OSCR

EOAD-Signature Atrophy Predicts Dementia in Early-Onset MCI due to Alzheimer Disease: An MRI-Based Prognostic Biomarker.

Code ↔ Paper

3 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 3 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Statistical Analysis ↔ GitHub_code/05_calibration_validation.R, lines 101–148 · score 0.77 · Optimism corrected, bootstrap corrected, Internal validation, calibration slope, Harrell, months
  2. [2] § Results › Neuroanatomical Predictors of Progression to Dementia ↔ GitHub_code/05_calibration_validation.R, lines 101–148 · score 0.74 · optimism corrected calibration, Bootstrap corrected calibration, Internal validation, calibration slope, Harrell, months
  3. [3] § Methods › Statistical Analysis ↔ GitHub_code/03_cox_models.R, the whole file · a weak match · score 0.54 · Cox proportional hazards, EOAD signature, variables, atrophy, models

Paper

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The authors' code

R · 148 lines · 5 KB · no license · 2 matches

  1. # ============================================================
  2. # Internal validation and calibration of the extended Cox model
  3. # Paranhos et al. - EOAD Prognostication
  4. # ============================================================
  6. library(survival)
  7. library(rms)
  8. library(dplyr)
  9. library(ggplot2)
  11. # -----------------------------
  12. # 1) Read data
  13. # -----------------------------
  14. # Replace with the path to your dataset
  15. data <- read.csv("path/to/your/data.csv", check.names = TRUE)
  17. # Keep model variables and remove missing values
  18. data2 <- data %>%
  19. select(Time_to_Event, Progressed_to_Mild,
  20. age_at_scan, gender, cdrsum, EOADsig_wscore) %>%
  21. filter(complete.cases(.))
  23. data2$gender <- as.factor(data2$gender)
  24. data2$Progressed_to_Mild <- as.integer(data2$Progressed_to_Mild)
  26. # -----------------------------
  27. # 2) Prediction horizon
  28. # -----------------------------
  29. u <- 24 # months
  31. # -----------------------------
  32. # 3) Fit model
  33. # -----------------------------
  34. # survival package version (used for Brier score and calibration slope)
  35. cox_fit <- coxph(
  36. Surv(Time_to_Event, Progressed_to_Mild) ~ age_at_scan + gender + cdrsum + EOADsig_wscore,
  37. data = data2, x = TRUE, y = TRUE
  38. )
  40. # rms package version (used for bootstrap validation)
  41. dd <- datadist(data2)
  42. options(datadist = "dd")
  44. cph_fit <- cph(
  45. Surv(Time_to_Event, Progressed_to_Mild) ~ age_at_scan + gender + cdrsum + EOADsig_wscore,
  46. data = data2, x = TRUE, y = TRUE, surv = TRUE, time.inc = u, units = "Month"
  47. )
  49. # -----------------------------
  50. # 4) Apparent C-index
  51. # -----------------------------
  52. conc <- summary(cox_fit)$concordance
  53. c_index <- unname(conc[1])
  54. c_se <- unname(conc[2])
  56. cat("\n--- Apparent C-index ---\n")
  57. cat("C-index:", round(c_index, 3),
  58. " (95% CI:", round(c_index - 1.96 * c_se, 3), "to",
  59. round(c_index + 1.96 * c_se, 3), ")\n")
  61. # -----------------------------
  62. # 5) Apparent Brier and scaled Brier
  63. # -----------------------------
  64. brier_obj <- brier(cox_fit, times = u)
  66. cat("\n--- Apparent Brier score ---\n")
  67. cat("Brier score at", u, "months:", round(brier_obj$brier[1], 3), "\n")
  68. cat("Scaled Brier score at", u, "months:", round(100 * brier_obj$rsquared[1], 1), "%\n")
  70. # -----------------------------
  71. # 6) Apparent calibration slope
  72. # -----------------------------
  73. lp <- predict(cox_fit, type = "lp")
  75. cal_slope_model <- coxph(Surv(Time_to_Event, Progressed_to_Mild) ~ lp, data = data2)
  76. cal_slope_sum <- summary(cal_slope_model)
  78. slope <- cal_slope_sum$coef[1, "coef"]
  79. slope_se <- cal_slope_sum$coef[1, "se(coef)"]
  81. cat("\n--- Apparent calibration slope ---\n")
  82. cat("Calibration slope:", round(slope, 3),
  83. " (95% CI:", round(slope - 1.96 * slope_se, 3), "to",
  84. round(slope + 1.96 * slope_se, 3), ")\n")
  86. # -----------------------------
  87. # 7) Observed-to-expected (O/E) ratio at u months
  88. # -----------------------------
  89. surv_func <- Survival(cph_fit)
  90. pred_risk <- 1 - surv_func(u, lp = lp)
  92. km_fit <- survfit(Surv(Time_to_Event, Progressed_to_Mild) ~ 1, data = data2)
  93. obs_risk <- 1 - summary(km_fit, times = u, extend = TRUE)$surv
  94. exp_risk <- mean(pred_risk)
  96. cat("\n--- O/E ratio at", u, "months ---\n")
  97. cat("Observed risk:", round(obs_risk, 3), "\n")
  98. cat("Mean predicted risk:", round(exp_risk, 3), "\n")
  99. cat("O/E ratio:", round(obs_risk / exp_risk, 3), "\n")
  101. # -----------------------------
  102. # 8) Bootstrap internal validation (1,000 iterations)
  103. # Harrell FE. Regression Modeling Strategies. 2nd ed. Springer; 2015.
  104. # -----------------------------
  105. set.seed(123)
  106. val_boot <- validate(cph_fit, method = "boot", B = 1000, dxy = TRUE, u = u)
  108. print(val_boot)
  110. # Optimism-corrected C-index
  111. dxy_corr <- val_boot["Dxy", "index.corrected"]
  112. c_corr <- dxy_corr / 2 + 0.5
  114. # Optimism-corrected calibration slope
  115. slope_corr <- val_boot["Slope", "index.corrected"]
  117. cat("\n--- Bootstrap-corrected estimates ---\n")
  118. cat("Optimism-corrected C-index :", round(c_corr, 3), "\n")
  119. cat("Optimism-corrected calibration slope:", round(slope_corr, 3), "\n")
  120. cat("Shrinkage factor :", round(slope_corr, 3), "\n")
  122. # -----------------------------
  123. # 9) Bootstrap-corrected calibration plot
  124. # -----------------------------
  125. cal_matrix <- val_boot # calibrate() output if used separately
  127. # Convert from survival to event probability scale
  128. cal_risk <- as.data.frame(cal_matrix) %>%
  129. mutate(
  130. pred_risk = 1 - pred,
  131. obs_risk = 1 - calibrated.corrected,
  132. lower_risk = pmax(1 - (calibrated.corrected + Upper), 0),
  133. upper_risk = pmin(1 - (calibrated.corrected + Lower), 1)
  134. )
  136. ggplot(cal_risk, aes(x = pred_risk, y = obs_risk)) +
  137. geom_ribbon(aes(ymin = lower_risk, ymax = upper_risk),
  138. fill = "gray70", alpha = 0.4) +
  139. geom_line(color = "red", linewidth = 1.4) +
  140. geom_abline(slope = 1, intercept = 0,
  141. linetype = "dashed", color = "black", linewidth = 1) +
  142. labs(
  143. x = "Predicted 24-month progression probability",
  144. y = "Observed 24-month progression probability",
  145. title = "Bootstrap-corrected calibration (t = 2 years)"
  146. ) +
  147. coord_cartesian(xlim = c(0, 1), ylim = c(0, 1)) +
  148. theme_classic(base_size = 16)

05_calibration_validation.R at commit 691a507, no license · at the source

Overview

Authors: Thiago Paranhos1, Yuta Katsumi1, Michael Joseph Brickhouse1, Ani Eloyan2, Ryan Eckbo1, Alexander Zaitsev1, Anna Du1, Renaud La Joie3, Maryanne Thangarajah2, Alexander Taurone2, Prashanthi Vemuri4, Clifford R. Jack4, as the LEADS Consortium, Dustin B. Hammers5, Paul S. Aisen6, Laurel A. Beckett7, Robert Koeppe8, Walter A. Kukull9, Arthur W. Toga10, Alireza Atri11
and 26 other authorsDavid Glenn Clark5, Gregory S. Day12, Ranjan Duara13, Neill R. Graff-Radford12, Ian M. Grant14, Lawrence S. Honig15, Erik Johnson16, David T. Jones4,12, Joseph C. Masdeu17, Mario F. Mendez18, Erik S. Musiek19, Chiadi U. Onyike20, Meghan Riddle21, Emily Rogalski22, Stephen Salloway21, Sharon J. Sha23, Raymond Scott Turner24, Thomas Wingo25, David A. Wolk26, Kyle B. Womack19, Maria C. Carrillo27, Gil Dan Rabinovici3, Liana G. Apostolova5,28,29, Bradford Clark Dickerson1, Mark C. Eldaief1,30,31, Alexandra Touroutoglou1
ORCID iDs: Thiago Paranhos, Yuta Katsumi, Michael Joseph Brickhouse, Renaud La Joie, Prashanthi Vemuri, Clifford R. Jack, Paul S. Aisen, Arthur W. Toga, Alireza Atri, David Glenn Clark, Gregory S. Day, Ranjan Duara, Ian M. Grant, Lawrence S. Honig, Erik Johnson, David T. Jones, Joseph C. Masdeu, Mario F. Mendez, Erik S. Musiek, Chiadi U. Onyike, Meghan Riddle, Stephen Salloway, Raymond Scott Turner, Thomas Wingo, David A. Wolk, Gil Dan Rabinovici, Liana G. Apostolova, Bradford Clark Dickerson, Mark C. Eldaief, Alexandra Touroutoglou
31 affiliations
  1. Frontotemporal Disorders Unit and Massachusetts Alzheimer's Disease Research Center, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston
  2. Department of Biostatistics, Center for Biostatistics and Health Data Science, Brown University, Providence, RI
  3. Department of Neurology, University of California—San Francisco
  4. Department of Radiology, Mayo Clinic, Rochester, MN
  5. Department of Neurology, Indiana University School of Medicine, Indianapolis
  6. Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego
  7. Department of Public Health Sciences, University of California—Davis
  8. Department of Radiology, University of Michigan, Ann Arbor
  9. Department of Neurology, Washington University School of Medicine, Saint Louis, MO
  10. Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, CA
  11. Banner Sun Health Research Institute, Sun City, AZ
  12. Department of Neurology, Mayo Clinic, Jacksonville, FL
  13. Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami, FL
  14. Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer's Disease, Feinberg School of Medicine, Northwestern University, Chicago, IL
  15. Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York
  16. Department of Neurology, Emory University School of Medicine, Atlanta, GA
  17. Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, TX
  18. Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA
  19. Department of Neurology, Washington University in St. Louis, MO
  20. Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD
  21. Department of Neurology, Alpert Medical School, Brown University, Providence, RI
  22. Department of Neurology, University of Chicago, IL
  23. Department of Neurology and Neurological Sciences, Stanford University, Palo Alto, CA
  24. Department of Neurology, Georgetown University, Washington, DC
  25. Department of Neurology, UC Davis Alzheimer's Disease Research Center, University of California—Davis
  26. Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia
  27. Medical & Scientific Relations Division, Alzheimer's Association, Chicago, IL
  28. Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis
  29. Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis
  30. Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston; and
  31. Center for Brain Sciences, Harvard University, Cambridge, MA
Institutions: Harvard University (United States); Massachusetts General Hospital (United States); Brown University (United States); University of California, San Francisco (United States); Mayo Clinic (United States); Indiana University Indianapolis (United States); Indiana University School of Medicine; University of Southern California (United States); University of California, Davis (United States); University of Michigan (United States); Washington University in St. Louis (United States); Banner Sun Health Research Institute (United States); Mayo Clinic in Florida (United States); Mount Sinai Medical Center (United States); Northwestern University (United States); Columbia University Irving Medical Center (United States); Emory University (United States); Houston Methodist (United States); Weill Cornell Medicine (United States); University of California, Los Angeles (United States); Johns Hopkins University (United States); Johns Hopkins Medicine (United States); University of Chicago (United States); Stanford University (United States); Georgetown University (United States); University of Pennsylvania (United States); Alzheimer's Association (United States)
Journal: Neurology, volume 107, issue 7, article e218519
Dates: received 16 October 2025; accepted 17 July 2026; published online 26 August 2026; in print 13 October 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1212/wnl.0000000000218519 · PMID 42647766 · PMCID PMC13528895 · OpenAlex W7204229719
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), Alzheimer's / dementia (population), clinical / translational (subfield)
Methods: Connectivity, Statistics, Preprocessing, fMRI & imaging
MeSH: Alzheimer Disease*, Cognitive Dysfunction*, Dementia*, Adult, Atrophy, Biomarkers, Disease Progression, Female, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Prognosis (* major topic)
Topic: Dementia and Cognitive Impairment Research (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: Alzheimer's Association (GENETICS-19-639372, LEADS GENETICS-19, 19‐639372, U01AG6057195, LEADS GENETICS-19-639372, LDRFP-21-824473, 21-824473, P30 AG062421, U24AG021886, LDRFP-21-818464, P30 AG062422, P30 AG010133, LDRFP-21-828356, R56 AG057195); Massachusetts General Hospital (P50 AG005134, P41EB015896); Centre d'Imagerie BioMédicale; National Institutes of Health (U01AG6057195); Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital (S10RR021110, P41-EB015896, S10 RR023401, S10 RR023043); National Institute on Aging (P30 AG066506, AG010133, P30AG062677, P30-AG066507, AG066507, U24‐AG021886, AG062421, R21 AG080588, AG072979, P41EB015896, R56AG057195, P30AG072977, AG005134, AG057195, P30-AG062421, P30 AG072980, U24AG072122, P30 AG010133, AG066511, P30AG066444, AG072977, R01 AG085377, P30AG066462, AG066444, R01DC014296, AG072122, P30-AG066511, U01‐AG6057195, P30-AG-072979, AG066462, AG072980, P30 AG066515, P50 AG 005134, AG066515, K23 DC016912, AG062422, P30 AG062422, AG062677, R21 AG073744); National Institute of Biomedical Imaging and Bioengineering (P30 AG062421, P41EB-015896, S10-RR023043, U24AG021886, S10RR023401, S10RR021110, R01 DC014296)
Citations: not cited yet (Europe PMC); 47 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

thiagoparanhos/EOAD-prognostication

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 691a507440da83c70221e5de5d7f67932ad806bc, 14 May 2026
Languages: R (5)
Size: 6 files, 5 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: survival (4 files), tidyverse (3 files), broom (1 file), ggplot2 (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
6 files
At the source: github.com/thiagoparanhos/EOAD-prognostication

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

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What the map holds:

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

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Code and data availability statement

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Read it in the paper: doi.org/10.1212/wnl.0000000000218519.

Versions

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Version 2, 28 September 2026

  • Publisher: n/a → Lippincott Williams & Wilkins
  • Funding: added Alzheimer's Association: GENETICS-19-639372, LEADS GENETICS-19, 19‐639372, U01AG6057195, LEADS GENETICS-19-639372, LDRFP-21-824473, 21-824473, P30 AG062421, U24AG021886, LDRFP-21-818464, P30 AG062422, P30 AG010133, LDRFP-21-828356, R56 AG057195; Massachusetts General Hospital: P50 AG005134, P41EB015896; Centre d'Imagerie BioMédicale; National Institutes of Health: U01AG6057195; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital: S10RR021110, P41-EB015896, S10 RR023401, S10 RR023043; National Institute on Aging: P30 AG066506, AG010133, P30AG062677, P30-AG066507, AG066507, U24‐AG021886, AG062421, R21 AG080588, AG072979, P41EB015896, R56AG057195, P30AG072977, AG005134, AG057195, P30-AG062421, P30 AG072980, U24AG072122, P30 AG010133, AG066511, P30AG066444, AG072977, R01 AG085377, P30AG066462, AG066444, R01DC014296, AG072122, P30-AG066511, U01‐AG6057195, P30-AG-072979, AG066462, AG072980, P30 AG066515, P50 AG 005134, AG066515, K23 DC016912, AG062422, P30 AG062422, AG062677, R21 AG073744; National Institute of Biomedical Imaging and Bioengineering: P30 AG062421, P41EB-015896, S10-RR023043, U24AG021886, S10RR023401, S10RR021110, R01 DC014296

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 46 authors, 14 MeSH terms, 45 references.

Cite

This paper

Paranhos, T., Katsumi, Y., Brickhouse, M. J., Eloyan, A., Eckbo, R., Zaitsev, A., Du, A., La Joie, R., Thangarajah, M., Taurone, A., Vemuri, P., Jack, C. R., as the LEADS Consortium, Hammers, D. B., Aisen, P. S., Beckett, L. A., Koeppe, R., Kukull, W. A., Toga, A. W., . . . Touroutoglou, A. (2026). EOAD-Signature Atrophy Predicts Dementia in Early-Onset MCI due to Alzheimer Disease: An MRI-Based Prognostic Biomarker. Neurology, 107(7), e218519. https://doi.org/10.1212/wnl.0000000000218519

BibTeX

@article{paranhos2026eoad,
author = {Paranhos, Thiago and Katsumi, Yuta and Brickhouse, Michael Joseph and Eloyan, Ani and Eckbo, Ryan and Zaitsev, Alexander and Du, Anna and La Joie, Renaud and Thangarajah, Maryanne and Taurone, Alexander and Vemuri, Prashanthi and Jack, Clifford R. and {as the LEADS Consortium} and Hammers, Dustin B. and Aisen, Paul S. and Beckett, Laurel A. and Koeppe, Robert and Kukull, Walter A. and Toga, Arthur W. and Atri, Alireza and Clark, David Glenn and Day, Gregory S. and Duara, Ranjan and Graff-Radford, Neill R. and Grant, Ian M. and Honig, Lawrence S. and Johnson, Erik and Jones, David T. and Masdeu, Joseph C. and Mendez, Mario F. and Musiek, Erik S. and Onyike, Chiadi U. and Riddle, Meghan and Rogalski, Emily and Salloway, Stephen and Sha, Sharon J. and Turner, Raymond Scott and Wingo, Thomas and Wolk, David A. and Womack, Kyle B. and Carrillo, Maria C. and Rabinovici, Gil Dan and Apostolova, Liana G. and Dickerson, Bradford Clark and Eldaief, Mark C. and Touroutoglou, Alexandra},
title = {{EOAD-Signature Atrophy Predicts Dementia in Early-Onset MCI due to Alzheimer Disease: An MRI-Based Prognostic Biomarker}},
journal = {Neurology},
year = {2026},
month = aug,
volume = {107},
number = {7},
pages = {e218519},
publisher = {Lippincott Williams \& Wilkins},
issn = {0028-3878},
doi = {10.1212/wnl.0000000000218519},
url = {https://doi.org/10.1212/wnl.0000000000218519},
pmid = {42647766},
pmcid = {PMC13528895}
}

RIS

TY - JOUR
AU - Paranhos, Thiago
AU - Katsumi, Yuta
AU - Brickhouse, Michael Joseph
AU - Eloyan, Ani
AU - Eckbo, Ryan
AU - Zaitsev, Alexander
AU - Du, Anna
AU - La Joie, Renaud
AU - Thangarajah, Maryanne
AU - Taurone, Alexander
AU - Vemuri, Prashanthi
AU - Jack, Clifford R.
AU - as the LEADS Consortium
AU - Hammers, Dustin B.
AU - Aisen, Paul S.
AU - Beckett, Laurel A.
AU - Koeppe, Robert
AU - Kukull, Walter A.
AU - Toga, Arthur W.
AU - Atri, Alireza
AU - Clark, David Glenn
AU - Day, Gregory S.
AU - Duara, Ranjan
AU - Graff-Radford, Neill R.
AU - Grant, Ian M.
AU - Honig, Lawrence S.
AU - Johnson, Erik
AU - Jones, David T.
AU - Masdeu, Joseph C.
AU - Mendez, Mario F.
AU - Musiek, Erik S.
AU - Onyike, Chiadi U.
AU - Riddle, Meghan
AU - Rogalski, Emily
AU - Salloway, Stephen
AU - Sha, Sharon J.
AU - Turner, Raymond Scott
AU - Wingo, Thomas
AU - Wolk, David A.
AU - Womack, Kyle B.
AU - Carrillo, Maria C.
AU - Rabinovici, Gil Dan
AU - Apostolova, Liana G.
AU - Dickerson, Bradford Clark
AU - Eldaief, Mark C.
AU - Touroutoglou, Alexandra
TI - EOAD-Signature Atrophy Predicts Dementia in Early-Onset MCI due to Alzheimer Disease: An MRI-Based Prognostic Biomarker
T2 - Neurology
J2 - Neurology
PY - 2026
DA - 2026/08/26
VL - 107
IS - 7
SP - e218519
SN - 0028-3878
PB - Lippincott Williams & Wilkins
DO - 10.1212/wnl.0000000000218519
UR - https://doi.org/10.1212/wnl.0000000000218519
LA - en
ER -

CSL-JSON

{
"id": "10.1212/wnl.0000000000218519",
"type": "article-journal",
"title": "EOAD-Signature Atrophy Predicts Dementia in Early-Onset MCI due to Alzheimer Disease: An MRI-Based Prognostic Biomarker",
"container-title": "Neurology",
"author": [
{
"family": "Paranhos",
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{
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{
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{
"family": "Aisen",
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{
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"family": "Grant",
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"family": "Honig",
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{
"family": "Johnson",
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{
"family": "Jones",
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"family": "Mendez",
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{
"family": "Salloway",
"given": "Stephen"
},
{
"family": "Sha",
"given": "Sharon J."
},
{
"family": "Turner",
"given": "Raymond Scott"
},
{
"family": "Wingo",
"given": "Thomas"
},
{
"family": "Wolk",
"given": "David A."
},
{
"family": "Womack",
"given": "Kyle B."
},
{
"family": "Carrillo",
"given": "Maria C."
},
{
"family": "Rabinovici",
"given": "Gil Dan"
},
{
"family": "Apostolova",
"given": "Liana G."
},
{
"family": "Dickerson",
"given": "Bradford Clark"
},
{
"family": "Eldaief",
"given": "Mark C."
},
{
"family": "Touroutoglou",
"given": "Alexandra"
}
],
"container-title-short": "Neurology",
"volume": "107",
"issue": "7",
"page": "e218519",
"DOI": "10.1212/wnl.0000000000218519",
"PMID": "42647766",
"PMCID": "PMC13528895",
"ISSN": "0028-3878",
"publisher": "Lippincott Williams & Wilkins",
"URL": "https://doi.org/10.1212/wnl.0000000000218519",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
26
]
]
}
}

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

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