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Revisiting the role of structural connectivity-based parcellation in thalamic nuclei segmentation: Benchmarking against recent state-of-the-art methods.

Code ↔ Paper

2 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 2 matches
  1. [1] § Materials and methods › Cortical parcellation schemes ↔ 8parcellation.sh, lines 42–84 · score 0.71 · antsApplyTransforms, antsRegistrationSyNQuick.sh, Motor Cortex, affine, warp, transformation
  2. [2] § Materials and methods › Cortical parcellation schemes ↔ 23parcellation.sh, lines 44–107 · score 0.70 · antsApplyTransforms, posterior opercular, Motor Cortex, affine, warp, transformation

Paper

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The authors' code

Shell · 317 lines · 14 KB · CC-BY-4.0 · 1 match

  1. #!/bin/bash
  3. # Check if the SUBJECT variable is provided
  4. if [ -z "$1" ]; then
  5. echo "No subject provided."
  6. exit 1
  7. fi
  9. # Get the subject name
  10. SUBJECT_DIR="/home/danieldude123/project"
  13. # Access the subject passed from the main script
  14. SUBJECT="$1"
  16. # Define directories and paths
  17. script_dir="/home/danieldude123/scripts/manoj_saranathan_pulvinar"
  18. SUBJECT_DIR="/home/danieldude123/data/${SUBJECT}/T1w/Diffusion/mrtrix_analysis"
  19. result_dir="/home/danieldude123/results/Papers/with_Manoj/results/${SUBJECT}"
  20. thomas_dir="/home/danieldude123/data/${SUBJECT}/T1w/THOMAS"
  21. LEFT_FILE="$SUBJECT_DIR/WTA_mrtrix_analysis_LEFT.nii.gz"
  22. RIGHT_FILE="$SUBJECT_DIR/WTA_mrtrix_analysis_RIGHT.nii.gz"
  24. echo "Processing ${SUBJECT}..."
  26. # Define input files with full paths
  27. T1="${SUBJECT_DIR}/T1TOnodif.nii.gz" ####please rename to the T1 file that is co-registered to diff space
  28. TCK="${SUBJECT_DIR}/10M_SIFT.tck" ####please run on 10M tracks s
  30. # Paths to common files
  31. FIXED_IMAGE="$script_dir/mni_icbm152_t1_tal_nlin_asym_09a.nii" ###please change to common directory containig these 3 files
  32. HCP_22="$script_dir/HCP-MMP1_cortices_1mm.nii.gz" ###please change to common directory containig these 3 files
  33. HCP_180="$script_dir/HCP-MMP_1mm.nii.gz" ###please change to common directory containig these 3 files
  36. LEFT_THALAMUS="${thomas_dir}/left/1-THALAMUS_NF.nii.gz"
  37. RIGHT_THALAMUS="${thomas_dir}/right/1-THALAMUS_NF.nii.gz"
  38. OUTPUT_DIR="${result_dir}/10M_SIFT_output/THOMAS_thalamus_mask/8parcellation"
  39. mkdir -p "$OUTPUT_DIR"
  42. # Transform T1 to MNI space
  43. antsRegistrationSyNQuick.sh -d 3 -f "${FIXED_IMAGE}" -m "${T1}" -o "${OUTPUT_DIR}/T1_to_MNI_"
  44. if [[ ! -f "${OUTPUT_DIR}/T1_to_MNI_0GenericAffine.mat" || ! -f "${OUTPUT_DIR}/T1_to_MNI_1InverseWarp.nii.gz" ]]; then
  45. echo "Failed MNI transform for ${SUBJECT}. Skipping..."
  46. exit 1
  47. fi
  49. # Apply HCP Atlases
  50. antsApplyTransforms -d 3 -i "${HCP_22}" -r "${T1}" -o "${OUTPUT_DIR}/HCP_in_T1_coreg.nii.gz" \
  51. -t ["${OUTPUT_DIR}/T1_to_MNI_0GenericAffine.mat", 1] -t "${OUTPUT_DIR}/T1_to_MNI_1InverseWarp.nii.gz" -n GenericLabel
  52. antsApplyTransforms -d 3 -i "${HCP_180}" -r "${T1}" -o "${OUTPUT_DIR}/Bigger_HCP_in_T1_coreg.nii.gz" \
  53. -t ["${OUTPUT_DIR}/T1_to_MNI_0GenericAffine.mat", 1] -t "${OUTPUT_DIR}/T1_to_MNI_1InverseWarp.nii.gz" -n GenericLabel
  55. HCP_22a="${OUTPUT_DIR}/HCP_in_T1_coreg.nii.gz"
  56. HCP_180a="${OUTPUT_DIR}/Bigger_HCP_in_T1_coreg.nii.gz"
  58. # Extract and binarize ROIs from both 22 and 180 atlases (Left Hemisphere)
  59. declare -A ROIS_180_LEFT=(
  60. ["left_hippocampus"]=120 ["motor_cortex"]=8 ["sensory_cortex"]=9
  61. ["sensory_1"]=51 ["sensory_2"]=52 ["sensory_3"]=53
  62. )
  64. for ROI in "${!ROIS_180_LEFT[@]}"; do
  65. fslmaths "${HCP_180a}" -thr "${ROIS_180_LEFT[$ROI]}" -uthr "${ROIS_180_LEFT[$ROI]}" \
  66. "${OUTPUT_DIR}/${ROI}_unbin.nii.gz"
  67. fslmaths "${OUTPUT_DIR}/${ROI}_unbin.nii.gz" -bin "${OUTPUT_DIR}/${ROI}.nii.gz"
  68. done
  73. declare -A ROIS_22_LEFT=(
  74. ["frontal"]=19 ["premotor_cortex"]=8 ["visual1"]=1 ["visual2"]=2 ["visual3"]=3
  75. ["visual4"]=4 ["visual5"]=5 ["temporal1"]=13
  76. ["temporal2"]=14 ["parietal1"]=15 ["parietal2"]=16 ["parietal3"]=17
  77. ["prefrontal1"]=20 ["prefrontal2"]=21 ["prefrontal3"]=22
  78. )
  80. for ROI in "${!ROIS_22_LEFT[@]}"; do
  81. fslmaths "${HCP_22a}" -thr "${ROIS_22_LEFT[$ROI]}" -uthr "${ROIS_22_LEFT[$ROI]}" \
  82. "${OUTPUT_DIR}/${ROI}_unbin.nii.gz"
  83. fslmaths "${OUTPUT_DIR}/${ROI}_unbin.nii.gz" -bin "${OUTPUT_DIR}/${ROI}.nii.gz"
  84. done
  88. # Step 4: Create Tracts with tckedit (Left Hemisphere)
  91. ## Visual Tracts
  92. for i in {1..5}; do
  93. tckedit "${TCK}" -include "${LEFT_THALAMUS}" -include "${OUTPUT_DIR}/visual${i}.nii.gz" \
  94. "${OUTPUT_DIR}/visual${i}_tracks.tck" -force
  95. done
  96. tckedit "${OUTPUT_DIR}/visual1_tracks.tck" "${OUTPUT_DIR}/visual2_tracks.tck" \
  97. "${OUTPUT_DIR}/visual3_tracks.tck" "${OUTPUT_DIR}/visual4_tracks.tck" \
  98. "${OUTPUT_DIR}/visual5_tracks.tck" "${OUTPUT_DIR}/visual.tck" -force
  100. ## Temporal Tracts
  101. tckedit "${TCK}" -include "${LEFT_THALAMUS}" -include "${OUTPUT_DIR}/temporal1.nii.gz" \
  102. "${OUTPUT_DIR}/temporal1_tracks.tck" -force
  103. tckedit "${TCK}" -include "${LEFT_THALAMUS}" -include "${OUTPUT_DIR}/temporal2.nii.gz" \
  104. "${OUTPUT_DIR}/temporal2_tracks.tck" -force
  105. tckedit "${TCK}" -include "${LEFT_THALAMUS}" -include "${OUTPUT_DIR}/left_hippocampus.nii.gz" \
  106. "${OUTPUT_DIR}/hippocampus_tracks.tck" -force
  107. tckedit "${OUTPUT_DIR}/temporal1_tracks.tck" "${OUTPUT_DIR}/temporal2_tracks.tck" \
  108. "${OUTPUT_DIR}/hippocampus_tracks.tck" "${OUTPUT_DIR}/temporal.tck" -force
  110. ## Parietal Tracts
  111. for i in {1..3}; do
  112. tckedit "${TCK}" -include "${LEFT_THALAMUS}" -include "${OUTPUT_DIR}/parietal${i}.nii.gz" \
  113. "${OUTPUT_DIR}/parietal${i}_tracks.tck" -force
  114. done
  115. tckedit "${OUTPUT_DIR}/parietal1_tracks.tck" "${OUTPUT_DIR}/parietal2_tracks.tck" \
  116. "${OUTPUT_DIR}/parietal3_tracks.tck" "${OUTPUT_DIR}/parietal.tck" -force
  118. ## Motor
  119. tckedit "${TCK}" -include "${LEFT_THALAMUS}" -include "${OUTPUT_DIR}/motor_cortex.nii.gz" \
  120. "${OUTPUT_DIR}/motor.tck" -force
  122. ## Sensory Tracts
  123. fslmaths "${OUTPUT_DIR}/sensory_cortex.nii.gz" -add "${OUTPUT_DIR}/sensory_1.nii.gz" \
  124. -add "${OUTPUT_DIR}/sensory_2.nii.gz" -add "${OUTPUT_DIR}/sensory_3.nii.gz" "${OUTPUT_DIR}/sensory_cortex_unbin.nii.gz"
  125. fslmaths "${OUTPUT_DIR}/sensory_cortex_unbin.nii.gz" -bin "${OUTPUT_DIR}/sensory_cortex.nii.gz"
  127. tckedit "${TCK}" -include "${LEFT_THALAMUS}" -include "${OUTPUT_DIR}/sensory_cortex.nii.gz" \
  128. "${OUTPUT_DIR}/sensory.tck" -force
  131. ## Premotor Tract
  132. tckedit "${TCK}" -include "${LEFT_THALAMUS}" -include "${OUTPUT_DIR}/premotor_cortex.nii.gz" \
  133. "${OUTPUT_DIR}/premotor.tck" -force
  135. ## Prefrontal Tract Creation
  137. tckedit "${TCK}" -include "${LEFT_THALAMUS}" -include "${OUTPUT_DIR}/prefrontal1.nii.gz" \
  138. "${OUTPUT_DIR}/prefrontal1.tck" -force
  139. tckedit "${TCK}" -include "${LEFT_THALAMUS}" -include "${OUTPUT_DIR}/prefrontal2.nii.gz" \
  140. "${OUTPUT_DIR}/prefrontal2.tck" -force
  141. tckedit "${TCK}" -include "${LEFT_THALAMUS}" -include "${OUTPUT_DIR}/prefrontal3.nii.gz" \
  142. "${OUTPUT_DIR}/prefrontal3.tck" -force
  143. tckedit "${OUTPUT_DIR}/prefrontal1.tck" "${OUTPUT_DIR}/prefrontal2.tck" "${OUTPUT_DIR}/prefrontal3.tck" \
  144. "${OUTPUT_DIR}/prefrontal.tck" -force
  146. # Create frontal tract
  147. tckedit "${TCK}" -include "${LEFT_THALAMUS}" -include "${OUTPUT_DIR}/frontal.nii.gz" \
  148. "${OUTPUT_DIR}/frontal.tck" -force
  150. # Step 5: Generate Density Maps for Each Tract (Left Hemisphere)
  151. TRACTS=("visual" "temporal" "parietal" "motor" "sensory" "premotor" "prefrontal" "frontal")
  153. for TRACT in "${TRACTS[@]}"; do
  154. tckmap -template "${LEFT_THALAMUS}" "${OUTPUT_DIR}/${TRACT}.tck" \
  155. "${OUTPUT_DIR}/${TRACT}_density.nii.gz" -force
  157. fslmaths "${OUTPUT_DIR}/${TRACT}_density.nii.gz" -mul "${LEFT_THALAMUS}" \
  158. "${OUTPUT_DIR}/${TRACT}_masked.nii.gz"
  160. mrcalc "${OUTPUT_DIR}/${TRACT}_masked.nii.gz" \
  161. $(mrstats "${OUTPUT_DIR}/${TRACT}_masked.nii.gz" -output mean -quiet) \
  162. -div "${OUTPUT_DIR}/${TRACT}_normalized.nii.gz" -force
  164. fslmaths "${OUTPUT_DIR}/${TRACT}_normalized.nii.gz" -thrP 25 \
  165. "${OUTPUT_DIR}/${TRACT}_thresh25.nii.gz"
  166. done
  168. # Step 6: Generate WTA Output (Left Hemisphere)
  169. find_the_biggest \
  170. "${OUTPUT_DIR}/temporal_normalized.nii.gz" \
  171. "${OUTPUT_DIR}/parietal_normalized.nii.gz" \
  172. "${OUTPUT_DIR}/motor_normalized.nii.gz" \
  173. "${OUTPUT_DIR}/sensory_normalized.nii.gz" \
  174. "${OUTPUT_DIR}/visual_normalized.nii.gz" \
  175. "${OUTPUT_DIR}/premotor_normalized.nii.gz" \
  176. "${OUTPUT_DIR}/prefrontal_normalized.nii.gz" \
  177. "${OUTPUT_DIR}/frontal_normalized.nii.gz" \
  178. "${OUTPUT_DIR}/WTA_${SUBJECT}_LEFT.nii.gz"
  180. # Add +1 to all IDs in the output
  181. fslmaths "${OUTPUT_DIR}/WTA_${SUBJECT}_LEFT.nii.gz" -add 1 "${OUTPUT_DIR}/WTA_${SUBJECT}_LEFT_incremented.nii.gz"
  182. fslmaths "${OUTPUT_DIR}/WTA_${SUBJECT}_LEFT_incremented.nii.gz" -sub 1 -thr 0 "${OUTPUT_DIR}/WTA_${SUBJECT}_LEFT_final.nii.gz"
  184. echo "Finished processing ${SUBJECT}. Left hemisphere completed."
  186. # Now, perform the same steps for the Right Hemisphere
  188. # Extract and binarize ROIs from both 22 and 180 atlases (Right Hemisphere)
  189. declare -A ROIS_180_RIGHT=(
  190. ["left_hippocampus"]=320 ["motor_cortex"]=208 ["sensory_cortex"]=209
  191. ["sensory_1"]=251 ["sensory_2"]=252 ["sensory_3"]=253
  192. )
  194. for ROI in "${!ROIS_180_RIGHT[@]}"; do
  195. fslmaths "${HCP_180a}" -thr "${ROIS_180_RIGHT[$ROI]}" -uthr "${ROIS_180_RIGHT[$ROI]}" \
  196. "${OUTPUT_DIR}/${ROI}_unbin.nii.gz"
  197. fslmaths "${OUTPUT_DIR}/${ROI}_unbin.nii.gz" -bin "${OUTPUT_DIR}/${ROI}.nii.gz"
  198. done
  200. declare -A ROIS_22_RIGHT=(
  201. ["frontal"]=119 ["premotor_cortex"]=108 ["visual1"]=101 ["visual2"]=102 ["visual3"]=103
  202. ["visual4"]=104 ["visual5"]=105 ["temporal1"]=113
  203. ["temporal2"]=114 ["parietal1"]=115 ["parietal2"]=116 ["parietal3"]=117
  204. ["prefrontal1"]=120 ["prefrontal2"]=121 ["prefrontal3"]=122
  205. )
  207. for ROI in "${!ROIS_22_RIGHT[@]}"; do
  208. fslmaths "${HCP_22a}" -thr "${ROIS_22_RIGHT[$ROI]}" -uthr "${ROIS_22_RIGHT[$ROI]}" \
  209. "${OUTPUT_DIR}/${ROI}_unbin.nii.gz"
  210. fslmaths "${OUTPUT_DIR}/${ROI}_unbin.nii.gz" -bin "${OUTPUT_DIR}/${ROI}.nii.gz"
  211. done
  213. # Step 4: Create Tracts with tckedit (Right Hemisphere)
  215. ## Visual Tracts
  216. for i in {1..5}; do
  217. tckedit "${TCK}" -include "${RIGHT_THALAMUS}" -include "${OUTPUT_DIR}/visual${i}.nii.gz" \
  218. "${OUTPUT_DIR}/visual${i}_tracks.tck" -force
  219. done
  220. tckedit "${OUTPUT_DIR}/visual1_tracks.tck" "${OUTPUT_DIR}/visual2_tracks.tck" \
  221. "${OUTPUT_DIR}/visual3_tracks.tck" "${OUTPUT_DIR}/visual4_tracks.tck" \
  222. "${OUTPUT_DIR}/visual5_tracks.tck" "${OUTPUT_DIR}/visual.tck" -force
  224. ## Temporal Tracts
  225. tckedit "${TCK}" -include "${RIGHT_THALAMUS}" -include "${OUTPUT_DIR}/temporal1.nii.gz" \
  226. "${OUTPUT_DIR}/temporal1_tracks.tck" -force
  227. tckedit "${TCK}" -include "${RIGHT_THALAMUS}" -include "${OUTPUT_DIR}/temporal2.nii.gz" \
  228. "${OUTPUT_DIR}/temporal2_tracks.tck" -force
  229. tckedit "${TCK}" -include "${RIGHT_THALAMUS}" -include "${OUTPUT_DIR}/left_hippocampus.nii.gz" \
  230. "${OUTPUT_DIR}/hippocampus_tracks.tck" -force
  231. tckedit "${OUTPUT_DIR}/temporal1_tracks.tck" "${OUTPUT_DIR}/temporal2_tracks.tck" \
  232. "${OUTPUT_DIR}/hippocampus_tracks.tck" "${OUTPUT_DIR}/temporal.tck" -force
  234. ## Parietal Tracts
  235. for i in {1..3}; do
  236. tckedit "${TCK}" -include "${RIGHT_THALAMUS}" -include "${OUTPUT_DIR}/parietal${i}.nii.gz" \
  237. "${OUTPUT_DIR}/parietal${i}_tracks.tck" -force
  238. done
  239. tckedit "${OUTPUT_DIR}/parietal1_tracks.tck" "${OUTPUT_DIR}/parietal2_tracks.tck" \
  240. "${OUTPUT_DIR}/parietal3_tracks.tck" "${OUTPUT_DIR}/parietal.tck" -force
  242. ## Motor
  243. tckedit "${TCK}" -include "${RIGHT_THALAMUS}" -include "${OUTPUT_DIR}/motor_cortex.nii.gz" \
  244. "${OUTPUT_DIR}/motor.tck" -force
  247. ## Sensory Tracts
  249. fslmaths "${OUTPUT_DIR}/sensory_cortex.nii.gz" -add "${OUTPUT_DIR}/sensory_1.nii.gz" \
  250. -add "${OUTPUT_DIR}/sensory_2.nii.gz" -add "${OUTPUT_DIR}/sensory_3.nii.gz" "${OUTPUT_DIR}/sensory_cortex_unbin.nii.gz"
  251. fslmaths "${OUTPUT_DIR}/sensory_cortex_unbin.nii.gz" -bin "${OUTPUT_DIR}/sensory_cortex.nii.gz"
  253. tckedit "${TCK}" -include "${RIGHT_THALAMUS}" -include "${OUTPUT_DIR}/sensory_cortex.nii.gz" \
  254. "${OUTPUT_DIR}/sensory.tck" -force
  257. ## Premotor Tract
  258. tckedit "${TCK}" -include "${RIGHT_THALAMUS}" -include "${OUTPUT_DIR}/premotor_cortex.nii.gz" \
  259. "${OUTPUT_DIR}/premotor.tck" -force
  261. ## Prefrontal Tract Creation
  263. tckedit "${TCK}" -include "${RIGHT_THALAMUS}" -include "${OUTPUT_DIR}/prefrontal1.nii.gz" \
  264. "${OUTPUT_DIR}/prefrontal1.tck" -force
  265. tckedit "${TCK}" -include "${RIGHT_THALAMUS}" -include "${OUTPUT_DIR}/prefrontal2.nii.gz" \
  266. "${OUTPUT_DIR}/prefrontal2.tck" -force
  267. tckedit "${TCK}" -include "${RIGHT_THALAMUS}" -include "${OUTPUT_DIR}/prefrontal3.nii.gz" \
  268. "${OUTPUT_DIR}/prefrontal3.tck" -force
  270. tckedit "${OUTPUT_DIR}/prefrontal1.tck" "${OUTPUT_DIR}/prefrontal2.tck" "${OUTPUT_DIR}/prefrontal3.tck" \
  271. "${OUTPUT_DIR}/prefrontal.tck" -force
  273. # Create frontal tract
  274. tckedit "${TCK}" -include "${RIGHT_THALAMUS}" -include "${OUTPUT_DIR}/frontal.nii.gz" \
  275. "${OUTPUT_DIR}/frontal.tck" -force
  277. # Step 5: Generate Density Maps for Each Tract (Right Hemisphere)
  278. for TRACT in "${TRACTS[@]}"; do
  279. tckmap -template "${RIGHT_THALAMUS}" "${OUTPUT_DIR}/${TRACT}.tck" \
  280. "${OUTPUT_DIR}/${TRACT}_density.nii.gz" -force
  282. fslmaths "${OUTPUT_DIR}/${TRACT}_density.nii.gz" -mul "${RIGHT_THALAMUS}" \
  283. "${OUTPUT_DIR}/${TRACT}_masked.nii.gz"
  285. mrcalc "${OUTPUT_DIR}/${TRACT}_masked.nii.gz" \
  286. $(mrstats "${OUTPUT_DIR}/${TRACT}_masked.nii.gz" -output mean -quiet) \
  287. -div "${OUTPUT_DIR}/${TRACT}_normalized.nii.gz" -force
  289. fslmaths "${OUTPUT_DIR}/${TRACT}_normalized.nii.gz" -thrP 25 \
  290. "${OUTPUT_DIR}/${TRACT}_thresh25.nii.gz"
  291. done
  293. # Step 6: Generate WTA Output (Right Hemisphere)
  294. find_the_biggest \
  295. "${OUTPUT_DIR}/temporal_normalized.nii.gz" \
  296. "${OUTPUT_DIR}/parietal_normalized.nii.gz" \
  297. "${OUTPUT_DIR}/motor_normalized.nii.gz" \
  298. "${OUTPUT_DIR}/sensory_normalized.nii.gz" \
  299. "${OUTPUT_DIR}/visual_normalized.nii.gz" \
  300. "${OUTPUT_DIR}/premotor_normalized.nii.gz" \
  301. "${OUTPUT_DIR}/prefrontal_normalized.nii.gz" \
  302. "${OUTPUT_DIR}/frontal_normalized.nii.gz" \
  303. "${OUTPUT_DIR}/WTA_${SUBJECT}_RIGHT.nii.gz"
  305. echo "Finished processing ${SUBJECT}. Right hemisphere completed."
  307. # Add +1 to all IDs in the output
  308. fslmaths "${OUTPUT_DIR}/WTA_${SUBJECT}_RIGHT.nii.gz" -add 1 "${OUTPUT_DIR}/WTA_${SUBJECT}_RIGHT_incremented.nii.gz"
  309. fslmaths "${OUTPUT_DIR}/WTA_${SUBJECT}_RIGHT_incremented.nii.gz" -sub 1 -thr 0 "${OUTPUT_DIR}/WTA_${SUBJECT}_RIGHT_final.nii.gz"
  311. echo "All processing completed for ${SUBJECT}."
  313. fi
  314. done

8parcellation.sh, under CC-BY-4.0 · at the source

Overview

Authors: Daniel H Nguyen1, Debottama Das2, Ali Bilgin2, Dianne Patterson3, Matthew Hook4, Chris Butson4, Alberto Cacciola5,6, Vinod Kumar Jangir7, Manojkumar Saranathan8
ORCID iDs: Debottama Das, Ali Bilgin, Chris Butson, Alberto Cacciola, Manojkumar Saranathan
  1. University of Massachusetts Chan Medical School, Worcester, Massachusetts, United States of America
  2. Department of Electrical and Computer Engineering, University of Arizona, Tucson, Arizona, United States of America
  3. University of Arizona at Tucson, Tucson, Arizona, United States of America
  4. Department of Neurology, University of Florida, GainesvilleFlorida, United States of America
  5. Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini, Pieve Emanuele, Milan, Italy
  6. IRCCS Humanitas Research Hospital, Via Manzoni, Rozzano, Milan, Italy
  7. Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
  8. Department of Radiology, University of Massachusetts Chan Medical School, Worcester, Massachusetts, United States of America
Institutions: University of Massachusetts Chan Medical School (United States); University of Arizona (United States); University of Florida (United States); Humanitas University (Italy); IRCCS Humanitas Research Hospital (Italy); Max Planck Institute for Biological Cybernetics (Germany)
Journal: PloS one, volume 21, issue 6, article e0351431
Dates: received 29 October 2025; accepted 25 May 2026; published online 15 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pone.0351431 · PMID 42296126 · PMCID PMC13268177 · OpenAlex W7164808153
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), histology / microscopy (modality), human (organism), methods / tools (subfield)
Methods: Connectivity, Machine learning, fMRI & imaging
MeSH: Connectome*, Image Processing, Computer-Assisted*, Thalamic Nuclei*, Adult, Algorithms, Benchmarking, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging, Female, Humans, Magnetic Resonance Imaging, Male, Young Adult (* major topic)
Topic: Advanced Neuroimaging Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: National Institute of Biomedical Imaging and Bioengineering (R01 EB032674); NIBIB NIH HHS (R01 EB032674)
Citations: not cited yet (Europe PMC); 70 references in the paper

Abstract

Leveraging diffusion tractography, connectivity-based parcellation (CBP) is one of the oldest methods for thalamic nuclei segmentation. The goal of this work was to reassess CBP using higher spatial resolution diffusion MRI data and reconstruction algorithms, and to compare it with recent state-of-the-art methods for thalamic nuclei segmentation. Furthermore, these methods were systematically evaluated against three histological atlases and one functional MRI–based atlas to examine their relative anatomical similarities and differences. High resolution diffusion and T1-weighted MRI data from 67 healthy individuals in the Human Connectome Project Young Adult database were analyzed. CBP was performed using probabilistic tractography with cortical targets derived from combining labels of the Human Connectome Project Multi-Modal Parcellation 1.0 atlas into 8, 11, and 23 regions. Results were compared against three recent methods: orientation distribution function clustering (ODF), track density imaging (TDI), and structural MRI-based segmentation. Group level analyses were conducted in the Montreal Neurological Institute space, and Dice overlap coefficients were calculated using four atlases (three histological, one functional). CBP results using newer data and methods were still remarkably similar to the original CBP parcellation results. Across atlases, a consistent hierarchy was observed: HIPS-THOMAS performed best, followed by TDI, ODF, and CBP (Kendall’s W = 1.00, p = 0.007). Histological atlases showed strong mutual agreement (Pearson r = 0.71–0.85), whereas the Zhang atlas demonstrated lower concordance (Pearson r = 0.51–0.63). Despite methodological advances, CBP remains constrained in its ability to delineate thalamic nuclei with histological accuracy. By contrast, structural and diffusion microstructural approaches provided better nuclear localization. These findings highlight the need for hybrid workflows that integrate structural and diffusion-based information to enable more reliable thalamic segmentation for neuroscience research.

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Zenodo 20473971

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: Shell (3)
Size: 8 files, 3 scripts
Software Heritage: not checked
Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ANTs (3 files), FSL (3 files), MRtrix3 (3 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
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3 files
At the source: doi.org/10.5281/zenodo.20473971

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

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Nguyen, D. H., Das, D., Bilgin, A., Patterson, D., Hook, M., Butson, C., Cacciola, A., Jangir, V. K., & Saranathan, M. (2026). Revisiting the role of structural connectivity-based parcellation in thalamic nuclei segmentation: Benchmarking against recent state-of-the-art methods. PloS one, 21(6), e0351431. https://doi.org/10.1371/journal.pone.0351431

BibTeX

@article{nguyen2026revisiting,
author = {Nguyen, Daniel H and Das, Debottama and Bilgin, Ali and Patterson, Dianne and Hook, Matthew and Butson, Chris and Cacciola, Alberto and Jangir, Vinod Kumar and Saranathan, Manojkumar},
title = {{Revisiting the role of structural connectivity-based parcellation in thalamic nuclei segmentation: Benchmarking against recent state-of-the-art methods}},
journal = {PloS one},
year = {2026},
month = jun,
volume = {21},
number = {6},
pages = {e0351431},
publisher = {PLOS},
issn = {1932-6203},
doi = {10.1371/journal.pone.0351431},
url = {https://doi.org/10.1371/journal.pone.0351431},
pmid = {42296126},
pmcid = {PMC13268177}
}

RIS

TY - JOUR
AU - Nguyen, Daniel H
AU - Das, Debottama
AU - Bilgin, Ali
AU - Patterson, Dianne
AU - Hook, Matthew
AU - Butson, Chris
AU - Cacciola, Alberto
AU - Jangir, Vinod Kumar
AU - Saranathan, Manojkumar
TI - Revisiting the role of structural connectivity-based parcellation in thalamic nuclei segmentation: Benchmarking against recent state-of-the-art methods
T2 - PloS one
J2 - PLoS One
PY - 2026
DA - 2026/06/15
VL - 21
IS - 6
SP - e0351431
SN - 1932-6203
PB - PLOS
DO - 10.1371/journal.pone.0351431
UR - https://doi.org/10.1371/journal.pone.0351431
LA - en
ER -

CSL-JSON

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"title": "Revisiting the role of structural connectivity-based parcellation in thalamic nuclei segmentation: Benchmarking against recent state-of-the-art methods",
"container-title": "PloS one",
"author": [
{
"family": "Nguyen",
"given": "Daniel H"
},
{
"family": "Das",
"given": "Debottama"
},
{
"family": "Bilgin",
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{
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{
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{
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{
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{
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"given": "Manojkumar"
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"issue": "6",
"page": "e0351431",
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"PMID": "42296126",
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"language": "en",
"issued": {
"date-parts": [
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}

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