OSCR

Autism subtypes identified using cross-species functional connectivity analyses.

Code ↔ Paper

14 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 14 matches · 6 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Resting-state fMRI connectivity mapping ↔ rsfmri_preprocess_08_regress_nuisance.sh, lines 24–56 · score 0.85 · fsl_regfilt, nuisance regressed, ventricular mask, motion traces, fslmeants, template
  2. [2] § Results › fMRI dysconnectivity in 20 autism mouse models clusters into dominant hypoconnectivity and hyperconnectivity subtypes ↔ cluster-analysis/rsfmri_etiologies_clustering_04_hierarchical_clustering.R, lines 1–45 · score 0.83 · Ube3a, p11.2, q11.2, hierarchical clustering, En2, Fmr1
  3. [3] § Methods › Brain decoding and gene enrichment analyses ↔ gene_decoding_and_enrichment_analysis/01_initialization.r, the whole file · a weak match · score 0.78 · NeuroVault, FDR corrected, spatially correlated, donor, hypergeometric, decoding
  4. [4] § Methods › Resting-state fMRI connectivity mapping ↔ rsfmri_connectivity_mapping_01_global_connectivity.sh, lines 1–43 · score 0.72 · weighted degree centrality, global connectivity mapping, Python, Pearson, preprocessed, voxelwise
  5. [5] § Methods › Gene enrichment analysis ↔ cluster-analysis/rsfmri_etiologies_clustering_04_hierarchical_clustering.R, lines 1–45 · score 0.67 · p11.2, q11.2, Cntnap2, Shank3, BTBR, etiology
  6. [6] § Methods › fMRI dysconnectivity subtyping in autism-relevant mouse models ↔ cluster-analysis/rsfmri_etiologies_clustering_04_hierarchical_clustering.R, lines 48–105 · score 0.66 · hierarchical clustering, dendrogram, Euclidean, agglomerative, rows, heatmap
  7. [7] § Methods › Gene enrichment analysis ↔ gene_decoding_and_enrichment_analysis/02_enrichmentAnalyses.r, the whole file · a weak match · score 0.66 · mTOR, protein interaction, DB, seed, enrichment, gene
  8. [8] § Methods › Autism subtyping using fMRI connectivity ↔ cognitive_maps/conjunction_maps.sh, the whole file · a weak match · score 0.64 · conjunction map, global connectivity maps, components, subcortical, discover, voxels
  9. [9] § Methods › Brain decoding and gene enrichment analyses ↔ gene_decoding_and_enrichment_analysis/02_enrichmentAnalyses.r, the whole file · a weak match · score 0.64 · co expression modules, NeuroVault, tissues, enrichment, decoding, enriched
  10. [10] § Methods › fMRI dysconnectivity subtyping in autism-relevant mouse models ↔ cluster-analysis/rsfmri_etiologies_clustering_02_NbClust.R, lines 41–93 · score 0.61 · NbClust, Euclidean, optimal, rows, distance, etiological
  11. [11] § Methods › Resting-state fMRI preprocessing ↔ rsfmri_preprocess_06_skull_stripping.sh, lines 18–70 · score 0.60 · skull stripped, Brain mask, FAST, FSL, preprocessed
  12. [12] § Methods › Replicability of subtyping ↔ cognitive_maps/conjunction_maps.sh, the whole file · a weak match · score 0.60 · conjunction maps, brain maps, connectivity maps, thresholding, discovery, cluster
  13. [13] § Results › fMRI dysconnectivity in 20 autism mouse models clusters into dominant hypoconnectivity and hyperconnectivity subtypes ↔ cluster-analysis/rsfmri_etiologies_clustering_01_feature_extraction.sh, the whole file · a weak match · score 0.55 · ube3a, En2, Fmr1, Sgsh, Tsc2, Voxelwise
  14. [14] § Methods › Resting-state fMRI preprocessing ↔ rsfmri_preprocess_00_extract_from_bruker.sh, lines 1–78 · score 0.52 · AFNI, tool, preprocessed, FSL, timeseries, MRI

Paper

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

The paper is loaded when this pane is shown.

The authors' code

R · 108 lines · 4.1 KB · no license · 3 matches

  1. # this procedure performs agglomerative cluster analysis and gives cluster membership
  2. #
  3. # quarda anche questo pacchetto nel caso
  4. # https://www.datanovia.com/en/lessons/heatmap-in-r-static-and-interactive-visualization/
  6. # https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/hclust
  7. # https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/dist
  8. #
  9. # ricordati che se hai troppe colonne (voxels), la visualizzazione crasha di conseguenza fai downsampling!)
  11. # https://www.rdocumentation.org/packages/grDevices/versions/3.4.1/topics/png
  14. # loading required libraries
  15. library('gplots')
  16. library('corrplot')
  17. library('RColorBrewer')
  18. library('ggplot2')
  21. # loading feature matrix
  22. feature_by_etiology_matrix <- as.matrix(read.table("/media/DATA1/rsfMRI_all_autism_mutations_Marie_Curie/06_20_etiologies_IL6/03_clustering/02/02_features_for_clustering_txt/feature_etiology_matrix.txt", sep = " "))
  25. # renaming etiologies
  26. colnames(feature_by_etiology_matrix)[1] <- '16p11.2'
  27. colnames(feature_by_etiology_matrix)[2] <- 'Btbr'
  28. colnames(feature_by_etiology_matrix)[3] <- 'Cdkl5 [ht]'
  29. colnames(feature_by_etiology_matrix)[4] <- 'Cdkl5 [ko]'
  30. colnames(feature_by_etiology_matrix)[5] <- 'Chd8'
  31. colnames(feature_by_etiology_matrix)[6] <- 'Cntnap2'
  32. colnames(feature_by_etiology_matrix)[7] <- 'En2'
  33. colnames(feature_by_etiology_matrix)[8] <- 'Fmr1'
  34. colnames(feature_by_etiology_matrix)[9] <- 'Il6'
  35. colnames(feature_by_etiology_matrix)[10] <- '22q11.2'
  36. colnames(feature_by_etiology_matrix)[11] <- 'Mecp2'
  37. colnames(feature_by_etiology_matrix)[12] <- 'Nlgn3-R451'
  38. colnames(feature_by_etiology_matrix)[13] <- 'Nlgn3 [ko]'
  39. colnames(feature_by_etiology_matrix)[14] <- 'Oxtr'
  40. colnames(feature_by_etiology_matrix)[15] <- 'Sgsh'
  41. colnames(feature_by_etiology_matrix)[16] <- 'Shank3'
  42. colnames(feature_by_etiology_matrix)[17] <- 'Syn2'
  43. colnames(feature_by_etiology_matrix)[18] <- 'Trem2'
  44. colnames(feature_by_etiology_matrix)[19] <- 'Tsc2'
  45. colnames(feature_by_etiology_matrix)[20] <- 'Ube3a'
  48. # transposing the matrix. Here I transpose because I want
  49. # etiologies to be the raws
  50. etiology_by_feature_matrix <- t(feature_by_etiology_matrix)
  53. # dimensions of the matrix
  54. dim(etiology_by_feature_matrix)
  57. # in case needed, this subsets the matrix - too large matrices don't get visualised by R
  58. # etiology_by_feature_matrix_subset <- etiology_by_feature_matrix[c(1:20),c(1:100)]
  61. # loading and reversing color palettes
  62. hmcol <- rev(colorRampPalette(brewer.pal(11, "RdBu"))(256))
  64. # set the maximum and minimum value of the colorbar
  65. range_colors = seq(from = -1.2, to = 1.2, by = 0.009375)
  68. # saving the figure in tiff
  69. png(filename = "/media/DATA1/rsfMRI_all_autism_mutations_Marie_Curie/06_20_etiologies_IL6/03_clustering/02/dendrogram.png",
  70. width = 15,
  71. height = 10,
  72. units = "cm",
  73. res= 600,
  74. pointsize = 4)
  77. # visualising the matrix with agglomerative clustering (about 1 minute to plot)
  78. matrix_with_dendrogram <- heatmap.2(etiology_by_feature_matrix,
  79. Rowv = T, # cluster rows
  80. Colv = T, # don't cluster columns
  81. symm = F, # matrix is not symmetrical
  82. revC = T,
  83. distfun = function(x) dist(x, method = "euclidean"), # define distance metrics
  84. hclustfun = function(x) hclust(x, method = "ward.D2"), # define clustering method (complete, ward.D2)
  85. trace = c("none"), # don't display trace
  86. dendrogram = "row", # show dendrogram
  87. col = hmcol, # color of the cells
  88. key = T,
  89. key.title = NA,
  90. key.xlab = "Cohen's d",
  91. density.info = "none",
  92. keysize = 1,
  93. cexRow = 3.5, # size labels x-axis
  94. cexCol = 0.01, # size labels y-axis, practically not visible
  95. breaks = range_colors,
  96. margins = c(1,15),
  97. labRow=as.expression(lapply(rownames(etiology_by_feature_matrix), function(a) bquote(italic(.(a)))))
  98. )
  100. dev.off()
  103. # save and write sorted matrix
  104. sorted_matrix_csv <- etiology_by_feature_matrix[rev(matrix_with_dendrogram$rowInd), matrix_with_dendrogram$colInd]
  105. write.table(sorted_matrix_csv, file = "sorted_etiology_by_feature_matrix.csv", sep = ",", dec = ".", row.names = TRUE, col.names = FALSE)

rsfmri_etiologies_clustering_04_hierarchical_clustering.R at commit ef58cf1, no license · at the source

Overview

Authors: Marco Pagani1,2,3, Valerio Zerbi4,5, Silvia Gini1,6, Filomena Grazia Alvino1, Abhishek Banerjee7,8,9, Andrea Barberis10, M. Albert Basson11,12, Yuri Bozzi6, Alberto Galbusera1, Jacob Ellegood13, Michela Fagiolini14,15, Jason P. Lerch16,17,18, Michela Matteoli19,20, Caterina Montani1,21, Davide Pozzi20, Giovanni Provenzano22, Maria Luisa Scattoni23, Nicole Wenderoth24, Ting Xu25, Michael V. Lombardo26, Michael P. Milham25,27, Adriana Di Martino2, Alessandro Gozzi1
ORCID iDs: Silvia Gini, M. Albert Basson, Yuri Bozzi, Alberto Galbusera, Caterina Montani, Nicole Wenderoth, Michael V. Lombardo, Michael P. Milham, Adriana Di Martino, Alessandro Gozzi
27 affiliations
  1. Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems,Rovereto, Italy
  2. Autism Center, Child Mind Institute,New York, NY USA
  3. IMT School for Advanced Studies,Lucca, Italy
  4. Department of Psychiatry, University of Geneva,Geneva, Switzerland
  5. Department of Basic Neurosciences, University of Geneva,Geneva, Switzerland
  6. Center for Mind and Brain Sciences (CIMeC), University of Trento,Rovereto, Italy
  7. Brain Research Institute, University of Zurich,Zurich, Switzerland
  8. Adaptive Decisions Lab, Blizard Institute, Queen Mary University of London,London, UK
  9. Department of Pharmacology, University of Oxford,Oxford, UK
  10. Synaptic Plasticity of Inhibitory Networks, Istituto Italiano di Tecnologia,Genova, Italy
  11. Centre for Craniofacial and Regenerative Biology, King’s College London,London, UK
  12. Department of Clinical and Biomedical Sciences, University of Exeter,Exeter, UK
  13. Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital,Toronto, Ontario Canada
  14. Boston Children’s Hospital, Harvard Medical School,Boston, MA USA
  15. International Research Center for Neurointelligence, University of Tokyo,Tokyo, Japan
  16. Oxford Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford,Oxford, UK
  17. Mouse Imaging Centre, The Hospital for Sick Children,Toronto, Ontario Canada
  18. Department of Medical Biophysics, University of Toronto,Toronto, Ontario Canada
  19. Department of Biomedical Sciences, Humanitas University,Milan, Italy
  20. IRCCS Humanitas Research Hospital,Milan, Italy
  21. Present Address: IRCCS Ospedale Policlinico San Martino,Genova, Italy
  22. Department of Cellular, Computational and Integrative Biology. University of Trento,Trento, Italy
  23. National Center for Rare Diseases, Istituto Superiore di Sanità,Rome, Italy
  24. Neural Control of Movement Lab, ETH Zürich,Zurich, Switzerland
  25. Center for Integrative Developmental Neuroscience, Child Mind Institute,New York, NY USA
  26. Laboratory for Autism and Neurodevelopmental Disorders, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems,Rovereto, Italy
  27. Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research,Orangeburg, NY USA
Institutions: Center for Neuroscience and Cognitive Systems (Italy); Child Mind Institute (United States); IMT School for Advanced Studies Lucca (Italy); University of Geneva (Switzerland); University of Trento (Italy); University of Zurich (Switzerland); Queen Mary University of London (United Kingdom); University of Oxford (United Kingdom); Italian Institute of Technology (Italy); King's College London (United Kingdom); University of Exeter (United Kingdom); Holland Bloorview Kids Rehabilitation Hospital (Canada); Boston Children's Hospital (United States); The University of Tokyo (Japan); Hospital for Sick Children (Canada); University of Toronto (Canada); Humanitas University (Italy); IRCCS Humanitas Research Hospital (Italy); Ospedale Policlinico San Martino (Italy); Istituto Superiore di Sanità (Italy); ETH Zurich (Switzerland); Nathan Kline Institute for Psychiatric Research (United States)
Journal: Nature neuroscience, volume 29, issue 6, pages 1476-1487
Dates: received 17 April 2025; accepted 30 March 2026; published online 15 May 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41593-026-02287-z · PMID 42141307 · PMCID PMC13246445 · OpenAlex W7161263408
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), mouse (organism), autism (population)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Graphs, fMRI & imaging
Keywords: Autism spectrum disorders, Functional magnetic resonance imaging
MeSH: Autistic Disorder*, Brain*, Animals, Brain Mapping, Disease Models, Animal, Female, Humans, Magnetic Resonance Imaging, Male, Mice, Neural Pathways, Species Specificity (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Swiss National Science Foundation (203005)
Citations: cited by 4 papers (Europe PMC); 125 references in the paper

Abstract

It is often assumed that phenotypic heterogeneity in autism reflects underlying pathobiological variation. However, direct evidence supporting this link is lacking. Leveraging cross-species functional neuroimaging, we show that brain dysconnectivity patterns in autism can be parsed into biologically dissociable subtypes. Specifically, we found that functional magnetic resonance imaging (fMRI) connectivity alterations in 20 distinct genetic mouse models of autism cluster into hypoconnectivity-dominant and hyperconnectivity-dominant subtypes. These subtypes are linked to distinct biological pathways, with hypoconnectivity being associated with synaptic dysfunction and hyperconnectivity reflecting transcriptional and immune-related alterations. Here we identified analogous hypoconnectivity and hyperconnectivity subtypes in a multicenter human fMRI dataset of n = 940 individuals with idiopathic autism and n = 1,036 neurotypical individuals. The human autism subtypes are highly replicable, are associated with distinct functional network architectures and behavioral profiles and recapitulate the synaptic and immune-related pathways identified in the rodent dataset. Our work provides a new empirical framework for targeted subtyping of the autism spectrum.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 14 matches between paragraphs and lines of code.

functional-neuroimaging/rsfMRI-preprocessing

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: fee4517511eb1ab7858ef0ec87e868df84e47852, 26 October 2022
Languages: Shell (11)
Size: 12 files, 11 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: AFNI (6 files), FSL (5 files), ANTs (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
12 files
At the source: github.com/functional-neuroimaging/rsfMRI-preprocessing

functional-neuroimaging/rsfMRI-global-local-connectivity

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: d93d2145761f46e2542bbec52b0976928164d659, 8 July 2021
Languages: Shell (4), Python (1)
Size: 5 files, 5 scripts
Software Heritage: not archived
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: AFNI (1 file), FSL (1 file), NiBabel (1 file), NumPy (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
5 files
At the source: github.com/functional-neuroimaging/rsfMRI-global-local-connectivity

functional-neuroimaging/gene_decoding_and_enrichment

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 3594ce91c8222e51f7d9ec16402ab26baf14baab, 17 September 2021
Languages: R (4)
Size: 10 files, 4 scripts
Software Heritage: archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (1 file), ggpubr (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
5 files
At the source: github.com/functional-neuroimaging/gene_decoding_and_enrichment

functional-neuroimaging/biological_subtyping

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: ef58cf1622e5b60885919d108b6033d78be84d52, 28 April 2026
Languages: Shell (6), R (4)
Size: 26 files, 10 scripts
Software Heritage: not archived
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FSL (4 files), AFNI (1 file), ggplot2 (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
10 files
At the source: github.com/functional-neuroimaging/biological_subtyping

Code availability

The code used for preprocessing mouse fMRI data is available at https://github.com/functional-neuroimaging/rsfMRI-preprocessing. The code for mapping global connectivity in mice and humans is available at https://github.com/functional-neuroimaging/rsfMRI-global-local-connectivity. The code employed for gene enrichment analysis is available at https://github.com/functional-neuroimaging/gene_decoding_and_enrichment. The code for site harmonization and cluster analysis is available at https://github.com/functional-neuroimaging/biological_subtyping.

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

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:

  • 4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 30 scripts, each with its path and the digest of its content;
  • 14 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

Raw mouse fMRI timeseries can be download from https://dataverse.iit.it/ (10.48557/AIO2LN). Human fMRI scans from ABIDE I and ABIDE II scans are available at https://fcon_1000.projects.nitrc.org/indi/abide/abide_I.html and https://fcon_1000.projects.nitrc.org/indi/abide/abide_II.html. Most CMI-based datasets are deposited in the National Database for Autism Research (NDAR) (10.15154/nnfr-4943) upon parent/legal guardian consent and are accessible through the NDAR (https://ndar.nih.gov/) in accordance with its data use policies. Source data are provided with this paper.

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 → Nature Portfolio

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 23 authors, 2 keywords, 12 MeSH terms, 1 funder, 124 references.

Cite

This paper

Pagani, M., Zerbi, V., Gini, S., Alvino, F. G., Banerjee, A., Barberis, A., Basson, M. A., Bozzi, Y., Galbusera, A., Ellegood, J., Fagiolini, M., Lerch, J. P., Matteoli, M., Montani, C., Pozzi, D., Provenzano, G., Scattoni, M. L., Wenderoth, N., Xu, T., . . . Gozzi, A. (2026). Autism subtypes identified using cross-species functional connectivity analyses. Nature neuroscience, 29(6), 1476-1487. https://doi.org/10.1038/s41593-026-02287-z

BibTeX

@article{pagani2026autism,
author = {Pagani, Marco and Zerbi, Valerio and Gini, Silvia and Alvino, Filomena Grazia and Banerjee, Abhishek and Barberis, Andrea and Basson, M. Albert and Bozzi, Yuri and Galbusera, Alberto and Ellegood, Jacob and Fagiolini, Michela and Lerch, Jason P. and Matteoli, Michela and Montani, Caterina and Pozzi, Davide and Provenzano, Giovanni and Scattoni, Maria Luisa and Wenderoth, Nicole and Xu, Ting and Lombardo, Michael V. and Milham, Michael P. and Di Martino, Adriana and Gozzi, Alessandro},
title = {{Autism subtypes identified using cross-species functional connectivity analyses}},
journal = {Nature neuroscience},
year = {2026},
month = may,
volume = {29},
number = {6},
pages = {1476--1487},
publisher = {Nature Portfolio},
issn = {1097-6256},
doi = {10.1038/s41593-026-02287-z},
url = {https://doi.org/10.1038/s41593-026-02287-z},
pmid = {42141307},
pmcid = {PMC13246445}
}

RIS

TY - JOUR
AU - Pagani, Marco
AU - Zerbi, Valerio
AU - Gini, Silvia
AU - Alvino, Filomena Grazia
AU - Banerjee, Abhishek
AU - Barberis, Andrea
AU - Basson, M. Albert
AU - Bozzi, Yuri
AU - Galbusera, Alberto
AU - Ellegood, Jacob
AU - Fagiolini, Michela
AU - Lerch, Jason P.
AU - Matteoli, Michela
AU - Montani, Caterina
AU - Pozzi, Davide
AU - Provenzano, Giovanni
AU - Scattoni, Maria Luisa
AU - Wenderoth, Nicole
AU - Xu, Ting
AU - Lombardo, Michael V.
AU - Milham, Michael P.
AU - Di Martino, Adriana
AU - Gozzi, Alessandro
TI - Autism subtypes identified using cross-species functional connectivity analyses
T2 - Nature neuroscience
J2 - Nat Neurosci
PY - 2026
DA - 2026/05/15
VL - 29
IS - 6
SP - 1476
EP - 1487
SN - 1097-6256
PB - Nature Portfolio
DO - 10.1038/s41593-026-02287-z
UR - https://doi.org/10.1038/s41593-026-02287-z
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41593-026-02287-z",
"type": "article-journal",
"title": "Autism subtypes identified using cross-species functional connectivity analyses",
"container-title": "Nature neuroscience",
"author": [
{
"family": "Pagani",
"given": "Marco"
},
{
"family": "Zerbi",
"given": "Valerio"
},
{
"family": "Gini",
"given": "Silvia"
},
{
"family": "Alvino",
"given": "Filomena Grazia"
},
{
"family": "Banerjee",
"given": "Abhishek"
},
{
"family": "Barberis",
"given": "Andrea"
},
{
"family": "Basson",
"given": "M. Albert"
},
{
"family": "Bozzi",
"given": "Yuri"
},
{
"family": "Galbusera",
"given": "Alberto"
},
{
"family": "Ellegood",
"given": "Jacob"
},
{
"family": "Fagiolini",
"given": "Michela"
},
{
"family": "Lerch",
"given": "Jason P."
},
{
"family": "Matteoli",
"given": "Michela"
},
{
"family": "Montani",
"given": "Caterina"
},
{
"family": "Pozzi",
"given": "Davide"
},
{
"family": "Provenzano",
"given": "Giovanni"
},
{
"family": "Scattoni",
"given": "Maria Luisa"
},
{
"family": "Wenderoth",
"given": "Nicole"
},
{
"family": "Xu",
"given": "Ting"
},
{
"family": "Lombardo",
"given": "Michael V."
},
{
"family": "Milham",
"given": "Michael P."
},
{
"family": "Di Martino",
"given": "Adriana"
},
{
"family": "Gozzi",
"given": "Alessandro"
}
],
"container-title-short": "Nat Neurosci",
"volume": "29",
"issue": "6",
"page": "1476-1487",
"DOI": "10.1038/s41593-026-02287-z",
"PMID": "42141307",
"PMCID": "PMC13246445",
"ISSN": "1097-6256",
"publisher": "Nature Portfolio",
"URL": "https://doi.org/10.1038/s41593-026-02287-z",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
15
]
]
}
}

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

Similar papers

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:10.64898/2026.03.12.710517 [code]
Cortical excitability inversely modulates fMRI connectivity via low-frequency neuronal coupling
Journal: bioRxiv (preprint)
In common: AFNI, ANTs, FSL, 1 other tool, fMRI, 10 references, 3 authors
[2] doi:10.1016/j.bpsgos.2026.100787 [code]
Dynamic Functional Synchronization Profiles in Autism Differ by Spatial Scale and Along Hierarchical Cortical Gradients.
Journal: Biological psychiatry global open science
In common: AFNI, FSL, NiBabel, 2 other tools, autism, fMRI, 12 references, author Michael V Lombardo
[3] doi:10.1038/s41593-026-02205-3 [code]
Competitive interactions shape mammalian brain network dynamics and computation.
Journal: Nature neuroscience
In common: ANTs, FSL, NiBabel, 1 other tool, mouse, 1 reference, 2 authors
[4] 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: AFNI, ANTs, FSL, 3 other tools, fMRI, 5 references
[5] doi:10.1038/s44220-026-00656-y [code]
Multiscale heterogeneity of atypical functional connectivity in autism.
Journal: Nature. Mental health
In common: autism, fMRI, 10 references
[6] doi:10.1038/s42003-026-10059-5 [code]
Transcriptomic analysis in autism spectrum disorder suggests three molecular subtypes with distinct phenotypic profiles and functional pathways.
Journal: Communications biology
In common: ggpubr, ggplot2, autism, 6 references
[7] doi:10.1038/s41467-026-73668-y [code]
Convergent and divergent brain-cognition development in early adolescence.
Journal: Nature communications
In common: AFNI, ANTs, FSL, 4 other tools, fMRI, 2 references
[8] doi:10.1038/s41467-026-76011-7 [code]
Human cortex organizes dynamic co-fluctuations along the sensorimotor-association axis.
Journal: Nature communications
In common: AFNI, ANTs, FSL, 4 other tools, 2 references
[9] doi:10.1038/s41467-026-71568-9 [code]
Convergent and selective representations of pain, appetitive processes, aversive processes, and cognitive control in the insula.
Journal: Nature communications
In common: ANTs, FSL, NiBabel, 1 other tool, 4 references
[10] doi:10.1126/sciadv.adq6577 [code]
Autism-like phenotypes and increased NMDAR2D expression in mice with KDM5B histone lysine demethylase deficiency.
Journal: Science advances
In common: NumPy, autism, mouse, 2 references, author M. Albert Basson

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.