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Record W3143018635

Characterizing Resting-State Brain Dynamics in Individuals with and without Autism Spectrum Disorder

2019· dissertation· en· W3143018635 on OpenAlexfundno aff
Amanda Easson

Bibliographic record

VenueTSpace · 2019
Typedissertation
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsAutism spectrum disorderResting state fMRIFunctional connectivityPsychologyDynamics (music)NeuroscienceSpectrum (functional analysis)AutismAudiologyMedicineDevelopmental psychologyPhysicsQuantum mechanics
DOInot available

Abstract

fetched live from OpenAlex

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is characterized by impairments in social communication as well as restricted, repetitive and stereotyped behaviours. ASD is a highly heterogeneous disorder, with a broad range of the types and severities of symptoms that can be displayed. It has been proposed that ASD is characterized by abnormal functional connectivity (FC) between brain regions, which can be defined as the correlations of functional magnetic resonance imaging (fMRI) time series between pairs of regions of interest. However, studies of FC in ASD have presented mixed results. Further, it has been suggested that there is a complex developmental trajectory of FC in ASD. Inconsistent results across studies may, in part, be related to differences in fMRI data processing strategies, as well as heterogeneity of sample characteristics. Other aspects of brain dynamics, including variability and complexity of blood oxygen-level dependent (BOLD) time series, are not well characterized in ASD. The goal of this dissertation is to analyze resting-state brain dynamics in ASD, and to address the inconsistencies in previous studies of resting-state fMRI in ASD. Study 1 involves the characterization of FC-based subtypes of ASD and typically developing (TD) participants to elucidate unique relationships between FC and behaviour. In Study 2, different fMRI data processing strategies are examined to determine the effects of these processing choices on group differences in FC in children and adolescents with and without ASD. Study 3 involves examining BOLD signal variability and complexity, and relating these metrics to structural connectivity, age, and behavioural severity. Overall, these studies reveal the importance of considering subtypes of ASD and TD individuals, the effects of preprocessing strategies, and relationships between brain dynamics and brain structure, age, and behavioural severity when analyzing resting-state brain dynamics in those with and without ASD.

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)0.002
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.017
GPT teacher head0.331
Teacher spread0.314 · 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
Published2019
Admission routes1
Has abstractyes

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