

Open access: gold, a free copy (OpenAlex)

Status: code verified (how the code is found and verified)

Categories: EEG (modality), human (organism), other condition (population), systems (subfield)

Methods: Statistics, Smoothing, state filtering, decompositions, Spectral & time-frequency, Preprocessing, Connectivity, Physiology & signal measures

MeSH: Frontal Lobe*, Motivation*, Obsessive-Compulsive Disorder*, Theta Rhythm*, Adult, Case-Control Studies, Electroencephalography, Female, Humans, Male, Reward, Young Adult (* major topic)

Topic: Transcranial Magnetic Stimulation Studies (Neurology, Neuroscience), according to OpenAlex

Funding: National Natural Science Foundation of China (32571283); National Science and Technology Innovation 2030 Major Program (2021ZD0203800)

Citations: not cited yet (Europe PMC); 152 references in the paper

Abstract

Obsessive‑compulsive disorder (OCD) is characterized by an insight‑action dissociation, in which people with OCD recognize that their behavior is irrational but still struggle to inhibit habitual responses. This dissociation may be related to abnormally strong motivational biases, reflected in excessive tendencies to approach reward and avoid punishment. We employed a motivational Go/NoGo learning task, combined with computational modeling and electroencephalography (EEG), to investigate how 36 people with OCD and 37 healthy controls (HC) regulate maladaptive biases during motivated action. People with OCD showed stronger Pavlovian bias and lower learning rates. Similar to HC, people with OCD also showed increased midfrontal theta power related to conflict detection and to the generation of a control demand to increase the weighting of instrumental action values during choice, suggesting that they were able to detect the mismatch between their behavior and task goals. However, in OCD, conflict‑related theta enhancement overlapped with the response window, indicating that control signals emerged or arrived too late to effectively influence choice. Midfrontal‑motor theta phase synchrony provided the strongest model evidence for the modulation of maladaptive biases in OCD, yet this pathway showed no significant conflict‑related enhancement and failed to effectively modulate motivational biases under conflict. Taken together, these findings suggest a neural mechanism underlying the insight‑action dissociation in OCD and identify midfrontal‑motor theta phase synchrony as a potential treatment target.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 12 matches between paragraphs and lines of code.

Zenodo 21964069

License: MIT

State: the link answers, verified on 26 September 2026

Evidence: files inventoried

Size: 1 file

Software Heritage: not checked

Found in: “Data Availability”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: Statistics and Machine Learning Toolbox (15 files), Stan (10 files), FieldTrip (7 files), EEGLAB (4 files), tidyverse (4 files), boundedline (3 files), ggplot2 (3 files), car (2 files), data.table (2 files), emmeans (2 files), lme4 (2 files), lmerTest (2 files), psych (2 files), afex (1 file), BayesFactor (1 file)

Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)

26 September 2026: the link answers (HTTP 200)

62 files

code/EEGprocessing/BayesFactor ANOVA.R, R, 147 lines
code/EEGprocessing/EEGpav_accuracy.sps, SPSS, 237 lines
code/EEGprocessing/EEGpav_mixedmodels.R, R, 623 lines
code/EEGprocessing/TF (group-level).m, MATLAB, 1,533 lines
code/EEGprocessing/TF (subject-level).m, MATLAB, 265 lines
code/EEGprocessing/eegfigureplot.m, MATLAB, 868 lines
code/EEGprocessing/eegpreprocess.m, MATLAB, 203 lines
code/EEGprocessing/feedback_spss.sps, SPSS, 63 lines
code/EEGprocessing/simpef.R, R, 15 lines
code/ISPS/EEGpav_icpc_hc.sps, SPSS, 126 lines
code/ISPS/EEGpav_icpc_ocddelete6.sps, SPSS, 109 lines
code/ISPS/EEGpav_icpchc&ocd.sps, SPSS, 102 lines
code/ISPS/ISPS (group-level).m, MATLAB, 1,327 lines
code/ISPS/ISPS (subject-level).m, MATLAB, 137 lines
code/ISPS/ISPSplot.m, MATLAB, 613 lines
code/ISPS/TF_singletrial_grouplevel.m, MATLAB, 718 lines
code/Regulatory orientation/relation.m, MATLAB, 486 lines
code/Regulatory orientation/Φreg Calculation.R, R, 185 lines
code/Regulatory orientation/Φreg plot.m, MATLAB, 470 lines, 2 matches
code/behavior/EEGpav_mixedmodels.R, R, 1,078 lines
code/behavior/EEGpav_prepStan4Reeg.m, MATLAB, 102 lines
code/behavior/behaviorplot.m, MATLAB, 236 lines
code/behavior/boundedline.m, MATLAB, 501 lines
code/behavior/data.m, MATLAB, 360 lines
code/behavior/simpef.R, R, 15 lines
code/computational_modelling/RunR.R, R, 144 lines
code/computational_modelling/eegRdatarun.R, R, 115 lines
code/computational_modelling/helpfunctions/boundedline.m, MATLAB, 385 lines
code/computational_modelling/helpfunctions/mycsvwrite.m, MATLAB, 14 lines
code/computational_modelling/helpfunctions/plot_model.m, MATLAB, 23 lines
code/computational_modelling/modelrun/extractEstimates.R, R, 34 lines
code/computational_modelling/modelrun/fitmodel.R, R, 117 lines
code/computational_modelling/modelrun/getWAIC.R, R, 23 lines
code/computational_modelling/modelrun/models4stan.R, R, 4,028 lines
code/computational_modelling/modelrun/plotFits.R, R, 36 lines
code/computational_modelling/modelrun/waic.R, R, 49 lines
code/computational_modelling/plot/M12M16M19M21beta_output.R, R, 134 lines
code/computational_modelling/plot/M3c_theta&conflictoutput.R, R, 346 lines
code/computational_modelling/plot/M5_hcvsocd/DrawCombined_AllInOne2.m, MATLAB, 554 lines
code/computational_modelling/plot/M5_hcvsocd/M5dataoutput.R, R, 306 lines
code/computational_modelling/plot/WAICplot/DrawHC_WAIC_Set1.m, MATLAB, 181 lines
code/computational_modelling/plot/WAICplot/DrawHC_WAIC_Set2.m, MATLAB, 186 lines
code/computational_modelling/plot/WAICplot/DrawHC_WAIC_Set3.m, MATLAB, 178 lines
code/computational_modelling/plot/WAICplot/DrawOCD_WAIC_Set1.m, MATLAB, 197 lines
code/computational_modelling/plot/WAICplot/DrawOCD_WAIC_Set2right.m, MATLAB, 211 lines
code/computational_modelling/plot/WAICplot/DrawOCD_WAIC_Set3right.m, MATLAB, 209 lines
code/computational_modelling/plot/WAICplot/WAIC_output.R, R, 128 lines
code/computational_modelling/plot/checkRhat.R, R, 52 lines
code/computational_modelling/plot/extractEstimates.R, R, 94 lines
code/computational_modelling/plot/extractPosteriors.R, R, 99 lines
code/computational_modelling/plot/rebuttal_computeR2.R, R, 116 lines
code/computational_modelling/plot/theta&conflictplot.m, MATLAB, 368 lines
code/computational_modelling/plot/βplot/HCdraw_m12_plot26.m, MATLAB, 276 lines
code/computational_modelling/plot/βplot/HCdraw_m16_plot26.m, MATLAB, 262 lines
code/computational_modelling/plot/βplot/HCdraw_m19_plot26.m, MATLAB, 261 lines
code/computational_modelling/plot/βplot/OCDdraw_m12_plot26.m, MATLAB, 213 lines
code/computational_modelling/plot/βplot/OCDdraw_m16_plot26.m, MATLAB, 264 lines
code/computational_modelling/plot/βplot/OCDdraw_m21_plot26.m, MATLAB, 284 lines
code/computational_modelling/plot/βplot/βdataoutput_plot.R, R, 63 lines
code/computational_modelling/prepEEG4stan.R, R, 148 lines
LICENSE.txt, License, 21 lines
README.md, Text, 143 lines

At the source: doi.org/10.5281/zenodo.21964069

yupang11/motivational-biases-in-ocd

License: MIT

State: the link answers, verified on 26 September 2026

Evidence: files inventoried

Commit: 3eae95c040fba849171a37264ea861c440dfe66d, 16 August 2026

Languages: MATLAB (24), R (22), SPSS (5)

Size: 288 files, 51 scripts

Software Heritage: not archived

Found in: the Zenodo archive record

Holds: README, license file

Not found: CITATION.cff, environment file, tests, continuous integration, documentation

Tools: Statistics and Machine Learning Toolbox (8 files), Stan (8 files), FieldTrip (7 files), EEGLAB (4 files), boundedline (3 files), ggplot2 (3 files), tidyverse (3 files), car (2 files), data.table (2 files), emmeans (2 files), lme4 (2 files), lmerTest (2 files), psych (2 files), afex (1 file), BayesFactor (1 file)

Availability: 1 check, the latest on 26 September 2026: the link answers

26 September 2026: the link answers

53 files

code/EEGprocessing/BayesFactor ANOVA.R, R, 147 lines
code/EEGprocessing/EEGpav_accuracy.sps, SPSS, 237 lines
code/EEGprocessing/EEGpav_mixedmodels.R, R, 623 lines
code/EEGprocessing/TF (group-level).m, MATLAB, 1,533 lines, 1 match
code/EEGprocessing/TF (subject-level).m, MATLAB, 265 lines
code/EEGprocessing/eegfigureplot.m, MATLAB, 868 lines
code/EEGprocessing/eegpreprocess.m, MATLAB, 203 lines, 1 match
code/EEGprocessing/feedback_spss.sps, SPSS, 63 lines
code/EEGprocessing/simpef.R, R, 15 lines
code/ISPS/EEGpav_icpc_hc.sps, SPSS, 126 lines
code/ISPS/EEGpav_icpc_ocddelete6.sps, SPSS, 109 lines
code/ISPS/EEGpav_icpchc&ocd.sps, SPSS, 102 lines
code/ISPS/ISPS (group-level).m, MATLAB, 1,327 lines
code/ISPS/ISPS (subject-level).m, MATLAB, 137 lines, 1 match
code/ISPS/ISPSplot.m, MATLAB, 613 lines
code/ISPS/TF_singletrial_grouplevel.m, MATLAB, 718 lines, 3 matches
code/Regulatory orientation/relation.m, MATLAB, 486 lines, 1 match
code/behavior/EEGpav_mixedmodels.R, R, 1,078 lines
code/behavior/EEGpav_prepStan4Reeg.m, MATLAB, 102 lines
code/behavior/behaviorplot.m, MATLAB, 236 lines
code/behavior/boundedline.m, MATLAB, 501 lines
code/behavior/data.m, MATLAB, 360 lines
code/behavior/simpef.R, R, 15 lines
code/computational_modelling/RunR.R, R, 144 lines
code/computational_modelling/eegRdatarun.R, R, 115 lines
code/computational_modelling/helpfunctions/boundedline.m, MATLAB, 385 lines
code/computational_modelling/helpfunctions/mycsvwrite.m, MATLAB, 14 lines
code/computational_modelling/helpfunctions/plot_model.m, MATLAB, 23 lines
code/computational_modelling/modelrun/extractEstimates.R, R, 34 lines
code/computational_modelling/modelrun/fitmodel.R, R, 117 lines
code/computational_modelling/modelrun/getWAIC.R, R, 23 lines
code/computational_modelling/modelrun/models4stan.R, R, 4,028 lines, 2 matches
code/computational_modelling/modelrun/plotFits.R, R, 36 lines
code/computational_modelling/modelrun/waic.R, R, 49 lines
code/computational_modelling/plot/M12M16M19M21beta_output.R, R, 134 lines
code/computational_modelling/plot/M3c_theta&conflictoutput.R, R, 346 lines
code/computational_modelling/plot/M5_hcvsocd/DrawCombined_AllInOne2.m, MATLAB, 554 lines, 1 match
code/computational_modelling/plot/M5_hcvsocd/M5dataoutput.R, R, 306 lines
code/computational_modelling/plot/WAICplot/DrawHC_WAIC_Set1.m, MATLAB, 181 lines
code/computational_modelling/plot/WAICplot/DrawHC_WAIC_Set2.m, MATLAB, 186 lines
code/computational_modelling/plot/WAICplot/DrawHC_WAIC_Set3.m, MATLAB, 178 lines
code/computational_modelling/plot/WAICplot/DrawOCD_WAIC_Set1.m, MATLAB, 197 lines
code/computational_modelling/plot/WAICplot/DrawOCD_WAIC_Set2right.m, MATLAB, 211 lines
code/computational_modelling/plot/WAICplot/DrawOCD_WAIC_Set3right.m, MATLAB, 209 lines
code/computational_modelling/plot/WAICplot/WAIC_output.R, R, 128 lines
code/computational_modelling/plot/checkRhat.R, R, 52 lines
code/computational_modelling/plot/extractEstimates.R, R, 94 lines
code/computational_modelling/plot/extractPosteriors.R, R, 99 lines
code/computational_modelling/plot/rebuttal_computeR2.R, R, 116 lines
code/computational_modelling/plot/theta&conflictplot.m, MATLAB, 368 lines
code/computational_modelling/prepEEG4stan.R, R, 148 lines
LICENSE.txt, License, 21 lines
README.md, Text, 143 lines

At the source: github.com/YuPang11/motivational-biases-in-OCD/tree/Version1.0.0

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:

2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
111 scripts, each with its path and the digest of its content;
12 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 individual numerical data underlying the main figures are provided in the Supporting information files (S1–S6 Data). All analysis code and raw data are publicly available on Zenodo (https://doi.org/10.5281/zenodo.21964069).

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, 7 authors, 12 MeSH terms, 2 funders, 148 references.

Cite

This paper

Pang, Y., Zhou, D., Peng, Z., Liu, W., Huang, R., Seger, C. A., & Chen, Q. (2026). Impaired midfrontal‑motor theta phase synchronization characterizes maladaptive motivational behavior in people with obsessive‑compulsive disorder. PLoS biology, 24(9), e3003979. https://doi.org/10.1371/journal.pbio.3003979

BibTeX

@article{pang2026impaired, author = {Pang, Yu and Zhou, Dongsheng and Peng, Ziwen and Liu, Wanting and Huang, Ruojie and Seger, Carol A and Chen, Qi}, title = {{Impaired midfrontal‑motor theta phase synchronization characterizes maladaptive motivational behavior in people with obsessive‑compulsive disorder}}, journal = {PLoS biology}, year = {2026}, month = sep, volume = {24}, number = {9}, pages = {e3003979}, publisher = {PLOS}, issn = {1544-9173}, doi = {10.1371/journal.pbio.3003979}, url = {https://doi.org/10.1371/journal.pbio.3003979}, pmid = {42678993}, pmcid = {PMC13568527} }

RIS

TY - JOUR AU - Pang, Yu AU - Zhou, Dongsheng AU - Peng, Ziwen AU - Liu, Wanting AU - Huang, Ruojie AU - Seger, Carol A AU - Chen, Qi TI - Impaired midfrontal‑motor theta phase synchronization characterizes maladaptive motivational behavior in people with obsessive‑compulsive disorder T2 - PLoS biology J2 - PLoS Biol PY - 2026 DA - 2026/09/01 VL - 24 IS - 9 SP - e3003979 SN - 1544-9173 PB - PLOS DO - 10.1371/journal.pbio.3003979 UR - https://doi.org/10.1371/journal.pbio.3003979 LA - en ER -

CSL-JSON

{ "id": "10.1371/journal.pbio.3003979", "type": "article-journal", "title": "Impaired midfrontal‑motor theta phase synchronization characterizes maladaptive motivational behavior in people with obsessive‑compulsive disorder", "container-title": "PLoS biology", "author": [ { "family": "Pang", "given": "Yu" }, { "family": "Zhou", "given": "Dongsheng" }, { "family": "Peng", "given": "Ziwen" }, { "family": "Liu", "given": "Wanting" }, { "family": "Huang", "given": "Ruojie" }, { "family": "Seger", "given": "Carol A" }, { "family": "Chen", "given": "Qi" } ], "container-title-short": "PLoS Biol", "volume": "24", "issue": "9", "page": "e3003979", "DOI": "10.1371/journal.pbio.3003979", "PMID": "42678993", "PMCID": "PMC13568527", "ISSN": "1544-9173", "publisher": "PLOS", "URL": "https://doi.org/10.1371/journal.pbio.3003979", "language": "en", "issued": { "date-parts": [ [ 2026, 9, 1 ] ] } }

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

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[3] doi:10.1371/journal.pbio.3003767 [code]: Ultrasound neuromodulation reveals distinct roles of the dorsal anterior cingulate cortex and anterior insula in learning.
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[5] doi:10.1016/j.dcn.2025.101614 [code]: Analysis of event-related potential difference waves can benefit from linear mixed effects modeling: Recommendations for analyses and general model fitting.
Journal: Developmental cognitive neuroscience
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[10] doi:10.1111/nyas.15407 [code]: Foreign cultural norms are better accepted in the second language.
Journal: Annals of the New York Academy of Sciences
In common: BayesFactor, afex, psych, 6 other tools, EEG, 3 references

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