Cited by 37Functional Brain Connectivity StudiesAutism Spectrum Disorder ResearchGenetics and Neurodevelopmental Disorders

Functional connectivity subtypes associate robustly with ASD diagnosis

Open access
Urchs, Sebastian GW, Tam, Angela, Orban, Pierre, Moreau, Clara, Benhajali, Yassine, Nguyen, Hien Duy, Evans, Alan C, Bellec, Pierre·2022·eLife

Published version of a preprint

Abstract

Our understanding of the changes in functional brain organization in autism is hampered by the extensive heterogeneity that characterizes this neurodevelopmental disorder. Data driven clustering offers a straightforward way to decompose autism heterogeneity into subtypes of connectivity and promises an unbiased framework to investigate behavioral symptoms and causative genetic factors. Yet, the robustness and generalizability of functional connectivity subtypes is unknown. Here, we show that a simple hierarchical cluster analysis can robustly relate a given individual and brain network to a connectivity subtype, but that continuous assignments are more robust than discrete ones. We also found that functional connectivity subtypes are moderately associated with the clinical diagnosis of autism, and these associations generalize to independent replication data. We explored systematically 18 different brain networks as we expected them to associate with different behavioral profiles as well as different key regions. Contrary to this prediction, autism functional connectivity subtypes converged on a common topography across different networks, consistent with a compression of the primary gradient of functional brain organization, as previously reported in the literature. Our results support the use of data driven clustering as a reliable data dimensionality reduction technique, where any given dimension only associates moderately with clinical manifestations.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.7554/elife.56257

Cites

11 of 11 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Abnormalities in the corpus callosum and interhemispheric connectivity are linked to many neurodevelopmental disorders, including ASD and SCZ.

    “a wealth of evidence links abnormalities in the corpus callosum and interhemispheric connectivity (driven by callosal projection neurons) to many neurodevelopmental disorders, among them ASD and SCZ2,3,111,112.”

    From CYFIP1 governs the development of cortical axons by modulating calcium availability (2025) · cites this paper as [111]

    The Results section reports functional-connectivity alterations associated with ASD diagnosis. But the passages do not identify those alterations as interhemispheric or callosal, and the paper's Discussion (Limitations) says its analysis focused on ASD rather than other disorders. The source therefore does not support the corpus-callosum or schizophrenia parts of the claim. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 57

  1. Abbott AE Nair A Keown CL Datko M Jahedi A Fishman I Müller RA 2016 Patterns of atypical functional connectivity and behavioral links in autism differ between default, salience, and executive networks Cerebral Cortex 26 4034 4045 10.1093/cercor/bhv191 26351318 PMC5027998 · DOI
  2. Abraham A Pedregosa F Eickenberg M Gervais P Mueller A Kossaifi J Gramfort A Thirion B Varoquaux G 2014 Machine learning for neuroimaging with scikit-learn Frontiers in Neuroinformatics 8 14 10.3389/fninf.2014.00014 24600388 PMC3930868 · DOI
  3. Baio J Wiggins L Christensen DL Maenner MJ Daniels J Warren Z Kurzius-Spencer M Zahorodny W Robinson Rosenberg C White T Durkin MS Imm P Nikolaou L Yeargin-Allsopp M Lee LC Harrington R Lopez M Fitzgerald RT Hewitt A Pettygrove S Constantino JN Vehorn A Shenouda J Hall-Lande J Van Naarden Braun K Dowling NF 2018 Prevalence of autism spectrum disorder among children aged 8 years-autism and developmental disabilities monitoring network, 11 sites, United States, 2014 Morbidity and Mortality Weekly Report 67 1 23 10.15585/mmwr.ss6706a1 29701730 PMC5919599 · DOI · Paper page
  4. Bedford SA Park MTM Devenyi GA Tullo S Germann J Patel R Anagnostou E Baron-Cohen S Bullmore ET Chura LR Craig MC Ecker C Floris DL Holt RJ Lenroot R Lerch JP Lombardo MV Murphy DGM Raznahan A Ruigrok ANV Smith E Spencer MD Suckling J Taylor MJ Thurm A Lai MC Chakravarty MM 2020 Large-Scale analyses of the relationship between sex, age and intelligence quotient heterogeneity and cortical morphometry in autism spectrum disorder Molecular Psychiatry 25 614 628 10.1038/s41380-019-0420-6 31028290 · DOI
  5. Beglinger LJ Smith TH 2001 A review of subtyping in autism and proposed dimensional classification model Journal of Autism and Developmental Disorders 31 411 422 10.1023/a:1010616719877 11569587 · DOI
  6. Bellec P Carbonell FM Perlbarg V Lepage C Lyttelton O Fonov V Janke A Tohka J Evans AC 2011 A neuroimaging analysis kit for matlab and octave Proceedings of the 17th International Conference on Functional Mapping of the Human Brain 2735 2746
  7. Benhajali Y Badhwar A Spiers H Urchs S Armoza J Ong T Pérusse D Bellec P 2020 A standardized protocol for efficient and reliable quality control of brain registration in functional MRI studies Frontiers in Neuroinformatics 14 7 10.3389/fninf.2020.00007 32180712 PMC7059806 · DOI
  8. Benjamini Y Hochberg Y 1995 Controlling the false discovery rate: a practical and powerful approach to multiple testing Journal of the Royal Statistical Society 57 289 300 10.1111/j.2517-6161.1995.tb02031.x · DOI · Paper page
  9. Castellanos FX Di Martino A Craddock RC Mehta AD Milham MP 2013 Clinical applications of the functional connectome NeuroImage 80 527 540 10.1016/j.neuroimage.2013.04.083 23631991 PMC3809093 · DOI · Paper page
  10. Cerliani L Mennes M Thomas RM Di Martino A Thioux M Keysers C 2015 Increased functional connectivity between subcortical and cortical resting-state networks in autism spectrum disorder JAMA Psychiatry 72 767 777 10.1001/jamapsychiatry.2015.0101 26061743 PMC5008437 · DOI · Paper page
  11. Cicchetti DV 1994 Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology Psychological Assessment 6 284 290 10.1037/1040-3590.6.4.284 · DOI · Paper page
  12. Dadi K Rahim M Abraham A Chyzhyk D Milham M Thirion B Varoquaux G 2019 Benchmarking functional connectome-based predictive models for resting-state fMRI NeuroImage 192 115 134 10.1016/j.neuroimage.2019.02.062 30836146 · DOI
  13. Dice LR 1945 Measures of the amount of ecologic association between species Ecology 26 297 302 10.2307/1932409 · DOI
  14. Dickie EW Ameis SH Shahab S Calarco N Smith DE Miranda D Viviano JD Voineskos AN 2018 Personalized intrinsic network topography mapping and functional connectivity deficits in autism spectrum disorder Biological Psychiatry 84 278 286 10.1016/j.biopsych.2018.02.1174 29703592 PMC6076333 · DOI
  15. Di Martino A. Zuo XN Kelly C Grzadzinski R Mennes M Schvarcz A Rodman J Lord C Castellanos FX Milham MP 2013 Shared and distinct intrinsic functional network centrality in autism and attention-deficit/hyperactivity disorder Biological Psychiatry 74 623 632 10.1016/j.biopsych.2013.02.011 23541632 PMC4508007 · DOI · Paper page
  16. Di Martino A O’Connor D Chen B Alaerts K Anderson JS Assaf M Balsters JH Baxter L Beggiato A Bernaerts S Blanken LME Bookheimer SY Braden BB Byrge L Castellanos FX Dapretto M Delorme R Fair DA Fishman I Fitzgerald J Gallagher L Keehn RJJ Kennedy DP Lainhart JE Luna B Mostofsky SH Müller RA Nebel MB Nigg JT O’Hearn K Solomon M Toro R Vaidya CJ Wenderoth N White T Craddock RC Lord C Leventhal B Milham MP 2017 Enhancing studies of the connectome in autism using the autism brain imaging data exchange II Scientific Data 4 170010 10.1038/sdata.2017.10 28291247 PMC5349246 · DOI
  17. Dinga R Schmaal L Penninx B van Tol MJ Veltman DJ van Velzen L Mennes M van der Wee NJA Marquand AF 2019 Evaluating the evidence for biotypes of depression: methodological replication and extension of NeuroImage. Clinical 22 101796 10.1016/j.nicl.2019.101796 30935858 PMC6543446 · DOI · Paper page
  18. Eaves LC Ho HH Eaves DM 1994 Subtypes of autism by cluster analysis Journal of Autism and Developmental Disorders 24 3 22 10.1007/BF02172209 8188572 · DOI
  19. Elliott ML Romer A Knodt AR Hariri AR 2018 A connectome-wide functional signature of transdiagnostic risk for mental illness Biological Psychiatry 84 452 459 10.1016/j.biopsych.2018.03.012 29779670 PMC6119080 · DOI
  20. Evans AC Kamber M Collins DL MacDonald D 1994 An MRI-based probabilistic atlas of neuroanatomy Shorvon SD Fish DR Andermann F Bydder GM Stefan H Magnetic Resonance Scanning and Epilepsy Boston, MA Springer 263 274 10.1007/978-1-4615-2546-2_48 · DOI
  21. Giove F Gili T Iacovella V Macaluso E Maraviglia B 2009 Images-based suppression of unwanted global signals in resting-state functional connectivity studies Magnetic Resonance Imaging 27 1058 1064 10.1016/j.mri.2009.06.004 19695814 · DOI
  22. Gordon EM Laumann TO Gilmore AW Newbold DJ Greene DJ Berg JJ Ortega M Hoyt-Drazen C Gratton C Sun H Hampton JM Coalson RS Nguyen AL McDermott KB Shimony JS Snyder AZ Schlaggar BL Petersen SE Nelson SM Dosenbach NUF 2017 Precision functional mapping of individual human brains Neuron 95 791 807 10.1016/j.neuron.2017.07.011 28757305 PMC5576360 · DOI · Paper page
  23. Gotham K Risi S Pickles A Lord C 2007 The autism diagnostic observation schedule: revised algorithms for improved diagnostic validity Journal of Autism and Developmental Disorders 37 613 627 10.1007/s10803-006-0280-1 17180459 · DOI · Paper page
  24. Hahamy A Behrmann M Malach R 2015 The idiosyncratic brain: distortion of spontaneous connectivity patterns in autism spectrum disorder Nature Neuroscience 18 302 309 10.1038/nn.3919 25599222 · DOI · Paper page
  25. Holiga Š Hipp JF Chatham CH Garces P Spooren W D’Ardhuy XL Bertolino A Bouquet C Buitelaar JK Bours C Rausch A Oldehinkel M Bouvard M Amestoy A Caralp M Gueguen S Ly-Le Moal M Houenou J Beckmann CF Loth E Murphy D Charman T Tillmann J Laidi C Delorme R Beggiato A Gaman A Scheid I Leboyer M d’Albis MA Sevigny J Czech C Bolognani F Honey GD Dukart J 2019 Patients with autism spectrum disorders display reproducible functional connectivity alterations Science Translational Medicine 11 481 10.1126/scitranslmed.aat9223 30814340 · DOI
  26. Hong SJ Valk SL Di Martino A Milham MP Bernhardt BC 2018 Multidimensional neuroanatomical subtyping of autism spectrum disorder Cerebral Cortex 28 3578 3588 10.1093/cercor/bhx229 28968847 PMC7190887 · DOI
  27. Hong SJ Vogelstein JT Gozzi A Bernhardt BC Yeo BTT Milham M Martino A 2019a Towards Neurosubtypes in Autism PsyArXiv 10.31234/osf.io/8az69 32553193 · DOI
  28. Hong SJ Vos de Wael R Bethlehem RAI Lariviere S Paquola C Valk SL Milham MP Di Martino A Margulies DS Smallwood J Bernhardt BC 2019b Atypical functional connectome hierarchy in autism Nature Communications 10 1022 10.1038/s41467-019-08944-1 30833582 PMC6399265 · DOI
  29. Hrdlicka M Dudova I Beranova I Lisy J Belsan T Neuwirth J Komarek V Faladova L Havlovicova M Sedlacek Z Blatny M Urbanek T 2005 Subtypes of autism by cluster analysis based on structural MRI data European Child & Adolescent Psychiatry 14 138 144 10.1007/s00787-005-0453-z 15959659 · DOI
  30. Jacob S Wolff JJ Steinbach MS Doyle CB Kumar V Elison JT 2019 Neurodevelopmental heterogeneity and computational approaches for understanding autism Translational Psychiatry 9 63 10.1038/s41398-019-0390-0 30718453 PMC6362076 · DOI
  31. Kernbach JM Satterthwaite TD Bassett DS Smallwood J Margulies D Krall S Shaw P Varoquaux G Thirion B Konrad K Bzdok D 2018 Shared endo-phenotypes of default mode dsfunction in attention deficit/hyperactivity disorder and autism spectrum disorder Translational Psychiatry 8 133 10.1038/s41398-018-0179-6 30018328 PMC6050263 · DOI · Paper page
  32. Khundrakpam BS Lewis JD Kostopoulos P Carbonell F Evans AC 2017 Cortical thickness abnormalities in autism spectrum disorders through late childhood, adolescence, and adulthood: a large-scale MRI study Cerebral Cortex 27 1721 1731 10.1093/cercor/bhx038 28334080 · DOI
  33. Kurtzer GM Sochat V Bauer MW 2017 Singularity: scientific containers for mobility of compute PLOS ONE 12 e0177459 10.1371/journal.pone.0177459 28494014 PMC5426675 · DOI · Paper page
  34. Lai MC Lombardo MV Baron-Cohen S 2014 Autism Lancet 383 896 910 10.1016/S0140-6736(13)61539-1 24074734 · DOI · Paper page
  35. Lake EMR Finn ES Noble SM Vanderwal T Shen X Rosenberg MD Spann MN Chun MM Scheinost D Constable RT 2019 The functional brain organization of an individual allows prediction of measures of social abilities transdiagnostically in autism and attention-deficit/hyperactivity disorder Biological Psychiatry 86 315 326 10.1016/j.biopsych.2019.02.019 31010580 PMC7311928 · DOI
  36. Lord C. Rutter M Le Couteur A 1994 Autism diagnostic interview-revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders Journal of Autism and Developmental Disorders 24 659 685 10.1007/BF02172145 7814313 · DOI · Paper page
  37. Lord C Risi S Lambrecht L Cook EH Leventhal BL DiLavore PC Pickles A Rutter M 2000 The autism diagnostic observation schedule-generic: a standard measure of social and communication deficits associated with the spectrum of autism Journal of Autism and Developmental Disorders 30 205 223 11055457
  38. Lund TE Madsen KH Sidaros K Luo WL Nichols TE 2006 Non-White noise in fMRI: does modelling have an impact? NeuroImage 29 54 66 10.1016/j.neuroimage.2005.07.005 16099175 · DOI
  39. Moreau CA Urchs SGW Kuldeep K Orban P Schramm C Dumas G Labbe A Huguet G Douard E Quirion PO Lin A Kushan L Grot S Luck D Mendrek A Potvin S Stip E Bourgeron T Evans AC Bearden CE Bellec P Jacquemont S 2020 Mutations associated with neuropsychiatric conditions delineate functional brain connectivity dimensions contributing to autism and schizophrenia Nature Communications 11 5272 10.1038/s41467-020-18997-2 33077750 PMC7573583 · DOI
  40. Müllner D 2011 Modern Hierarchical, Agglomerative Clustering Algorithms arXiv https://arxiv.org/abs/1109.2378 · arXiv
  41. Noble S Scheinost D Constable RT 2019 A decade of test-retest reliability of functional connectivity: a systematic review and meta-analysis NeuroImage 203 116157 10.1016/j.neuroimage.2019.116157 31494250 PMC6907736 · DOI
  42. Nunes AS Peatfield N Vakorin V Doesburg SM 2019 Idiosyncratic organization of cortical networks in autism spectrum disorder NeuroImage 190 182 190 10.1016/j.neuroimage.2018.01.022 29355768 · DOI
  43. Oldehinkel M Mennes M Marquand A Charman T Tillmann J Ecker C Dell’Acqua F Brandeis D Banaschewski T Baumeister S Moessnang C Baron-Cohen S Holt R Bölte S Durston S Kundu P Lombardo MV Spooren W Loth E Murphy DGM Beckmann CF Buitelaar JK EU-AIMS LEAP group 2019 Altered connectivity between cerebellum, visual, and sensory-motor networks in autism spectrum disorder: results from the EU-AIMS longitudinal European autism project Biological Psychiatry. Cognitive Neuroscience and Neuroimaging 4 260 270 10.1016/j.bpsc.2018.11.010 30711508 · DOI
  44. Park MTM Raznahan A Shaw P Gogtay N Lerch JP Chakravarty MM 2018 Neuroanatomical phenotypes in mental illness: identifying convergent and divergent cortical phenotypes across autism, ADHD and schizophrenia Journal of Psychiatry & Neuroscience 43 201 212 29688876 PMC5915241
  45. Power JD Barnes KA Snyder AZ Schlaggar BL Petersen SE 2012 Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion NeuroImage 59 2142 2154 10.1016/j.neuroimage.2011.10.018 22019881 PMC3254728 · DOI · Paper page
  46. Rosenbaum PR Rubin DB 1985 Constructing a control group using multivariate matched sampling methods that incorporate the propensity score The American Statistician 39 33 38 10.1080/00031305.1985.10479383 · DOI
  47. Rudie JD Shehzad Z Hernandez LM Colich NL Bookheimer SY Iacoboni M Dapretto M 2012 Reduced functional integration and segregation of distributed neural systems underlying social and emotional information processing in autism spectrum disorders Cerebral Cortex 22 1025 1037 10.1093/cercor/bhr171 21784971 PMC3328339 · DOI
  48. Sathyanesan A Zhou J Scafidi J Heck DH Sillitoe RV Gallo V 2019 Emerging connections between cerebellar development, behaviour and complex brain disorders Nature Reviews. Neuroscience 20 298 313 10.1038/s41583-019-0152-2 30923348 PMC7236620 · DOI
  49. Sha Z Wager TD Mechelli A He Y 2019 Common dysfunction of large-scale neurocognitive networks across psychiatric disorders Biological Psychiatry 85 379 388 10.1016/j.biopsych.2018.11.011 30612699 · DOI · Paper page
  50. Shehzad Z Kelly AMC Reiss PT Gee DG Gotimer K Uddin LQ Lee SH Margulies DS Roy AK Biswal BB Petkova E Castellanos FX Milham MP 2009 The resting brain: unconstrained yet reliable Cerebral Cortex 19 2209 2229 10.1093/cercor/bhn256 19221144 PMC3896030 · DOI
  51. Tang S Sun N Floris DL Zhang X Di Martino A Yeo BTT 2020 Reconciling dimensional and categorical models of autism heterogeneity: a brain connectomics and behavioral study Biological Psychiatry 87 1071 1082 10.1016/j.biopsych.2019.11.009 31955916 · DOI
  52. Thompson WH Wright J Bissett PG Poldrack RA 2020 Dataset decay and the problem of sequential analyses on open datasets eLife 9 e53498 10.7554/eLife.53498 32425159 PMC7237204 · DOI
  53. Urchs S Armoza J Benhajali Y St-Aubin J Orban P Bellec P 2017 Mist: a multi-resolution parcellation of functional brain networks MNI Open Research 1 3 10.12688/mniopenres.12767.1 · DOI
  54. Urchs S Armoza J Benhajali Y Bellec P 2018 dashqc-fmri - an interactive web dashboard for manual quality control Sixth Biennial Conference on Resting State and Brain Connectivity
  55. Volkmar FR McPartland JC 2014 From kanner to DSM-5: autism as an evolving diagnostic concept Annual Review of Clinical Psychology 10 193 212 10.1146/annurev-clinpsy-032813-153710 24329180 · DOI
  56. Vul E Pashler H 2012 Voodoo and circularity errors NeuroImage 62 945 948 10.1016/j.neuroimage.2012.01.027 22270348 · DOI
  57. Zuo XN Anderson JS Bellec P Birn RM Biswal BB Blautzik J Breitner JCS Buckner RL Calhoun VD Castellanos FX Chen A Chen B Chen J Chen X Colcombe SJ Courtney W Craddock RC Di Martino A Dong HM Fu X Gong Q Gorgolewski KJ Han Y He Y He Y Ho E Holmes A Hou XH Huckins J Jiang T Jiang Y Kelley W Kelly C King M LaConte SM Lainhart JE Lei X Li HJ Li K Li K Lin Q Liu D Liu J Liu X Liu Y Lu G Lu J Luna B Luo J Lurie D Mao Y Margulies DS Mayer AR Meindl T Meyerand ME Nan W Nielsen JA O’Connor D Paulsen D Prabhakaran V Qi Z Qiu J Shao C Shehzad Z Tang W Villringer A Wang H Wang K Wei D Wei GX Weng XC Wu X Xu T Yang N Yang Z Zang YF Zhang L Zhang Q Zhang Z Zhang Z Zhao K Zhen Z Zhou Y Zhu XT Milham MP 2014 An open science resource for establishing reliability and reproducibility in functional connectomics Scientific Data 1 140049 10.1038/sdata.2014.49 25977800 PMC4421932 · DOI

Cited by

Sorted by citations

Authors
Urchs, S. G., Tam, A., Orban, P., Moreau, C., Benhajali, Y., Nguyen, H. D., Evans, A. C., & Bellec, P.
Affiliations
  • Montreal Neurological Institute and Hospital, McGill University
  • Institut Universitaire de Gériatrie de Montréal (CA)
  • Institut universitaire en santé mentale de Montréal (CA)
  • Centre National de la Recherche Scientifique (FR)
  • The University of Queensland (AU)
  • Montreal Neurological Institute and Hospital (CA)
Corresponding author
Bellec, Pierre
Venue
eLife
Published
2022-11-29
Volume
11
ISSN
2050-084X (ISSN-L)
OpenAlex
W4300716292
Cited by
37
References
11
Citations by year
2026: 8 · 2025: 12 · 2024: 10 · 2023: 5 · 2020: 2
FWCI
3.47
Type
journal article
Language
en
Open access
Locations
  • eLife (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Australian Research Council (DE170101134, DP180101192)
  • Brain Canada Multi Investigator Research Initiative (MIRI) and Azrieli Foundation (3388)
  • Canadian Open Neuroscience Plastudenttform (Student Scholar Award)
  • Courtouis Neuromod Foundation (Graduate Student Funding)
  • Fonds de Recherche du Québec - Santé (Salary Award)
  • Centre de recherche de l'Institut universitaire de geriatrie de Montreal (Graduate Student Funding)
  • Canadian Consortium on Neurodegeneration in Aging (Graduate Student Funding)
  • Healthy Brains, Healthy Lives (Graduate Student Funding)
  • Brain Canada Multi investigator research initiative (Graduate Student Funding)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Neurodevelopmental Disorders
Keywords
autism spectrum disorder, functional connectivity, data-driven clustering, hierarchical clustering, brain network organization, autism heterogeneity, diagnosis, dimensionality reduction
MeSH terms
Autistic Disorder, Brain, Humans, Research Personnel, Cluster Analysis, Neurodevelopmental Disorders

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review