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A longitudinal model for functional connectivity networks using resting-state fMRI

2018· article· en· W2952741788 on OpenAlexafffund
Brian Hart, Ivor Cribben, Mark Fiecas

Bibliographic record

VenueNeuroImage · 2018
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsUniversity of Alberta
FundersNational Institute on AgingNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchNational Institutes of HealthGenentechTakeda Pharmaceutical CompanyIXICOH. Lundbeck A/SServierEisaiAlzheimer's AssociationFujirebio USDoD Alzheimer's Disease Neuroimaging InitiativeNatural Sciences and Engineering Research Council of CanadaPfizerBioClinicaBiogenNovartis Pharmaceuticals CorporationU.S. Department of DefenseEli Lilly and CompanyBristol-Myers SquibbF. Hoffmann-La RocheRocheMerckAlzheimer's Drug Discovery FoundationJanssen Alzheimer Immunotherapy Research And DevelopmentAbbVieGE HealthcareAlzheimer's Disease Neuroimaging InitiativeMeso Scale Diagnostics
KeywordsFunctional magnetic resonance imagingNeuroimagingPopulationResting state fMRIAutocorrelationAlzheimer's Disease Neuroimaging InitiativeComputer scienceSet (abstract data type)Statistical parametric mappingRepeated measures designArtificial intelligencePsychologyCognitionMagnetic resonance imagingNeuroscienceStatisticsMathematicsMedicineCognitive impairment

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.005
metaresearch head score (Gemma)0.008
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: none
Teacher disagreement score0.035
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0040.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.003

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 head0.150
GPT teacher head0.317
Teacher spread0.167 · 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

Citations26
Published2018
Admission routes2
Has abstractno

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