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Basal Ganglia and Prefrontal Regions Influence Postural Control and Cognitive Impairment in Parkinson's Disease

2018· article· en· W3177048831 on OpenAlexaboutno aff
Wannipat Buated, Praween Lolekha, Shohei Hidaka, Tsutomu Fujinami

Bibliographic record

VenueThe FASEB Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsParkinson's diseaseBalance (ability)Physical medicine and rehabilitationCognitionGaitMontreal Cognitive AssessmentRating scalePsychologyLevodopaPosturographyForce platformPostural instabilityDiseaseBasal gangliaPhysical therapyMedicineCognitive impairmentInternal medicineNeuroscienceDevelopmental psychology

Abstract

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Objective Postural instability is one of clinical manifestations caused by the deterioration of basal ganglia (BG) in Parkinson's disease (PD). It has frequently been found in moderate and advanced stages of the disease, simultaneously, impairments of cognition and executive function are also commonly present. Several studies reported interferences between postural control and cognitive tasks passing the prefrontal regions. We aimed to reveal the roles of BG and prefrontal regions in postural control and cognitive impairment in PD patients who exhibit freezing of gait (FOG). Methods We measured postural control in 60 patients with Parkinson's disease (PD) in on‐medication (39 PD patients with FOG and 21 PD patients without FOG). A Nintendo Wii balance board was utilized to measure postural stability in terms of center of pressure (CoP). A modified Hoehn and Yahr scale (H&Y) was used to classify clinical staging of PD. Subjects' disease severities were evaluated by using Unified Parkinson's Disease Rating Scale (UPDRS) (part III). Levodopa Equivalent Dose (LED), New Freezing of Gait questionnaire (NFOG‐Q), and Montreal Cognitive Assessment were also assessed. The participants were instructed to stand naturally on the balance platform and look at a marker, which was 3 meters from the board. The subjects were asked to perform counting days backward for a total of 170 seconds. A computer program collected the data automatically (Buated, W., et.al. Gerontology and Geriatric Medicine, 2016). Regression analysis were performed for posturographic and clinical variables to identify relationships of mild cognitive impairment, postural instability and FOG. An important parameter of CoP; path length (PL), which was found to be dominant factor of the balance analysis (Buated, W., et al. The 19 th International Congress of Parkinson's Disease and Movement Disorders, 2015) , was also calculated. Principal component analysis was used to analyze all clinical variables and determined the statistical significance of the variance. Results Associations between PL and clinical variables were observed in all patients; LED (R 2 = 0.150, p = 0.001 ), H&Y (R 2 = 0.060, p = 0.060 ), duration of disease (R 2 = 0.065, p < 0.049 ) and NFOG‐Q (R 2 = 0.108, p = 0.010 ). In PD patients with FOG, relationship was found in LED (R 2 = 0.125, p = 0.032 ). On the other hand, no relationship was noticed in PD patients without FOG. Conclusions Basal ganglia and prefrontal regions play important roles in postural control and cognitive function in PD patients. Freezing of gait (FOG), cognitive impairment and postural control are associated with the effects of medication. Among the clinical variables, the correlation between LED and PL was found to be most important. These influences of the interruption of the neural circuitry were expressed in the postural control of PD patients, especially those subjects with FOG symptoms. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.010
GPT teacher head0.254
Teacher spread0.244 · 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 designBench or experimental
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
Published2018
Admission routes1
Has abstractyes

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