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Record W2609905986 · doi:10.1002/mp.12300

Manganese concentration mapping in the rat brain with MRI, PET, and autoradiography

2017· article· en· W2609905986 on OpenAlexafffund
Geoffrey J. Topping, Andrew Yung, Paul Schaffer, Cornelia Hoehr, Rick Kornelsen, Piotr Kozłowski, Vesna Sossi

Bibliographic record

VenueMedical Physics · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy Metal Exposure and Toxicity
Canadian institutionsTRIUMFUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaTRIUMF
KeywordsIn vivoPositron emission tomographyMagnetic resonance imagingNuclear medicineEx vivoChemistryNuclear magnetic resonanceGadoliniumMaterials scienceMedicineRadiologyPhysicsBiology

Abstract

fetched live from OpenAlex

Purpose Mn2+ is used as a contrast agent and marker for neuronal activity with magnetic resonance imaging (MRI) in rats and mice, but its accumulation is generally not assessed quantitatively. In this work, nonradioactive Mn and 52Mn are injected simultaneously in rats, and imaged with MRI, positron emission tomography (PET) and autoradiography (AR). Mn distributions are compared between modalities, to assess the potential and limitations on quantification of Mn with MRI, and to investigate the potential of multimodal measurement of Mn accumulation. Methods MRI (in vivo), PET (in vivo and post mortem), and AR (ex vivo) were acquired of rat brains, for which animals received simultaneous intraperitoneal (IP) or intracerebrovertricular (ICV)‐targeted injections containing the positron‐emitting radionuclide 52Mn and additional nonradioactive MnCl2, which acts as an MRI contrast agent. Pre and postinjection MR images were fit for the longitudinal relaxation rate (R1), coregistered, and subtracted to generate R1 difference maps, which are expected to be proportional to change in Mn concentration in tissue. AR and PET images were coregistered to smoothed R1 difference maps. Results Similar spatial distributions were seen across modalities, with Mn accumulation in the colliculus, near the injection site, and in the 4th ventricle. There was no 52Mn accumulation measurable with PET in the brain after IP injection. In areas of very highly localized and concentrated 52Mn accumulation in PET or AR, consistent increases of R1 were not seen with MRI. Scatter plots of corresponding voxel R1 difference and PET or AR signal intensity were generated and fit with least squares linear models within anatomical regions. Linear correlations were observed, particularly in regions away from very highly localized and concentrated Mn accumulation at the injection site and the 4th ventricle. Accounting for radioactive decay of 52Mn, the MnCl2 longitudinal relaxivity was between 4.0 and 5.1 s−1/mM, which is within 22% of the in vitro relaxivity. Conclusions This proof‐of‐concept study demonstrates that MR has strong potential for quantitative assessment of Mn accumulation in the brain, although local discrepancies from linear correlation suggest limitations to this use of MR in areas of inflammation or very high concentrations of Mn. These discrepancies also suggest that a combination of modalities may have additional utility for discriminating between different pools of Mn accumulation in tissue.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.241
Teacher spread0.228 · 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 designBench or experimental
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

Citations8
Published2017
Admission routes2
Has abstractyes

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