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From Genes to Neurochemistry: Excitation and Inhibition Mechanisms of Sensory Differences in Autism

Code ↔ Paper

3 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 3 matches
  1. [1] § Materials and Methods › Vibrotactile psychophysics ↔ R/BATD_analyze.R, lines 233–286 · score 0.93 · DDTdown, DDTup, static detection threshold, ADTssa, dynamic detection threshold, simultaneous amplitude discrimination
  2. [2] § Materials and Methods › Vibrotactile psychophysics ↔ R/BATD_extract_OF.R, lines 140–194 · score 0.73 · static detection threshold, dynamic detection threshold, simultaneous amplitude discrimination, 350 um, judgement, temporal
  3. [3] § Materials and Methods › Vibrotactile psychophysics ↔ R/BATD_analyze.R, lines 233–286 · score 0.53 · ADTssa, DDT, SDT, SQAD, adaptation, thresholds

Paper

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

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The authors' code

R · 286 lines · 15 KB · no license · 2 matches

  1. #' BATD_analyze
  2. #'
  3. #' Analyze extracted data for from "BATD_extract_NF" or "BATD_extract_OF" for a single participant. This function only requires a dataframe containing the performance output of participants who have had their data extracted using either BATD_extract_NF or BATD_extract_OF.
  4. #' BATD_analyze is used to analyze the datafrom a single participant. In order to analyze data from multiple participants, we recommend users use "BATD_analyze_all".
  5. #'
  6. #' @param dataframe refers to a dataframe produced from "BATD_extract_NF" or "BATD_extract_OF"
  7. #'
  8. #' @return BATD_analyze will return a dataframe with a single row, containing the performance metrics for all the protocols completed by a given participant.
  9. #'
  10. #' @examples
  11. #' Examples are currently NA
  12. #'
  13. #' @export
  15. BATD_analyze <- function(dataframe){
  17. ##VERSION ----
  18. Version <- c("BATD_V.1.7")
  20. #DEBUGGING ----
  21. debugging <- "off"
  22. if(debugging=="on"){
  23. print("Note: Debugging on")
  24. dataframe <- temp2
  25. }
  28. # Setup -------------------------------------------------------------------
  30. '%ni%' <- Negate('%in%') #create the function for %ni% (not in)
  31. library(dplyr) #for some reason I can't call 'lead' or 'lag' without reading in the dplyr library
  33. dataframe <- dataframe #redundant code but useful for debugging (ignore)
  34. unique_number_of_runs <- unique(dataframe$run) #identify the unique number of runs completed
  36. list_of_protocols_by_run <- list()
  37. for(r in unique_number_of_runs){
  38. #Subset the data by the run and analyze the data
  40. data <- dataframe[dataframe$run==r,] #Subset to the current run
  41. protocolsCompleted <- as.character(unique(data$protocolName)) #identify the number of protocols completed
  42. protocolsCompleted <- protocolsCompleted[!is.na(protocolsCompleted)] #legacy: remove any NAs (haven't tested without this line yet)
  44. ## SECTION 2 (extract the participant and protocol details) ----
  46. # ___ 1.1 Participant Details -----------------------------------------------------
  48. id <- as.character(data$id[1])
  49. race <- as.character(data$race[1])
  50. gender <- as.character(data$gender[1])
  51. handedness <- as.character(data$handedness[1])
  52. birthYear <- as.character(data$birthYear[1])
  54. participant_details <- cbind(id, race, gender, handedness, birthYear)
  57. # ___ 1.2 Protocol Details --------------------------------------------------------
  59. dateTested <- as.character(dataframe$date[1])
  60. extractedBy <- Version
  61. run <- r
  62. site <- data$Site[1]
  64. protocolDetails <- cbind(dateTested, extractedBy, site, run)
  66. ## SECTION 2 (Analyze each of the protocols completed identified in SECTION 1)----
  68. analyzed_protocols_list <- list() # Create an external list to put the analyzed protocols
  69. for(p in 1:length(protocolsCompleted)){ #For loops which subsets into the nth protocol completed for a given session
  71. #Basic cleaning of dataframe ----
  72. protocol <- protocolsCompleted[p] #state the current protocol (legacy: haven't tried running without this yet)
  73. protocolData <- data[data$protocolName==protocolsCompleted[p],] #Subset to relevant protocol
  75. if(nrow(protocolData) < 1){next} #(legacy)
  77. sessionData <- protocolData
  79. #Change performance column values to numeric ----
  80. sessionData <- sessionData[!is.na(sessionData$trialNumber),] #remove any trials without a trial number (legacy)
  81. sessionData$responseTime <- as.numeric(as.character(sessionData$responseTime)) #turn responseTime to numeric
  82. sessionData$correctResponse <- as.numeric(as.character(sessionData$correctResponse)) #turn correctResponse to numeric
  83. sessionData$value <- as.numeric(as.character(sessionData$value)) #turn string variables into numeric
  85. numberofPracticeTrials <- as.numeric(as.character(sessionData$numberofPracticeTrials[1]))
  87. #adding one so that the trials start AFTER the n of practice trials
  88. if(numberofPracticeTrials > 1){
  89. numberofPracticeTrials <- numberofPracticeTrials + 1
  90. }
  92. #here we remove the practice trials if the n > 10, this is because some sites actually ran a whole protocol as a practice, rather than the first number of trials (usually 3)
  93. #If practice trials were ran as a whole protocol, they are just treated as a protocol
  94. if(numberofPracticeTrials < 9){
  95. sessionData <- sessionData[numberofPracticeTrials:nrow(sessionData),] #remove practice trials
  96. sessionData$trialNumber <- 1:nrow(sessionData) #reset trial numbers
  97. }
  99. sessionData_for_thresholds <- sessionData
  100. sessionData <- sessionData[!is.na(sessionData$response),] #some protocols had a last trial where a response was not made, this response is not included (CONSIDER MAKING THIS BASED ON SITE)
  102. #General variables ----
  103. medianRT <- median(sessionData$responseTime, na.rm = TRUE)
  104. sdRT <- sd(sessionData$responseTime, na.rm = TRUE)
  105. accuracy <- (sum(sessionData$correctResponse, na.rm = TRUE)/nrow(sessionData))*100
  107. #Reversals ----
  108. a <- sessionData$value
  109. b <- lag(sessionData$value,1)
  110. reversals <- as.data.frame(cbind(b,a))
  111. colnames(reversals) <- c("valueprior", "value")
  112. reversals$valuediff <- reversals$value-reversals$valueprior #value difference
  113. reversals$valuediffprior <- lead(reversals$valuediff,1)
  114. reversals$sign <- sign(reversals$valuediff)
  115. reversals$signs <- lead(sign(reversals$valuediff),1)
  116. reversals$signs[reversals$signs==0] <- -1
  117. reversals$reversals <-reversals$sign+reversals$signs
  118. reversals$reversals[reversals$reversals==0] <- "Reversals"
  119. reversals <- length(which(reversals$reversals=="Reversals"))
  121. #Key variables ----
  122. #For the reaction time protocols
  123. if(protocol %in% c("Simple Reaction Time","Choice Reaction Time")){
  124. if(sum(sessionData$correctResponse) >= 6){
  125. correctResponses <- sessionData$responseTime[sessionData$correctResponse==1]
  126. middle <- length(correctResponses)/2
  127. median6 <- correctResponses[(middle-3):(middle+2)]
  128. meanRT <- mean(median6)
  129. } else {
  130. meanRT <- NA}}
  132. #For the tactile threshold protocols
  133. #Estimate Threshold
  134. if(protocol %ni% c("Simple Reaction Time","Choice Reaction Time")){
  136. sessionData <- sessionData_for_thresholds
  137. if(nrow(sessionData) > 5){
  138. threshold <- mean(sessionData$value[(nrow(sessionData)-4):(nrow(sessionData))])
  139. }else{
  140. threshold <- NA
  141. }
  142. }
  144. #Estimate Threshold for dynamic detection threshold (done differently)
  145. if (protocol %in% c(
  146. "Dynamic Detection Threshold",
  147. "Dynamic Detection Threshold (up)",
  148. "Dynamic Detection Threshold (down)"
  149. )) {
  150. sessionData <- sessionData_for_thresholds
  151. threshold <- mean(sessionData$value[sessionData$correctResponse == 1]) #mean of hte correct responses
  152. }
  154. #Else, if it is an amplitude discrimination protocol
  155. if(protocol %in% c("Simultaneous Amplitude Discrimination",
  156. "Sequential Amplitude Discrimination",
  157. "Dual Staircase Amplitude Discrimination (up)",
  158. "Dual Staircase Amplitude Discrimination (down)",
  159. "Amplitude Discrimination with Single Site Adaptation",
  160. "Amplitude Discrimination with Dual Site Adaptation",
  161. "Sequential Amplitude Challenge",
  162. "Simultaneous Amplitude Discrimination with adaptation"
  163. )){
  164. threshold <- threshold - 100 #remove the standard stimulus from threshold
  165. }
  167. #If frequency discrimination protocol
  168. if(protocol %in% c(
  169. "Simultaneous Frequency Discrimination",
  170. "Sequential Frequency Discrimination"
  171. )){
  172. threshold <- threshold - 30 #remove the standard stimulus from threshold
  173. }
  175. #If duration discrimination protocol
  176. if(protocol %in% c("Duration Discrimination")){
  177. threshold <- threshold - 500
  178. }
  180. #If protocol is SSA 2 Block from the STES experiments at KCL, we need to re-estimate everything because there should be two sets of thresholds (before and after adaptation),
  182. if(protocol %in% c("SSA 2 Block")){
  183. sessionDataOne <- sessionData[1:24,]
  184. sessionDataTwo <- sessionData[25:nrow(sessionData),]
  186. sessionDataX_list <- list()
  187. for(x in 1:2){
  189. #if x == 1, then set sessionDataX to be the first half of the protocol
  190. if(x == 1){
  191. sessionDataX <- sessionDataOne
  192. }
  193. # else, set it to be the second half of the protocol
  194. if(x == 2){
  195. sessionDataX <- sessionDataTwo
  196. }
  198. #estimate medianRT, sdRT and accuracy for the given sessionDataX
  199. medianRT <- median(sessionDataX$responseTime, na.rm = TRUE)
  200. sdRT <- sd(sessionDataX$responseTime, na.rm = TRUE)
  201. accuracy <- (sum(sessionDataX$correctResponse, na.rm = TRUE)/nrow(sessionData))*100
  203. #estimate reversals for given sessionDataX
  204. a <- sessionDataX$value
  205. b <- lag(sessionDataX$value,1)
  206. reversals <- as.data.frame(cbind(b,a))
  207. colnames(reversals) <- c("valueprior", "value")
  208. reversals$valuediff <- reversals$value-reversals$valueprior #value difference
  209. reversals$valuediffprior <- lead(reversals$valuediff,1)
  210. reversals$sign <- sign(reversals$valuediff)
  211. reversals$signs <- lead(sign(reversals$valuediff),1)
  212. reversals$signs[reversals$signs==0] <- -1
  213. reversals$reversals <-reversals$sign+reversals$signs
  214. reversals$reversals[reversals$reversals==0] <- "Reversals"
  215. reversals <- length(which(reversals$reversals=="Reversals"))
  217. #estimate thresholds for given sessionDataX
  218. threshold <- mean(sessionDataX$value[(nrow(sessionDataX)-4):(nrow(sessionDataX))]) - 100 #note 100 being subtracted
  220. sessionDataX_list[[x]] <- cbind(medianRT, sdRT, accuracy, reversals, threshold)
  221. }
  223. #combine and add tags to each session
  224. sessionDataOne_output <- as.data.frame(sessionDataX_list[[1]])
  225. colnames(sessionDataOne_output) <- paste0(colnames(sessionDataOne_output), "_SSA2_pt1")
  227. sessionDataTwo_output <- as.data.frame(sessionDataX_list[[2]])
  228. colnames(sessionDataTwo_output) <- paste0(colnames(sessionDataTwo_output), "_SSA2_pt2")
  230. }
  233. #Column bind variables -----
  234. if(protocol %in% c("Simple Reaction Time","Choice Reaction Time")){
  235. outPut <- cbind(accuracy, medianRT, sdRT, meanRT)}else{outPut <- cbind(accuracy, medianRT, sdRT, threshold, reversals)}
  237. #Add a tag to the end of the column names to specify which protocol the outPut is from ----
  239. tag <- ifelse(protocol=="Simple Reaction Time", "_SRT",
  240. ifelse(protocol=="Choice Reaction Time", "_CRT",
  241. ifelse(protocol=="Static Detection Threshold", "_SDT",
  242. ifelse(protocol=="Static Detection Threshold with Adaptation ISI 30", "_SDT30",
  243. ifelse(protocol=="Static Detection Threshold with Adaptation ISI 100", "_SDT100",
  244. ifelse(protocol=="Dynamic Detection Threshold", "_DDT",
  245. ifelse(protocol=="Amplitude Discrimination Threshold without Adaptation", "_ADT",
  246. ifelse(protocol=="Amplitude Discrimination with Single Site Adaptation", "_ADTssa",
  247. ifelse(protocol=="Amplitude Discrimination with Dual Site Adaptation", "_ADTdsa",
  248. ifelse(protocol=="Dual Staircase Amplitude Discrimination (up)", "_ADTdsa_up",
  249. ifelse(protocol=="Dual Staircase Amplitude Discrimination (down)", "_ADTdsa_down",
  250. ifelse(protocol=="Simultaneous Frequency Discrimination", "_SMFD",
  251. ifelse(protocol=="Sequential Frequency Discrimination", "_SQFD",
  252. ifelse(protocol=="Simultaneous Amplitude Discrimination", "_SMAD",
  253. ifelse(protocol=="Sequential Amplitude Discrimination", "_SQAD",
  254. ifelse(protocol=="Temporal Order Judgement", "_TOJ",
  255. ifelse(protocol=="Temporal Order Judgement with Carrier", "_TOJwc",
  256. ifelse(protocol=="Duration Discrimination", "_DD",
  257. ifelse(protocol=="Dynamic Detection Threshold (up)", "_DDTup",
  258. ifelse(protocol=="Dynamic Detection Threshold (down)", "_DDTdown",
  259. ifelse(protocol=="Sequential Amplitude Challenge", "_SQAD",
  260. ifelse(protocol=="Simultaneous Amplitude Discrimination with adaptation", "_SMADadp", NA))))))))))))))))))))))
  261. colnames(outPut) <- paste0(colnames(outPut), tag)
  264. if(protocol %in% c("SSA 2 Block")){
  265. outPut <- cbind(sessionDataOne_output, sessionDataTwo_output)
  266. }
  268. analyzed_protocols_list[[p]] <- outPut
  269. }
  271. participant_output <- do.call(cbind, analyzed_protocols_list)
  273. #Convert the analyzed data to numeric ----
  274. participant_output <- suppressWarnings(as.data.frame(participant_output))
  275. participant_output[,1:ncol(participant_output)] <- suppressWarnings(sapply(participant_output[,1:ncol(participant_output)], suppressWarnings(as.character)))
  276. participant_output[,1:ncol(participant_output)] <- suppressWarnings(sapply(participant_output[,1:ncol(participant_output)], suppressWarnings(as.numeric)))
  278. #Append additional details ----
  279. participant_output <- cbind(participant_details, protocolDetails, participant_output)
  280. list_of_protocols_by_run[[r]] <- participant_output
  281. }
  283. participant_output <- plyr::rbind.fill(list_of_protocols_by_run)
  285. return(participant_output)
  286. }

BATD_analyze.R at commit a8def81, no license · at the source

Overview

Authors: Alice R. Thomson1,2, Viola Hollestein1, Martina Arenella1,3, Helen Powell1, Jason He1, Beth Oakley1, Eva Loth1, Rosemary Holt4, Jan K. Buitelaar5, Laura Colomar1, Natalie J. Forde5, Thomas Bourgeron6, Terje Falck-Ytter7,8, Giorgia Bussu7, Tobias Banaschweski9,10, Pascal-M Aggensteiner9,10,11, Richard Edden12,13, Tony Charman14, Charlotte Pretzsch1, Declan Murphy1, Tomoki Arichi2,15, Nicolaas A. Puts1,2
ORCID iDs: Alice R. Thomson, Nicolaas A. Puts
15 affiliations
  1. Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
  2. MRC Centre for Neurodevelopmental Disorders, King’s College London, London, UK
  3. Department of Human genetics, Radboud University Medical Center, Nijmegen, Netherlands
  4. Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
  5. Radboud University Nijmegen Medical Center, Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Netherlands
  6. Human Genetics and Cognitive Functions Unit, Institut Pasteur, University de Paris, France
  7. Development and Neurodiversity Lab, Department of Psychology, Uppsala University, Uppsala, Sweden
  8. Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women’s and Children’s Health, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
  9. Central Institute of Mental Health, Department of Child and Adolescent Psychiatry and Psychotherapy, Mannheim, Germany
  10. German Center for Mental Health (DZPG), partner site Mannheim-Heidelberg-Ulm, Germany
  11. Department of Clinical Psychology of Childhood and Adolescence, Institute of Psychology, Friedrich Schiller University Jena, Semmelweisstraße 12, Jena, 07743, Germany
  12. Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, 601 North Caroline Street, Baltimore, MD, 21287, United States
  13. F.M. Kirby Research Centre for Functional Brain Imaging, Kennedy Krieger Institute, 707 North Broadway, Baltimore, MD, 21205, United States
  14. Department of Psychology, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
  15. Research Department of Early life Imaging, School of Biomedical Engineering and Imaging Sciences, London, UK
Institutions: King's College London (United Kingdom); MRC Centre for Neurodevelopmental Disorders (United Kingdom); Radboud University Medical Center (Netherlands); University of Cambridge (United Kingdom); Donders Institute for Brain, Cognition and Behaviour (Netherlands); Institut Pasteur (France); Université Paris Cité (France); Uppsala University (Sweden); Karolinska Institutet (Sweden); Central Institute of Mental Health (Germany); Deutsches Zentrum für Psychische Gesundheit (Germany); Friedrich Schiller University Jena (Germany); Johns Hopkins University (United States); Kennedy Krieger Institute (United States)
Journal: medRxiv (preprint)
Dates: published online 16 July 2026
Type: Preprint
License: CC BY-NC-ND
Identifiers: DOI 10.64898/2026.07.14.26358047 · OpenAlex W7168765399
Open access: green, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), autism (population), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing, fMRI & imaging
Topic: Autism Spectrum Disorder Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Autism Speaks; European Federation of Pharmaceutical Industries and Associations; Simons Foundation; European Regional Development Fund
Citations: not cited yet (Europe PMC); 127 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

HeJasonL/BATD

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: a8def8144aff27d30ab5010ad98b1facb09cdb0d, 13 February 2026
Languages: R (26)
Size: 112 files, 26 scripts
Software Heritage: not archived
Found in: the text, “Vibrotactile psychophysics”
Holds: README, environment (DESCRIPTION), tests, documentation
Not found: license file, CITATION.cff, continuous integration
Tools: data.table (6 files), tidyverse (5 files), ggpubr (4 files), ggplot2 (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
27 files
At the source: github.com/HeJasonL/BATD

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 26 scripts, each with its path and the digest of its content;
  • 3 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 statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.64898/2026.07.14.26358047.

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

  • Authors: added Nicolaas A. Puts (0000-0003-1024-1927); removed Nicolaas A. Puts
  • Funding: added Autism Speaks; European Federation of Pharmaceutical Industries and Associations; Simons Foundation Autism Research Initiative; European Commission

Version 1, 27 September 2026: the first record

Recorded: type, journal, dates, 22 authors, 125 references.

Cite

This paper

Thomson, A. R., Hollestein, V., Arenella, M., Powell, H., He, J., Oakley, B., Loth, E., Holt, R., Buitelaar, J. K., Colomar, L., Forde, N. J., Bourgeron, T., Falck-Ytter, T., Bussu, G., Banaschweski, T., Aggensteiner, P.-M., Edden, R., Charman, T., Pretzsch, C., . . . Puts, N. A. (2026). From Genes to Neurochemistry: Excitation and Inhibition Mechanisms of Sensory Differences in Autism. medRxiv (preprint). https://doi.org/10.64898/2026.07.14.26358047

BibTeX

@article{thomson2026genes,
author = {Thomson, Alice R. and Hollestein, Viola and Arenella, Martina and Powell, Helen and He, Jason and Oakley, Beth and Loth, Eva and Holt, Rosemary and Buitelaar, Jan K. and Colomar, Laura and Forde, Natalie J. and Bourgeron, Thomas and Falck-Ytter, Terje and Bussu, Giorgia and Banaschweski, Tobias and Aggensteiner, Pascal-M and Edden, Richard and Charman, Tony and Pretzsch, Charlotte and Murphy, Declan and Arichi, Tomoki and Puts, Nicolaas A.},
title = {{From Genes to Neurochemistry: Excitation and Inhibition Mechanisms of Sensory Differences in Autism}},
journal = {medRxiv (preprint)},
year = {2026},
month = jul,
publisher = {medRxiv},
doi = {10.64898/2026.07.14.26358047},
url = {https://doi.org/10.64898/2026.07.14.26358047}
}

RIS

TY - JOUR
AU - Thomson, Alice R.
AU - Hollestein, Viola
AU - Arenella, Martina
AU - Powell, Helen
AU - He, Jason
AU - Oakley, Beth
AU - Loth, Eva
AU - Holt, Rosemary
AU - Buitelaar, Jan K.
AU - Colomar, Laura
AU - Forde, Natalie J.
AU - Bourgeron, Thomas
AU - Falck-Ytter, Terje
AU - Bussu, Giorgia
AU - Banaschweski, Tobias
AU - Aggensteiner, Pascal-M
AU - Edden, Richard
AU - Charman, Tony
AU - Pretzsch, Charlotte
AU - Murphy, Declan
AU - Arichi, Tomoki
AU - Puts, Nicolaas A.
TI - From Genes to Neurochemistry: Excitation and Inhibition Mechanisms of Sensory Differences in Autism
T2 - medRxiv (preprint)
J2 - medRxiv
PY - 2026
DA - 2026/07/16
PB - medRxiv
DO - 10.64898/2026.07.14.26358047
UR - https://doi.org/10.64898/2026.07.14.26358047
ER -

CSL-JSON

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"id": "10.64898/2026.07.14.26358047",
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"title": "From Genes to Neurochemistry: Excitation and Inhibition Mechanisms of Sensory Differences in Autism",
"container-title": "medRxiv (preprint)",
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The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1038/s41398-026-04036-z [code]
Distinct sensory atypicalities bridge the gap between brain chemistry and motor dysfunction in autism.
Journal: Translational psychiatry
In common: ggplot2, tidyverse, autism, 7 references, author Nicolaas A. Puts
[2] doi:10.1002/advs.202519479 [code]
Diminished Signal-to-Noise Ratio Disrupts Somatosensory Population Encoding and Drives Tactile Hyposensitivity in the Fmr1&lt;sup&gt;-/y&lt;/sup&gt; Autism Model.
Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany)
In common: autism, 6 references
[3] doi:10.1038/s41598-026-42120-y [code]
Qualitative EEG abnormalities in ASD reflect inhibition-dominated brain dynamics.
Journal: Scientific reports
In common: autism, 5 references
[4] doi:10.1002/mrm.70380 [code]
The Impact and Reliability of Tissue Segmentation on In Vivo Magnetic Resonance Spectroscopy Metabolite Quantification.
Journal: Magnetic resonance in medicine
In common: 5 references
[5] doi:10.1016/j.xhgg.2026.100652 [code]
CRISPR-engineered deletion of POGZ alters transcription factor binding at promoters of genes involved in synaptic signaling.
Journal: HGG advances
In common: ggpubr, data.table, ggplot2, 1 other tool, autism, cellular / molecular, 1 reference
[6] doi:10.1162/imag.a.1252 [code]
Does the brain's E:I balance really shape long-range temporal correlations? Lessons learned from 3T MRI.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: tidyverse, 4 references
[7] doi:10.1126/sciadv.aec9291 [code]
Computational mechanisms of perception in autism revealed using games inspired by rodent operant tasks.
Journal: Science advances
In common: ggpubr, data.table, ggplot2, 1 other tool, autism, 1 reference
[8] doi:10.1038/s41593-026-02287-z [code]
Autism subtypes identified using cross-species functional connectivity analyses.
Journal: Nature neuroscience
In common: ggpubr, ggplot2, tidyverse, autism, 2 references
[9] doi:10.1186/s40168-026-02342-8 [code]
Impacts of host genetics on gut microbiome composition in Alzheimer's disease.
Journal: Microbiome
In common: ggpubr, data.table, ggplot2, 1 other tool, genetics / omics, 1 reference
[10] doi:10.1038/s42003-026-10045-x [code]
Spatiotemporal brain transcriptomics reveal risk gene hot-spots in major neuropsychiatric disorders.
Journal: Communications biology
In common: ggpubr, data.table, ggplot2, 1 other tool, genetics / omics, cellular / molecular, 1 reference

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