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Record W4391838314 · doi:10.3389/fnagi.2024.1301826

Diffusion kurtosis imaging of brain white matter alteration in patients with coronary artery disease based on the TBSS method

2024· article· en· W4391838314 on OpenAlexaboutno aff
Tong Li, Rui Qin, Cuicui Li, Lin Li, Ximing Wang, Li Wang

Bibliographic record

VenueFrontiers in Aging Neuroscience · 2024
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsCorona radiata (embryology)Internal capsuleFractional anisotropySuperior longitudinal fasciculusCorpus callosumDiffusion MRIMedicineWhite matterOptic radiationCardiologyKurtosisAnatomyInternal medicineRadiologyMagnetic resonance imaging

Abstract

fetched live from OpenAlex

Object The aim of our study was to examine the alterations in microstructure in patients with coronary artery disease (CAD) and cognitive impairment (CI) using diffusion kurtosis imaging (DKI). Additionally, we aimed to investigate the potential correlation between DKI parameters and cognitive function. Materials and methods A total of 28 CAD patients and 30 healthy controls (HC) were prospectively enrolled in our study. All participants underwent routine and diffusion sequences of head imaging. DKE software was utilized to generate various diffusion kurtosis imaging parameters (DKI), including kurtosis fractional anisotropy (KFA), mean kurtosis (MK), axial kurtosis (AK), radial kurtosis (RK), fractional anisotropy (FA), and mean diffusivity (MD). Nonparametric tests were conducted using tract-based spatial statistics (TBSS) to compare the parameter values between the two groups. The parameter values of the significantly different fiber tracts were extracted and correlated with the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores. Results Compared to the HC group, patients with coronary artery disease exhibited significant reductions in FA values in the bilateral Superior corona radiata, bilateral Anterior corona radiata, bilateral Posterior corona radiata, corpus callosum, left Posterior thalamic radiation, right Posterior limb of internal capsule, Anterior limb of internal capsule, and Cerebral peduncle, as well as in the left Superior longitudinal fasciculus. Additionally, KFA values decreased in the bilateral Anterior corona radiata, bilateral Anterior limb of internal capsule, and Genu of the corpus callosum. The MK values decreased in the right Posterior corona radiata, Retrolenticular part of the internal capsule, Posterior thalamic radiation (including optic radiation), Superior longitudinal fasciculus, and left Posterior thalamic radiation (including optic radiation). Moreover, the RK values decreased in the bilateral Retrolenticular part of the internal capsule, right Posterior thalamic radiation (including optic radiation), and Superior longitudinal fasciculus, as well as in the left Superior longitudinal fasciculus and Posterior thalamic radiation (including optic radiation) (p < 0.01, TFCE corrected), while no significant differences were observed in other parameter values (p > 0.01, TFCE corrected). The FA values of the right posterior limb of the internal capsule (r = 0.610, p = 0.001) and the right cerebral peduncle (r = 0.622, p < 0.001) were positively correlated with MMSE scores. Additionally, a significant correlation between kurtosis and diffusion coefficient parameters (FA and KFA) was observed. Conclusion CAD patients showed radial shrinkage and complexity of brain white matter microstructure. Whole-brain white matter analysis based on TBSS DKI can objectively reflect the characteristics of white matter damage in CAD patients, providing a basis for the auxiliary diagnosis of CAD with CI.

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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.017
GPT teacher head0.298
Teacher spread0.281 · 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

Citations11
Published2024
Admission routes1
Has abstractyes

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