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Record W7061617005

Reorganization of auditory cortex in deaf people: functional, behavioural, and anatomical correlates

2015· dissertation· en· W7061617005 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2015
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsAuditory cortexFunctional magnetic resonance imagingVisual cortexFunctional connectivityNeuroplasticityCortex (anatomy)Functional imagingTemporal cortexCognitive neuroscience of musicBrain activity and meditation
DOInot available

Abstract

fetched live from OpenAlex

After early deafness, the brain can reorganize so that the sensory-deprived auditory cortex processes visual information. This cross-modal activity has been demonstrated numerous times in functional brain imaging research, but we still know little about the underlying mechanisms, the factors that constrain it, and its behavioural consequences. In this thesis, we address these unknowns in three studies that examine the functional, behavioural, and anatomical correlates of cross-modal reorganization in early-deaf adults. In study one, we used functional magnetic resonance imaging to identify cross-modal activity in the posterior superior temporal cortex of deaf people. With functional connectivity analysis, we explored the cortical network of this reorganized area and found enhanced interactions with primary visual cortex. Here, both the amplitude of the cross-modal activity and the strength of the audio-visual functional connectivity correlated with residual hearing, as measured by duration of hearing aid use: deaf people with more residual hearing showed less cross-modal reorganization. This study gives insight into the mechanism of cross-modal plasticity after deafness, revealing that it involves a network-level change and that it varies with the degree of auditory deprivation over the lifetime. In study two, we hypothesized that auditory deprivation causes compensatory changes to vision, and predicted that this would manifest as an improved ability in deaf people to detect visual motion. We designed a visual psychophysical test, which determined that people with early and profound deafness have lower motion detection thresholds than hearing controls. In study three, we followed up on the effect of study two, with the hypothesis that enhanced vision in deaf people is supported by cross-modal reorganization of auditory cortex. We measured cortical thickness with magnetic resonance imaging in early and profoundly deaf people, and found a correlation between visual motion detection thresholds and the thickness of the right posterior superior temporal cortex. This correlation implicates this area in compensatory vision and indicates an anatomical correlate – increased cortical thickness – of cross-modal plasticity. Based on the findings of these three studies, we conclude that cross-modal activity and increased cortical thickness in the right superior temporal cortex of deaf people reflect increased visual input to this area, and that these adaptations support enhanced visual motion detection, perhaps for sensory reorienting. We suggest that this plasticity may occur as a result of decreased activity-driven pruning after early auditory deprivation, and that it is interrupted when individuals exploit their residual hearing. This work illuminates the principles that govern cortical plasticity, and informs us on the organization and function of the right posterior superior temporal cortex.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.014
GPT teacher head0.247
Teacher spread0.233 · 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
Published2015
Admission routes1
Has abstractyes

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