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Brain extraction for fixed tissue banking: a technical report.

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The authors' code

R · 208 lines · 8 KB · MIT

  1. # Load required libraries
  2. library(ggplot2)
  3. library(readr)
  4. library(dplyr)
  5. library(tidyr)
  7. cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
  9. # Load data
  10. df <- read_tsv("Brain Extraction Data and Stats - Raw Data Updated Anonymized.tsv")
  12. # Clean up column names
  13. names(df)[names(df) == "Case ID"] <- "Case_ID"
  14. names(df)[names(df) == "Perfusion?"] <- "Perfusion"
  15. names(df)[names(df) == "PMI (hours)"] <- "PMI_hours"
  16. names(df)[names(df) == "Soft Tissue Removal Time (minutes)"] <- "Soft_Tissue_Time"
  17. names(df)[names(df) == "Craniectomy Time (minutes)"] <- "Craniectomy_Time"
  18. names(df)[names(df) == "Dura Removal Time (minutes)"] <- "Dura_Time"
  19. names(df)[names(df) == "Brain Isolation Time (minutes)"] <- "Brain_Isolation_Time"
  20. names(df)[names(df) == "Total Removal Time (minutes)"] <- "Total_Time"
  21. names(df)[names(df) == "Traction Injury Count"] <- "Traction_Injuries"
  22. names(df)[names(df) == "Laceration Count"] <- "Lacerations"
  23. names(df)[names(df) == "Total Injuries"] <- "Total_Injuries"
  24. names(df)[names(df) == "Injury Level"] <- "Injury_Level"
  25. names(df)[names(df) == "PMI Length"] <- "PMI_Length"
  27. # Check it loaded correctly
  28. head(df)
  29. names(df)
  31. # ===== FIGURE Procedure Time Distribution =====
  32. # Reshape the data
  33. timing_long <- df %>%
  34. select(Case_ID, Soft_Tissue_Time, Craniectomy_Time, Dura_Time, Brain_Isolation_Time) %>%
  35. pivot_longer(cols = c(Soft_Tissue_Time, Craniectomy_Time, Dura_Time, Brain_Isolation_Time),
  36. names_to = "Step",
  37. values_to = "Time") %>%
  38. mutate(Step = case_when(
  39. Step == "Soft_Tissue_Time" ~ "Soft Tissue",
  40. Step == "Craniectomy_Time" ~ "Craniectomy",
  41. Step == "Dura_Time" ~ "Dura Removal",
  42. Step == "Brain_Isolation_Time" ~ "Brain Isolation"
  43. )) %>%
  44. mutate(Step = factor(Step, levels = c("Soft Tissue", "Craniectomy", "Dura Removal", "Brain Isolation"))) %>%
  45. filter(Time <= 75)
  47. # Create the plot
  48. ggplot(timing_long, aes(x = Step, y = Time, fill = Step, color = Step)) +
  49. geom_violin(alpha = 0.7) +
  50. geom_jitter(width = 0.2, alpha = 0.5) +
  51. stat_summary(fun = mean, geom = "crossbar", width = 0.5, color = "#FF7F7F", linewidth = 0.3) +
  52. labs(
  53. title = "Time Distribution by Procedure Step",
  54. x = "Procedure Step",
  55. y = "Time (minutes)"
  56. ) +
  57. scale_fill_manual(values = cbPalette[1:4]) +
  58. scale_color_manual(values = cbPalette[1:4]) +
  59. theme_bw() +
  60. theme(
  61. plot.title = element_text(face = "bold", hjust = 0.5),
  62. axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
  63. axis.text.y = element_text(size = 11),
  64. legend.position = "none"
  65. )
  67. ggplot(df, aes(x = PMI_hours, y = Total_Time)) +
  68. geom_point(alpha = 0.6, size = 2, color = "darkblue") +
  69. geom_smooth(method = "lm", se = TRUE, color = "#FF7F7F", linewidth = 1) +
  70. labs(
  71. x = "Post-Mortem Interval (Hours)",
  72. y = "Total Removal Time (Minutes)"
  73. ) +
  74. scale_x_log10() + # This creates the log scale
  75. theme_bw()
  77. ## Learning curve analysis
  79. # Filter out Tech 4 (who had significant experience), keep techs with 10+ cases, and create sequential case numbers
  80. df_sequential <- df %>%
  81. filter(Tech != "Tech 4") %>%
  82. group_by(Tech) %>%
  83. filter(n() >= 10) %>%
  84. arrange(Tech, Case_ID) %>%
  85. mutate(Case_Number = row_number()) %>%
  86. ungroup() %>%
  87. # Assign anonymous tech numbers
  88. mutate(Tech_Number = paste0("Tech ", as.numeric(factor(Tech, levels = unique(Tech)))))
  90. # Plot learning curves - cleaner version with facets
  91. ggplot(df_sequential, aes(x = Case_Number, y = Total_Time)) +
  92. geom_point(alpha = 0.5, size = 1.5) +
  93. geom_smooth(method = "loess", se = TRUE, color = "#0072B2", fill = "#0072B2", alpha = 0.2) +
  94. facet_wrap(~Tech_Number, scales = "free_x") +
  95. labs(
  96. x = "Sequential Case Number",
  97. y = "Total Removal Time (minutes)"
  98. ) +
  99. scale_x_continuous(limits = c(0, NA), expand = expansion(mult = c(0, 0.05))) +
  100. theme_minimal() +
  101. theme(
  102. panel.spacing = unit(1.5, "lines"),
  103. strip.text = element_text(face = "bold")
  104. )
  106. # Sample size
  107. cat("Total cases: n =", nrow(df), "\n\n")
  109. # Craniectomy Time
  110. cat("CRANIECTOMY TIME:\n")
  111. cat(" Mean:", round(mean(df$Craniectomy_Time), 1), "minutes\n")
  112. cat(" SD:", round(sd(df$Craniectomy_Time), 1), "minutes\n")
  113. cat(" Range:", min(df$Craniectomy_Time), "-", max(df$Craniectomy_Time), "minutes\n")
  115. # Soft Tissue Removal Time
  116. cat("SOFT TISSUE REMOVAL TIME:\n")
  117. cat(" Mean:", round(mean(df$Soft_Tissue_Time), 1), "minutes\n")
  118. cat(" SD:", round(sd(df$Soft_Tissue_Time), 1), "minutes\n")
  119. cat(" Range:", min(df$Soft_Tissue_Time), "-", max(df$Soft_Tissue_Time), "minutes\n\n")
  121. # Dura Removal Time
  122. cat("DURA REMOVAL TIME:\n")
  123. cat(" Mean:", round(mean(df$Dura_Time), 1), "minutes\n")
  124. cat(" SD:", round(sd(df$Dura_Time), 1), "minutes\n")
  125. cat(" Range:", min(df$Dura_Time), "-", max(df$Dura_Time), "minutes\n\n")
  127. # Brain Isolation Time
  128. cat("BRAIN ISOLATION TIME:\n")
  129. cat(" Mean:", round(mean(df$Brain_Isolation_Time), 1), "minutes\n")
  130. cat(" SD:", round(sd(df$Brain_Isolation_Time), 1), "minutes\n")
  131. cat(" Range:", min(df$Brain_Isolation_Time), "-", max(df$Brain_Isolation_Time), "minutes\n\n")
  133. # Total Procedure Time
  134. cat("TOTAL PROCEDURE TIME:\n")
  135. cat(" Mean:", round(mean(df$Total_Time), 1), "minutes\n")
  136. cat(" SD:", round(sd(df$Total_Time), 1), "minutes\n")
  137. cat(" Range:", min(df$Total_Time), "-", max(df$Total_Time), "minutes\n\n")
  139. # PMI statistics
  140. cat("PMI STATISTICS:\n")
  141. cat(" Mean:", round(mean(df$PMI_hours), 1), "hours\n")
  142. cat(" Median:", round(median(df$PMI_hours), 1), "hours\n")
  143. cat(" Range:", round(min(df$PMI_hours), 1), "-", round(max(df$PMI_hours), 1), "hours\n\n")
  145. cor.test(df$Total_Time, df$PMI_hours, method = "spearman")
  147. cat("=== TISSUE DAMAGE STATISTICS ===\n\n")
  149. # Lacerations
  150. cat("LACERATIONS:\n")
  151. cat(" Mean:", round(mean(df$Lacerations), 2), "\n")
  152. cat(" SD:", round(sd(df$Lacerations), 2), "\n")
  153. cat(" Range:", min(df$Lacerations), "-", max(df$Lacerations), "\n")
  154. cat(" Cases with 1+:", sum(df$Lacerations > 0),
  155. "(", round(100*sum(df$Lacerations > 0)/nrow(df), 1), "%)\n")
  157. # Traction Injuries
  158. cat("TRACTION INJURIES:\n")
  159. cat(" Mean:", round(mean(df$Traction_Injuries), 2), "\n")
  160. cat(" SD:", round(sd(df$Traction_Injuries), 2), "\n")
  161. cat(" Range:", min(df$Traction_Injuries), "-", max(df$Traction_Injuries), "\n")
  162. cat(" Cases with 1+:", sum(df$Traction_Injuries >= 1),
  163. "(", round(100*sum(df$Traction_Injuries >= 1)/nrow(df), 1), "%)\n")
  165. # ===== TECHNICIAN SUMMARY TABLE =====
  166. cat("=== TECHNICIAN COMPARISON: TIME AND LACERATIONS ===\n\n")
  168. tech_summary <- df %>%
  169. group_by(Tech) %>%
  170. summarise(
  171. N_Cases = n(),
  172. Time_Mean = mean(Total_Time),
  173. Time_Median = median(Total_Time),
  174. Time_SD = sd(Total_Time),
  175. Lac_Mean = mean(Lacerations),
  176. Lac_Total = sum(Lacerations),
  177. Trac_Mean = mean(Traction_Injuries),
  178. TotInj_Mean = mean(Total_Injuries),
  179. PMI_Median = median(PMI_hours)
  180. ) %>%
  181. arrange(desc(N_Cases))
  183. print(tech_summary, n = Inf)
  185. # ===== STATISTICAL TESTS =====
  186. cat("\n\n=== STATISTICAL TESTS ===\n\n")
  188. # Filter to technicians with >= 5 cases
  189. techs_5plus <- tech_summary %>% filter(N_Cases >= 5) %>% pull(Tech)
  190. df_filtered <- df %>% filter(Tech %in% techs_5plus)
  192. # Kruskal-Wallis test for lacerations across technicians
  193. kw_lac <- kruskal.test(Lacerations ~ Tech, data = df_filtered)
  194. cat("Kruskal-Wallis test for lacerations across technicians (n>=5):\n")
  195. cat(sprintf(" H = %.2f, p = %.4f\n", kw_lac$statistic, kw_lac$p.value))
  197. # Kruskal-Wallis test for total time across technicians
  198. kw_time <- kruskal.test(Total_Time ~ Tech, data = df_filtered)
  199. cat("\nKruskal-Wallis test for total time across technicians (n>=5):\n")
  200. cat(sprintf(" H = %.2f, p = %.4f\n", kw_time$statistic, kw_time$p.value))
  202. # ===== TECH 4 =====
  204. tech4 <- df %>% filter(Tech == "Tech 4")
  206. cat(sprintf("Tech 4 (n=%d):\n", nrow(tech4)))
  207. cat(sprintf(" Time: mean=%.1f, median=%.1f\n", mean(tech4$Total_Time), median(tech4$Total_Time)))
  208. cat(sprintf(" Lacerations: mean=%.2f, total=%d\n", mean(tech4$Lacerations), sum(tech4$Lacerations)))

figures_code.R at commit 937e25d, under MIT · at the source

Overview

Authors: Mads Wolf1, Autumn Beck1, Laura Paredes1, Sarah Darcy1, Alexander Parra1, Gabriel A. Taylor1, Macy Garrood1, Emma L. Thorn2,3, Claudia De Sanctis2,3, John F. Crary2,3, Kurt Farrell2,3, Andrew T. McKenzie1
  1. Apex Neuroscience, Salem, Oregon, USA
  2. Friedman Brain Institute, Departments of Pathology, Neuroscience, and Artificial Intelligence & Human Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
  3. Neuropathology Brain Bank & Research Core and Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, New York, USA
Institutions: Icahn School of Medicine at Mount Sinai (United States)
Journal: Free neuropathology, volume 7, article 11
Dates: received 2 March 2026; accepted 15 May 2026; published online 26 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.17879/freeneuropathology-2026-9411 · PMID 42222515 · PMCID PMC13217454 · OpenAlex W7162530775
Open access: green, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality)
Keywords: Brain extraction, Tissue preservation, Brain banking, Immersion fixation, Postmortem changes
Topic: Anatomy and Medical Technology (Biomedical Engineering, Engineering), according to OpenAlex
Citations: not cited yet (Europe PMC); 33 references in the paper

Abstract

Brain banking enables the study of human neural tissue and is essential for research on disease, physiology, and neuroanatomy. An essential step for whole brain studies is the extraction of the brain from the skull. Yet detailed technical descriptions of brain removal are rare, perhaps contributing to the artifactual tissue disruption sometimes observed as a result of the procedure. Here, we describe a method for whole brain extraction that could be implemented in the context of a whole-body donation, focused on the use case of fixed tissue banking. The method involves a sequential craniectomy that uses both circumferential and midline sagittal cuts, followed by a posterior approach to cutting the dura. We report the application of this protocol across n = 105 human whole-body donors. We document the time required for each procedural step and the frequency of craniectomy artifacts and skull edge artifacts at the brain surface. When the brain tissue is particularly soft, we also describe the potential use of in situ immersion fixation to increase tissue stiffness before removal, finding however that this also slows the diffusion of chemicals into the interior of the brain. Our experience suggests that the effectiveness of brain extraction can be improved via procedural optimizations. Technicians can become comfortable with the method after approximately 5–10 cases. We anticipate that improving brain extraction quality may support downstream work in the development of diagnostics and treatments for neurological and psychiatric disease.

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

Repository

Its files are read in the Code ↔ Paper reader above.

andymckenzie/Brain_extraction

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 937e25d1c7fe9618be2d342d1994bae33bbf8eef, 2 March 2026
Languages: R (1)
Size: 3 files, 1 script
Software Heritage: not archived
Found in: “Data availability”
Holds: license file
Not found: README, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
2 files
At the source: github.com/andymckenzie/Brain_extraction

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.

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1 script, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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

Datasets cited

Data availability

Whole slide image data can be accessed in a public repository on Zenodo, available here: https://zenodo.org/communities/brainextraction. Code and data is available at https://github.com/andymckenzie/Brain_extraction.

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 12 authors, 5 keywords, 32 references.

Cite

This paper

Wolf, M., Beck, A., Paredes, L., Darcy, S., Parra, A., Taylor, G. A., Garrood, M., Thorn, E. L., Sanctis, C. D., Crary, J. F., Farrell, K., & McKenzie, A. T. (2026). Brain extraction for fixed tissue banking: a technical report. Free neuropathology, 7, 11. https://doi.org/10.17879/freeneuropathology-2026-9411

BibTeX

@article{wolf2026brain,
author = {Wolf, Mads and Beck, Autumn and Paredes, Laura and Darcy, Sarah and Parra, Alexander and Taylor, Gabriel A. and Garrood, Macy and Thorn, Emma L. and Sanctis, Claudia De and Crary, John F. and Farrell, Kurt and McKenzie, Andrew T.},
title = {{Brain extraction for fixed tissue banking: a technical report}},
journal = {Free neuropathology},
year = {2026},
month = may,
volume = {7},
pages = {11},
publisher = {Free Neuropathology General Assembly},
issn = {2699-4445},
doi = {10.17879/freeneuropathology-2026-9411},
url = {https://doi.org/10.17879/freeneuropathology-2026-9411},
pmid = {42222515},
pmcid = {PMC13217454}
}

RIS

TY - JOUR
AU - Wolf, Mads
AU - Beck, Autumn
AU - Paredes, Laura
AU - Darcy, Sarah
AU - Parra, Alexander
AU - Taylor, Gabriel A.
AU - Garrood, Macy
AU - Thorn, Emma L.
AU - Sanctis, Claudia De
AU - Crary, John F.
AU - Farrell, Kurt
AU - McKenzie, Andrew T.
TI - Brain extraction for fixed tissue banking: a technical report
T2 - Free neuropathology
J2 - Free Neuropathol
PY - 2026
DA - 2026/05/26
VL - 7
SP - 11
SN - 2699-4445
PB - Free Neuropathology General Assembly
DO - 10.17879/freeneuropathology-2026-9411
UR - https://doi.org/10.17879/freeneuropathology-2026-9411
LA - en
ER -

CSL-JSON

{
"id": "10.17879/freeneuropathology-2026-9411",
"type": "article-journal",
"title": "Brain extraction for fixed tissue banking: a technical report",
"container-title": "Free neuropathology",
"author": [
{
"family": "Wolf",
"given": "Mads"
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{
"family": "Beck",
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{
"family": "Paredes",
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{
"family": "Darcy",
"given": "Sarah"
},
{
"family": "Parra",
"given": "Alexander"
},
{
"family": "Taylor",
"given": "Gabriel A."
},
{
"family": "Garrood",
"given": "Macy"
},
{
"family": "Thorn",
"given": "Emma L."
},
{
"family": "Sanctis",
"given": "Claudia De"
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{
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"given": "John F."
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{
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}
],
"container-title-short": "Free Neuropathol",
"volume": "7",
"page": "11",
"DOI": "10.17879/freeneuropathology-2026-9411",
"PMID": "42222515",
"PMCID": "PMC13217454",
"ISSN": "2699-4445",
"publisher": "Free Neuropathology General Assembly",
"URL": "https://doi.org/10.17879/freeneuropathology-2026-9411",
"language": "en",
"issued": {
"date-parts": [
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}

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