Characterizing Resting-State Brain Dynamics in Individuals with and without Autism Spectrum Disorder
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".