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Record W4390194207 · doi:10.1002/alz.080668

Assessment of quantitative susceptibility mapping (QSM) and oxygen extraction fraction (OEF) in the spectrum of Alzheimer’s disease clinical presentations

2023· article· en· W4390194207 on OpenAlexaff
Seyyed Ali Hosseini, Stijn Servaes, Joseph Therriault, Cécile Tissot, Nesrine Rahmouni, Arthur C. Macedo, Firoza Z Lussier, Jenna Stevenson, Yi‐Ting Wang, Jaime Fernández Arias, Étienne Aumont, Kely Quispialaya Socualaya, Tahnia Nazneen, Alyssa Stevenson, Serge Gauthier, Tharick A. Pascoal, Pedro Rosa‐Neto

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversité du Québec à MontréalMcGill University
Fundersnot available
KeywordsQuantitative susceptibility mappingRegion of interestMedicineAlzheimer's diseaseAnalysis of varianceNuclear medicineMagnetic resonance imagingPathologyInternal medicineDiseaseRadiology

Abstract

fetched live from OpenAlex

Abstract Background Different methods have been proposed to assess vascular dysfunction in Alzheimer’s disease (AD), mild cognitive impairment (MCI), and cognitively unimpaired (CU) subjects including quantitative susceptibility mapping (QSM), and recently oxygen extraction fraction (OEF). QSM is sensitive to the presence of iron and is a non‐invasive MR approach to measure local tissue susceptibility with high spatial resolution. OEF is a measure of the percentage of oxygen taken from the brain’s blood supply and relates directly to brain oxygen metabolism. Here, we aimed to examine QSM (Figure1) and OEF (Figure1) potential in differentiating AD, MCI, and CU. Method A cohort of 310 subjects with AD (n = 48), MCI (n = 80), and CU (n = 182) were recruited. All the patients received 3D gradient‐recalled echo sequence MRI. All QSM images were constructed by MEDI+0 and OEF images were constructed by QSM+qBOLD model with CCTV (Temporal clustering, tissue composition, and total variation). Eighty‐two various brain regions of interest (ROI) and 6 Braak ROI were investigated in the current study. ANOVA and Posthoc least significant difference (LSD) tests were implemented for each ROI over AD, MCI, and CU. Result Over Braak ROI analysis, the ANOVA test showed that the average susceptibility of QSM with Braak 4 ROI is significantly different (p<0.001). LSD test showed that Braak 4 ROI of QSM is significantly different between AD vs. MCI and AD vs. CU (p<0.001). Braak 2 ROI of OEF was significantly different between AD vs. MCI and AD vs. CU (p = 0.21 and p = 0.32, respectively). Over brain ROI, the ANOVA test showed QSM right and left posterior cingulate and right Caudate were significantly different (p<0.001). Besides, these ROIs were significantly different between AD vs. MCI and AD vs. CU, according to the LSD test. Over brain ROI, OEF right Caudate was significantly different (p<0.001) followed by left Caudate and Right transverse temporal with p = 0.002 based on the ANOVA test. However, right and left Caudate were just significantly different between AD vs. CU (p<0.001) based on the LSD test (Table 1 and 2). Conclusion QSM and OEF are affordable non‐invasive MR approaches to assess vascular dysfunction in the spectrum of clinical presentations of AD.

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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.135
GPT teacher head0.468
Teacher spread0.333 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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