OSCR

Intracranial volume: To adjust or not to adjust? It is not a matter of if, but how.

Code ↔ Paper

8 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 8 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 › Image processing ↔ PELICAN.sh, lines 117–164 · score 0.71 · linearly registered, intensity normalization, linear registration, ANTs, denoising, preprocessing
  2. [2] § Methods › Image processing ↔ app_cicLngPipeline_ants_asym.sh, lines 61–115 · score 0.71 · linearly registered, intensity normalization, linear registration, ANTs, denoising, preprocessing
  3. [3] § Methods › Additional validations ↔ code/01_wrangling/07_healthandbeh.r, lines 243–318 · score 0.61 · body mass, income, educational, socioeconomic, BMI, height
  4. [4] § Methods › Matching process ↔ code/01_wrangling/02_full_data.r, the whole file · a weak match · score 0.58 · demographic information, birth, months, date, fields, age
  5. [5] § Methods › Additional validations ↔ code/01_wrangling/05_get_nonimag_ukbb.r, lines 1–50 · score 0.58 · body mass, UKBB, BMI, disease, field, height
  6. [6] § Results › Voxelwise DBM ↔ code/03_analyses_and_viz/dbm/03_brain_plot_data.r, lines 1–50 · score 0.54 · deep GM, Cortical GM, ventricles, WM, BISON, brain
  7. [7] § Results › Voxelwise DBM ↔ code/03_analyses_and_viz/dbm/04_compare_voxel_adjStats.r, lines 1–68 · score 0.54 · deep GM, Cortical GM, ventricles, WM, BISON, DBM
  8. [8] § Results › Voxelwise DBM ↔ code/03_analyses_and_viz/dbm/04_compare_voxel_adjStats.r, lines 124–202 · score 0.51 · deep GM, cortical GM, WM, voxel, tissues, DBM

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

R · 293 lines · 10 KB · no license · 2 matches

  1. library(dplyr)
  2. library(tidyr)
  3. library(ggplot2)
  4. library(readr)
  5. library(stringr)
  6. library(rstatix)
  7. library(purrr)
  8. library(googlesheets4)
  9. library(here)
  11. source("code/helpers.r")
  13. summaries_folder <- file.path("outputs", "goodness_fit_summaries")
  14. supplementary_material_file <- sheet_id("supplementary_material")
  16. ####
  17. # Load voxel data
  19. adjustment_vox_files <- list.files(path = "outputs/in_revision/voxel_stats_v2",
  20. pattern = "adjustment", full.names = TRUE)
  21. bison_atlas <- read_csv(here("data", "atlases", "bison_icbm.csv"))
  23. bison_labels <-
  24. tibble(
  25. bison = 0:8,
  26. tissues = c("bg", "ventricles", "csf", "cerebellar_gm", "cerebellar_wm",
  27. "brainstem", "deep_gm", "cortical_gm", "wm"),
  28. clean_tissues = c("BG",
  29. "Ventricles",
  30. "CSF",
  31. "Cerebellar GM",
  32. "Cerebellar WM",
  33. "Brainstem",
  34. "Deep GM",
  35. "Cortical GM",
  36. "White Matter")
  37. )
  39. #metrics <- vertex_metrics$metric
  40. adj_methods <- methods_ref$vert_methods
  42. adj_files <-
  43. tibble(full_file = adjustment_vox_files) |>
  44. mutate(
  45. ext = basename(full_file),
  46. ext = gsub("adjustment_stats_", "", ext),
  47. ext = gsub("\\.csv", "", ext),
  48. chunk = gsub(paste(dfs_n_c, collapse = "|"), "", ext),
  49. chunk = gsub(paste(adj_methods, collapse = "|"), "", chunk),
  50. chunk = gsub("_", "", chunk),
  51. nchunk = as.numeric(gsub("chunk", "", chunk)),
  52. sample = str_extract(ext, paste(dfs_n_c, collapse = "|")),
  53. method = str_extract(ext, paste(adj_methods, collapse = "|"))
  54. )
  55. glimpse(adj_files)
  57. adj_data <-
  58. adj_files |>
  59. arrange(sample, method, nchunk) |>
  60. mutate(data = map(full_file, read_csv)) |>
  61. unnest(cols = data) |>
  62. select(-full_file)
  63. glimpse(adj_data); gc()
  65. bison_use <-
  66. bison_atlas |>
  67. rename(bison = flat_bison) |>
  68. left_join(bison_labels, by = "bison")
  70. get_comparisons <- function(data, tissue_sel, sample_sel) {
  71. tmp <-
  72. adj_data |>
  73. filter(sample == sample_sel) |>
  74. group_by(method) |>
  75. nest() |>
  76. mutate(data = map(data, ~ bind_cols(.x, bison_use))) |>
  77. unnest(cols = data) |>
  78. ungroup() |>
  79. filter(tissues %in% tissue_sel)
  81. data_summary <-
  82. tmp |>
  83. group_by(method) |>
  84. summarise(
  85. r2adj_mean = mean(adj_r2),
  86. r2adj_min = min(adj_r2),
  87. r2adj_max = max(adj_r2),
  88. r2adj_sd = sd(adj_r2),
  89. rmse_mean = mean(rmse),
  90. rmse_min = min(rmse),
  91. rmse_max = max(rmse),
  92. rmse_sd = sd(rmse),
  93. #aic_mean = mean(AIC),
  94. #aic_sd = sd(AIC),
  95. #bic_mean = mean(BIC),
  96. #bic_sd = sd(BIC)
  97. )
  100. print(data_summary)
  102. pairwise_adjr2 <- rstatix::pairwise_t_test(adj_r2 ~ method, data = tmp, p.adjust.method = "bonferroni", paired = TRUE)
  103. cohen_d_adj_r2 <- rstatix::cohens_d(adj_r2 ~ method, data = tmp, paired = TRUE)
  105. pairwise_rmse <- rstatix::pairwise_t_test(rmse ~ method, data = tmp, p.adjust.method = "bonferroni", paired = TRUE)
  106. cohen_d_rmse <- rstatix::cohens_d(rmse ~ method, data = tmp, paired = TRUE)
  108. final_adj_r2 <-
  109. left_join(pairwise_adjr2, cohen_d_adj_r2) |>
  110. select(-n1, -n2) |>
  111. arrange(-abs(effsize))
  113. final_rmse <-
  114. left_join(pairwise_rmse, cohen_d_rmse) |>
  115. select(-n1, -n2) |>
  116. arrange(-abs(effsize))
  118. print(final_adj_r2, width=Inf)
  119. print(final_rmse, width=Inf)
  121. return(list(dat_summary = data_summary, adj_r2 = final_adj_r2, rmse = final_rmse))
  122. }
  124. random_corticalgm <- get_comparisons(adj_data, tissue_sel = "cortical_gm", sample_sel = "random")
  125. random_deepgm <- get_comparisons(adj_data, tissue_sel = "deep_gm", sample_sel = "random")
  126. random_gm <- get_comparisons(adj_data, tissue_sel = c("cortical_gm", "deep_gm"), sample_sel = "random")
  127. random_wm <- get_comparisons(adj_data, tissue_sel = "wm", sample_sel = "random")
  129. random_sums <- list(
  130. random_corticalgm = random_corticalgm,
  131. random_deepgm = random_deepgm,
  132. random_gm = random_gm,
  133. random_wm = random_wm
  134. )
  137. random_dat_summary_df <- purrr::imap_dfr(random_sums, ~ (.x[[1]] %>% dplyr::mutate(source = .y)))
  138. random_adjr2_df <- purrr::imap_dfr(random_sums, ~ (.x[[2]] %>% dplyr::mutate(source = .y)))
  139. random_rmse_df <- purrr::imap_dfr(random_sums, ~ (.x[[3]] %>% dplyr::mutate(source = .y)))
  141. random_clean_summary <-
  142. random_dat_summary_df |>
  143. left_join(methods_ref, by = c("method" = "vert_methods")) |>
  144. transmute(
  145. Sample = "Not Matched",
  146. Metric = "Deformation based morphometry",
  147. Tissue = case_when(
  148. grepl("corticalgm", source) ~ "Cortical Gray Matter",
  149. grepl("deepgm", source) ~ "Deep Gray Matter",
  150. grepl("gm", source) ~ "Gray Matter (combined)",
  151. grepl("wm", source) ~ "White Matter"
  152. ),
  153. Method = adj,
  154. `Adjusted R-squared` = glue::glue("{round(r2adj_mean, 3)} ({round(r2adj_sd, 3)}), [{round(r2adj_min, 3)}, {round(r2adj_max, 3)}]"),
  155. RMSE = glue::glue("{round(rmse_mean, 3)} ({round(rmse_sd, 3)}), [{round(rmse_min, 3)}, {round(rmse_max, 3)}]")
  156. )
  158. random_adjr2_clean <-
  159. random_adjr2_df |>
  160. left_join(methods_ref |> select(group1 = vert_methods, Method1 = adj)) |>
  161. left_join(methods_ref |> select(group2 = vert_methods, Method2 = adj)) |>
  162. transmute(
  163. Sample = "Not Matched",
  164. Metric = "Deformation based morphometry",
  165. Tissue = case_when(
  166. grepl("corticalgm", source) ~ "Cortical Gray Matter",
  167. grepl("deepgm", source) ~ "Deep Gray Matter",
  168. grepl("gm", source) ~ "Gray Matter (combined)",
  169. grepl("wm", source) ~ "White Matter"
  170. ),
  171. Method1 = Method1,
  172. Method2 = Method2,
  173. `t-statistic` = round(statistic, 3),
  174. `Adjusted p-value` = ifelse(p.adj < 0.001, "< 0.001", round(p.adj, 3)),
  175. `Cohen's d` = round(effsize, 3),
  176. `Cohen's d magnitude` = magnitude
  177. )
  179. random_rmse_clean <-
  180. random_rmse_df |>
  181. left_join(methods_ref |> select(group1 = vert_methods, Method1 = adj)) |>
  182. left_join(methods_ref |> select(group2 = vert_methods, Method2 = adj)) |>
  183. transmute(
  184. Sample = "Not Matched",
  185. Metric = "Deformation based morphometry",
  186. Tissue = case_when(
  187. grepl("corticalgm", source) ~ "Cortical Gray Matter",
  188. grepl("deepgm", source) ~ "Deep Gray Matter",
  189. grepl("gm", source) ~ "Gray Matter (combined)",
  190. grepl("wm", source) ~ "White Matter"
  191. ),
  192. Method1 = Method1,
  193. Method2 = Method2,
  194. `t-statistic` = round(statistic, 3),
  195. `Adjusted p-value` = ifelse(p.adj < 0.001, "< 0.001", round(p.adj, 3)),
  196. `Cohen's d` = round(effsize, 3),
  197. `Cohen's d magnitude` = magnitude
  198. )
  200. write_csv(random_clean_summary, file.path(summaries_folder, "voxel_random_clean_summary.csv"))
  201. write_csv(random_adjr2_clean, file.path(summaries_folder, "voxel_random_adjr2_clean.csv"))
  202. write_csv(random_rmse_clean, file.path(summaries_folder, "voxel_random_rmse_clean.csv"))
  205. ######## Supplementary material --------
  206. tmp <-
  207. adj_data |>
  208. group_by(sample, method) |>
  209. nest() |>
  210. mutate(data = map(data, ~ bind_cols(.x, bison_use))) |>
  211. unnest(cols = data) |>
  212. ungroup()
  215. supplementary_voxel <-
  216. tmp |>
  217. filter(bison %in% c(6, 7, 8)) |>
  218. group_by(sample, method, clean_tissues) |>
  219. summarise(
  220. `Mean adjusted R2` = mean(adj_r2),
  221. `Min adjusted R2` = min(adj_r2),
  222. `Max adjusted R2` = max(adj_r2),
  223. `SD adjusted R2` = sd(adj_r2)
  224. ) |>
  225. ungroup() |>
  226. left_join(methods_ref |> select(vert_methods, adj), by = c("method" = "vert_methods")) |>
  227. left_join(samples_ref) |>
  228. rename(
  229. "Sample" = clean_sample,
  230. "Method" = adj,
  231. "Tissue" = clean_tissues
  232. ) |>
  233. mutate(
  234. Sample = factor(Sample, levels = c(samples_ref$clean_sample)),
  235. Method = factor(Method, levels = methods_ref$adj)
  236. ) |>
  237. arrange(Tissue, Sample, Method) |>
  238. select(Tissue, Sample, Method, `Mean adjusted R2`,
  239. `Min adjusted R2`, `Max adjusted R2`, `SD adjusted R2`)
  241. write_sheet(supplementary_voxel, ss = supplementary_material_file, sheet = "voxel_dbm_summary")
  243. stats <-
  244. tmp |>
  245. filter(bison %in% c(6, 7, 8)) |>
  246. left_join(
  247. methods_ref |> select(vert_methods, adj) |>
  248. mutate(Method = factor(adj, levels = methods_ref$adj)),
  249. by = c("method" = "vert_methods")
  250. ) |>
  251. left_join(
  252. samples_ref |> select(sample, clean_sample) |>
  253. mutate(Sample = factor(clean_sample,
  254. levels = samples_ref$clean_sample)),
  255. ) |>
  256. arrange(Sample, Method) |>
  257. group_by(Tissue = clean_tissues, Sample) |>
  258. nest() |>
  259. mutate(
  260. t = map(data, ~ rstatix::pairwise_t_test(adj_r2 ~ Method, data = ., p.adjust.method = "fdr", paired = TRUE)),
  261. d = map(data, ~ rstatix::cohens_d(adj_r2 ~ Method, data = ., paired = TRUE))
  262. ) |>
  263. select(-data) |>
  264. unnest(cols = c(t, d), names_sep = "_")
  266. stats_sc <-
  267. stats |>
  268. transmute(
  269. g1 = ifelse(t_group1 == d_group1, 1, 0),
  270. g2 = ifelse(t_group2 == d_group2, 1, 0)
  271. )
  273. if(sum(stats_sc$g1) != nrow(stats) | sum(stats_sc$g2) != nrow(stats)) {
  274. print("Group names do not match between t-test and Cohen's d results.")
  275. }
  277. stats <-
  278. stats |>
  279. mutate(
  280. t_p.adj = ifelse(t_p.adj < 0.001, "< 0.001", round(t_p.adj, 4)),
  281. t_statistic = round(t_statistic, 4),
  282. d_effsize = round(d_effsize, 4)
  283. ) |>
  284. select(Tissue, Sample, `Method 1` = t_group1, `Method 2` = t_group2,
  285. `t-statistic` = t_statistic, `Adjusted p-value` = t_p.adj,
  286. `Cohen's d` = d_effsize, `Cohen's d magnitude` = d_magnitude) |>
  287. mutate(
  288. `Method 1` = factor(`Method 1`, levels = methods_ref$adj),
  289. `Method 2` = factor(`Method 2`, levels = methods_ref$adj)
  290. ) |>
  291. arrange(Tissue, Sample, `Method 1`)
  293. write_sheet(stats, ss = supplementary_material_file, sheet = "voxel_dbm_stats")

04_compare_voxel_adjStats.r at commit c411374, no license · at the source

Overview

Authors: Aliza Brzezinski-Rittner1,2,3, Roqaie Moqadam1,4,5, Yashar Zeighami1,2,3, Mahsa Dadar1,2,3
ORCID iDs: Aliza Brzezinski-Rittner, Roqaie Moqadam, Yashar Zeighami, Mahsa Dadar
  1. Cerebral Imaging Center, Douglas Mental Health University Institute, Québec, Canada
  2. Department of Psychiatry, McGill University, Montréal, QC, Canada
  3. Integrated Program in Neuroscience, McGill University, Montréal, QC, Canada
  4. Faculty of Medicine, University of Montreal, Montréal, QC, Canada
  5. Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montréal, QC, Canada
Institutions: Douglas Mental Health University Institute (Canada); McGill University (Canada); Institut Universitaire de Gériatrie de Montréal (Canada); Université de Montréal (Canada)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1235
Dates: received 1 October 2025; accepted 16 April 2026; published online 22 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1235 · PMID 42206219 · PMCID PMC13206449 · OpenAlex W7155568096
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism)
Methods: Statistics, Preprocessing, fMRI & imaging
Keywords: aging, sex, magnetic resonance imaging, cortical thickness, surface area, volume, deformation-based morphometry, head size, brain size, intracranial volume, correction
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Fonds de recherche du Québec - Santé
Citations: not cited yet (Europe PMC); 91 references in the paper

Abstract

Total intracranial volume (TIV) is a major confounding factor in neuroimaging studies, particularly when studying sex differences in the brain. Different methods have been proposed to adjust for this effect; however, their impact has not been directly studied and compared. Furthermore, when studying cortical metrics at the vertex level, the choice of smoothing level can impact analysis outcomes which can in turn impact the degree of TIV-based biases and the effectiveness of the correction methods. In this study, we sought to evaluate the impact of four most commonly used adjustment methods in the literature on the estimations of neuroanatomical sex differences. These methods included the proportions method, the residuals method, the power-corrected proportions method, and adding TIV as a covariate in a regression analysis. Leveraging data from the UK Biobank, we employed a matching approach to devise a gold standard as reference for comparing TIV correction methods. To achieve this, we matched the male and female participants based on age and TIV to remove the impact of TIV differences between sexes. We further modeled aging trajectories at the regional level, vertexwise using data with different smoothing levels, and voxelwise, using raw and adjusted values, and compared the obtained estimates against the gold standard. We found that across different metrics, adding TIV as a covariate was the best-performing method for removing the effect of TIV, in terms of the correlation between the estimates of the different subsamples and the gold standard as well as the degree of estimation bias. Furthermore, we showed that the commonly used smoothing of the morphometric measures can result in biased estimation of sex differences in these measures. Finally, we showed that while small in effect size, there still remains some neuroanatomically specific uncorrected effects for all adjustment methods.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 8 matches between paragraphs and lines of code.

VANDAlab/Preprocessing_Pipeline

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 1e2f4abd90baa404fedf0d91f914fa3be3051820, 15 April 2026
Languages: Shell (10)
Size: 12 files, 10 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ANTs (10 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
11 files
At the source: github.com/VANDAlab/Preprocessing_Pipeline

AgingLab/tiv_adjustment_brzezinskirittner_2026

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: c411374479543f3b488699a0cb844bd85b2f1860, 7 May 2026
Languages: R (50), Python (1)
Size: 58 files, 51 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (43 files), ggplot2 (16 files), rstatix (13 files), broom (8 files), ggpubr (6 files), cowplot (3 files), ggseg (2 files), patchwork (2 files), lme4 (1 file), lmerTest (1 file), netneurotools (1 file), NumPy (1 file), pandas (1 file), SciPy (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
52 files
At the source: github.com/AgingLab/tiv_adjustment_brzezinskirittner_2026

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 61 scripts, each with its path and the digest of its content;
  • 8 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

Datasets cited

Data and Code Availability

The UK Biobank dataset is open access and can be requested from https://www.ukbiobank.ac.uk/use-our-data/apply-for-access/. FreeSurfer is also open source and freely available at https://surfer.nmr.mgh.harvard.edu/. Similarly, PELICAN (Dadar et al., 2025), the image processing pipeline used to derive TIV and DBM measurements, is open source and freely available at https://github.com/VANDAlab/Preprocessing_Pipeline. The code used during this project is available at https://github.com/AgingLab/tiv_adjustment_brzezinskirittner_2026. Estimates and t-value maps obtained for the matched and not matched sample without adjustments and adding TIV as a covariate are available at https://doi.org/10.5281/zenodo.20029615.

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, pages, dates, 4 authors, 11 keywords, 1 funder, 91 references.

Cite

This paper

Brzezinski-Rittner, A., Moqadam, R., Zeighami, Y., & Dadar, M. (2026). Intracranial volume: To adjust or not to adjust? It is not a matter of if, but how. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1235. https://doi.org/10.1162/imag.a.1235

BibTeX

@article{brzezinskirittner2026intracranial,
author = {Brzezinski-Rittner, Aliza and Moqadam, Roqaie and Zeighami, Yashar and Dadar, Mahsa},
title = {{Intracranial volume: To adjust or not to adjust? It is not a matter of if, but how}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = may,
volume = {4},
pages = {IMAG.a.1235},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1235},
url = {https://doi.org/10.1162/imag.a.1235},
pmid = {42206219},
pmcid = {PMC13206449}
}

RIS

TY - JOUR
AU - Brzezinski-Rittner, Aliza
AU - Moqadam, Roqaie
AU - Zeighami, Yashar
AU - Dadar, Mahsa
TI - Intracranial volume: To adjust or not to adjust? It is not a matter of if, but how
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/05/22
VL - 4
SP - IMAG.a.1235
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1235
UR - https://doi.org/10.1162/imag.a.1235
LA - en
ER -

CSL-JSON

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"id": "10.1162/imag.a.1235",
"type": "article-journal",
"title": "Intracranial volume: To adjust or not to adjust? It is not a matter of if, but how",
"container-title": "Imaging neuroscience (Cambridge, Mass.)",
"author": [
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"family": "Brzezinski-Rittner",
"given": "Aliza"
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"family": "Moqadam",
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"family": "Zeighami",
"given": "Yashar"
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{
"family": "Dadar",
"given": "Mahsa"
}
],
"container-title-short": "Imaging Neurosci (Camb)",
"volume": "4",
"page": "IMAG.a.1235",
"DOI": "10.1162/imag.a.1235",
"PMID": "42206219",
"PMCID": "PMC13206449",
"ISSN": "2837-6056",
"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/imag.a.1235",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
22
]
]
}
}

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

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BrainEnrich: Revealing Biological Insights for Imaging-Derived Phenotypes Through Transcriptomic Enrichment.
Journal: Human brain mapping
In common: netneurotools, ggseg, broom, 9 other tools, 3 references
[3] doi:10.1162/imag.a.1264 [code]
Data-driven subtyping and staging of ALS: A multicenter, longitudinal, deformation-based morphometry study.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: ANTs, pandas, SciPy, 1 other tool, structural MRI / diffusion, 9 references
[4] doi:10.1162/imag.a.1245 [code]
Towards precision EEG connectomics: Evaluating the benefits of dense sampling.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: rstatix, ANTs, lmerTest, 9 other tools, 1 reference
[5] doi:10.1038/s41467-026-73262-2 [code]
Robust but independent sex differences in human brain function, structure, and behavior.
Journal: Nature communications
In common: broom, lmerTest, lme4, 4 other tools, structural MRI / diffusion, 4 references
[6] doi:10.1038/s41398-026-04010-9 [code]
Bullying victimization and brain development: a longitudinal structural magnetic resonance imaging study from adolescence to early adulthood.
Journal: Translational psychiatry
In common: ggseg, rstatix, broom, 6 other tools, structural MRI / diffusion, 1 reference
[7] doi:10.1038/s41593-026-02363-4 [code]
Cortical thickness changes precede high levels of amyloid by at least 7 years.
Journal: Nature neuroscience
In common: ggseg, broom, lmerTest, 6 other tools, structural MRI / diffusion, 1 reference
[8] doi:10.1073/pnas.2603114123 [code]
The human hippocampus can pattern separate memories by meaning.
Journal: Proceedings of the National Academy of Sciences of the United States of America
In common: rstatix, broom, lmerTest, 8 other tools, 1 reference
[9] doi:10.1093/nc/niag029 [code]
A data-driven approach to identifying and evaluating connectivity-based neural correlates of conscious visual perception.
Journal: Neuroscience of consciousness
In common: ggseg, broom, lmerTest, 8 other tools
[10] doi:10.1038/s41467-026-73865-9 [code]
Histamine shapes the neurocomputational dynamics of human learning.
Journal: Nature communications
In common: rstatix, broom, lmerTest, 8 other tools

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