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Record W4224946110 · doi:10.1101/2022.01.27.477925

High spatial overlap but diverging age-related trajectories of cortical MRI markers aiming to represent intracortical myelin and microstructure

2022· preprint· en· W4224946110 on OpenAlexafffund
Olivier Parent, Emily Olafson, Aurélie Bussy, Stéphanie Tullo, Nadia Blostein, Alyssa Salaciak, Saashi A. Bedford, Sarah Farzin, Marie‐Lise Béland, Vanessa Valiquette, Christine Tardif, Gabriel A. Devenyi, M. Mallar Chakravarty

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsMontreal Neurological Institute and HospitalMcGill UniversityDouglas Mental Health University Institute
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchWeston Brain Institute
KeywordsWhite matterMagnetic resonance imagingMyelinCorrelationNuclear magnetic resonanceNeurosciencePsychologyMedicineMathematicsPhysicsRadiologyCentral nervous systemGeometry

Abstract

fetched live from OpenAlex

Abstract Cortical thickness (CT), gray-white matter contrast (GWC), boundary sharpness coefficient (BSC), and T1-weighted/T2-weighted ratio (T1w/T2w) are cortical metrics derived from standard T1- and T2-weighted magnetic resonance imaging (MRI) images that are often interpreted as representing or being influenced by intracortical myelin content. However, there is little empirical evidence to justify these interpretations nor have the homologies or differences between these measures been examined. We examined differences and similarities in group mean and age-related trends with the underlying hypothesis that different measures sensitive to similar changes in underlying myelo- and microstructural processes should be highly related. We further probe their sensitivity to cellular organization using the BigBrain, a high-resolution digitized volume stemming from a whole human brain histologically stained for cell bodies with the Merker stain. The measures were generated on both the MRI-derived images of 127 healthy subjects, aged 18 to 81, and on the BigBrain volume using cortical surfaces that were generated with the CIVET 2.1.0 pipeline. Comparing MRI markers between themselves, our results revealed generally high overlap in spatial distribution (i.e., group mean), but mostly divergent age trajectories in the shape, direction, and spatial distribution of the linear age effect. Significant spatial relationships were found between the BSC and GWC and their BigBrain equivalent, as well as a correlation approaching significance between the BigBrain intensities and the T1w/T2w ratio in gray matter (GM) both sampled at half cortical depth. We conclude that the microstructural properties at the source of spatial distributions of MRI cortical markers (e.g. GM myelin) can be different from microstructural changes that affect these markers in aging. While our findings highlight a discrepancy in the interpretation of the biological underpinnings of the cortical markers, they also highlight their potential complementarity, as they are largely independent in aging. Our BigBrain results indicate a general trend of GM T1w signal and myelin being spatially related to the density of cells, which is possibly more pronounced in superficial cortical layers. Highlights – Different MRI cortical markers aim to represent myelin and microstructure – These markers show high spatial overlap, but mostly divergent age trajectories – It is unlikely that myelin changes are the source of the age effect for all markers – Trend of MRI signal being related to cell density in more superficial cortical layers

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)0.001
metaresearch head score (Gemma)0.002
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
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.018
GPT teacher head0.270
Teacher spread0.253 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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