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Sampling bias corrections for discrete and Gaussian partial information decompositions.

Code ↔ Paper

10 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 10 matches · 4 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Three-source PID analysis of real MEG data ↔ Real_data_analyses/MEG_Analysis_3SourcePID_gauss.m, lines 37–78 · score 0.70 · prefrontal cortex, visual cortex, motor, somatosensory, source PID, MEG
  2. [2] § Methods › Details of the simulations used to test the three-source PID ↔ Real_data_analyses/fMRIanalysis/main_code_bias_corrs_on_data.py, lines 153–186 · score 0.66 · block diagonal covariance, multivariate normal, zero, matrix, Gaussian
  3. [3] § Results › The bias of three-source PID ↔ Real_data_analyses/MEG_Analysis_3SourcePID_gauss.m, lines 37–78 · score 0.64 · prefrontal cortex, visual cortex, motor, somatosensory, source PID, MEG
  4. [4] § Methods › Details of two-source PID analysis of real neural data › Mouse auditory cortex data recorded during a sound discrimination task ↔ Real_data_analyses/analysis_functions/data_for_information_analysis.m, the whole file · a weak match · score 0.60 · imaging frame, licking, onset, fluorescence, behavioral, mice
  5. [5] § Methods › Details of simulations used to test the bias properties › Details of discrete simulations ↔ Real_data_analyses/analysis_functions/correlated_analyses_bias_PID.m, lines 4–54 · score 0.59 · standard deviation, shared spikes, firing, correlated, neurons, stimuli
  6. [6] § Methods › Details of simulations used to test the bias properties › Details of discrete simulations ↔ Real_data_analyses/analysis_functions/simulate_edges.m, the whole file · a weak match · score 0.58 · standard deviation, shared spikes, firing, correlated, neurons, simulations
  7. [7] § Results › Background: PID and its neuroscience use ↔ Real_data_analyses/analysis_functions/generate_dataset_neural_activity_and_stimuli_to_analyze.m, the whole file · a weak match · score 0.56 · neural activity, spiking activity, LFPs, electroencephalography, field, window
  8. [8] § Results › Background: PID and its neuroscience use ↔ Functions/pid_lattice.m, lines 1–146 · score 0.54 · joint source, joint probability, mutual information, theoretical, properties, marginals
  9. [9] § Methods › Three-source PID analysis of real MEG data ↔ Functions/glasser_group.m, the whole file · a weak match · score 0.53 · motor, prefrontal, somatosensory, V2, V3, V1
  10. [10] § Methods › Definitions of Shannon information and PID › Shannon information and relationships with PID components ↔ Functions/pid_lattice.m, lines 1–146 · score 0.51 · information theoretical, covariance matrix, sum, variables, probability, dimensions

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 203 lines · 6.4 KB · GPL-3.0 · 2 matches

  1. %% PID lattice + three corrections: demo
  2. % Make sure the following are on your path:
  3. % pid_lattice.m
  4. % resample.m % uses pid.is_gaussian internally
  5. % shuffsub.m % uses pid.is_gaussian internally
  6. % qe.m % uses pid.is_gaussian internally
  8. rng(0);
  9. clc;
  10. clear;
  11. % addpath(genpath('...')); Add Path to data
  12. load('subjects.mat', 'subj');
  13. if ~iscell(subj)
  14. subj = cellstr(subj);
  15. end
  16. load('behav_data_idx.mat', 'behav_data_idx');
  17. conditions = {'attn'};
  18. SAVE_DIR = pwd;
  21. for c_idx = 1:length(conditions)
  22. condition = conditions{c_idx};
  24. fprintf('Processing condition: %s\n', upper(condition));
  25. condition_save_dir = fullfile(SAVE_DIR, condition);
  26. if ~exist(condition_save_dir, 'dir')
  27. mkdir(condition_save_dir);
  28. end
  30. for s_idx = 1:length(subj)
  31. s = subj{s_idx};
  32. behav_data = behav_data_idx.(condition).(s);
  33. epochs = behav_data_idx.(condition).epochs;
  34. process_subject_matched(s, behav_data, epochs, condition, condition_save_dir);
  35. end
  36. end
  37. function process_subject_matched(s, behav, epochs, condition, condition_save_dir)
  38. sf = 200;
  39. etimep = -0.5 : 1/sf : 4.5;
  41. opts_II.bias = 'shuffSub';
  42. opts_II.shuff = 20;
  43. opts_II.suppressWarnings = true;
  44. opts_II.bin_method = {'eqpop'};
  45. opts_II.n_bins = {3};
  46. epochs_to_process = [1];
  47. areas_all = [3 11 16];
  48. d0s = [0.05, 0.20, 0.35, 0.50, 0.65, 0.80, 2.70, 2.85, 3.00, 3.15, 3.30, 3.45];
  49. d0 = arrayfun(@(x) find(abs(etimep - x) == min(abs(etimep - x)), 1), d0s);
  51. % Visual cortex (indices 1–5)
  52. areas1 = [];
  53. for idx = 1:5
  54. [groi, ~, ~] = glasser_group(idx);
  55. areas1 = [areas1, groi]; % concatenate all ROIs
  56. end
  58. % Prefrontal cortex (indices 19–22)
  59. areas2 = [];
  60. for idx = 19:22
  61. [groi, ~, ~] = glasser_group(idx);
  62. areas2 = [areas2, groi];
  63. end
  65. % Somatosensory/motor (indices 6–8, 24–26)
  66. areas3 = [];
  67. for idx = [6 7 8 24 25 26]
  68. [groi, ~, ~] = glasser_group(idx);
  69. areas3 = [areas3, groi];
  70. end
  73. PID_plugin = cell(1,length(epochs_to_process));
  74. PID_res = cell(1,length(epochs_to_process));
  75. PID_shuff = cell(1,length(epochs_to_process));
  77. trials_idx = behav.upTrials | behav.downTrials;
  78. did = find(trials_idx);
  80. % X_trials = 175;
  81. % if numel(did) > X_trials
  82. % random_indices = randperm(numel(did));
  83. % subsampled_indices = random_indices(1:X_trials);
  84. % did = did(subsampled_indices);
  85. % end
  87. data_area1 = zeros(length(trials_idx), length(areas1), length(etimep));
  88. data_area2 = zeros(length(trials_idx), length(areas2), length(etimep));
  89. data_area3 = zeros(length(trials_idx), length(areas3), length(etimep));
  91. save_path = fullfile(condition_save_dir, sprintf('PID3Source_%s_%s.mat', s, condition));
  93. for p = 1:length(areas1)
  94. area_name = ['HCPMMP1_' areas1{p}];
  95. pca_filename = fullfile('',s, sprintf('PCA_%s_%s_%s.mat', s, condition, area_name)); % ADD PATH TO DATA
  96. if ~exist(pca_filename, 'file')
  97. fprintf('Missing file: %s\n', pca_filename);
  98. continue;
  99. end
  100. loaded_data = load(pca_filename, 'score');
  101. score = loaded_data.score;
  102. data = cellfun(@(x) permute(x, [3 1 2]), score, 'UniformOutput', false);
  103. data = cell2mat(data);
  104. data_tmp = permute(data, [2 3 1]);
  105. assert(size(data_tmp, 3)==length(etimep))
  106. data_tmp = zscore(data_tmp, 0, 3);
  107. data_area1(:,p,:) = data_tmp(:,1,:);
  108. end
  109. for p = 1:length(areas2)
  110. area_name = ['HCPMMP1_' areas2{p}];
  111. pca_filename = fullfile('',s, sprintf('PCA_%s_%s_%s.mat', s, condition, area_name)); % ADD PATH TO DATA
  112. if ~exist(pca_filename, 'file')
  113. fprintf('Missing file: %s\n', pca_filename);
  114. continue;
  115. end
  116. loaded_data = load(pca_filename, 'score');
  117. score = loaded_data.score;
  118. data = cellfun(@(x) permute(x, [3 1 2]), score, 'UniformOutput', false);
  119. data = cell2mat(data);
  120. data_tmp = permute(data, [2 3 1]);
  121. assert(size(data_tmp, 3)==length(etimep))
  122. data_tmp = zscore(data_tmp, 0, 3);
  123. data_area2(:,p,:) = data_tmp(:,1,:);
  124. end
  126. for p = 1:length(areas3)
  127. area_name = ['HCPMMP1_' areas3{p}];
  128. pca_filename = fullfile('',s, sprintf('PCA_%s_%s_%s.mat', s, condition, area_name)); % ADD PATH TO DATA
  129. if ~exist(pca_filename, 'file')
  130. fprintf('Missing file: %s\n', pca_filename);
  131. continue;
  132. end
  133. loaded_data = load(pca_filename, 'score');
  134. score = loaded_data.score;
  135. data = cellfun(@(x) permute(x, [3 1 2]), score, 'UniformOutput', false);
  136. data = cell2mat(data);
  137. data_tmp = permute(data, [2 3 1]);
  138. assert(size(data_tmp, 3)==length(etimep))
  139. data_tmp = zscore(data_tmp, 0, 3);
  140. data_area3(:,p,:) = data_tmp(:,1,:);
  141. end
  143. for d_idx = 1:length(epochs_to_process)
  144. d = epochs_to_process(d_idx);
  146. R1 = data_area1(did, :, epochs(d, 1):epochs(d, 2));
  147. R2 = data_area2(did, :, epochs(d, 1):epochs(d, 2));
  148. R3 = data_area3(did, :, epochs(d, 1):epochs(d, 2));
  149. S = behav.sample(:, d);S = S(did);
  151. [~, N_reg1, Nt] = size(R1);
  152. [~, N_reg2, ~] = size(R2);
  153. [~, N_reg3, ~] = size(R3);
  155. n_blocks = floor(Nt / 4);
  157. PIDttmp_plugin = nan(18,n_blocks);
  158. PIDttmp_shuff = nan(18,n_blocks);
  159. PIDttmp_res = nan(18,n_blocks);
  161. for b = 1:n_blocks
  162. t_start = (b-1) * 4 + 1;
  163. t_end = t_start + 3;
  164. R1_avg = mean(R1(:, :, t_start:t_end), 3);
  165. R2_avg = mean(R2(:, :, t_start:t_end), 3);
  166. R3_avg = mean(R3(:, :, t_start:t_end), 3);
  168. % opts_bin.bin_method = {'eqpop'};
  169. % opts_bin.n_bins = {3};
  170. %
  171. % binnedData = binning({R1_avg', R2_avg', R3_avg', S'}, opts_bin);
  173. data_PID = cat(1, R1_avg', R2_avg', R3_avg', S');
  174. pid_lat = pid_lattice(3, 'IMMI', true);
  176. idx1 = 1:N_reg1;
  177. idx2 = N_reg1 + (1:N_reg2);
  178. idx3 = N_reg1 + N_reg2 + (1:N_reg3);
  179. idy = N_reg1 + N_reg2 + N_reg3 + (1:1);
  180. pid_lat.source_dims = {idx1, idx2, idx3};
  181. pid_lat.target_dims = idy;
  183. obsG = data_PID.'; % N x V
  184. Sigma = cov(obsG, 1); % 1/N normalization
  186. lat_emp_gauss = pid_lat.calculate_latvals(Sigma);
  188. lat_res_gauss = resample(data_PID, 10, pid_lat); % vector
  189. lat_sh_gauss = shuffsub(data_PID, 10, pid_lat); % atoms x nshuff
  191. PIDttmp_plugin(:,b) = lat_emp_gauss;
  192. PIDttmp_res(:,b) = lat_res_gauss;
  193. PIDttmp_shuff(:,b) = lat_sh_gauss;
  194. end
  195. PID_plugin{d_idx} = PIDttmp_plugin;
  196. PID_res{d_idx} = PIDttmp_res;
  197. PID_shuff{d_idx} = PIDttmp_shuff;
  198. end
  199. % Save results
  200. save(save_path, 'PID_plugin', 'PID_res', 'PID_shuff', 's', 'condition');
  201. fprintf('Saved: %s\n', save_path);
  202. end

MEG_Analysis_3SourcePID_gauss.m at commit c738f2e, under GPL-3.0 · at the source

Overview

Authors: Gabriel Matías Lorenz1, Nicola Marie Engel1, Loren Koçillari1,2, Marco Celotto1,3, Davide Orsenigo4, Sebastiano Curreli5, Simone Blanco Malerba1, Andreas K. Engel2, Christoph Kayser6, Tommaso Fellin5, Andrea I. Luppi7,8,9, Stefano Panzeri1
ORCID iDs: Gabriel Matías Lorenz, Nicola Marie Engel, Loren Koçillari, Marco Celotto, Davide Orsenigo, Sebastiano Curreli, Simone Blanco Malerba, Andreas K. Engel, Christoph Kayser, Tommaso Fellin, Andrea I. Luppi, Stefano Panzeri
  1. Institute for Neural Information Processing, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf (UKE), 20251 Hamburg, Germany
  2. Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf (UKE), 20246 Hamburg, Germany
  3. Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  4. Department of Psychology, University of Torino, 10124 Turin, Italy
  5. Optical Approaches to Brain Function Laboratory, Istituto Italiano di Tecnologia, 16163 Genova, Italy
  6. Department of Cognitive Neuroscience, Bielefeld University, 33501 Bielefeld, Germany
  7. Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK
  8. St. John’s College, University of Cambridge, Cambridge CB2 1TP, UK
  9. Division of Information Engineering, University of Cambridge, Cambridge CB2 1PZ, UK
Institutions: University Medical Center Hamburg-Eppendorf (Germany); Massachusetts Institute of Technology (United States); University of Turin (Italy); Italian Institute of Technology (Italy); Bielefeld University (Germany); University of Cambridge (United Kingdom); University of Oxford (United Kingdom)
Journal: Patterns (New York, N.Y.), volume 7, issue 9, article 101619
Dates: received 17 January 2026; accepted 26 June 2026; published online 23 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.patter.2026.101619 · PMID 42746280 · PMCID PMC13576656 · OpenAlex W7170149077
Open access: gold, a free copy (OpenAlex)
Status: code verified
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: information theory, neural coding, synergy, redundancy, brain communication, limited-sampling bias
Topic: Distributed Sensor Networks and Detection Algorithms (Computer Networks and Communications, Computer Science), according to OpenAlex
Funding: Simons Foundation Autism Research Initiative (982347); German Federal Ministry of Education and Research (BMBF) (01GQ2404); European Union’s European Research Council NEUROPATTERNS (647725); cICMs (ERC-2022-AdG-101097402); European Union's Horizon research and innovation programme (101206609); Marie Sklodowska-Curie Global Fellowship (101152984); EU Marie Sklodowska-Curie Action; Wellcome Early Career Award (226924/Z/23/Z); St. John’s College, Cambridge
Citations: not cited yet (Europe PMC); 112 references in the paper

Abstract

Partial information decomposition (PID) has emerged as a principled way to decompose the information carried by neural activity into components identifying whether interactions among neurons or brain areas generate synergistic or redundant information. Here, we demonstrate that empirical measures of synergy and redundancy based on either Gaussian or discrete probability estimators suffer from a substantial limited-sampling estimation bias. This bias is much larger for synergy than for redundancy. The gap between them increases with the number of parameters specifying the probability distributions. We develop procedures that effectively correct for the bias and provide rules of thumb for the sample sizes required to obtain unbiased estimates. We show that, when used on empirical brain datasets, they successfully remove large synergy biases across species, recording modalities, and experimental designs. Our bias corrections extend the range of neuroscience questions and experimental designs addressable with PID and allow accurate comparisons between synergy and redundancy.

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

Repositories

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Zenodo 20085438

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source: doi.org/10.5281/zenodo.20085438

panzerilab/sampling_bias_corrections_syn_red

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: c738f2e65d5d5c2a97fcaa00d620d1c867dd0121, 8 May 2026
Languages: MATLAB (79), Python (18)
Size: 173 files, 97 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file, environment (environment.yml, Real_data_analyses/fMRIanalysis/Dockerfile, Real_data_analyses/fMRIanalysis/environment.yml, Real_data_analyses/fMRIanalysis/requirements.txt)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (17 files), NumPy (15 files), Matplotlib (13 files), SciPy (12 files), pandas (9 files), scikit-learn (4 files), NiBabel (2 files), Nilearn (2 files), seaborn (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
99 files
At the source: github.com/panzerilab/Sampling_bias_corrections_Syn_Red/tree/v1.2

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;
  • 97 scripts, each with its path and the digest of its content;
  • 10 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

Datasets cited

Data and code availability

Code for simulating and analyzing data is made available at our repository at https://doi.org/10.5281/zenodo.20085438.112 The code uses the MINT toolbox.64 The real neural data used here in Figures 6 and S16 were published previously. Dataset 1 was released with the original publication.36 Dataset 2 was first published in Curreli et al.63 and is available for download from Lorenz et al.64 Dataset 3 was published in Kayser et al.11 and is made available in our repository at https://doi.org/10.5281/zenodo.20085438.112 Dataset 4 is publicly available from the original publication.86 Dataset 5 is publicly available from the original publication.77

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 2, 28 September 2026

  • Authors: added Nicola Marie Engel (0009-0001-9530-1126); Loren Koçillari (0000-0001-7301-1796); Sebastiano Curreli (0000-0003-4490-6835); Simone Blanco Malerba (0000-0002-4467-5988); Andreas K. Engel (0000-0003-4899-8466); Christoph Kayser (0000-0001-7362-5704); Andrea I. Luppi (0000-0002-3461-6431); Stefano Panzeri (0000-0003-1700-8909); removed Nicola Marie Engel; Loren Koçillari; Sebastiano Curreli; Simone Blanco Malerba; Andreas K. Engel; Christoph Kayser; Andrea I. Luppi; Stefano Panzeri

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 6 keywords, 9 funders, 105 references.

Cite

This paper

Lorenz, G. M., Engel, N. M., Koçillari, L., Celotto, M., Orsenigo, D., Curreli, S., Malerba, S. B., Engel, A. K., Kayser, C., Fellin, T., Luppi, A. I., & Panzeri, S. (2026). Sampling bias corrections for discrete and Gaussian partial information decompositions. Patterns (New York, N.Y.), 7(9), 101619. https://doi.org/10.1016/j.patter.2026.101619

BibTeX

@article{lorenz2026sampling,
author = {Lorenz, Gabriel Matías and Engel, Nicola Marie and Koçillari, Loren and Celotto, Marco and Orsenigo, Davide and Curreli, Sebastiano and Malerba, Simone Blanco and Engel, Andreas K. and Kayser, Christoph and Fellin, Tommaso and Luppi, Andrea I. and Panzeri, Stefano},
title = {{Sampling bias corrections for discrete and Gaussian partial information decompositions}},
journal = {Patterns (New York, N.Y.)},
year = {2026},
month = jul,
volume = {7},
number = {9},
pages = {101619},
publisher = {Elsevier},
issn = {2666-3899},
doi = {10.1016/j.patter.2026.101619},
url = {https://doi.org/10.1016/j.patter.2026.101619},
pmid = {42746280},
pmcid = {PMC13576656}
}

RIS

TY - JOUR
AU - Lorenz, Gabriel Matías
AU - Engel, Nicola Marie
AU - Koçillari, Loren
AU - Celotto, Marco
AU - Orsenigo, Davide
AU - Curreli, Sebastiano
AU - Malerba, Simone Blanco
AU - Engel, Andreas K.
AU - Kayser, Christoph
AU - Fellin, Tommaso
AU - Luppi, Andrea I.
AU - Panzeri, Stefano
TI - Sampling bias corrections for discrete and Gaussian partial information decompositions
T2 - Patterns (New York, N.Y.)
J2 - Patterns (N Y)
PY - 2026
DA - 2026/07/23
VL - 7
IS - 9
SP - 101619
SN - 2666-3899
PB - Elsevier
DO - 10.1016/j.patter.2026.101619
UR - https://doi.org/10.1016/j.patter.2026.101619
LA - en
ER -

CSL-JSON

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"id": "10.1016/j.patter.2026.101619",
"type": "article-journal",
"title": "Sampling bias corrections for discrete and Gaussian partial information decompositions",
"container-title": "Patterns (New York, N.Y.)",
"author": [
{
"family": "Lorenz",
"given": "Gabriel Matías"
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{
"family": "Koçillari",
"given": "Loren"
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{
"family": "Celotto",
"given": "Marco"
},
{
"family": "Orsenigo",
"given": "Davide"
},
{
"family": "Curreli",
"given": "Sebastiano"
},
{
"family": "Malerba",
"given": "Simone Blanco"
},
{
"family": "Engel",
"given": "Andreas K."
},
{
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},
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{
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},
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"given": "Stefano"
}
],
"container-title-short": "Patterns (N Y)",
"volume": "7",
"issue": "9",
"page": "101619",
"DOI": "10.1016/j.patter.2026.101619",
"PMID": "42746280",
"PMCID": "PMC13576656",
"ISSN": "2666-3899",
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"issued": {
"date-parts": [
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In common: seaborn, SciPy, Matplotlib, 1 other tool, humanconnectome.org/study/hcp-young-adult, 3 references, author Andrea I Luppi
[7] doi:10.1016/j.isci.2026.116903 [code]
Neurobiological and behavioral relevance of intrinsic functional connectome constraints on task-evoked neural activation.
Journal: iScience
In common: Nilearn, NiBabel, Statistics and Machine Learning Toolbox, 4 other tools, humanconnectome.org/study/hcp-young-adult, 4 references
[8] doi:10.1371/journal.pone.0348005 [code]
THOI: An efficient and accessible library for computing higher-order interactions enhanced by batch-processing.
Journal: PloS one
In common: seaborn, scikit-learn, pandas, 3 other tools, 6 references
[9] doi:10.1038/s41467-026-72931-6 [code]
Three parsimonious spatiotemporal patterns in cerebellum reveal individual traits in function and behavior.
Journal: Nature communications
In common: Nilearn, NiBabel, Statistics and Machine Learning Toolbox, 6 other tools, humanconnectome.org/study/hcp-young-adult, 1 reference
[10] doi:10.1002/hbm.70557 [code]
Efficient Deep Learning Models for Predicting Individualized Task Activation From Resting-State Functional Connectivity.
Journal: Human brain mapping
In common: Nilearn, NiBabel, seaborn, 5 other tools, humanconnectome.org/study/hcp-young-adult, 2 references

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