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Failure to detect entorhinal grid-like signals in a passive navigation human fMRI study.

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] § Results › Unimodal signal interference ↔ Analyses_GridLikeSignals.R, lines 319–373 · score 0.54 · behavioural task scores, fold signal, predicted, magnitude, Unimodal, hemisphere
  2. [2] § Methods › Statistical analyses ↔ Analyses_GridLikeSignals.R, lines 319–373 · score 0.51 · behavioral task, GLS magnitudes, scoring, hemisphere, Welch, fold

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The authors' code

R · 373 lines · 13 KB · MIT · 2 matches

  1. # grid_analysis.R
  2. # Analysis script for Kransberg et al. (2025) - GLS fMRI Study
  3. # Author: Jonas Kransberg
  4. # Date last modified: 02.05.2025
  5. # Author contact information: [email hidden]
  7. # 0. Load required libraries
  8. library(tidyverse)
  9. library(lmerTest)
  11. # 1. Generate dummy data
  12. set.seed(123)
  13. # 1.1 Create participant-level info for manual segmentation
  14. n_manual <- 110
  15. manual_ids <- factor(1:n_manual)
  16. participant_manual <- tibble(
  17. subject_id = manual_ids,
  18. visit_age = runif(n_manual, 16, 80),
  19. subject_sex = factor(sample(c("Male","Female"), n_manual, replace = TRUE)),
  20. total_correct_combined = sample(1:7, n_manual, replace = TRUE)
  21. ) %>%
  22. mutate(
  23. age_group = factor(ifelse(visit_age < 40, "Under40", "Above40"),
  24. levels = c("Under40","Above40"))
  25. )
  27. # 1.2 Expand to row-level for manual segmentation
  28. manual <- expand_grid(
  29. participant_manual,
  30. segmentation = "manual_segmentation",
  31. xfoldsym = c(1,5,6,7),
  32. roi = factor(c("rh_erc","lh_erc")),
  33. smoothing = c(0,4)
  34. ) %>%
  35. mutate(
  36. beta_gridcode_mean_combined = rnorm(n(), 0, 0.1),
  37. rayleigh_z = runif(n(), 0, 1), # spatial stability metric
  38. temporally_stable = runif(n(), 0, 100) # temporal stability metric (percent)
  39. )
  41. # 1.3 Create participant-level info for automatic segmentation
  42. n_auto <- 207
  43. auto_ids <- factor(1:n_auto)
  44. participant_auto <- tibble(
  45. subject_id = auto_ids,
  46. visit_age = runif(n_auto, 18.2, 78.6),
  47. subject_sex = factor(sample(c("Male","Female"), n_auto, replace = TRUE)),
  48. total_correct_combined = sample(1:7, n_auto, replace = TRUE)
  49. ) %>%
  50. mutate(
  51. age_group = factor(ifelse(visit_age < 40, "Under40", "Above40"),
  52. levels = c("Under40","Above40"))
  53. )
  55. automatic <- expand_grid(
  56. participant_auto,
  57. segmentation = "automatic_segmentation",
  58. xfoldsym = c(1,5,6,7),
  59. roi = factor(c("rh_erc","lh_erc")),
  60. smoothing = c(0,4)
  61. ) %>%
  62. mutate(
  63. beta_gridcode_mean_combined = rnorm(n(), 0, 0.1)
  64. )
  66. # Combine datasets
  67. combined <- bind_rows(manual, automatic)
  69. # 2. Grid-Like Signal (GLS) Analyses
  70. ## 2.1 6-Fold GLS vs Zero
  71. for(region in c("rh_erc","lh_erc")){
  72. cat("6-fold GLS vs zero in", region, "\n")
  73. print(t.test(filter(manual, xfoldsym==6, roi==region, smoothing == 0)$beta_gridcode_mean_combined,
  74. mu=0, alternative = "greater", na.rm = TRUE))
  75. }
  77. ## 2.2 Control Symmetry Checks (5- and 7-fold)
  78. for(sym in c(5,7)){
  79. for(region in c("rh_erc","lh_erc")){
  80. cat(sym, "-fold GLS vs zero in", region, "\n")
  81. print(t.test(filter(manual, xfoldsym==sym, roi==region, smoothing == 0)$beta_gridcode_mean_combined,
  82. mu=0, alternative = "greater", na.rm = TRUE))
  83. }
  84. }
  86. ## 2.3 Age-Group Stratification (<40 vs ≥40)
  87. ### Younger group (<40)
  88. for(sym in c(6,5,7)){
  89. for(region in c("rh_erc","lh_erc")){
  90. cat("Younger (<40):", sym, "-fold GLS vs zero in", region, "\n")
  91. data_y <- filter(manual, age_group=="Under40", xfoldsym==sym, roi==region, smoothing == 0)
  92. print(t.test(data_y$beta_gridcode_mean_combined, mu=0, alternative = "greater", na.rm = TRUE))
  93. }
  94. }
  96. ### Older group (≥40)
  97. for(sym in c(6,5,7)){
  98. for(region in c("rh_erc","lh_erc")){
  99. cat("Older (≥40):", sym, "-fold GLS vs zero in", region, "\n")
  100. data_o <- filter(manual, age_group=="Above40", xfoldsym==sym, roi==region, smoothing == 0)
  101. print(t.test(data_o$beta_gridcode_mean_combined, mu=0, alternative = "greater", na.rm = TRUE))
  102. }
  103. }
  105. ## 2.4 Between-Group Comparison for 6-Fold
  106. for(region in c("rh_erc","lh_erc")){
  107. cat("Between-group Welch t-test for 6-fold in", region, "\n")
  108. dt <- filter(manual, xfoldsym==6, roi==region, smoothing == 0)
  109. print(t.test(beta_gridcode_mean_combined ~ age_group, data=dt, alternative = "greater"))
  110. }
  112. ## 2.5 Continuous Age Effects (LME)
  113. model_6f <- lmer(beta_gridcode_mean_combined ~ visit_age * roi + subject_sex +
  114. (1|subject_id), data = filter(manual, xfoldsym==6, smoothing == 0))
  115. summary(model_6f)
  117. # 3. Temporal and Spatial Stability Analyses
  118. temp <- manual %>%
  119. filter(xfoldsym == 6, smoothing == 0)
  120. ## 3.1 Temporal Stability Analyses
  121. for(region in c("rh_erc","lh_erc")){
  122. df <- filter(temp, roi == region)
  123. cat("Temporal stability one-sample t-test in", region, "(H0: mean = 50%)\n")
  124. print(t.test(df$temporally_stable, mu = 50))
  125. cat("Temporal stability age-group comparison in", region, "\n")
  126. print(t.test(temporally_stable ~ age_group, data = df))
  127. }
  129. ## 3.2 Spatial Stability Analyses
  130. # Using Rayleigh's Z, test age-group differences
  131. for(region in c("rh_erc","lh_erc")){
  132. df <- filter(temp, roi == region)
  133. cat("Spatial stability age-group t-test for rayleigh_z in", region, "\n")
  134. print(t.test(rayleigh_z ~ age_group, data = df))
  135. }
  138. # 4 Control Analyses: Smoothing
  139. ## 4.1 6-Fold GLS vs Zero
  140. for(region in c("rh_erc","lh_erc")){
  141. cat("6-fold GLS vs zero in", region, "\n")
  142. print(t.test(filter(manual, xfoldsym==6, roi==region, smoothing == 4)$beta_gridcode_mean_combined,
  143. mu=0))
  144. }
  146. ## 4.2 Control Symmetry Checks (5- and 7-fold)
  147. for(sym in c(5,7)){
  148. for(region in c("rh_erc","lh_erc")){
  149. cat(sym, "-fold GLS vs zero in", region, "\n")
  150. print(t.test(filter(manual, xfoldsym==sym, roi==region, smoothing == 4)$beta_gridcode_mean_combined,
  151. mu=0))
  152. }
  153. }
  155. ## 4.3 Age-Group Stratification (<40 vs ≥40)
  156. ### Younger group (<40)
  157. for(sym in c(6,5,7)){
  158. for(region in c("rh_erc","lh_erc")){
  159. cat("Younger (<40):", sym, "-fold GLS vs zero in", region, "\n")
  160. data_y <- filter(manual, age_group=="Under40", xfoldsym==sym, roi==region, smoothing == 4)
  161. print(t.test(data_y$beta_gridcode_mean_combined, mu=0))
  162. }
  163. }
  165. ### Older group (≥40)
  166. for(sym in c(6,5,7)){
  167. for(region in c("rh_erc","lh_erc")){
  168. cat("Older (≥40):", sym, "-fold GLS vs zero in", region, "\n")
  169. data_o <- filter(manual, age_group=="Above40", xfoldsym==sym, roi==region, smoothing == 4)
  170. print(t.test(data_o$beta_gridcode_mean_combined, mu=0))
  171. }
  172. }
  174. ## 4.4 Between-Group Comparison for 6-Fold
  175. for(region in c("rh_erc","lh_erc")){
  176. cat("Between-group Welch t-test for 6-fold in", region, "\n")
  177. dt <- filter(manual, xfoldsym==6, roi==region, smoothing == 4)
  178. print(t.test(beta_gridcode_mean_combined ~ age_group, data=dt))
  179. }
  181. ## 4.5 Correlate GLS by smoothing type
  182. manualsmoothing_rh <- manual %>%
  183. filter(xfoldsym == 6, roi == "rh_erc") %>%
  184. select(subject_id, smoothing, beta_gridcode_mean_combined) %>%
  185. pivot_wider(names_from = smoothing,
  186. values_from = beta_gridcode_mean_combined,
  187. names_prefix = "smoothing_")
  190. cor.test(manualsmoothing_rh$smoothing_0, manualsmoothing_rh$smoothing_4)
  193. manualsmoothing_lh <- manual %>%
  194. filter(xfoldsym == 6, roi == "lh_erc") %>%
  195. select(subject_id, smoothing, beta_gridcode_mean_combined) %>%
  196. pivot_wider(names_from = smoothing,
  197. values_from = beta_gridcode_mean_combined,
  198. names_prefix = "smoothing_")
  200. cor.test(manualsmoothing_lh$smoothing_0, manualsmoothing_lh$smoothing_4)
  203. # 5 Control Analyses: Segmentation
  204. #Re-do primary analyses with automatically segmented EC masks.
  205. ## 5.1 6-Fold GLS vs Zero
  206. for(region in c("rh_erc","lh_erc")){
  207. cat("6-fold GLS vs zero in", region, "\n")
  208. print(t.test(filter(automatic, xfoldsym==6, roi==region, smoothing == 0)$beta_gridcode_mean_combined,
  209. mu=0))
  210. }
  212. ## 5.2 Control Symmetry Checks (5- and 7-fold)
  213. for(sym in c(5,7)){
  214. for(region in c("rh_erc","lh_erc")){
  215. cat(sym, "-fold GLS vs zero in", region, "\n")
  216. print(t.test(filter(automatic, xfoldsym==sym, roi==region, smoothing == 0)$beta_gridcode_mean_combined,
  217. mu=0))
  218. }
  219. }
  221. ## 5.3 Age-Group Stratification (<40 vs ≥40)
  222. ### Younger group (<40)
  223. for(sym in c(6,5,7)){
  224. for(region in c("rh_erc","lh_erc")){
  225. cat("Younger (<40):", sym, "-fold GLS vs zero in", region, "\n")
  226. data_y <- filter(automatic, age_group=="Under40", xfoldsym==sym, roi==region, smoothing == 0)
  227. print(t.test(data_y$beta_gridcode_mean_combined, mu=0))
  228. }
  229. }
  231. ### Older group (≥40)
  232. for(sym in c(6,5,7)){
  233. for(region in c("rh_erc","lh_erc")){
  234. cat("Older (≥40):", sym, "-fold GLS vs zero in", region, "\n")
  235. data_o <- filter(automatic, age_group=="Above40", xfoldsym==sym, roi==region, smoothing == 0)
  236. print(t.test(data_o$beta_gridcode_mean_combined, mu=0))
  237. }
  238. }
  240. ## 5.4 Between-Group Comparison for 6-Fold
  241. for(region in c("rh_erc","lh_erc")){
  242. cat("Between-group Welch t-test for 6-fold in", region, "\n")
  243. dt <- filter(automatic, xfoldsym==6, roi==region, smoothing == 0)
  244. print(t.test(beta_gridcode_mean_combined ~ age_group, data=dt))
  245. }
  247. ## 5.5 Correlate GLS by smoothing type
  248. # Right hemisphere
  249. combinedcorrh <- combined %>%
  250. filter(xfoldsym == 6, roi == "rh_erc", smoothing == 0) %>%
  251. select(subject_id, segmentation, beta_gridcode_mean_combined) %>%
  252. pivot_wider(names_from = segmentation,
  253. values_from = beta_gridcode_mean_combined,
  254. names_prefix = "seg_")
  257. cor.test(combinedcorrh$seg_manual_segmentation, combinedcorrh$seg_automatic_segmentation)
  259. # Left hemisphere
  260. combinedcorlh <- combined %>%
  261. filter(xfoldsym == 6, roi == "lh_erc", smoothing == 0) %>%
  262. select(subject_id, segmentation, beta_gridcode_mean_combined) %>%
  263. pivot_wider(names_from = segmentation,
  264. values_from = beta_gridcode_mean_combined,
  265. names_prefix = "seg_")
  268. cor.test(combinedcorlh$seg_manual_segmentation, combinedcorlh$seg_automatic_segmentation)
  271. # 6 High-Performance Participants Analysis
  272. ## 6. 1 Behavioral Performance Ceiling Effects
  273. behavrh <- manual %>% filter(xfoldsym == 6, roi == "rh_erc", smoothing == 0)
  274. behavlh <- manual %>% filter(xfoldsym == 6, roi == "lh_erc", smoothing == 0)
  275. table(behavrh$age_group, behavrh$total_correct_combined >= 6)
  277. ## 6.2 Age-Group Difference in Task Scores
  278. print(t.test(total_correct_combined ~ age_group, data=behavrh))
  281. ## 6.3 GLS After Excluding Low Scorers (<=6)
  282. high_perf <- filter(manual, total_correct_combined >= 6)
  284. ## 6.4 6-Fold GLS vs Zero
  285. for(region in c("rh_erc","lh_erc")){
  286. cat("6-fold GLS vs zero in", region, "\n")
  287. print(t.test(filter(high_perf, xfoldsym==6, roi==region, smoothing == 0)$beta_gridcode_mean_combined,
  288. mu=0))
  289. }
  291. ## 6.5 Control Symmetry Checks (5- and 7-fold)
  292. for(sym in c(5,7)){
  293. for(region in c("rh_erc","lh_erc")){
  294. cat(sym, "-fold GLS vs zero in", region, "\n")
  295. print(t.test(filter(high_perf, xfoldsym==sym, roi==region, smoothing == 0)$beta_gridcode_mean_combined,
  296. mu=0))
  297. }
  298. }
  300. ## 6.6 Age-Group Stratification (<40 vs ≥40)
  301. ### Younger group (<40)
  302. for(sym in c(6,5,7)){
  303. for(region in c("rh_erc","lh_erc")){
  304. cat("Younger (<40):", sym, "-fold GLS vs zero in", region, "\n")
  305. data_y <- filter(high_perf, age_group=="Under40", xfoldsym==sym, roi==region, smoothing == 0)
  306. print(t.test(data_y$beta_gridcode_mean_combined, mu=0))
  307. }
  308. }
  310. ### Older group (≥40)
  311. for(sym in c(6,5,7)){
  312. for(region in c("rh_erc","lh_erc")){
  313. cat("Older (≥40):", sym, "-fold GLS vs zero in", region, "\n")
  314. data_o <- filter(high_perf, age_group=="Above40", xfoldsym==sym, roi==region, smoothing == 0)
  315. print(t.test(data_o$beta_gridcode_mean_combined, mu=0))
  316. }
  317. }
  319. ## 6.7 Between-Group Comparison for 6-Fold
  320. for(region in c("rh_erc","lh_erc")){
  321. cat("Between-group Welch t-test for 6-fold in", region, "\n")
  322. dt <- filter(high_perf, xfoldsym==6, roi==region, smoothing == 0)
  323. print(t.test(beta_gridcode_mean_combined ~ age_group, data=dt))
  324. }
  327. ## 6.8 Correlation Analyses
  328. # Behavioural task scores and Age
  329. print(cor.test(behavrh$visit_age, behavrh$total_correct_combined))
  331. # Behavioural task scores and GLS magnitude
  332. # Right hemisphere
  333. print(cor.test(behavrh$beta_gridcode_mean_combined, behavrh$total_correct_combined))
  335. # Left hemisphere
  336. print(cor.test(behavlh$beta_gridcode_mean_combined, behavlh$total_correct_combined))
  339. # 7. Unimodal (1-Fold) Signal Analyses
  340. ## 7.1 1-Fold Signal vs Behavior
  341. filtered <- manual %>% filter(smoothing == 0)
  342. #rh_cor
  343. print(cor.test(filter(filtered, xfoldsym==1, roi == "rh_erc")$beta_gridcode_mean_combined,
  344. filter(filtered, xfoldsym==1, roi == "rh_erc")$total_correct_combined))
  346. #lh_cor
  347. print(cor.test(filter(filtered, xfoldsym==1, roi == "lh_erc")$beta_gridcode_mean_combined,
  348. filter(filtered, xfoldsym==1, roi == "lh_erc")$total_correct_combined))
  350. ## 7.2 1-Fold Signal
  351. lme_1f <- lmer(beta_gridcode_mean_combined ~ visit_age * roi + subject_sex +
  352. (1|subject_id), data = filter(filtered, xfoldsym==1))
  353. summary(lme_1f)
  355. ## 7.3 1-Fold Predicting 7-Fold & 6-Fold
  356. wide <- manual %>%
  357. filter(smoothing == 0) %>%
  358. select(subject_id, roi, xfoldsym, beta_gridcode_mean_combined, visit_age) %>%
  359. pivot_wider(
  360. names_from = xfoldsym,
  361. names_prefix = "fold_",
  362. values_from = beta_gridcode_mean_combined
  363. )
  365. lme_1v7 <- lmer(fold_7 ~
  366. fold_1 * roi + visit_age +
  367. (1|subject_id), data=wide)
  368. summary(lme_1v7)
  370. lme_1v6 <- lmer(fold_6 ~
  371. fold_1 * roi + visit_age +
  372. (1|subject_id), data=wide)
  373. summary(lme_1v6)

Analyses_GridLikeSignals.R at commit de7a08f, under MIT · at the source

Overview

Authors: Jonas Kransberg1, Anne Cecilie Sjøli Bråthen1, Emilie Sogn Falch1, Knut E.Ø. Øverbye1, Pablo F. Garrido1,2, Anders M. Fjell1,3, Matthias Stangl4,5, Thomas Wolbers6,7, Markus H. Sneve1, Kristine B. Walhovd1,3
ORCID iDs: Jonas Kransberg, Anne Cecilie Sjøli Bråthen, Emilie Sogn Falch, Knut E.Ø. Øverbye, Pablo F. Garrido, Anders M. Fjell, Matthias Stangl, Thomas Wolbers, Markus H. Sneve, Kristine B. Walhovd
  1. Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Norway
  2. Department of Physics, University of Oslo, Oslo, Norway
  3. Computational Radiology and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
  4. Department of Biomedical Engineering and Department of Psychological & Brain Sciences, Center for Systems Neuroscience, Cognitive Neuroimaging Center, Neurophotonics Center, Boston University, Boston, MA, United States
  5. Department of Neurosurgery, Boston Medical Center, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
  6. Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany
  7. Aging, Cognition & Technology Research Group, German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
Institutions: University of Oslo (Norway); Oslo University Hospital (Norway); Boston University (United States); Boston Medical Center (United States); German Center for Neurodegenerative Diseases (Germany); Center for Behavioral Brain Sciences (Germany)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1196
Dates: received 6 May 2025; accepted 14 February 2026; published online 7 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1196 · PMID 41958631 · PMCID PMC13058850 · OpenAlex W7137844700
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Connectivity, Graphs, fMRI & imaging
Keywords: aging, entorhinal cortex, grid cells, fMRI, spatial navigation
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: European Research Council (771375); Norges Forskningsråd (301395)
Citations: not cited yet (Europe PMC); 55 references in the paper

Abstract

Grid cells in the human entorhinal cortex (EC) play a critical role in spatial navigation and memory. The EC is also one of the first regions affected by ageing and Alzheimer’s disease. This pre-registered functional magnetic resonance imaging (fMRI) study aimed to detect grid-cell-like signals (GLS) in a passive virtual navigation task. Contrary to our hypotheses and previous findings, we did not observe significant GLS at a population level, even in younger participants. Further exploratory analyses investigated the impact of task-engagement, as inferred from object-location memory performance, and showed no relationship with GLS magnitude. We also examined potential influences of a confounding one-fold directional signal and various data-processing choices but observed no consistent patterns. Our findings, consistent with recent null results from similar studies, suggest that passive navigation paradigms may be insufficient for reliably eliciting grid-like signals in human fMRI.

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

Repository

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jokran/GLS_fMRI_Analysis_Kransberg_2025

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: de7a08fd0593e7f7645913ff16899121b0473fc3, 2 May 2025
Languages: R (1)
Size: 4 files, 1 script
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: lmerTest (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
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3 files
At the source: github.com/jokran/GLS_fMRI_Analysis_Kransberg_2025

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

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Data

No dataset and no data link were found in the paper.

Data and Code Availability

The study pre-registration can be found at https://aspredicted.org/rfx5-gmmq.pdf. The code used for the analyses is available at https://github.com/jokran/GLS_fMRI_Analysis_Kransberg_2025. The LCBC dataset has restricted access, but requests can be made to the corresponding author, and some of the data can be made available given appropriate ethical and data protection approvals.

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Recorded: type, language, journal, volume, pages, dates, 10 authors, 5 keywords, 2 funders, 55 references.

Cite

This paper

Kransberg, J., Sjøli Bråthen, A. C., Falch, E. S., Øverbye, K. E., Garrido, P. F., Fjell, A. M., Stangl, M., Wolbers, T., Sneve, M. H., & Walhovd, K. B. (2026). Failure to detect entorhinal grid-like signals in a passive navigation human fMRI study. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1196. https://doi.org/10.1162/imag.a.1196

BibTeX

@article{kransberg2026failure,
author = {Kransberg, Jonas and Sjøli Bråthen, Anne Cecilie and Falch, Emilie Sogn and Øverbye, Knut E.Ø. and Garrido, Pablo F. and Fjell, Anders M. and Stangl, Matthias and Wolbers, Thomas and Sneve, Markus H. and Walhovd, Kristine B.},
title = {{Failure to detect entorhinal grid-like signals in a passive navigation human fMRI study}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = apr,
volume = {4},
pages = {IMAG.a.1196},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1196},
url = {https://doi.org/10.1162/imag.a.1196},
pmid = {41958631},
pmcid = {PMC13058850}
}

RIS

TY - JOUR
AU - Kransberg, Jonas
AU - Sjøli Bråthen, Anne Cecilie
AU - Falch, Emilie Sogn
AU - Øverbye, Knut E.Ø.
AU - Garrido, Pablo F.
AU - Fjell, Anders M.
AU - Stangl, Matthias
AU - Wolbers, Thomas
AU - Sneve, Markus H.
AU - Walhovd, Kristine B.
TI - Failure to detect entorhinal grid-like signals in a passive navigation human fMRI study
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/04/07
VL - 4
SP - IMAG.a.1196
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1196
UR - https://doi.org/10.1162/imag.a.1196
LA - en
ER -

CSL-JSON

{
"id": "10.1162/imag.a.1196",
"type": "article-journal",
"title": "Failure to detect entorhinal grid-like signals in a passive navigation human fMRI study",
"container-title": "Imaging neuroscience (Cambridge, Mass.)",
"author": [
{
"family": "Kransberg",
"given": "Jonas"
},
{
"family": "Sjøli Bråthen",
"given": "Anne Cecilie"
},
{
"family": "Falch",
"given": "Emilie Sogn"
},
{
"family": "Øverbye",
"given": "Knut E.Ø."
},
{
"family": "Garrido",
"given": "Pablo F."
},
{
"family": "Fjell",
"given": "Anders M."
},
{
"family": "Stangl",
"given": "Matthias"
},
{
"family": "Wolbers",
"given": "Thomas"
},
{
"family": "Sneve",
"given": "Markus H."
},
{
"family": "Walhovd",
"given": "Kristine B."
}
],
"container-title-short": "Imaging Neurosci (Camb)",
"volume": "4",
"page": "IMAG.a.1196",
"DOI": "10.1162/imag.a.1196",
"PMID": "41958631",
"PMCID": "PMC13058850",
"ISSN": "2837-6056",
"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/imag.a.1196",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
7
]
]
}
}

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