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Neuroimaging and behavioural biomarkers of post-stroke cognitive recovery outcomes

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The authors' code

R · 212 lines · 9.2 KB · no license

  1. #OCSPred Project Data Cleaning/Finding -------------------------
  2. #load relevant packages
  3. library(tidyverse)
  4. library(readxl)
  6. #possible method notes ---
  7. # -SCCAN/LDA
  8. #- build model best predicting change over time?
  9. # - or build models separating recovered/persistent?
  11. #load in raw data
  12. volumes <- read_xlsx('/Users/uqmmoo13/Desktop/OCSPred/Data/LSMMulti_Volumes.xlsx')
  13. volumes<- subset(volumes, !grepl('SP', ScanFile))
  15. acute_raw <- read_xlsx('/Users/uqmmoo13/Desktop/OCSPred/Data/OCSPred_OxCont_Acute.xlsx')
  16. chronic_raw <- read_xlsx('/Users/uqmmoo13/Desktop/OCSPred/Data/OCSPred_OxCont_Chronic.xlsx')
  17. extras_raw <- read_xlsx('/Users/uqmmoo13/Desktop/OCSPred/Data/OCSPred_Data_extras.xlsx')
  19. load('/Users/uqmmoo13/Desktop/OCSPred/Data/LSMMulti_Cleaned_SampleData.R')
  20. dems <- subset(Data, !grepl('S', ID))
  22. #create variables for matching
  23. volumes <- mutate(volumes, IDshort = substr(ScanFile, 6, 9))
  24. extras_raw <- mutate(extras_raw, IDshort = substr(ID, nchar(ID)-3, nchar(ID)))
  25. extras_raw <- subset(extras_raw, !is.na(IDshort))
  26. chronic_raw <- mutate(chronic_raw, IDshort = substr(StudyID, nchar(StudyID)-3, nchar(StudyID)))
  27. chronic_raw <- subset(chronic_raw, !is.na(IDshort))
  28. acute_raw <- mutate(acute_raw, IDshort = substr(ID, nchar(ID)-3, nchar(ID)))
  29. acute_raw <- subset(acute_raw, !is.na(IDshort))
  30. dems <- mutate(dems, IDshort = substr(ID, nchar(ID)-3, nchar(ID)))
  32. #join all data
  33. data <- left_join(acute_raw, chronic_raw, by = 'IDshort')
  34. data <- left_join(data, dems, by = 'IDshort')
  35. data <- left_join(data, volumes, by = 'IDshort')
  36. data <- left_join(data, extras_raw, by = 'IDshort')
  38. #removes any NA or Impute
  39. data[data == "Impute"] <- NA
  40. data[data == "NA"] <- NA
  42. #cleans up duplicate variables
  43. data$ScanType.x[is.na(data$ScanType.x)] <- data$ScanType.y[is.na(data$ScanType.x)]
  44. data$StrokeScan[is.na(data$StrokeScan)] <- data$Intv[is.na(data$StrokeScan)]
  45. data$Multiple.x[is.na(data$Multiple.x)] <- data$Multiple.y[is.na(data$Multiple.x)]
  46. data$Sex[is.na(data$Sex)] <- data$Gender[is.na(data$Sex)]
  47. data$YearsEdu[is.na(data$YearsEdu)] <- data$Education[is.na(data$YearsEdu)]
  48. data$StrokeAge[is.na(data$StrokeAge)] <- data$Age[is.na(data$StrokeAge)]
  49. data$Handedness.x[is.na(data$Handedness.x)] <- data$Handedness.y[is.na(data$Handedness.x)]
  50. data$StrokeType.x[is.na(data$StrokeType.x)] <- data$StrokeType.y[is.na(data$StrokeType.x)]
  51. data$LesionHem[is.na(data$LesionHem)] <- data$StrokeSide[is.na(data$LesionHem)]
  52. data$Volume.x[is.na(data$Volume.x)] <- data$Volume.y[is.na(data$Volume.x)]
  54. #remove uneccesary columns
  55. data <- dplyr::select(data, !(ID.y) &!(Intv) & !(ScanTest) & !(Multiple.y)
  56. & !(Gender) & !(Education) & !(Age) & !(StrokeType.y) & !(Handedness.y)
  57. & !(StrokeSide) &!(ScanFile.y) &!(IntvCat) & !(age) & !(sex) & !(hand)
  58. & !(years_education) & !(nihss) &!(Version) & !(hadsa_total) & !(hadsd_total)
  59. & !(barthel_lf_total) &(!ID) & !(Volume.y) & !(moca_total) & !(StrokeTest))
  61. #renames remaining columns
  62. name_list <- names(data)
  63. name_list[name_list == "ID.x"] <- "ID"
  64. name_list[name_list == "ScanType.x"] <- "ScanType"
  65. name_list[name_list == "Multiple.x"] <- "Multiple"
  66. name_list[name_list == "Handedness.x"] <- "Hand"
  67. name_list[name_list == "StrokeType.x"] <- "StrokeType"
  68. names(data) <- name_list
  70. #creates group indexes
  71. test_list <- c("PIC", "SEM", "SNT", "BHT",
  72. "EGO", "ALLO", "EXC",
  73. "ORT","RCL", "RCG", "PRX", "CAL", "NUM")
  75. #creates standard scores for each subtest -----------------------------------
  76. for (i in 1:length(test_list)){
  77. test <- test_list[i]
  79. acute_total <- paste("A_", test, "_T", sep ="")
  80. chronic_total <- paste("C_", test, "_T", sep ="")
  81. acute_max <- paste("A_", test, "_M", sep ="")
  82. chronic_max <- paste("C_", test, "_M", sep ="")
  83. a_norm <- paste("A_", test, "_N", sep ="")
  84. c_norm <- paste("C_", test, "_N", sep ="")
  86. if (test == "ALLO" | test == "EGO"){
  87. acute_bin <- paste("A_", test, "_L_B", sep ="")
  88. chron_bin <- paste("C_", test, "_L_B", sep ="")
  89. test_temp = paste(test, "_L", sep ="")
  90. change_score <- paste(test, "_change", sep ="")
  92. data[test_temp] <- "NA"
  93. data[[test_temp]][as.numeric(data[[acute_bin]]) == 1 & as.numeric(data[[chron_bin]]) == 1] <- "Persistent"
  94. data[[test_temp]][as.numeric(data[[acute_bin]]) == 1 & as.numeric(data[[chron_bin]]) == 0] <- "Recovered"
  95. data[[test_temp]][as.numeric(data[[acute_bin]]) == 0] <- "Control"
  97. acute_bin <- paste("A_", test, "_R_B", sep ="")
  98. chron_bin <- paste("C_", test, "_R_B", sep ="")
  99. test_temp <- paste(test, "_R", sep ="")
  101. data[test_temp] <- "NA"
  102. data[[test_temp]][as.numeric(data[[acute_bin]]) == 1 & as.numeric(data[[chron_bin]]) == 1] <- "Persistent"
  103. data[[test_temp]][as.numeric(data[[acute_bin]]) == 1 & as.numeric(data[[chron_bin]]) == 0] <- "Recovered"
  104. data[[test_temp]][as.numeric(data[[acute_bin]]) == 0] <- "Control"
  106. data <- mutate(data, placeholder = as.numeric(data[[acute_total]])/as.numeric(data[[acute_max]]))
  107. names(data)[names(data)== "placeholder"] <- a_norm
  108. data <- mutate(data, placeholder = as.numeric(data[[chronic_total]])/as.numeric(data[[chronic_max]]))
  109. names(data)[names(data)== "placeholder"] <- c_norm
  110. data <- mutate(data, placeholder = as.numeric(data[[c_norm]]) - as.numeric(data[[a_norm]]))
  111. names(data)[names(data)== "placeholder"] <- change_score
  113. }else{
  114. acute_bin <- paste("A_", test, "_B", sep ="")
  115. chron_bin <- paste("C_", test, "_B", sep ="")
  116. change_score <- paste(test, "_change", sep ="")
  118. data[test] <- "NA"
  119. data[[test]][as.numeric(data[[acute_bin]]) == 1 & as.numeric(data[[chron_bin]]) == 1] <- "Persistent"
  120. data[[test]][as.numeric(data[[acute_bin]]) == 1 & as.numeric(data[[chron_bin]]) == 0] <- "Recovered"
  121. data[[test]][as.numeric(data[[acute_bin]]) == 0] <- "Control"
  123. data <- mutate(data, placeholder = as.numeric(data[[acute_total]])/as.numeric(data[[acute_max]]))
  124. names(data)[names(data)== "placeholder"] <- a_norm
  125. data <- mutate(data, placeholder = as.numeric(data[[chronic_total]])/as.numeric(data[[chronic_max]]))
  126. names(data)[names(data)== "placeholder"] <- c_norm
  127. data <- mutate(data, placeholder = as.numeric(data[[c_norm]]) - as.numeric(data[[a_norm]]))
  128. names(data)[names(data)== "placeholder"] <- change_score
  129. }
  130. }
  132. #make egocentric and allocentric variables -
  133. data$C_EGO_L_N <- data$C_EGO_N
  134. data$C_EGO_L_N[data$C_EGO_N < 0] <- 0
  135. data$C_EGO_R_N <- data$C_EGO_N
  136. data$C_EGO_R_N[data$C_EGO_N > 0] <- 0
  137. data$C_ALLO_R_N <- data$C_ALLO_N
  138. data$C_ALLO_R_N[data$C_ALLO_N > 0] <- 0
  139. data$C_ALLO_L_N <- data$C_ALLO_N
  140. data$C_ALLO_L_N[data$C_ALLO_N < 0] <- 0
  141. data$A_EGO_L_N <- data$A_EGO_N
  142. data$A_EGO_L_N[data$A_EGO_N < 0] <- 0
  143. data$A_EGO_R_N <- data$A_EGO_N
  144. data$A_EGO_R_N[data$A_EGO_N > 0] <- 0
  145. data$A_ALLO_R_N <- data$A_ALLO_N
  146. data$A_ALLO_R_N[data$A_ALLO_N > 0] <- 0
  147. data$A_ALLO_L_N <- data$A_ALLO_N
  148. data$A_ALLO_L_N[data$A_ALLO_N < 0] <- 0
  150. data$A_ALLO_L_N <- 1 - abs(data$A_ALLO_L_N )
  151. data$A_ALLO_R_N <- 1 - abs(data$A_ALLO_R_N )
  152. data$A_EGO_L_N <- 1 - abs(data$A_EGO_L_N )
  153. data$A_EGO_R_N <- 1 - abs(data$A_EGO_R_N )
  154. data$C_ALLO_L_N <- 1 - abs(data$C_ALLO_L_N )
  155. data$C_ALLO_R_N <- 1 - abs(data$C_ALLO_R_N )
  156. data$C_EGO_L_N <- 1 - abs(data$C_EGO_L_N )
  157. data$C_EGO_R_N <- 1 - abs(data$C_EGO_R_N )
  159. #invert BHT scores too
  160. data$A_BHT_N <- 1 - abs(data$A_BHT_N )
  161. data$C_BHT_N <- 1 - abs(data$C_BHT_N )
  163. #removes excluded patients
  164. grouped_data <- subset(data, !is.na(LesionFile))
  166. test_list <- c("PIC", "SEM", "SNT", "BHT",
  167. "EGO_L", "ALLO_L", "EGO_R", "ALLO_R", "EXC",
  168. "ORT","RCL", "RCG", "PRX", "CAL", "NUM")
  170. #summarise numbers in each gorup
  171. temp_dat <- pivot_longer(grouped_data, cols = test_list, names_to = "Test", values_to = "Cat")
  172. temp_dat <- summarise(group_by(temp_dat, Test,Cat), n = n())
  174. #for each test - plot proportions spared/impaired
  175. #plot_data <- pivot_longer(grouped_data, cols = test_list, names_to = "Test", values_to = "Cat")
  176. #plot_data <- subset(plot_data, !is.na(Cat) & Cat != "NA")
  178. #ggplot(plot_data, aes(x=Cat)) +
  179. #geom_bar()+
  180. #geom_hline(yintercept = 10, colour = 'red') +
  181. #facet_wrap(facets = vars(Test)) +
  182. #theme_classic()
  184. #load in lesion data!
  185. harox <- read_xlsx('/Users/uqmmoo13/Desktop/OCSPred/Data/summary_fraction_HarOx.xlsx')
  186. harox<- mutate(harox, IDshort = substr(LesionFile, 6, 9))
  187. harox <- dplyr::select(harox, !('LesionFile'))
  188. grouped_data <- left_join(grouped_data, harox, by = "IDshort")
  190. jhu<- read_xlsx('/Users/uqmmoo13/Desktop/OCSPred/Data/summary_fraction_JHU.xlsx')
  191. jhu<- mutate(jhu, IDshort = substr(LesionFile, 6, 9))
  192. jhu <- dplyr::select(jhu, !('LesionFile'))
  193. grouped_data <- left_join(grouped_data, jhu, by = "IDshort")
  195. #remove duplicate IDs
  196. grouped_data <- distinct(grouped_data)
  197. grouped_data <- mutate(grouped_data, dupcheck = duplicated(grouped_data$ID))
  198. grouped_data <- subset(grouped_data, dupcheck == FALSE)
  201. #inverts BHT and EXC
  202. grouped_data$A_BHT_N <- 1 - grouped_data$A_BHT_N
  203. grouped_data$C_BHT_N <- 1 - grouped_data$C_BHT_N
  205. grouped_data$A_EXC_N[grouped_data$A_EXC_N < 0] <- 0
  206. grouped_data$C_EXC_N[grouped_data$C_EXC_N < 0] <- 0
  207. grouped_data$A_EXC_N <- 1 - grouped_data$A_EXC_N
  208. grouped_data$C_EXC_N <- 1 - grouped_data$C_EXC_N
  210. #save data for later use
  211. clean_data <- grouped_data
  212. save('clean_data', file = '/Users/uqmmoo13/Desktop/OCSPred/Data/LSMMulti_CleanData_V1.RData')

OCSPred_DataCleaning_V2.R, no license · at the source

Overview

Authors: Margaret Jane Moore1, Stephanie J. Forkel2, Nele Demeyere3
ORCID iDs: Stephanie J. Forkel
  1. Queensland Brain Institute, University of Queensland, Brisbane, Australia
  2. Donders Research Institute for Brain, Cognition, and Behaviour, Radbound University, Nijmegen, Netherlands
  3. Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
Institutions: The University of Queensland (Australia); Radboud University Nijmegen (Netherlands); University of Oxford (United Kingdom)
Journal: medRxiv (preprint)
Dates: published online 15 May 2026
Type: Preprint · Language: English
License: CC BY
Identifiers: DOI 10.64898/2026.05.12.26353056 · OpenAlex W7161266650
Open access: green, a free copy (OpenAlex)
Preprint: osf.io/9zkyh
Status: code verified
Categories: behavior only (modality), stroke (population), clinical / translational (subfield)
Methods: Statistics, fMRI & imaging
Keywords: stroke, outcome prediction, lesion mapping, post-stroke cognitive impairment, cognitive recovery, disconnection
Topic: Acute Ischemic Stroke Management (Epidemiology, Medicine), according to OpenAlex
Funding: National Institute for Health and Care Research (NIHR302224); Department of Health and Social Care
Citations: not cited yet (Europe PMC); 84 references in the paper

Abstract

Lesion anatomy has been widely used to study post stroke cognitive outcomes, but it is unclear whether lesion-based measures provide clinically meaningful prognostic information beyond established predictors. Stroke survivors (n = 408) completed the Oxford Cognitive Screen (OCS) during acute hospitalisation and at chronic (6-month) follow-up. Lesion characteristics and structural disconnection profiles associated with chronic OCS scores were identified using ROI-level, voxel-level and structural network disconnection lesion mapping approaches. The incremental predictive value of these measures, relative to acute behaviour and pre-morbid brain health, was evaluated using regression analyses, receiver operating curve (ROC) and support vector regression (SVR) models predicting continuous chronic scores.

Significant lesion and disconnection correlates of chronic cognitive impairment were identified for 9/10 OCS subtests. The extent of damage to these correlates was significantly associated with chronic cognitive scores, but their diagnostic utility for identifying persistent impairment was low under conventional thresholds (AUC mean = 0.59, range= 0.46–0.66). Acute cognitive task performance was the single best predictor of chronic cognition (AUC mean = 0.66, range = 0.41–0.95). In multivariate analyses, SVR models trained on acute cognitive performance and regional atrophy severity scores both outperformed models trained on lesion anatomy or structural disconnection across most cognitive domains. SVR models combining anatomical, disconnection and behavioural predictors did not improve predictions accuracy relative to behaviour- or atrophy-only models.

Together, these findings demonstrate that statistically significant lesion–outcome relationships do not necessarily translate into clinically useful prognostic indicators. In a large, clinically representative stroke cohort, detailed lesion-based measures provided limited incremental prognostic value beyond acute cognitive assessment and coarse brain health markers. These results highlight the importance of explicitly evaluating predictive utility when developing prognostic models for post-stroke cognitive outcomes.

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

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OSF 9zkyh

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: R (7)
Size: 11 files, 7 scripts
Software Heritage: not checked
Found in: “Data Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (6 files), ggpubr (3 files), cowplot (2 files), pROC (2 files), BayesFactor (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
7 files
At the source: osf.io/9zkyh/

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;
  • 7 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
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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 Availability Statement

All data and code associated with this project is openly available on the Open Science Framework (https://osf.io/9zkyh/).

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

Versions

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Version 2, 28 September 2026

  • Language: n/a → en
  • Funding: added National Institute for Health and Care Research: NIHR302224; Department of Health and Social Care

Version 1, 28 September 2026: the first record

Recorded: type, journal, dates, 3 authors, 6 keywords, 80 references.

Cite

This paper

Jane Moore, M., Forkel, S. J., & Demeyere, N. (2026). Neuroimaging and behavioural biomarkers of post-stroke cognitive recovery outcomes. medRxiv (preprint). https://doi.org/10.64898/2026.05.12.26353056

BibTeX

@article{janemoore2026neuroimaging,
author = {Jane Moore, Margaret and Forkel, Stephanie J. and Demeyere, Nele},
title = {{Neuroimaging and behavioural biomarkers of post-stroke cognitive recovery outcomes}},
journal = {medRxiv (preprint)},
year = {2026},
month = may,
publisher = {medRxiv},
doi = {10.64898/2026.05.12.26353056},
url = {https://doi.org/10.64898/2026.05.12.26353056}
}

RIS

TY - JOUR
AU - Jane Moore, Margaret
AU - Forkel, Stephanie J.
AU - Demeyere, Nele
TI - Neuroimaging and behavioural biomarkers of post-stroke cognitive recovery outcomes
T2 - medRxiv (preprint)
J2 - medRxiv
PY - 2026
DA - 2026/05/15
PB - medRxiv
DO - 10.64898/2026.05.12.26353056
UR - https://doi.org/10.64898/2026.05.12.26353056
LA - en
ER -

CSL-JSON

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"type": "article",
"title": "Neuroimaging and behavioural biomarkers of post-stroke cognitive recovery outcomes",
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"author": [
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"family": "Jane Moore",
"given": "Margaret"
},
{
"family": "Forkel",
"given": "Stephanie J."
},
{
"family": "Demeyere",
"given": "Nele"
}
],
"container-title-short": "medRxiv",
"DOI": "10.64898/2026.05.12.26353056",
"publisher": "medRxiv",
"URL": "https://doi.org/10.64898/2026.05.12.26353056",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
15
]
]
}
}

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