OSCR

Behavioural and clinical biomarkers of the human bed nucleus of stria terminalis from direct neural recordings.

Code ↔ Paper

7 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 7 matches
  1. [1] § Materials and methods › Data acquisition › Behavioural rating analysis and scatter plots › Cross-frequency coupling phase-amplitude coupling ↔ phase-amplitude-coupling-master/ModIndex_v1.m, lines 28–62 · score 0.82 · amplitude envelope, phase bins, phase amplitude coupling, amplitude frequency, phase frequency, quantifying
  2. [2] § Materials and methods › Data acquisition › Behavioural rating analysis and scatter plots › Task-evoked activity—event-related spectral perturbation ↔ pop_newtimef.m, lines 126–204 · score 0.69 · event related spectral, EEGLAB, FFT, perturbation, power, baseline
  3. [3] § Materials and methods › Data acquisition › Surgical procedure, electrode mapping and BNST localization ↔ affect_ratings_v1.m, lines 156–208 · score 0.67 · fixation cross, 1–1.5 s, neutral, stimulus, 1 s
  4. [4] § Materials and methods › Data acquisition › Behavioural rating analysis and scatter plots › Cross-frequency coupling phase-amplitude coupling ↔ phase-amplitude-coupling-master/eegfilt.m, lines 106–237 · score 0.61 · bandpass filtered, phase amplitude coupling, matrix, signals
  5. [5] § Materials and methods › Data acquisition › Data pre-processing and analysis ↔ phase-amplitude-coupling-master/eegfilt.m, lines 106–237 · score 0.58 · notch filtered, bandpass filtered, 0.1 Hz
  6. [6] § Materials and methods › Data acquisition › Experimental paradigm › Pain perception paradigm ↔ affect_ratings_v1.m, lines 50–83 · score 0.58 · 1–1.5 s, jittered, 1 s
  7. [7] § Materials and methods › Data acquisition › Data pre-processing and analysis ↔ affect_ratings_v1.m, lines 156–208 · score 0.53 · fixation cross, stimulus onset

Paper

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

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The authors' code

MATLAB · 290 lines · 9.8 KB · no license · 3 matches

  1. % BNST Affect Task paradigm
  2. %
  3. %
  4. % Saurabh Sonkusare, March 2021
  5. % University of Cambridge
  6. % Dept of Psychiatry
  8. sca; close all; clearvars;
  9. clc;
  10. Screen('Preference', 'SkipSyncTests', 1);
  11. %% definitions %%
  13. PsychDefaultSetup(2); % load default settings
  15. p = mfilename('fullpath'); i = strfind(p,'\');
  16. workdir = p(1:i(end));
  17. clear p i
  18. %% Screen Conditions
  19. screens = Screen('Screens'); % get the screen numbers
  20. screenNumber = max(screens); % select the maximum (possibly external)
  21. HideCursor;
  23. white = WhiteIndex(screenNumber);
  24. black = BlackIndex(screenNumber);
  26. pointspos = .9;
  28. lineWidthPix = 10; % line width for fixation cross
  29. fixCrossDimPix = 40; % size of fixation cross arms
  30. xCoords = [-fixCrossDimPix fixCrossDimPix 0 0];
  31. yCoords = [0 0 -fixCrossDimPix fixCrossDimPix];
  32. allCoords = [xCoords; yCoords]; % set coordinates for fixation cross
  34. heightScalers = .75; % set image height to fraction of screen height
  36. wrapatLong = 60; % set max. width for text
  37. vSpacing = 1.7; % set line spacing for text
  39. Screen('Preference', 'DefaultFontSize', 40); % default font size = 40 pt
  41. rect = Screen('Rect',screenNumber);
  42. screenRatio = rect(3)/rect(4);
  43. pixelSizes = Screen('PixelSizes', 0);
  44. startPosition = round([rect(3)/2, rect(4)/2]);
  46. %% Enter the details of the subject and retrieve the image
  47. subj_num = input('Enter the subject number\n');
  48. name = input('Subjects initials: ','s');
  49. % stimfreq= input('enter stimulation frequency');
  50. %% Blocks, jitters and trials
  51. totaltrials= 135;
  52. flag=0;
  54. % Generating ITI between 1-1.5s
  55. iti=linspace(1,1.5,totaltrials);
  56. iti=iti(randperm(totaltrials));
  58. % Variable initialization for ratings
  59. pos_Valence = 1;% Variable that keep track of valence in each condition
  60. pos_Arousal=1; % Variable that keep track of arousal in each condition
  62. neg_Valence=1;
  63. neg_Arousal=1;
  65. neu_Valence=1;
  66. neu_Arousal=1;
  68. % Variables for storing data of ratings and RT
  69. neg_ns_response_valence=[];neu_ns_response_valence=[];neg_s1_response_valence=[];neu_s1_response_valence=[];
  70. neg_ns_RT_all_valence=[];neu_ns_RT_all_valence=[];neg_s1_RT_all_valence=[];neu_s1_RT_all_valence=[];
  72. neg_ns_response_Arousal=[];neu_ns_response_Arousal=[];neg_s1_response_Arousal=[];neu_s1_response_Arousal=[];
  73. neg_ns_RT_all_Arousal=[];neu_ns_RT_all_Arousal=[];neg_s1_RT_all_Arousal=[];neu_s1_RT_all_Arousal=[];
  75. % Trigger numbers per condition
  76. %1= negative nostim
  77. %2= neutral no stim
  78. %3 = negative stim1
  79. %5= ITI normal
  80. %64= valence
  81. %65= arousal
  82. %33= stim on image
  83. %0 = stim off
  85. %% finding the dimensions of the image
  86. stim1 = imread('1.jpg'); % stimulus
  87. [s1, s2, s3] = size(stim1); % get size of one stimulus (all stimuli have same size in this experiment)
  89. aspectRatio = s2/s1; % get aspect ratio for stimuli so it doesn't appear warped / stretched
  91. [window, windowRect] = Screen('OpenWindow', screenNumber, black); % black background window
  92. [screenXpixels, screenYpixels] = Screen('WindowSize', window); % get size of on screen window
  93. [xCenter, yCenter] = RectCenter(windowRect); % get centre of screen
  94. Screen('BlendFunction', window, 'GL_SRC_ALPHA', 'GL_ONE_MINUS_SRC_ALPHA'); % set up alpha-blending for smooth (anti-aliased) lines
  96. imageHeights = screenYpixels .* heightScalers;
  97. imageWidths = imageHeights .* aspectRatio;
  99. centralRect1 = CenterRectOnPointd([0 0 imageWidths imageHeights], xCenter, yCenter); % for fruit
  102. %% start instructions %%
  103. % line1 = '\n you will be shown a series of Images. Sometimes, you will be asked to rate the valence and sometimes arousal level.';
  104. % line2 = '\n\n Please use the mouse to rate it on the slider.';
  105. % line3 = '\n Press any key to start the session';
  106. % DrawFormattedText(window, [line1 line2 line3], 'center', 'center', [255 255 255], wrapatLong, [], [], vSpacing);
  108. % line1 = '\n If High frequency = 160 Hz.';
  109. % line2 = '\n\n If alpha burst mode settings = 100Hz with 25ms ON and 75ms OFF.';
  110. % % line3 = '\n Press any key to start the session';
  111. % DrawFormattedText(window, [line1 line2], 'center', 'center', [255 255 255], wrapatLong, [], [], vSpacing);
  112. % Screen('Flip', window);
  113. % KbStrokeWait;
  115. instruct= imread('INSTRUCTION.jpg');
  116. i1=Screen('MakeTexture',window,instruct);
  117. Screen('DrawTexture',window,i1,[],[],0);
  118. Screen('Flip', window);
  119. KbStrokeWait;
  121. % %% Trigger
  122. object=io64; % put this at the start of the script.
  123. status=io64(object);
  124. address=hex2dec('3FF8');
  127. % ---------------------Randomizing the images--------------------%
  128. image_num = randperm(totaltrials);
  130. %-------- Rating trials for valence and arousal ---------%
  131. valence_trials=[];
  132. arousal_trials=[];
  134. % Defining valence and arousal trial numbers randomly
  135. neg_temp_trials=91:135;
  136. neg_temp_trials= Shuffle(neg_temp_trials);
  138. neu_temp_trials=46:90;
  139. neu_temp_trials= Shuffle(neu_temp_trials);
  141. pos_temp_trials=1:45;
  142. pos_temp_trials= Shuffle(pos_temp_trials);
  146. if rem(subj_num,2)== 0
  147. valence_trials= [neg_temp_trials(1:5) neu_temp_trials(1:5) pos_temp_trials(1:5)];
  148. arousal_trials = [neg_temp_trials(6:10) neu_temp_trials(6:10) pos_temp_trials(6:10)];
  149. else
  150. arousal_trials= [neg_temp_trials(1:5) neu_temp_trials(1:5) pos_temp_trials(1:5)];
  151. valence_trials = [neg_temp_trials(6:10) neu_temp_trials(6:10) pos_temp_trials(6:10)];
  152. end
  154. trig_array=[];
  155. iti_condt_all=[];
  156. %% load images and define default screen %%
  157. % Images type and stimulation specification
  158. % No stim neg images= 1:30
  159. % No stim neu images= 31:60
  160. % Stim1 neg images= 61:75
  161. % Stim1 neu images= 76:90
  163. for condt= 1:totaltrials
  165. %-------------------------- Image presentation-----------%
  166. image = image_num(condt);
  168. %------------ Determing the condition,trigger value based on image type------------------------%
  169. if image>=1 && image<=45
  170. condt_temp=1;
  171. trig = 1; % Trigger for pos image nostim condition
  173. elseif image>=46 && image<=90
  174. condt_temp=2;
  175. trig = 2; % Trigger for neutral image nostim condition
  177. else
  178. condt_temp=3;
  179. trig = 3; % Trigger for negative nostim condition
  181. end
  184. %----------------------Display the ITI with fixation-----------%
  186. Screen('DrawLines', window, allCoords, lineWidthPix, [255 255 255], [xCenter yCenter], 2); % draw fixation cross
  187. Screen('Flip', window);
  189. % Trigger
  190. io64(object,address,5);
  191. WaitSecs(iti(condt)); % for 1-1.5 s
  192. io64(object,address,0); % Triggers stimulation off
  196. %-----------------------------Presenting the image-----------------------------%
  197. temp= sprintf('%d.jpg',image);
  198. [stim1,map1] = imread(temp); % stimulus
  199. temp2 = imresize(stim1,0.3);
  201. stim1Texture = Screen('MakeTexture', window, stim1);
  202. Screen('DrawTexture', window, stim1Texture,[], centralRect1); %show 1st stim
  203. StimulusOnsetTime = Screen('Flip', window);
  205. % % Trigger
  206. io64(object,address,trig);
  207. WaitSecs(2); % for 2s
  208. io64(object,address,0); % Triggers stimulation off
  214. %% -------- Define when to rate based on valence and arousal-----%
  216. if ismember(image,valence_trials)
  218. %-------Valence Question-------%
  219. question = 'How Positive or Negative is this image';
  220. endPoints = {'Very Negative','Very Positive'};
  222. [position, RT, answer] = slideScale(window, question, rect, endPoints, 'device', 'mouse', 'scalaposition', 0.9, 'startposition', 'center', 'displayposition', true, 'range', 2,'linelength',50,'scalaposition',0.5,'scalacolor',[255 0 255],'width',10,'aborttime',60,'image',temp2);
  224. %%Trigger
  225. io64(object,address,64);
  226. WaitSecs(0.001); %
  227. io64(object,address,0);
  228. %
  229. % Segrating valence raings and RT based on the condition
  230. if image>=1 && image<=45
  231. pos_response_valence(pos_Valence)= position;
  232. pos_RT_all_valence(pos_Valence)=RT;
  233. pos_Valence=pos_Valence+1;
  234. elseif image>=46 && image<=90
  235. neu_response_valence(neu_Valence)= position;
  236. neu_RT_all_valence(neu_Valence)=RT;
  237. neu_Valence=neu_Valence+1;
  238. else
  239. neg_response_valence(neg_Valence)= position;
  240. neg_RT_all_valence(neg_Valence)=RT;
  241. neg_Valence=neg_Valence+1;
  242. end
  244. position=[]; RT=[];
  246. elseif ismember(image,arousal_trials)
  247. %-------Arousal Question-------%
  249. question = 'How exciting is this image';
  250. endPoints = {'Not Exciting','Very Exciting'};
  252. [position, RT, answer] = slideScale(window, question, rect, endPoints, 'device', 'mouse', 'scalaposition', 0.9, 'startposition', 'center', 'displayposition', true, 'range', 2,'linelength',50,'scalaposition',0.5,'scalacolor',[0 0 255],'width',10,'aborttime',60,'image',temp2);
  254. %%Trigger
  255. io64(object,address,65);
  256. WaitSecs(0.001);
  257. io64(object,address,0);
  259. % Segrating arousal raings and RT based on the condition
  260. if image>=1 && image<=45
  261. pos_response_Arousal(pos_Arousal)= position;
  262. pos_RT_all_Arousal(pos_Arousal)=RT;
  263. pos_Arousal=pos_Arousal+1;
  265. elseif image>=46 && image<=90
  266. neu_response_Arousal(neu_Arousal)= position;
  267. neu_RT_all_Arousal(neu_Arousal)=RT;
  268. neu_Arousal=neu_Arousal+1;
  270. else
  271. neg_response_Arousal(neg_Arousal)= position;
  272. neg_RT_all_Arousal(neg_Arousal)=RT;
  273. neg_Arousal=neg_Arousal+1;
  274. end
  275. position=[]; RT=[];
  278. end
  282. end
  284. %% save data %%
  285. mkdir(sprintf(name));
  286. cd(name);
  287. save(sprintf('Affect_Ratings_subj_%s_number_%d.mat',name,subj_num)); %
  289. sca; % clear the screen

affect_ratings_v1.m at commit 5b6bec3, no license · at the source

Overview

Authors: Saurabh Sonkusare1,2,3, Yingying Zhang3, Qiong Ding1,2, Yashu Feng1, Yijie Zhao3, Linbin Wang3, Violeta Casero2, Kuanghao Ye1, Yijie Lai1, Xin Lv1, Peng Huang1, Xian Qiu1, Luling Dai1, Xiaoxiao Zhang1, Yuhan Wang1, Kejia Hu1, Yixin Pan1, Dianyou Li1, Wei Liu3, Shikun Zhan1, Bomin Sun1, Valerie Voon1,2,3
ORCID iDs: Saurabh Sonkusare, Yijie Zhao, Dianyou Li
  1. Department of Neurosurgery, Centre for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
  2. Department of Psychiatry, Addenbrookes Hospital, University of Cambridge, Cambridge CB2 0QQ, UK
  3. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
Institutions: Shanghai Jiao Tong University (China); University of Cambridge (United Kingdom); Fudan University (China); Ruijin Hospital (China); Addenbrooke's Hospital (United Kingdom)
Journal: Brain : a journal of neurology, volume 149, issue 7, pages 2308-2322
Dates: received 15 February 2025; accepted 17 October 2025; published online 14 November 2025; in print July 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1093/brain/awaf431 · PMID 41234186 · PMCID PMC13337240 · OpenAlex W4416231039
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), other condition (population), depression (population), pain (population)
Methods: Spectral & time-frequency, Preprocessing, Statistics, fMRI & imaging, Connectivity
Keywords: BNST, deep brain stimulation, emotion, pain perception, depression, anxiety
MeSH: Pain Perception*, Septal Nuclei*, Adult, Anxiety, Biomarkers, Cross-Sectional Studies, Deep Brain Stimulation, Depression, Emotions, Female, Humans, Male, Middle Aged (* major topic)
Topic: Neurological disorders and treatments (Neurology, Medicine), according to OpenAlex
Funding: NIHR Applied Research Centre; University of Cambridge; SJTU Trans-med Awards Research (20190105); Medical Research Council Senior Clinical (MR/P008747/1, MR/W020408/1); Shanghai Clinical Research Centre for Mental Health (19MC1911100); NIHR Cambridge Biomedical Research Centre (BRC-1215-20014)
Citations: cited by 2 papers (Europe PMC); 91 references in the paper

Abstract

Identifying novel neuromodulatory targets for deep brain stimulation (DBS) in psychiatric disorders is an urgent clinical need. Equally critical is the discovery of simple oscillatory biomarkers that bridge behaviour and clinical symptoms, enabling personalized treatment strategies. The bed nucleus of the stria terminalis (BNST), a pivotal output structure of the amygdala, is a potential candidate for DBS due to its key role in regulating fear, emotional valence and prosocial behaviour. However, owing to the small size, its neural dynamics and functional contributions are poorly understood, precluding behavioural-clinical relevance.

In a cross-sectional design, we acquired BNST neural recordings from 23 patients with depression undergoing DBS during two tasks: pain perception with painful/non-painful scenarios and an affect task with emotionally valenced images. We first localized the electrode contacts in the BNST and using their neural recordings for further analysis. We subjected the preprocessed data to time-frequency decompositions to find condition differences. The significant clusters were then used to link to the behavioural ratings and clinical symptom severity. Furthermore, cross-frequency interactions were undertaken.

Pain perception elicited late theta and alpha activity (∼1 s), with theta activity linked to subjective pain ratings and alpha correlating with anxiety/depression scores and anxiety symptoms post-DBS. Negative imagery induced early theta (∼250 ms), resembling previously reported amygdalar responses, and which link to valence ratings, depression and anxiety symptom severity.

These results reveal distinct BNST dynamics in depression: early theta for rapid threat processing and late theta/alpha for complex socio-cognitive responses. Task-dependent theta and alpha activity linked behavioural profiles and symptom severity, highlighting BNST’s role in behaviourally and clinically relevant oscillatory patterns, contributing novel insights for advancing precision neuromodulation strategies.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

srbsonkusare/bnst_task_dynamics

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 5b6bec31d17ab3c571c2510db5197d8954a07655, 12 November 2024
Languages: MATLAB (8)
Size: 11 files, 8 scripts
Software Heritage: not checked
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (3 files), EEGLAB (2 files), Psychtoolbox (2 files), Image Processing Toolbox (1 file), Statistics and Machine Learning Toolbox (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/srbsonkusare/BNST_task_dynamics/tree/main

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

The data for this project were acquired from patients undergoing clinical care and consenting to additional research protocols. Researchers wishing to access these data will require local ethics approval and a data sharing agreement with Ruijin Hospital, Shanghai, China. Open-source toolboxes have been used for analyses of this study. Specific toolboxes and code are made available on github https://github.com/srbsonkusare/BNST_task_dynamics/tree/main.

Reproduced under the paper's license (CC BY-NC), 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, 22 authors, 6 keywords, 13 MeSH terms, 6 funders, 86 references.

Cite

This paper

Sonkusare, S., Zhang, Y., Ding, Q., Feng, Y., Zhao, Y., Wang, L., Casero, V., Ye, K., Lai, Y., Lv, X., Huang, P., Qiu, X., Dai, L., Zhang, X., Wang, Y., Hu, K., Pan, Y., Li, D., Liu, W., . . . Voon, V. (2026). Behavioural and clinical biomarkers of the human bed nucleus of stria terminalis from direct neural recordings. Brain : a journal of neurology, 149(7), 2308-2322. https://doi.org/10.1093/brain/awaf431

BibTeX

@article{sonkusare2026behavioural,
author = {Sonkusare, Saurabh and Zhang, Yingying and Ding, Qiong and Feng, Yashu and Zhao, Yijie and Wang, Linbin and Casero, Violeta and Ye, Kuanghao and Lai, Yijie and Lv, Xin and Huang, Peng and Qiu, Xian and Dai, Luling and Zhang, Xiaoxiao and Wang, Yuhan and Hu, Kejia and Pan, Yixin and Li, Dianyou and Liu, Wei and Zhan, Shikun and Sun, Bomin and Voon, Valerie},
title = {{Behavioural and clinical biomarkers of the human bed nucleus of stria terminalis from direct neural recordings}},
journal = {Brain : a journal of neurology},
year = {2026},
month = jul,
volume = {149},
number = {7},
pages = {2308--2322},
publisher = {Oxford University Press},
issn = {0006-8950},
doi = {10.1093/brain/awaf431},
url = {https://doi.org/10.1093/brain/awaf431},
pmid = {41234186},
pmcid = {PMC13337240}
}

RIS

TY - JOUR
AU - Sonkusare, Saurabh
AU - Zhang, Yingying
AU - Ding, Qiong
AU - Feng, Yashu
AU - Zhao, Yijie
AU - Wang, Linbin
AU - Casero, Violeta
AU - Ye, Kuanghao
AU - Lai, Yijie
AU - Lv, Xin
AU - Huang, Peng
AU - Qiu, Xian
AU - Dai, Luling
AU - Zhang, Xiaoxiao
AU - Wang, Yuhan
AU - Hu, Kejia
AU - Pan, Yixin
AU - Li, Dianyou
AU - Liu, Wei
AU - Zhan, Shikun
AU - Sun, Bomin
AU - Voon, Valerie
TI - Behavioural and clinical biomarkers of the human bed nucleus of stria terminalis from direct neural recordings
T2 - Brain : a journal of neurology
J2 - Brain
PY - 2026
DA - 2026/07/01
VL - 149
IS - 7
SP - 2308
EP - 2322
SN - 0006-8950
PB - Oxford University Press
DO - 10.1093/brain/awaf431
UR - https://doi.org/10.1093/brain/awaf431
LA - en
ER -

CSL-JSON

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"id": "10.1093/brain/awaf431",
"type": "article-journal",
"title": "Behavioural and clinical biomarkers of the human bed nucleus of stria terminalis from direct neural recordings",
"container-title": "Brain : a journal of neurology",
"author": [
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{
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{
"family": "Feng",
"given": "Yashu"
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{
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