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Record W2911932709

Resting Network Synchronization in Atypical Development

2017· dissertation· en· W2911932709 on OpenAlexafffund
Annette Ye

Bibliographic record

VenueTSpace (University of Toronto) · 2017
Typedissertation
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsUniversity of Toronto
FundersUniversity of TorontoHospital for Sick ChildrenHSBC Bank USA
KeywordsSynchronization (alternating current)Computer scienceNeurosciencePsychologyComputer network
DOInot available

Abstract

fetched live from OpenAlex

The development of the human brain, which extends well beyond adolescence, is characterized by maturational processes underlying significant improvements in cognition and perception. Neural synchronization represents a versatile mechanism for flexible communication within and between cortical areas, and is thought to be disrupted in atypical brain development. To date, most of our knowledge concerning human brain maturation has come from studies using functional or structural magnetic resonance imaging. While these techniques have been crucial to our understanding of typical and atypical brain development, work investigating synchronized spontaneous brain activity using magnetoencephalography (MEG) may provide additional insight into the spatiotemporal organization of brain networks relevant to cognition. The present work examines the synchronization of spontaneous MEG activity in atypical neurodevelopment, specifically, in autism spectrum disorder (ASD) and following very premature birth. In both these populations, neural synchronization was disrupted across a range of frequencies. These changes in neural communication affected functional integration and segregation within large-scale brain networks related to executive function in very preterm children and social cognition in ASD. Children born very preterm showed global reductions in neural synchronization, resulting in disconnected networks implicating executive function abilities. In ASD, a different pattern of disruption was detected, such that the brain was hypo-connected in posterior regions but over-connected in frontal and subcortical areas. With increasing age, over-connectivity was observed at faster frequencies, implicating intrinsic segregation and integration of large-scale functional connectivity in the brain. These disruptions in functional connectivity vary by frequency throughout childhood to adulthood, and affect different brain regions in addition to large-scale resting state networks (RSNs). These findings highlight the importance of understanding functional connectivity using a neurophysiological perspective, as the interplay of oscillatory activity throughout the brain changes over development and implicates functional network organization within the connectome. This work opens dialogue between cellular and cognitive neuroscience which is critical to the development of detection and therapeutic interventions in populations with atypical development, where altered neuromagnetic connectivity in multiple frequencies may be linked to mechanistic models of pathology and injury affecting brain development and behavioural outcomes.

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 score0.005

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 communication0.0000.000
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 head0.032
GPT teacher head0.267
Teacher spread0.236 · 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
Published2017
Admission routes2
Has abstractyes

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