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Updated: Nov 12, 2025

Applying the RatWalker System for Gait Analysis in a Genetic Rat Model of Parkinson's Disease
Published on: January 18, 2021
Reduction of falls in a rat model of PD falls by the M1 PAM TAK-071
Aaron Kucinski1, Martin Sarter2
1Department of Psychology, University of Michigan, 530 Church Street, Ann Arbor, MI, 48103, USA.
Rationale:
In addition to the disease-defining motor symptoms, patients with Parkinson's disease (PD) exhibit gait dysfunction, postural instability, and a propensity for falls. These dopamine (DA) replacement-resistant symptoms in part have been attributed to loss of basal forebrain (BF) cholinergic neurons and, in interaction with striatal dopamine (DA) loss, to the resulting disruption of the attentional control of balance and complex movements. Rats with dual cholinergic-DA losses ("DL rats") were previously demonstrated to model PD falls and associated impairments of gait and balance.
Objectives:
We previously found that the muscarinic M1-positive allosteric modulator (PAM) TAK-071 improved the attentional performance of rats with BF cholinergic losses. Here, we tested the hypotheses that TAK-071 reduces fall rates in DL rats.
Results:
Prior to DL surgery, female rats were trained to traverse a rotating straight rod as well as a rod with two zigzag segments. DL rats were refamiliarized with such traversals post-surgery and tested over 7 days on increasingly demanding testing conditions. TAK-071 (0.1, 0.3 mg/kg, p.o.) was administered prior to daily test sessions over this 7-day period. As before, DL rats fell more frequently than sham-operated control rats. Treatment of DL rats with TAK-071 reduced falls from the rotating rod and the rotating zigzag rod, specifically when the angled part of the zigzag segment, upon entering, was at a steep, near vertical angle.
Conclusions:
TAK-071 may benefit complex movement control, specifically in situations which disrupt the patterning of forward movement and require the interplay between cognitive and motor functions to modify movement based on information about the state of dynamic surfaces, balance, and gait.
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