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Record W4285034202 · doi:10.21037/qims-22-7

Associations of cortical iron accumulation with cognition and cerebral atrophy in Alzheimer’s disease

2022· article· en· W4285034202 on OpenAlexaboutno aff
Aocai Yang, Lei Du, Wenwen Gao, Bing Liu, Yue Chen, Yige Wang, Xiuxiu Liu, Kuan Lv, Wenwei Zhang, Hui Xia, Kai Wu, Guolin Ma

Bibliographic record

VenueQuantitative Imaging in Medicine and Surgery · 2022
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsQuantitative susceptibility mappingCerebral cortexMedicineAtrophyMontreal Cognitive AssessmentPutamenCortex (anatomy)Cognitive declineNeurosciencePathologyDementiaPsychologyMagnetic resonance imagingInternal medicineDiseaseRadiology

Abstract

fetched live from OpenAlex

Background: In Alzheimer's disease (AD), cerebral iron accumulation colocalizes with the pathological proteins amyloid-β (Aβ) and tau. Furthermore, tau-induced cortical thinning is associated with cognitive decline. In this study, quantitative susceptibility mapping (QSM) was used to investigate the whole-brain distribution pattern of cortical iron deposition and its relationships with cognition and cortical thickness in AD. Methods: -weighted examinations on a 3.0T MRI scanner. Global cognition was assessed using the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Whole-brain cross-sectional QSM analysis and whole-brain QSM regression analyses against the MMSE and MoCA scores were performed. Surface-based morphometry analysis was also performed. Subsequently, in regions with significant atrophy, magnetic susceptibility was compared between the AD and HC groups, and the association between magnetic susceptibility and cortical thickness was assessed. Results: Whole-brain QSM cross-sectional analysis in the AD group demonstrated widespread increased susceptibility across the cortical ribbon, asymmetrically covering the left hemisphere cerebral cortex, caudate nucleus, putamen, and partial cerebellar cortex. Whole-brain QSM regression analyses in the AD group showed that increased susceptibility covaried with lower MMSE and MoCA scores, and was predominantly located in the right parietal cortex and lateral occipital cortex. In the AD group, cortical thickness was reduced in the left superior temporal gyrus, right frontal pole, fusiform gyus, and pars opercularis, and there were increases in susceptibility in the right frontal pole (AD: mean ± SD 0.034±0.007 ppm, 95% CI: 0.032-0.037 ppm; HC: 0.030±0.005 ppm, 95% CI: 0.028-0.032 ppm; P=0.016) and pars opercularis (AD: 0.020±0.003 ppm, 95% CI: 0.018-0.021 ppm; HC: 0.017±0.002 ppm, 95% CI: 0.017-0.018 ppm; P=0.002). Susceptibility was negatively correlated with cortical thickness in the right pars opercularis in the entire cohort (r=-0.521, P<0.001) and AD group (r=-0.510, P=0.005). Conclusions: Widespread cortical iron, as measured by QSM, accumulated in AD and iron deposition was associated with poor cognitive performance. Increased iron content was also associated with brain atrophy. Our study suggests QSM may be a useful imaging biomarker for monitoring the neurodegenerative progression 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.005
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.101
GPT teacher head0.394
Teacher spread0.293 · 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 teacher head, 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

Citations36
Published2022
Admission routes1
Has abstractyes

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