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Record W2955094487 · doi:10.1080/13554794.2019.1631853

A MEG study of the neural substrates of semantic processing in semantic variant primary progressive aphasia

2019· article· en· W2955094487 on OpenAlexafffund
Jessica Pineault, Pierre Jolicœur, Stéphan Grimault, Jacinthe Lacombe, Simona M. Brambati, Nathalie Bier, Céline Chayer, Sven Joubert

Bibliographic record

VenueNeurocase · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsHôpital Maisonneuve-RosemontUniversité de MontréalInstitut Universitaire de Gériatrie de Montréal
FundersCanadian Institutes of Health ResearchAlzheimer Society
KeywordsPrimary progressive aphasiaMagnetoencephalographySemantic memoryCategorizationPsychologyAphasiaTemporal lobeNeuroscienceCognitive psychologyAudiologyMedicineComputer scienceCognitionArtificial intelligencePathologyElectroencephalographyDementiaFrontotemporal dementiaEpilepsy

Abstract

fetched live from OpenAlex

Despite a well-documented pattern of semantic impairment, the patterns of brain activation during semantic processing in semantic variant primary progressive aphasia (svPPA) still remain poorly understood. In the current study, one svPPA patient (EC) and six elderly controls carried out a general-level semantic categorization task while their brain activity was recorded using magnetoencephalography (MEG). Despite similar behavioral performance, EC showed hyperactivation of the left inferior temporal gyrus (ITG) and right anterior temporal lobe (ATL) relative to controls. This suggests that periatrophic regions within the ATL region may support preserved semantic abilities in svPPA.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.021
GPT teacher head0.276
Teacher spread0.255 · 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

Citations6
Published2019
Admission routes2
Has abstractyes

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