Quantification of early learning and movement sub-structure predictive of motor performance

Abstract

Time-to-fall off an accelerating rotating rod (rotarod) is widely utilized to evaluate rodent motor performance. We reasoned that this simple outcome could be refined with additional measures explicit in the task (however inconspicuously) to examine what we call movement sub-structure. Our goal was to characterize normal variation or motor impairment more robustly than by using time-to-fall. We also hypothesized that measures (or features) early in the sub-structure could anticipate the learning expected of a mouse undergoing serial trials. Using normal untreated and baclofen-treated movement-impaired mice, we defined these features and automated their analysis using paw video-tracking in three consecutive trials, including paw location, speed, acceleration, variance and approximate entropy. Spectral arc length yielded speed and acceleration uniformity. We found that, in normal mice, paw movement smoothness inversely correlated with rotarod time-to-fall for the three trials. Greater approximate entropy in vertical movements, and opposite changes in horizontal movements, correlated with greater first-trial time-to-fall. First-trial horizontal approximate entropy in the first few seconds predicted subsequent time-to-fall. This allowed for the separation, after only one rotarod trial, of different-weight, untreated mouse groups, and for the detection of mice otherwise unimpaired after baclofen, which displayed a time-to-fall similar to control. A machine-learning support vector machine classifier corroborated these findings. In conclusion, time-to-fall off a rotarod correlated well with several measures, including some obtained during the first few seconds of a trial, and some responsive to learning over the first two trials, allowing for predictions or preemptive experimental manipulations before learning completion.

Neutrophil-derived cathelicidin promotes cerebral angiogenesis after ischemic stroke

“…In addition, sustained depletion of neutrophils until late phase increased infarct volume with the extent similar to CAMP −/− mice (Figure 2(f) to (g)). In terms of the neurological function after MCAO, we found that the modified Garcia score and time-to-fall off an accelerating rotating rod (rotarod test) 34 were both significantly decreased in the CAMP −/− mice compared to the WT mice 7 days after MCAO (Figure 2(h)). In the grid-walking test, the percentage of forelimb foot fault (right impaired forelimb foot fault/total amount of right forelimb step) were significantly higher in CAMP −/− mice compared to WT mice (Figure 2(h)) during 14 days after MCAO.…”

Low-intensity transcranial focused ultrasound suppresses pain by modulating pain processing brain circuits

“…8a, left panel). The rotarod test has been well-established to evaluate motor coordination and balance by assessing the time of falling from a programmed rotating rod 52, 53 . After the single-session tFUS with a PRF of 40 Hz at S1HL or thalamus and multi-session tFUS stimulation with the PRF of 40 Hz at S1HL or insula for 14 days, we did not find any significant difference in the averaged latency to fall compared with pre-stimulation baseline (Fig.…”

Identification of Glucose Transport Modulators In Vitro and Method for Their Deep Learning Neural Network Behavioral Evaluation in Glucose Transporter 1–Deficient Mice

“…Rate of change of speed: Difference in the instantaneous speed between two successive frames divided by the inter-frame duration. Speed Smoothness/Spectral Arc Length: Smoothness of speed of a limb using MATLAB's 'fft' function (Jakkamsetti et al, 2021).…”

“…While measuring these relations for the first two drugs (acetazolamide and baclofen), we observed differential effects on various measures of gait, indicating that individual body part movements are not uniformly amenable to modulation. Thus, to facilitate and expand this analysis, we drew upon a machine learning assisted method that we previously developed for rotarod motor evaluation (Jakkamsetti et al, 2021) using deep learning neural networks. This allowed for the identification of 49 locomotor parameters that differentiated G1D from control, which were differentially sensitive to treatment with saline as control or glucose as therapy.…”