Evidence map›Paper›PMID 30792396›Full record

ArticleScientific reports2019

Translational methods to detect asymmetries in temporal and spatial walking metrics in parkinsonian mouse models and human subjects with Parkinson's disease.

Lauren Broom, Audrey Worley, Fay Gao, Laura D Hernandez, Christine E Ashton, Ludy C Shih, Veronique G VanderHorst

Open access · goldAbstract readValidation Study
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
0.5field-weighted citation impact, top 35% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
  2. Article
  3. COMPREHENSIVE ANALYSIS OF MURINE GAIT DURING SKELETAL MATURATION.bioRxiv : the preprint server for biology · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Hemispheric asymmetries in mental disorders: evidence from rodent studies.Journal of neural transmission (Vienna, Austria : 1996) · 2023
    Review
  10. Article
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Lauren BroomDepartment of Neurology, Division of Movement Disorders, Beth Israel Deaconess Medical Center and Harvard Medical School, 3 Blackfan Circle, Boston, MA, 02115, USA.
Audrey WorleyDepartment of Neurology, Division of Movement Disorders, Beth Israel Deaconess Medical Center and Harvard Medical School, 3 Blackfan Circle, Boston, MA, 02115, USA.
Fay GaoDepartment of Neurology, Division of Movement Disorders, Beth Israel Deaconess Medical Center and Harvard Medical School, 3 Blackfan Circle, Boston, MA, 02115, USA.
Laura D HernandezDepartment of Neurology, Division of Movement Disorders, Beth Israel Deaconess Medical Center and Harvard Medical School, 3 Blackfan Circle, Boston, MA, 02115, USA.
Christine E AshtonDepartment of Neurology, Division of Movement Disorders, Beth Israel Deaconess Medical Center and Harvard Medical School, 3 Blackfan Circle, Boston, MA, 02115, USA.ORCID 0000-0002-3966-2111
Ludy C ShihDepartment of Neurology, Division of Movement Disorders, Beth Israel Deaconess Medical Center and Harvard Medical School, 3 Blackfan Circle, Boston, MA, 02115, USA.
Veronique G VanderHorstDepartment of Neurology, Division of Movement Disorders, Beth Israel Deaconess Medical Center and Harvard Medical School, 3 Blackfan Circle, Boston, MA, 02115, USA. vvanderh@bidmc.harvard.edu.ORCID 0000-0002-4089-5393
Beth Israel Deaconess Medical Center · US

Funding

Harvard Clinical and Translational Science CenterUL1TR001102 · NCATS · HARVARD MEDICAL SCHOOL · PI NADLER, LEE MARSHALL · 2013 to 2017
$106.2M
Functions and connections of inhibitory reticulospinal locomotor systemsR01NS079623 · NINDS · BETH ISRAEL DEACONESS MEDICAL CENTER · PI VANDERHORST, VERONIQUE · 2012 to 2016
$1.9M
NCATS NIH HHS UL1 TR001102NINDS NIH HHS R01 NS079623
6 · The paper itself

Abstract

Clinical signs in Parkinson's disease (PD), including parkinsonian gait, are often asymmetric, but mechanisms underlying gait asymmetries in PD remain poorly understood. A translational toolkit, a set of standardized measures to capture gait asymmetries in relevant mouse models and patients, would greatly facilitate research efforts. We validated approaches to quantify asymmetries in placement and timing of limbs in mouse models of parkinsonism and human PD subjects at speeds that are relevant for human walking. In mice, we applied regression analysis to compare left and right gait metrics within a condition. To compare alternation ratios of left and right limbs before and after induction of parkinsonism, we used circular statistics. Both approaches revealed asymmetries in hind- and forelimb step length in a unilateral PD model, but not in bilateral or control models. In human subjects, a similar regression approach showed a step length asymmetry in the PD but not control group. Sub-analysis of cohorts with predominant postural instability-gait impairment and with predominant tremor revealed asymmetries for step length in both cohorts and for swing time only in the former cohort. This translational approach captures asymmetries of gait in mice and patients. Application revealed striking differences between models, and that spatial and temporal asymmetries may occur independently. This approach will be useful to investigate circuit mechanisms underlying the heterogeneity between models.

Indexed as

Diagnostic Techniques, NeurologicalAgedAnimalsBiomechanical PhenomenaCase-Control StudiesCohort StudiesDisease Models, AnimalFemaleGaitGait Disorders, NeurologicHumansMaleMiceMice, Inbred C57BLMiddle AgedParkinson Disease

Identifiers

PMID30792396
PMCPMC6385183
OpenAlexW2916602575

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.