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Record W2536924620 · doi:10.1016/j.jalz.2016.06.160

IC‐P‐130: Morphometric Cortical Normative Data Throughout Aging

2016· article· en· W2536924620 on OpenAlexaff
Olivier Potvin, Abderazzak Mouiha, Louis Dieumegarde, Simon Duchesne

Bibliographic record

VenueAlzheimer s & Dementia · 2016
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsUniversité LavalInstitut Universitaire en Santé Mentale de Québec
Fundersnot available
KeywordsNormativeNeuroimagingNormalityMagnetic resonance imagingStandard deviationResidualExplained variationPsychologySegmentationBrain sizeStatisticsMedicineDemographyMathematicsRadiologyArtificial intelligenceComputer scienceNeuroscience

Abstract

fetched live from OpenAlex

Structural neuroimaging data from cognitively healthy individuals across the lifespan are necessary if one is to assess deviation from normality. Unfortunately, such normative data are found lacking, especially in such a form as to be widely applicable, allowing cross-studies comparisons. Our objective was to produce normative values for regional cortical surfaces, thicknesses and volumes using a large sample of cognitively healthy adults and a free, widely available segmentation technique. We merged 3D T1-weighted MRI scans from 2,194 healthy adults (1,078 women; 49.1%) aged 18 to 94 years old (mean: 49.6; SD: 20.9). Data originated from fifteen datasets (Table 1). We used FreeSurfer (Version 5.3) to extract cortical surfaces, thicknesses, and volumes based on the “Desikan–Killiany–Tourville” (DKT) cortical atlas containing 31 bilateral subdivisions. We generated predictive models for each region with age, sex, intracranial volume (TIV), magnetic field strength (MFS), and manufacturer as predictors. Predictors’ selection was based on the predicted residual sum of squares statistic using a 10-fold cross-validation. The mean R of the 186 cortical measures revealed that the predictors explained a substantial amount of variance (.40). The mean R of each predictor showed that age (.17), sex (.08), and TIV (.12) explained most of the variance while MFS (.01), manufacturer (.02) and interactions between predictors (.01) explained only a limited amount. We report results for both hemisphere of the cortex as examples (formulas are included in Table 2). R for surface (L: .76, R: .77), thickness (L: .46, R: .44), and volumes (L: .80, R: .80) were considerably larger than that for cortical regional models. The strongest effects were TIV (L: .35, R: .35) and sex (R: L: .24, R: .25) for surface, age (R: L: .40, R: .39) and MFS (L: .02, R: 02) for thickness, and age (L: .38, R: .38) and TIV (L: .23, R: .23) for volume. Figure 1 displays age and sex effects on each measure.

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)0.005
metaresearch head score (Gemma)0.015
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 score0.008
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.004

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 head0.121
GPT teacher head0.330
Teacher spread0.209 · 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
Published2016
Admission routes1
Has abstractyes

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