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Record W2891919159 · doi:10.1002/jmri.26328

Characterizing Structural Changes With Evolving Remyelination Following Experimental Demyelination Using High Angular Resolution Diffusion MRI and Texture Analysis

2018· article· en· W2891919159 on OpenAlexafffund
Tim Luo, Olayinka Oladosu, Khalil S. Rawji, Peng Zhai, Glen Pridham, Shahnewaz Hossain, Yunyan Zhang

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2018
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsHotchkiss Brain InstituteOntario Brain InstituteUniversity of Calgary
FundersAlberta InnovatesHealth Research BoardNatural Sciences and Engineering Research Council of CanadaFondation Brain Canada
KeywordsRemyelinationDiffusion imagingDiffusion MRITexture (cosmology)High resolutionDiffusionMedicineComputer scienceMagnetic resonance imagingPhysicsRadiologyArtificial intelligenceGeologyMyelinInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Changes in myelin integrity are associated with the pathophysiology of many neurological diseases, including multiple sclerosis. However, noninvasive measurement of myelin injury and repair remains challenging. Advanced MRI techniques including diffusion tensor imaging (DTI), neurite orientation dispersion and density index (NODDI), and texture analysis have shown promise in quantifying subtle abnormalities in white matter structure. PURPOSE: To determine whether and how these advanced imaging methods help understand remyelination changes after demyelination using a mouse model. STUDY TYPE: Prospective, longitudinal. ANIMAL MODEL: Demyelination was induced in the thoracic spinal cord of 21 mice using the chemical toxin lysolecithin. FIELD STRENGTH/SEQUENCES: 9.4T ASSESSMENT: Imaging was done at day 7 (demyelination) and days 14 to 35 (ongoing remyelination) postsurgery, followed by histology. Image analysis focused on both lesions and peri-lesional areas where remyelination began. In histology, we quantified the complexity of tissue alignment using angular entropy, in addition to staining area. STATISTICAL ANALYSIS: Two-way analysis of variance was performed for assessing differences between tissue types and across timepoints, followed by post-hoc analysis to correct for multiple comparisons (P < 0.05). RESULTS: All diffusion and texture parameters were worse in lesions than the control tissue (P < 0.05) except orientation dispersion index (ODI) and neurite density index (NDI) over late remyelination. Longitudinally, ODI decreased and NDI increased persistently in both lesions and peri-lesion regions (P < 0.05). Fractional anisotropy showed a mild decrease at day 35 after increase, when lesion texture heterogeneity showed a trend to decrease (P > 0.05). Both lesion size and angular entropy decreased over time, and no change in any measure in the control tissue. DATA CONCLUSION: Diffusion and MRI texture metrics may provide compensatory information on myelin repair and ODI and NDI could be sensitive measures of evolving remyelination, deserving further validation. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:1750-1759.

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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
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 head0.020
GPT teacher head0.319
Teacher spread0.299 · 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

Citations19
Published2018
Admission routes2
Has abstractyes

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