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Functional neuroanatomy of musical object processing in Alzheimer's disease and frontotemporal dementia.

Code ↔ Paper

4 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 4 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] § Materials and methods › Participants ↔ Scripts/4_DemNpsych.R, lines 1–69 · score 0.82 · formal musical training, music listening, svPPA, week, demographics, hours
  2. [2] § Results › General characteristics of participant groups ↔ Scripts/4_DemNpsych.R, lines 1–69 · score 0.69 · music listening, symptom duration, musical training, educational, score, age
  3. [3] § Materials and methods › Data analyses › Behavioural data analysis ↔ Scripts/5_PostscanTasks.R, lines 43–109 · score 0.63 · rank sum, Shapiro Wilk, rfit, covariance, detection, pitch
  4. [4] § Materials and methods › Experimental protocol › Brain image acquisition ↔ Scripts/2_fMRIStimulusPresentation.m, the whole file · a weak match · score 0.55 · fMRI, TR, multiband, silent, scanner, stimulus

Paper

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The authors' code

R · 288 lines · 9.2 KB · no license · 2 matches

  1. #Script for calculating summary statistics and group differences for demographics and npsych variables for
  2. #participants in musical object paradigm (MOP) study
  3. #Script written by Lucy Core
  5. #Clear R environment
  6. rm(list = ls())
  8. #Load packages
  9. library(readxl)
  10. library(dplyr)
  11. library(rstatix)
  13. #Import and view dataset
  14. MOP_DemNpsych <- read_excel() #insert file path
  15. View(MOP_DemNpsych)
  17. #DATA ORGANIZATION
  19. #Update column names to add underscores in place of spaces (to make calling variables easier later on)
  20. names(MOP_DemNpsych) <- gsub(" ", "_", names(MOP_DemNpsych))
  22. #Add diagnosisFTD column to combine svPPA and rtvFTD participants in one FTD group
  23. MOP_DemNpsych$DiagnosisFTD <- ifelse(MOP_DemNpsych$Diagnosis %in% c("svPPA", "rtvFTD"), "FTD", MOP_DemNpsych$Diagnosis)
  25. #Reorder factor levels
  26. MOP_DemNpsych$DiagnosisFTD <- factor(MOP_DemNpsych$DiagnosisFTD,
  27. levels = c("HV", "AD", "FTD"))
  29. #Rearrange order of columns so that DiagnosisFTD appears third rather than last
  30. MOP_DemNpsych <- MOP_DemNpsych %>%
  31. select(DRC_Code, Diagnosis, DiagnosisFTD, everything())
  33. #Look at column names
  34. names(MOP_DemNpsych)
  36. #Make list of all continuous variables
  37. continuous_variables <- c(
  38. "Age_at_fMRI",
  39. "Symptom_Duration_(Years)",
  40. "Highest_education_level",
  41. "Years_of_Formal_Musical_Training",
  42. "Total_hours_of_music_listening_per_week",
  43. "BMRQ_(/35)",
  44. "MMSE",
  45. "WASI:_Matrices",
  46. "WMSR-Digit_Span:_Forward_Score",
  47. "WMSR-Digit_Span:_Backward_Score",
  48. "Spatial_Span:_Forward_Score",
  49. "Spatial_Span:_Backward_Score",
  50. "BPVS:_Ceiling/Basal_Score",
  51. "BNT",
  52. "GDA:_Total",
  53. "CPAL",
  54. "JOL",
  55. "NART",
  56. "Fluency:_Phonemic",
  57. "Fluency:_Category",
  58. "VOSP_Object_Decision",
  59. "Trails_A:_Time",
  60. "Trails_B:_Time",
  61. "WAIS-R_Digit_Symbol",
  62. "ECAS-Social_Cognition_Screen",
  63. "Elevator_Attention:_Counting",
  64. "Elevator_Attention:_With_Distraction",
  65. "CFMT_(/18)",
  66. "Emotional_Hexagon:_Experiment_(/24)",
  67. "Pitch_detection_(QSTAC)_(/20)",
  68. "Better_Ear_Average_(250-4k)"
  69. )
  71. #Make list of all categorical variables
  72. categorical_variables <- c(
  73. "Sex",
  74. "Handedness",
  75. "Hearing_aids?"
  76. )
  78. #Convert continuous variables to numeric. Warnings will appear indicating that missing data codes (e.g., LT or NT) get turned into NA values
  79. MOP_DemNpsych[continuous_variables] <- lapply(MOP_DemNpsych[continuous_variables],
  80. function(x) as.numeric(as.character(x)))
  82. #FOR ALL PARTICIPANTS
  84. #Calculate summary statistics (range, mean, st dev) for age
  85. allparticipants_range_age <- range(MOP_DemNpsych$Age_at_fMRI)
  86. allparticipants_mean_age <- mean(MOP_DemNpsych$Age_at_fMRI)
  87. allparticipants_sd_age <- sd(MOP_DemNpsych$Age_at_fMRI)
  89. #Print summary stats for age
  90. print(allparticipants_range_age)
  91. print(allparticipants_mean_age)
  92. print(allparticipants_sd_age)
  94. #Count number of females and males
  95. allparticipants_num_females <- sum(MOP_DemNpsych$Sex == "F")
  96. allparticipants_num_males <- sum(MOP_DemNpsych$Sex == "M")
  98. #Print counts for sex
  99. print(allparticipants_num_females)
  100. print(allparticipants_num_males)
  102. #FOR PATIENTS ONLY
  103. patientsonly <- MOP_DemNpsych$Age_at_fMRI[MOP_DemNpsych$DiagnosisFTD %in% c("AD", "FTD")]
  105. patients_range_age <- range(patientsonly, na.rm = TRUE)
  106. patients_mean_age <- mean(patientsonly, na.rm = TRUE)
  107. patients_sd_age <- sd(patientsonly, na.rm = TRUE)
  109. print(patients_range_age)
  110. print(patients_mean_age)
  111. print(patients_sd_age)
  113. #MEAN AND STANDARD DEVIATION FOR CONTINUOUS VARIABLES SPLIT BY GROUP
  114. Mean_SD_ContinuousVariables <- MOP_DemNpsych %>%
  115. group_by(DiagnosisFTD) %>%
  116. summarise(across(all_of(continuous_variables),
  117. list(mean = ~ mean(., na.rm = TRUE),
  118. sd = ~ sd(., na.rm = TRUE)),
  119. .names = "{.col}_{.fn}"))
  121. #Flip rows and columns for easier readability
  122. Mean_SD_ContinuousVariables <- t(Mean_SD_ContinuousVariables)
  124. #Print means and st dev for each continuous variable
  125. print(Mean_SD_ContinuousVariables)
  127. #MISSING DATA FOR CONTINUOUS VARIABLES
  129. #Initialize a list to store NA counts
  130. na_counts <- list()
  132. #Loop through each continuous variable to calculate NA counts by group
  133. for (var in continuous_variables) {
  134. #Group by DiagnosisFTD and count NAs for the current variable
  135. na_count <- MOP_DemNpsych %>%
  136. group_by(DiagnosisFTD) %>%
  137. summarise(NA_Count = sum(is.na(.data[[var]]))) %>%
  138. mutate(Variable = var)
  140. #Add to the list
  141. na_counts[[var]] <- na_count
  142. }
  144. #Combine all results into a single data frame
  145. na_counts_df <- do.call(rbind, na_counts)
  147. #Reorder columns for better readability
  148. na_counts_df <- na_counts_df %>% select(Variable, DiagnosisFTD, NA_Count)
  150. # Print the counts
  151. print(na_counts_df)
  153. #GROUP DIFFERENCES FOR CONTINUOUS VARIABLES
  155. #Ensure p-values are displayed with appropriate decimal points
  156. options(scipen = 999) #Avoid scientific notation globally
  158. #Assess group differences using Kruskal-Wallis ANOVA
  159. kruskal_results <- lapply(continuous_variables, function(var) {
  160. #Ensure the variable is wrapped in backticks if it contains special characters
  161. result <- kruskal.test(reformulate("DiagnosisFTD", paste0("`", var, "`")), data = MOP_DemNpsych)
  163. #Format the p-value to show appropriate decimal points
  164. formatted_p_value <- format(result$p.value, digits = 4, scientific = FALSE)
  166. return(data.frame(Variable = var,
  167. p.value = formatted_p_value,
  168. Statistic = result$statistic))
  169. })
  171. # Combine results into a single data frame
  172. kruskal_results_df <- do.call(rbind, kruskal_results)
  174. #Print Kruskal Wallis ANOVA results
  175. print(kruskal_results_df)
  177. # Extract significant variables from Kruskal-Wallis results
  178. significant_kruskal_variables <- kruskal_results_df$Variable[kruskal_results_df$p.value < 0.05]
  180. # Initialize a list to store Dunn's test results
  181. dunn_results <- list()
  183. for (var in significant_kruskal_variables) {
  185. # Dunn's test
  186. dunn_test <- MOP_DemNpsych %>%
  187. dunn_test(as.formula(paste0("`", var, "` ~ DiagnosisFTD")),
  188. p.adjust.method = "none")
  190. # Compute medians
  191. medians <- MOP_DemNpsych %>%
  192. group_by(DiagnosisFTD) %>%
  193. summarise(median_value = median(.data[[var]], na.rm = TRUE), .groups = "drop")
  195. # Compute mean ranks
  196. meanranks <- MOP_DemNpsych %>%
  197. mutate(rank_val = rank(.data[[var]], na.last = "keep")) %>%
  198. group_by(DiagnosisFTD) %>%
  199. summarise(mean_rank = mean(rank_val, na.rm = TRUE), .groups = "drop")
  201. # Join medians + mean ranks for group1 and group2
  202. dunn_test <- dunn_test %>%
  203. left_join(medians, by = c("group1" = "DiagnosisFTD")) %>%
  204. rename(group1_median = median_value) %>%
  205. left_join(medians, by = c("group2" = "DiagnosisFTD")) %>%
  206. rename(group2_median = median_value) %>%
  207. left_join(meanranks, by = c("group1" = "DiagnosisFTD")) %>%
  208. rename(group1_meanrank = mean_rank) %>%
  209. left_join(meanranks, by = c("group2" = "DiagnosisFTD")) %>%
  210. rename(group2_meanrank = mean_rank) %>%
  211. mutate(direction = if_else(group1_meanrank < group2_meanrank,
  212. paste0(group1, " < ", group2),
  213. if_else(group1_meanrank > group2_meanrank,
  214. paste0(group1, " > ", group2),
  215. paste0(group1, " = ", group2))))
  217. dunn_results[[var]] <- dunn_test
  218. }
  220. # Print results
  221. for (var in significant_kruskal_variables) {
  222. cat("\nPost hoc Dunn's test results for variable:", var, "\n")
  223. print(dunn_results[[var]])
  224. }
  227. #Test for symptom duration to compare the two patient groups
  229. #Subset the data for DiagnosisFTD groups AD and svPPA
  230. patient_data <- MOP_DemNpsych %>%
  231. filter(DiagnosisFTD %in% c("AD", "FTD"))
  233. # Perform the Wilcoxon test for Symptom_Duration_(Years) between AD and svPPA
  234. sympduration_result <- wilcox.test(
  235. `Symptom_Duration_(Years)` ~ DiagnosisFTD,
  236. data = patient_data,
  237. exact = FALSE
  238. )
  240. #Print the test result
  241. print(sympduration_result)
  243. #Extract and print W statistic
  244. cat("W statistic:", sympduration_result$statistic, "\n")
  246. #COUNTS FOR CATEGORICAL VARIABLES SPLIT BY GROUP
  247. Counts_CategoricalVariables <- MOP_DemNpsych %>%
  248. group_by(DiagnosisFTD) %>%
  249. summarise(
  250. M_in_Sex = sum(Sex == "M", na.rm = TRUE),
  251. F_in_Sex = sum(Sex == "F", na.rm = TRUE),
  252. R_in_Handedness = sum(Handedness == "R", na.rm = TRUE),
  253. L_in_Handedness = sum(Handedness == "L", na.rm = TRUE),
  254. Yes_in_HearingAids = sum(`Hearing_aids?` == "Yes", na.rm = TRUE),
  255. No_in_HearingAids = sum(`Hearing_aids?` == "No", na.rm = TRUE)
  256. )
  258. #Print counts for each categorical variable
  259. print(Counts_CategoricalVariables)
  261. #GROUP DIFFERENCES IN CATEGORICAL VARIABLES
  263. #Initialize a list to store Fisher's Exact Test results
  264. fisher_results <- list()
  266. #Perform Fisher's Exact Test for each categorical variable
  267. for (var in categorical_variables) {
  268. #Create a contingency table for the variable and DiagnosisFTD
  269. contingency_table <- table(MOP_DemNpsych[[var]], MOP_DemNpsych$DiagnosisFTD)
  271. #Perform Fisher's Exact Test
  272. fisher_test <- fisher.test(contingency_table)
  274. #Store the results, conditionally including the estimate
  275. result <- data.frame(
  276. Variable = var,
  277. p.value = fisher_test$p.value
  278. )
  280. #Add to the results list
  281. fisher_results[[var]] <- result
  282. }
  284. #Combine results into a single data frame
  285. fisher_results_df <- do.call(rbind, fisher_results)
  287. #Print Fisher test results
  288. print(fisher_results_df)

4_DemNpsych.R, no license · at the source

Overview

Authors: Lucy B Core1, Sophie A Froud1, Stephen Wastling2,3, Jessica Jiang1, Benjamin A Levett1, Laura Mancini2, Barbara Dymerska4, Chris J D Hardy1, Peter Zeidman4, Jason D Warren1
ORCID iDs: Peter Zeidman, Jason D Warren
  1. Brain Behaviour Group, Dementia Research Centre, Queen Square Institute of Neurology, University College London, London WC1N 3AR, United Kingdom
  2. National Hospital for Neurology and Neurosurgery, University College London Hospitals National Health Service Foundation Trust, London WC1N 3BG, United Kingdom
  3. Department of Translational Neuroscience and Stroke, Queen Square Institute of Neurology, University College London, London WC1N 3AR, United Kingdom
  4. Department of Imaging Neuroscience, Queen Square Institute of Neurology, University College London, London WC1N 3AR, United Kingdom
Institutions: UCL Queen Square Institute of Neurology (United Kingdom); University College London (United Kingdom); National Health Service (United Kingdom); University College London Hospitals NHS Foundation Trust (United Kingdom); National Hospital for Neurology and Neurosurgery (United Kingdom)
Journal: Brain communications, volume 8, issue 3, article fcag161
Dates: received 7 July 2025; accepted 2 May 2026; published online 5 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/braincomms/fcag161 · PMID 42146858 · PMCID PMC13178109 · OpenAlex W7160421161
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), Alzheimer's / dementia (population), cognitive (subfield)
Methods: Statistics, fMRI & imaging, Preprocessing
Keywords: music, Alzheimer’s disease, frontotemporal dementia, fMRI, auditory object
Topic: Neuroscience and Music Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Alzheimer's Society (627); Wellcome Trust (226793/Z/22/Z)
Citations: not cited yet (Europe PMC); 137 references in the paper

Abstract

Music, besides its emotional and social resonance, models a complex sensory environment exemplifying auditory objects corresponding to sources (musical instruments) and information streams (melodies). These musical dimensions can be variably preserved or blighted by neurodegenerative disease. Music is, therefore, an attractive way to investigate the neural mechanisms of sensory object processing in these diseases. Here, we assessed the functional neuroanatomy underlying the perceptual, semantic, and apperceptive processing of musical objects in Alzheimer’s disease and temporal variant frontotemporal dementia. We studied 35 patients (20 Alzheimer’s disease, 15 temporal variant frontotemporal dementia; 14 females; mean [standard deviation] age 70.3 [8.4] years) in relation to 25 cognitively healthy volunteers (16 females; age 69.5 [6.8] years). In a functional MRI experiment with sparse image acquisition to minimise the impact of scanner noise, participants passively listened to monophonic melodies. We varied timbre (same/change), timbre familiarity (natural/artificial instruments), melody familiarity (familiar/novel), and apperception (melodies with interpolated timbre changes); these manipulations allowed us to assess the functional neuroanatomical correlates of musical feature perception, melody familiarity, constancy, novelty, and instrument familiarity. Behavioural correlates were assessed in post-scan tasks and disease-related atrophy patterns using voxel-based morphometry of participants’ structural scans. All contrasts were assessed at P < 0.05, corrected for multiple voxel-wise comparisons within pre-specified anatomical regions of interest. For timbre change perception, all participant groups demonstrated comparable temporo-parietal cortical activation anchored in planum temporale. For melodies, processing of semantic familiarity in all participant groups engaged a common network including supplementary motor area and inferior frontal gyrus, with reduced supplementary motor area activation in the Alzheimer’s disease group compared with other groups, while melody novelty comparably engaged postero-medial cortical circuitry across groups. Apperceptive coding of melody constancy was associated with activation of the posterior superior temporal cortex in the healthy volunteers, but greater activation of the temporal polar cortex in the temporal variant frontotemporal dementia group than in healthy volunteers. For instrument familiarity, the temporal variant frontotemporal dementia group showed reduced activation of the temporal polar cortex, but increased activation of the anterior insula compared to healthy volunteers. Brain activation profiles were not influenced by behavioural performance on post-scan tasks and did not coincide with regional atrophy. Our findings delineate complex, differentiated functional neuroanatomical profiles of musical object processing in Alzheimer’s disease and frontotemporal dementia, with implications for our understanding of the neural mechanisms that decode complex sensory environments and the design and evaluation of interventions in these diseases.

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 4 matches between paragraphs and lines of code.

OSF azcf7

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: R (3), MATLAB (2)
Size: 175 files, 5 scripts
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (3 files), rstatix (2 files), car (1 file), Psychtoolbox (1 file), survival (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
5 files
At the source: osf.io/azcf7/overview?view_only=12925453e1234bc9b4f3c512f5fc1b54

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

The data that support the findings of this study are available on request from the corresponding author. In line with the ethics approvals governing the study, the data are not fully publicly available as they include information that could compromise the confidentiality of the research participants. Audio files of the musical stimuli and relevant scripts can be found on the Open Science Framework website here (https://osf.io/azcf7/overview?view_only=12925453e1234bc9b4f3c512f5fc1b54); see also Supplementary material.

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, issue, pages, dates, 10 authors, 5 keywords, 2 funders, 126 references.

Cite

This paper

Core, L. B., Froud, S. A., Wastling, S., Jiang, J., Levett, B. A., Mancini, L., Dymerska, B., Hardy, C. J. D., Zeidman, P., & Warren, J. D. (2026). Functional neuroanatomy of musical object processing in Alzheimer's disease and frontotemporal dementia. Brain communications, 8(3), fcag161. https://doi.org/10.1093/braincomms/fcag161

BibTeX

@article{core2026functional,
author = {Core, Lucy B and Froud, Sophie A and Wastling, Stephen and Jiang, Jessica and Levett, Benjamin A and Mancini, Laura and Dymerska, Barbara and Hardy, Chris J D and Zeidman, Peter and Warren, Jason D},
title = {{Functional neuroanatomy of musical object processing in Alzheimer's disease and frontotemporal dementia}},
journal = {Brain communications},
year = {2026},
month = may,
volume = {8},
number = {3},
pages = {fcag161},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/braincomms/fcag161},
url = {https://doi.org/10.1093/braincomms/fcag161},
pmid = {42146858},
pmcid = {PMC13178109}
}

RIS

TY - JOUR
AU - Core, Lucy B
AU - Froud, Sophie A
AU - Wastling, Stephen
AU - Jiang, Jessica
AU - Levett, Benjamin A
AU - Mancini, Laura
AU - Dymerska, Barbara
AU - Hardy, Chris J D
AU - Zeidman, Peter
AU - Warren, Jason D
TI - Functional neuroanatomy of musical object processing in Alzheimer's disease and frontotemporal dementia
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/05/05
VL - 8
IS - 3
SP - fcag161
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/braincomms/fcag161
UR - https://doi.org/10.1093/braincomms/fcag161
LA - en
ER -

CSL-JSON

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