OSCR

A single Citrobacter rodentium infection in Pink1 knockout and wild-type mice leads to regional blood-brain-barrier perturbation and limited microglial activation without dopamine neuron axon terminal loss.

Code ↔ Paper

5 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 5 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and methods › Magnetic Resonance Imaging (MRI) of the brain and image processing ↔ Mukherjee-2026/MRI/rarevtr_pipeline.py, lines 170–191 · score 0.78 · bias field correction, affine registration, T1 maps, skull, fitting, segmented
  2. [2] § Results › Citrobacter rodentium infection leads to a regional increase in blood brain barrier permeability in both Pink1 WT and KO mice ↔ Mukherjee-2026/MRI/rarevtr_roi_analysis.m, lines 219–332 · score 0.77 · Earth Mover, post CA, dentate gyrus, T1 mapping, ROI, Distance
  3. [3] § Materials and methods › Magnetic Resonance Imaging (MRI) of the brain and image processing ↔ sherm.m, the whole file · a weak match · score 0.75 · descriptor selected extremal, morphologically filtering, SHERM, volumes, distance, resolution
  4. [4] § Materials and methods › Magnetic Resonance Imaging (MRI) of the brain and image processing ↔ Mukherjee-2026/MRI/rarevtr_t1fit.m, the whole file · a weak match · score 0.71 · RARE VTR, T1 mapping, repetition, scan, MRI
  5. [5] § Results › Citrobacter rodentium infection leads to a regional increase in blood brain barrier permeability in both Pink1 WT and KO mice ↔ Mukherjee-2026/MRI/rarevtr_roi_analysis.m, lines 333–412 · score 0.70 · primary somatosensory cortex, pre CA, post CA, dentate gyrus, ROI, thalamus

Paper

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The authors' code

MATLAB · 685 lines · 39 KB · MIT · 2 matches

  1. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  2. % RARE VTR ROI ANALYSIS PIPELINE
  3. % ANALYZES COHORT 2 OF THE PD MOUSE STUDY
  4. % ADAPTED TO ALSO ANALYZE COHORT 1
  5. % WRITTEN BY VG 2022
  6. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  8. % cohort 1
  9. data_path_cohort0 = ['/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/170/niftis/RAREVTR_14'; ...
  10. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/170/niftis/RAREVTR_20'; ...
  11. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/174/niftis/RAREVTR_12'; ...
  12. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/174/niftis/RAREVTR_16'; ...
  13. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/175/niftis/RAREVTR_14'; ...
  14. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/175/niftis/RAREVTR_19'; ...
  15. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/181/niftis/RAREVTR_14'; ...
  16. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/181/niftis/RAREVTR_19'; ...
  17. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/354/niftis/RAREVTR_15'; ...
  18. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/354/niftis/RAREVTR_20'; ...
  19. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/356/niftis/RAREVTR_14'; ...
  20. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/356/niftis/RAREVTR_20'; ...
  21. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/388/niftis/RAREVTR_14'; ...
  22. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/388/niftis/RAREVTR_19'; ...
  23. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/432/niftis/RAREVTR_12'; ...
  24. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/432/niftis/RAREVTR_16'; ...
  25. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/434/niftis/RAREVTR_12'; ...
  26. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/434/niftis/RAREVTR_16'; ...
  27. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/435/niftis/RAREVTR_13'; ...
  28. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/435/niftis/RAREVTR_20'; ...
  29. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/436/niftis/RAREVTR_12'; ...
  30. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/436/niftis/RAREVTR_17'; ...
  31. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/441/niftis/RAREVTR_12'; ...
  32. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/441/niftis/RAREVTR_16'; ...
  33. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/442/niftis/RAREVTR_14'; ...
  34. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/442/niftis/RAREVTR_17'; ...
  35. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/444/niftis/RAREVTR_13'; ...
  36. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/444/niftis/RAREVTR_19'; ...
  37. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/446/niftis/RAREVTR_13'; ...
  38. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/446/niftis/RAREVTR_17'; ...
  39. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/449/niftis/RAREVTR_15'; ...
  40. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/449/niftis/RAREVTR_18'; ...
  41. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/476/niftis/RAREVTR_12'; ...
  42. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/476/niftis/RAREVTR_16'; ...
  43. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/477/niftis/RAREVTR_15'; ...
  44. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_0/477/niftis/RAREVTR_19'];
  48. data_path_cohort1 = ['/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/131/niftis/RAREVTR_19'; ...
  49. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/131/niftis/RAREVTR_25'; ...
  50. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/155/niftis/RAREVTR_10'; ...
  51. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/155/niftis/RAREVTR_16'; ...
  52. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/261/niftis/RAREVTR_14'; ...
  53. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/261/niftis/RAREVTR_18'; ...
  54. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/263/niftis/RAREVTR_16'; ...
  55. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/263/niftis/RAREVTR_22'; ...
  56. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/270/niftis/RAREVTR_25'; ...
  57. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/270/niftis/RAREVTR_31'; ...
  58. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/271/niftis/RAREVTR_14'; ...
  59. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/271/niftis/RAREVTR_21'; ...
  60. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/274/niftis/RAREVTR_12'; ...
  61. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/274/niftis/RAREVTR_17'; ...
  62. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/275/niftis/RAREVTR_13'; ...
  63. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/275/niftis/RAREVTR_18'; ...
  64. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/276/niftis/RAREVTR_10'; ...
  65. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/276/niftis/RAREVTR_15'; ...
  66. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/428/niftis/RAREVTR_12'; ...
  67. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/428/niftis/RAREVTR_18'; ...
  68. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/433/niftis/RAREVTR_13'; ...
  69. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/433/niftis/RAREVTR_19'; ...
  70. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/435/niftis/RAREVTR_14'; ...
  71. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/435/niftis/RAREVTR_20'; ...
  72. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/438/niftis/RAREVTR_13'; ...
  73. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/438/niftis/RAREVTR_19'; ...
  74. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/440/niftis/RAREVTR_14'; ...
  75. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/440/niftis/RAREVTR_19'; ...
  76. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/444/niftis/RAREVTR_12'; ...
  77. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/444/niftis/RAREVTR_18'; ...
  78. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/445/niftis/RAREVTR_14'; ...
  79. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/445/niftis/RAREVTR_22'; ...
  80. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/449/niftis/RAREVTR_15'; ...
  81. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/449/niftis/RAREVTR_23'; ...
  82. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/453/niftis/RAREVTR_10'; ...
  83. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/453/niftis/RAREVTR_15'; ...
  84. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/464/niftis/RAREVTR_10'; ...
  85. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/464/niftis/RAREVTR_17'; ...
  86. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/508/niftis/RAREVTR_17'; ...
  87. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/508/niftis/RAREVTR_26'; ...
  88. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/509/niftis/RAREVTR_10'; ...
  89. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/509/niftis/RAREVTR_16'; ...
  90. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/513/niftis/RAREVTR_15'; ...
  91. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/513/niftis/RAREVTR_21'; ...
  92. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/514/niftis/RAREVTR_16'; ...
  93. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/514/niftis/RAREVTR_23'; ...
  94. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/519/niftis/RAREVTR_10'; ...
  95. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/519/niftis/RAREVTR_16'; ...
  96. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/535/niftis/RAREVTR_14'; ...
  97. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/535/niftis/RAREVTR_22'; ...
  98. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/536/niftis/RAREVTR_13'; ...
  99. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/536/niftis/RAREVTR_19'; ...
  100. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/538/niftis/RAREVTR_13'; ...
  101. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/538/niftis/RAREVTR_22'; ...
  102. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/542/niftis/RAREVTR_16'; ...
  103. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/542/niftis/RAREVTR_22'; ...
  104. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/543/niftis/RAREVTR_12'; ...
  105. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/543/niftis/RAREVTR_22'; ...
  106. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/544/niftis/RAREVTR_12'; ...
  107. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/544/niftis/RAREVTR_21'; ...
  108. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/592/niftis/RAREVTR_13'; ...
  109. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/592/niftis/RAREVTR_25'; ...
  110. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/593/niftis/RAREVTR_10'; ...
  111. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/593/niftis/RAREVTR_16'; ...
  112. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/594/niftis/RAREVTR_13'; ...
  113. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/594/niftis/RAREVTR_19'; ...
  114. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/664/niftis/RAREVTR_13'; ...
  115. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/664/niftis/RAREVTR_20'; ...
  116. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/665/niftis/RAREVTR_17'; ...
  117. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/665/niftis/RAREVTR_23'; ...
  118. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/674/niftis/RAREVTR_12'; ...
  119. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/674/niftis/RAREVTR_18'; ...
  120. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/676/niftis/RAREVTR_13'; ...
  121. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/676/niftis/RAREVTR_18'; ...
  122. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/677/niftis/RAREVTR_10'; ...
  123. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/677/niftis/RAREVTR_16'; ...
  124. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/678/niftis/RAREVTR_10'; ...
  125. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/678/niftis/RAREVTR_21'; ...
  126. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/681/niftis/RAREVTR_12'; ...
  127. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_1/681/niftis/RAREVTR_19'];
  130. data_path_cohort2 = ['/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/698/niftis/RAREVTR_14'; ...
  131. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/698/niftis/RAREVTR_17'; ...
  132. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/702/niftis/RAREVTR_12'; ...
  133. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/702/niftis/RAREVTR_22'; ...
  134. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/703/niftis/RAREVTR_12'; ...
  135. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/703/niftis/RAREVTR_18'; ...
  136. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/706/niftis/RAREVTR_14'; ...
  137. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/706/niftis/RAREVTR_18'; ...
  138. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/707/niftis/RAREVTR_16'; ...
  139. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/707/niftis/RAREVTR_24'; ...
  140. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/711/niftis/RAREVTR_12'; ...
  141. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/711/niftis/RAREVTR_21'; ...
  142. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/713/niftis/RAREVTR_12'; ...
  143. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/713/niftis/RAREVTR_18'; ...
  144. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/725/niftis/RAREVTR_12'; ...
  145. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/725/niftis/RAREVTR_19'; ...
  146. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/726/niftis/RAREVTR_13'; ...
  147. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/726/niftis/RAREVTR_17'; ...
  148. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/727/niftis/RAREVTR_12'; ...
  149. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/727/niftis/RAREVTR_16'; ...
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  163. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/778/niftis/RAREVTR_23'; ...
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  165. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/779/niftis/RAREVTR_24'; ...
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  171. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/788/niftis/RAREVTR_19'; ...
  172. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/812/niftis/RAREVTR_13'; ...
  173. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/812/niftis/RAREVTR_20'; ...
  174. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/817/niftis/RAREVTR_13'; ...
  175. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/817/niftis/RAREVTR_24'; ...
  176. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/818/niftis/RAREVTR_12'; ...
  177. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/818/niftis/RAREVTR_21'; ...
  178. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/821/niftis/RAREVTR_16'; ...
  179. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/821/niftis/RAREVTR_24'; ...
  180. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/825/niftis/RAREVTR_15'; ...
  181. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/825/niftis/RAREVTR_19'; ...
  182. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/833/niftis/RAREVTR_14'; ...
  183. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/833/niftis/RAREVTR_18'; ...
  184. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/834/niftis/RAREVTR_12'; ...
  185. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/834/niftis/RAREVTR_19'; ...
  186. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/838/niftis/RAREVTR_12'; ...
  187. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/838/niftis/RAREVTR_16'; ...
  188. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/839/niftis/RAREVTR_12'; ...
  189. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/839/niftis/RAREVTR_22'; ...
  190. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/843/niftis/RAREVTR_15'; ...
  191. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/843/niftis/RAREVTR_24'; ...
  192. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/845/niftis/RAREVTR_12'; ...
  193. '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2/845/niftis/RAREVTR_21'];
  196. %% Run the analysis
  198. % add niftitools and EMD toolbox to path
  199. addpath(genpath('/data/rudko/vgrouza/invivomouse/niftitools'));
  200. addpath(genpath('/data/rudko/vgrouza/invivomouse/emd-2005-02'));
  201. addpath(genpath('/data/rudko/vgrouza/invivomouse/jsdiv'));
  203. allSubjectPars = [];
  204. allSubjectDir = '/data/rudko/vgrouza/invivomouse/pddata/data/cohort_2';
  205. allSubjectFileName = 't1_Cohort2SubjectParsBS.csv';
  206. currDataPath = data_path_cohort2;
  207. cohort_num = 2;
  209. for i = 1:2:size(currDataPath, 1)
  210. % Don't forget to change the cohort!
  211. currSubjectPars = struct2table(run_analysis(currDataPath(i,:), currDataPath(i+1,:), cohort_num));
  212. allSubjectPars = [allSubjectPars; currSubjectPars];
  213. end
  214. writetable(allSubjectPars, fullfile(allSubjectDir, allSubjectFileName), 'delimiter',',');
  215. fprintf('Completed Analysis of %d subjects. \n', size(currDataPath, 1)/2);
  217. %% MAIN ANALYSIS FUNCTION
  219. function currSubjectPars = run_analysis(input_path_pre, input_path_post, cohort_number)
  221. % get subject summary information
  222. currSubjectPars = parse_subject_parameters(input_path_pre, cohort_number);
  223. currSubjectPars.PathToPre = input_path_pre;
  224. currSubjectPars.PathToPost = input_path_post;
  225. fprintf('Processing subject %d... \n', currSubjectPars.ID);
  227. % get T1 distributions within ROIS pre- and post-ca injection
  228. T1HistogramsPre = segment_t1_map(input_path_pre);
  229. T1HistogramsPost = segment_t1_map(input_path_post);
  231. % compute wasserstein/earth mover's distance for each roi
  233. % STRIATUM
  234. % Simple Mean
  235. currSubjectPars.T1pre_striatum = T1HistogramsPre.T1_Striatum;
  236. currSubjectPars.T1post_striatum = T1HistogramsPost.T1_Striatum;
  239. % EMD
  240. [~, currSubjectPars.EMDstriatum] = emd(T1HistogramsPre.BinSupport', ...
  241. T1HistogramsPost.BinSupport', ...
  242. T1HistogramsPre.Striatum', ...
  243. T1HistogramsPost.Striatum', ...
  244. @gdf);
  245. % Z value
  246. [p, h, stats] = ranksum(T1HistogramsPre.ROI_Striatum, T1HistogramsPost.ROI_Striatum, ...
  247. 'alpha',0.01, ...
  248. 'tail','right');
  249. currSubjectPars.zval_striatum = stats.zval;
  252. % T Test
  253. [h, p, ~, stats_t] = ttest2(T1HistogramsPre.ROI_Striatum, T1HistogramsPost.ROI_Striatum, 'Vartype','unequal')
  255. % THALAMUS
  256. % Simple Mean
  257. currSubjectPars.T1pre_thalamus = T1HistogramsPre.T1_Thalamus;
  258. currSubjectPars.T1post_thalamus = T1HistogramsPost.T1_Thalamus;
  261. % EMD
  262. [~, currSubjectPars.EMDthalamus] = emd(T1HistogramsPre.BinSupport', ...
  263. T1HistogramsPost.BinSupport', ...
  264. T1HistogramsPre.Thalamus', ...
  265. T1HistogramsPost.Thalamus', ...
  266. @gdf);
  267. % Z value
  268. [p, h, stats] = ranksum(T1HistogramsPre.ROI_Thalamus, T1HistogramsPost.ROI_Thalamus, ...
  269. 'alpha',0.01, ...
  270. 'tail','right');
  271. currSubjectPars.zval_thalamus = stats.zval;
  274. % T Test
  275. [h, p, ~, stats_t] = ttest2(T1HistogramsPre.ROI_Thalamus, T1HistogramsPost.ROI_Thalamus, 'Vartype','unequal')
  277. % PRIMARY SSC
  278. % Simple Mean
  279. currSubjectPars.T1pre_pssc = T1HistogramsPre.T1_PrimarySSC;
  280. currSubjectPars.T1post_pssc = T1HistogramsPost.T1_PrimarySSC;
  283. % EMD
  284. [~, currSubjectPars.EMDpssc] = emd(T1HistogramsPre.BinSupport', ...
  285. T1HistogramsPost.BinSupport', ...
  286. T1HistogramsPre.PrimarySSC', ...
  287. T1HistogramsPost.PrimarySSC', ...
  288. @gdf);
  289. % Z value
  290. [p, h, stats] = ranksum(T1HistogramsPre.ROI_PrimarySSC, T1HistogramsPost.ROI_PrimarySSC, ...
  291. 'alpha',0.01, ...
  292. 'tail','right');
  293. currSubjectPars.zval_pssc = stats.zval;
  295. % T Test
  296. [h, p, ~, stats_t] = ttest2(T1HistogramsPre.ROI_PrimarySSC, T1HistogramsPost.ROI_PrimarySSC, 'Vartype','unequal')
  298. % DENTATE GYRUS
  299. % Simple Mean
  300. currSubjectPars.T1pre_dg = T1HistogramsPre.T1_DentateGyrus;
  301. currSubjectPars.T1post_dg = T1HistogramsPost.T1_DentateGyrus;
  304. % EMD
  305. [~, currSubjectPars.EMDdg] = emd(T1HistogramsPre.BinSupport', ...
  306. T1HistogramsPost.BinSupport', ...
  307. T1HistogramsPre.DentateGyrus', ...
  308. T1HistogramsPost.DentateGyrus', ...
  309. @gdf);
  310. % Z value
  311. [p, h, stats] = ranksum(T1HistogramsPre.ROI_DentateGyrus, T1HistogramsPost.ROI_DentateGyrus, ...
  312. 'alpha',0.01, ...
  313. 'tail','right');
  314. currSubjectPars.zval_dg = stats.zval;
  316. % T Test
  317. [h, p, ~, stats_t] = ttest2(T1HistogramsPre.ROI_DentateGyrus, T1HistogramsPost.ROI_DentateGyrus, 'Vartype','unequal')
  320. % generate a summary figure
  321. % generate_figure(currSubjectPars, T1HistogramsPre, T1HistogramsPost);
  322. %generate_figure_bs(currSubjectPars, T1HistogramsPre, T1HistogramsPost);
  324. % save Pars and Histograms as .csv
  325. currSubjectPars = orderfields(currSubjectPars, [1 2 3 4 5 6 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 7 8 9]);
  326. fooTable = struct2table(currSubjectPars);
  327. writetable(fooTable, fullfile(currSubjectPars.PathToStudy, 't1_SubjectPars.csv'), 'delimiter',',');
  328. disp('Done.')
  329. end
  332. %% SUPPORTING FUNCTIONS
  333. function [] = generate_figure(currSubjectPars, T1HistogramsPre, T1HistogramsPost)
  335. close all;
  336. curr_fig = figure(1);
  337. set(gcf, 'Color', 'w', 'Position',[0 0 1200 800]);
  338. sgtitle(sprintf('VTR T_1 Summary for Subject %s %d', currSubjectPars.Genotype, currSubjectPars.ID), 'FontWeight', 'b');
  340. subplot(2,2,1); hold on;
  341. % Thalamus
  342. t1mean_pre = T1HistogramsPre.T1_Thalamus;
  343. t1mean_post = T1HistogramsPost.T1_Thalamus;
  344. bar(T1HistogramsPre.BinSupport, T1HistogramsPre.Thalamus, 'FaceColor', [0.8 0.8 0.8], 'FaceAlpha', 1);
  345. bar(T1HistogramsPost.BinSupport, T1HistogramsPost.Thalamus, 'r', 'FaceAlpha', 0.5);
  346. text(1750, 0.15, sprintf('EMD = %2.1f', currSubjectPars.EMDthalamus));
  347. text(2500, 0.1, sprintf('T1_{pre} = %2.1f msec', t1mean_pre))
  348. text(1600, 0.1, sprintf('T1_{post} = %2.1f msec', t1mean_post))
  349. title('(i). Thalamus');
  351. subplot(2,2,2); hold on;
  352. % Striatum
  353. t1mean_pre = T1HistogramsPre.T1_Striatum;
  354. t1mean_post = T1HistogramsPost.T1_Striatum;
  355. bar(T1HistogramsPre.BinSupport, T1HistogramsPre.Striatum, 'FaceColor', [0.8 0.8 0.8], 'FaceAlpha', 1);
  356. bar(T1HistogramsPost.BinSupport, T1HistogramsPost.Striatum, 'r', 'FaceAlpha', 0.5);
  357. text(1750, 0.15, sprintf('EMD = %2.1f', currSubjectPars.EMDstriatum));
  358. text(2500, 0.1, sprintf('T1_{pre} = %2.1f msec', t1mean_pre))
  359. text(1600, 0.1, sprintf('T1_{post} = %2.1f msec', t1mean_post))
  360. title('(ii). Striatum');
  362. subplot(2,2,3); hold on;
  363. % Primary Somatosensory Cortex
  364. t1mean_pre = T1HistogramsPre.T1_PrimarySSC;
  365. t1mean_post = T1HistogramsPost.T1_PrimarySSC;
  366. bar(T1HistogramsPre.BinSupport, T1HistogramsPre.PrimarySSC, 'FaceColor', [0.8 0.8 0.8], 'FaceAlpha', 1);
  367. bar(T1HistogramsPost.BinSupport, T1HistogramsPost.PrimarySSC, 'r', 'FaceAlpha', 0.5);
  368. text(1750, 0.15, sprintf('EMD = %2.1f', currSubjectPars.EMDpssc));
  369. text(2500, 0.1, sprintf('T1_{pre} = %2.1f msec', t1mean_pre))
  370. text(1600, 0.1, sprintf('T1_{post} = %2.1f msec', t1mean_post))
  371. title('(iii). Primary SSC');
  373. subplot(2,2,4); hold on;
  374. % Dentate Gyrus
  375. t1mean_pre = T1HistogramsPre.T1_DentateGyrus;
  376. t1mean_post = T1HistogramsPost.T1_DentateGyrus;
  377. bar(T1HistogramsPre.BinSupport, T1HistogramsPre.DentateGyrus, 'FaceColor', [0.8 0.8 0.8], 'FaceAlpha', 1);
  378. bar(T1HistogramsPost.BinSupport, T1HistogramsPost.DentateGyrus, 'r', 'FaceAlpha', 0.5);
  379. text(1750, 0.15, sprintf('EMD = %2.1f', currSubjectPars.EMDdg));
  380. text(2500, 0.1, sprintf('T1_{pre} = %2.1f msec', t1mean_pre))
  381. text(1600, 0.1, sprintf('T1_{post} = %2.1f msec', t1mean_post))
  382. title('(iv). Dentate Gyrus');
  383. for i = 1:4
  384. subplot(2,2,i);
  385. set(gca, 'LineWidth', 2, 'FontWeight', 'b', 'FontSize', 10);
  386. xlabel('T_1 (msec)'); ylabel('Density');
  387. xlim([1500, 3000]); ylim([0, 0.2]);
  388. legend('Pre-CA', 'Post-CA', 'Location', 'NorthEast')
  389. end
  391. % save figure to main Bruker study directory
  392. if currSubjectPars.Infected == 0 && currSubjectPars.Timepoint > 0
  393. fig_string = sprintf('%d_%s_noninfected_t1_summary.png', ...
  394. currSubjectPars.ID, ...
  395. currSubjectPars.Genotype);
  396. elseif currSubjectPars.Infected == 1
  397. fig_string = sprintf('%d_%s_infected_t1_summary.png', ...
  398. currSubjectPars.ID, ...
  399. currSubjectPars.Genotype);
  400. elseif currSubjectPars.Timepoint == -1
  401. fig_string = sprintf('%d_%s_ptx_treated_t1_summary.png', ...
  402. currSubjectPars.ID, ...
  403. currSubjectPars.Genotype);
  404. elseif currSubjectPars.Timepoint == 0
  405. fig_string = sprintf('%d_%s_ptx_control_t1_summary.png', ...
  406. currSubjectPars.ID, ...
  407. currSubjectPars.Genotype);
  409. end
  410. saveas(curr_fig, fullfile(fileparts(currSubjectPars.PathToStudy), fig_string));
  412. end
  416. function [] = generate_figure_bs(currSubjectPars, T1HistogramsPre, T1HistogramsPost)
  418. curr_fig = figure(2);
  419. set(gcf, 'Color', 'w', 'Position',[0 0 1200 800]);
  420. sgtitle(sprintf('VTR T_1 Summary for Subject %s %d', currSubjectPars.Genotype, currSubjectPars.ID), 'FontWeight', 'b');
  422. subplot(2,2,1); hold on;
  423. % Thalamus
  424. t1mean_pre = T1HistogramsPre.T1_Thalamus;
  425. t1mean_post = T1HistogramsPost.T1_Thalamus;
  426. bar(T1HistogramsPre.BinSupport, T1HistogramsPre.ThalamusBS, 'FaceColor', [0.8 0.8 0.8], 'FaceAlpha', 1);
  427. bar(T1HistogramsPost.BinSupport, T1HistogramsPost.ThalamusBS, 'r', 'FaceAlpha', 0.5);
  429. %[h, p] = ttest2(T1HistogramsPre.ThalamusBS, T1HistogramsPost.ThalamusBS, 'alpha', 0.05, 'Vartype','unequal')
  430. [p, h, stats] = ranksum(T1HistogramsPre.ROI_Thalamus, T1HistogramsPost.ROI_Thalamus, 'alpha',0.01,...
  431. 'tail','right')
  434. text(1750, 0.15, sprintf('EMD = %2.1f', currSubjectPars.EMDthalamus));
  435. text(2500, 0.1, sprintf('T1_{pre} = %2.1f msec', t1mean_pre))
  436. text(1600, 0.1, sprintf('T1_{post} = %2.1f msec', t1mean_post))
  437. title('(i). Thalamus');
  439. subplot(2,2,2); hold on;
  440. % Striatum
  441. t1mean_pre = T1HistogramsPre.T1_Striatum;
  442. t1mean_post = T1HistogramsPost.T1_Striatum;
  443. bar(T1HistogramsPre.BinSupport, T1HistogramsPre.StriatumBS, 'FaceColor', [0.8 0.8 0.8], 'FaceAlpha', 1);
  444. bar(T1HistogramsPost.BinSupport, T1HistogramsPost.StriatumBS, 'r', 'FaceAlpha', 0.5);
  446. % [h, p] = ttest2(T1HistogramsPre.StriatumBS, T1HistogramsPost.StriatumBS, 'alpha', 0.05, 'Vartype','unequal')
  447. [p, h, stats] = ranksum(T1HistogramsPre.ROI_Striatum, T1HistogramsPost.ROI_Striatum, 'alpha',0.01,...
  448. 'tail','right')
  450. text(1750, 0.15, sprintf('EMD = %2.1f', currSubjectPars.EMDstriatum));
  451. text(2500, 0.1, sprintf('T1_{pre} = %2.1f msec', t1mean_pre))
  452. text(1600, 0.1, sprintf('T1_{post} = %2.1f msec', t1mean_post))
  453. title('(ii). Striatum');
  455. subplot(2,2,3); hold on;
  456. % Primary Somatosensory Cortex
  457. t1mean_pre = T1HistogramsPre.T1_PrimarySSC;
  458. t1mean_post = T1HistogramsPost.T1_PrimarySSC;
  459. bar(T1HistogramsPre.BinSupport, T1HistogramsPre.PrimarySSCBS, 'FaceColor', [0.8 0.8 0.8], 'FaceAlpha', 1);
  460. bar(T1HistogramsPost.BinSupport, T1HistogramsPost.PrimarySSCBS, 'r', 'FaceAlpha', 0.5);
  462. %[h, p] = ttest2(T1HistogramsPre.PrimarySSCBS, T1HistogramsPost.PrimarySSCBS, 'alpha', 0.05, 'Vartype','unequal')
  463. [p, h, stats] = ranksum(T1HistogramsPre.ROI_PrimarySSC, T1HistogramsPost.ROI_PrimarySSC,'alpha',0.01,...
  464. 'tail','right')
  466. text(1750, 0.15, sprintf('EMD = %2.1f', currSubjectPars.EMDpssc));
  467. text(2500, 0.1, sprintf('T1_{pre} = %2.1f msec', t1mean_pre))
  468. text(1600, 0.1, sprintf('T1_{post} = %2.1f msec', t1mean_post))
  469. title('(iii). Primary SSC');
  471. subplot(2,2,4); hold on;
  472. % Dentate Gyrus
  473. t1mean_pre = T1HistogramsPre.T1_DentateGyrus;
  474. t1mean_post = T1HistogramsPost.T1_DentateGyrus;
  475. bar(T1HistogramsPre.BinSupport, T1HistogramsPre.DentateGyrusBS, 'FaceColor', [0.8 0.8 0.8], 'FaceAlpha', 1);
  476. bar(T1HistogramsPost.BinSupport, T1HistogramsPost.DentateGyrusBS, 'r', 'FaceAlpha', 0.5);
  478. %[h, p] = ttest2(T1HistogramsPre.DentateGyrusBS, T1HistogramsPost.DentateGyrusBS, 'alpha', 0.05, 'Vartype','unequal')
  479. [p, h, stats] = ranksum(T1HistogramsPre.ROI_DentateGyrus, T1HistogramsPost.ROI_DentateGyrus, 'alpha',0.01,...
  480. 'tail','right')
  482. text(1750, 0.15, sprintf('EMD = %2.1f', currSubjectPars.EMDdg));
  483. text(2500, 0.1, sprintf('T1_{pre} = %2.1f msec', t1mean_pre))
  484. text(1600, 0.1, sprintf('T1_{post} = %2.1f msec', t1mean_post))
  485. title('(iv). Dentate Gyrus');
  486. for i = 1:4
  487. subplot(2,2,i);
  488. set(gca, 'LineWidth', 2, 'FontWeight', 'b', 'FontSize', 10);
  489. xlabel('T_1 (msec)'); ylabel('Density');
  490. xlim([1500, 3000]); ylim([0, 0.2]);
  491. legend('Pre-CA', 'Post-CA', 'Location', 'NorthEast')
  492. end
  494. % save figure to main Bruker study directory
  495. if currSubjectPars.Infected == 0 && currSubjectPars.Timepoint > 0
  496. fig_string = sprintf('%d_%s_noninfected_t1_summaryBS.png', ...
  497. currSubjectPars.ID, ...
  498. currSubjectPars.Genotype);
  499. elseif currSubjectPars.Infected == 1
  500. fig_string = sprintf('%d_%s_infected_t1_summaryBS.png', ...
  501. currSubjectPars.ID, ...
  502. currSubjectPars.Genotype);
  503. elseif currSubjectPars.Timepoint == -1
  504. fig_string = sprintf('%d_%s_ptx_treated_t1_summaryBS.png', ...
  505. currSubjectPars.ID, ...
  506. currSubjectPars.Genotype);
  507. elseif currSubjectPars.Timepoint == 0
  508. fig_string = sprintf('%d_%s_ptx_control_t1_summaryBS.png', ...
  509. currSubjectPars.ID, ...
  510. currSubjectPars.Genotype);
  512. end
  513. saveas(curr_fig, fullfile(fileparts(currSubjectPars.PathToStudy), fig_string));
  515. end
  520. function T1Histograms = segment_t1_map(input_path)
  521. % segment the T1 map using DSURQE atlas labels warped to native space
  523. % load t1 map and labels
  524. t1map = load_nii(fullfile(input_path, 't1_map_corrected.nii.gz')).img;
  525. labels = round(load_nii(fullfile(input_path, 'labels_inv.nii.gz')).img);
  527. % specify bin widths for t1 histograms
  528. bin_width = 25;
  529. bin_edges = 1500:bin_width:3000;
  530. bin_support = (bin_edges(1)+bin_width/2):bin_width:(bin_edges(end)-bin_width/2);
  532. % set roi labels based on DSURQE atlas
  533. thalamus_idx = [204, 4];
  534. striatum_idx = [7, 17];
  535. primary_ssc_idx = [111, 280];
  536. dentate_gyrus_idx = 326:331;
  538. % pur roi labels into a cell so we can cycle through them
  539. roi_cell{1} = thalamus_idx;
  540. roi_cell{2} = striatum_idx;
  541. roi_cell{3} = primary_ssc_idx;
  542. roi_cell{4} = dentate_gyrus_idx;
  544. % cycle through rois
  545. bin_density_matrix = zeros(length(roi_cell), length(bin_support));
  546. bin_density_bs_matrix = zeros(length(roi_cell), length(bin_support));
  547. t1_mean_matrix = zeros(length(roi_cell), 1);
  548. NBTSTRP = 1000;
  549. for i = 1:length(roi_cell)
  550. roi_nums = roi_cell{i};
  551. % segment roi within the whole t1 map
  552. roi_labels = zeros(size(labels));
  553. for j = 1:length(roi_nums)
  554. roi_labels = roi_labels + (labels == roi_nums(j));
  555. end
  556. % bin the t1 values into predefined histogram support
  557. t1_roi = rmoutliers(t1map(roi_labels > 0));
  558. [bin_density, ~] = histcounts(t1_roi, ...
  559. bin_edges, ...
  560. 'Normalization', 'probability');
  561. bin_density_matrix(i, :) = bin_density;
  563. t1_mean_matrix(i, 1) = mean(t1_roi);
  565. for j = 1:NBTSTRP
  566. [bin_density_bs(i,:), ~] = histcounts(datasample(t1_roi, NBTSTRP), ...
  567. bin_edges, ...
  568. 'Normalization', 'probability');
  569. end
  570. bin_density_bs = mean(bin_density_bs, 1);
  572. bin_density_bs_matrix(i, :) = bin_density_bs;
  574. if i == 1
  575. T1Histograms.ROI_Thalamus = t1_roi;
  576. elseif i == 2
  577. T1Histograms.ROI_Striatum = t1_roi;
  578. elseif i == 3
  579. T1Histograms.ROI_PrimarySSC = t1_roi;
  580. elseif i == 4
  581. T1Histograms.ROI_DentateGyrus = t1_roi;
  582. end
  583. end
  585. T1Histograms.BinSupport = bin_support;
  586. T1Histograms.Thalamus = bin_density_matrix(1,:);
  587. T1Histograms.ThalamusBS = bin_density_bs_matrix(1,:);
  588. T1Histograms.T1_Thalamus = t1_mean_matrix(1,1);
  590. T1Histograms.Striatum = bin_density_matrix(2,:);
  591. T1Histograms.StriatumBS = bin_density_bs_matrix(2,:);
  592. T1Histograms.T1_Striatum = t1_mean_matrix(2,1);
  595. T1Histograms.PrimarySSC = bin_density_matrix(3,:);
  596. T1Histograms.PrimarySSCBS = bin_density_bs_matrix(3,:);
  597. T1Histograms.T1_PrimarySSC = t1_mean_matrix(3,1);
  599. T1Histograms.DentateGyrus = bin_density_matrix(4,:);
  600. T1Histograms.DentateGyrusBS = bin_density_bs_matrix(4,:);
  601. T1Histograms.T1_DentateGyrus = t1_mean_matrix(4,1);
  604. end
  607. function SubjectPars = parse_subject_parameters(input_path_pre, cohort_number)
  608. % gets summary information about the current subject
  610. % aggregate identifying subject parameters from Bruker "./subject" file
  611. pathparts = strsplit(input_path_pre,filesep);
  612. study_path = strcat('/', fullfile(pathparts{1:end-2}));
  614. % load subject file into string array
  615. fid = fopen(fullfile(study_path, 'subject'),'r');
  616. subject_file = fread(fid,Inf,'*char');
  617. fclose(fid);
  618. clear fid;
  620. % parse subject file indices for subject name
  621. index1 = strfind(subject_file','##$SUBJECT_name=(<>, <') + length('##$SUBJECT_name=(<>, <');
  622. index2temp = strfind(subject_file', '>');
  623. index2 = index2temp(find(index2temp > index1, 1));
  624. subject_name = lower(subject_file(index1:index2-1)');
  625. clear index1 index2 index2temp;
  627. % subject ID
  628. subject_id = str2double(pathparts{end-2});
  630. % subject sex
  631. index1 = strfind(subject_file','##$SUBJECT_sex=( 8 )') + length('##$SUBJECT_sex=( 8 )') + 2;
  632. index2temp = strfind(subject_file', '>');
  633. index2 = index2temp(find(index2temp > index1, 1));
  634. subject_sex_str = lower(subject_file(index1:index2-1)');
  635. if contains(subject_sex_str, 'female')
  636. subject_sex = 'F';
  637. else
  638. subject_sex = 'M';
  639. end
  640. clear index1 index2 index2temp;
  642. % genotype
  643. if contains(subject_name, 'ko')
  644. genotype = 'KO';
  645. elseif contains(subject_name, 'wt')
  646. genotype = 'WT';
  647. else
  648. genotype = 'WT';
  649. end
  651. % InfectionStatus
  652. if contains(subject_name, 'non')
  653. infected = false;
  654. elseif contains(subject_name, 'control')
  655. infected = false;
  656. elseif contains(subject_name, 'ptx')
  657. infected = false;
  658. else
  659. infected = true;
  660. end
  662. % TimePoint
  663. if contains(subject_name, 'day13')
  664. timepoint = 1;
  665. elseif contains(subject_name, 'day26')
  666. timepoint = 2;
  667. elseif contains(subject_name, 'control')
  668. timepoint = 0;
  669. elseif contains(subject_name, 'ptx')
  670. timepoint = -1;
  671. else
  672. timepoint = 1;
  673. end
  675. % summarize into a data structure
  676. SubjectPars.ID = subject_id;
  677. SubjectPars.Sex = subject_sex;
  678. SubjectPars.Genotype = genotype;
  679. SubjectPars.Infected = infected;
  680. SubjectPars.Timepoint = timepoint;
  681. SubjectPars.Cohort = cohort_number;
  682. SubjectPars.PathToStudy = study_path;
  684. end

rarevtr_roi_analysis.m at commit b2d1ac0, under MIT · at the source

Overview

Authors: Sriparna Mukherjee1,2, Vladimir Grouza3, Alex Tchung1, Amandine Even1,2, Moein Yaqubi2,3, Marius Tuznik3, Tyler Cannon2,4, Sherilyn Junelle Recinto2,3, Christina Gavino2,4, Marie-Josée Bourque1,2, Nicolas Giguère1, Heidi McBride2,3, Michel Desjardins2,5, Samantha Gruenheid2,4, Jo Anne Stratton2,3, David A Rudko3, Louis-Eric Trudeau1,2,6,7
ORCID iDs: Sriparna Mukherjee, Jo Anne Stratton, Louis-Eric Trudeau
  1. Department of Pharmacology and Physiology, Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada
  2. Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, Maryland, United States of America
  3. Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal Neurological Institute, Montreal, Quebec, Canada
  4. Department of Microbiology and Immunology, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
  5. Department of Pathology and Cell Biology, Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada
  6. Department of Neurosciences, Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada
  7. Courtois Institute for Biomedical Innovation and SNC and CIRCA Research Groups, Université de Montréal, Montreal, Quebec, Canada
Institutions: Aligning Science Across Parkinson's (United States); Université de Montréal (Canada); Montreal Neurological Institute and Hospital (Canada); McGill University (Canada)
Journal: PLoS pathogens, volume 22, issue 6, article e1014315
Dates: received 9 July 2025; accepted 28 May 2026; published online 30 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.ppat.1014315 · PMID 42378309 · PMCID PMC13318054 · OpenAlex W7166716914
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), other condition (population), Parkinson's (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, fMRI & imaging
MeSH: Blood-Brain Barrier*, Citrobacter rodentium*, Dopaminergic Neurons*, Enterobacteriaceae Infections*, Microglia*, Parkinson Disease*, Protein Kinases*, Animals, Axons, Male, Mice, Mice, Inbred C57BL, Mice, Knockout, PTEN-Induced Putative Kinase (* major topic)
Topic: Neuroinflammation and Neurodegeneration Mechanisms (Neurology, Neuroscience), according to OpenAlex
Funding: CIHR; Aligning Science Across Parkinson’s; Foundation for Parkinson’s Research (MJFF); FRQS (Fonds de recherche du Québec, Santé)
Citations: not cited yet (Europe PMC); 78 references in the paper
Research resources: GFAP RRID:AB_10013382, RRID:AB_10015282, LY6C-AF700 RRID:AB_10612017, CD86-BV650 RRID:AB_11126147, CD11b-BV605 RRID:AB_11126744, NK1.1-BV650 RRID:AB_11147949, RRID:AB_141778, RRID:AB_143165, CD11C-PECY7 RRID:AB_2033997, RRID:AB_2044003, DAT RRID:AB_2190413, TH RRID:AB_2201528, ZO-2 RRID:AB_2203575, RRID:AB_2307392, Peroxidase-AffiniPure Goat Anti-Rat IgG RRID:AB_2338128, 647 were used for fluorescence detection RRID:AB_2534077, 647 were used for fluorescence detection RRID:AB_2534117, RRID:AB_2535812, LY6G-BV510 RRID:AB_2562937, CD45-BV711 RRID:AB_2564383, β actin peroxidase RRID:AB_262011, RRID:AB_2725776, CD19-BV786 RRID:AB_2738141, ZO-1 RRID:AB_2798287, IBA1 RRID:AB_2924932, CD4-APC Cy7 RRID:AB_312726, CD45-FITC RRID:AB_312972, CD8a-A647 RRID:AB_389326, TH RRID:AB_390204, IBA1 RRID:AB_839504, IBA1 RRID:AB_839506, CD3e-PerCp Cy5.5 RRID:AB_893318, F4/80-A647 RRID:AB_893492, RRID:IMSR_JAX:006660, RRID:IMSR_JAX:007909, Hansruedi Bueler RRID:IMSR_JAX:017946, https://github.com/dipy/dipy RRID:SCR_000029, RRID:SCR_001622, The DESeq2 package in R RRID:SCR_001905, 1 http://www.graphpad.com/ RRID:SCR_002798, RRID:SCR_003070, RRID:SCR_003201, RRID:SCR_004463, RRID:SCR_006809, RRID:SCR_008394, RRID:SCR_008520, RRID:SCR_011867, RRID:SCR_014215, RRID:SCR_015687, RRID:SCR_019037, RRID:SCR_025285, RRID:SCR_027284

Abstract

A growing body of research suggests a link between immune system activation and the development of Parkinson’s disease (PD). Previous work showed that repeated gastrointestinal infection with Citrobacter rodentium can induce PD-like motor dysfunction in Pink1 knockout (KO) mice, along with immune cell infiltration into the brain. To better understand mechanisms underlying immune-mediated brain attack in this model, we tested whether mild infections are sufficient to increase blood-brain barrier (BBB) permeability and trigger brain inflammation. Pink1 wild-type (WT) and KO mice were infected with C. rodentium, and gadolinium-enhanced magnetic resonance imaging (MRI) was performed at days 13 and 26 post-infection to assess BBB integrity. Quantitative MRI analysis revealed increased BBB permeability at day 26 in both WT and KO mice, particularly in the striatum, dentate gyrus, somatosensory cortex, and thalamus. Notably, this permeability was not associated with changes in tight junction protein expression or dopamine system markers in the striatum at either time point. However, persistent microglial activation was observed at day 26 post-infection, along with elevated levels of inflammatory mediators such as eotaxin, IFN-γ, CXCL9, IL-17, and MIP-2 in the striatum. Additionally, serum levels of IL-17 and CXCL1 were increased in infected Pink1 KO mice. Flow cytometry revealed neutrophil infiltration in the brain at day 26 post-infection. Finally, a bulk RNA-seq transcriptome analysis revealed that gene sets related to synaptic function were particularly influenced by the infection and that inflammation-related genes were upregulated by the infection in the Pink1 KO mice. These findings support the hypothesis that even mild gastrointestinal infections can increase BBB permeability, disrupt brain homeostasis, and promote chronic neuroinflammation. In genetically susceptible individuals, such as those with Pink1 deficiency, this may represent a first hit that contributes to subsequent induction of PD pathology with aging.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

liu-yikang/sherm-rodentskullstrip

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: c44afe8a302672a92c0fcc4a3c333d6a40ba3d26, 17 February 2021
Languages: MATLAB (8)
Size: 37 files, 8 scripts
Software Heritage: not archived
Found in: the text, “Magnetic Resonance Imaging (MRI) of the brain an”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (5 files), Statistics and Machine Learning Toolbox (1 file), Tools for NIfTI and ANALYZE image (MATLAB) (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
10 files
At the source: github.com/liu-yikang/SHERM-rodentSkullStrip/tree/master

louis-erictrudeau/trudeau-lab

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: b2d1ac0f24c21f434ed3d740397fcb46fd218838, 14 August 2026
Languages: Python (6), MATLAB (3)
Size: 41 files, 9 scripts
Software Heritage: not archived
Found in: the text, “Confocal imaging and image processing”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (4 files), pandas (4 files), Statistics and Machine Learning Toolbox (2 files), Tools for NIfTI and ANALYZE image (MATLAB) (2 files), ANTs (1 file), DIPY (1 file), FSL (1 file), Optimization Toolbox (1 file), Nipype (1 file), SciPy (1 file), statsmodels (1 file), Violinplot-Matlab (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
11 files
At the source: github.com/Louis-EricTrudeau/Trudeau-lab/tree/main/Mukherjee-2026

The paper's code and data availability statement is in the Data section.

Tracing map

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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;
  • 17 scripts, each with its path and the digest of its content;
  • 5 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

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Data Availability

The data, code, and key lab materials used and generated in this study are listed in a Key Resource Table alongside their persistent identifiers at https://doi.org/10.5281/zenodo.20091139.

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, 17 authors, 14 MeSH terms, 4 funders, 77 references, 52 RRIDs.

Cite

This paper

Mukherjee, S., Grouza, V., Tchung, A., Even, A., Yaqubi, M., Tuznik, M., Cannon, T., Recinto, S. J., Gavino, C., Bourque, M.-J., Giguère, N., McBride, H., Desjardins, M., Gruenheid, S., Stratton, J. A., Rudko, D. A., & Trudeau, L.-E. (2026). A single Citrobacter rodentium infection in Pink1 knockout and wild-type mice leads to regional blood-brain-barrier perturbation and limited microglial activation without dopamine neuron axon terminal loss. PLoS pathogens, 22(6), e1014315. https://doi.org/10.1371/journal.ppat.1014315

BibTeX

@article{mukherjee2026single,
author = {Mukherjee, Sriparna and Grouza, Vladimir and Tchung, Alex and Even, Amandine and Yaqubi, Moein and Tuznik, Marius and Cannon, Tyler and Recinto, Sherilyn Junelle and Gavino, Christina and Bourque, Marie-Josée and Giguère, Nicolas and McBride, Heidi and Desjardins, Michel and Gruenheid, Samantha and Stratton, Jo Anne and Rudko, David A and Trudeau, Louis-Eric},
title = {{A single Citrobacter rodentium infection in Pink1 knockout and wild-type mice leads to regional blood-brain-barrier perturbation and limited microglial activation without dopamine neuron axon terminal loss}},
journal = {PLoS pathogens},
year = {2026},
month = jun,
volume = {22},
number = {6},
pages = {e1014315},
publisher = {PLOS},
issn = {1553-7366},
doi = {10.1371/journal.ppat.1014315},
url = {https://doi.org/10.1371/journal.ppat.1014315},
pmid = {42378309},
pmcid = {PMC13318054}
}

RIS

TY - JOUR
AU - Mukherjee, Sriparna
AU - Grouza, Vladimir
AU - Tchung, Alex
AU - Even, Amandine
AU - Yaqubi, Moein
AU - Tuznik, Marius
AU - Cannon, Tyler
AU - Recinto, Sherilyn Junelle
AU - Gavino, Christina
AU - Bourque, Marie-Josée
AU - Giguère, Nicolas
AU - McBride, Heidi
AU - Desjardins, Michel
AU - Gruenheid, Samantha
AU - Stratton, Jo Anne
AU - Rudko, David A
AU - Trudeau, Louis-Eric
TI - A single Citrobacter rodentium infection in Pink1 knockout and wild-type mice leads to regional blood-brain-barrier perturbation and limited microglial activation without dopamine neuron axon terminal loss
T2 - PLoS pathogens
J2 - PLoS Pathog
PY - 2026
DA - 2026/06/30
VL - 22
IS - 6
SP - e1014315
SN - 1553-7366
PB - PLOS
DO - 10.1371/journal.ppat.1014315
UR - https://doi.org/10.1371/journal.ppat.1014315
LA - en
ER -

CSL-JSON

{
"id": "10.1371/journal.ppat.1014315",
"type": "article-journal",
"title": "A single Citrobacter rodentium infection in Pink1 knockout and wild-type mice leads to regional blood-brain-barrier perturbation and limited microglial activation without dopamine neuron axon terminal loss",
"container-title": "PLoS pathogens",
"author": [
{
"family": "Mukherjee",
"given": "Sriparna"
},
{
"family": "Grouza",
"given": "Vladimir"
},
{
"family": "Tchung",
"given": "Alex"
},
{
"family": "Even",
"given": "Amandine"
},
{
"family": "Yaqubi",
"given": "Moein"
},
{
"family": "Tuznik",
"given": "Marius"
},
{
"family": "Cannon",
"given": "Tyler"
},
{
"family": "Recinto",
"given": "Sherilyn Junelle"
},
{
"family": "Gavino",
"given": "Christina"
},
{
"family": "Bourque",
"given": "Marie-Josée"
},
{
"family": "Giguère",
"given": "Nicolas"
},
{
"family": "McBride",
"given": "Heidi"
},
{
"family": "Desjardins",
"given": "Michel"
},
{
"family": "Gruenheid",
"given": "Samantha"
},
{
"family": "Stratton",
"given": "Jo Anne"
},
{
"family": "Rudko",
"given": "David A"
},
{
"family": "Trudeau",
"given": "Louis-Eric"
}
],
"container-title-short": "PLoS Pathog",
"volume": "22",
"issue": "6",
"page": "e1014315",
"DOI": "10.1371/journal.ppat.1014315",
"PMID": "42378309",
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"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
30
]
]
}
}

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