SCIENTIFIC HUB

Follow the evidence.

SCIENTIFIC LITERATURE · MED

State transitions in the substantia nigra reticulata predict the onset of motor deficits in models of progressive dopamine depletion in mice.

Elife · 2019-03-06

DOI: 10.7554/elife.42746PMID: 30839276

Open accessResearch Support, Non-U.S. Gov't; research-article; Journal Article; Research Support, N.I.H., ExtramuralOriginal source ↗

Research overview

The text below is the source article’s original abstract under a verified reuse license. cc by

Parkinson's disease (PD) is a progressive neurodegenerative disorder whose cardinal motor symptoms are attributed to dysfunction of basal ganglia circuits under conditions of low dopamine. Despite well-established physiological criteria to define basal ganglia dysfunction, correlations between individual parameters and motor symptoms are often weak, challenging their predictive validity and causal contributions to behavior. One limitation is that basal ganglia pathophysiology is studied only at end-stages of depletion, leaving an impoverished understanding of when deficits emerge and how they evolve over the course of depletion. In this study, we use toxin- and neurodegeneration-induced mouse models of dopamine depletion to establish the physiological trajectory by which the substantia nigra reticulata (SNr) transitions from the healthy to the diseased state. We find that physiological progression in the SNr proceeds in discrete state transitions that are highly stereotyped across models and correlate well with the prodromal and symptomatic stages of behavior.

Scientific assessment

01

Research question

Assess the question within the original study’s scope.

02

Methods & sample

Check the source article for study design and sample details.

03

Key findings

An editorially approved findings summary is not yet published for this record.

04

Limitations

Generalizability, bias and clinical meaning require separate assessment.

Methods, samples and clinical conclusions are not inferred from bibliographic metadata.