OSCR

Movement Disorder Patients with Depression Have Altered Corticostriatal Alpha-Beta Power Response to Reward and Loss.

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] § Materials and Methods › Neurophysiology and statistical analysis ↔ VerbWM_SecondLevelClusters_permutestref_func.m, lines 1–121 · score 0.70 · shuffled permutations, absolute, randomizations, predictor, sum, matrix
  2. [2] § Materials and Methods › Neurophysiology and statistical analysis ↔ VerbWMBDIDiagANOVALME.m, lines 88–171 · score 0.56 · linear mixed, way ANOVAs, diagnosis, models, interaction, BDI
  3. [3] § Materials and Methods › Neurophysiology and statistical analysis ↔ VerbWMBDIDiagANOVALME_OtherVarStats.m, lines 358–420 · score 0.56 · linear mixed, way ANOVAs, diagnosis, models, interaction, BDI

Paper

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The authors' code

MATLAB · 256 lines · 11 KB · no license · 1 match

  1. function VerbWM_SecondLevelClusters_permutestref_func(brain_rgn,aligntype,trialtype,data_structure,datatype,subjID,powrange,varargin)
  2. savename_add=''; % Default is empty
  3. if ~isempty(varargin)
  4. for i = 1:2:length(varargin)
  5. if strcmp(varargin{i},'savename_add')
  6. savename_add=varargin{i+1};
  7. end
  8. end
  9. end
  11. % Compute cluster correction threshold
  12. random_nb=60000; % 60,000 random combinations of all contacts in an ROI, drawn from the 300 shuffles calculated previously for each site
  13. alpha=0.05;
  15. % Load data for all subjects
  16. tMatrix_perm_allsubj=[]; tMatrix_allsubj=[];
  17. for su=1:length(subjID)
  18. if strcmp(data_structure,'timefreq')
  19. if strcmp(datatype,'Power')
  20. filename=[brain_rgn,'_',aligntype,'_',trialtype,'_',subjID{su},'_FirstLevel_tTest_alldata_Cat'];
  21. elseif strcmp(datatype,'Bursts')
  22. filename=[brain_rgn,'_Bursts_',aligntype,'_',trialtype,'_',subjID{su},'_FirstLevel_tTest_alldata_Cat'];
  23. end
  24. else
  25. if strcmp(datatype,'Power')
  26. filename=[powrange,'_',brain_rgn,'_',aligntype,'_',trialtype,'_',subjID{su},'_FirstLevel_tTest_alldata_Cat'];
  27. elseif strcmp(datatype,'Bursts')
  28. filename=[powrange,'_',brain_rgn,'_Bursts_',aligntype,'_',trialtype,'_',subjID{su},'_FirstLevel_tTest_alldata_Cat'];
  29. end
  30. end
  32. tempload = load(filename);
  34. tMatrix_perm_ch=tempload.tMatrix_perm_ch;
  35. tMatrix_ch=tempload.tMatrix_ch;
  37. for ch=1:length(tMatrix_perm_ch)
  38. % For shuffled permutations: Concatenate frequency x time x permutation x channel matrix
  39. tMatrix_perm_allsubj = cat(4,tMatrix_perm_allsubj,tMatrix_perm_ch{ch});
  41. % For non-shuffled: Concatenate frequency x time x channel matrix
  42. tMatrix_allsubj = cat(3,tMatrix_allsubj,tMatrix_ch{ch});
  43. end
  44. clear tempload
  45. end
  47. permstat_correct = NaN(random_nb, 1); % Initialization (randomizations x 1), original code had it as randomizations x 1 x number of predictors (which is just 1 in our case)
  49. % Randomization loop
  50. tic
  51. parfor rd = 1:random_nb
  52. disp(['Randomization # ',num2str(rd)]); tic
  53. h_matrix = NaN(size(tMatrix_perm_allsubj,1),size(tMatrix_perm_allsubj,2)); % Initialize frequency x time matrix
  54. t_matrix = NaN(size(tMatrix_perm_allsubj,1),size(tMatrix_perm_allsubj,2)); % Initialize frequency x time matrix
  56. for ff=1:size(tMatrix_perm_allsubj,1)
  57. rd_perm = NaN(size(tMatrix_perm_allsubj,4),size(tMatrix_perm_allsubj,2)); % Initialize (channels x time)
  59. for chan = 1:size(tMatrix_perm_allsubj,4) % Loop over channels
  60. ichan = randperm(size(tMatrix_perm_allsubj,3), 1); % We take a random permutation for each contact
  61. rd_perm(chan,:) = squeeze(tMatrix_perm_allsubj(ff, :, ichan, chan)); % Channel x Time
  62. end
  64. % MATLAB default is two-tailed test w/ alpha of 0.05
  65. [h,~,~,stats] = ttest(rd_perm); % T-test (over channels, returns a t-value for each time index)
  67. h_matrix(ff,:)=h;
  68. t_matrix(ff,:)=stats.tstat;
  69. end
  70. h=[]; stats=[];
  72. % Compute t-value threshold from p-value threshold (two-sided, dependent samples)
  73. num_channels = size(tMatrix_perm_allsubj,4);
  74. tThreshold = abs(tinv((1-alpha/2), num_channels-1)); %using total N-1 number of channels for DF
  76. % Find positive clusters (must be done separately in case positive and
  77. % negative clusters are connected)
  78. pos_t_idx = find(t_matrix > tThreshold);
  79. pos_h_matrix = zeros(size(h_matrix));
  80. pos_h_matrix(pos_t_idx) = 1;
  82. L_pos = spm_bwlabel(pos_h_matrix); %% Find clusters (number indices of each identified cluster with a number)
  85. % Find negative clusters
  86. neg_t_idx = find(t_matrix < -tThreshold);
  87. neg_h_matrix = zeros(size(h_matrix));
  88. neg_h_matrix(neg_t_idx) = 1;
  90. L_neg = spm_bwlabel(neg_h_matrix); %% Find clusters (number indices of each identified cluster with a number), includes both negative and positive clusters
  93. % Analyze all clusters (numbered in L)
  94. clusternum_pos = max(L_pos,[],'all'); % Count positive clusters
  95. clusternum_neg = max(L_neg,[],'all'); % Count negative clusters
  97. stats_summary_pos = NaN(clusternum_pos,1); % Initialization
  98. stats_summary_neg = NaN(clusternum_neg,1); % Initialization
  99. stats_summary_tmp = [];
  101. for k = 1:clusternum_pos % Positive clusters
  102. stats_summary_pos(k,:) = sum(t_matrix(L_pos==k)); % Sum t-values of cluster
  103. end
  104. for k = 1:clusternum_neg % Negative clusters
  105. stats_summary_neg(k,:) = sum(t_matrix(L_neg==k)); % Sum t-values of cluster
  106. end
  107. stats_summary_tmp = [stats_summary_pos;stats_summary_neg];
  109. % Fill the cluster matrix
  110. if isempty(stats_summary_tmp) % There is no cluster
  111. permstat_correct(rd) = 0;
  112. else % Find the biggest cluster
  113. [~,max_idx] = max(abs(stats_summary_tmp));
  114. permstat_correct(rd) = abs(stats_summary_tmp(max_idx)); % Sum t-values of the biggest cluster
  115. % Storing absolute value of t-value sum for ease of use of permstat_correct
  116. % to get absolute threshold from percentile function
  118. end
  119. toc;
  120. end
  121. toc
  123. %% Non-shuffled statistics
  125. % Loop over frequencies
  126. h_matrix = NaN(size(tMatrix_allsubj,1),size(tMatrix_allsubj,2)); % Initialize frequency x time matrix
  127. t_matrix = NaN(size(tMatrix_allsubj,1),size(tMatrix_allsubj,2)); % Initialize frequency x time matrix
  128. avg_tMatrix = NaN(size(tMatrix_allsubj,1),size(tMatrix_allsubj,2)); % Initialize frequency x time matrix
  129. for ff=1:size(tMatrix_allsubj,1)
  130. avg_tMatff = squeeze(mean(tMatrix_allsubj(ff,:,:),3,'omitnan')); % Keep time course, average t-values over channels
  131. [h,~,~,stats] = ttest(squeeze(tMatrix_allsubj(ff,:,:))'); % T-test (over channels, returns a t-value for each time index)
  133. h_matrix(ff,:)=h;
  134. t_matrix(ff,:)=stats.tstat;
  135. avg_tMatrix(ff,:)=avg_tMatff;
  136. end
  137. h=[]; stats=[];
  139. % Compute t-value threshold from p-value threshold (two-sided, dependent samples)
  140. num_channels = size(tMatrix_allsubj,3);
  141. tThreshold = abs(tinv((1-alpha/2), num_channels-1)); %using total N-1 number of channels for DF
  144. % Find positive clusters (must be done separately in case positive and
  145. % negative clusters are connected)
  146. pos_t_idx = find(t_matrix > tThreshold);
  147. pos_h_matrix = zeros(size(h_matrix));
  148. pos_h_matrix(pos_t_idx) = 1;
  150. L_pos = spm_bwlabel(pos_h_matrix); %% Find clusters (number indices of each identified cluster with a number)
  153. % Find negative clusters
  154. neg_t_idx = find(t_matrix < -tThreshold);
  155. neg_h_matrix = zeros(size(h_matrix));
  156. neg_h_matrix(neg_t_idx) = 1;
  158. L_neg = spm_bwlabel(neg_h_matrix); %% Find clusters (number indices of each identified cluster with a number), includes both negative and positive clusters
  161. %% Cecchi verson of p-threshold
  162. % Count all clusters (numbered in L)
  163. clusternum_pos = max(L_pos,[],'all'); % Count positive clusters
  164. clusternum_neg = max(L_neg,[],'all'); % Count negative clusters
  165. clusternum_tot = clusternum_pos + clusternum_neg;
  167. % Initialization
  168. stats_summary_pos = cell(clusternum_pos,3);
  169. stats_summary_neg = cell(clusternum_neg,3);
  171. clusteridx_store = {};
  172. mean_tVal=[];
  173. t_stat_sig=[]; p_val_sig=[]; % Initialization of cluster statistics
  175. % Positive clusters
  176. for k = 1:clusternum_pos % Analyze all clusters (numbered in L_pos)
  177. stats_summary_pos{k,1} = mean(avg_tMatrix(L_pos == k)); % (1) Mean of cluster
  178. if strcmp(data_structure,'bandavg')
  179. time_idx = find(L_pos == k); % Get cluster time indices
  180. stats_summary_pos{k,2} = time_idx; % (2) Cluster indices
  181. else
  182. [row_idx,col_idx] = find(L_pos == k); % Get 2D cluster indices
  183. stats_summary_pos{k,2} = [row_idx,col_idx]; % (2) Cluster indices
  184. end
  185. stats_summary_pos{k,3} = sum(t_matrix(L_pos == k)); % (3) Sum of t-values of cluster
  187. if ~isempty(permstat_correct) % Permutation correction
  189. abs_thresh = prctile(permstat_correct(:, 1), 100-(alpha/clusternum_tot)*100); % Bonferroni correct for number of clusters
  191. if abs(stats_summary_pos{k,3}) > abs_thresh % Cluster significant
  192. t_stat_sig(end+1) = stats_summary_pos{k,3}; % Sum t-values of cluster
  194. randgreater_count = length(find(permstat_correct(:,1) > abs(stats_summary_pos{k,3})));
  195. p_val_sig(end+1) = (randgreater_count+1) / (length(permstat_correct(:,1))+1);
  196. clusteridx_store{end+1} = stats_summary_pos{k,2};
  197. mean_tVal(end+1) = stats_summary_pos{k,1};
  198. end
  200. else % No correction
  201. disp('No correction, empty permstat_correct')
  202. end
  203. end
  205. % Negative clusters
  206. for k = 1:clusternum_neg % Analyze all clusters (numbered in L_neg)
  207. stats_summary_neg{k,1} = mean(avg_tMatrix(L_neg == k)); % (1) Mean of cluster
  208. if strcmp(data_structure,'bandavg')
  209. time_idx = find(L_neg == k); % Get cluster time indices
  210. stats_summary_neg{k,2} = time_idx; % (2) Cluster indices
  211. else
  212. [row_idx,col_idx] = find(L_neg == k); % Get 2D cluster indices
  213. stats_summary_neg{k,2} = [row_idx,col_idx]; % (2) Cluster indices
  214. end
  215. stats_summary_neg{k,3} = sum(t_matrix(L_neg == k)); % (3) Sum of t-values of cluster
  217. if ~isempty(permstat_correct) % Permutation correction
  219. abs_thresh = prctile(permstat_correct(:, 1), 100-(alpha/clusternum_tot)*100); % Bonferroni correct for number of clusters
  221. if abs(stats_summary_neg{k,3}) > abs_thresh % Cluster significant
  222. t_stat_sig(end+1) = stats_summary_neg{k,3}; % Sum t-values of cluster
  224. randgreater_count = length(find(permstat_correct(:,1) > abs(stats_summary_neg{k,3})));
  225. p_val_sig(end+1) = (randgreater_count+1) / (length(permstat_correct(:,1))+1);
  226. clusteridx_store{end+1} = stats_summary_neg{k,2};
  227. mean_tVal(end+1) = stats_summary_neg{k,1};
  228. end
  230. else % No correction
  231. disp('No correction, empty permstat_correct')
  232. end
  233. end
  235. % Sort clusters in order of significance
  236. [t_stat_sig_sort,sort_idx] = sort(abs(t_stat_sig),'descend');
  237. p_val_sig_sort = p_val_sig(sort_idx);
  238. sig_clusters = clusteridx_store(sort_idx);
  239. mean_tVal_sort = mean_tVal(sort_idx);
  241. if strcmp(data_structure,'timefreq')
  242. if strcmp(datatype,'Power')
  243. SaveName=[brain_rgn,'_',aligntype,'_',trialtype,'_Clusters_permutestref_Cecchi',savename_add];
  244. elseif strcmp(datatype,'Bursts')
  245. SaveName=[brain_rgn,'_Bursts_',aligntype,'_',trialtype,'_Clusters_permutestref_Cecchi',savename_add];
  246. end
  247. else
  248. if strcmp(datatype,'Power')
  249. SaveName=[powrange,'_',brain_rgn,'_',aligntype,'_',trialtype,'_Clusters_permutestref_Cecchi',savename_add];
  250. elseif strcmp(datatype,'Bursts')
  251. SaveName=[powrange,'_',brain_rgn,'_Bursts_',aligntype,'_',trialtype,'_Clusters_permutestref_Cecchi',savename_add];
  252. end
  253. end
  254. save(SaveName,'t_stat_sig_sort','p_val_sig_sort','sig_clusters','mean_tVal_sort','avg_tMatrix','clusternum_tot');
  256. end

VerbWM_SecondLevelClusters_permutestref_func.m at commit 63e6cc6, no license · at the source

Overview

Authors: Helen Qian1,2, Graham W. Johnson3,4, Natasha C. Hughes3,5, Astoria Chao3, Isabel C. Long2, Danika L. Paulo6, Zixiang Zhao2, Deeptha Subramanian2, Warren D. Taylor7,8, Kaltra Dhima9,10, Sarah K. Bick2,7,11
ORCID iDs: Helen Qian, Astoria Chao, Sarah K. Bick
  1. Harvard Medical School, Boston, Massachusetts 02115
  2. Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee 37212
  3. Vanderbilt University School of Medicine, Vanderbilt University, Nashville, Tennessee 37232
  4. Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota 55905
  5. Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts 02114
  6. Department of Neurosurgery, Henry Ford Hospital, Detroit, Michigan 48202
  7. Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee 37232
  8. Geriatric Research, Education and Clinical Center (GRECC), Tennessee Valley Healthcare System Veterans Administration, Nashville, Tennessee 37212
  9. Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee 37232
  10. Department of Psychiatry, UT Southwestern Medical Center, Dallas, Texas 75390
  11. Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee 37212
Institutions: Harvard University (United States); Vanderbilt University Medical Center (United States); Mayo Clinic (United States); Vanderbilt University (United States); Massachusetts General Hospital (United States); Henry Ford Hospital (United States); VA Tennessee Valley Healthcare System (United States); The University of Texas Southwestern Medical Center (United States)
Journal: eNeuro, volume 13, issue 7, pages ENEURO.0008-26.2026
Dates: received 6 January 2026; accepted 23 June 2026; published online 9 July 2026; in print July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1523/eneuro.0008-26.2026 · PMID 42386530 · PMCID PMC13364504 · OpenAlex W7166879189
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), intracranial EEG (iEEG / ECoG / SEEG) (modality), human (organism), other condition (population), Parkinson's (population), depression (population), systems (subfield)
Methods: Preprocessing, Statistics, Spectral & time-frequency, Connectivity
Keywords: corticostriatal, depression, essential tremor, intracranial EEG, Parkinson’s disease, reward
MeSH: Alpha Rhythm*, Beta Rhythm*, Caudate Nucleus*, Depression*, Dorsolateral Prefrontal Cortex*, Essential Tremor*, Parkinson Disease*, Reward*, Aged, Deep Brain Stimulation, Female, Humans, Male, Memory, Short-Term, Middle Aged, Neural Pathways, Psychiatric Status Rating Scales (* major topic)
Journal subjects: Research Article: New Research, Cognition and Behavior
Topic: Neurological disorders and treatments (Neurology, Medicine), according to OpenAlex
Funding: HHS | NIH | National Institute of Neurological Disorders and Stroke (NINDS) (NIH NINDS K12 NS080223, NIH NINDS K08 NS140767); SyBBURE Searle Undergraduate Research Program
Citations: not cited yet (Europe PMC); 59 references in the paper

Abstract

Depression is a common comorbidity in movement disorders such as Parkinson's disease (PD) and essential tremor (ET). Altered reward signaling contributes to core depression symptoms such as anhedonia, but the specific neural activity patterns underlying these processes and how they manifest in comorbid movement disorders are incompletely understood. Fourteen PD and 16 ET patients (22 male, 8 female) participated while undergoing deep brain stimulation surgery. Subjects completed a working memory task and received visual feedback about response accuracy while signals were recorded from traversed structures [caudate and/or dorsolateral prefrontal cortex (DLPFC)]. Preoperative Beck Depression Inventory-II (BDI-II) scores of ≥14 indicated elevated depression symptoms. Using cluster-based permutation testing, we identified time and frequency ranges in which oscillatory power significantly differed during reward versus loss feedback. We then used two-way ANOVAs and linear mixed effects models to assess how these power changes differed based on movement disorder and depression severity. Caudate and DLPFC alpha-beta (8–30 Hz) power increased during reward feedback. In both regions, this increase was attenuated in depressed subjects (caudate difference = −0.22, 95% CI = −0.32 to −0.13; DLPFC difference = −0.10, 95% CI = −0.16 to −0.045). BDI-II score was a negative predictor of reward- and loss-related corticostriatal alpha-beta power (caudate estimate = −0.014, 95% CI = −0.020 to −0.0078; DLPFC estimate = −0.0075, 95% CI = −0.012 to −0.0029). Specific to PD, depressed patients had greater decreases in DLPFC alpha-beta power following loss feedback than nondepressed patients (difference = −0.10, 95% CI = −0.17 to −0.027). Our findings suggest that altered corticostriatal alpha-beta power may contribute to reward dysfunction in depression in patients with movement disorders.

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.

qhelen/MD-Depression-Reward

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 63e6cc66e3584bd1558aed55d1f3968f9c509b56, 9 January 2026
Languages: MATLAB (9)
Size: 9 files, 9 scripts
Software Heritage: not archived
Found in: “Code accessibility”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (8 files), Violinplot-Matlab (3 files), SPM (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
9 files
At the source: github.com/qhelen/MD-Depression-Reward

Code accessibility

The code/software described in the paper is freely available online at https://github.com/qhelen/MD-Depression-Reward. The code is available as Extended Data. Code was run on a 2019 MacBook Pro and the Sonoma 14.1 operating system.

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

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;
  • 9 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);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 6 keywords, 17 MeSH terms, 2 funders, 59 references.

Cite

This paper

Qian, H., Johnson, G. W., Hughes, N. C., Chao, A., Long, I. C., Paulo, D. L., Zhao, Z., Subramanian, D., Taylor, W. D., Dhima, K., & Bick, S. K. (2026). Movement Disorder Patients with Depression Have Altered Corticostriatal Alpha-Beta Power Response to Reward and Loss. eNeuro, 13(7), ENEURO.0008-26.2026. https://doi.org/10.1523/eneuro.0008-26.2026

BibTeX

@article{qian2026movement,
author = {Qian, Helen and Johnson, Graham W. and Hughes, Natasha C. and Chao, Astoria and Long, Isabel C. and Paulo, Danika L. and Zhao, Zixiang and Subramanian, Deeptha and Taylor, Warren D. and Dhima, Kaltra and Bick, Sarah K.},
title = {{Movement Disorder Patients with Depression Have Altered Corticostriatal Alpha-Beta Power Response to Reward and Loss}},
journal = {eNeuro},
year = {2026},
month = jul,
volume = {13},
number = {7},
pages = {ENEURO.0008--26.2026},
publisher = {Society for Neuroscience},
issn = {2373-2822},
doi = {10.1523/eneuro.0008-26.2026},
url = {https://doi.org/10.1523/eneuro.0008-26.2026},
pmid = {42386530},
pmcid = {PMC13364504}
}

RIS

TY - JOUR
AU - Qian, Helen
AU - Johnson, Graham W.
AU - Hughes, Natasha C.
AU - Chao, Astoria
AU - Long, Isabel C.
AU - Paulo, Danika L.
AU - Zhao, Zixiang
AU - Subramanian, Deeptha
AU - Taylor, Warren D.
AU - Dhima, Kaltra
AU - Bick, Sarah K.
TI - Movement Disorder Patients with Depression Have Altered Corticostriatal Alpha-Beta Power Response to Reward and Loss
T2 - eNeuro
J2 - eNeuro
PY - 2026
DA - 2026/07/13
VL - 13
IS - 7
SP - ENEURO.0008
EP - 26.2026
SN - 2373-2822
PB - Society for Neuroscience
DO - 10.1523/eneuro.0008-26.2026
UR - https://doi.org/10.1523/eneuro.0008-26.2026
LA - en
ER -

CSL-JSON

{
"id": "10.1523/eneuro.0008-26.2026",
"type": "article-journal",
"title": "Movement Disorder Patients with Depression Have Altered Corticostriatal Alpha-Beta Power Response to Reward and Loss",
"container-title": "eNeuro",
"author": [
{
"family": "Qian",
"given": "Helen"
},
{
"family": "Johnson",
"given": "Graham W."
},
{
"family": "Hughes",
"given": "Natasha C."
},
{
"family": "Chao",
"given": "Astoria"
},
{
"family": "Long",
"given": "Isabel C."
},
{
"family": "Paulo",
"given": "Danika L."
},
{
"family": "Zhao",
"given": "Zixiang"
},
{
"family": "Subramanian",
"given": "Deeptha"
},
{
"family": "Taylor",
"given": "Warren D."
},
{
"family": "Dhima",
"given": "Kaltra"
},
{
"family": "Bick",
"given": "Sarah K."
}
],
"container-title-short": "eNeuro",
"volume": "13",
"issue": "7",
"page": "ENEURO.0008-26.2026",
"DOI": "10.1523/eneuro.0008-26.2026",
"PMID": "42386530",
"PMCID": "PMC13364504",
"ISSN": "2373-2822",
"publisher": "Society for Neuroscience",
"URL": "https://doi.org/10.1523/eneuro.0008-26.2026",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
13
]
]
}
}

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[8] doi:10.1162/imag.a.1229 [code]
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[9] doi:10.7554/elife.103689 [code]
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[10] doi:10.1038/s41467-026-75265-5 [code]
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