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Castration-induced nigrostriatal deficits are linked to reduced TrkB and loss of mature spines in the dorsal striatum.

Code ↔ Paper

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  1. [1] § Materials and methods › Behavior › Open field ↔ bsoid/bsoid_mt.m, the whole file · a weak match · score 0.93 · left hind paw, right hind paw, left forepaw, right forepaw, DeepLabCut, Open field
  2. [2] § Materials and methods › Behavior › Open field ↔ bsoid/bsoid_assign.m, lines 1–68 · score 0.84 · Behavioral Segmentation, hind paw, DeepLabCut, Open field, track, tail
  3. [3] § Results › Motor behavior in a castration-based model of PD ↔ analyses/getkin.m, the whole file · a weak match · score 0.59 · peak speed, bout duration, forelimb, pixels, tracking, distance
  4. [4] § Results › Motor behavior in a castration-based model of PD ↔ bsoid/bsoid_mt.m, the whole file · a weak match · score 0.50 · Open field, Conversely, pixels, locomotive, tracking, cross

Paper

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The authors' code

MATLAB · 126 lines · 9.3 KB · GPL-3.0 · 2 matches

  1. function [g_num,perc_unk,trans_p] = bsoid_mt(data,fps,pix_cm,smth_hstry,smth_futr,min_trans) % data{m}, pixel/cm
  2. %BSOID_MT Classifies 3D pose estimation (DeepLabCut) output for rodent open field behavior using manual thresholding criteria.
  3. %
  4. % [G_LABEL,G_NUM,PERC_UNK] = BSOID_MT(DATA,PIX_CM) outputs classified behaviors based on DeepLabCut analysis
  5. %
  6. % INPUTS:
  7. % DATA A cell-array containing either one or multiple rodents' estimated positions of the 6-body parts (snout, 4 paws, and base of tail)
  8. % over time recorded from the bottom-up perspective. Rows represent chronological frame numbers. Columns represent x and y coordinates of
  9. % individual body parts as follows:
  10. % Columns 1 & 2 tracks snout; columns 3 to 6 tracks the two front paws; columns 7 to 10 tracks the two hind paws;
  11. % columns 11 & 12 tracks the base of the tail. Tested on tracking data generated by DeepLabCut 2.0.
  12. % FPS Rounded frame rate, can use VideoReader/ffmpeg(linux command) to automatically detect the input video fps.
  13. % PIX_CM Pixel-to-cm conversion to adapt our manual thresholds to the user's set-up.
  14. % SMTH_HSTRY BOXCAR smoothing using number of frames from before. Default ~40ms before.
  15. % SMTH_FUTR BOXCAR smoothing using number of frames from after. Default ~40ms after.
  16. % MIN_TRANS Minimum number of frames for transition to occur. Default ~100ms.
  17. %
  18. % G_NUM Classified behavioral state, numbered.
  19. % PERC_UNK Percentage of total number of frames that are undefined.
  20. % TRANS_P Transition probability matrix with Markov assumption (rows as current, columns as next).
  21. %
  22. % Created by Alexander Hsu, Date: 051519
  23. % Contact [email hidden]
  25. if nargin < 3
  26. error('Please input dataset, frame rate, & pixel-to-cm conversion!')
  27. end
  28. if nargin < 4
  29. smth_hstry = round(0.05/(1/fps));
  30. smth_futr = round(0.05/(1/fps));
  31. end
  32. if nargin < 6
  33. min_trans = round(0.1/(1/fps))-1;
  34. end
  35. fprintf('Obtaining features from dataset... \n');
  36. for m = 1:length(data) % For each csv file you uploaded.
  37. data{m} = data{m}*24/pix_cm; % With our zoom and resolution, our threshold was set according to the ~24 pixel/cm set-up.
  38. %% Obtain features
  39. clear fpd_norm cfp_pt_norm sn_pt_norm sn_pt_ang sn_disp pt_disp hpR_disp hpL_disp fpR_disp fpL_disp;
  40. cfp = [mean([data{m}(:,3),data{m}(:,5)],2),mean([data{m}(:,4),data{m}(:,6)],2)]; % Center of front paws x,y
  41. cfp_pt = [cfp(:,1) - data{m}(:,11), cfp(:,2) - data{m}(:,12)]; % Center of front paw to proximal tail x,y
  42. sn_pt = [data{m}(:,1) - data{m}(:,11), data{m}(:,2) - data{m}(:,12)]; % Snout to proximal tail x,y
  43. for i = 1:length(data{m}) % Euclidean distance of x,y, since position means nothing
  44. fpd_norm(i) = norm(data{m}(i,3:4)-data{m}(i,5:6)); % Front paw R to L euclidean distance
  45. cfp_pt_norm(i) = norm(cfp_pt(i,:)); % Center of front paws to proximal tail euclidean distance
  46. sn_pt_norm(i) = norm(sn_pt(i,:)); % Snout to proximal tail euclidean distance, i.e. body length
  47. end
  48. fpd_norm_smth{m} = movmean(fpd_norm,[smth_hstry,smth_futr]); % Reduce label noise
  49. sn_cfp_norm_smth{m} = movmean(sn_pt_norm-cfp_pt_norm,[smth_hstry,smth_futr]); % Reduce label noise
  50. sn_pt_norm_smth{m} = movmean(sn_pt_norm,[smth_hstry,smth_futr]); % Reduce label noise
  51. for k = 1:length(data{m})-1 % Velocity and angle over time
  52. b_3d = [sn_pt(k+1,:),0]; a_3d = [sn_pt(k,:),0]; c = cross(b_3d,a_3d);
  53. sn_pt_ang(k) = sign(c(3))*180/pi*atan2(norm(c),dot(sn_pt(k,:),sn_pt(k+1,:))); % Body angle, arctan between body
  54. sn_disp(k) = norm(data{m}(k+1,1:2)-data{m}(k,1:2)); % Snout displacement over time
  55. pt_disp(k) = norm(data{m}(k+1,11:12)-data{m}(k,11:12)); % Proximal tail displacement over time
  56. hpR_disp(k) = norm(data{m}(k+1,7:8)-data{m}(k,7:8)); % Right hind paw displacement over time
  57. hpL_disp(k) = norm(data{m}(k+1,9:10)-data{m}(k,9:10)); % Left hind paw displacement over time
  58. fpR_disp(k) = norm(data{m}(k+1,3:4)-data{m}(k,3:4)); % Right forepaw displacement over time
  59. fpL_disp(k) = norm(data{m}(k+1,5:6)-data{m}(k,5:6)); % Left forepaw displacement over time
  60. end
  61. sn_pt_ang_smth{m} = movmean(sn_pt_ang,[smth_hstry,smth_futr]); % Reduce label noise
  62. sn_disp_smth{m} = movmean(sn_disp,[smth_hstry,smth_futr]); % Reduce label noise
  63. pt_disp_smth{m} = movmean(pt_disp,[smth_hstry,smth_futr]); % Reduce label noise
  64. hpR_disp_smth{m} = movmean(hpR_disp,[smth_hstry,smth_futr]); % Reduce label noise
  65. hpL_disp_smth{m} = movmean(hpL_disp,[smth_hstry,smth_futr]); % Reduce label noise
  66. fpR_disp_smth{m} = movmean(fpR_disp,[smth_hstry,smth_futr]); % Reduce label noise
  67. fpL_disp_smth{m} = movmean(fpL_disp,[smth_hstry,smth_futr]); % Reduce label noise
  68. %% Classify action based on parameters specified above
  69. %%% Rest/Pause is defined as minimal change in body angle, and minimal displacement of all labels.
  70. idx_rest{m} = find(abs(sn_pt_ang_smth{m}) < 1 & sn_disp_smth{m} > 0 & sn_disp_smth{m} < 2 & pt_disp_smth{m} < 2 & ...
  71. fpR_disp_smth{m} < 2 & fpL_disp_smth{m} < 2 & sn_cfp_norm_smth{m}(:,2:end) > 0);
  72. %%% Rear is defined as either snout is gone, front paws are gone (reared up), or front paws coming together (rearing up).
  73. idx_rear{m} = find(sn_disp_smth{m} == 0 & abs(sn_pt_ang_smth{m}) < 1 | ...
  74. fpR_disp_smth{m} == 0 & abs(sn_pt_ang_smth{m}) < 1 | ...
  75. fpL_disp_smth{m} == 0 & abs(sn_pt_ang_smth{m}) < 1 | ...
  76. sn_disp_smth{m} > 2 & abs(fpR_disp_smth{m}-fpL_disp_smth{m}) < 5 & sn_cfp_norm_smth{m}(:,2:end) > 0 & ...
  77. fpd_norm_smth{m}(:,2:end) < 20 & pt_disp_smth{m} >= 2 & fpR_disp_smth{m} >= 2 & fpL_disp_smth{m} >= 2);
  78. %%% Groom is defined as either snout invisible, front paws visible and close, and no change in angle or tail position
  79. %%%% snout is visible, front paws in/visible and far, hands are in front of snout, and no change in angle or tail position
  80. %%%% or snout visibly moving, both front paws visible, one of which moves, and no change in angle or tail position
  81. idx_groom{m} = find(sn_disp_smth{m} == 0 & fpd_norm_smth{m}(:,2:end) < 10 & fpR_disp_smth{m} >= 2 & abs(sn_pt_ang_smth{m}) < 1 & ...
  82. pt_disp_smth{m} < 2 | sn_disp_smth{m} == 0 & fpd_norm_smth{m}(:,2:end) < 10 & fpL_disp_smth{m} >= 2 & ...
  83. abs(sn_pt_ang_smth{m}) < 1 & pt_disp_smth{m} < 2 | ...
  84. sn_disp_smth{m} > 0 & fpR_disp_smth{m} >= 2 & abs(sn_pt_ang_smth{m}) < 1 & hpR_disp_smth{m} < 2 & hpL_disp_smth{m} < 2 & pt_disp_smth{m} < 2 | ...
  85. sn_disp_smth{m} > 0 & fpL_disp_smth{m} >= 2 & abs(sn_pt_ang_smth{m}) < 1 & hpR_disp_smth{m} < 2 & hpL_disp_smth{m} < 2 & pt_disp_smth{m} < 2 | ...
  86. sn_disp_smth{m} > 0 & abs(sn_pt_ang_smth{m}) < 1 & sn_cfp_norm_smth{m}(:,2:end) < 0 & pt_disp_smth{m} < 2 | ...
  87. sn_disp_smth{m} >= 2 & fpR_disp_smth{m} > 0 & fpL_disp_smth{m} > 0 & abs(sn_pt_ang_smth{m}) < 1 & pt_disp_smth{m} < 2);
  88. %%% Orientation of body is defined as non-locomoting body angle change
  89. idx_ori_right{m} = find(pt_disp_smth{m} < 2 & sn_pt_ang_smth{m} >= 1);
  90. idx_ori_left{m} = find(pt_disp_smth{m} < 2 & sn_pt_ang_smth{m} <= -1);
  91. %%% Locomotion is defined as body displacement with either front paw
  92. idx_loc{m} = find(pt_disp_smth{m} >= 2 & fpR_disp_smth{m} >= 2 | pt_disp_smth{m} >= 2 & fpL_disp_smth{m} >= 2 | ...
  93. pt_disp_smth{m} >= 2 & hpR_disp_smth{m} >= 2 | pt_disp_smth{m} >= 2 & hpL_disp_smth{m} >= 2);
  94. %%% Head movement is defined as only snout movement with stationary body, and no change in angle or tail position
  95. idx_headmov{m} = find(abs(sn_pt_ang_smth{m}) < 1 & sn_disp_smth{m} >= 2 & hpR_disp_smth{m} < 2 & hpL_disp_smth{m} < 2);
  96. %% Low pass filter of unreasonable fast state changes
  97. MC = []; MC(:) = 0; MC(idx_rest{m},1) = 1; MC(idx_loc{m},1) = 5; MC(idx_ori_left{m},1) = 6; MC(idx_ori_right{m},1) = 7;
  98. MC(idx_rear{m},1) = 2; MC(idx_groom{m},1) = 3; MC(idx_headmov{m},1) = 4;
  99. stch = find(diff(MC)~=0); % Remove short transitions
  100. for i = 1:length(stch)-1
  101. if stch(i+1) - stch(i) <= min_trans
  102. MC(stch(i):stch(i+1)) = MC(stch(i));
  103. end
  104. end
  105. %% Reassign sandwiched unidentified behavior to prior and posterior, if the same
  106. for k = 1:length(stch)-2
  107. if MC(stch(k+1)) == 0 && MC(stch(k)) == MC(stch(k+2))
  108. MC(stch(k):stch(k+2)) = MC(stch(k));
  109. end
  110. end
  111. g_num{m} = MC; % Store behavior per .csv
  112. %% Pull out transitional probability
  113. perc_unk{m} = length(find(g_num{m}==0))/(length(data{m})-1); % Undefined percentage
  114. binEdges = 1:length(MC); [~,states] = histc(nonzeros(MC),binEdges); % Histc just asks which bin each pose belongs to
  115. binTicks = binEdges(1:end-1); % Don't need the last bin edge
  116. ActMat = zeros(length(unique(MC))-1); fromBinNos = states(1:end-1); nextBinNos = states(2:end);
  117. for i = 1:length(fromBinNos)
  118. ActMat(fromBinNos(i),nextBinNos(i)) = ActMat(fromBinNos(i),nextBinNos(i)) + 1;
  119. end
  120. for j = 1:length(unique(MC))-1
  121. trans_p{m}(j,:) = ActMat(j,:)/sum(ActMat(j,:));
  122. end
  123. end
  125. return

bsoid_mt.m at commit a19b935, under GPL-3.0 · at the source

Overview

Authors: Gerald J Donahue1,2, James A Lundari2, Patrick W Kane1,2, Samantha M Matamorose-Patrick3, Julia E Klotz3, Nicholas L Carr1,2, Stephen C Kudriavetz2, Joe C Brague1,2
  1. Neuroscience Program, University of Scranton, Scranton, PA, United States
  2. Department of Biology, University of Scranton, Scranton, PA, United States
  3. Department of Chemistry, University of Scranton, Scranton PA, United States
Institutions: University of Scranton (United States)
Journal: Frontiers in endocrinology, volume 17, article 1828487
Dates: received 11 March 2026; accepted 14 May 2026; published online 3 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fendo.2026.1828487 · PMID 42318211 · PMCID PMC13272008 · OpenAlex W7163385326
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality), mouse (organism), Parkinson's (population), cellular / molecular (subfield)
Methods: Statistics, Physiology & signal measures
Keywords: castration, dendritic spines, Parkinson’s disease, testosterone, TrkB
MeSH: Castration*, Corpus Striatum*, Dendritic Spines*, Membrane Glycoproteins*, Orchiectomy*, Receptor, trkB*, Substantia Nigra*, Animals, Brain-Derived Neurotrophic Factor, Dopaminergic Neurons, Male, Mice, Mice, Inbred C57BL (* major topic)
Topic: Parkinson's Disease Mechanisms and Treatments (Neurology, Medicine), according to OpenAlex
Funding: National Science Foundation (2406615)
Citations: not cited yet (Europe PMC); 44 references in the paper
Research resources: 1:1000 mouse α-ERK2 RRID:AB_1122622, RRID:AB_2313584, 000 Donkey α-mouse HRP RRID:AB_2340770, RRID:AB_2492288, 000 mouse α-GAPDH RRID:AB_627679, RRID:AB_628422

Abstract

Parkinson’s disease is a neurodegenerative disorder characterized by degeneration of nigrostriatal dopamine neurons and basal ganglia dysfunction, with sex differences in risk and progression that are not yet fully understood. Here, we investigated how peripubertal loss of gonadal hormones influences nigrostriatal integrity, striatal synaptic architecture, and motor behavior in male mice. Using a castration model, we show that early androgen deprivation recapitulates key neuropathological and behavioral features of Parkinsonian hypodopaminergia, including reduced tyrosine hydroxylase–positive neurons in the substantia nigra pars compacta, impaired motor performance on the vertical pole and rotarod, and disrupted forelimb kinematics. At the circuit level, androgen loss markedly reduced mushroom spine density on both direct- and indirect pathway striatal spiny projection neurons, with a concomitant increase in immature spine classes, consistent with impaired synaptic maturation. These structural and behavioral deficits were accompanied by reduced striatal Tyrosine receptor kinase-B (TrkB) protein, suggesting attenuation of brain derived neurotrophic factor (BDNF)/TrkB trophic signaling. Testosterone replacement preserved nigral dopaminergic neurons, normalized motor behavior, prevented mushroom spine loss across both striatal pathways, and selectively increased long/thin spines on direct-pathway neurons, suggesting enhanced synaptic plasticity. Together, these findings support a hormone-trophic model in which androgen signaling maintains nigrostriatal integrity and striatal synaptic maturation through androgen receptor-dependent BDNF/TrkB mechanisms. Although Parkinson’s disease predominantly manifests with aging, these results suggest that early disruption of androgen signaling can produce lasting circuit-level alterations that influence vulnerability to degeneration over time, providing mechanistic insight into sex differences in Parkinson’s Disease.

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

Repository

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YttriLab/B-SOID

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: a19b935142ac75d4a2991fbde1f9211df8f69e43, 9 February 2023
Languages: Python (100), MATLAB (18), Jupyter (2), C (1), Shell (1)
Size: 295 files, 122 scripts
Software Heritage: not archived
Found in: the text, “Open field”
Holds: README, license file, environment (requirements.txt), documentation, 2 notebooks
Not found: CITATION.cff, tests, continuous integration
Tools: NumPy (55 files), Matplotlib (28 files), pandas (18 files), seaborn (10 files), scikit-learn (9 files), h5py (5 files), SciPy (5 files), OpenCV (4 files), Statistics and Machine Learning Toolbox (3 files), UMAP (2 files), imageio (1 file), Signal Processing Toolbox (1 file), NetworkX (1 file), Plotly (1 file), scikit-image (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
124 files
At the source: github.com/YttriLab/B-SOID

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;
  • 122 scripts, each with its path and the digest of its content;
  • 4 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 statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

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

  • Funding: added National Science Foundation: 2406615

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 8 authors, 5 keywords, 13 MeSH terms, 44 references, 6 RRIDs.

Cite

This paper

Donahue, G. J., Lundari, J. A., Kane, P. W., Matamorose-Patrick, S. M., Klotz, J. E., Carr, N. L., Kudriavetz, S. C., & Brague, J. C. (2026). Castration-induced nigrostriatal deficits are linked to reduced TrkB and loss of mature spines in the dorsal striatum. Frontiers in endocrinology, 17, 1828487. https://doi.org/10.3389/fendo.2026.1828487

BibTeX

@article{donahue2026castration,
author = {Donahue, Gerald J and Lundari, James A and Kane, Patrick W and Matamorose-Patrick, Samantha M and Klotz, Julia E and Carr, Nicholas L and Kudriavetz, Stephen C and Brague, Joe C},
title = {{Castration-induced nigrostriatal deficits are linked to reduced TrkB and loss of mature spines in the dorsal striatum}},
journal = {Frontiers in endocrinology},
year = {2026},
month = jun,
volume = {17},
pages = {1828487},
publisher = {Frontiers Media SA},
issn = {1664-2392},
doi = {10.3389/fendo.2026.1828487},
url = {https://doi.org/10.3389/fendo.2026.1828487},
pmid = {42318211},
pmcid = {PMC13272008}
}

RIS

TY - JOUR
AU - Donahue, Gerald J
AU - Lundari, James A
AU - Kane, Patrick W
AU - Matamorose-Patrick, Samantha M
AU - Klotz, Julia E
AU - Carr, Nicholas L
AU - Kudriavetz, Stephen C
AU - Brague, Joe C
TI - Castration-induced nigrostriatal deficits are linked to reduced TrkB and loss of mature spines in the dorsal striatum
T2 - Frontiers in endocrinology
J2 - Front Endocrinol (Lausanne)
PY - 2026
DA - 2026/06/03
VL - 17
SP - 1828487
SN - 1664-2392
PB - Frontiers Media SA
DO - 10.3389/fendo.2026.1828487
UR - https://doi.org/10.3389/fendo.2026.1828487
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Castration-induced nigrostriatal deficits are linked to reduced TrkB and loss of mature spines in the dorsal striatum",
"container-title": "Frontiers in endocrinology",
"author": [
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"family": "Donahue",
"given": "Gerald J"
},
{
"family": "Lundari",
"given": "James A"
},
{
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{
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"given": "Samantha M"
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{
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"given": "Julia E"
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{
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"given": "Nicholas L"
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{
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"given": "Stephen C"
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{
"family": "Brague",
"given": "Joe C"
}
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"container-title-short": "Front Endocrinol (Lausanne)",
"volume": "17",
"page": "1828487",
"DOI": "10.3389/fendo.2026.1828487",
"PMID": "42318211",
"PMCID": "PMC13272008",
"ISSN": "1664-2392",
"publisher": "Frontiers Media SA",
"URL": "https://doi.org/10.3389/fendo.2026.1828487",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
3
]
]
}
}

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