Skip to content
DraconiticResearch
Search Sources API
203,234 studies31 sourcesUpdated 2 min ago
PreprintCC BY 4.0arXiv

Backspace as a Natural Experiment: An Accelerated Failure Time Model of Selective Post-Error Motor Impairment in Parkinsons Disease

Abstract

Parkinson's disease (PD) selectively impairs distinct stages of motor control. Using backspace events as natural error-correction episodes in the public neuroQWERTY MIT-CSXPD dataset (n=57 subjects, 27 PD with UPDRS-III scores), we test whether passively-collected keystroke timing dissociates a variability-based pre-error monitoring signal from a speed-based post-error recovery signal. Pre-error typing instability does not track PD severity (r=-0.072, p=0.721), while post-error pause duration does (r=+0.656, p=0.0002; subject-level OLS p=1.2x10^-4, n=27, primary analysis given within-subject event clustering). A mixed-effects log-normal model with a random subject intercept confirms this while retaining full event-level power (coef=0.0250, p=1.2x10^-4, n=1,563 events), closely matching the subject-level OLS despite an unrelated estimation strategy. Error-detection latency also correlates with UPDRS-III (r=+0.660), confirming the dissociation is between variability and speed, not detection and correction per se. A log-normal accelerated failure time model yields a coefficient of 0.0255 (bootstrap 95% CI [0.0146, 0.0357]), a 2.6% increase per UPDRS-III point. After controlling the immediately thecoefficient attenuates to 0.0133 (p=3.1x10^-21),consistent w timingincrement above general bradykinesia. Results reacross both S2:r=+0.645), survive jackknife exclusion of every subject, andnatingfinger-tapping and mPower smartphone tapping (group AUC=0.836). cellent(ICC(2,1)=0.945, n=20).

Related

  1. Pallidal beta oscillations underlying locomotor adaptation in Parkinsons disease
    J. T. Choi, A. Gurrala, D. D. Wang +2
    bioRxivPreprintAll rights reservedFull textNeurosciencebioRxiv
  2. Intrinsic dimensionality of deep learning representations reveals cell death-associated heterogeneity in Parkinsons disease iPSC-derived neurons
    M. L. Choi, S. Kang, J. Park
    bioRxivPreprintAll rights reservedFull textCell biologybioRxiv
  3. Targeting microglial P2Y12-receptor signalling attenuates neurodegeneration in experimental Parkinsons disease
    A. Iring, D. J. Kiss, M. Baranyi +9
    bioRxivPreprintAll rights reservedFull textNeurosciencebioRxiv
  4. Neuroinflammation is an early pathological process in a mouse model of prodromal Parkinsons disease
    Gabriela Mercado, Lucas Stetzik, Emily Schulz +8
    bioRxivPreprintAll rights reservedFull textNeurosciencebioRxivPMC
  5. Signed Dopaminergic Asymmetry Tracks Contralateral Motor Laterality in Parkinsons Disease
    H. Tirhekar, P. Yadav, C. Bajaj
    bioRxivPreprintCC BY-NC-NDFull textNeurosciencebioRxiv
BibTeX
@misc{bondade2026backspace,
  title = {{Backspace as a Natural Experiment: An Accelerated Failure Time Model of Selective Post-Error Motor Impairment in Parkinsons Disease}},
  author = {Navin Bondade},
  howpublished = {arXiv},
  year = {2026},
  month = aug,
  doi = {10.48550/arxiv.2607.24796},
  eprint = {2607.24796},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2607.24796},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Backspace as a Natural Experiment: An Accelerated Failure Time Model of Selective Post-Error Motor Impairment in Parkinsons Disease
AU  - Navin Bondade
PY  - 2026
DA  - 2026/08/31
PB  - arXiv
DO  - 10.48550/arxiv.2607.24796
UR  - https://arxiv.org/abs/2607.24796
AB  - Parkinson's disease (PD) selectively impairs distinct stages of motor control. Using backspace events as natural error-correction episodes in the public neuroQWERTY MIT-CSXPD dataset (n=57 subjects, 27 PD with UPDRS-III scores), we test whether passively-collected keystroke timing dissociates a variability-based pre-error monitoring signal from a speed-based post-error recovery signal. Pre-error typing instability does not track PD severity (r=-0.072, p=0.721), while post-error pause duration does (r=+0.656, p=0.0002; subject-level OLS p=1.2x10^-4, n=27, primary analysis given within-subject event clustering). A mixed-effects log-normal model with a random subject intercept confirms this while retaining full event-level power (coef=0.0250, p=1.2x10^-4, n=1,563 events), closely matching the subject-level OLS despite an unrelated estimation strategy. Error-detection latency also correlates with UPDRS-III (r=+0.660), confirming the dissociation is between variability and speed, not detection and correction per se. A log-normal accelerated failure time model yields a coefficient of 0.0255 (bootstrap 95% CI [0.0146, 0.0357]), a 2.6% increase per UPDRS-III point. After controlling the immediately thecoefficient attenuates to 0.0133 (p=3.1x10^-21),consistent w timingincrement above general bradykinesia. Results reacross both S2:r=+0.645), survive jackknife exclusion of every subject, andnatingfinger-tapping and mPower smartphone tapping (group AUC=0.836). cellent(ICC(2,1)=0.945, n=20).
ER  -