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Ambiguity tolerance and resting-state functional connectivity: A preregistered conceptual replication in a Japanese sample.

Code ↔ Paper

3 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 3 matches
  1. [1] § Method › Data analysis › Statistical analysis ↔ Matlab Script/conn_second_level.m, lines 63–105 · score 0.77 · transformed correlation, left IPL left, anterior insula, left OFC, Fisher, H1
  2. [2] § Method › Data analysis › Regions of interest ↔ Matlab Script/conn_create_rois.m, lines 15–52 · score 0.77 · anterior insula, left MFG, left OFC, left IPL, MNI, bilateral
  3. [3] § Method › Data analysis › Regions of interest ↔ Matlab Script/conn_create_rois_v2.m, lines 31–68 · score 0.77 · anterior insula, left MFG, left OFC, left IPL, MNI, bilateral

Paper

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The authors' code

MATLAB · 323 lines · 10 KB · no license · 1 match

  1. %% HAIKU resting-state fMRI: 2nd-level Analysis スクリプト（修正版v4）
  2. % 3つの仮説を検証：
  3. % H1: DA → Amygdala ↔ Anterior Insula
  4. % H2: AB → Left OFC ↔ ACC
  5. % H3: NC → Left IPL ↔ MFG/MCC
  6. % 共変量: age, sex, mean FD
  8. %% 設定
  9. conn_project = 'D:\mri_toolbox\HAIKU_rest_conn\conn_project01.mat';
  10. data_dir = 'D:\mri_toolbox';
  12. % 結果フォルダ
  13. results_dir = 'D:\mri_toolbox\HAIKU_rest_conn\conn_project01\results\firstlevel\SBC_01';
  15. % 有効な被験者ID
  16. valid_ids = [3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44];
  17. n_subjects = length(valid_ids);
  19. %% Step 1: Mean FD（Framewise Displacement）の計算
  20. disp('=== Mean FD 計算中 ===');
  22. mean_fd = zeros(n_subjects, 1);
  24. for i = 1:n_subjects
  25. subj_id = valid_ids(i);
  26. subj_folder = sprintf('HAIKU_%02d', subj_id);
  28. % rpファイルのパス
  29. rp_folder = fullfile(data_dir, subj_folder, '2_mb_ep_bold_mb4_sl76_tr2_rest');
  30. rp_files = dir(fullfile(rp_folder, 'rp_*.txt'));
  32. if isempty(rp_files)
  33. warning('Subject %d: rp file not found', subj_id);
  34. continue;
  35. end
  37. rp_file = fullfile(rp_folder, rp_files(1).name);
  38. rp = load(rp_file);
  40. % FD計算（Power et al., 2012）
  41. rp_mm = rp;
  42. rp_mm(:, 4:6) = rp(:, 4:6) * 50; % radian to mm (r=50mm)
  43. diff_rp = diff(rp_mm);
  44. fd = sum(abs(diff_rp), 2);
  45. mean_fd(i) = mean(fd);
  47. fprintf('Subject %02d: mean FD = %.4f mm\n', subj_id, mean_fd(i));
  48. end
  50. disp(' ');
  51. fprintf('Mean FD across subjects: %.4f (SD = %.4f) mm\n', mean(mean_fd), std(mean_fd));
  53. %% Step 2: ROIインデックスの設定（修正済み）
  54. roi_idx = struct();
  55. roi_idx.amygdala = 1; % Amygdala_bilateral
  56. roi_idx.ai = 2; % Anterior_Insula_bilateral
  57. roi_idx.lofc = 3; % Left_OFC
  58. roi_idx.acc = 4; % ACC_bilateral
  59. roi_idx.lipl = 5; % Left_IPL
  60. roi_idx.lmfg = 6; % Left_MFG
  61. roi_idx.mcc = 7; % MCC
  63. %% Step 3: 各被験者のROI-to-ROI相関を抽出
  64. disp(' ');
  65. disp('=== 各被験者のROI間相関を抽出 ===');
  67. % 各被験者のZ値を格納
  68. fc_h1 = zeros(n_subjects, 1); % Amygdala - AI
  69. fc_h2 = zeros(n_subjects, 1); % Left OFC - ACC
  70. fc_h3_mfg = zeros(n_subjects, 1); % Left IPL - Left MFG
  71. fc_h3_mcc = zeros(n_subjects, 1); % Left IPL - MCC
  73. for i = 1:n_subjects
  74. % Subject番号は1から39の連番
  75. roi_file = fullfile(results_dir, sprintf('resultsROI_Subject%03d_Condition001.mat', i));
  77. if exist(roi_file, 'file')
  78. data = load(roi_file);
  79. Z = data.Z; % Fisher's Z transformed correlation matrix
  81. % H1: Amygdala - Anterior Insula
  82. fc_h1(i) = Z(roi_idx.amygdala, roi_idx.ai);
  84. % H2: Left OFC - ACC
  85. fc_h2(i) = Z(roi_idx.lofc, roi_idx.acc);
  87. % H3: Left IPL - Left MFG, Left IPL - MCC
  88. fc_h3_mfg(i) = Z(roi_idx.lipl, roi_idx.lmfg);
  89. fc_h3_mcc(i) = Z(roi_idx.lipl, roi_idx.mcc);
  91. fprintf('Subject %03d (ID%02d): H1=%.4f, H2=%.4f, H3_MFG=%.4f, H3_MCC=%.4f\n', ...
  92. i, valid_ids(i), fc_h1(i), fc_h2(i), fc_h3_mfg(i), fc_h3_mcc(i));
  93. else
  94. warning('Subject %d: ROI results file not found: %s', i, roi_file);
  95. end
  96. end
  98. % H3は MFG と MCC の平均を使用
  99. fc_h3 = (fc_h3_mfg + fc_h3_mcc) / 2;
  101. disp(' ');
  102. disp('FC values summary:');
  103. fprintf('H1 (Amygdala-AI): mean = %.4f, SD = %.4f\n', mean(fc_h1), std(fc_h1));
  104. fprintf('H2 (OFC-ACC): mean = %.4f, SD = %.4f\n', mean(fc_h2), std(fc_h2));
  105. fprintf('H3 (IPL-MFG/MCC): mean = %.4f, SD = %.4f\n', mean(fc_h3), std(fc_h3));
  107. %% Step 4: 質問紙データ読み込み
  108. disp(' ');
  109. disp('=== 質問紙データ読み込み ===');
  111. csv_file = 'D:\mri_toolbox\HAIKU_rest_conn\trait_MAAS.csv';
  112. data_table = readtable(csv_file);
  113. data_table = sortrows(data_table, 'ID');
  115. DA = data_table.DA;
  116. AB = data_table.AB;
  117. NC = data_table.NC;
  118. age = data_table.age;
  119. sex = data_table.sex;
  121. fprintf('DA: mean = %.4f, SD = %.4f\n', mean(DA), std(DA));
  122. fprintf('AB: mean = %.4f, SD = %.4f\n', mean(AB), std(AB));
  123. fprintf('NC: mean = %.4f, SD = %.4f\n', mean(NC), std(NC));
  125. %% Step 5: t分布・F分布のp値計算関数（Statistics Toolbox不要）
  127. function p = my_tcdf(t, df)
  128. % t分布の累積分布関数（片側）
  129. x = df ./ (df + t.^2);
  130. p = 0.5 * betainc(x, df/2, 0.5);
  131. p(t > 0) = 1 - p(t > 0);
  132. end
  134. function p = my_fcdf(F, df1, df2)
  135. % F分布の累積分布関数
  136. x = df2 ./ (df2 + df1 .* F);
  137. p = 1 - betainc(x, df2/2, df1/2);
  138. end
  140. %% Step 6: 回帰分析（Statistics Toolbox完全不要版）
  141. disp(' ');
  142. disp('========================================');
  143. disp('=== 回帰分析結果 ===');
  144. disp('========================================');
  146. function [b, t_vals, p_vals, R2, F_stat, p_F, se] = my_regress(y, X)
  147. n = length(y);
  148. k = size(X, 2);
  150. % 最小二乗法
  151. b = (X' * X) \ (X' * y);
  153. % 予測値と残差
  154. y_hat = X * b;
  155. residuals = y - y_hat;
  157. % 平方和
  158. SS_res = sum(residuals.^2);
  159. SS_tot = sum((y - mean(y)).^2);
  161. % R-squared
  162. R2 = 1 - SS_res / SS_tot;
  164. % MSE
  165. df = n - k;
  166. MSE = SS_res / df;
  168. % 標準誤差
  169. var_b = MSE * inv(X' * X);
  170. se = sqrt(diag(var_b));
  172. % t値
  173. t_vals = b ./ se;
  175. % p値（両側検定）
  176. p_vals = zeros(size(t_vals));
  177. for j = 1:length(t_vals)
  178. p_vals(j) = 2 * my_tcdf(-abs(t_vals(j)), df);
  179. end
  181. % F統計量
  182. MS_reg = (SS_tot - SS_res) / (k - 1);
  183. F_stat = MS_reg / MSE;
  184. p_F = 1 - my_fcdf(F_stat, k - 1, df);
  185. end
  187. % 共変量行列
  188. covariates = [age, sex, mean_fd];
  190. % --- H1: DA → Amygdala-AI ---
  191. disp(' ');
  192. disp('--- H1: DA → Amygdala-Anterior Insula ---');
  193. X_h1 = [ones(n_subjects, 1), DA, covariates];
  194. [b_h1, t_h1, p_h1, R2_h1, F_h1, pF_h1, se_h1] = my_regress(fc_h1, X_h1);
  196. fprintf('DA coefficient (β): %.4f (SE = %.4f)\n', b_h1(2), se_h1(2));
  197. fprintf('t-value: %.4f\n', t_h1(2));
  198. fprintf('p-value: %.6f\n', p_h1(2));
  199. fprintf('R-squared: %.4f\n', R2_h1);
  200. fprintf('F(%d,%d) = %.4f, p = %.6f\n', size(X_h1,2)-1, n_subjects-size(X_h1,2), F_h1, pF_h1);
  202. % --- H2: AB → Left OFC-ACC ---
  203. disp(' ');
  204. disp('--- H2: AB → Left OFC-ACC ---');
  205. X_h2 = [ones(n_subjects, 1), AB, covariates];
  206. [b_h2, t_h2, p_h2, R2_h2, F_h2, pF_h2, se_h2] = my_regress(fc_h2, X_h2);
  208. fprintf('AB coefficient (β): %.4f (SE = %.4f)\n', b_h2(2), se_h2(2));
  209. fprintf('t-value: %.4f\n', t_h2(2));
  210. fprintf('p-value: %.6f\n', p_h2(2));
  211. fprintf('R-squared: %.4f\n', R2_h2);
  212. fprintf('F(%d,%d) = %.4f, p = %.6f\n', size(X_h2,2)-1, n_subjects-size(X_h2,2), F_h2, pF_h2);
  214. % --- H3: NC → Left IPL-MFG/MCC ---
  215. disp(' ');
  216. disp('--- H3: NC → Left IPL-MFG/MCC ---');
  217. X_h3 = [ones(n_subjects, 1), NC, covariates];
  218. [b_h3, t_h3, p_h3, R2_h3, F_h3, pF_h3, se_h3] = my_regress(fc_h3, X_h3);
  220. fprintf('NC coefficient (β): %.4f (SE = %.4f)\n', b_h3(2), se_h3(2));
  221. fprintf('t-value: %.4f\n', t_h3(2));
  222. fprintf('p-value: %.6f\n', p_h3(2));
  223. fprintf('R-squared: %.4f\n', R2_h3);
  224. fprintf('F(%d,%d) = %.4f, p = %.6f\n', size(X_h3,2)-1, n_subjects-size(X_h3,2), F_h3, pF_h3);
  226. %% Step 7: 結果サマリー
  227. disp(' ');
  228. disp('========================================');
  229. disp('=== 結果サマリー ===');
  230. disp('========================================');
  231. disp('Bonferroni補正閾値: p < 0.017 (0.05/3)');
  232. disp(' ');
  234. fprintf('H1 (DA → Amygdala-AI): β = %+.4f, t(%d) = %+.4f, p = %.4f', b_h1(2), n_subjects-size(X_h1,2), t_h1(2), p_h1(2));
  235. if p_h1(2) < 0.017
  236. fprintf(' **\n');
  237. elseif p_h1(2) < 0.05
  238. fprintf(' *\n');
  239. else
  240. fprintf('\n');
  241. end
  243. fprintf('H2 (AB → OFC-ACC): β = %+.4f, t(%d) = %+.4f, p = %.4f', b_h2(2), n_subjects-size(X_h2,2), t_h2(2), p_h2(2));
  244. if p_h2(2) < 0.017
  245. fprintf(' **\n');
  246. elseif p_h2(2) < 0.05
  247. fprintf(' *\n');
  248. else
  249. fprintf('\n');
  250. end
  252. fprintf('H3 (NC → IPL-MFG/MCC): β = %+.4f, t(%d) = %+.4f, p = %.4f', b_h3(2), n_subjects-size(X_h3,2), t_h3(2), p_h3(2));
  253. if p_h3(2) < 0.017
  254. fprintf(' **\n');
  255. elseif p_h3(2) < 0.05
  256. fprintf(' *\n');
  257. else
  258. fprintf('\n');
  259. end
  261. disp(' ');
  262. disp('** = significant at Bonferroni-corrected threshold (p < .017)');
  263. disp('* = significant at uncorrected threshold (p < .05)');
  265. %% Step 8: 結果をCSVに保存
  266. results_table = table(valid_ids', DA, AB, NC, age, sex, mean_fd, fc_h1, fc_h2, fc_h3, fc_h3_mfg, fc_h3_mcc, ...
  267. 'VariableNames', {'ID', 'DA', 'AB', 'NC', 'age', 'sex', 'mean_FD', 'FC_Amyg_AI', 'FC_OFC_ACC', 'FC_IPL_MFG_MCC', 'FC_IPL_MFG', 'FC_IPL_MCC'});
  269. output_csv = 'D:\mri_toolbox\HAIKU_rest_conn\analysis_results.csv';
  270. writetable(results_table, output_csv);
  271. fprintf('\n結果をCSVに保存しました: %s\n', output_csv);
  273. %% Step 9: 散布図の作成
  274. disp(' ');
  275. disp('=== 散布図を作成中 ===');
  277. figure('Position', [100, 100, 1200, 400]);
  279. % H1: DA vs Amygdala-AI
  280. subplot(1, 3, 1);
  281. scatter(DA, fc_h1, 50, 'filled');
  282. hold on;
  283. p = polyfit(DA, fc_h1, 1);
  284. x_line = linspace(min(DA), max(DA), 100);
  285. y_line = polyval(p, x_line);
  286. plot(x_line, y_line, 'r-', 'LineWidth', 2);
  287. xlabel('Discomfort with Ambiguity (DA)');
  288. ylabel('FC: Amygdala - Anterior Insula (Z)');
  289. title(sprintf('H1: \\beta = %.3f, p = %.3f', b_h1(2), p_h1(2)));
  290. grid on;
  292. % H2: AB vs OFC-ACC
  293. subplot(1, 3, 2);
  294. scatter(AB, fc_h2, 50, 'filled');
  295. hold on;
  296. p = polyfit(AB, fc_h2, 1);
  297. x_line = linspace(min(AB), max(AB), 100);
  298. y_line = polyval(p, x_line);
  299. plot(x_line, y_line, 'r-', 'LineWidth', 2);
  300. xlabel('Absolutism (AB)');
  301. ylabel('FC: Left OFC - ACC (Z)');
  302. title(sprintf('H2: \\beta = %.3f, p = %.3f', b_h2(2), p_h2(2)));
  303. grid on;
  305. % H3: NC vs IPL-MFG/MCC
  306. subplot(1, 3, 3);
  307. scatter(NC, fc_h3, 50, 'filled');
  308. hold on;
  309. p = polyfit(NC, fc_h3, 1);
  310. x_line = linspace(min(NC), max(NC), 100);
  311. y_line = polyval(p, x_line);
  312. plot(x_line, y_line, 'r-', 'LineWidth', 2);
  313. xlabel('Need for Complexity (NC)');
  314. ylabel('FC: Left IPL - MFG/MCC (Z)');
  315. title(sprintf('H3: \\beta = %.3f, p = %.3f', b_h3(2), p_h3(2)));
  316. grid on;
  318. % 図を保存
  319. saveas(gcf, 'D:\mri_toolbox\HAIKU_rest_conn\scatter_plots.png');
  320. fprintf('散布図を保存しました: D:\\mri_toolbox\\HAIKU_rest_conn\\scatter_plots.png\n');
  322. disp(' ');
  323. disp('=== 解析完了 ===');

conn_second_level.m, no license · at the source

Overview

Authors: Jimpei Hitsuwari1,2
ORCID iDs: Jimpei Hitsuwari
  1. Experimental Psychology Unit, Faculty of Humanities and Social Sciences, Helmut Schmidt University, Germany
  2. Japan Society for the Promotion of Science, Japan
Institutions: Helmut Schmidt University (Germany); Japan Society for the Promotion of Science (Japan)
Journal: Neuroimage. Reports, volume 6, issue 3, article 100376
Dates: received 25 February 2026; accepted 19 June 2026; published online 22 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.ynirp.2026.100376 · PMID 42381863 · PMCID PMC13315806 · OpenAlex W4417368985
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.21203/rs.3.rs-8351701/v1
Status: code verified
Categories: fMRI (modality), systems (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Preprocessing, fMRI & imaging
Keywords: Ambiguity tolerance, Resting-state functional connectivity, Multidimensional attitude toward ambiguity scale, Neural correlate, Conceptual replication
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Kyoto University; Japan Society for the Promotion of Science
Citations: not cited yet (Europe PMC); 30 references in the paper

Abstract

Objective: To examine how three dimensions of the Multidimensional Attitude toward Ambiguity Scale (MAAS)—Discomfort with Ambiguity, Absolutism, and Need for Complexity—relate to resting-state functional connectivity, conceptually replicating and extending the work of Liu et al. (2023) in a Japanese sample. Liu et al. (2023) reported that higher ambiguity tolerance was associated with stronger connectivity in integration and control networks, whereas lower ambiguity tolerance was associated with stronger connectivity in threat- and error-monitoring circuits. Of the three MAAS dimensions, Need for Complexity was the one most closely aligned with their measure.

Methods: Thirty-nine participants underwent resting-state MRI and completed the MAAS. Region-of-interest (ROI)-to-ROI analyses were used to test the associations between each MAAS dimension and its hypothesized connectivity pair, controlling for age, sex, and head motion.

Results: No MAAS dimension was significantly associated with its corresponding connectivity pair. Effect sizes were negligible, although the Need for Complexity showed a small zero-order correlation with inferior parietal lobule–middle cingulate cortex connectivity.

Conclusions: These findings contrast with earlier reports using unidimensional measures, suggesting that previously observed neural correlates may not map directly onto specific MAAS dimensions. Larger, well-powered, cross-cultural studies are needed.

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 3 matches between paragraphs and lines of code.

OSF 84ev5

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: MATLAB (7)
Size: 9 files, 7 scripts
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: CONN (4 files), SPM (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
7 files
At the source: osf.io/84ev5

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;
  • 7 scripts, each with its path and the digest of its content;
  • 3 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

Behavioral data and analysis scripts are publicly available in the Open Science Framework (https://osf.io/84ev5), with a persistent DOI assigned upon public release at acceptance. Raw MRI data are not publicly available because ethics approval and participant consent did not include provisions for the open release of imaging data; de-identified data are available from the author upon reasonable request and are subject to institutional approval.

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, 1 author, 5 keywords, 2 funders, 29 references.

Cite

This paper

Hitsuwari, J. (2026). Ambiguity tolerance and resting-state functional connectivity: A preregistered conceptual replication in a Japanese sample. Neuroimage. Reports, 6(3), 100376. https://doi.org/10.1016/j.ynirp.2026.100376

BibTeX

@article{hitsuwari2026ambiguity,
author = {Hitsuwari, Jimpei},
title = {{Ambiguity tolerance and resting-state functional connectivity: A preregistered conceptual replication in a Japanese sample}},
journal = {Neuroimage. Reports},
year = {2026},
month = jun,
volume = {6},
number = {3},
pages = {100376},
publisher = {Elsevier},
issn = {2666-9560},
doi = {10.1016/j.ynirp.2026.100376},
url = {https://doi.org/10.1016/j.ynirp.2026.100376},
pmid = {42381863},
pmcid = {PMC13315806}
}

RIS

TY - JOUR
AU - Hitsuwari, Jimpei
TI - Ambiguity tolerance and resting-state functional connectivity: A preregistered conceptual replication in a Japanese sample
T2 - Neuroimage. Reports
J2 - Neuroimage Rep
PY - 2026
DA - 2026/06/22
VL - 6
IS - 3
SP - 100376
SN - 2666-9560
PB - Elsevier
DO - 10.1016/j.ynirp.2026.100376
UR - https://doi.org/10.1016/j.ynirp.2026.100376
LA - en
ER -

CSL-JSON

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Journal: Nature neuroscience
In common: CONN, SPM, fMRI, 1 reference
[2] doi:10.1162/imag.a.1283 [code]
The language network responds robustly to sentences across tasks.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: CONN, SPM, fMRI
[3] doi:10.3389/fnhum.2026.1887834 [code]
Neural substrates of ambiguity and beauty in haiku poetry: an fMRI study.
Journal: Frontiers in human neuroscience
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[4] doi:10.21203/rs.3.rs-9326213/v1 [code]
Multi-task fMRI outperforms resting-state fMRI for revealing task-invariant organization of the human brain
Journal: Research Square (preprint)
In common: SPM, fMRI, 2 references
[5] doi:10.64898/2026.03.09.710558 [code]
Multi-task fMRI outperforms resting-state fMRI for revealing task-invariant organization of the human brain
Journal: bioRxiv (preprint)
In common: SPM, fMRI, 2 references
[6] doi:10.3389/fnins.2026.1803897 [code]
Multimodal imaging-based targeting approach for network-level brain stimulation.
Journal: Frontiers in neuroscience
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[7] doi:10.1186/s40708-026-00312-2 [code]
Synergistic and redundant information dynamics exhibit dissociable alterations across schizophrenia and neurodevelopmental conditions.
Journal: Brain informatics
In common: SPM, fMRI, 2 references
[8] doi:10.1111/ejn.70511 [code]
The Effect of Caffeine Consumption and Acute Withdrawal on Resting-State fMRI Brain Connectivity, Mood and Cognition.
Journal: The European journal of neuroscience
In common: fMRI, 3 references
[9] doi:10.1093/brain/awaf443 [code]
Cellular signatures underlying functional resilience in presymptomatic frontotemporal dementia.
Journal: Brain : a journal of neurology
In common: SPM, fMRI, 2 references
[10] doi:10.1038/s41467-026-74215-5 [code]
Multi-metric evaluations of acute psychedelic effects on fMRI brain entropy.
Journal: Nature communications
In common: fMRI, 3 references

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