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Cortical similarity networks in the rat brain: Postnatal development and sensitivity to early life stress.

Code ↔ Paper

19 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 19 matches · 5 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § MATERIALS AND METHODS › Rat Atlas Mapping ↔ code/03_data-analysis/02_validate-benchmark-MIND/01_withOB/02_mouse-transcriptomics.R, lines 2–43 · score 0.83 · Allen Mouse Brain, spatial transcriptomic expression, atlas mappings, WHS atlas, alignment, AMBA
  2. [2] § MATERIALS AND METHODS › Rat Atlas Mapping ↔ code/03_data-analysis/02_validate-benchmark-MIND/00_withoutOB/05_mouse-transcriptomics.R, lines 2–40 · score 0.83 · Allen Mouse Brain, spatial transcriptomic expression, atlas mappings, WHS atlas, alignment, AMBA
  3. [3] § MATERIALS AND METHODS › Image Registration ↔ code/01_preprocessing/03_register-to-atlas.sh, the whole file · a weak match · score 0.80 · animal_warper, WHS template, init_scale, AFNI, deobliqued, preprocessing
  4. [4] § MATERIALS AND METHODS › MIND Network Calculation › Input data generation. ↔ code/01_preprocessing/06b_subset-csv-for-MIND.R, the whole file · a weak match · score 0.68 · excludes olfactory bulb, Cerebral cortex, hierarchical, MTR, scan, atlas
  5. [5] § MATERIALS AND METHODS › Cortex Type Network Thresholding ↔ code/03_data-analysis/02_validate-benchmark-MIND/00_withoutOB/03_cortical_type.R, lines 44–119 · score 0.65 · MIND edge, thresholded, Paleocortical, agranular, archicortical, allocortex
  6. [6] § RESULTS › Network Validation and Reliability › Mouse transcriptional relationships to MIND similarity. ↔ code/03_data-analysis/02_validate-benchmark-MIND/00_withoutOB/05_mouse-transcriptomics.R, lines 2–40 · score 0.65 · mouse brain, spatial transcriptomic, gene expression, AMBA, atlas
  7. [7] § RESULTS › Network Validation and Reliability › Mouse transcriptional relationships to MIND similarity. ↔ code/03_data-analysis/02_validate-benchmark-MIND/01_withOB/02_mouse-transcriptomics.R, lines 2–43 · score 0.65 · mouse brain, spatial transcriptomic, gene expression, AMBA, atlas
  8. [8] § MATERIALS AND METHODS › Image Registration ↔ code/01_preprocessing/02_align-to-atlas.sh, the whole file · a weak match · score 0.64 · WHS template, AFNI, oriented, deobliqued, preprocessing, atlas
  9. [9] § MATERIALS AND METHODS › MIND Network Calculation › MIND network phenotypes. ↔ code/03_data-analysis/02_validate-benchmark-MIND/00_withoutOB/02_rich-club.R, lines 2–44 · score 0.62 · rich club coefficient, normative network, hubs, sum, nodes, weight
  10. [10] § RESULTS › Network Validation and Reliability › Cytoarchitectonic relationships to MIND similarity. ↔ code/03_data-analysis/02_validate-benchmark-MIND/00_withoutOB/03_cortical_type.R, lines 44–119 · score 0.62 · allo meso, network densities, allocortical, mesocortical, thresholding, Intraclass
  11. [11] § RESULTS › Normative Network Topology ↔ code/03_data-analysis/02_validate-benchmark-MIND/00_withoutOB/02_rich-club.R, lines 2–44 · score 0.61 · rich club coefficient, normative network, stronger, Hubs, sum, median
  12. [12] § RESULTS › Network Validation and Reliability › Tract-tracing relationships to MIND similarity. ↔ code/04_figures-markdown/Fig4.Rmd, lines 174–199 · score 0.59 · tract tracing Jaccard, Spearman correlation, MIND edge weights, Figure 4
  13. [13] § MATERIALS AND METHODS › Edge Distance Calculation ↔ code/02_data-prep/00b_WHS-3dCM-find-centers.R, the whole file · a weak match · score 0.56 · right hemispheres, Icent, AFNI, dcalc, WHS, atlas
  14. [14] § MATERIALS AND METHODS › Statistical Analyses › Correlation calculation. ↔ code/04_figures-markdown/Fig4.Rmd, lines 57–84 · score 0.54 · linear relationships, MIND edge weight, Spearman, Pearson, distance, correlation
  15. [15] § RESULTS › Network Validation and Reliability › MIND similarity–distance relationship. ↔ code/04_figures-markdown/Fig4.Rmd, lines 148–172 · score 0.53 · network density thresholds, class edges, intraclass, normative MIND, maroon, Figure 4
  16. [16] § MATERIALS AND METHODS › MTR Calculation ↔ code/01_preprocessing/04-3dcalc-MTR.sh, lines 59–86 · score 0.53 · native space, AFNI, dcalc, MTR, scan
  17. [17] § RESULTS › Network Validation and Reliability › Tract-tracing relationships to MIND similarity. ↔ code/03_data-analysis/02_validate-benchmark-MIND/00_withoutOB/04_tract-tracing.R, lines 2–46 · score 0.52 · axonal connectivity profiles, tract tracing, hypothesized
  18. [18] § MATERIALS AND METHODS › MTR Calculation ↔ code/01_preprocessing/06a_generate-csv-for-MIND.R, the whole file · a weak match · score 0.50 · native space, native scans, voxel, MTR
  19. [19] § RESULTS › Network Validation and Reliability › Tract-tracing relationships to MIND similarity. ↔ code/03_data-analysis/02_validate-benchmark-MIND/00_withoutOB/04_tract-tracing.R, lines 289–328 · score 0.50 · tract tracing Jaccard, Spearman correlation, weights, edge

Paper

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The authors' code

R Markdown · 276 lines · 9.8 KB · no license · 3 matches

  1. ---
  2. title: "Figure 4: Validation of the normative rat network against other neurobiological properties"
  3. author: "Rachel Smith"
  4. output: pdf_document
  5. ---
  7. ```{r setup, include = FALSE}
  8. knitr::opts_chunk$set(fig.width = 2.5, fig.height = 2, collapse = TRUE, warning = FALSE, message = FALSE, echo = FALSE)
  10. ## Set paths and load data
  11. base_dir <- "~/Documents/PhD/projects/CamRat/CamRat/"
  12. source(paste0(base_dir, "code/03.data_analysis/setup.R"))
  13. figures_dir <- paste0(base_dir, "outputs/figures2.0/Fig4/")
  14. tables_dir <- paste0(base_dir, "outputs/tables/")
  15. analysis_objects_dir <- paste0(base_dir, "outputs/objects/") # to save analysis objects for figures
  17. ## Load data objects for figures
  18. objects <- list.files(analysis_objects_dir)
  19. objects <- objects[!str_detect(objects, "null|archive")]
  20. for (obj in objects){
  21. print(paste0("loading ", obj, "...."))
  22. load(paste0(analysis_objects_dir, obj))
  23. }
  25. ## Write function to calculate the Pearson and Spearman correlations of two vectors, as well as the respective P values
  26. f_calc_pearson_spearman <- function(vec1, vec2) {
  28. ## Calculate the Pearson correlation
  29. pearson_res <- cor.test(vec1, vec2, method = "pearson")
  30. pearson_cor <- pearson_res$estimate
  31. pearson_tstat <- pearson_res$statistic
  32. pearson_df <- pearson_res$parameter
  33. pearson_pvalue <- 2 * pt(abs(pearson_tstat), df = pearson_df, lower.tail = FALSE)
  35. ## Calculate the Spearman correlation
  36. spearman_res <- cor.test(vec1, vec2, method = "spearman")
  37. spearman_cor <- spearman_res$estimate
  38. n <- length(vec1)
  39. spearman_df <- n - 2
  40. #spearman_sstat <- spearman_res$statistic
  41. spearman_tstat <- spearman_cor * sqrt(spearman_df / (1 - spearman_cor^2))
  42. spearman_pvalue <- 2 * pt(abs(spearman_tstat), df = spearman_df, lower.tail = FALSE)
  44. ## Return results as a tibble
  45. res <- tibble(
  46. test = c("Pearson", "Spearman"),
  47. correlation = c(pearson_cor, spearman_cor),
  48. t_stat = c(pearson_tstat, spearman_tstat),
  49. p_value = c(pearson_pvalue, spearman_pvalue)
  50. )
  51. return(res)
  52. }
  54. ```
  57. ### A | Edge-distance relationship
  59. ```{r Fig4a, fig.width = 2.15, fig.height = 2}
  60. ## Pearson & Spearman correlation (plot Pearson)
  61. f_calc_pearson_spearman(df_normative_mind_distance$median_weight, df_normative_mind_distance$distance)
  63. ## Plot
  64. df_normative_mind_distance %>%
  65. ggplot(aes(x = distance, y = median_weight)) +
  66. geom_point(size = 0.3) +
  67. geom_smooth(method = "lm", color = "maroon", linewidth = 0.5) +
  68. stat_cor(aes(label = ..r.label..), color = "maroon", label.x.npc = 0.62) +
  69. scale_fill_viridis() +
  70. coord_cartesian(clip = "off") +
  71. labs(x = "Distance between nodes", y = "Edge weight (w)",
  72. title = "A | Distance by weight"
  73. #title = "Linear relationship between normative MIND edge weight and distance"
  74. ) +
  75. theme(
  76. legend.position = "none"
  77. )
  79. ## Save
  80. map(
  81. .x = c(".png", ".pdf"),
  82. .f = ~ ggsave(paste0(figures_dir, "A.edge_distance", .x), width = 2.15, height = 2)
  83. )
  84. ```
  86. ### B | Anatomy of cortical types
  88. ```{r Fig4b, fig.width = 2.15, fig.height = 2.0}
  89. ## Load cortical type assignments (Zilles --> WHS)
  90. data_dir <- paste0(base_dir, "data/WHS_analysis/")
  91. df_cortical_type_all <- read_xlsx(paste0(data_dir, "GarciaCabeza2023_all_cortical_types_RLS.xlsx")) %>%
  92. clean_names
  94. ## Assign colors
  95. cortical_type_colors <- c(
  96. "eulaminate" = "#5050FFFF",
  97. "agranular" = "#CE3D32FF",
  98. "dysgranular" = "#749B58FF",
  99. "archicortex" = "#F0E685FF",
  100. "paleocortex" = "#D595A7FF",
  102. "isocortex" = "#E69F00",
  103. "mesocortex" = "#009E73",
  104. "allocortex" = "#9966FF"
  105. )
  107. ## Rotate coordinates by 90 degrees (to plot upright)
  108. rotate_180 <- function(geometry) {
  109. rotation_matrix <- matrix(c(-1, 0, 0, -1), ncol = 2) # 180-degree counterclockwise rotation
  110. st_geometry(geometry) <- st_geometry(geometry) * rotation_matrix
  111. return(geometry)
  112. }
  113. df_flatmap_rot <- rotate_180(df_flatmap)
  115. ## Add system hierarchy
  116. df_cortical_type_flatmap <- df_flatmap_rot %>%
  117. filter(!is.na(Swanson_name)) %>%
  118. left_join(df_cortical_type_all, by = join_by("region_of_interest" == "whs")) %>%
  119. mutate(color = ifelse(is.na(region_of_interest), "gray", "black")) %>%
  120. mutate(cortex = ifelse(!is.na(cortex), paste0(cortex, "cortex"), cortex)) %>%
  121. mutate(fill = ifelse(hemisphere == "Right", cortical_type, cortex)) %>%
  122. mutate(fill = factor(fill, levels = names(cortical_type_colors)))
  124. ## Plot
  125. df_cortical_type_flatmap %>%
  126. filter(hemisphere == "Left") %>% # Plot only cortex type
  128. ggplot(mapping = aes(geometry = geometry)) +
  129. geom_sf(aes(fill = fill, color = I(color))) +
  130. scale_fill_manual(values = cortical_type_colors,
  131. na.value = "white", guide = guide_legend(title = NULL, nrow = 2)) +
  132. labs(x = NULL, y = NULL, title = "B | Cortical type topography") +
  133. theme(
  134. legend.position = "bottom",
  135. legend.justification = "center",
  136. legend.key.size = unit(0.25, "cm"),
  137. legend.margin = margin(t = -20),
  138. axis.line = element_blank(),
  139. axis.text = element_blank(),
  140. axis.ticks = element_blank()
  141. )
  142. map(
  143. .x = c(".png", ".pdf"),
  144. .f = ~ ggsave(paste0(figures_dir, "B.cortical_type_topography", .x), width = 2.15, height = 2.0)
  145. )
  146. ```
  148. ### C | Cortical type: Intra-class edge proportion across network density thresholds
  150. ```{r Fig4c, fig.width = 2.15, fig.height = 2}
  151. df_intraclass_overlap <- df_intraclass_overlap %>% mutate(color = ifelse(color != "gray", "maroon", color))
  152. ggplot(mapping = aes(x = density, y = percent_edge*100)) +
  153. #geom_point(aes(fill = I(color)), shape = 21) +
  154. geom_line(data = df_intraclass_overlap %>% filter(network == "normative MIND"), linewidth = 0.75,
  155. mapping = aes(group = network, color = I(color))
  156. ) +
  157. geom_smooth(data = df_intraclass_overlap %>% filter(network != "normative MIND"),
  158. mapping = aes(color = I(color)), se = TRUE
  159. ) +
  160. annotate(geom = "text", label = "Normative network", color = "maroon", x = 0.06, y = 100, size = 3.5) +
  161. annotate(geom = "text", label = "10000 null nets", color = "#595959", x = 0.06, y = 25, size = 3.5) +
  163. scale_x_continuous(breaks = c(0, 0.05, 0.1)) +
  164. ylim(c(0, 100)) +
  165. coord_cartesian(clip = "off") +
  166. labs(x = "Network density", y = "Intra-class edge %",
  167. title = "C | Intra-class similarity")
  168. map(
  169. .x = c(".png", ".pdf"),
  170. .f = ~ ggsave(paste0(figures_dir, "C.intraclass_similarity", .x), width = 2.15, height = 2)
  171. )
  172. ```
  174. ### D | Tract-tracing jaccard vs MIND edge weight
  176. ```{r Fig4d.cor, fig.height = 2.0, fig.width = 2.15}
  177. ## Pearson & Spearman correlation (plot Spearman)
  178. f_calc_pearson_spearman(df_jaccard_mind$mind_weight, df_jaccard_mind$jaccard)
  180. ## Plot
  181. df_jaccard_mind %>%
  182. ggplot(aes(x = jaccard, y = mind_weight)) +
  183. geom_point(size = 0.25) +
  184. geom_smooth(method = "lm", color = "maroon", linewidth = 0.5) +
  185. stat_cor(method = "spearman", cor.coef.name = "rho", p.digits = NA, label.y = 0.625, label.x = 0.5,
  186. label.sep = "\n", hjust = 0, size = 3.5, color = "maroon") +
  187. coord_cartesian(clip = "off") +
  188. labs(x = "Tract-tracing Jaccard index", y = "Edge weight (w)",
  189. title = "D | Tract-tracing similarity") +
  190. theme(
  191. legend.position = "none"
  192. )
  194. ## Save
  195. map(
  196. .x = c(".png", ".pdf"),
  197. .f = ~ ggsave(paste0(figures_dir, "D.tracttracing_jaccard_mind_cor", .x), width = 2.15, height = 2.0)
  198. )
  199. ```
  201. ### E | Side-by-side comparison of network heatmaps
  203. ```{r Fi4e, fig.height = 2.0, fig.width = 2.2}
  204. # Format tibble to plot
  205. df_norm_exp_plot <- df_norm_exp_mind %>%
  206. mutate(
  207. R1 = factor(R1, levels = roi_order),
  208. R2 = factor(R2, levels = roi_order)
  209. ) %>%
  210. mutate(
  211. x_num = as.numeric(R1),
  212. y_num = as.numeric(R2),
  213. triangle = ifelse( x_num >= y_num, "lower", "upper")
  214. ) %>%
  215. pivot_longer(c(norm_weight, exp_weight), names_to = "network", values_to = "value") %>%
  216. filter( (triangle == "upper" & str_detect(network, "exp")) | (triangle == "lower" & str_detect(network, "norm")) )
  218. # Plot
  219. df_norm_exp_plot %>%
  220. ggplot(aes(x = R1, y = R2)) +
  221. geom_tile(aes(fill = value)) +
  222. geom_abline(color = "black") +
  224. # bottom bar annotation
  225. annotate(xmin = c(0.5, system_lines),
  226. xmax = c(system_lines, length(roi_order) + 0.5),
  227. ymin = -1.25, ymax = 0,
  228. geom = "rect",
  229. fill = system_colors) +
  231. # plot aesthetics
  232. scale_fill_viridis(guide = guide_colorbar(title = NULL)) +
  233. coord_equal() +
  234. labs(x = "Control PND63 (N=19)", y = "Normative PND63 (N=41)",
  235. title = "E | Cohort comparison") +
  236. theme(
  237. axis.text = element_blank(),
  238. axis.ticks = element_blank(),
  239. axis.title.x = element_text(vjust = 67),
  240. axis.title.y = element_text(vjust = 66, angle = 270),
  241. legend.position = "left",
  242. legend.margin = margin(r = -30),
  243. legend.text = element_text(margin = margin(l = 2)),
  244. legend.key.height = unit(0.75, "cm"),
  245. legend.key.width = unit(0.15, "cm"),
  246. plot.margin = margin(b = -10, r = 10),
  247. axis.line = element_blank()
  248. )
  249. map(
  250. .x = c(".png", ".pdf"),
  251. .f = ~ ggsave(paste0(figures_dir, "E.normative_control_heatmaps", .x), width = 2.2, height = 2.0)
  252. )
  253. ```
  255. ### F | Scatterplot correlation of edge weights & edge weight distributions
  257. ```{r Fig4f, fig.height = 2.0, fig.width = 2.15}
  258. ## Pearson & Spearman correlation (plot Pearson)
  259. f_calc_pearson_spearman(df_norm_exp_mind$norm_weight, df_norm_exp_mind$exp_weight)
  261. ## Plot
  262. df_norm_exp_mind %>%
  263. ggplot(aes(x = norm_weight, y = exp_weight)) +
  264. geom_point(size = 0.25) +
  265. geom_smooth(method = "lm", color = "maroon", linewidth = 0.5, se = FALSE) +
  266. stat_cor(aes(label = ..r.label..), vjust = 1, color = "maroon", size = 3.5) +
  267. labs(x = "Normative PND63 (N=41)", y = "Control PND63 (N=19)",
  268. title = "F | Edge weight correlation")
  270. ## Save
  271. map(
  272. .x = c(".png", ".pdf"),
  273. .f = ~ ggsave(paste0(figures_dir, "F.edge_weight_cor", .x), width = 2.15, height = 2.0)
  274. )
  275. ```

Fig4.Rmd at commit 6286f65, no license · at the source

Overview

Authors: Rachel L. Smith1,2, Stephen J. Sawiak3,4, Lena Dorfschmidt1, Ethan G. Dutcher5, Jolyon A. Jones3,5, Joel D. Hahn6, Olaf Sporns7,8, Larry W. Swanson6, Paul A. Taylor9, Daniel R. Glen9, Jeffrey W. Dalley1,3,5, Francis J. McMahon2, Armin Raznahan2, Petra E. Vértes1, Edward T. Bullmore1
ORCID iDs: Rachel L. Smith, Paul A. Taylor, Edward T. Bullmore
  1. Department of Psychiatry, University of Cambridge, Cambridge, UK
  2. Human Genetics Branch, National Institute of Mental Health, Bethesda, MD, USA
  3. Behavioural and Clinical Neuroscience Institute, University of Cambridge, Downing Site, Cambridge, UK
  4. Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
  5. Department of Psychology, University of Cambridge, Cambridge, UK
  6. Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA
  7. Indiana University Network Science Institute, Indiana University, Bloomington, IN, USA
  8. Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
  9. Scientific and Statistical Computing Core, National Institute of Mental Health, NIH, Bethesda, MD, USA
Institutions: University of Cambridge (United Kingdom); National Institute of Mental Health (United States); University of Southern California (United States); Indiana University Bloomington (United States); Indiana University (United States); National Institutes of Health (United States)
Journal: Network neuroscience (Cambridge, Mass.), volume 10, issue 2, pages 418-443
Dates: received 11 September 2025; accepted 11 January 2026; published online 22 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/netn.a.546 · PMID 42039103 · PMCID PMC13108504 · OpenAlex W7124468690
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), rat (organism), developmental (subfield)
Methods: Connectivity, Statistics, Graphs, fMRI & imaging, Physiology & signal measures
Keywords: Connectome, Architectome, Micro-structural MRI, Translational neuroscience, Structural similarity
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: GlaxoSmithKline (300034212); Medical Research Council (PJAG/371); MQ: Transforming Mental Health (MQF17_24); Gates Cambridge Trust; HHS | NIH | National Institute of Mental Health (NIMH) (1ZIAMH002810, 1ZIAMH002949, ZICMH002888); NIHR Cambridge Biomedical Research Centre (BRC-1215-20014); NIH Oxford-Cambridge Scholars Program
Citations: cited by 2 papers (Europe PMC); 98 references in the paper

Abstract

Network models are a key tool in human neuroscience, and translation into animal models is essential for interrogating mechanistic drivers of network organization. Using magnetic resonance imaging (MRI), we present the first in vivo network representation of the individual rat brain, a key animal model in neuroscience. We measured magnetization transfer ratio (MTR) at each of 53 distinct cortical areas and estimated a cortical similarity network for each scan across two independent cohorts. We characterized normative network development in rats scanned repeatedly between postnatal days 20 (weanling) and 290 (mid-adulthood; N = 47) and then contrasted these findings with a cohort exposed to early life stress (repeated maternal separation [RMS]; N = 40). The normative rat cortical similarity network exhibited biologically meaningful organization, consistent with cytoarchitectonic and tract-tracing data, and displayed complex topological features. Developmental analyses revealed increasing interregional similarity during early postnatal and adolescent periods, followed by divergence in mid-adulthood, particularly within fronto-hippocampal systems. RMS disrupted these trajectories, especially between frontal and parahippocampal regions that were also most dynamic during development and aging. These findings introduce a new network-based methodology for studying cortical organization in a model organism, providing a translational framework to understand how environmental risk factors alter brain network development.

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

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davidmcclure/svg-to-wkt

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Evidence: files inventoried
Commit: f37f45cc8dd5dd8130a060a309c7989fe75314f9, 15 September 2015
Languages: JavaScript (15)
Size: 21 files, 15 scripts
Software Heritage: archived
Found in: the text, “Flatmap rendering.”
Holds: README, tests
Not found: license file, CITATION.cff, environment file, continuous integration, documentation
Availability: 1 check, the latest on 29 September 2026: the link answers
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At the source: github.com/davidmcclure/svg-to-wkt

rlsmith1/rat_MRI_similarity_networks

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Commit: 6286f65e3f0678ffc26f4830d4492a98be097f86, 10 March 2026
Languages: R (52), Shell (14), Python (2)
Size: 3,670 files, 68 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 (49 files), patchwork (5 files), AFNI (4 files), RNifti (3 files), lme4 (2 files), NumPy (2 files), pandas (2 files), SciPy (2 files), cowplot (1 file), ggpubr (1 file), igraph (1 file), nlme (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
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69 files
At the source: github.com/rlsmith1/rat_MRI_similarity_networks

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

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Data

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Data and Code Availability

All data were made available through previous publications (Dutcher et al., 2023; Jones et al., 2024). All visualizations were rendered using the R package ggplot version 3.5.1 (Wickham, 2016). Code for data preprocessing (including the rat MRI preprocessing pipeline), data analysis, and figure construction is available at https://github.com/rlsmith1/rat_MRI_similarity_networks.

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Smith, R. L., Sawiak, S. J., Dorfschmidt, L., Dutcher, E. G., Jones, J. A., Hahn, J. D., Sporns, O., Swanson, L. W., Taylor, P. A., Glen, D. R., Dalley, J. W., McMahon, F. J., Raznahan, A., Vértes, P. E., & Bullmore, E. T. (2026). Cortical similarity networks in the rat brain: Postnatal development and sensitivity to early life stress. Network neuroscience (Cambridge, Mass.), 10(2), 418-443. https://doi.org/10.1162/netn.a.546

BibTeX

@article{smith2026cortical,
author = {Smith, Rachel L. and Sawiak, Stephen J. and Dorfschmidt, Lena and Dutcher, Ethan G. and Jones, Jolyon A. and Hahn, Joel D. and Sporns, Olaf and Swanson, Larry W. and Taylor, Paul A. and Glen, Daniel R. and Dalley, Jeffrey W. and McMahon, Francis J. and Raznahan, Armin and Vértes, Petra E. and Bullmore, Edward T.},
title = {{Cortical similarity networks in the rat brain: Postnatal development and sensitivity to early life stress}},
journal = {Network neuroscience (Cambridge, Mass.)},
year = {2026},
month = apr,
volume = {10},
number = {2},
pages = {418--443},
publisher = {MIT Press},
issn = {2472-1751},
doi = {10.1162/netn.a.546},
url = {https://doi.org/10.1162/netn.a.546},
pmid = {42039103},
pmcid = {PMC13108504}
}

RIS

TY - JOUR
AU - Smith, Rachel L.
AU - Sawiak, Stephen J.
AU - Dorfschmidt, Lena
AU - Dutcher, Ethan G.
AU - Jones, Jolyon A.
AU - Hahn, Joel D.
AU - Sporns, Olaf
AU - Swanson, Larry W.
AU - Taylor, Paul A.
AU - Glen, Daniel R.
AU - Dalley, Jeffrey W.
AU - McMahon, Francis J.
AU - Raznahan, Armin
AU - Vértes, Petra E.
AU - Bullmore, Edward T.
TI - Cortical similarity networks in the rat brain: Postnatal development and sensitivity to early life stress
T2 - Network neuroscience (Cambridge, Mass.)
J2 - Netw Neurosci
PY - 2026
DA - 2026/04/22
VL - 10
IS - 2
SP - 418
EP - 443
SN - 2472-1751
PB - MIT Press
DO - 10.1162/netn.a.546
UR - https://doi.org/10.1162/netn.a.546
LA - en
ER -

CSL-JSON

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"DOI": "10.1162/netn.a.546",
"PMID": "42039103",
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"date-parts": [
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