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Record W4408279656 · doi:10.1101/2025.03.04.641443

Assigning Targetable Molecular Pathways to Transdiagnostic Subgroups Across Autism and Related Neurodevelopmental Disorders

2025· preprint· en· W4408279656 on OpenAlexaff
Jacob Ellegood, Antoine Beauchamp, Yohan Yee, Justine Ziolkowski, Lily R. Qiu, Rand Askalan, Muhammad Ayub, Philipp Suetterlin, Alex P. A. Donovan, M. Albert Basson, Katherine M Quesnel, Nathalie G. Bérubé, Taeseon Woo, David Q. Beversdorf, Hans T. Björnsson, Randy Blakely, Jacqueline N. Crawley, Jennifer Crosbie, B. Orr, Graeme W. Davis, Emanuel DiCicco‐Bloom, Sean E. Egan, Kyle D. Fink, Sarah Asbury, Jonathan Lai, Kelly C. Rilett, Jane A. Foster, John B. Vincent, Paul W. Frankland, Stelios Georgiades, Olga Peñagarikano, Daniel H. Geschwind, Roman J. Giger, Sander Markx, Joseph A. Gogos, Christelle Golzio, Marco Pagani, Alessandro Gozzi, Laura Pacey, David R. Hampson, Tzyy‐Nan Huang, Tzu‐Li Yen, Yi‐Ping Hsueh, Alana Iaboni, Megha Amar, Lilia M. Iakoucheva, Jessica Jones, Elizabeth A. Kelly, Brigette Kieffer, Mihyun Bae, Hwajin Jung, Hyosang Kim, Haram Park, Junye Daniel Roh, Eunjoon Kim, Geneviève Konopka, Christine Laliberté, Julie L. Lefebvre, Kathie L. Eagleson, Pat Levitt, Aurea Martins Bach, Thomas J. Cunningham, Elizabeth Fisher, Karla L. Miller, Alea A. Mills, Alysson R. Muotri, Rob Nicolson, Leigh Spencer Noakes, Brian J. Nieman, César P. Canales, Alex S. Nord, Lauryl M. J. Nutter, Elaine Tam, Lucy R. Osborne, Amy E. Clipperton‐Allen, Damon T. Page, Brooke A. Babineau, Theo D. Palmer, Keqin Yan, David J. Picketts, Qiang‐qiang Xia, Craig M. Powell, Armin Raznahan, Diane M. Robins, Gavin Rumbaugh, Ameet S. Sengar, Michael W. Salter, Russell Schachar, Lia D’Abate, Clarissa A. Bradley, Stephen W. Scherer, Nycole A. Copping, S. P. Petkova, Jill L. Silverman, Karun K. Singh, Namshik Kim, Ki‐Jun Yoon, Guo‐li Ming, Hong-Jun Song, Shoshana Spring, Jin Nakatani, Nobuhiro Nakai, Jun Nomura, Toru Takumi, Margot J. Taylor, Peter T. Tsai, Matthew Bruce, Karen L. Jones, Judy Van de Water, Matthijs C. van Eede, Travis M. Kerr, Christopher L. Muller, Jeremy Veenstra- Vanderweele, Marlee M. Vandewouw, Rosanna Weksberg, Rachel Wevrick, Haim Belinson, Anthony Wynshaw‐Boris, Konstantinos S. Zarbalis, Brett Trost, Rogier B. Mars, M. Mallar Chakravarty, Azadeh Kushki, Evdokia Anagnostou, Jason P. Lerch

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsUniversity of AlbertaUniversity Health NetworkOttawa HospitalCentre for Addiction and Mental HealthUniversity of TorontoWestern UniversityKootenay Association for Science & TechnologyMcMaster UniversityQueen's UniversitySickKids FoundationMcGill UniversityDouglas CollegeHospital for Sick ChildrenHolland Bloorview Kids Rehabilitation Hospital
Fundersnot available
KeywordsNeuroanatomyAutismAutism spectrum disorderNeuroscienceGenetic heterogeneityBiologyCognitionWnt signaling pathwayNeuroimagingAxon guidanceDevelopmental cognitive neurosciencePhenotypePsychologyComputational biologyGeneticsGeneCognitive neuroscienceAxonDevelopmental psychology

Abstract

fetched live from OpenAlex

Significant genetic, behavioural and neuroanatomic heterogeneity is common in autism spectrum- and related- neurodevelopmental disorders (NDDs). This heterogeneity constrains the development of effective therapies for diverse patients in precision medicine paradigms. This has led to the search for subgroups of individuals having common etiologic factors/biology (e.g., genetic pathways), thus creating potential uniformity in prognosis and/or treatment response. Despite NDDs having a strong genetic component, only ~15-20% of individuals will present with a specific rare genetic variant considered clinically pathogenic, and therefore, subtyping efforts tend to focus on using clinical, cognitive, and/or brain imaging phenotypes to group individuals. Here we delineated mechanisms via mouse to human translational neuroscience. Using MRI derived structural neuroanatomy and a spatial transcriptomic comparison, we linked subgroups of 135 NDD relevant mouse models (3,515 individual mice) separately to two human databases, with 1,234 and 1,015 human individuals with NDDs, composed of autism, attention-deficit/hyperactivity disorder (ADHD), obsessive compulsive disorder (OCD), other related NDDs, and typically developing controls. Subgroups were significantly linked by consistent neuroanatomy across all three datasets, mouse and human, indicating that direct cross-species subgrouping and translation is consistent and reproducible. Ultimately, four specific neuroanatomical clusters were found and linked to precise molecular mechanisms: two showing a chromatin/transcription motif, with one of those showing specific links to G-protein coupled receptors (GPCR) and Notch signalling, and another two being mainly synaptic in origin, with one off those showing specific connections to axon guidance and Wnt signaling. Assigning molecular pathways, and thus genetic information, from the mouse to individual participants provides an insight into undetected and/or related genetic variants that could be working in combination or interacting with an environmental influence. Moreover, the subgroups found are transdiagnostic, including participants with autism, ADHD, and OCD, which indicates that NDDs as a whole can be subdivided into consistent neuroanatomical clusters with cohesive underlying biological mechanisms. This work allows us to bridge the gap between preclinical models and human disorders, linking previously idiopathic human patients to pertinent genetics, molecular mechanisms, and pathways.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.244
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

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