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Correlation between white matter microstructural integrity and cognitive function in patients with hypertension

2019· article· en· W3031133393 on OpenAlexaboutno aff
Junhui Zou, Xin Chen, Yu‐Cheng Gu, Haifeng Chen, Renyuan Liu, Ruomeng Qin, Yongcheng Jiang

Bibliographic record

VenueInt J Cerebrovasc Dis · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsCorpus callosumWhite matterDiffusion MRIMedicineFractional anisotropyNeuropsychologyInternal medicineCardiologySpleniumCognitionMagnetic resonance imagingAnatomyPsychiatryRadiology

Abstract

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Objective To investigate the effect of hypertension on white matter microstructure and its correlation with cognitive function using automated fiber-tract quantification. Methods Consecutive subjects visited Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School between January 2017 and July 2018 were collected. They were divided into hypertension without cognitive impairment (HTN-nonCI) group (n=44), hypertension with cognitive impairment (HTN-CI) group (n=50), and control group (n=25). The imaging data and neuropsychological scale test results of the subjects were collected. The automated fiber-tract quantification was used to obtain the diffusion parameters of 100 nodes on 20 fiber tracts in the whole brain. The differential segments of each fiber tract diffusion parameter between the control group and HTN-nonCI group, and the HTN-nonCI group and HTN-CI group were compared. Correlation analysis was performed in white matter fiber tracts with significant differences and each cognitive domain between the HTN-nonCI group and the HTN-CI group. Results The fractional anisotropy (FA) of white matter fiber tracts in 3 groups showed a decreasing trend, and the mean diffusion diffusivity (MD) showed an increasing trend. The comparison between the control group and the HTN-nonCI group showed that there were significant differences in the FA values of the midpoint of left thalamic radiation tract partial to brainstem, the genus of corpus callosum near brainstem, and frontal part and and proximal ventricle of splenium of corpus callosum (all P<0.05); there were significant differences in the MD values of the middle part of left thalamic radiation tract and near the brainstem, the right thalamic radiation tract near the brainstem, the top of left corticospinal tract and near brainstem, the middle hippocampus of right cingulate tract, the middle part of genu of corpus callosum, the splenium of the corpus callosum near the lateral ventricle, the left uncinate tract near the forehead (all P<0.05). A comparison between the HTN-nonCI group and the HTN-CI group showed that there was significant difference in the FA value between the distributed segments of cingulate gyrus of left cingulate tract and the left inferior fronto-occipital tract near the occipital lobe, in which cingulate gyrus of left cingulate tract was significantly correlated with the Montreal Cognitive Assessment Scale score (standardized β=0.268, P=0.029); there were significant differences in the right thalamic radiation tract near the brainstem, the forehead and proximal ventricle of splenium of the corpus callosum, and the scattered distribution segments of the right inferior longitudinal fasciculus, in which the right inferior longitudinal fasciculus was significantly correlated with memory (standardized β=-0.243, P=0.047) and executive function (standardized β=-0.284, P=0.021). Conclusions Microstructural integrity of white matter was generally destroyed in patients with hypertension, but some segments were more susceptible to hypertension. The integrity of cingulate gyrus of cingulate tract was significantly correlated with the overall cognitive function. The integrity of inferior longitudinal fasciculus was significantly correlated with the executive function and memory. Key words: White matter; Neural pathways; Cognition; Cognition disorders; Hypertension; Magnetic resonance imaging; Diffusion tensor imaging; Risk factors

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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)0.000
metaresearch head score (Gemma)0.000
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 score0.002
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.013
GPT teacher head0.207
Teacher spread0.194 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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