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Functional connectivity is linked to symbolic BOLD patterns: Replication, extension, and clinical application of the human "Complexome".

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 · 7 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § RESULTS › Extending the “Complexome”: Complexity Cofluctuations and Pattern Incongruency ↔ analysis_code/main_functions/calc_wpe_cofluctuations.m, lines 1–112 · score 0.84 · root sum square, downward cofluctuations, drop coincidence, sliding window, edge timeseries, metric
  2. [2] § MATERIALS AND METHODS › WPE Calculation ↔ analysis_code/main_functions/permEntropy_motif_distribution.m, lines 123–211 · score 0.80 · Shannon entropy, weighting factor, weighted pattern frequency, factorial, log2, unity
  3. [3] § MATERIALS AND METHODS › WPE Calculation ↔ Code/permEntropy.m, lines 104–169 · score 0.79 · Shannon entropy, weighting factor, weighted pattern frequency, factorial, log2, unity
  4. [4] § RESULTS › Extending the “Complexome”: Complexity Cofluctuations and Pattern Incongruency ↔ analysis_code/main_functions/calc_wpe_cofluctuations.m, lines 1–112 · score 0.79 · root sum square, downward cofluctuation, cofluctuation timeseries, sliding window, edge timeseries, BOLD timeseries
  5. [5] § MATERIALS AND METHODS › Complexity Timeseries Cofluctuation ↔ analysis_code/main_functions/calc_wpe_cofluctuations.m, lines 149–228 · score 0.68 · temporal unwrapping procedure, frame, edge timeseries, fluctuate, pairwise, cofluctuations
  6. [6] § RESULTS › FC Is Linked to Complexity Dynamics and BOLD Signal Patterns ↔ Code/demo_complexity_timeseries.m, the whole file · a weak match · score 0.62 · Young Adults, window parameters, HCP, BOLD timeseries, scan, brain regions
  7. [7] § MATERIALS AND METHODS › WPE Calculation ↔ analysis_code/wrapper_functions/wrapper_wpe_coflux_ipi.m, the whole file · a weak match · score 0.61 · slide length, WPE timeseries, sliding window, segmented, BOLD timeseries, ROI
  8. [8] § MATERIALS AND METHODS › Complexity Timeseries Cofluctuation ↔ analysis_code/main_functions/calc_dynamic_wpe.m, the whole file · a weak match · score 0.56 · spatiotemporal complexity architecture, brain activity, brain regions, dynamics, windows, WPE
  9. [9] § MATERIALS AND METHODS › Complexity Timeseries Cofluctuation ↔ analysis_code/main_functions/timeresolved_permEntropy_motifs.m, the whole file · a weak match · score 0.55 · spatiotemporal complexity architecture, brain activity, dynamics, signals, windows, Timeseries
  10. [10] § MATERIALS AND METHODS › WPE Calculation ↔ analysis_code/main_functions/timeresolved_permEntropy_motifs.m, the whole file · a weak match · score 0.54 · slide length, sliding window, overlapping, BOLD timeseries, TR, dynamic
  11. [11] § MATERIALS AND METHODS › Index of Pattern Incongruency ↔ analysis_code/main_functions/calc_ipi.m, the whole file · a weak match · score 0.54 · Euclidean distance, pattern incongruency, brain regions, scalar, symbolic, windows
  12. [12] § RESULTS › FC Is Linked to Complexity Dynamics and BOLD Signal Patterns ↔ analysis_code/wrapper_functions/wrapper_wpe_coflux_ipi.m, the whole file · a weak match · score 0.53 · sliding window parameters, slide lengths, segmentation, BOLD timeseries, brain regions, AUC SCD

Paper

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The authors' code

MATLAB · 322 lines · 16 KB · no license · 3 matches

  1. function [wpe_ts_zscore_i, ets_wpe_i_vec, rms_wpe_i_vec, mean_ets_wpe_i_vec, corr_wpe_ts_i_vec, ...
  2. count_bidirect_coflux_i, auc_scd, idx_auc_scd, rms_scd, rms_auc_zeroed, scd_count, ...
  3. rms_scd_count, linear_ind] = calc_wpe_cofluctuations(WPE_norm, n_edges, nr_windows, n_rois)
  4. % CALC_WPE_COFLUCTUATIONS
  5. %
  6. % Estimate cofluctuations between WPE timeseries using temporal dot product
  7. % and root sum square of resultant (i.e., like edge timeseries but not on BOLD data).
  8. % Subsequently, find instances of simultaneous complexity decrease (SCD) and
  9. % downward cofluctation of WPE timeseries. The temporal union of these
  10. % instances serves as an alternative to the original 'drop coincidence'
  11. % measure from Krohn et al. (2023).
  12. %
  13. % Finally, the area under the curve (AUC) of the cofluctuation timeseries,
  14. % indexed for these instances (i.e., AUC-SCD), serves as a summary measure of
  15. % the magnitude and number of SCD over time, between a pair of brain regions.
  16. %
  17. % See Esfahliani et al. (2020) for introduction to temporal unwrapping procedure
  18. % applied to product-moment correlations between BOLD timeseries, used to generate 'edge timeseries':
  19. %
  20. % F. Z. Esfahlani, Y. Jo, J. Faskowitz, L. Byrge, D. P. Kennedy, O. Sporns, and R. F. Betzel,
  21. % “High-amplitude cofluctuations in cortical activity drive functional connectivity,”
  22. % Proc. Natl. Acad. Sci., vol. 117, no. 45, pp. 28393–28401, Nov. 2020, doi: 10.1073/pnas.2005531117.
  23. %
  24. %
  25. % INPUTS:
  26. %
  27. % WPE_norm (matrix) - Numeric array containing weighted permutation entropy values in bits,
  28. % for every window and brain region, normalized to [0,1].
  29. % (i.e., the WPE timeseries)
  30. % Dimensions: nr_windows -by- n_rois.
  31. %
  32. %
  33. %
  34. % n_edges (scalar) - Number of edges in the whole brain network.
  35. % Here, 29646 - i.e., (n_rois*n_rois - n_rois)/2
  36. % nr_windows (scalar) - Number of consecutive sliding windows per BOLD TS.
  37. % Calculated in single_wpe_coflux_ipi.m from n_tps, window_size, and slide_samples.
  38. % n_rois (scalar) - Number of brain regions in atlas.
  39. % Here, 244 (Romanello et al., 2026)
  40. %
  41. % OUTPUTS:
  42. %
  43. % wpe_ts_zscore_i (matrix) - Z-scored WPE_norm.
  44. % Numeric array, dimensions: nr_windows -by- n_rois,
  45. %
  46. % ets_wpe_i_vec (matrix) - Temporal, element-wise dot product between a pair of z-scored WPE timeseries.
  47. % Numeric array, dimensions: n_edges -by- nr_windows.
  48. % Analogous to "edge timeseries" between pair of z-scored BOLD timeseries.
  49. %
  50. % rms_wpe_i_vec (matrix) - Root-sum-squared of "edge timeseries" between a pair of z-scored WPE timeseries.
  51. % Numeric array, dimensions: n_edges -by- nr_windows.
  52. % Analogous to amplitude of cofluctuation between pair of z-scored BOLD timeseries.
  53. %
  54. % mean_ets_wpe_i_vec (vector) - Mean over windows of WPE 'edge timeseries'.
  55. % Numeric vector of length n_edges.
  56. %
  57. % corr_wpe_ts_i_vec (vector) - Each element contains product-moment correlation over windows of edge
  58. % timeseries for a given edge. Should be approximately equal to
  59. % mean_ets_wpe_i_vec.
  60. % Numeric vector of length n_edges.
  61. %
  62. % count_bidirect_coflux_i (vector) - Each element contains count of number bidirectional positive
  63. % cofluctuations (both WPE TS go up OR down).
  64. % Numeric vector of length n_edges.
  65. %
  66. % idx_auc_scd (matrix) - Each element indicates if regions of given edge at given window meet
  67. % criteria for simultaneous complexity decrease (1 = yes, 0 = no)
  68. % Logical matrix with dims: n_edges x nr_windows.
  69. %
  70. %
  71. % rms_scd (matrix) - Amplitude of cofluctuation in instances of SCD. Cell array of length n_edges.
  72. % Each cell contains a numeric vector whose length
  73. % corresponds to the number of windows where SCD criteria was met. Values contain
  74. % coflcutuation values for SCD windows.
  75. %
  76. % rms_auc_zeroed (matrix) - Each row contains root sum square of WPE 'edge timeseries', but non-SCD
  77. % windows are zeroes. Used for area-under-curve calculation.
  78. % Numeric matrix with dims n_edges x nr_windows
  79. %
  80. % auc_scd (vector) - Each element contains area-under-the-curve of indexed cofluctuation
  81. % timeseries (SCD instances) for a given edge. This value is our analogous drop coincidence
  82. % metric.
  83. % Numeric vector with length n_edges.
  84. %
  85. % scd_count (vector) - Each element contains count of number of windows in which both regions showed decreases in
  86. % complexity, relative to their previous window.
  87. % Numeric vector of length n_edges.
  88. %
  89. % rms_scd_count (vector) - Each element contains count of number of windows in which both regions
  90. % showed decreases in complexity AND downward cofluctuations (same direction).
  91. % Numeric vector of length n_edges.
  92. %
  93. % linear_ind (vector) - Used for transforming between matrix/vector (lower triangle) form of symmetrical data.
  94. % Numeric vector of length n_edges.
  95. %
  96. %
  97. % Note: there are additional local functions at the bottom of this file!
  98. % get_wpe_ets()
  99. % get_wpe_decrease_indices()
  100. % get_coflux_scd()
  101. %
  102. %------------------------------------------------------------------------------------------------------------------------------
  103. % Amy Romanello, 2026.
  104. %
  105. % Original publication:
  106. % A. Romanello, N. von Schwanenflug, M. Franka, F. Paul, H. Prüss, S. Krohn, and C. Finke,
  107. % “Functional connectivity is linked to symbolic BOLD patterns: Replication,
  108. % extension, and clinical application of the human ‘complexome,’” Netw. Neu-rosci.,
  109. % pp. 1–26, Apr. 2026, doi: 10.1162/NETN.a.572.
  110. %
  111. % Please cite the above publication when using or adapting this code for subsequent work.
  112. % ------------------------------------------------------------------------------------------------------------------------------
  115. %% Step 1: Set up and compute WPE 'edge timeseries' & root-sum-square of cofluctuations
  116. % This is a generalized application of the temporal unwrapping procedure described in Esfahlani et al. (2020).
  117. % Here, applied to the standardized covariance between WPE timeseries, instead of BOLD timeseries.
  119. % z-score wpe ts --> each col is zscored separately to have mean 0, sd 1
  120. wpe_ts_zscore_i = zscore(WPE_norm);
  122. % compute cofluctuations between all pairs of WPE TS
  123. % this is a local function at the bottom
  124. [ets_wpe_i_vec, rms_wpe_i_vec, mean_ets_wpe_i_vec, ...
  125. corr_wpe_ts_i_vec, linear_ind] = get_wpe_ets(wpe_ts_zscore_i, n_rois, nr_windows);
  128. %% Step 2: Get indices of decreases in WPE from window t to t+1
  130. [neg_delta_wpe_i] = get_wpe_decrease_indices(WPE_norm, nr_windows, n_rois);
  133. %% Step 3: Get indices of positive cofluctuation (= both(!) WPE TS go up or down in same window)
  135. idx_ets_pos_i = ets_wpe_i_vec>=0;
  136. % save number of positive cofluctuations per edge (for later)
  137. count_bidirect_coflux_i = sum(idx_ets_pos_i,2);
  139. %% Step 4: Get cofluctuations for instances of simultaneous complexity decreases (SCD) only
  141. [auc_scd, idx_auc_scd, rms_scd, rms_auc_zeroed, scd_count,...
  142. rms_scd_count] = get_coflux_scd(neg_delta_wpe_i, idx_ets_pos_i, ...
  143. rms_wpe_i_vec, n_edges, n_rois, nr_windows, linear_ind);
  149. %%%%%% LOCAL FUNCTIONS %%%%%%
  151. function [ets_wpe_i_vec, rms_wpe_i_vec, mean_ets_wpe_i_vec, ...
  152. corr_wpe_scale_i_vec, linear_ind] = get_wpe_ets(wpe_ts_zscore_i, n_rois, nr_windows)
  154. % Apply temporal unwrapping procedure to pairwise WPE timeseries. This includes z-scoring of
  155. % WPE timeseries, taking the element-wise dot product between them, and calculating the amplitude
  156. % of cofluctuation using the root-sum-squared.
  158. % pre-allocate vars
  159. ets_wpe_i = nan(n_rois, n_rois, nr_windows);
  160. fc_wpe_scalar_i = nan(n_rois, n_rois);
  161. mean_ets_wpe_i = nan(n_rois, n_rois); % should be a matrix now
  162. rms_wpe_i = nan(n_rois, n_rois, nr_windows);
  164. % loop over roi pairs
  165. for row = 1:n_rois
  166. for col = 1:n_rois
  168. % get pair of regions
  169. cofluct_roi1 = col ;
  170. cofluct_roi2 = row ;
  172. % - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  173. % - - - - Cofluctuations / Edge timeseries of WPE timeseries
  174. % - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  176. % grab for legibility purposes
  177. ts_wpe_roi1_2_z = wpe_ts_zscore_i(:,[cofluct_roi1 cofluct_roi2]);
  179. % get dimensions - output vars can be over-written on each iter
  180. [ntime_wpe,nnodes_wpe] = size(ts_wpe_roi1_2_z) ; % should be: nr windows, 2
  182. % calculate number of edges
  183. nedges_wpe = nnodes_wpe*(nnodes_wpe - 1)/2 ; % should always be 1 for us
  185. % indices of unique edges (upper triangle)
  186. [u_wpe,v_wpe] = find(triu(ones(nnodes_wpe),1));
  187. idx_wpe = (v_wpe - 1)*nnodes_wpe + u_wpe ;
  189. % calculate "static fc" - correlation b/t z-scored complexity time-series of 2 rois,
  190. % should be equivalent (or nearly) to the mean of the edge-time series between the 2 rois
  191. fc_wpe = corr(ts_wpe_roi1_2_z) ;
  193. % generate edge time series
  194. ets_wpe_i(row,col,:) = ts_wpe_roi1_2_z(:,u_wpe).*ts_wpe_roi1_2_z(:,v_wpe) ;
  196. % save for legibility
  197. ets_wpe = squeeze(ets_wpe_i(row, col, :));
  199. % sanity check?
  200. fc_wpe_scalar_i(row, col) = fc_wpe(u_wpe,v_wpe);
  201. mean_ets_wpe_i(row, col) = mean(ets_wpe);
  203. % calculate co-fluctuation amplitude at each frame
  204. rms_wpe_i(row,col,:) = sum(ets_wpe.^2,2).^0.5 ;
  206. end
  207. end
  209. % % sanity check
  210. % is_symmetric_ets = nan(nr_windows,1);
  211. % is_symmetric_rms = nan(nr_windows,1);
  212. % for s=1:nr_windows
  213. % is_symmetric_ets(s) = issymmetric(ets_wpe_i(:,:,s));
  214. % is_symmetric_rms(s) = issymmetric(rms_wpe_i(:,:,s));
  215. % end
  216. %
  217. % sum(is_symmetric_ets)==nr_windows
  218. % sum(is_symmetric_rms)==nr_windows
  221. % save just lower triangle of symmetric matrices
  222. ets_wpe_i_vec = icatb_mat2vec(ets_wpe_i);
  223. rms_wpe_i_vec = icatb_mat2vec(rms_wpe_i);
  224. mean_ets_wpe_i_vec = icatb_mat2vec(mean_ets_wpe_i);
  225. [corr_wpe_scale_i_vec, linear_ind] = icatb_mat2vec(fc_wpe_scalar_i); % save linear indices for later
  228. end
  233. function [neg_delta_wpe_i] = get_wpe_decrease_indices(WPE_norm, nr_windows, n_rois)
  234. % Compute delta wpe to indetify windows where wpe goes down from t to t+1
  236. % pre-allocate vars
  237. wpe_delta_i = nan(nr_windows, n_rois); % we want the first row to remain NaN
  239. % shift windows by 1 (rows)
  240. wpe_ts_2_end = WPE_norm(2:end,:);
  241. wpe_ts_1_endmin1 = WPE_norm(1:end-1,:);
  243. wpe_delta_i(2:end,:) = wpe_ts_2_end-wpe_ts_1_endmin1; % first row (win 1) remains NaN (no delta)
  245. % now binarize that matrix to get an index for negative delta wpe windows
  246. neg_delta_wpe_i = wpe_delta_i<0;
  247. end
  254. function [auc_scd, idx_auc_scd, rms_scd, rms_auc_zeroed, scd_count,...
  255. rms_scd_count] = get_coflux_scd(neg_delta_wpe_i, idx_ets_pos_i, rms_wpe_i_vec, n_edges, n_rois, nr_windows, linear_ind)
  257. % Using indices of complexity decrease in both WPE TS (SCD) + instances of downward coflucuations, find their intersection and
  258. % compute area-under-the-curve of the cofluctuation amplitude during those moments. Here, AUC-SCD serves as a continuous measure,
  259. % analogous to complexity "drop coincidence" in Krohn et al. (2023)
  261. % convert linear indices to subscripts
  262. [rows, cols] = ind2sub([n_rois, n_rois], linear_ind);
  264. % pre-allocate vars
  265. auc_scd = nan(n_edges,1);
  266. idx_auc_scd = nan(n_edges, nr_windows);
  268. rms_scd = cell(n_edges,1);
  269. rms_auc_zeroed = nan(n_edges, nr_windows);
  271. scd_count = nan(n_edges,1);
  272. rms_scd_count = nan(n_edges,1);
  274. for e=1:n_edges % loop over edges
  276. % get roi ids for edge e
  277. roi_1 = rows(e);
  278. roi_2 = cols(e);
  280. % get neg delta indices for these 2 rois (over all windows)
  281. neg_delta_roi_1 = neg_delta_wpe_i(:,roi_1);
  282. neg_delta_roi_2 = neg_delta_wpe_i(:,roi_2);
  284. % get positive poitions of ets for edge e, over all windows
  285. ets_wpe_pos_edge_e = idx_ets_pos_i(e,:)';
  287. % find windows where both WPE TS decrease - relative to previous
  288. % window (simultaneous complexity decrease = SCD)
  289. scd_idx = neg_delta_roi_1&neg_delta_roi_2;
  291. % save sum of SCD - sum over windows
  292. scd_count(e,1) = sum(scd_idx);
  294. % get indx for SCD = 1 AND ets_pos = 1
  295. idx_pos_coflux_pos_scd = ets_wpe_pos_edge_e&scd_idx;
  296. rms_scd_count(e,1) = sum(idx_pos_coflux_pos_scd);
  298. % now use that boolean to index into the rms vector
  299. rms_edge_e = rms_wpe_i_vec(e,:)';
  301. % rms where SCD + ETS = 1
  302. rms_scd_edge_e = rms_edge_e(idx_pos_coflux_pos_scd);
  304. % make vector for area-under-curve (auc) calculation - zero non-SCD windows
  305. rms_auc_vec = rms_edge_e; % copy
  306. rms_auc_vec(~idx_pos_coflux_pos_scd) = 0;
  308. % calc auc for edge e
  309. auc_scd_edge_e = trapz(rms_auc_vec);
  311. % store vars for later
  312. auc_scd(e) = auc_scd_edge_e;
  313. idx_auc_scd(e,:) = idx_pos_coflux_pos_scd';
  315. rms_scd{e} = rms_scd_edge_e;
  316. rms_auc_zeroed(e, :) = rms_auc_vec';
  318. end
  319. idx_auc_scd = logical(idx_auc_scd);
  320. end
  322. end

calc_wpe_cofluctuations.m, no license · at the source

Overview

Authors: Amy Romanello1,2, Nina von Schwanenflug1,2, Michelle Franka1,2, Friedemann Paul3,4,5, Harald Prüss1,6, Stephan Krohn1,2, Carsten Finke1,2
ORCID iDs: Amy Romanello, Nina von Schwanenflug, Michelle Franka, Friedemann Paul, Harald Prüss, Stephan Krohn, Carsten Finke
  1. Department of Neurology and Experimental Neurology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität Berlin, Berlin, Germany
  2. Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
  3. Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
  4. Experimental and Clinical Research Center, a cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and Charité-Universitätsmedizin Berlin, Berlin, Germany
  5. NeuroCure Clinical Research Center, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
  6. German Center for Neurodegenerative Diseases (DZNE) Berlin, Berlin, Germany
Institutions: Humboldt-Universität zu Berlin (Germany); Charité - Universitätsmedizin Berlin (Germany); Max Delbrück Center (Germany); Freie Universität Berlin (Germany); German Center for Neurodegenerative Diseases (Germany)
Journal: Network neuroscience (Cambridge, Mass.), volume 10, issue 3, pages 761-786
Dates: received 18 November 2025; accepted 3 March 2026; published online 25 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/netn.a.572 · PMID 42730197 · PMCID PMC13569379 · OpenAlex W4414054133
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.09.05.674447
Status: code verified
Categories: fMRI (modality), human (organism), other condition (population)
Methods: Spectral & time-frequency, Statistics, Preprocessing, Connectivity, fMRI & imaging
Keywords: Functional magnetic resonance imaging, Functional connectivity, Complexity, Neural dynamics, Autoimmune encephalitis
Topic: Psychotherapy Techniques and Applications (Clinical Psychology, Psychology), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (327654276 (CRC 1315), 504745852 (Clinical Research Unit KFO 5023 'BecauseY'), FI 2309/1-1 (Heisenberg Program), FI 2309/2-1); Bundesministerium für Bildung und Forschung (Federal Ministry of Education and Research) (01GM1908D, 01GM2208C, 01GM2102)
Citations: not cited yet (Europe PMC); 30 references in the paper

Abstract

Functional connectivity (FC) quantifies the temporal coherence of blood-oxygen-level-dependent (BOLD) signals across brain regions. Recently, the information-theoretic “complexome” framework has linked FC to coinciding “complexity drops”: transient moments in which regional BOLD signals simultaneously become regular. Here, we replicate this relationship in an independent dataset and extend the framework by (a) integrating it with signal cofluctuation analysis; (b) extending the previous binary concept of simultaneous complexity drops to a continuous, threshold-free calculation based on a temporal unwrapping procedure; (c) providing evidence of clinical relevance in the model disease of anti-N-methyl-D-aspartate-receptor encephalitis; and (d) deriving a new measure of pairwise dissimilarity in local BOLD patterns. This “index of pattern incongruency” (IPI) explains clinically relevant FC reductions and maps onto distinct associations with cognition beyond FC. These findings show that global FC is closely related to local patterns within underlying BOLD signals, strengthening the link between complexity dynamics and the brain’s functional organization as a large-scale network.

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

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OSF mr8f7

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OSF wjua7

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Languages: MATLAB (10)
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Found in: “DATA AND MATERIALS AVAILABILITY”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: GIFT (1 file), Parallel Computing Toolbox (1 file), Statistics and Machine Learning Toolbox (1 file)
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The paper's code and data availability statement is in the Data section.

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Data

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Data and materials availability

Analysis code to reproduce the main findings of this study is made available on the Open Science Framework: https://osf.io/wjua7/. Upon reasonable request, clinical data can be provided pending scientific review and a completed material transfer agreement. Requests should be submitted to the corresponding author.

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 5 keywords, 2 funders, 27 references.

Cite

This paper

Romanello, A., von Schwanenflug, N., Franka, M., Paul, F., Prüss, H., Krohn, S., & Finke, C. (2026). Functional connectivity is linked to symbolic BOLD patterns: Replication, extension, and clinical application of the human "Complexome". Network neuroscience (Cambridge, Mass.), 10(3), 761-786. https://doi.org/10.1162/netn.a.572

BibTeX

@article{romanello2026functional,
author = {Romanello, Amy and von Schwanenflug, Nina and Franka, Michelle and Paul, Friedemann and Prüss, Harald and Krohn, Stephan and Finke, Carsten},
title = {{Functional connectivity is linked to symbolic BOLD patterns: Replication, extension, and clinical application of the human "Complexome"}},
journal = {Network neuroscience (Cambridge, Mass.)},
year = {2026},
month = aug,
volume = {10},
number = {3},
pages = {761--786},
publisher = {MIT Press},
issn = {2472-1751},
doi = {10.1162/netn.a.572},
url = {https://doi.org/10.1162/netn.a.572},
pmid = {42730197},
pmcid = {PMC13569379}
}

RIS

TY - JOUR
AU - Romanello, Amy
AU - von Schwanenflug, Nina
AU - Franka, Michelle
AU - Paul, Friedemann
AU - Prüss, Harald
AU - Krohn, Stephan
AU - Finke, Carsten
TI - Functional connectivity is linked to symbolic BOLD patterns: Replication, extension, and clinical application of the human "Complexome"
T2 - Network neuroscience (Cambridge, Mass.)
J2 - Netw Neurosci
PY - 2026
DA - 2026/08/25
VL - 10
IS - 3
SP - 761
EP - 786
SN - 2472-1751
PB - MIT Press
DO - 10.1162/netn.a.572
UR - https://doi.org/10.1162/netn.a.572
LA - en
ER -

CSL-JSON

{
"id": "10.1162/netn.a.572",
"type": "article-journal",
"title": "Functional connectivity is linked to symbolic BOLD patterns: Replication, extension, and clinical application of the human \"Complexome\"",
"container-title": "Network neuroscience (Cambridge, Mass.)",
"author": [
{
"family": "Romanello",
"given": "Amy"
},
{
"family": "von Schwanenflug",
"given": "Nina"
},
{
"family": "Franka",
"given": "Michelle"
},
{
"family": "Paul",
"given": "Friedemann"
},
{
"family": "Prüss",
"given": "Harald"
},
{
"family": "Krohn",
"given": "Stephan"
},
{
"family": "Finke",
"given": "Carsten"
}
],
"container-title-short": "Netw Neurosci",
"volume": "10",
"issue": "3",
"page": "761-786",
"DOI": "10.1162/netn.a.572",
"PMID": "42730197",
"PMCID": "PMC13569379",
"ISSN": "2472-1751",
"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/netn.a.572",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
25
]
]
}
}

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