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Record W4200321044 · doi:10.1016/j.bpsgos.2021.12.004

A Prospective Evaluation of Infant Cerebellar-Cerebral Functional Connectivity in Relation to Behavioral Development in Autism Spectrum Disorder

2021· article· en· W4200321044 on OpenAlexafffund
Zoë Hawks, Alexandre A. Todorov, Natasha Marrus, Tomoyuki Nishino, Muhamed Talović, Mary Beth Nebel, Jessica B. Girault, Savannah Davis, Scott Marek, Benjamin A. Seitzman, Adam T. Eggebrecht, Jed T. Elison, Stephen R. Dager, Matthew W. Mosconi, Lawrence Tychsen, Abraham Z. Snyder, Kelly N. Botteron, Annette Estes, Alan C. Evans, Guido Gerig, Heather C. Hazlett, Robert C. McKinstry, Juhi Pandey, Robert T. Schultz, Martin Styner, Jason J. Wolff, Lonnie Zwaigenbaum, Lori Markson, Steven E. Petersen, John N. Constantino, Desirée A. White, Joseph Piven, John R. Pruett

Bibliographic record

VenueBiological Psychiatry Global Open Science · 2021
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsUniversity of AlbertaMontreal Neurological Institute and Hospital
FundersNational Center for Advancing Translational SciencesNational Center for Research ResourcesNational Institute of Neurological Disorders and StrokeInstitute of Clinical and Translational SciencesUniversity of North Carolina WilmingtonNational Institutes of HealthAutism Science FoundationNational Institute of Mental HealthUniversity of AlbertaUniversity of WashingtonJohns Hopkins UniversityUniversity of MinnesotaEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentWashington University School of Medicine in St. LouisUniversity of California
KeywordsAutism spectrum disorderFunctional magnetic resonance imagingCerebellumPsychologyNeuroscienceAutismFunctional connectivityNeuroimagingMagnetic resonance imagingDevelopmental psychologyMedicine

Abstract

fetched live from OpenAlex

Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder diagnosed based on social impairment, restricted interests, and repetitive behaviors. Contemporary theories posit that cerebellar pathology contributes causally to ASD by disrupting error-based learning (EBL) during infancy. The present study represents the first test of this theory in a prospective infant sample, with potential implications for ASD detection. Methods: 94, 68 male) were used to examine 6-month cerebellar functional connectivity magnetic resonance imaging in relation to later (12/24-month) ASD-associated behaviors and outcomes. Hypothesis-driven univariate analyses and machine learning-based predictive tests examined cerebellar-frontoparietal network (FPN; subserves error signaling in support of EBL) and cerebellar-default mode network (DMN; broadly implicated in ASD) connections. Cerebellar-FPN functional connectivity was used as a proxy for EBL, and cerebellar-DMN functional connectivity provided a comparative foil. Data-driven functional connectivity magnetic resonance imaging enrichment examined brain-wide behavioral associations, with post hoc tests of cerebellar connections. Results: Cerebellar-FPN and cerebellar-DMN connections did not demonstrate associations with ASD. Functional connectivity magnetic resonance imaging enrichment identified 6-month correlates of later ASD-associated behaviors in networks of a priori interest (FPN, DMN), as well as in cingulo-opercular (also implicated in error signaling) and medial visual networks. Post hoc tests did not suggest a role for cerebellar connections. Conclusions: We failed to identify cerebellar functional connectivity-based contributions to ASD. However, we observed prospective correlates of ASD-associated behaviors in networks that support EBL. Future studies may replicate and extend network-level positive results, and tests of the cerebellum may investigate brain-behavior associations at different developmental stages and/or using different neuroimaging modalities.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.097
GPT teacher head0.388
Teacher spread0.291 · 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 designObservational
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

Citations12
Published2021
Admission routes2
Has abstractyes

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