OSCR

Distinct sensory atypicalities bridge the gap between brain chemistry and motor dysfunction in autism.

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] § Results › Relationship between sensory functioning scores and motor ability is generally group indifferent ↔ codes/Figure_2.Rmd, lines 386–481 · score 0.81 · static detection threshold, simultaneous amplitude discrimination, feedforward inhibition, single site adaptation, PANESS Overflow, Gaits Stations
  2. [2] § Results › Sensory functioning scores are related to motor ability in both autism and TDC: evidence of path b ↔ codes/Figure_2.Rmd, lines 386–481 · score 0.76 · Static detection threshold, Simultaneous amplitude discrimination, feedforward inhibition, discrimination threshold, Gaits Stations, Manual Dexterity
  3. [3] § Participants and methods › Participants ↔ codes/summary.Rmd, lines 741–799 · score 0.69 · Verbal Comprehension, full scale IQ, Perceptual Reasoning, ADOS, scores
  4. [4] § Results ↔ codes/summary.Rmd, lines 741–799 · score 0.61 · Verbal Comprehension, full scale IQ, Perceptual Reasoning, scores

Paper

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The authors' code

R Markdown · 491 lines · 16 KB · no license · 2 matches

  1. ---
  2. title: "Figure3"
  3. author: "Mingrun Shi"
  4. date: "2025-01-09"
  5. output:
  6. pdf_document: default
  7. html_document: default
  8. ---
  10. ## 1. Setup
  12. ```{r}
  13. # SETUP -------------------------------------------------------------------
  14. rm(list = ls()) #Clear everything in environment
  16. library(ggside)
  18. # _packages ---------------------------------------------------------------
  19. #create a function to read in and load packages
  20. load.packages <- function(pkg) {
  21. new.pkg <- pkg[!(pkg %in% installed.packages()[ , "Package"])]
  22. if (length(new.pkg)) {
  23. install.packages(new.pkg, dependencies = T)
  24. }
  25. sapply(pkg, require, character.only = T)
  26. }
  28. packages <- c(
  29. "rstatix",
  30. "tidyverse",
  31. "nlme",
  32. "ggpubr",
  33. "Hmisc",
  34. "plyr",
  35. "Rmisc",
  36. "retimes",
  37. "data.table",
  38. "lme4",
  39. "multcomp",
  40. "pastecs",
  41. "effects",
  42. "DataCombine",
  43. "gridExtra",
  44. "leaps",
  45. "ppcor",
  46. "ggm",
  47. "readxl",
  48. "emmeans",
  49. "eeptools",
  50. "psych",
  51. "weights",
  52. "here",
  53. "cowplot",
  54. "reghelper",
  55. "sjstats",
  56. "lubridate"
  57. )
  59. load.packages(packages) # load in the packages
  62. # _functions --------------------------------------------------------------
  64. #https://www.r-bloggers.com/2011/11/outersect-the-opposite-of-rs-intersect-function/
  65. #returns what is not in either
  66. outersect <- function(x, y) {
  67. sort(c(x[!x%in%y],
  68. y[!y%in%x]))
  69. }
  71. `%notin%` <- Negate(`%in%`)
  73. mid_date <- function(startdate, enddate) {
  74. stopifnot(class(startdate) == "Date" & class(enddate) == "Date")
  75. stopifnot(length(startdate) == length(enddate))
  76. idx <- enddate < startdate
  77. if (any(idx, na.rm = TRUE)) {
  78. enddate[idx] <- NULL
  79. startdate[idx] <- NULL
  80. cat(paste0("\nWARNING: NAs assigned to ", sum(idx, na.rm = TRUE),
  81. " inconsistent date pairs\n"))
  82. }
  83. res <- NULL
  84. if (length(startdate > 0)) {
  85. intobj <- lubridate::interval(startdate, enddate)
  86. res <- lubridate::as_date(lubridate::int_start(intobj) +
  87. ((lubridate::int_end(intobj) -
  88. lubridate::int_start(intobj)) / 2))
  89. }
  90. res
  91. }
  93. descriptives_table <- function(x, grouping_variable, list_of_variables){
  94. # x <- df
  95. # grouping_variable <- "group"
  96. # list_of_variables <- demographic_variables
  97. #establish the number of unique groups
  98. unique_groups <- unique(x[[grouping_variable]])
  100. #create two lists external to the for loops below
  101. list_of_descriptives <- list()
  102. list_of_summaries <- list()
  104. #outer for loop: loop through each of the demographic variables
  105. for(d in list_of_variables){
  107. #establish the variable class
  108. variable_class <- class(x[[d]])
  109. print(paste("Variable name:", d)) #print variable name to console
  110. print(paste("Variable type:", class(x[[d]]))) #print variable type to console
  112. #IF CHARACTER ----
  113. if(variable_class=="character"){
  114. variable_table <- table(x[[d]], x[["group"]])
  115. chisq_result <- chisq.test(variable_table)
  117. #inner for loop if character
  118. for(u in unique_groups){
  119. variable_table_df <- as.data.frame(variable_table)
  120. freqs_for_current_group <- variable_table_df[variable_table_df[["group"]]==u,]
  122. N <- paste(freqs_for_current_group$Freq, collapse=":")
  123. M <- "-"
  124. SD <- "-"
  126. list_of_summaries[[u]] <- cbind(N, M, SD)
  127. }
  129. summary <- as.data.frame(bind_cols(list_of_summaries))
  130. summary$p <- round(chisq_result$p.value, 6)
  131. }
  133. #IF NUMERIC OR INTEGER ----
  134. if(variable_class %in% c("numeric", "integer")){
  136. x[[d]] <- as.numeric(x[[d]]) #ensures the DV is numeric (some functions won't treat integer as numeric)
  137. formula <- as.formula(paste(d, "~", grouping_variable)) #establish the formula required for the t.tests
  139. #inner for loop if numeric (retrieve N, M and SD for current_variable "d" for each unique_group "u")
  140. for(u in unique_groups){
  141. current_group <- x[x[grouping_variable]== u,]
  143. N <- length(current_group[[d]][!is.na(current_group[[d]])])
  144. M <- round(mean(current_group[[d]], na.rm = TRUE),2)
  145. SD <- round(sd(current_group[[d]], na.rm = TRUE), 2)
  147. list_of_summaries[[u]] <- cbind(N, M, SD)
  148. }
  150. #combine the N, M and SD for each group into a single variable
  151. summary <- as.data.frame(bind_cols(list_of_summaries))
  153. #Unsure what this was for, but leaving it here and will annotate later
  154. if(TRUE %in% (as.list(is.na(summary)))){
  155. summary$p <- "-"
  156. } else {
  157. ttest_result <- t.test(formula, data = x)
  158. summary$p <- round(ttest_result$p.value, 3)
  159. }
  161. }
  163. #store the summaries from either for loop into the list_of_descriptives list created outside the major for loop
  164. list_of_descriptives[[d]] <- summary
  165. }
  167. table_of_descriptives <- rbind.fill(list_of_descriptives)
  168. table_of_descriptives <- as.data.frame(cbind("variables" = list_of_variables, table_of_descriptives))
  169. table_of_descriptives <- as.data.frame(as.matrix(table_of_descriptives))
  171. return(table_of_descriptives)
  172. }
  174. geom_flat_violin <-
  175. function(mapping = NULL,
  176. data = NULL,
  177. stat = "ydensity",
  178. position = "dodge",
  179. trim = TRUE,
  180. scale = "area",
  181. show.legend = NA,
  182. inherit.aes = TRUE,
  183. ...) {
  184. layer(
  185. data = data,
  186. mapping = mapping,
  187. stat = stat,
  188. geom = GeomFlatViolin,
  189. position = position,
  190. show.legend = show.legend,
  191. inherit.aes = inherit.aes,
  192. params = list(trim = trim,
  193. scale = scale,
  194. ...)
  195. )
  196. }
  197. GeomFlatViolin <-
  198. ggproto(
  199. "GeomFlatViolin",
  200. Geom,
  201. setup_data = function(data, params) {
  202. data$width <- data$width %||%
  203. params$width %||% (resolution(data$x, FALSE) * 0.9)
  205. # ymin, ymax, xmin, and xmax define the bounding rectangle for each group
  206. data %>%
  207. group_by(group) %>%
  208. mutate(
  209. ymin = min(y),
  210. ymax = max(y),
  211. xmin = x,
  212. xmax = x + width / 2
  213. )
  215. },
  217. draw_group = function(data, panel_scales, coord) {
  218. # Find the points for the line to go all the way around
  219. data <- transform(data,
  220. xminv = x,
  221. xmaxv = x + violinwidth * (xmax - x))
  223. # Make sure it's sorted properly to draw the outline
  224. newdata <-
  225. rbind(plyr::arrange(transform(data, x = xminv), y),
  226. plyr::arrange(transform(data, x = xmaxv),-y))
  228. # Close the polygon: set first and last point the same
  229. # Needed for coord_polar and such
  230. newdata <- rbind(newdata, newdata[1,])
  232. ggplot2:::ggname("geom_flat_violin",
  233. GeomPolygon$draw_panel(newdata, panel_scales, coord))
  234. },
  236. draw_key = draw_key_polygon,
  238. default_aes = aes(
  239. weight = 1,
  240. colour = "grey20",
  241. fill = "white",
  242. size = 0.5,
  243. alpha = NA,
  244. linetype = "solid"
  245. ),
  247. required_aes = c("x", "y")
  248. )
  250. extract_mediation_summary <- function (x) {
  252. clp <- 100 * x$conf.level
  253. isLinear.y <- ((class(x$model.y)[1] %in% c("lm", "rq")) ||
  254. (inherits(x$model.y, "glm") && x$model.y$family$family ==
  255. "gaussian" && x$model.y$family$link == "identity") ||
  256. (inherits(x$model.y, "survreg") && x$model.y$dist ==
  257. "gaussian"))
  259. printone <- !x$INT && isLinear.y
  261. if (printone) {
  263. smat <- c(x$d1, x$d1.ci, x$d1.p)
  264. smat <- rbind(smat, c(x$z0, x$z0.ci, x$z0.p))
  265. smat <- rbind(smat, c(x$tau.coef, x$tau.ci, x$tau.p))
  266. smat <- rbind(smat, c(x$n0, x$n0.ci, x$n0.p))
  268. rownames(smat) <- c("ACME", "ADE", "Total Effect", "Prop. Mediated")
  270. } else {
  271. smat <- c(x$d0, x$d0.ci, x$d0.p)
  272. smat <- rbind(smat, c(x$d1, x$d1.ci, x$d1.p))
  273. smat <- rbind(smat, c(x$z0, x$z0.ci, x$z0.p))
  274. smat <- rbind(smat, c(x$z1, x$z1.ci, x$z1.p))
  275. smat <- rbind(smat, c(x$tau.coef, x$tau.ci, x$tau.p))
  276. smat <- rbind(smat, c(x$n0, x$n0.ci, x$n0.p))
  277. smat <- rbind(smat, c(x$n1, x$n1.ci, x$n1.p))
  278. smat <- rbind(smat, c(x$d.avg, x$d.avg.ci, x$d.avg.p))
  279. smat <- rbind(smat, c(x$z.avg, x$z.avg.ci, x$z.avg.p))
  280. smat <- rbind(smat, c(x$n.avg, x$n.avg.ci, x$n.avg.p))
  282. rownames(smat) <- c("ACME (control)", "ACME (treated)",
  283. "ADE (control)", "ADE (treated)", "Total Effect",
  284. "Prop. Mediated (control)", "Prop. Mediated (treated)",
  285. "ACME (average)", "ADE (average)", "Prop. Mediated (average)")
  287. }
  289. colnames(smat) <- c("Estimate", paste(clp, "% CI Lower", sep = ""),
  290. paste(clp, "% CI Upper", sep = ""), "p-value")
  291. smat
  293. }
  296. ```
  298. ## 2. Themes
  301. ```{r}
  302. # THEMES ------------------------------------------------------------------
  303. group_colours <- c("#0072B2", "#f37735", "#D55E00", "#00BA38", "#F8766D")
  305. theme_MR = theme(
  306. text = element_text(family = "Helvetica", color = "black"),
  307. plot.tag = element_text(face = "bold"),
  308. plot.title = element_text(hjust = 0.5),
  309. # axis.title.x = element_text(size = 14, margin = unit(c(2, 0, 0, 0), "mm")),
  310. # axis.title.y = element_text(size = 14, margin = unit(c(0, 1, 0, 0), "mm")),
  311. # axis.text.x = element_text(size = 14),
  312. # axis.text = element_text(angle = 0, vjust = 0, color = "black"),
  313. #legend.title = element_text(size=16),
  314. #legend.text = element_text(size=16),
  315. legend.position = "bottom",
  316. #plot.title = element_text(lineheight=.8, face= "bold", size = 16),
  317. panel.border = element_blank(),
  318. panel.grid.minor = element_blank(),
  319. panel.grid.major = element_blank(),
  320. panel.background = element_blank(),
  321. axis.line = element_line(color = "black"),
  322. axis.ticks = element_line(colour = "black"))
  324. ```
  326. ## 3. Read Data and Filter Data
  328. ```{r}
  329. df <- read.csv("F:/codes/data/SMM.csv")
  331. #We need a list of the MABC variables
  332. MABC_variables <- c(
  333. "mABC_2_Total_Standard_Score",
  334. "mABC_2_Balance_Component_Standard_Score",
  335. "mABC_2_Manual_Dexterity_Component_Standard_Score",
  336. "mABC_2_Aiming_and_Catching_Component_Standard_Score"
  337. )
  339. PANESS_variables <- c(
  340. "Total_PANESS",
  341. "PANESS_Total_Timed",
  342. "PANESS_Total_Overflow",
  343. "PANESS_Total_Gaits_Stations"
  344. )
  346. #.. and tactile variables
  347. tactile_variables <- c(
  348. "SEQ.Seeking.Mean.Score",
  349. "SEQ.Hypo.Mean.Score",
  350. "SEQ.Hyper.Mean.Score",
  351. "threshold_SDT", #"threshold_DDT",
  352. "FFI", #detection tasks
  353. "threshold_SMAD", #"threshold_ADTssa",
  354. "SS_adaptation", #single-site adaptation
  355. "threshold_SMFD", "threshold_SQFD", "SMFD_SQFD"
  356. )
  358. #The comparisons control for gender and age, therefore we need to create residuals for the plots
  359. for(i in c(MABC_variables, PANESS_variables)){
  360. formula <- as.formula(paste0(i, "~ gender + age"))
  361. model <- lm(formula, data = df)
  362. residuals <- as.data.frame(model$residuals)
  363. residuals$name<- rownames(residuals)
  364. df$name <- rownames(df)
  365. df$temp <- NA
  366. for (n in residuals$name){
  367. df$temp[df$name == n] <- residuals$`model$residuals`[residuals$name == n]
  368. }
  369. names(df)[names(df) == 'temp'] <- paste0("residual_", i)
  370. }
  372. for (j in 1:106){
  373. if(df[j,]$Group == "Control"){
  374. df[j,]$Group <- "TDC"
  375. }
  376. if(df[j,]$Group == "ASD"){
  377. df[j,]$Group <- "Autism"
  378. }
  379. }
  381. df_a <- df[df$Group == "Autism",]
  382. df_t <- df[df$Group == "TDC",]
  384. ```
  386. ## 4. Including Plots
  388. ```{r}
  389. # Figure_3 --------------------------------------------------------
  391. pdf3 <- df[c(paste0("residual_", MABC_variables), tactile_variables, "Group")]
  392. pdf4 <- pdf3[pdf3$Group=="Autism",]
  393. pdf5 <- pdf3[pdf3$Group=="TDC",]
  394. figure_3a <- ggplot(data = pdf3, aes(x = threshold_SDT, y = residual_mABC_2_Total_Standard_Score, colour = Group)) +
  395. geom_point() +
  396. geom_smooth(method = "lm") +
  397. scale_colour_manual(values=group_colours) + #manually adjust the colors of the dots
  398. scale_fill_manual(values=group_colours) + #manually adjust the colors of the dots
  399. labs(tag = "A", y = "MABC-2-Total (residuals)", x = "Static Detection Threshold (μm)") +
  400. stat_cor(show.legend = FALSE)+
  401. annotate("text", x=8, y=15, label= " ",colour = "#00BA38") +
  402. annotate("text", x=8, y=12, label= "z = -1.72, p = 0.085",colour = "#00BA38") +
  403. geom_ysidedensity(aes(y = residual_mABC_2_Total_Standard_Score, color = Group, fill = Group, position = "stack", alpha = 0.4), data = pdf3) +
  404. scale_ysidex_continuous(guide = guide_axis(angle = 90), minor_breaks = NULL) +
  405. guides(alpha = 'none') + # Remove alpha legend
  406. theme_MR
  407. # theme(legend.position = "bottom")
  410. figure_3b <- ggplot(data = pdf3, aes(x = SS_adaptation, y = residual_mABC_2_Manual_Dexterity_Component_Standard_Score, colour = Group)) +
  411. geom_point() +
  412. geom_smooth(method = "lm") +
  413. scale_colour_manual(values=group_colours) + #manually adjust the colors of the dots
  414. scale_fill_manual(values=group_colours) + #manually adjust the colors of the dots
  415. labs(tag = "B", y = "MABC-2-Manual (residuals)", x = "Single-Site Adaptation (%)") +
  416. stat_cor(show.legend = FALSE)+
  417. annotate("text", x=304, y=15, label= " ",colour = "#00BA38") +
  418. annotate("text", x=304, y=11.55, label= "z = -1.93, p = 0.054",colour = "#00BA38") +
  419. geom_ysidedensity(aes(y = residual_mABC_2_Manual_Dexterity_Component_Standard_Score, color = Group, fill = Group, position = "stack", alpha = 0.4), data = pdf3) +
  420. scale_ysidex_continuous(guide = guide_axis(angle = 90), minor_breaks = NULL) +
  421. guides(alpha = 'none') + # Remove alpha legend
  422. theme_MR
  423. # theme(legend.position = "bottom")
  425. pdf3 <- df[c(paste0("residual_", PANESS_variables), tactile_variables, "Group")]
  426. pdf4 <- pdf3[pdf3$Group=="Autism",]
  427. pdf5 <- pdf3[pdf3$Group=="TDC",]
  429. figure_3c <- ggplot(data = pdf3, aes(x = FFI, y = residual_PANESS_Total_Overflow, colour = Group)) +
  430. geom_point() +
  431. geom_smooth(method = "lm") +
  432. scale_colour_manual(values=group_colours) + #manually adjust the colors of the dots
  433. scale_fill_manual(values=group_colours) + #manually adjust the colors of the dots
  434. labs(tag = "C", y = "PANESS-Overflow (residuals)", x = "Feedforward Inhibition Index (%)") +
  435. stat_cor(show.legend = FALSE)+
  436. annotate("text", x=-7, y=20, label= " ",colour = "#00BA38") +
  437. annotate("text", x=-7, y=15.3, label= "z = 3.12, p = 0.002",colour = "#00BA38") +
  438. geom_ysidedensity(aes(y = residual_PANESS_Total_Overflow, color = Group, fill = Group, position = "stack", alpha = 0.4), data = pdf3) +
  439. scale_ysidex_continuous(guide = guide_axis(angle = 90), minor_breaks = NULL) +
  440. guides(alpha = 'none') + # Remove alpha legend
  441. theme_MR
  442. # theme(legend.position = "bottom")
  444. figure_3d <- ggplot(data = pdf3, aes(x = threshold_SMAD, y = residual_PANESS_Total_Gaits_Stations, colour = Group)) +
  445. geom_point() +
  446. geom_smooth(method = "lm") +
  447. scale_colour_manual(values=group_colours) + #manually adjust the colors of the dots
  448. scale_fill_manual(values=group_colours) + #manually adjust the colors of the dots
  449. labs(tag = "D", y = "PANESS-Gaits_Stations (residuals)", x = "Simultaneous Amplitude Discrimination Thresholds (μm)") +
  450. stat_cor(show.legend = FALSE)+
  451. annotate("text", x=43.4, y=20, label= " ",colour = "#00BA38") +
  452. annotate("text", x=43.4, y=16, label= "z = -0.34, p = 0.732",colour = "#00BA38") +
  453. geom_ysidedensity(aes(y = residual_PANESS_Total_Gaits_Stations, color = Group, fill = Group, position = "stack", alpha = 0.4), data = pdf3) +
  454. scale_ysidex_continuous(guide = guide_axis(angle = 90), minor_breaks = NULL) +
  455. guides(alpha = 'none') + # Remove alpha legend
  456. theme_MR
  457. # theme(legend.position = "bottom")
  460. figure_3 <-
  461. ggarrange(
  462. figure_3a,
  463. figure_3b,
  464. figure_3c,
  465. figure_3d,
  466. ncol = 2,
  467. nrow = 2,
  468. common.legend = TRUE,
  469. legend = "bottom",
  470. align = "hv"
  471. )
  473. ggsave(
  474. plot = figure_3,
  475. filename = here("F:/codes/figures/Figure_3_revised.pdf"),
  476. width = 10,
  477. height = 10
  478. )
  480. ## We used Adobe illustrator to modify fonts, text, and for visualisation purposes but not to manipulate or affect the data presented within the figure
  481. ```
  484. ```{r}
  485. # Filter the data for rows with non-missing values in both variables
  486. valid_data <- df[!is.na(pdf3$SS_adaptation) & !is.na(pdf3$residual_mABC_2_Manual_Dexterity_Component_Standard_Score), ]
  488. # Count participants by group
  489. table(valid_data$Group)
  490. ```

Figure_2.Rmd, no license · at the source

Overview

Authors: Mingrun Shi1, Jason L He1, Helen Powell1, Oliver Lack1, Georg Oeltzschner2,3, Alyssa Deronda4, Deana Crocetti4, Ericka L Wodka5, Richard A Edden2,3, Jonathan O’Muircheartaigh1,6, Stewart H Mostofsky4,7,8, Nicolaas A J Puts1,6
ORCID iDs: Mingrun Shi, Jason L He, Oliver Lack, Georg Oeltzschner, Alyssa Deronda, Jonathan O’Muircheartaigh, Nicolaas A J Puts
  1. Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
  2. Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD US
  3. F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD US
  4. Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD US
  5. Center for Autism Science, Services and Innovation, Kennedy Krieger Institute, Baltimore, MD US
  6. MRC Centre for Neurodevelopmental Disorders, King’s College London, London, UK
  7. Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD US
  8. Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD US
Institutions: King's College London (United Kingdom); Kennedy Krieger Institute (United States); Johns Hopkins University (United States); Johns Hopkins Medicine (United States); MRC Centre for Neurodevelopmental Disorders (United Kingdom)
Journal: Translational psychiatry, volume 16, issue 1, article 320
Dates: received 13 March 2025; accepted 30 March 2026; published online 8 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41398-026-04036-z · PMID 42103716 · PMCID PMC13324517 · OpenAlex W7160608858
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), autism (population), systems (subfield)
Methods: Statistics
Keywords: Human behaviour, Molecular neuroscience
MeSH: Autistic Disorder*, Brain Chemistry*, Sensorimotor Cortex*, Thalamus*, Child, Female, gamma-Aminobutyric Acid, Glutamic Acid, Glutamine, Humans, Magnetic Resonance Spectroscopy, Male (* major topic)
Topic: Autism Spectrum Disorder Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) (R21HD100869, R01MH106564, R01MH078160, P41EB031771, R00MH107719)
Citations: not cited yet (Europe PMC); 69 references in the paper

Abstract

Sensory and motor difficulties are common in autism. Altered excitation-inhibition (E-I) balance is a putative framework for understanding atypical sensory and motor function. We investigated whether sensory differences of autism mediate motor difficulties of autism via differences in E-I balance. 106 children were included in the study (Autism n = 44, Typical development children (TDC) n = 62, age 10.32 ± 1.49). E-I balance was assessed through magnetic resonance spectroscopy (MRS), quantifying Glutamate and Glutamine (Glx) and Gamma-Aminobutyric Acid (GABA) in primary sensorimotor cortex (SM1) and thalamus (Thal). Sensory function was evaluated using both objective vibrotactile perceptual sensitivity assessments and subjective parent ratings via the Sensory Experience Questionnaire (SEQ). Motor ability was assessed objectively through the Movement Assessment Battery for Children-second edition (MABC-2) and the Physical and Neurological Examination for Subtle Signs (PANESS). Our findings reveal that lower sensory reactivity and lower tactile thresholds are both predictive of better motor ability (Rsig range between 0.32 and 0.57) with higher sensory scores reflecting poorer sensory filtering predicting worse motor function (Rsig range −0.22 and −0.63). We identified significant associations between MRS-measured Glx and GABA+ levels and sensory reactivity (p < 0.001). Importantly, sensory reactivity sub-scores were found to fully mediate E-I balance to motor associations in domain-specific patterns: Hyper-reactivity mediated the impact of SM1 Glx levels, while hypo-reactivity mediated the impact of SM1 GABA levels. Additionally, sensory seeking mediated the impact of Thalamic GABA levels with all indirect paths ab p < 0.01. These results propose a model where regional metabolite-specific markers of E-I balance explain patterns of autism-associated sensory and motor difficulties, and where subsequently, distinct sensory phenotypes differentially mediate metabolite-motor associations (see Graphical Abstract for detail).

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Repository

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OSF 5unmc

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: R (10)
Size: 23 files, 10 scripts
Software Heritage: not checked
Found in: “Data availability”
Holds: 10 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (10 files), tidyverse (10 files)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
10 files
At the source: osf.io/5unmc/overview

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Data

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Data availability

The code supporting the findings of this study is publicly available on the Open Science Framework (OSF) at https://osf.io/5unmc/overview. The repository includes scripts for reproducing the figures and all associated output figures. Due to General Data Protection Regulation (GDPR) and data transfer agreements, the datasets analysed during the current study are not publicly available but can be requested from CNIR (Mostofsky) through standard project and data access procedures.

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 2 keywords, 12 MeSH terms, 1 funder, 59 references.

Cite

This paper

Shi, M., He, J. L., Powell, H., Lack, O., Oeltzschner, G., Deronda, A., Crocetti, D., Wodka, E. L., Edden, R. A., O’Muircheartaigh, J., Mostofsky, S. H., & Puts, N. A. J. (2026). Distinct sensory atypicalities bridge the gap between brain chemistry and motor dysfunction in autism. Translational psychiatry, 16(1), 320. https://doi.org/10.1038/s41398-026-04036-z

BibTeX

@article{shi2026distinct,
author = {Shi, Mingrun and He, Jason L and Powell, Helen and Lack, Oliver and Oeltzschner, Georg and Deronda, Alyssa and Crocetti, Deana and Wodka, Ericka L and Edden, Richard A and O’Muircheartaigh, Jonathan and Mostofsky, Stewart H and Puts, Nicolaas A J},
title = {{Distinct sensory atypicalities bridge the gap between brain chemistry and motor dysfunction in autism}},
journal = {Translational psychiatry},
year = {2026},
month = may,
volume = {16},
number = {1},
pages = {320},
publisher = {Nature Publishing Group},
issn = {2158-3188},
doi = {10.1038/s41398-026-04036-z},
url = {https://doi.org/10.1038/s41398-026-04036-z},
pmid = {42103716},
pmcid = {PMC13324517}
}

RIS

TY - JOUR
AU - Shi, Mingrun
AU - He, Jason L
AU - Powell, Helen
AU - Lack, Oliver
AU - Oeltzschner, Georg
AU - Deronda, Alyssa
AU - Crocetti, Deana
AU - Wodka, Ericka L
AU - Edden, Richard A
AU - O’Muircheartaigh, Jonathan
AU - Mostofsky, Stewart H
AU - Puts, Nicolaas A J
TI - Distinct sensory atypicalities bridge the gap between brain chemistry and motor dysfunction in autism
T2 - Translational psychiatry
J2 - Transl Psychiatry
PY - 2026
DA - 2026/05/08
VL - 16
IS - 1
SP - 320
SN - 2158-3188
PB - Nature Publishing Group
DO - 10.1038/s41398-026-04036-z
UR - https://doi.org/10.1038/s41398-026-04036-z
LA - en
ER -

CSL-JSON

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