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Record W4401933761 · doi:10.1093/brain/awae210

Neurophysiological markers of motor compensatory mechanisms in early Parkinson’s disease

2024· article· en· W4401933761 on OpenAlexaff
Massimiliano Passaretti, Roberto Cilia, Sara Rinaldo, Davide Rossi Sebastiano, Eva Orunesu, Grazia Devigili, Arianna Braccia, Giulia Paparella, Martina De Riggi, Thilo van Eimeren, Antonio P. Strafella, Paola Lanteri, Alfredo Berardelli, Matteo Bologna, Roberto Eleopra

Bibliographic record

VenueBrain · 2024
Typearticle
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsOntario Brain InstituteCentre for Addiction and Mental HealthUniversity Health Network
FundersH. Lundbeck A/SLundbeckfondenMinisteriet Sundhed ForebyggelseUniversitätsklinikum KölnNvidiaDeutsche ForschungsgemeinschaftEli Lilly and Company
KeywordsParkinson's diseaseTranscranial magnetic stimulationDopamine transporterNeuroscienceMotor cortexPsychologyDopaminePrimary motor cortexDopaminergicPhysical medicine and rehabilitationMedicineDiseaseInternal medicineStimulation

Abstract

fetched live from OpenAlex

Compensatory mechanisms in Parkinson's disease are defined as the changes that the brain uses to adapt to neurodegeneration and progressive dopamine reduction. Motor compensation in early Parkinson's disease could, in part, be responsible for a unilateral onset of clinical motor signs despite the presence of bilateral nigrostriatal degeneration. Although several mechanisms have been proposed for compensatory adaptations in Parkinson's disease, the underlying pathophysiology is unclear. Here, we investigate motor compensation in Parkinson's disease by investigating the relationship between clinical signs, dopamine transporter imaging data and neurophysiological measures of the primary motor cortex (M1), using transcranial magnetic stimulation in presymptomatic and symptomatic hemispheres of patients. In this cross-sectional, multicentre study, we screened 82 individuals with Parkinson's disease. Patients were evaluated clinically in their medication OFF state using standardized scales. Sixteen Parkinson's disease patients with bilateral dopamine transporter deficit in the putamina but unilateral symptoms were included. Twenty-eight sex- and age-matched healthy controls were also investigated. In all participants, we tested cortical excitability using single- and paired-pulse techniques, interhemispheric inhibition and cortical plasticity with paired associative stimulation. Data were analysed with ANOVAs, multiple linear regression and logistic regression models. Individual coefficients of motor compensation were defined in patients based on clinical and imaging data, i.e. the motor compensation coefficient. The motor compensation coefficient includes an asymmetry score to balance motor and dopamine transporter data between the two hemispheres, in addition to a hemispheric ratio accounting for the relative mismatch between the magnitude of motor signs and dopaminergic deficit. In patients, corticospinal excitability and plasticity were higher in the presymptomatic compared with the symptomatic M1. Also, interhemispheric inhibition from the presymptomatic to the symptomatic M1 was reduced. Lower putamen binding was associated with higher plasticity and reduced interhemispheric inhibition in the presymptomatic hemisphere. The motor compensation coefficient distinguished the presymptomatic from the symptomatic hemisphere. Finally, in the presymptomatic hemisphere, a higher motor compensation coefficient was associated with lower corticospinal excitability and interhemispheric inhibition and with higher plasticity. In conclusion, the present study suggests that motor compensation involves M1-striatal networks and intercortical connections becoming more effective with progressive loss of dopaminergic terminals in the putamen. The balance between these motor networks seems to be driven by cortical plasticity.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.982
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.031
GPT teacher head0.265
Teacher spread0.235 · 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.

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

Citations33
Published2024
Admission routes1
Has abstractyes

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