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Record W7132927895

The Structural and Functional Connectivity of the Cerebellum in Autism Spectrum Disorder

2023· dissertation· W7132927895 on OpenAlexaff
Felipe Morgado

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCerebellumAutism spectrum disorderFunctional connectivityFunctional magnetic resonance imagingContext (archaeology)Neurodevelopmental disorderAutismNeurophysiology
DOInot available

Abstract

fetched live from OpenAlex

The cerebellum coordinates motor, cognitive, and affective function in the brain, largely via its connectivity to the cerebral cortex. Perinatal injury to the cerebellum and mutations to genes integral for cerebellar development are strongly associated with Autism Spectrum Disorder (ASD), a highly prevalent neurodevelopmental disorder with an unclear etiology and heterogeneous behaviour presentation. Disrupted cerebellar development results in changes to its coordinated growth and neurophysiological activity with other brain regions, heretofore referred to as structural and functional connectivity. Recurrent patterns in cerebellar-cerebral connectivity in the context of atypical neurodevelopment have been observed. However, much is still unknown regarding the extent to which these patterns correspond to particular ASD-related behaviours and genetic mutations. In chapter 2, behaviour-correlated functional connectivity profiles - measured using resting-state functional magnetic resonance imaging (MRI) - were identified using a neurodevelopmental disorder (NDD) imaging and behaviour dataset. The findings were then assessed for replication using an independent dataset from a second consortium. Two functional connectivity components were observed in the original and replication dataset: a first component maximally correlated to obsessive-compulsive behaviour and a second component characterized by social communication deficit contrasted against attention deficit. Statistically stable features of these components mainly included nodes pertaining to the attentional, central executive, and default mode networks. Having identified functional connectivity profiles correlated to ASD-related behaviours, structural connectivity profiles were then characterized and compared to those of clustered mouse models, where each model bore a genetic mutation associated with ASD. Structural connectivity was defined as correlations between regional brain volumes measured via MRI. Two mouse model clusters were significantly similar to human clusters following False Discovery Rate correction: the cluster with elevated protein product expression in the cerebellum versus the cerebrum, and the cluster with genes implicated mTOR pathway biomolecule synthesis. Structural correlations were most similar between mice and humans for correlations between cerebellar Crus II or vermal lobule VI and all cerebral regions. Similar to the findings of chapter 2, large structural correlation differences mainly involved regions pertaining to the attentional, central executive, and default-mode networks.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.025
GPT teacher head0.294
Teacher spread0.269 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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