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Impaired consolidation of spatial memory during sleep in patients with leucine-rich glioma-inactivated 1-associated limbic encephalitis.

Code ↔ Paper

2 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 2 matches
  1. [1] § Materials and methods › Virtual Morris water maze task › Statistical analysis ↔ Maze_stats_paper.Rmd, lines 169–192 · score 0.65 · ART ANOVAs, post hoc, rank, pairwise, interaction, phases
  2. [2] § Materials and methods › Virtual Morris water maze task › Sleep analysis ↔ Analysis_paper.py, lines 144–185 · score 0.60 · Morlet wavelets, frontal channels, cycle, power, 0.5 Hz, spindles

Paper

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The authors' code

R Markdown · 293 lines · 8.1 KB · MIT · 1 match

  1. ---
  2. title: "Maze_Statistics"
  3. output: html_document
  4. date: "2025-01-23"
  5. ---
  7. ```{r setup, include=FALSE}
  8. rm(list = ls())
  9. knitr::opts_chunk$set(echo = TRUE)
  10. library(ggplot2)
  11. library(tidyverse)
  12. library(dplyr)
  13. library(ggpubr)
  14. library(rstatix)
  15. ```
  18. ```{r}
  19. library(emmeans)
  20. data_maze_raw <- read.csv2("./data/Maze/ANOVA_LGI1.csv")
  22. data_maze <- data_maze_raw %>% pivot_longer(cols = B1_distance_learn:R4_latency_revlearn, names_to = c("Timepoint", "Variable", "Test"), names_sep = "_")
  24. ```
  27. ```{r}
  28. library(afex)
  29. library(emmeans)
  30. library(dplyr)
  33. calc_anova <- function(dat_aov, test, param){
  34. # Fit the repeated measures ANOVA using afex
  35. aov1 <- aov_ez(
  36. id = "ID", # Subject identifier
  37. dv = "value", # Dependent variable
  38. within = "Timepoint", # Within-subject factor
  39. between = "Group", # Between-subject factor
  40. data = dat_aov, # Data frame
  41. type = 2 # Type II sum of squares
  42. )
  44. # Extract the ANOVA table from the aov_ez model object
  45. anova_table <- as.data.frame(aov1$anova_table) %>%
  46. mutate(
  47. p.adj = p.adjust(`Pr(>F)`, method = "BH"), # Adjust p-values using BH method
  48. )
  49. anova_table$Test <- test
  50. anova_table$Parameter <- param
  52. # Pairwise comparisons using emmeans
  53. pwc_emmeans <- emmeans(aov1, pairwise ~ Group * Timepoint) %>% summary(infer = TRUE, adjust = "BH")
  55. # Extract pairwise comparisons table
  56. pwc_table <- as.data.frame(pwc_emmeans$contrasts)
  57. pwc_table$Test <- test
  58. pwc_table$Parameter <- param
  60. return(list(anova_table, pwc_table))
  61. }
  63. ```
  66. Calculate ANOVAs:
  68. ```{r}
  69. dat_aov <- data_maze %>% filter(Test == "learn" & Variable == "distance")
  70. aov <- calc_anova(dat_aov, test = "learning", param = "distance")
  71. aov1 <- aov[[1]]
  72. pwc1 <- aov[[2]]
  75. dat_aov <- data_maze %>% filter(Test == "learn" & Variable == "latency")
  76. aov <- calc_anova(dat_aov,test = "learning", param = "latency")
  77. aov2 <- aov[[1]]
  78. pwc2 <- aov[[2]]
  81. dat_aov <- data_maze %>% filter(Test == "revlearn" & Variable == "latency")
  82. aov <- calc_anova(dat_aov, test = "revlearn", param = "latency")
  83. aov3 <- aov[[1]]
  84. pwc3 <- aov[[2]]
  87. dat_aov <- data_maze %>% filter(Test == "revlearn" & Variable == "distance")
  88. aov <- calc_anova(dat_aov, test = "revlearn", param = "distance")
  89. aov4 <- aov[[1]]
  90. pwc4 <- aov[[2]]
  93. dat_aov <- data_maze %>% filter(Test == "revlearn" & Variable == "reldwtimenew")
  94. aov <- calc_anova(dat_aov, test = "revlearn", param = "reldwtimenew")
  95. aov5 <- aov[[1]]
  96. pwc5 <- aov[[2]]
  99. dat_aov <- data_maze %>% filter(Test == "revlearn" & Variable == "reldwtimeold")
  100. aov <- calc_anova(dat_aov, test = "revlearn", param = "reldwtimeold")
  101. aov6 <- aov[[1]]
  102. pwc6 <- aov[[2]]
  105. dat_aov <- data_maze %>% filter(Test == "disc" & Variable == "reldwtime")
  106. aov <- calc_anova(dat_aov, test = "disc", param = "reldwtime")
  107. aov7 <- aov[[1]]
  108. pwc7 <- aov[[2]]
  111. dat_aov <- data_maze %>% filter(Test == "cons" & Variable == "reldwtime")
  112. aov <- calc_anova(dat_aov, test = "cons", param = "reldwtime")
  113. aov8 <- aov[[1]]
  114. pwc8 <- aov[[2]]
  117. pwc <- rbind(pwc1,pwc2,pwc3, pwc4, pwc5, pwc6, pwc7, pwc8)
  118. aovs <- rbind(aov1,aov2,aov3,aov4,aov5,aov6,aov7,aov8)
  120. library(xlsx)
  121. write.xlsx(aovs, "./data/Maze/ANOVA_Result.xlsx")
  122. write.xlsx(pwc, "./data/Maze/PWC_Result.xlsx")
  125. ```
  128. ```{r}
  129. calc_anova_nonparametric <- function(dat_aov, test, param){
  130. library(rstatix)
  131. library(dplyr)
  133. # Perform Friedman test for within-subject comparisons (if more than 2 timepoints)
  134. friedman_res <- dat_aov %>%
  135. group_by(Group) %>%
  136. friedman_test(value ~ Timepoint | ID) %>%
  137. ungroup()
  139. # Perform Kruskal-Wallis test for between-group comparisons at each timepoint
  140. kruskal_res <- dat_aov %>%
  141. group_by(Timepoint) %>%
  142. kruskal_test(value ~ Group) %>%
  143. ungroup()
  145. # Merge the results into a single ANOVA table
  146. anova_table <- bind_rows(
  147. mutate(friedman_res, Test = "Friedman Test"),
  148. mutate(kruskal_res, Test = "Kruskal-Wallis Test")
  149. ) %>%
  150. mutate(
  151. p.adj = p.adjust(p, method = "BH"), # Adjust p-values using BH method
  152. Parameter = param
  153. )
  155. # Post hoc pairwise comparisons using Dunn’s test with BH correction
  156. pwc_table <- dat_aov %>%
  157. dunn_test(value ~ Group, p.adjust.method = "BH") %>%
  158. mutate(
  159. Test = "Dunn’s Test",
  160. Parameter = param
  161. )
  163. return(list(anova_table, pwc_table))
  164. }
  166. ```
  169. ```{r}
  170. calc_anova_nonparametric <- function(dat_aov, test, param){
  171. library(ARTool)
  172. library(rstatix)
  173. library(dplyr)
  175. # Perform ART ANOVA to test main effects and interaction
  176. dat_aov$Group <- as.factor(dat_aov$Group)
  177. dat_aov$Timepoint <- as.factor(dat_aov$Timepoint)
  179. art_model <- art(value ~ Group * Timepoint + (1 | ID), data = dat_aov)
  181. # ANOVA table
  182. anova_res <- anova(art_model) %>%
  183. mutate(Test = "ART Contrasts", Parameter = param, Phase = test)
  185. # Post hoc pairwise comparisons using ART-adjusted ranks
  186. posthoc_res <- summary(art.con(art_model, "Group:Timepoint")) %>%
  187. mutate(Test = "ART Contrasts", Parameter = param, Phase = test)
  189. return(list(anova_table = anova_res, posthoc_table = posthoc_res))
  190. }
  192. ```
  195. ```{r}
  197. dat_aov <- data_maze %>% filter(Test == "learn" & Variable == "distance")
  198. aov <- calc_anova_nonparametric(dat_aov, test = "learning", param = "distance")
  199. aov1 <- aov[[1]]
  200. pwc1 <- aov[[2]]
  203. dat_aov <- data_maze %>% filter(Test == "learn" & Variable == "latency")
  204. aov <- calc_anova_nonparametric(dat_aov,test = "learning", param = "latency")
  205. aov2 <- aov[[1]]
  206. pwc2 <- aov[[2]]
  208. dat_aov <- data_maze %>% filter(Test == "revlearn" & Variable == "latency" & ID != "30")
  209. aov <- calc_anova_nonparametric(dat_aov, test = "revlearn", param = "latency")
  210. aov3 <- aov[[1]]
  211. pwc3 <- aov[[2]]
  214. dat_aov <- data_maze %>% filter(Test == "revlearn" & Variable == "distance")
  215. aov <- calc_anova_nonparametric(dat_aov, test = "revlearn", param = "distance")
  216. aov4 <- aov[[1]]
  217. pwc4 <- aov[[2]]
  220. dat_aov <- data_maze %>% filter(Test == "revlearn" & Variable == "reldwtimenew")
  221. aov <- calc_anova_nonparametric(dat_aov, test = "revlearn", param = "reldwtimenew")
  222. aov5 <- aov[[1]]
  223. pwc5 <- aov[[2]]
  226. dat_aov <- data_maze %>% filter(Test == "revlearn" & Variable == "reldwtimeold")
  227. aov <- calc_anova_nonparametric(dat_aov, test = "revlearn", param = "reldwtimeold")
  228. aov6 <- aov[[1]]
  229. pwc6 <- aov[[2]]
  232. dat_aov <- data_maze %>% filter(Test == "disc" & Variable == "reldwtime")
  233. aov <- calc_anova_nonparametric(dat_aov, test = "disc", param = "reldwtime")
  234. aov7 <- aov[[1]]
  235. pwc7 <- aov[[2]]
  238. dat_aov <- data_maze %>% filter(Test == "cons" & Variable == "reldwtime")
  239. aov <- calc_anova_nonparametric(dat_aov, test = "cons", param = "reldwtime")
  240. aov8 <- aov[[1]]
  241. pwc8 <- aov[[2]]
  244. pwc <- rbind(pwc1,pwc2,pwc3, pwc4, pwc5, pwc6, pwc7, pwc8)
  245. aovs <- rbind(aov1,aov2,aov3,aov4,aov5,aov6,aov7,aov8)
  247. library(xlsx)
  248. write.xlsx(aovs, "./data/Maze/ART_Result.xlsx")
  249. write.xlsx(pwc, "./data/Maze/ART_PostHoc_Result.xlsx")
  251. ```
  255. ```{r}
  256. bxp <- ggboxplot(
  257. selfesteem2, x = "Timepoint", y = "score",
  258. color = "treatment", palette = "jco"
  259. )
  260. bxp
  262. plt <- dat_aov %>% group_by(Timepoint, Group) %>% summarise(value = mean(value)) %>%
  263. mutate(Timepoint= as.factor(Timepoint), Group = as.factor(Group)) %>%
  264. ggplot(aes(x = Timepoint, y = value, group = Group)) +
  265. geom_point() +
  266. geom_line(aes(linetype = Group)) +
  267. labs(
  268. linetype = "Group", # Change legend title for linetype
  269. x = "Phase", # Optional: Change x-axis label
  270. y = "Value" # Optional: Change y-axis label
  271. ) +
  272. scale_linetype_manual(
  273. values = c(1, 2), # Customize line types
  274. labels = c("Controls", "Patients") # Change legend labels
  275. )+
  276. theme_minimal()
  277. plt
  279. # Visualization: box plots with p-values
  280. plt+geom_signif(
  281. comparisons = list(c("B2", "R1")), # Groups to compare (if across timepoints)
  282. annotations = aov8$p.adj.signif[2], # Add significance level
  283. y_position = c(8), # Adjust height of annotation
  284. tip_length = 0.01
  285. ) +
  286. stat_pvalue_manual(
  287. data = pwc8,
  288. label = "p.adj.signif",
  289. y.position = c(6, 7), # Adjust y-positions for pairwise annotations
  290. tip.length = 0.01
  291. )
  292. ```

Maze_stats_paper.Rmd at commit 18f0807, under MIT · at the source

Overview

Authors: Julius Rave1,2, Annika Hanert1, Younes Adam Tabi1,2, Yasmin Fiedler1, Sarah Philippen1, Oliver Granert1, Frank Leypoldt1,3, Jan Born4, Andrea Burgalossi5, Robby Schönfeld6, Carsten Finke7, Thorsten Bartsch1,2
ORCID iDs: Sarah Philippen, Frank Leypoldt, Jan Born, Carsten Finke, Thorsten Bartsch
  1. Department of Neurology, Memory Disorders and Plasticity Group, University Hospital Schleswig-Holstein, University of Kiel, 24105 Kiel, Germany
  2. Clinician Scientist Program in Evolutionary Medicine, University of Kiel, 24105 Kiel, Germany
  3. Department of Neuroimmunology, Institute for Clinical Chemistry UKSH Campus Kiel and Lübeck, 24105 Kiel and 23538 Lübeck, Germany
  4. Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, 72076 Tübingen, Germany
  5. Institute of Neurobiology, Werner-Reichardt Center for Integrative Neuroscience, University of Tübingen, 72076 Tübingen, Germany
  6. Department of Psychology, Martin-Luther-Universität Halle-Wittenberg, 06108 Halle (Saale), Germany
  7. Department of Neurology, Cognition in Neurological Disorders Group, University Hospital Charité & Berlin School of Mind and Brain, 10117 Berlin, Germany
Institutions: Christian-Albrechts-Universität zu Kiel (Germany); University Hospital Schleswig-Holstein (Germany); University of Tübingen (Germany); Martin Luther University Halle-Wittenberg (Germany); Stiftung Charité; Charité - Universitätsmedizin Berlin (Germany)
Journal: Brain communications, volume 8, issue 4, article fcag255
Dates: received 15 December 2025; accepted 14 May 2026; published online 6 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/braincomms/fcag255 · PMID 42465729 · PMCID PMC13373790 · OpenAlex W7167415273
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), other condition (population), cognitive (subfield)
Methods: Statistics, Spectral & time-frequency, Connectivity, Graphs, Smoothing, state filtering, decompositions
Keywords: Memory consolidation, Sleep, Spatial navigation, Hippocampus, LGI1
Topic: Autoimmune Neurological Disorders and Treatments (Neurology, Medicine), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (413490537)
Citations: cited by 1 paper (Europe PMC); 150 references in the paper

Abstract

Sleep promotes the systems consolidation of hippocampus (HC)-dependent spatial memories by reprocessing of previously encoded hippocampal representations. Hippocampal reprocessing involves pattern separation and pattern completion as central hippocampal functions performed by the dentate gyrus (DG) and cornu ammonis region 3 (CA3), respectively. The leucine-rich, glioma inactivated 1 (LGI1)-associated limbic encephalitis (LE) is an autoimmune brain disorder particularly affecting the DG and CA3 regions, thereby impairing hippocampal function. We studied 15 LGI1 patients (and matched healthy controls) to examine hippocampal contributions to the sleep-associated consolidation of spatial memory. Spatial memory was assessed using the virtual Morris water maze (VWM) during learning before nocturnal sleep. Spatial retrieval of target locations (as indicated by dwell time in target area) was tested in the next morning, with separate trials testing pattern separation and pattern completion functions, as well as place memory precision and reversal learning capabilities. Leucine-rich, glioma inactivated 1-associated limbic encephalitis (LGI1-LE) patients were able to learn and retrieve spatial locations, albeit to a lesser extent than controls. Recall of place memories was decreased in LGI1-LE patients in comparison with learning performance before sleep and with healthy controls, especially in trials assessing pattern separation. Moreover, at recall, LGI1 patients showed a less flexible adaptation to the reversal learning task, in comparison with the controls. Sleep quality, macro-sleep architecture and EEG slow oscillations (SOs) and spindles were comparable in both groups. However, in LGI1-LE patients, phase–amplitude coupling of SO–spindle events appeared diminished although the group difference did not remain significant after correction for multiple comparisons. In addition, a negative correlation between spindle density and retrieval of target locations was observed. Magnetic resonance imaging confirmed smaller volumes of the HC and its subfields (subiculum, CA1, CA3, DG) in the patients. Divergent structure–function relationships emerged between patients with LGI1-associated encephalitis and healthy controls: In patients, larger volumes of DG and CA3 were associated with weaker sleep-dependent consolidation but greater stability under cue deprivation. In controls, larger hippocampal, CA1, and subicular volumes correlated with better memory retrieval and reversal learning performance. Our results show an impaired sleep-associated consolidation of spatial memory in LGI1-LE patients highlighting the involvement of DG and CA3 areas in sleep-associated spatial memory formation and cognitive flexibility.

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 2 matches between paragraphs and lines of code.

yf-neuro/LGI1-coupling-analysis

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 18f0807dbd3edb9db030d1e10ef1ffccb6194a73, 11 March 2026
Languages: R (3), Python (2)
Size: 6 files, 5 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: license file, 3 notebooks
Not found: README, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (2 files), MNE-Python (2 files), tidyverse (2 files), afex (1 file), BayesFactor (1 file), emmeans (1 file), ggpubr (1 file), Matplotlib (1 file), NumPy (1 file), pandas (1 file), rstatix (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/yf-neuro/LGI1-coupling-analysis.git

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;
  • 2 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 presented in this work are available upon reasonable request. Analysis code (including scripts used for the time–frequency and coupling analyses) is publicly available at https://github.com/yf-neuro/LGI1-coupling-analysis.git.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 5 keywords, 1 funder, 142 references.

Cite

This paper

Rave, J., Hanert, A., Tabi, Y. A., Fiedler, Y., Philippen, S., Granert, O., Leypoldt, F., Born, J., Burgalossi, A., Schönfeld, R., Finke, C., & Bartsch, T. (2026). Impaired consolidation of spatial memory during sleep in patients with leucine-rich glioma-inactivated 1-associated limbic encephalitis. Brain communications, 8(4), fcag255. https://doi.org/10.1093/braincomms/fcag255

BibTeX

@article{rave2026impaired,
author = {Rave, Julius and Hanert, Annika and Tabi, Younes Adam and Fiedler, Yasmin and Philippen, Sarah and Granert, Oliver and Leypoldt, Frank and Born, Jan and Burgalossi, Andrea and Schönfeld, Robby and Finke, Carsten and Bartsch, Thorsten},
title = {{Impaired consolidation of spatial memory during sleep in patients with leucine-rich glioma-inactivated 1-associated limbic encephalitis}},
journal = {Brain communications},
year = {2026},
month = jul,
volume = {8},
number = {4},
pages = {fcag255},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/braincomms/fcag255},
url = {https://doi.org/10.1093/braincomms/fcag255},
pmid = {42465729},
pmcid = {PMC13373790}
}

RIS

TY - JOUR
AU - Rave, Julius
AU - Hanert, Annika
AU - Tabi, Younes Adam
AU - Fiedler, Yasmin
AU - Philippen, Sarah
AU - Granert, Oliver
AU - Leypoldt, Frank
AU - Born, Jan
AU - Burgalossi, Andrea
AU - Schönfeld, Robby
AU - Finke, Carsten
AU - Bartsch, Thorsten
TI - Impaired consolidation of spatial memory during sleep in patients with leucine-rich glioma-inactivated 1-associated limbic encephalitis
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/07/06
VL - 8
IS - 4
SP - fcag255
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/braincomms/fcag255
UR - https://doi.org/10.1093/braincomms/fcag255
LA - en
ER -

CSL-JSON

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"PMID": "42465729",
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