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Autor(en) / Beteiligte
Titel
Association between the Degree of Pre-Synaptic Dopaminergic Pathway Degeneration and Motor Unit Firing Behavior in Parkinson’s Disease Patients
Ist Teil von
  • Sensors (Basel, Switzerland), 2021-10, Vol.21 (19), p.6615
Ort / Verlag
Basel: MDPI AG
Erscheinungsjahr
2021
Link zum Volltext
Quelle
EZB Electronic Journals Library
Beschreibungen/Notizen
  • The relationship between motor unit (MU) firing behavior and the severity of neurodegeneration in Parkinson’s disease (PD) is not clear. This study aimed to elucidate the association between degeneration with dopaminergic pathways and MU firing behavior in people with PD. Fourteen females with PD (age, 72.6 ± 7.2 years, disease duration, 3.5 ± 2.1 years) were enrolled in this study. All participants performed a submaximal, isometric knee extension ramp-up contraction from 0% to 80% of their maximal voluntary contraction strength. We used high-density surface electromyography with 64 electrodes to record the muscle activity of the vastus lateralis muscle and decomposed the signals with the convolution kernel compensation technique to extract the signals of individual MUs. We calculated the degree of degeneration of the central lesion-specific binding ratio by dopamine transporter single-photon emission computed tomography. The primary, novel results were as follows: (1) moderate-to-strong correlations were observed between the degree of degeneration of the central lesion and MU firing behavior; (2) a moderate correlation was observed between clinical measures of disease severity and MU firing behavior; and (3) the methods of predicting central nervous system degeneration from MU firing behavior abnormalities had a high detection accuracy with an area under the curve >0.83. These findings suggest that abnormalities in MU activity can be used to predict central nervous system degeneration following PD.
Sprache
Englisch
Identifikatoren
ISSN: 1424-8220
eISSN: 1424-8220
DOI: 10.3390/s21196615
Titel-ID: cdi_doaj_primary_oai_doaj_org_article_d6e2c9f5c9e544cb9e3e4fde0c68e77d

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