MétaCan
Menu
Back to cohort
Record W2589631167 · doi:10.3174/ajnr.a5109

Cognitive Implications of Deep Gray Matter Iron in Multiple Sclerosis

2017· article· en· W2589631167 on OpenAlexafffund
Esther Fujiwara, J.A. Kmech, Dana Cobzaş, Hongfu Sun, Peter Seres, Gregg Blevins, Alan H. Wilman

Bibliographic record

VenueAmerican Journal of Neuroradiology · 2017
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsWiLAN (Canada)Multiple Sclerosis Society of CanadaNorthern Alberta Institute of TechnologyUniversity of Alberta HospitalUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of CanadaGenentechMultiple Sclerosis Society of CanadaEuropean Genomic Institute for DiabetesAlberta Gambling Research Institute, University of CalgaryCanadian Institutes of Health ResearchTeva Pharmaceutical IndustriesMultiple Sclerosis SocietyBiogen
KeywordsGlobus pallidusPutamenWhite matterMedicineAtrophyGrey matterThalamusNeuroscienceMagnetic resonance imagingPathologyInternal medicineBasal gangliaPsychologyRadiologyCentral nervous system

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: Deep gray matter iron accumulation is increasingly recognized in association with multiple sclerosis and can be measured in vivo with MR imaging. The cognitive implications of this pathology are not well-understood, especially vis-à-vis deep gray matter atrophy. Our aim was to investigate the relationships between cognition and deep gray matter iron in MS by using 2 MR imaging–based iron-susceptibility measures. MATERIALS AND METHODS: Forty patients with multiple sclerosis (relapsing-remitting, n = 16; progressive, n = 24) and 27 healthy controls were imaged at 4.7T by using the transverse relaxation rate and quantitative susceptibility mapping. The transverse relaxation rate and quantitative susceptibility mapping values and volumes (atrophy) of the caudate, putamen, globus pallidus, and thalamus were determined by multiatlas segmentation. Cognition was assessed with the Brief Repeatable Battery of Neuropsychological Tests. Relationships between cognition and deep gray matter iron were examined by hierarchic regressions. RESULTS: Compared with controls, patients showed reduced memory (P < .001) and processing speed (P = .02) and smaller putamen (P < .001), globus pallidus (P = .002), and thalamic volumes (P < .001). Quantitative susceptibility mapping values were increased in patients compared with controls in the putamen (P = .003) and globus pallidus (P = .003). In patients only, thalamus (P < .001) and putamen (P = .04) volumes were related to cognitive performance. After we controlled for volume effects, quantitative susceptibility mapping values in the globus pallidus (P = .03; trend for transverse relaxation rate, P = .10) were still related to cognition. CONCLUSIONS: Quantitative susceptibility mapping was more sensitive compared with the transverse relaxation rate in detecting deep gray matter iron accumulation in the current multiple sclerosis cohort. Atrophy and iron accumulation in deep gray matter both have negative but separable relationships to cognition in multiple sclerosis.

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)0.001
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.086
GPT teacher head0.351
Teacher spread0.265 · 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

Citations38
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueAmerican Journal of NeuroradiologySame topicMultiple Sclerosis Research StudiesFrench-language works237,207