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Brain structure and function in Homo naledi.

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. [1] § Materials and methods › Multiple reconstructions ↔ Lesedi_Endocast_R_publication.R, lines 1–79 · score 0.86 · fixLMtps, missing surfaces, missing landmarks, LES1 reconstruction, fossil hominins, declared
  2. [2] § Materials and methods › Comparative sample ↔ Lesedi_Endocast_R_publication.R, lines 1–79 · score 0.77 · KNM WT, recent humans, KNM ER, fossil hominins, human adults, LES1 endocast
  3. [3] § Results ↔ Lesedi_Endocast_R_publication.R, lines 201–251 · score 0.70 · KNM WT, adult sample, full human, KNM ER, Homo erectus, OH
  4. [4] § Materials and methods › Multiple reconstructions ↔ Lesedi_Endocast_R_publication.R, lines 129–199 · score 0.62 · meshDist, Procrustes distance, morphotypes, pairwise, UPGMA, modern human

Paper

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The authors' code

R · 291 lines · 14 KB · CC-BY-4.0 · 4 matches

  1. # R code for "Brain structure and function in Homo naledi"
  2. # (Cofran et al., Brain Structure & Function)
  3. # Estimate missing landmarks for LES1 endocast
  4. # geometric morphometric analyses
  5. # comparing LES1 to recent humans and fossil hominins
  7. # First, download landmark data Neubauer et al. 2018 dataset: < https://edmond.mpg.de/file.xhtml?fileId=102415&version=1.0 >
  8. # Also download "Lesedi_Virtual_Endocast.csv" from Zenodo. DOI: 10.5281/zenodo.20136569
  9. # Save these in the default working directory
  11. library(geomorph); library(Morpho); library(abind); library(Rvcg); library(Arothron); library(dendextend)
  13. #################### #########################
  14. # Landmark data input
  15. #################### #########################
  16. #----Read and prep Neubauer et al. 2018 landmark data
  17. d <- read.table('NeubauerEtAl2018-ER42700-endolmdata.txt', header=T)
  18. # extract individual IDs and convert landmarks array
  19. id <- d[,1]
  20. dl <- d[,2:2806]
  21. dd <- matrix(t(dl), ncol=3, byrow=T)
  22. da <- arrayspecs(dd, 935, 3)
  23. # Omit non-adult humans and extra fossil reconstructions
  24. da <- da[,,c(7:98)]
  25. id <- id[7:98]
  27. #----Read and symmetrize Sts 5 & LES1 landmark data
  28. e <- read.csv("LES1_Sts5_endocast_landmarks_Cofran&al2026.csv")
  29. ea <- arrayspecs(as.matrix(e[,3:5]), 935, 3)
  30. eid <- unique(e[,1])
  31. # symmetrize - vectors for L + R then symmetrize()
  32. l <- c(9:18,64:101,140:537); r <- c(19:28,102:139, 538:935)
  33. pairedLM <- cbind(l,r)
  34. ea <- symmetrize(ea, pairedLM)
  36. #----Combine and cull landmark datasets to only human adults and fossils
  37. all <- abind(da,ea)
  38. ids <- c(id,eid)
  39. # vectorize fossil and human numbers for subsequent analysis and plots
  40. f <- c(1,82:94)
  41. h <- c(2:81)
  43. ######## ###########
  44. # Multiple LES1 reconstructions (missing LM estimation)
  45. ######## ###########
  47. #----Specify the landmarks missing from the mirrored LES1 endocast
  48. lmf <- c(1,4:15,17:25,27:29) # missing fixed
  49. lmc <- c(45:72,77,85:91,100:110,115,123:129,138:139) # missing curve
  50. # missing surface, left & right sides separate
  51. lmsl <- c(150,151,152,153,154,156,157,158,204,209,210,211,214,217,218,220,221,225,229,237,238,239,240,245,246,247,247,248,249,250,251,252,254,259,263,264,265,267,270,273,283,284,296,299,300,306,312,313,317,318,322,352,353,356,358,360,365,381,382,383,385,386,387,388,389,390,393,394,414,424,426,430,431,432,433,434,435,436,439,440,441,442,443,444,444,445,446,447,447,448,449,450,452,455,456,457,458,459,460,461,462,463,464,465,466,468,469,470,471,473,475,479,480,482,483,484,485,488,489,490,491,495,498,499,500,501,502,503,505,506,508,510,513,514,516,517,537)
  52. lmsr <- c(lmsl+398)
  54. #----declare missing data as NA
  55. all[c(lmf,lmc,lmsl,lmsr),,f[14]] <- NA
  57. #----estimate missing LMs from fossils
  58. les.fm <- fixLMtps(all[,,f],weight=T)$out[,,14] # all fossils, weighted by PD
  59. les.sts <- fixLMtps(all[,,f[13:14]])$out[,,2] # Sts 5 reference
  60. les.1470 <- fixLMtps(all[,,f[c(11,14)]])$out[,,2] # KNM-ER 1470 reference
  61. les.1813 <- fixLMtps(all[,,f[c(10,14)]])$out[,,2] # KNM-ER 1813 reference
  62. les.4270 <- fixLMtps(all[,,f[c(1,14)]])$out[,,2] # KNM-ER 42700 reference
  63. les.3733 <- fixLMtps(all[,,f[c(2,14)]])$out[,,2] # KNM-ER 3733 reference
  64. les.3883 <- fixLMtps(all[,,f[c(3,14)]])$out[,,2] # KNM-ER 3883 reference
  65. les.wt15 <- fixLMtps(all[,,f[c(4,14)]])$out[,,2] # KNM-WT 15000 reference
  66. les.oh9 <- fixLMtps(all[,,f[c(5,14)]])$out[,,2] # OH9 reference
  67. les.ng14 <- fixLMtps(all[,,f[c(6,14)]])$out[,,2] # Ngandong 14 reference
  68. les.ngaw <- fixLMtps(all[,,f[c(7,14)]])$out[,,2] # Ngawi 1 reference
  69. les.sm3 <- fixLMtps(all[,,f[c(8,14)]])$out[,,2] # Sambungmacan 3 reference
  70. les.sg2 <- fixLMtps(all[,,f[c(9,14)]])$out[,,2] # Sangiran 2 reference
  71. les.mojo <- fixLMtps(all[,,f[c(12,14)]])$out[,,2] # Mojokerto reference
  72. # Estimate missing LMs from humans
  73. les.hs <- fixLMtps(all[,,c(h,f[14])],weight=T)$out[,,81] # all human adults as reference
  75. ################ #########################
  76. # Shape analyses & Figures 5-7
  77. ################ #########################
  78. #----Procrustes alignment of 80 humans, 13 fossils, 15 recons of LES1 in order of ids[f]
  79. gpa <- gpagen(abind(all[,,1:93],les.fm,les.4270,les.3733,les.3883, les.wt15,les.oh9,les.ng14,les.ngaw,les.sm3,les.sg2,les.1813,les.1470,les.mojo,les.sts,les.hs))
  81. #------------------------------------------------------
  82. # Compare Procrustes distance bw groups (FIG 5, left)
  83. #------------------------------------------------------
  84. # Function to calc Procrustes distance between pairs of individuals
  85. PD <- function(x,y){sqrt(sum((gpa$coords[,,x]-gpa$coords[,,y])^2))}
  87. # Adult humans first
  88. pdh <- NA
  89. for (i in 1:5000) {
  90. x <- sample(h,2, replace=F) # randomly pick 2
  91. pdh[i] <- PD(x[1],x[2])
  92. }
  93. # Homo erectus adults (excluding Mojo & ER 42700)
  94. pde <- NA; e1 <- NA; e2 <- NA
  95. for (i in 1:1000) {
  96. ee <- sample(f[2:9], 2, replace=F); e1[i] <- min(ee); e2[i] <- max(ee)
  97. pde[i] <- PD(e1[i],e2[i])
  98. }
  99. pex <- data.frame(unique(cbind(ids[e1],ids[e2],pde)))
  101. # Lesedi (fossil mean recon.) vs. other fossils
  102. pdi <- NA #; pdi.2 <- NA
  103. for (i in 1:13) {
  104. pdi[i] <- PD(94,f[i])
  105. #pdi.2[i] <- PD(94+i,f[i])
  106. }
  107. # pdi.2 compares fossils with fossil-specific LES1 recons
  108. # Results are similar whether fossil-specific or avg. recon,
  109. # but Mojokerto- and 42700-specific PDs are much lower
  111. # Lesedi reconstructions compared to one another
  112. pdl <- NA
  113. for (i in 1:500) {
  114. x <- sample(c(95:108),2)
  115. pdl[i] <- PD(x[1],x[2])
  116. }
  117. pdl <- unique(pdl)
  119. #----------------------------------------------------
  120. # Hierarchical cluster analysis (UPGMA, FIG 5, right)
  121. #----------------------------------------------------
  122. # cluster dendrogram with human average and fossil adults only
  123. clusts <- c(18,f[c(2:11,13:14)]) # individual 18 closest to sample avg
  124. x <- two.d.array(gpa$coords[,,clusts])
  125. row.names(x) <- ids[clusts]
  126. endro <- as.dendrogram(hclust(dist(x), method="average"))
  127. labels_colors(endro) <- c(cola[c(5,3,1,4,2)],"deeppink",cola[rep(2,7)])
  129. # FIGURE 5
  130. # Plot comparison of PD, labeling fossil comparisons
  131. # 1=human, 2=erectus, 3=ER 1813, 4=ER 1470, 5=Sts 5, 6=LES1
  132. groups <- c(2, rep(1,80), rep(2,8), 3, 4, 2, 5, 6)
  133. cola <- c(hcl.colors(5,"Zissou"), "deeppink")
  134. pchs <- c(15:18,1,8)
  136. par(mar=c(3,4.5,1,2), mfrow=c(1,2))
  137. boxplot(pdh, pde, pdi, ylab="Procrustes distance", border=c(1,1,0), col=0, names=c("Modern humans", "Homo erectus","LES1"), lty=1, cex=0.5, lwd=c(2,2,2), range=0, cex.lab=1.5, cex.axis=1, main="Pairwise Shape Differences", ylim=c(min(pdl), max(pdi)))
  138. points(rnorm(91,3,0.01), pdl, col="deeppink", cex=0.5) # LES1 recons
  139. gx <- rnorm(13,3,0.15)
  140. points(gx, pdi, col=cola[groups[f]], cex=4, pch=pchs[groups[f]])
  141. text(gx,pdi, labels=seq(1:13))
  142. #text(rnorm(13,3,0.05), pdi, labels=ids[f[1:13]], cex=0.8, col=cola[groups[f]]) # color-coded fossil ID labels
  144. par(mar=c(10,2,2,0))
  145. plot(as.dendrogram(endro), lwd=2, main="UPGMA cluster analysis", , nodePar = list(lab.cex = 1, pch = NA))
  147. #-------------------------------------------------
  148. # Compare LES1 and Aust/Homo morphotypes (FIG 6)
  149. #-------------------------------------------------
  150. # Make Human and Homo erectus group averages
  151. havg <- mshape(gpa$coords[,,h]) # human adult average
  152. ham <- mshape(gpa$coords[,,f[2:9]]) # all erectus
  154. # Make triangle meshes for morphotypes
  155. lem <- vcgBallPivoting(mesh3d(gpa$coords[,,94])) # LES1
  156. hamm <- vcgBallPivoting(mesh3d(ham)) # erectus average
  157. hhmm <- vcgBallPivoting(mesh3d(gpa$coords[,,f[10]])) # ER 1813
  158. hrmm <- vcgBallPivoting(mesh3d(gpa$coords[,,f[11]])) # ER 1470
  159. amm <- vcgBallPivoting(mesh3d(gpa$coords[,,93])) # sts5
  160. hsm <- vcgBallPivoting(mesh3d(havg)) # human average
  162. cold <- c(hcl.colors(3,"Cividis")[1],"white",hcl.colors(3,"Cividis")[3])
  164. les.st5 <- meshDist(lem,amm, from=-0.00623, to=0.00623, rampcolors = cold)
  165. les.hab <- meshDist(lem,hhmm, from=-0.00623, to=0.00623, rampcolors = cold)
  166. les.rud <- meshDist(lem,hrmm, from=-0.00623, to=0.00623, rampcolors = cold)
  167. les.ere <- meshDist(lem,hamm, from=-0.00623, to=0.00623, rampcolors = cold)
  168. les.hsa <- meshDist(lem,hsm, from=-0.00623, to=0.00623, rampcolors = cold)
  170. mfrow3d(2,5, sharedMouse=T)
  171. shade3d(lem, col='whitesmoke', specular="black")
  172. shade3d(amm, col=cola[5], alpha=0.4, specular="black")
  173. next3d()
  174. shade3d(lem, col='whitesmoke', specular="black")
  175. shade3d(hhmm, col=cola[3], alpha=0.4, specular="black")
  176. next3d()
  177. shade3d(lem, col='whitesmoke', specular="black")
  178. shade3d(hrmm, col=cola[4], alpha=0.4, specular="black")
  179. next3d()
  180. shade3d(lem, col='whitesmoke', specular="black")
  181. shade3d(hamm, col=cola[2], alpha=0.4, specular="black")
  182. next3d()
  183. shade3d(lem, col='whitesmoke', specular="black")
  184. shade3d(hsm, col=cola[1], alpha=0.4, specular="black")
  185. next3d()
  186. shade3d(lem, col='whitesmoke', specular="black")
  187. spheres3d(gpa$coords[,,95], col=les.st5$cols, r=0.0015, specular="black")
  188. next3d()
  189. shade3d(lem, col='whitesmoke', specular="black")
  190. spheres3d(gpa$coords[,,95], col=les.hab$cols, r=0.0015, specular="black")
  191. next3d()
  192. shade3d(lem, col='whitesmoke', specular="black")
  193. spheres3d(gpa$coords[,,95], col=les.rud$cols, r=0.0015, specular="black")
  194. next3d()
  195. shade3d(lem, col='whitesmoke', specular="black")
  196. spheres3d(gpa$coords[,,95], col=les.ere$cols, r=0.0015, specular="black")
  197. next3d()
  198. shade3d(lem, col='whitesmoke', specular="black")
  199. spheres3d(gpa$coords[,,95], col=les.hsa$cols, r=0.0015, specular="black")
  201. # (can also color-code triangles rather than landmarks if preferred)
  202. #mfrow3d(1,5, sharedMouse=T)
  203. # shade3d(les.st5$colMesh, specular="black"); next3d()
  204. # shade3d(les.hab$colMesh, specular="black"); next3d()
  205. # shade3d(les.rud$colMesh, specular="black"); next3d()
  206. # shade3d(les.ere$colMesh, specular="black"); next3d()
  207. # shade3d(les.hsa$colMesh, specular="black")
  209. #-----------------------------------------------------
  210. # Compare cerebrum vs. cerebellum (PCF) size (FIG 7)
  211. #-----------------------------------------------------
  212. # PCF landmarks
  213. c1 <- c(6,7,15,17,18,25,27,28,29,55:62,64:69,91:101,102:107,129:139)
  214. c2 <- c(111,144,308,374:376,381,95,382,129,338,94,314,299,96,155,315,333,335,105,332,312,104,97,316,113,310,313,98,309,324,105,312,332,339,328,337,103,168,109,336,334,102,320,329,327,108,331,295,99,323,319,317,291,101,322,321,100,330,157,318,124,326,107,106,325,311,292,214,300,294,112,293,301)+139
  215. c3 <- c2+398
  216. pcf <- sort(unique(c(c1,c2,c3)))
  218. # cerebrum GPA
  219. cgpa <- gpagen(abind(all[-pcf,,1:93],les.fm[-pcf,]))
  220. # cerebellum GPA
  221. pgpa <- gpagen(abind(all[pcf,,1:93],les.fm[pcf,]))
  223. par(mar=c(4.5,4.5,1,1))
  224. plot(log10(cgpa$Csize)~log10(pgpa$Csize), col=cola[groups], cex=2, pch=pchs[groups], xlab="log(Cerebellum size)", ylab="log(Cerebrum size)", cex.lab=1.25)
  225. text(log10(cgpa$Csize)[94]~log10(pgpa$Csize)[94], labels="H. naledi", cex=2, pos=4, col=cola[6])
  226. legend('topleft',legend=c("Modern human","Homo erectus","KNM-ER 1813","KNM-ER 1470","Australopithecus"), text.col=cola, bty='n', pch=(pchs), col=(cola), cex=1.25)
  227. abline(lm(log10(cgpa$Csize[h])~log10(pgpa$Csize[h])), col=cola[1], lty=2)
  229. #shade3d(vcgBallPivoting(mesh3d(les.fm)), col="whitesmoke")
  230. spheres3d(les.fm[-pcf,],r=1.5,col="pink")
  231. spheres3d(les.fm[pcf,],r=1.5,col="deeppink")
  233. #################### ########################
  234. # SUPPORTING INFORMATION
  235. #################### ########################
  237. #----------------------------------------------------------
  238. # Figure S1: Cluster dendrogram with full human adult sample
  239. #----------------------------------------------------------
  240. clusts <- c(h,f[c(2:11,13:14)])
  241. x <- two.d.array(gpa$coords[,,clusts])
  242. #row.names(x) <- ids[clusts] # original IDs
  243. # row.names(x)[17] <- "HumanAdult"
  244. row.names(x) <- c(rep("Modern human", 80), "KNM-ER 3733", "KNM-ER 3883", "KNM-WT 15000", "OH 9", "Ngandong 14", "Ngawi", "Sambungmacan 3", "Sangiran 2", "KNM-ER 1813", "KNM-ER 1470", "Sts 5", "LES1")
  245. endro <- as.dendrogram(hclust(dist(x), method="average"))
  247. labels_colors(endro) <- c(cola[c(5, rep(1, 80), 3, 2,2,2,2, 4, 2)],"deeppink",cola[rep(2,3)])
  248. labels_cex(endro) <- 0.75#c(1, rep(0.75,80), rep(1,7))
  250. par(mar=c(6,3,2,0))
  251. plot(as.dendrogram(endro), main="UPGMA cluster analysis")
  253. #--------------------------------------------
  254. # Figure S2: PCA of fossil sample
  255. #--------------------------------------------
  256. pca <- gm.prcomp(gpa$coords[,,c(1:94)])
  257. # includes all fossils + LES1 fossil average recon (les.fm)
  259. par(mfrow=c(1,1), mar=c(4,4,1,1))
  260. plot(pca, col = cola[groups], pch=pchs[groups], cex=c(2, rep(1.5,80), rep(2,13)), lty=0)
  261. # text(pca$x[f[-c(10:11,13:14)],1:2], labels=seq(1:10), cex=1, col='white')
  262. text(pca$x[f[-c(10:11,13:14)],1:2], labels=ids[f[-c(10:11,13:14)]], cex=0.5, pos=c(1,4,4,3,2,1,1,1,1,3))
  263. legend('topleft', pch=c(pchs,1), col=c(cola,"deeppink"), text.col=c(cola,"deeppink"), legend=c("Modern human", "H. erectus", "H. habilis (ER1813)", "H. rudolfensis (ER1470)", "A. africanus (Sts 5)", "LES1 average"), bty='n')
  265. # PC shape warps
  266. pcshape <- pca$shapes
  267. avg <- vcgBallPivoting(mesh3d(mshape(gpa$coords))) # sample average triangle mesh
  268. pc1min <- pcshape$shapes.comp1$min
  269. pc1max <- pcshape$shapes.comp1$max
  270. pc2min <- pcshape$shapes.comp2$min
  271. pc2max <- pcshape$shapes.comp2$max
  272. min1 <- warpRefMesh(avg, mshape(gpa$coords), pc1min)
  273. max1 <- warpRefMesh(avg, mshape(gpa$coords), pc1max)
  274. min2 <- warpRefMesh(avg, mshape(gpa$coords), pc2min)
  275. max2 <- warpRefMesh(avg, mshape(gpa$coords), pc2max)
  277. mfrow3d(2,2, sharedMouse=T)
  278. shade3d(min1, col="white", specular='black'); next3d()
  279. shade3d(max1, col='darkgrey', specular='black'); next3d()
  280. shade3d(min2, col="white", specular='black'); next3d()
  281. shade3d(max2, col='darkgrey', specular='black')
  283. #################### #####################
  284. # Just for fun, LES1 vs human average
  285. # No size scaling and aligned by basicranium fixed and curve LMs
  286. #################### #####################
  287. # just for fun, make a pretty picture of
  288. bgpa <- procSym(abind(all[,,1:93], les.fm), use.lm=c(1:2,5:29,54:139), CSinit=F, scale=F)
  290. shade3d(vcgBallPivoting(mesh3d(mshape(bgpa$rotated[,,h]))), col='whitesmoke', alpha=0.2, specular='black')
  291. shade3d(vcgBallPivoting(mesh3d(bgpa$rotated[,,94]), radius=0), col="#D7FF04", alpha=1, specular='black')

Lesedi_Endocast_R_publication.R, under CC-BY-4.0 · at the source

Overview

Authors: Zachary Cofran1, Shawn Hurst2, John Hawks3
ORCID iDs: Zachary Cofran, Shawn Hurst, John Hawks
  1. Anthropology Department, Vassar College,Poughkeepsie, New York USA
  2. Department of Biology, University of Indianapolis,Indianapolis, Indiana USA
  3. Department of Anthropology, University of Wisconsin-Madison,Madison, Wisconsin USA
Institutions: Vassar College (United States); University of Indianapolis (United States); University of Wisconsin–Madison (United States)
Journal: Brain structure & function, volume 231, issue 6, article 89
Dates: received 16 March 2026; accepted 13 May 2026; published online 15 June 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1007/s00429-026-03129-1 · PMID 42295457 · PMCID PMC13269317 · OpenAlex W7164831474
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), cognitive (subfield)
Keywords: Endocast, Geometric morphometrics, Cognition, Life history, Cerebellum
MeSH: Brain*, Hominidae*, Animals, Biological Evolution, Fossils, Humans, Skull (* major topic)
Topic: Pleistocene-Era Hominins and Archaeology (Anthropology, Social Sciences), according to OpenAlex
Funding: Jane Rosenthal Helmerdinger Fund from Vassar College; Anne McNiff Tatlock ‘61 Fund from Vassar College
Citations: not cited yet (Europe PMC); 185 references in the paper

Abstract

Endocasts are casts of the internal surface of skull bones created by the brain and surrounding tissues. These fossil phantoms provide the most direct evidence of the brains of extinct organisms, and are one of the many puzzle pieces that help paleoneurologists reconstruct brain evolution. The recent discovery of a small-brained, recently-extinct human species, Homo naledi, creates a timely opportunity to review what endocasts can and cannot tell us about ancient brains. We first review published evidence about the brain and behavior of H. naledi, including the suggestion that the species may have practiced mortuary behaviors over 230,000 years ago. We next use geometric morphometric methods to reconstruct and the most complete H. naledi endocast and compare it to those of modern humans and Pleistocene hominins. Our results corroborate previous evidence that the brain of H. naledi presents a unique combination of ancestral and modern human-like characteristics, specifically displaying a derived frontal lobe while retaining ancestral sizes, morphology, and cerebro-cerebellar proportions. Finally, we discuss these results in the context of recent paleoanthropological data, advances in neuroimaging, and theoretical frameworks linking brain morphology, structure, and function.

Supplementary Information: The online version contains supplementary material available at 10.1007/s00429-026-03129-1.

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

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Zenodo 20136569

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At the source: zenodo.org/records/20136569

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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;
  • 1 script, 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

We used landmark data from Neubauer, Simon, 2018, “KNM-ER 42700: endocranial data”, accessible at https://doi.org/10.17617/3.18. Landmark data and R code novel to this study are available in the Zenodo repository, accessible at https://zenodo.org/records/20136569.

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 2, 28 September 2026

  • Publisher: n/a → Springer Science+Business Media

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 5 keywords, 7 MeSH terms, 2 funders, 174 references.

Cite

This paper

Cofran, Z., Hurst, S., & Hawks, J. (2026). Brain structure and function in Homo naledi. Brain structure & function, 231(6), 89. https://doi.org/10.1007/s00429-026-03129-1

BibTeX

@article{cofran2026brain,
author = {Cofran, Zachary and Hurst, Shawn and Hawks, John},
title = {{Brain structure and function in Homo naledi}},
journal = {Brain structure \& function},
year = {2026},
month = jun,
volume = {231},
number = {6},
pages = {89},
publisher = {Springer Science+Business Media},
issn = {1863-2653},
doi = {10.1007/s00429-026-03129-1},
url = {https://doi.org/10.1007/s00429-026-03129-1},
pmid = {42295457},
pmcid = {PMC13269317}
}

RIS

TY - JOUR
AU - Cofran, Zachary
AU - Hurst, Shawn
AU - Hawks, John
TI - Brain structure and function in Homo naledi
T2 - Brain structure & function
J2 - Brain Struct Funct
PY - 2026
DA - 2026/06/15
VL - 231
IS - 6
SP - 89
SN - 1863-2653
PB - Springer Science+Business Media
DO - 10.1007/s00429-026-03129-1
UR - https://doi.org/10.1007/s00429-026-03129-1
LA - en
ER -

CSL-JSON

{
"id": "10.1007/s00429-026-03129-1",
"type": "article-journal",
"title": "Brain structure and function in Homo naledi",
"container-title": "Brain structure & function",
"author": [
{
"family": "Cofran",
"given": "Zachary"
},
{
"family": "Hurst",
"given": "Shawn"
},
{
"family": "Hawks",
"given": "John"
}
],
"container-title-short": "Brain Struct Funct",
"volume": "231",
"issue": "6",
"page": "89",
"DOI": "10.1007/s00429-026-03129-1",
"PMID": "42295457",
"PMCID": "PMC13269317",
"ISSN": "1863-2653",
"publisher": "Springer Science+Business Media",
"URL": "https://doi.org/10.1007/s00429-026-03129-1",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
15
]
]
}
}

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

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