Corticothalamic dynamics during postictal recovery of self-orientation after electroconvulsive therapy.

Code ↔ Paper

1 match 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 1 match

[1] § Materials and methods › Model parameter estimation ↔ +bt_utils/head_imager.m, lines 105–170 · score 0.63 · power spectral densities, power spectrum, baseline, electrode

Paper

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The authors' code

MATLAB · 252 lines · 5.8 KB · BSD-2-Clause · 1 match

function p_smoothed = head_imager(varargin)
% Possible usage
% head_imager(x,y,P)
% head_imager(x,y,P,label)
% head_imager(x,y,f,P) % For an explorer
% head_imager(x,y,f,P,label)
% head_imager(x,y,f,P,label,f_secondary,P_secondary)
smooth_hf = false;
plot_secondary = false;
if nargin < 4 || (nargin == 4 && (isempty(varargin{4}) || iscell(varargin{4})))
explorer_mode = false;
else
explorer_mode = true;
end
if ~explorer_mode && nargin < 4
label = [];
end
x = varargin{1};
y = varargin{2};
if ~explorer_mode
P = varargin{3};
if nargin < 4
label = [];
else
label = varargin{4};
end
else
f = varargin{3};
P = varargin{4};
if nargin >= 5
label = varargin{5};
else
label = [];
end
if nargin > 5
plot_secondary = true;
f_secondary = varargin{6};
P_secondary = varargin{7};
assert(size(P,1)==size(P_secondary,1) && size(P,2)==size(P_secondary,2));
end
end
if ~explorer_mode && (size(P,3) > 1 || numel(P)~=numel(x))
error('Must provide frequencies if frequency-dependent power is specified');
end
Lx = 0.5;
Ly = 0.5;
x1 = x-Lx/2;
y1 = y-Ly/2;
x = x(:);
y = y(:);
rmax = bt.data.electrode_positions()/2; % Special mode to set head radius
valid = sqrt((x1(:).^2+y1(:).^2))<rmax; % These are the only electrodes to include
[xg,yg] = meshgrid(linspace(0,Lx,40),linspace(0,Ly,40)); % Grid for background colors
x = x(valid);
y = y(valid);
P = reshape_validate(P,valid,explorer_mode);
if plot_secondary
P_secondary = reshape_validate(P_secondary,valid,explorer_mode);
end
if explorer_mode && smooth_hf
f_filt = f > 25; % Set this to 5 to smooth the alpha peak and ensure that the frequencies don't change
% THIS IS WORKING CODE FOR INDEPENDENT SMOOTHING
% for j = 1:size(P,2)
% P(f_filt,j) =smooth(P(f_filt,j),10);
% end
% if plot_secondary
% f_filt = f_secondary > 25;
% for j = 1:size(P,2)
% P_secondary(f_filt,j) =smooth(P_secondary(f_filt,j),10);
% end
% end
% 3D GRID INTERPOLATION - SMOOTHING IN SPACE AND FREQUENCY
pp = P(f_filt,:);
xp = repmat(x(:)',size(pp,1),1);
yp = repmat(y(:)',size(pp,1),1);
fp = repmat(f(f_filt),1,size(pp,2));
fl = linspace(fp(1,1),fp(end,1),50).';
fl = f(f_filt);
[xp1,yp1,fp1] = meshgrid(xg(1,:),yg(:,1),fl);
%pp1 = griddata(xp(:),yp(:),fp(:),pp(:),xp1,yp1,fp1);
pp_spec = scatteredInterpolant(xp(:),yp(:),fp(:),pp(:));
pp1 = pp_spec(xp1,yp1,fp1);
pp1a = smooth3(pp1,'box',[5 5 5]);
% DIAGNOSTIC PLOT SHOWING SMOOTHED SURFACE
% figure
% idx = 20;
% f_test = fp(idx,1);
% [~,idx2] = min(abs(fl-f_test));
% f_test
% fl(idx2)
% surf(xp1(:,:,20),yp1(:,:,20),pp1a(:,:,20))
% hold on
% P2 = P(f_filt,:);
% scatter3(x,y,P2(20,:),'ro')
pp2 = griddata(xp1(:),yp1(:),fp1(:),pp1a(:),xp,yp,fp);
P(f_filt,:) = pp2;
end
p_smoothed = P;
figure
if explorer_mode % Do the initialization and all that jazz
subplot(1,2,1);
end
img = imagesc([min(xg(:)) max(xg(:))],[min(yg(:)) max(yg(:))],zeros(size(xg)));
hold on
utils.draw_head(rmax,0.4,[Lx/2 Ly/2])
draw_electrodes(x,y,label)
axis equal
set(gca,'XLim',[0 0.5],'YLim',[0 0.5])
colorbar('SouthOutside')
img.HitTest = 'off';
title('Spatial power distribution')
xlabel('X (m)');
ylabel('Y (m)');
hold off
if explorer_mode % Do the initialization and all that jazz
ax_1 = gca;
hold on
elec_marker = scatter(x(1),y(1),'ro','hittest','off');
hold off
subplot(1,2,2);
ax_2 = gca;
spec = loglog(f,squeeze(P(:,1)));
spec.HitTest = 'off';
ax_2.XLim = [1 45];
ax_2.YLim = [min(P(:)) max(P(:))];
fb = ax_2.XBaseline;
fb.Color = 'r';
fb.BaseValue = 0;
fb.Visible = 'on';
title('Power spectrum')
xlabel('Frequency (Hz)')
ylabel('Power spectral density')
if plot_secondary
hold on
spec_secondary = loglog(f_secondary,squeeze(P_secondary(:,1)),'r--');
spec_secondary.HitTest = 'off';
end
set(ax_1,'ButtonDownFcn', @(a,b) update_spec(b) )
set(ax_2,'ButtonDownFcn', @(a,b) update_map(b) )
else
[Xi,Yi,Zi] = griddata(x,y,P,xg,yg,'cubic');
img.CData = Zi;
end
function update_spec(b)
click_value = b.IntersectionPoint(1:2);
[~,idx] = min((sum(bsxfun(@minus,[x(:) y(:)],click_value).^2,2)));
spec.YData = P(:,idx); % ... so they are reversed here into (row,col) = (y,x)
if plot_secondary
spec_secondary.YData = P_secondary(:,idx);
end
elec_marker.XData = x(idx);
elec_marker.YData = y(idx);
end
function update_map(b)
f_click = b.IntersectionPoint(1);
[~,f_idx] = min(abs(f-f_click));
fb.BaseValue = f(f_idx);
cdata = P(f_idx,:);
xt = x;
yt = y;
if length(unique(x))==1
xt = [xt(:)-0.05; xt(:)+0.05];
yt = [yt(:); yt(:)];
cdata = [cdata(:); cdata(:)];
end
if length(unique(yt)) == 1
yt = [yt(:)-0.05; yt(:)+0.05];
xt = [xt(:); xt(:)];
cdata = [cdata(:); cdata(:)];
end
[~,~,Zi] = griddata(xt,yt,cdata(:),xg,yg,'cubic');
img.CData = Zi;
img.AlphaData = isfinite(Zi);
ax_1.CLim = [min(cdata(:)) max(cdata(:))];
end
end
function P2 = reshape_validate(P,valid,explorer_mode)
if explorer_mode % 2D output
if length(size(P)) == 2 % The first dimension is frequency
P2 = zeros(size(P,1),sum(valid));
else
P2 = zeros(size(P,3),sum(valid));
end
else % 1D output
P2 = zeros(sum(valid),1);
end
count = 1;
for j = 1:length(valid)
if valid(j)
if explorer_mode && length(size(P)) == 2 % 2D output, and it is frequency dependent
P2(:,count) = P(:,j);
elseif explorer_mode % 3D output, remap array indexes
[a,b] = ind2sub([size(P,1) size(P,2)],j);
P2(:,count) = P(a,b,:);
else % 1D output
P2(count) = P(j);
end
count = count + 1;
end
end
end
function draw_electrodes(x,y,label)
scatter(x,y,'HitTest','off')
if ~isempty(label)
for j = 1:length(label)
text(x(j),y(j)-0.007,label{j},'HorizontalAlignment','center','VerticalAlignment','middle','FontSize',8,'hittest','off')
end
end
end

head_imager.m at commit 1681a89, under BSD-2-Clause · at the source

Overview

Authors: Sven Stuiver^1,2, Prejaas K B Tewarie³, Julia C M Pottkämper^2,4, Joey P A J Verdijk^1,4, Jeannette Hofmeijer^1,5, Guido A van Wingen⁴, Michel J A M van Putten^1,6, Jeroen A van Waarde²

ORCID iDs: Sven Stuiver, Prejaas K B Tewarie, Michel J A M van Putten

Institutions: Rijnstate Hospital (Netherlands); University of Twente (Netherlands); Université Laval (Canada); Amsterdam UMC Location University of Amsterdam (Netherlands); Amsterdam University Medical Centers (Netherlands); Medisch Spectrum Twente (Netherlands)

Journal: Brain communications, volume 8, issue 3, article fcag144

Dates: received 3 November 2025; accepted 21 April 2026; published online 22 April 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1093/braincomms/fcag144 · PMID 42109688 · PMCID PMC13152015 · OpenAlex W7155203707

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: EEG (modality), human (organism), clinical / translational (subfield)

Methods: Spectral & time-frequency, Statistics, Single-unit activity, calcium imaging

Keywords: EEG, postictal state, corticothalamic model, consciousness, self-orientation

Topic: Electroconvulsive Therapy Studies (Psychiatry and Mental health, Medicine), according to OpenAlex

Funding: Epilepsiefonds; EpilepsieNL (WAR 19-02)

Citations: cited by 1 paper (Europe PMC); 40 references in the paper

Abstract

The mechanisms underlying recovery of consciousness after its transient loss remain incompletely understood. Electroconvulsive therapy (ECT) induced generalized seizures disrupt responsiveness and orientation—key functional dimensions related to consciousness—and are followed by a stereotyped postictal state of temporary unresponsiveness, offering a unique model to study the dynamics of connected, report-capable cognition in humans. We performed a post hoc analysis of prospectively collected data from a randomized controlled trial, comprising 345 continuous postictal electroencephalography (EEG) recordings of up to 1 h from 33 patients (median age 53 years, interquartile range 21.3 years; n = 19 [56%] female) undergoing a course of ECT. Using a corticothalamic mean-field model, we estimated cortical (X), corticothalamic loop (Y) and intrathalamic (Z) gain parameters that quantify the responsiveness of cortical and thalamic populations to synaptic input. We examined whether recovery of self-orientation, indexed by the time to reorientation in person, was associated with specific parameter regimes. Immediately after seizure termination, cortical gain was elevated, the corticothalamic loop gain was negative and intrathalamic gain was near zero—suggesting that the thalamus exerted a suppressive effect on the cortex while exhibiting minimal intrinsic activity. Bayesian mixed-effects models showed that during postictal recovery cortical gain × decreased (β = −0.11, CrI95 = [−0.16, −0.07]), while corticothalamic loop gain Y (β = 0.05, CrI95 = [0.02, 0.07]) as well as intrathalamic gain Z (β = 0.05, CrI95 = [0.02, 0.08]) increased, reflecting progressive restoration of thalamic excitatory drive. Across patients and ECT sessions, recovery of self-orientation occurred when model parameters approached towards a characteristic regime, i.e. βX = 0.81 (CrI95 = [0.74, 0.88]), βY = −0.11 (CrI95 = [−0.16, −0.07]) and βZ = 0.01 (CrI95 = [−0.04, 0.05]), for cortical, corticothalamic loop and intrathalamic gains, respectively. No associations were found between model parameters at which self-orientation was regained and reorientation time, showing relatively small inter-subject variability (sdX = 0.04, sdY = 0.03 and sdZ = 0.02), suggesting that these values were consistent across patients. To conclude, these findings suggest that restoration of effective corticothalamic coupling represents a critical regime for the re-emergence of self-orientation and reportable responsiveness following postictal unresponsiveness. Our results provide time-resolved modelling of human thalamocortical circuit dynamics during postictal recovery, offering mechanistic insight into how large-scale neural interactions reorganize to restore self-orientation during the postictal state.

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 1 match between paragraphs and lines of code.

BrainDynamicsUSYD/braintrak

License: BSD-2-Clause

State: the link answers, verified on 29 September 2026

Evidence: files inventoried

Commit: 1681a892c8635e31bdf6463e81d0460768068bf2, 18 April 2018

Languages: MATLAB (90)

Size: 124 files, 90 scripts

Software Heritage: not archived

Found in: “Data availability”

Holds: README, license file, documentation

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

Tools: Parallel Computing Toolbox (2 files), Statistics and Machine Learning Toolbox (1 file)

Availability: 1 check, the latest on 29 September 2026: the link answers

29 September 2026: the link answers

92 files

+bt/+core/chain.m, MATLAB, 186 lines
+bt/+core/chain_diagnostics.m, MATLAB, 106 lines
+bt/+core/chain_diagnostics_parallel.m, MATLAB, 31 lines
+bt/+core/chain_parallel.m, MATLAB, 64 lines
+bt/+core/fit_spectrum.m, MATLAB, 121 lines
+bt/+core/load_subject.m, MATLAB, 52 lines
+bt/+data/electrode_positions.m, MATLAB, 167 lines
+bt/+data/get_tfs.m, MATLAB, 125 lines
+bt/+data/import_raw_eeg.m, MATLAB, 55 lines
+bt/+model/dummy.m, MATLAB, 129 lines
+bt/+model/full.m, MATLAB, 193 lines
+bt/+model/full_b_ratio.m, MATLAB, 67 lines
+bt/+model/full_emgf.m, MATLAB, 103 lines
+bt/+model/full_gdiff.m, MATLAB, 195 lines
+bt/+model/full_gee_extended.m, MATLAB, 193 lines
+bt/+model/full_norm.m, MATLAB, 59 lines
+bt/+model/gab_mass.m, MATLAB, 173 lines
+bt/+model/nus_mass.m, MATLAB, 196 lines
+bt/+model/reduced.m, MATLAB, 194 lines
+bt/+model/reduced_alpha_emphasized.m, MATLAB, 15 lines
+bt/+model/reduced_b_ratio.m, MATLAB, 58 lines
+bt/+model/reduced_chisqweight.m, MATLAB, 26 lines
+bt/+model/reduced_emgf.m, MATLAB, 99 lines
+bt/+model/reduced_l.m, MATLAB, 191 lines
+bt/+model/reduced_ln.m, MATLAB, 59 lines
+bt/+model/reduced_ln_b_ratio.m, MATLAB, 58 lines
+bt/+model/reduced_ly.m, MATLAB, 184 lines
+bt/+model/reduced_ly_no_n.m, MATLAB, 64 lines
+bt/+model/spatial_emg.m, MATLAB, 119 lines
+bt/+model/spatial_emg_xyz.m, MATLAB, 90 lines
+bt/+model/spatial_express_gains.m, MATLAB, 76 lines
+bt/+model/spatial_express_superphase.m, MATLAB, 96 lines
+bt/+model/spatial_express_t0.m, MATLAB, 78 lines
+bt/+model/spatial_express_t0_noemg.m, MATLAB, 81 lines
+bt/+model/spatial_express_t0_noemg_alphaweighted.m, MATLAB, 48 lines
+bt/+model/spatial_express_t0_phased.m, MATLAB, 110 lines
+bt/+model/spatial_express_xyz_all.m, MATLAB, 85 lines
+bt/+model/spatial_express_xyz_t0.m, MATLAB, 67 lines
+bt/+model/spatial_express_xyz_x.m, MATLAB, 67 lines
+bt/+model/template.m, MATLAB, 180 lines
+bt/+model/template_spatial.m, MATLAB, 123 lines
+bt/@feather/blobs.m, MATLAB, 19 lines
+bt/@feather/chisq.m, MATLAB, 6 lines
+bt/@feather/clouds.m, MATLAB, 25 lines
+bt/@feather/cones.m, MATLAB, 103 lines
+bt/@feather/feather.m, MATLAB, 187 lines
+bt/@feather/fitted_params.m, MATLAB, 3 lines
+bt/@feather/fitted_params_from_posterior.m, MATLAB, 37 lines
+bt/@feather/head_plot.m, MATLAB, 15 lines
+bt/@feather/import.m, MATLAB, 77 lines
+bt/@feather/interactive_fit.m, MATLAB, 144 lines
+bt/@feather/loadobj.m, MATLAB, 15 lines
+bt/@feather/plot_statecolored.m, MATLAB, 33 lines
+bt/@feather/plot_track.m, MATLAB, 28 lines
+bt/@feather/point_cloud.m, MATLAB, 6 lines
+bt/@feather/scatter_statecolored.m, MATLAB, 28 lines
+bt/@feather/spectrum.m, MATLAB, 22 lines
+bt/@feather/state_blocks.m, MATLAB, 14 lines
+bt/@feather/state_colors.m, MATLAB, 38 lines
+bt/@feather/state_str.m, MATLAB, 3 lines
+bt/@feather/subrange.m, MATLAB, 20 lines
+bt/@feather/timecourse.m, MATLAB, 75 lines
+bt/@feather/xyz.m, MATLAB, 3 lines
+bt/@viewer/viewer.m, MATLAB, 580 lines
+bt/fit.m, MATLAB, 11 lines
+bt/fit_track.m, MATLAB, 51 lines
+bt_utils/alphavol_wrapper.m, MATLAB, 12 lines
+bt_utils/chisq_outliers.m, MATLAB, 5 lines
+bt_utils/head_imager.m, MATLAB, 252 lines, 1 match
+bt_utils/kde.m, MATLAB, 133 lines
+bt_utils/state_cdata.m, MATLAB, 26 lines
+db_fit/animate_fit.m, MATLAB, 94 lines
+db_fit/animate_fit_old.m, MATLAB, 112 lines
+db_fit/assemble_master_pp.m, MATLAB, 51 lines
+db_fit/convert_derived_pp.m, MATLAB, 20 lines
+db_fit/db_derived_fit.m, MATLAB, 61 lines
+db_fit/db_fit.m, MATLAB, 90 lines
+db_fit/db_fit_make_all_states_pp.m, MATLAB, 73 lines
+db_fit/db_fit_make_pp.m, MATLAB, 63 lines
+db_fit/nd_cutdown.m, MATLAB, 72 lines
+db_fit/quick_fit.m, MATLAB, 38 lines
Published/Abeysuriya_2015/fit_br_bic.m, MATLAB, 61 lines
Published/Abeysuriya_2015/fit_cluster.m, MATLAB, 106 lines
data_examples/fit_br_spatial.m, MATLAB, 11 lines
data_examples/fit_kaggle.m, MATLAB, 52 lines
data_examples/fit_seizure.m, MATLAB, 58 lines
data_examples/import_unresponsive.m, MATLAB, 36 lines
data_examples/load_br_data.m, MATLAB, 28 lines
data_examples/load_kaggle_data.m, MATLAB, 25 lines
data_examples/load_seizure_data.m, MATLAB, 46 lines
README.md, Text, 15 lines
license.txt, License, 54 lines

At the source: github.com/BrainDynamicsUSYD/braintrak

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;
90 scripts, each with its path and the digest of its content;
1 match 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

Raw data were generated at Rijnstate. Derived and anonymized data supporting the findings of this study are available from the corresponding author on request.

The code for the parameter estimation method can be found on https://github.com/BrainDynamicsUSYD/braintrak.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 5 keywords, 2 funders, 31 references.

Cite

This paper

Stuiver, S., Tewarie, P. K. B., Pottkämper, J. C. M., Verdijk, J. P. A. J., Hofmeijer, J., van Wingen, G. A., van Putten, M. J. A. M., & van Waarde, J. A. (2026). Corticothalamic dynamics during postictal recovery of self-orientation after electroconvulsive therapy. Brain communications, 8(3), fcag144. https://doi.org/10.1093/braincomms/fcag144

BibTeX

@article{stuiver2026corticothalamic, author = {Stuiver, Sven and Tewarie, Prejaas K B and Pottkämper, Julia C M and Verdijk, Joey P A J and Hofmeijer, Jeannette and van Wingen, Guido A and van Putten, Michel J A M and van Waarde, Jeroen A}, title = {{Corticothalamic dynamics during postictal recovery of self-orientation after electroconvulsive therapy}}, journal = {Brain communications}, year = {2026}, month = apr, volume = {8}, number = {3}, pages = {fcag144}, publisher = {Oxford University Press}, issn = {2632-1297}, doi = {10.1093/braincomms/fcag144}, url = {https://doi.org/10.1093/braincomms/fcag144}, pmid = {42109688}, pmcid = {PMC13152015} }

RIS

TY - JOUR AU - Stuiver, Sven AU - Tewarie, Prejaas K B AU - Pottkämper, Julia C M AU - Verdijk, Joey P A J AU - Hofmeijer, Jeannette AU - van Wingen, Guido A AU - van Putten, Michel J A M AU - van Waarde, Jeroen A TI - Corticothalamic dynamics during postictal recovery of self-orientation after electroconvulsive therapy T2 - Brain communications J2 - Brain Commun PY - 2026 DA - 2026/04/22 VL - 8 IS - 3 SP - fcag144 SN - 2632-1297 PB - Oxford University Press DO - 10.1093/braincomms/fcag144 UR - https://doi.org/10.1093/braincomms/fcag144 LA - en ER -

CSL-JSON

{ "id": "10.1093/braincomms/fcag144", "type": "article-journal", "title": "Corticothalamic dynamics during postictal recovery of self-orientation after electroconvulsive therapy", "container-title": "Brain communications", "author": [ { "family": "Stuiver", "given": "Sven" }, { "family": "Tewarie", "given": "Prejaas K B" }, { "family": "Pottkämper", "given": "Julia C M" }, { "family": "Verdijk", "given": "Joey P A J" }, { "family": "Hofmeijer", "given": "Jeannette" }, { "family": "van Wingen", "given": "Guido A" }, { "family": "van Putten", "given": "Michel J A M" }, { "family": "van Waarde", "given": "Jeroen A" } ], "container-title-short": "Brain Commun", "volume": "8", "issue": "3", "page": "fcag144", "DOI": "10.1093/braincomms/fcag144", "PMID": "42109688", "PMCID": "PMC13152015", "ISSN": "2632-1297", "publisher": "Oxford University Press", "URL": "https://doi.org/10.1093/braincomms/fcag144", "language": "en", "issued": { "date-parts": [ [ 2026, 4, 22 ] ] } }

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