OSCR

Intracortical microstructure profiling: A cross-modal method for indexing cortical lamination.

Code ↔ Paper

8 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 8 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § The Intracortical Microstructure Profiling Workflow ↔ microstructure_profiling.sh, lines 198–264 · score 0.79 · co registered, equivolumetric surfaces, surface space, micro image, intracortical surfaces, microstructure profile
  2. [2] § The Intracortical Microstructure Profiling Workflow ↔ functions/generate_equivolumetric_surfaces.py, lines 94–108 · score 0.69 · volume fraction, cortical volume, equivolumetric surface, vertex
  3. [3] § The Intracortical Microstructure Profiling Workflow ↔ functions/compute_t1t2_ratio.sh, the whole file · a weak match · score 0.67 · ratio image, bias corrected, T2w image, micapipe, T1w, MRI
  4. [4] § The Intracortical Microstructure Profiling Workflow ↔ functions/compute_t1t2_ratio_predefined.sh, the whole file · a weak match · score 0.66 · ratio image, bias corrected, T2w image, micapipe, T1w, MRI
  5. [5] § The Intracortical Microstructure Profiling Workflow ↔ microstructure_profiling.sh, lines 198–264 · score 0.64 · generating equivolumetric, surface space, intracortical surfaces, microstructure profiles, FreeSurfer
  6. [6] § The Intracortical Microstructure Profiling Workflow ↔ functions/collate_MP.py, lines 60–92 · score 0.62 · cortical depths, u3, kurtosis, skewness, u4, u0
  7. [7] § The Intracortical Microstructure Profiling Workflow ↔ functions/collate_MP.py, lines 60–92 · score 0.53 · image intensities, cortical depths, SD, profile, microstructural
  8. [8] § The Intracortical Microstructure Profiling Workflow ↔ functions/compute_snr.sh, lines 1–39 · score 0.52 · T1 weighted imaging, micro image, computation, SPACE

Paper

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

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The authors' code

Shell · 403 lines · 18 KB · MIT · 2 matches

  1. #!/bin/bash
  3. # Auto-detect location of the toolbox bin directory
  4. SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
  5. TOOLBOX_BIN="${SCRIPT_DIR}/functions"
  6. export TOOLBOX_BIN
  8. # -----------------------------
  9. # Microstructure Profiling Toolbox Wrapper
  10. # -----------------------------
  11. show_help() {
  12. echo "Usage: $0 [--micro-image FILE] [--anat-dir DIR] --subject-id ID --subjects-dir DIR --output-dir DIR --fs-dir DIR --sing-dir DIR [--num_surfaces 14]"
  13. echo
  14. echo "Arguments:"
  15. echo " --micro-image FILE Path to a precomputed microstructural image (optional)"
  16. echo " --anat-dir DIR Path to a BIDS anat/ directory (optional, necessary for creation of T1wDividedByT2w image and cortical surface construction)"
  17. echo " --subject-id ID Subject ID (e.g., sub-001) [required]"
  18. echo " --subjects-dir DIR Path to Freesurfer-style SUBJECTS_DIR [required] (if the directory doesn't contain surfaces for the specified subject, Fastsurfer will be run)"
  19. echo " --output-dir DIR Output directory for toolbox results [required]"
  20. echo " --fs-dir DIR Path to the FreeSurfer directory [required] (should contain standard license file, 'license.txt')"
  21. echo " --sing-dir DIR Path to the directory with singularities [required] (must contain cortpro.sif and, if Freesurfer output is not yet available, fastsurfer_gpu.sif)"
  22. echo " --num-surfaces N Number of intracortical surfaces (default: 14)"
  23. echo " --surface-output NAME Name of standard surface for output, currently compatible with any fsaverage number (default: fsaverage5), native or fsLR32k"
  24. echo " --ratio-type NAME Type of image for ratio with T1w (default: T2w). In principle, accepts any BIDS suffix that is housed in anat"
  25. echo " --t1-file PATH Custom T1w (must be paired with --t2-file)"
  26. echo " --t2-file PATH Custom T2w (must be paired with --t1-file)"
  27. echo " --skip-bias-correct Option to turn off bias correction on T1w and T2w images"
  28. echo " --keep-inter-files Option to keep all intermediary files, which are otherwise removed in a final clean up"
  29. echo " --run-snr Option to compute SNR profiles"
  30. echo " -h, --help Display this help message"
  31. }
  33. # -----------------------------
  34. # Parse Arguments
  35. # -----------------------------
  36. MICRO_IMAGE=""
  37. ANAT_DIR=""
  38. SUBJECT_ID=""
  39. SUBJECTS_DIR=""
  40. OUTPUT_DIR=""
  41. FREESURFER_HOME=""
  42. SING_DIR=""
  43. NUM_SURFACES=14 # default
  44. SURF_OUT=fsaverage5 # default
  45. RATIO_TYPE=T2w # default
  46. T1_FILE=""
  47. T2_FILE=""
  48. SKIP_BC=0
  49. CLEAN_UP=1
  50. RUN_SNR=0
  51. RESLICE_MICRO=0
  52. REGISTER_T1=0
  54. while [[ $# -gt 0 ]]; do
  55. case "$1" in
  56. --micro-image)
  57. MICRO_IMAGE="$2"
  58. shift 2
  59. ;;
  60. --anat-dir)
  61. ANAT_DIR="$2"
  62. shift 2
  63. ;;
  64. --subject-id)
  65. SUBJECT_ID="$2"
  66. shift 2
  67. ;;
  68. --subjects-dir)
  69. SUBJECTS_DIR="$2"
  70. shift 2
  71. ;;
  72. --output-dir)
  73. OUTPUT_DIR="$2"
  74. shift 2
  75. ;;
  76. --fs-dir)
  77. FREESURFER_HOME="$2"
  78. shift 2
  79. ;;
  80. --sing-dir)
  81. SING_DIR="$2"
  82. shift 2
  83. ;;
  84. --num-surfaces)
  85. NUM_SURFACES="$2"
  86. shift 2
  87. ;;
  88. --surface-output)
  89. SURF_OUT="$2"
  90. shift 2
  91. ;;
  92. --ratio-type)
  93. RATIO_TYPE="$2"
  94. shift 2
  95. ;;
  96. --t1-file)
  97. T1_FILE="$2"
  98. shift 2
  99. ;;
  100. --t2-file)
  101. T2_FILE="$2"
  102. shift 2
  103. ;;
  104. --skip-bias-correct)
  105. SKIP_BC=1
  106. shift
  107. ;;
  108. --keep-inter-files)
  109. CLEAN_UP=0
  110. shift
  111. ;;
  112. --run-snr)
  113. RUN_SNR=1
  114. shift
  115. ;;
  116. -h|--help)
  117. show_help
  118. exit 0
  119. ;;
  120. *)
  121. echo "[ERROR] Unknown option: $1"
  122. show_help
  123. exit 1
  124. ;;
  125. esac
  126. done
  129. # -----------------------------
  130. # Validate Required Inputs
  131. # -----------------------------
  132. if [[ -z "$SUBJECT_ID" || -z "$SUBJECTS_DIR" || -z "$OUTPUT_DIR" || -z "$FREESURFER_HOME" || -z "$SING_DIR" ]]; then
  133. echo "[ERROR] --subject-id, --subjects-dir, --output-dir, --fs-dir and --sing-dir are all required."
  134. exit 1
  135. fi
  136. export FREESURFER_HOME
  137. export SUBJECTS_DIR
  138. export SING_DIR
  140. if ! command -v singularity &> /dev/null; then
  141. echo "[ERROR] Singularity not found - unable to continue"
  142. exit 1
  143. fi
  145. SING_IMG="$SING_DIR/cortpro.sif"
  146. if [[ ! -f $SING_IMG ]]; then
  147. echo "[ERROR] cortpro.sif not found at: $SING_DIR"
  148. exit 1
  149. fi
  151. # Validate NUM_SURFACES is a positive integer
  152. if ! [[ "$NUM_SURFACES" =~ ^[0-9]+$ ]]; then
  153. echo "[ERROR] --num-surfaces must be a positive integer."
  154. exit 1
  155. fi
  157. # Create output directory if needed
  158. mkdir -p "$OUTPUT_DIR"/"$SUBJECT_ID" || {
  159. echo "[ERROR] Failed to create output directory: "$OUTPUT_DIR"/"$SUBJECT_ID""
  160. exit 1
  161. }
  163. # -----------------------------
  164. # Compile microstructure image - creates new file with standardised naming for micro image in its native space (Nb: T1-space for T1w/T2w)
  165. # -----------------------------
  167. if [[ -n "$MICRO_IMAGE" ]]; then
  168. # pre-made micro image case
  169. echo "[INFO] Using precomputed microstructure image: $MICRO_IMAGE"
  170. cp ${MICRO_IMAGE} $OUTPUT_DIR/$SUBJECT_ID/"$SUBJECT_ID"_space-native_desc-micro.nii.gz
  171. elif [[ -n "$T1_FILE" || -n "$T2_FILE" ]]; then
  172. # predefined T1/T2 case
  173. if [[ -z "$T1_FILE" || -z "$T2_FILE" ]]; then
  174. echo "[ERROR] --t1-file and --t2-file must be provided together."
  175. exit 1
  176. fi
  177. if [[ ! -f "$T1_FILE" || ! -f "$T2_FILE" ]]; then
  178. echo "[ERROR] Provided T1 or T2 file does not exist."
  179. exit 1
  180. fi
  181. bash "$TOOLBOX_BIN/compute_t1t2_ratio_predefined.sh" \
  182. "$T1_FILE" "$T2_FILE" "$SUBJECT_ID" "$OUTPUT_DIR" "$SKIP_BC"
  183. cp "$OUTPUT_DIR"/"$SUBJECT_ID"/T1wDividedByT2w.nii.gz $OUTPUT_DIR/$SUBJECT_ID/"$SUBJECT_ID"_space-native_desc-micro.nii.gz
  184. REGISTER_T1=1
  185. else
  186. # BIDS-derived case
  187. if [[ -z "$ANAT_DIR" ]]; then
  188. echo "[ERROR] No --anat-dir provided to compute T1/T2 ratio."
  189. exit 1
  190. fi
  191. bash "$TOOLBOX_BIN/compute_t1t2_ratio.sh" \
  192. "$ANAT_DIR" "$SUBJECT_ID" "$OUTPUT_DIR" "$RATIO_TYPE" "$SKIP_BC"
  193. cp "$OUTPUT_DIR"/"$SUBJECT_ID"/T1wDividedBy${RATIO_TYPE}.nii.gz $OUTPUT_DIR/$SUBJECT_ID/"$SUBJECT_ID"_space-native_desc-micro.nii.gz
  194. REGISTER_T1=1
  195. fi
  198. # -----------------------------
  199. # Check for Freesurfer output and/or run Fastsurfer
  200. # -----------------------------
  201. if [[ ! -f "$SUBJECTS_DIR"/"$SUBJECT_ID"/surf/lh.pial ]]; then
  202. echo "[WARNING] Freesurfer data not found at "$SUBJECTS_DIR"/"$SUBJECT_ID". Will try to run Fastsurfer"
  203. if [[ -z "$ANAT_DIR" ]]; then
  204. echo "[ERROR] Freesurfer output missing, and no --anat-dir provided to run Fastsurfer on."
  205. exit 1
  206. fi
  207. bash "$TOOLBOX_BIN/run_fastsurfer.sh" "$ANAT_DIR" "$SUBJECT_ID" "$SUBJECTS_DIR" "$OUTPUT_DIR"
  208. if [[ ! -n "$MICRO_IMAGE" ]]; then
  209. RESLICE_MICRO=1 # Defines whether reslicing of affine registration will be used for co-registration of micro-image. Dependent on surface generation from T1 in micro-image.
  210. fi
  211. else
  212. echo "[INFO] Found Freesurfer directory: "$SUBJECTS_DIR"/"$SUBJECT_ID""
  213. fi
  215. # -----------------------------
  216. # Generate intracortical surfaces
  217. # -----------------------------
  218. echo "[INFO] Creating intracortical surfaces"
  219. total_surfaces=$((NUM_SURFACES + 2))
  220. for hemi in lh rh ; do
  221. python3 ${TOOLBOX_BIN}/generate_equivolumetric_surfaces.py \
  222. ${SUBJECTS_DIR}/${SUBJECT_ID}/surf/${hemi}.pial \
  223. ${SUBJECTS_DIR}/${SUBJECT_ID}/surf/${hemi}.white \
  224. $total_surfaces \
  225. ${OUTPUT_DIR}/${SUBJECT_ID}/${hemi}. \
  226. /tmp/ \
  227. --software freesurfer --subject_id $SUBJECT_ID
  228. done
  229. rm -rfv ${OUTPUT_DIR}/${SUBJECT_ID}/*.0.0.pial ${OUTPUT_DIR}/${SUBJECT_ID}/*.1.0.pial # removing pial and wm surfaces
  232. # -----------------------------
  233. # Co-register microstructure image
  234. # -----------------------------
  235. if [[ "$RESLICE_MICRO" == 1 ]]; then
  236. echo "[INFO] Reslicing micro-image to surface space"
  237. mri_vol2vol --mov $OUTPUT_DIR/$SUBJECT_ID/"$SUBJECT_ID"_space-native_desc-micro.nii.gz \
  238. --targ ${SUBJECTS_DIR}/${SUBJECT_ID}/mri/rawavg.mgz \
  239. --regheader \
  240. --o "$OUTPUT_DIR"/"$SUBJECT_ID"/"$SUBJECT_ID"_space-fsnative_desc-micro.nii.gz \
  241. --no-save-reg
  242. else
  243. if [[ "$REGISTER_T1" == 1 ]]; then
  244. echo "[INFO] Performing affine registration of T1 to surface space"
  245. # define image to use as template for transformation (micro volume to surface space)
  246. if [[ "$SKIP_BC" == 1 ]]; then
  247. cp $OUTPUT_DIR/$SUBJECT_ID/T1w.nii.gz $OUTPUT_DIR/$SUBJECT_ID/"$SUBJECT_ID"_space-native_desc-template.nii.gz
  248. else
  249. cp $OUTPUT_DIR/$SUBJECT_ID/T1w_BC.nii.gz $OUTPUT_DIR/$SUBJECT_ID/"$SUBJECT_ID"_space-native_desc-template.nii.gz
  250. fi
  251. else
  252. echo "[INFO] Performing affine registration of micro-image to surface space"
  253. cp ${MICRO_IMAGE} $OUTPUT_DIR/$SUBJECT_ID/"$SUBJECT_ID"_space-native_desc-template.nii.gz
  254. cp ${MICRO_IMAGE} $OUTPUT_DIR/$SUBJECT_ID/"$SUBJECT_ID"_space-native_desc-micro.nii.gz
  255. fi
  256. if [[ ! -f "$OUTPUT_DIR"/"$SUBJECT_ID"/"$SUBJECT_ID"_space-fsnative_desc-micro.nii.gz ]] ; then
  257. singularity exec -B $SUBJECTS_DIR/:/subjects_dir \
  258. -B $OUTPUT_DIR/:/out_dir \
  259. -B $TOOLBOX_BIN/:/toolbox_bin \
  260. -B $FREESURFER_HOME/:/freesurfer \
  261. "${SING_IMG}" \
  262. /toolbox_bin/coregister_micro.sh "$SUBJECT_ID"
  263. fi
  264. fi
  267. # -----------------------------
  268. # Compute SNR of microstructure image
  269. # -----------------------------
  270. if [[ "$RUN_SNR" == 1 ]]; then
  271. echo "[INFO] Computing SNR along profiles"
  272. singularity exec -B $SUBJECTS_DIR/:/subjects_dir \
  273. -B $OUTPUT_DIR/:/out_dir \
  274. -B $TOOLBOX_BIN/:/toolbox_bin \
  275. "${SING_IMG}" \
  276. /toolbox_bin/compute_snr.sh "$SUBJECT_ID" 9
  277. fi
  280. # -----------------------------
  281. # Sample microstructure profiles
  282. # -----------------------------
  283. # create symbolic link to fsaverage
  284. if [[ "$SURF_OUT" == *"fsaverage"* ]] ; then
  285. ln -s $FREESURFER_HOME/subjects/$SURF_OUT $SUBJECTS_DIR
  286. fi
  287. if [[ "$SURF_OUT" == "fsLR32k" ]] ; then
  288. ln -s $FREESURFER_HOME/subjects/fsaverage $SUBJECTS_DIR
  289. fi
  291. for hemi in lh rh ; do
  292. [[ $hemi == lh ]] && HEMI=L || HEMI=R
  293. # find all intracortical surfaces, list by creation time, sample intensities and convert to fsaverage
  294. x=$(ls -t ${OUTPUT_DIR}/${SUBJECT_ID}/${hemi}.0.*)
  295. for n in $(seq 1 1 ${NUM_SURFACES}) ; do
  297. which_surf=$(sed -n "${n}p" <<< "$x")
  298. filename=${which_surf##*/}
  299. if [[ ! -f ${SUBJECTS_DIR}/${SUBJECT_ID}/surf/$filename ]] ; then
  300. cp $which_surf ${SUBJECTS_DIR}/${SUBJECT_ID}/surf/$filename
  301. fi
  302. shortname=${filename#*.}
  304. # sample microstructure along intracortical surface
  305. mri_vol2surf --mov "$OUTPUT_DIR"/"$SUBJECT_ID"/"$SUBJECT_ID"_space-fsnative_desc-micro.nii.gz \
  306. --regheader ${SUBJECT_ID} \
  307. --hemi ${hemi} \
  308. --surf $shortname \
  309. --o "$OUTPUT_DIR"/"$SUBJECT_ID"/"$SUBJECT_ID"_hemi-${HEMI}_surf-fsnative_MP-${n}.mgh \
  310. --interp trilinear
  312. # transform to desired output space
  313. if [[ "$SURF_OUT" == *"fsaverage"* ]] ; then
  314. # transform to fsaverage
  315. mri_surf2surf --hemi ${hemi} \
  316. --srcsubject $SUBJECT_ID --srcsurfval "$OUTPUT_DIR"/"$SUBJECT_ID"/"$SUBJECT_ID"_hemi-${HEMI}_surf-fsnative_MP-${n}.mgh \
  317. --trgsubject $SURF_OUT --trgsurfval "$OUTPUT_DIR"/"$SUBJECT_ID"/"$SUBJECT_ID"_hemi-${HEMI}_surf-${SURF_OUT}_MP-${n}.mgh
  318. fi
  319. if [[ "$SURF_OUT" == "fsLR32k" ]] ; then
  320. # transform to fsaverage
  321. mri_surf2surf --hemi ${hemi} \
  322. --srcsubject $SUBJECT_ID --srcsurfval "$OUTPUT_DIR"/"$SUBJECT_ID"/"$SUBJECT_ID"_hemi-${HEMI}_surf-fsnative_MP-${n}.mgh \
  323. --trgsubject fsaverage --trgsurfval "$OUTPUT_DIR"/"$SUBJECT_ID"/"$SUBJECT_ID"_hemi-${HEMI}_surf-fsaverage_MP-${n}.shape.gii
  324. # transform to fsLR32k using wb_command
  325. singularity exec -B $OUTPUT_DIR:/out_dir \
  326. -B $SCRIPT_DIR/templates:/templates \
  327. "${SING_IMG}" \
  328. wb_command -metric-resample \
  329. /out_dir/"$SUBJECT_ID"/"$SUBJECT_ID"_hemi-${HEMI}_surf-fsaverage_MP-${n}.shape.gii \
  330. /templates/fsaverage_std_sphere.${HEMI}.164k_fsavg_${HEMI}.surf.gii \
  331. /templates/fs_LR-deformed_to-fsaverage.${HEMI}.sphere.32k_fs_LR.surf.gii \
  332. ADAP_BARY_AREA \
  333. /out_dir/"$SUBJECT_ID"/"$SUBJECT_ID"_hemi-${HEMI}_surf-fsLR32k_MP-${n}.shape.gii \
  334. -area-metrics \
  335. /templates/fsaverage.${HEMI}.midthickness_va_avg.164k_fsavg_${HEMI}.shape.gii \
  336. /templates/fs_LR.${HEMI}.midthickness_va_avg.32k_fs_LR.shape.gii
  337. fi
  339. if [[ "$RUN_SNR" == 1 ]]; then
  340. # sample SNR along intracortical surface
  341. mri_vol2surf --mov "$OUTPUT_DIR"/"$SUBJECT_ID"/"$SUBJECT_ID"_space-fsnative_desc-micro_SNR.nii.gz \
  342. --regheader ${SUBJECT_ID} \
  343. --hemi ${hemi} \
  344. --surf $shortname \
  345. --o "$OUTPUT_DIR"/"$SUBJECT_ID"/"$SUBJECT_ID"_hemi-${HEMI}_surf-fsnative_SNR-${n}.mgh \
  346. --interp trilinear
  348. if [[ "$SURF_OUT" == *"fsaverage"* ]] ; then
  349. # transform to fsaverage
  350. mri_surf2surf --hemi ${hemi} \
  351. --srcsubject $SUBJECT_ID --srcsurfval "$OUTPUT_DIR"/"$SUBJECT_ID"/"$SUBJECT_ID"_hemi-${HEMI}_surf-fsnative_SNR-${n}.mgh \
  352. --trgsubject $SURF_OUT --trgsurfval "$OUTPUT_DIR"/"$SUBJECT_ID"/"$SUBJECT_ID"_hemi-${HEMI}_surf-${SURF_OUT}_SNR-${n}.mgh
  353. fi
  354. if [[ "$SURF_OUT" == "fsLR32k" ]] ; then
  355. # transform to fsaverage
  356. mri_surf2surf --hemi ${hemi} \
  357. --srcsubject $SUBJECT_ID --srcsurfval "$OUTPUT_DIR"/"$SUBJECT_ID"/"$SUBJECT_ID"_hemi-${HEMI}_surf-fsnative_SNR-${n}.mgh \
  358. --trgsubject fsaverage --trgsurfval "$OUTPUT_DIR"/"$SUBJECT_ID"/"$SUBJECT_ID"_hemi-${HEMI}_surf-fsaverage_SNR-${n}.mgh
  359. # transform to fsLR32k using wb_command
  360. singularity exec -B $OUTPUT_DIR:/out_dir \
  361. -B $SCRIPT_DIR/templates:/templates \
  362. "${SING_IMG}" \
  363. wb_command -metric-resample \
  364. /out_dir/"$SUBJECT_ID"/"$SUBJECT_ID"_hemi-${HEMI}_surf-fsaverage_SNR-${n}.shape.gii \
  365. /templates/fsaverage_std_sphere.${HEMI}.164k_fsavg_${HEMI}.surf.gii \
  366. /templates/fs_LR-deformed_to-fsaverage.${HEMI}.sphere.32k_fs_LR.surf.gii \
  367. ADAP_BARY_AREA \
  368. /out_dir/"$SUBJECT_ID"/"$SUBJECT_ID"_hemi-${HEMI}_surf-fsLR32k_SNW-${n}.shape.gii \
  369. -area-metrics \
  370. /templates/fsaverage.${HEMI}.midthickness_va_avg.164k_fsavg_${HEMI}.shape.gii \
  371. /templates/fs_LR.${HEMI}.midthickness_va_avg.32k_fs_LR.shape.gii
  372. fi
  373. fi
  374. done
  375. ((Nsteps++))
  376. done
  378. ##------------------------------------------------------------------------------#
  379. # Generate MPs for easy reading
  380. echo "[INFO] Collating microstructure profiles and computing moments for shape analysis"
  381. singularity exec -B $OUTPUT_DIR/:/out_dir \
  382. -B $TOOLBOX_BIN/:/toolbox_bin \
  383. "${SING_IMG}" \
  384. python3 /toolbox_bin/collate_MP.py --output_dir /out_dir/ --subject_id "$SUBJECT_ID" --num_surfaces "$NUM_SURFACES" --surface_output "$SURF_OUT"
  386. if [[ "$RUN_SNR" == 1 ]]; then
  387. singularity exec -B $OUTPUT_DIR/:/out_dir \
  388. -B $TOOLBOX_BIN/:/toolbox_bin \
  389. "${SING_IMG}" \
  390. python3 /toolbox_bin/collate_SNR.py --output_dir /out_dir/ --subject_id "$SUBJECT_ID" --num_surfaces "$NUM_SURFACES" --surface_output "$SURF_OUT"
  391. fi
  393. ##------------------------------------------------------------------------------#
  394. # Clean up tmp folder and drop datalad files
  395. if [[ "$CLEAN_UP" == 1 ]]; then
  396. rm -rf "$OUTPUT_DIR"/"$SUBJECT_ID"/*.mgh
  397. rm -rf "$OUTPUT_DIR"/"$SUBJECT_ID"/*.pial
  398. rm -rf "$OUTPUT_DIR"/"$SUBJECT_ID"/*synthseg*
  399. rm -rf "$OUTPUT_DIR"/"$SUBJECT_ID"/*Warped*
  400. rm -rf "$OUTPUT_DIR"/"$SUBJECT_ID"/*.mat
  401. rm -rf "$OUTPUT_DIR"/"$SUBJECT_ID"/*tmp*.nii.gz
  402. fi
  403. echo "[INFO] Toolbox completed for subject $SUBJECT_ID."

microstructure_profiling.sh at commit 38d4148, under MIT · at the source

Overview

Authors: Casey Paquola1,2, Jessica Royer3,4, Thanos Tsigaras1,2, Donna Gift Cabalo3, Youngeun Hwang3, Felix Hoffstaedter1,2, Simon B Eickhoff1,2, Boris C Bernhardt3
ORCID iDs: Casey Paquola
  1. Institute for Neuroscience and Medicine (INM-7), Forschungszentrum Juelich, Juelich, Germany
  2. Institute for Systems Neuroscience, Heinrich Heine Universität Dusseldorf, Dusseldorf, Germany
  3. McConnell Brain Imaging Centre (BIC) and Centre for Excellence in Epilepsy at the Neuro (CEEN), Montreal Neurological Institute, McGill University, Montreal, Canada
  4. Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
Institutions: Forschungszentrum Jülich (Germany); Heinrich Heine University Düsseldorf (Germany); Montreal Neurological Institute and Hospital (Canada); Max Planck Institute for Human Cognitive and Brain Sciences (Germany); McGill University (Canada)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1212
Dates: received 16 October 2025; accepted 23 March 2026; published online 21 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1212 · PMID 42027740 · PMCID PMC13100671 · OpenAlex W7143464860
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), histology / microscopy (modality), methods / tools (subfield)
Keywords: quantitative MRI, cytoarchitecture, myeloarchitecture, in vivo histology, surface-based analysis, open-source pipeline
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: SickKids Foundation (NI17-039); National Science and Engineering Research Council of Canada (NSERC RGPIN-2025-05932); Healthy Brains and Healthy Lives; Brain Canada Foundation; CIHR (PJT-203761, PJT-191853, PJT-174995, FDN-154298); FRQS, the Tier-2 Canada Research Chairs Program; HIBALL; Deutsche Forschungsgemeinschaft (524408221); Centre for Excellence in Epilepsy at the Neuro
Citations: not cited yet (Europe PMC); 60 references in the paper

Abstract

Intracortical microstructure profiling represents a powerful, scalable approach for investigating the laminar organisation of the human cortex on both in-vivo and post-mortem datasets. Building upon a long tradition of histological analysis, this method leverages surface-based intracortical sampling to generate profiles of tissue properties across cortical depths. The present work outlines a standardised workflow for intracortical microstructural profiling, newly packaged as the open-source toolbox “CortPro” (https://github.com/caseypaquola/cortpro). Here, we explore the utility of central moments as descriptors of profile shape. Using these measures, we quantify (i) the extent to which in-vivo MRI can capture laminar differentiation, (ii) the test-retest reliability of profiles, and (iii) their replicability across sites and studies. Our results demonstrate that intracortical profiles are remarkably robust and effectively mitigate bias-field related limitations of non-quantitative MRI. As applications of microstructure-sensitive imaging expand across development, aging, and disease, microstructure profiling provides a principled means of linking microstructural neuroanatomy with systems-level brain organisation.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 8 matches between paragraphs and lines of code.

caseypaquola/CortPro

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 38d4148ec04bc4fab48cdf9fc1e3ec86324e0b66, 6 August 2026
Languages: Python (9), Shell (8), Jupyter (1)
Size: 60 files, 18 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README, license file, environment (Dockerfile, Singularity.def, docs/requirements.txt), continuous integration, documentation
Not found: CITATION.cff, tests
Tools: FreeSurfer (7 files), NumPy (5 files), NiBabel (4 files), FSL (3 files), pandas (2 files), ANTs (1 file), neuromaps (1 file), SciPy (1 file), Connectome Workbench (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
20 files
At the source: github.com/caseypaquola/CortPro

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 18 scripts, each with its path and the digest of its content;
  • 8 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 and Code Availability

Raw data for our analyses are available via the following open datasets:

Lüsebrink: https://open-science.ub.ovgu.de/items/5d8e3df2-f60f-44a9-b2c7-710fc1a142a0/full

Shams: https://data.donders.ru.nl/collections/di/dccn/DSC_3015046.03_479?26

MICA-MICs: https://osf.io/j532r/

MICA-PNI: https://osf.io/mhq3f/

ABCD: https://www.nbdc-datahub.org/

Preprocessed microstructure profiles are additionally available on the Microstructure Marketplace (https://osf.io/e6f7d/overview). Code used to conduct the analyses are available as part of CortPro (https://github.com/caseypaquola/CortPro).

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 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 8 authors, 6 keywords, 9 funders, 59 references.

Cite

This paper

Paquola, C., Royer, J., Tsigaras, T., Cabalo, D. G., Hwang, Y., Hoffstaedter, F., Eickhoff, S. B., & Bernhardt, B. C. (2026). Intracortical microstructure profiling: A cross-modal method for indexing cortical lamination. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1212. https://doi.org/10.1162/imag.a.1212

BibTeX

@article{paquola2026intracortical,
author = {Paquola, Casey and Royer, Jessica and Tsigaras, Thanos and Cabalo, Donna Gift and Hwang, Youngeun and Hoffstaedter, Felix and Eickhoff, Simon B and Bernhardt, Boris C},
title = {{Intracortical microstructure profiling: A cross-modal method for indexing cortical lamination}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = apr,
volume = {4},
pages = {IMAG.a.1212},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1212},
url = {https://doi.org/10.1162/imag.a.1212},
pmid = {42027740},
pmcid = {PMC13100671}
}

RIS

TY - JOUR
AU - Paquola, Casey
AU - Royer, Jessica
AU - Tsigaras, Thanos
AU - Cabalo, Donna Gift
AU - Hwang, Youngeun
AU - Hoffstaedter, Felix
AU - Eickhoff, Simon B
AU - Bernhardt, Boris C
TI - Intracortical microstructure profiling: A cross-modal method for indexing cortical lamination
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/04/21
VL - 4
SP - IMAG.a.1212
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1212
UR - https://doi.org/10.1162/imag.a.1212
LA - en
ER -

CSL-JSON

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"date-parts": [
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21
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}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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