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Record W2104897820 · doi:10.1109/iembs.2008.4650527

Is undiagnosed synaesthesia a confounding factor in the interpretation of MRI images?

2008· review· en· W2104897820 on OpenAlexaff
Nourhan Zayed, Brad Goodyear, Michael R. Smith

Bibliographic record

Venuenot available
Typereview
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsFunctional magnetic resonance imagingConfoundingSensationSensory systemStimulus modalityPsychologyMagnetic resonance imagingSensory stimulation therapyModality (human–computer interaction)AudiologyNeuroimagingNeuroscienceCognitionBrain activity and meditationCognitive psychologyMedicineElectroencephalographyComputer scienceArtificial intelligenceRadiologyPathology

Abstract

fetched live from OpenAlex

Synaesthesia is a condition in which stimulation of a sensory modality evokes another sensation in the same or a different sensory modality. Currently, synaesthesia is considered a neurological condition that involves crosstalk between brain regions. Given the numerous anatomical and functional connections within the brain, it is possible that undiagnosed synaesthesia may influence the results of functional magnetic resonance imaging (fMRI) studies or even structural MRI. In this paper, we investigated the currently available literature to determine if and how the sensations invoked by synaesthesia could impact fMRI and structural MRI. Our investigation found that synaesthesia can have a profound impact on fMRI studies of sensory and cognitive functions, and there is evidence to suggest structural connections in the brain are also altered. Given the low prevalence of synaesthesia, the likelihood of synaesthesia being a confounding factor in fMRI studies of patient groups is small; however, determining the presence of synaesthesia is important for investigating individual patients.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.001

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 head0.134
GPT teacher head0.450
Teacher spread0.315 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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

Citations2
Published2008
Admission routes1
Has abstractyes

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