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Record W4390439047

Analysis the relationship of cholinergic pathway damage and cerebral cortex structure change in patients with cognitive impairment accompanied by white matter hypertensity

2020· article· en· W4390439047 on OpenAlexaboutno aff
S. Wang, Jin⁃fang WANG, Qingli Shi, Yue⁃xiu LI, Hong⁃yan CHEN, Yumei Zhang

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsCholinergicWhite matterNeuroscienceCognitive impairmentCerebral cortexCognitionPsychologyMedicineCortex (anatomy)AudiologyMagnetic resonance imaging
DOInot available

Abstract

fetched live from OpenAlex

Objective To investigate the relationship of cholinergic pathway (CP) damage and cerebral cortex structure change in patients with different degree of cognitive impairment accompanied by white matter hyperintensity (WMH). Methods From March 2016 to December 2018, a total of 80 patients with WMH were rolled into WMH with no cognitive impairment group (WMH⁃CN group, n = 43), WMH with vascular cognitive impairment⁃no dementia group (WMH⁃VCIND group, n = 21) and WMH with vascular dementia group (WMH ⁃ VaD group, n = 16), according to Montreal Cognitive Assessment (MoCA) and Clinical Dementia Rating Scale (CDR). White matter damage in CP were evaluated by Cholinergic Pathways Hyperintensities Scale (CHIPS). MRI was performed on all patients, 34 regions of interest (ROIs) in cerebral cortex CP were marked, and layer thickness and volume were measured. Spearman rank correlation analysis and partial correlation analysis were performed to explore the correlation between the total CHIPS score of left and right hemispheres and ipsilateral cortical ROIs thickness and volume. Results 1) The CHIPS scores of WMH ⁃ CN group, WMH ⁃ VCIND group and WMH ⁃ VaD group had statistically significant differences in the whole brain (P = 0.023), the left hemisphere (P = 0.039) and the right hemisphere (P = 0.004), respectively. Among them, the CHIPS scores of WMH⁃VCIND group (P = 0.002, 0.000, 0.001) and WMH⁃VaD group (P = 0.000, 0.003, 0.000) were all higher than those of WMH⁃CN group. The CHIPS scores of three regions in WMH⁃VaD group were also higher than those in WMH⁃VCIND group (P = 0.008, 0.013, 0.020). 2) The differences of the left hemisphere ROIs layer thickness (P =0.000) and volume (P = 0.000), and the right hemisphere ROIs layer thickness (P = 0.000) were statistically significant between WMH patients and normal controls. The left hemisphere ROIs layer thickness (P =0.000, for all) and volume (P = 0.000, for all), and the right hemisphere ROIs layer thickness (P = 0.000, for all) in WMH⁃CN group, WMH⁃VCIND group and WMH⁃VaD group were all lower than those in control group. In WMH⁃VaD group, the thickness of left ROIs was higher than that of WMH⁃CN group (P = 0.000) and WMH⁃VCIND group (P = 0.036). In WMH⁃CN group, the volume of left ROIs was higher than that in WMH⁃VCIND group (P = 0.033) and lower than that in WMH⁃VaD group (P = 0.025), and the thickness of right ROIs in WMH⁃VCIND group (P = 0.001) and WMH⁃VaD group (P = 0.000) were both higher than that in WMH ⁃ CN group. 3) Correlation analysis showed that only the WMH ⁃ VCIND group had a positive correlation between left hemispheric CHIPS score and ipsilateral cortical ROIs (r = 0.439, P = 0.047). Conclusions When WMH patients with cognitive impairment, as the degree is aggravating, the thickness and volume of 34 ROIs on left hemisphere CP have a certain degree of decline. The cerebral cortex structure change was positively associated with the degree of left hemisphere CP damage, but the structural change is not obvious in right hemisphere. DOI:10.3969/j.issn.1672⁃6731.2020.10.009

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 score0.001
Threshold uncertainty score0.004

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 studies0.0000.000
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 head0.175
GPT teacher head0.431
Teacher spread0.256 · 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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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