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Record W4404894771

Analysis of correlation between cerebral perfusion and KIM score of white matter lesions in patients with Alzheimer’s disease

2019· article· en· W4404894771 on OpenAlexaboutno aff
LeVier Rr, He Ys, Mingyao Liu, Nie ZY, LH Huang, Lu Zhang, Jin LJ, Li YX

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2019
Typearticle
Languageen
FieldNeuroscience
TopicBrain Tumor Detection and Classification
Canadian institutionsnot available
Fundersnot available
KeywordsWhite matterCerebral hypoperfusionDiseaseMedicineCorrelationInternal medicinePerfusionRadiologyMagnetic resonance imagingMathematics
DOInot available

Abstract

fetched live from OpenAlex

Ren-Ren Li,1,* Yu-Sheng He,1,* Meng Liu,1 Zhi-Yu Nie,1 Li-He Huang,2 Zheng Lu,3 Ling-Jing Jin,1 Yun-Xia Li1 1Department of Neurology, Tongji Hospital, Tongji University School of Medicine, Shanghai 200065, People’s Republic of China; 2School of Foreign Languages, Research Center for Ageing, Language and Care, Tongji University, Shanghai 200092, People’s Republic of China; 3Department of Psychiatry, Tongji Hospital, Tongji University School of Medicine, Shanghai 200065, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yun-Xia LiDepartment of Neurology, Tongji Hospital, Tongji University School of Medicine, 389 Xincun Road, Shanghai 200065, People’s Republic of ChinaEmail doctorliyunxia@163.comPurpose: This study aimed to characterize white matter lesions (WMLs) and regional cerebral perfusion, and evaluate their correlations with cognitive deficits in Alzheimer’s disease (AD) patients.Patient and methods: One hundred and twenty-eight patients with AD (AD group) and 75 subjects without AD (control group) were recruited. The medical information was collected from each subject. Montreal cognitive assessment (MoCA) was employed for the assessment of cognition. Cranial MRI was performed, and the KIM scoring system was used to evaluate the white matter hyperintensity. The CT perfusion (CTP) imaging was employed to assess the whole cerebral perfusion, and the region of interest (ROI) was selected to determine the blood perfusion at different parts.Results: The education level and MoCA score in AD group were significantly lower than in control group (P<0.001). The KIM score of juxtaventricular WML (JVWMLs) was significantly different between two groups (P<0.05) and AD group showed a higher incidence of severe JVWML and periventricular WML (PVWMLs); in AD group, the total KIM score and KIM scores of JVWMLs, PVWMLs and deep WML (DWMLs) showed negative relationships with the MoCA score (P<0.001). As compared to control group, the blood perfusion of either whole brain or different parts in the AD group reduced significantly (P<0.05). In the AD group, there was a negative correlations of blood perfusion at JVWM and PVWM with corresponding KIM scores (P<0.05 or 0.01). In the AD group, the blood perfusions of the whole brain, JVWMLs, PVWMLs and deep WML were negatively related to MoCA score (P<0.05).Conclusion: In conclusion, the cognitive deficits in the AD patients are associated with the degree of WMLs, especially the JVWML, PVWML and DWML as well as with the reduced perfusion of JVWM, PVWM and deep WM.Keywords: Alzheimer’s disease, cerebral blood flow, white matter lesion, KIM score

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.166
GPT teacher head0.449
Teacher spread0.283 · 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.

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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