Biology subjects

Uusisaari, M. Y.

Publications and source records attributed to Uusisaari, M. Y..

3 recordsLinked to original sources

bioRxiv2024

Deep Imputation for Skeleton Data (DISK) for Behavioral Science

AbstractPose estimation methods and motion capture systems have opened doors to quan- titative measurements of animal kinematics. However, these methods are not perfect and contain missing data. Our method, Deep Imputation for Skeleton data (DISK), leverages deep learning algorithms to learn dependencies between keypoints and their dynamics to impute missing tracking data. We developed an unsupervised training scheme, which does not rely on manual annotations, and tested several neural network architectures for the imputation task. We found that transformer outperforms other architectures including graph con- volutional networks that were developed specifically for skeleton-based action recognition. We demonstrate the usability and performance of our imputation method on seven different animal skeletons including two multi-animal set-ups. With an optional estimated imputation error, DISK enables behavior scien- tists to assess the reliability of the imputed data. The imputed recordings allow to detect more episodes of motion, such as steps, and to obtain more sta- tistically robust results when comparing these episodes between experimental conditions. While animal behavior experiments are expensive and complex, track- ing errors make sometimes large portions of the experimental data unusable. DISK allows for filling in the missing information and for taking full advantage of the rich behavioral data. This stand-alone imputation package, freely available at https://github.com/bozeklab/DISK.git, is applicable to results of any track- ing method (marker-based or markerless) and allows for any type of downstream analysis.

animal behavior and cognition↗

bioRxiv2024

Mice tails function in response to external and self-generated balance perturbation on the roll plane

The functionality of mouse tails has been unexplored in the scientific literature, to the extent that they might seem to be considered as a passive appendage. Previous research on mouse locomotion has largely omitted tail dynamics, but hints at its potential use in balancing can be seen in the natural habitats and behaviors of these rodents. Here, leveraging high-speed videography, a novel naturalistic locomotory task and a simple biomechanical model analysis, we investigated the behavioral utility of the mouse tail. We observed that mice engage their tails on narrow ridge environments that mimic tree branches with narrow footholds prone to roll-plane perturbations, using different control strategies under two defined conditions: during external perturbations of the ridge where they primarily work to maintain posture and avoid falling, and during non-perturbated locomotion on the ridge, where the challenge is to dynamically control the center of mass while progressing forward. These results not only advance the existing understanding of mouse tail functionality but also open avenues for more nuanced explorations in neurobiology and biomechanics. Furthermore, we call for inclusions of tail dynamics for a holistic understanding of mammalian locomotor strategies. Author summaryWe describe and quantify the rapid mouse tail movements in response to external balance perturbations, possibly constituting a novel balance-compensatory motor program. Furthermore, we bring to light the consistent, context-dependent movements of the tail during increasingly precarious locomotion. The observations highlight the tail as an integral component of the mouse locomotory system, contributing to balancing and putatively movement efficacy, and call for inclusion of the tail in future works examining motor (dys)function.

neuroscience↗

bioRxiv2023

Endocannabinoid system modulation alters fine motor behavior in mice: insights from 3D motion capture

The neuromodulatory endocannabinoid system is a promising target for therapeutic interventions. One of the well-known behavioral effects of cannabinoid CB1 receptor activation with exogenous ligands such as THC is the inhibition of locomotor activity. However, the behavioral effects of endogenous cannabinoids are not understood. Enhancing endocannabinoid signaling offers an advantageous therapeutic strategy with limited cannabimimetic side effects, but their effects on motor function remain unclear. To reveal even the finest changes in motor function during voluntary locomotor tasks in mice, we adapted a high-speed, high-resolution marker-based motion capture, which so far has not been available in freely moving mice. Here we show that inhibition of distinct endocannabinoid metabolic pathways produces opposite effects on locomotor behavior that differ from those induced by exogenous cannabinoid receptor ligands. Selective upregulation of endocannabinoids 2-arachidonoylglycerol (2-AG) or N-arachidonoylethanolamine (AEA, anandamide) with inhibitors of their degradation (MJN110 and PF3845, respectively), produced bidirectional effects: MJN110 enhanced and PF3845 suppressed locomotor activity. Consistent differences in whole-body movement and precise step kinematics were found under distinct treatments, while analysis of locomotory episodes revealed invariant temporal microstructure, pointing towards motivational rather than motor-related mechanisms of action. The results show that the effects of manipulations of endocannabinoid system on locomotion are more diverse than previously assumed and result in distinct kinematic phenotypes.

animal behavior and cognition↗