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The effect of morphometric brain changes on gait-cognitive impairment of patients with Parkinson’s disease

2023· article· en· W4386700642 on OpenAlexaboutno aff
Christiane Malá, Slávka Neťuková, Tereza Duspivová, Petr Dušek, Ondřej Bezdíček, Anna Vážná, Evžen Růžička, Radim Krupička

Bibliographic record

VenueGait & Posture · 2023
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsGaitCadenceMontreal Cognitive AssessmentPhysical medicine and rehabilitationCognitionParkinson's diseaseCognitive impairmentMedicineAtrophySTRIDEPsychologyDiseaseInternal medicinePsychiatry

Abstract

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Introduction With the progression of Parkinson`s Disease (PD), patients suffer from impairments of gait and cognition [1,2]. More severe cognitive impairment is often accompanied by worse gait performance [3]. It is widely believed that ongoing neurodegeneration underlies both disorders. While several studies published their findings regarding brain morphology changes correlating either with cognitive or gait parameters [4], it stays open if there are volumetric differences in brain-regions related to gait between patients with and without cognitive impairment . Research question Is there a relationship between dual-task gait results and brain atrophy in PD patients and is it depending on the existence of cognitive impairment? Methods We included 79 drug-naive PD patients (59.5±12 years) and 54 healthy controls (HC) (60.5 ±9.0 years). All subjects underwent the MoCA test and a single-task (ST) and dual-task (DT) gait assessment. In DT the subjects had to count down from 100 by sevens. The DT cost was calculated for velocity, stride length and cadence, as follows: [(DT − ST)/ST × 100]. Additionally, brain MRI including a T1-weighted images with 1mm3 isotropic resolution was done on a 3 T scanner Based on gait and MoCA results, the subjects in both groups were further divided into cognitively unimpaired subjects, with MoCA ≥24 and first PCA component of DT cost >-0.178 (PD 44, 58.63±12.2 years; HC 43, 61.05±9.3 years), and cognitively impaired subjects with MoCA <24 and first PCA component of DT cost of ≤ -0.178 (PD 35, 60.65±12.3 years; HC 11, 58.55±7.5 years). A voxel-based-morphometry analysis using a multiple regression model with the covariates DT-cost, total intracranial volume, age and sex was performed using the CAT12 software. Results For cognitively unimpaired PD patients, a cluster in the primary motor cortex positively correlated with the stride-length DT cost (pFWEcorr=0.027). This correlation was not detectable for either the cognitively impaired PD group or both HC subgroups. No significant correlations were found for velocity DT-cost and cadence DT-cost. Fig. 1: Results of VBM, positive correlation of DT-cost for stride length for cognitive unimpaired PD patients (significant peak at -21,-12,78) Download: Download high-res image (120KB) Download: Download full-size image Discussion By filtering the group of PD patients into a cognitively impaired and cognitively unimpaired group, we were able to detect a correlation between the precentral gyrus volume and stride-length DT cost in patients without cognitive impairment. This indicates that the poorer DT performance in these patients is mainly driven by degeneration of motor brain regions. This correlation is not detectable in the whole PD dataset, pointing to the fact that more extensive brain atrophy involving different brain regions, might be responsible for worse results in DT in cognitively impaired subjects. In conclusion, the impact of cognitive impairment on gait analysis in PD patients should be considered an important influencing factor. Supported by the Czech Ministry of Health (Grant No. NU20-04-00327)

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.240
Teacher spread0.234 · 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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Citations0
Published2023
Admission routes1
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