Skip to content
DraconiticResearch
Search Sources API
197,768 studies31 sourcesUpdated 20 min ago
PreprintCC BY-NC-NDbioRxiv

Computational markers of rhythmic timing in Parkinson's disease

  1. 1Aarhus University & The Royal Academy of Music Aarhus/Aalborg

Abstract

Sensorimotor synchronization requires the nervous system to predict upcoming events while continuously correcting the timing of ongoing actions. Parkinson's disease (PD) is associated with impaired temporal processing, but it remains unclear which cognitive-computational mechanisms underlie rhythmic timing deficits. Here, we applied the Adaptation and Anticipation Model (ADAM) to synchronized finger-tapping data from participants with PD and controls across four steady tempi. After data screening, analyzes included 35 participants with PD and 28 controls. PD was associated with reduced synchronization accuracy and stability, reflected in larger absolute asynchronies and higher variability of asynchronies. ADAM parameter estimates indicated lower phase correction (coupling strength) and higher central timekeeper noise in the PD group, whereas period correction (tempo matching) and peripheral motor noise showed no robust group differences. Across participants, timekeeper noise predicted synchronization accuracy. Phase correction, timekeeper noise, and motor noise predicted synchronization stability, but their associations differed between groups: phase correction was more closely associated with stability in PD, whereas timekeeper and motor noise were more closely associated with stability in controls. These findings suggest that sensorimotor synchronization deficits in PD reflect reduced use of recent timing errors to guide upcoming movements and less precise internal timekeeping. Error correction and timekeeper noise thus emerge as candidate computational markers of altered rhythmic timing in PD, with potential relevance for understanding individual differences in responses to rhythmic auditory cueing.

Related

  1. Dopaminergic tone predicts rhythmic temporal attention in Parkinson's disease
    B. Yosef, B. Balamurugan, M. Miura +1
    bioRxivPreprintCC BYFull textNeurosciencebioRxiv
  2. Hindmarsh-Rose neuronal network with spike-timing-dependent plasticity demonstrates coordinated reset neuromodulation for Parkinson's disease
    S. Sharafi, J. Gilmer, M. Al Borno +1
    bioRxivPreprintCC BY-NC-NDFull textNeurosciencebioRxiv
  3. Computational modelling of acetylcholine deficiency-mediated dysfunction in the basal ganglia-thalamocortical network related to Parkinson’s disease dementia
    Hao Yang
    PLOS OnePeer-reviewedCC BY 4.0Full textGeneral biochemistryCell biologyPLOSPMC
  4. Cross-disorder Learning of Imaging-derived Joint Mophometric Markers in Alzheimer's and Parkinson's Diseases
    Yuji Zhao, Boris Gutman
    arXivPreprintarXiv licenceFull textBioinformatics & computational biologyNeurosciencearXiv
  5. HD-DRUM Rhythmic Training in Huntington's Disease: Feasibility, Motor, and Brain Microstructural Pilot Signals.
    Pedro Luque Laguna, Vasileios Ioakeimidis, Cheney J G Drew +6
    Movement disorders clinical practicePeer-reviewedCC BYFull textNeuroscienceEurope PMC
BibTeX
@misc{pandonaude2026computational,
  title = {{Computational markers of rhythmic timing in Parkinson's disease}},
  author = {V. Pando-Naude and V. H. Kuang and K. L. Christensen and J. Stupacher and P. Vuust and P. E. Keller},
  howpublished = {bioRxiv},
  year = {2026},
  month = oct,
  doi = {10.64898/2026.10.22.760665},
  url = {https://www.biorxiv.org/content/10.64898/2026.10.22.760665v1},
  note = {CC BY-NC-ND},
}
RIS
TY  - UNPB
TI  - Computational markers of rhythmic timing in Parkinson's disease
AU  - V. Pando-Naude
AU  - V. H. Kuang
AU  - K. L. Christensen
AU  - J. Stupacher
AU  - P. Vuust
AU  - P. E. Keller
PY  - 2026
DA  - 2026/10/22
PB  - bioRxiv
DO  - 10.64898/2026.10.22.760665
UR  - https://www.biorxiv.org/content/10.64898/2026.10.22.760665v1
AB  - Sensorimotor synchronization requires the nervous system to predict upcoming events while continuously correcting the timing of ongoing actions. Parkinson's disease (PD) is associated with impaired temporal processing, but it remains unclear which cognitive-computational mechanisms underlie rhythmic timing deficits. Here, we applied the Adaptation and Anticipation Model (ADAM) to synchronized finger-tapping data from participants with PD and controls across four steady tempi. After data screening, analyzes included 35 participants with PD and 28 controls. PD was associated with reduced synchronization accuracy and stability, reflected in larger absolute asynchronies and higher variability of asynchronies. ADAM parameter estimates indicated lower phase correction (coupling strength) and higher central timekeeper noise in the PD group, whereas period correction (tempo matching) and peripheral motor noise showed no robust group differences. Across participants, timekeeper noise predicted synchronization accuracy. Phase correction, timekeeper noise, and motor noise predicted synchronization stability, but their associations differed between groups: phase correction was more closely associated with stability in PD, whereas timekeeper and motor noise were more closely associated with stability in controls. These findings suggest that sensorimotor synchronization deficits in PD reflect reduced use of recent timing errors to guide upcoming movements and less precise internal timekeeping. Error correction and timekeeper noise thus emerge as candidate computational markers of altered rhythmic timing in PD, with potential relevance for understanding individual differences in responses to rhythmic auditory cueing.
ER  -