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Record W4292836161 · doi:10.3389/fnagi.2022.975329

Association between physical exercise, executive function, and cerebellar cortex: A cross-sectional study among the elderly in Chinese communities

2022· article· en· W4292836161 on OpenAlexaboutno aff
Wei Li, Yong Li, Yaopian Chen, Ling Yue, Shifu Xiao

Bibliographic record

VenueFrontiers in Aging Neuroscience · 2022
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersShanghai Jiao Tong UniversityScience and Technology Commission of Shanghai MunicipalityChinese Academy of SciencesShanghai Clinical Research CenterNational Natural Science Foundation of ChinaShanghai Center for Brain Science and Brain-Inspired Technology
KeywordsDementiaMontreal Cognitive AssessmentCognitionCross-sectional studyCognitive declineMedicinePsychologyStepwise regressionPhysical exerciseGerontologyPhysical therapyPhysical medicine and rehabilitationDiseaseInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Background Previous studies have confirmed that physical exercise may be beneficial for brain health, but there is little data on this among older Chinese. Objective The purpose of this study was to explore the relationship between physical exercise and cognitive impairment, and to explore the possible mechanism by which physical exercise prevents cognitive decline. Materials and methods 192 older adults with dementia, 610 older adults with mild cognitive impairment (MCI), and 2,218 healthy older adults were included in the study. Through standardized questionnaires, we obtained their general demographic information (such as gender, age, education, etc.), disease-related information (hypertension and diabetes) and physical exercise information (such as whether they did physical exercise and the frequency of physical exercise, etc.). The mini-mental state examination (MMSE) and Montreal Cognitive Assessment (MoCA) were used to assess their overall cognitive function, while the Wechsler block diagram was used to assess their executive function. Moreover, 164 healthy, randomly selected older adults also underwent brain MRI scans at the same time, and the target brain regions included hippocampus, gray matter, and cerebellar cortex. Results By using stepwise multiple logistics regression analysis, we found that physical exercise was associated with both MCI (p = 0.001*, OR = 0.689, 95%CI: 0.553–0.859) and dementia (p < 0.001*, OR = 0.501, 95%CI: 0.354–0.709), independent of gender, age, education, and other factors. The results of ROC curve showed that the area under the curve of physical exercise in predicting MCI and dementia was 0.551 (p < 0.001*, 95%CI: 0.525–0.577) and 0.628 (p = 0.001*, 95%CI: 0.585–0.671), respectively. The results of partial correlation analysis showed that physical exercise was associated with left cerebellar cortex (r = 0.163, p = 0.023), right cerebellar cortex (r = 0.175, p = 0.015) and Wechsler block diagram score (r = 0.235, p = 0.011). Moreover, the results of linear regression analysis mediation model showed that physical exercise may affect Wechsler block diagram score through influencing the thickness of right cerebellum cortex, and the latter may play a partial mediation effect (indirect B = 0.001, p = 0.045). Conclusion Physical exercise might be a protective factor for mild cognitive impairment and dementia among the Chinese elderly, and there might be an association among physical exercise, executive function, and the thickness of the cerebellar cortex.

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)0.001
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 score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.305
Teacher spread0.288 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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