bioRxiv Science⌕ Search

Biology subjects

Ghimire, A.

Publications and source records attributed to Ghimire, A..

2 recordsLinked to original sources

Sleep alterations in a mouse model of Spinocerebellar ataxia type 3

BackgroundSpinocerebellar ataxia type 3 (SCA3) is a neurodegenerative disorder showing progressive neuronal loss in several brain areas and a broad spectrum of motor and non-motor symptoms, including ataxia and altered sleep. While sleep disturbances are known to play pathophysiologic roles in other neurodegenerative disorders, their impact on SCA3 is unknown. ObjectivesUsing state-of-the art spectrographic measurements, we sought to quantitatively characterize sleep electroencephalography (EEG) in a SCA3 transgenic mouse model with confirmed disease phenotype. MethodsWe first measured motor phenotypes in 18-31-week-old homozygous and hemizygous SCA3 YACMJD84.2 mice and non-transgenic wild-type littermate mice during lights-on and lights-off periods. We next implanted electrodes to obtain 12-hour (zeitgeber time 0-12) EEG recordings for three consecutive days when the mice were 26-36 weeks old. We then analyzed EEG-based sleep structure data to quantify differences between homozygous, hemizygous, and wild-type mice. ResultsCompared to wild-type littermates, SCA3 homozygous mice display: i) increased duration of rapid-eye movement sleep (REM) and fragmentation in all sleep and wake states; ii) higher beta power oscillations during REM and non-REM (NREM); and iii) additional spectral power band alterations during REM and wake. ConclusionsOur data show that sleep architecture and EEG spectral power are dysregulated in homozygous SCA3 mice, indicating that common sleep-related etiologic factors may underlie mouse and human SCA3 phenotypes.

neuroscience↗

LabGym: A versatile computational tool for automatic quantification of user-defined animal behaviors

Quantifying animal behavior is important for many branches of biological research. Current computational tools for behavioral quantification typically rely on a few pre-defined, simplified features to identify a behavior. However, such an approach restricts the information used and the tools applicability to a limited range of behavior types or species. Here we report a new tool, LabGym, for quantifying animal behaviors without such limitations. Combining a novel approach for effective evaluation of animal motion with customizable convolutional recurrent networks for capturing spatiotemporal details, LabGym provides holistic behavioral assessment and accurately identify user-defined animal behaviors without restrictions on behavior types or animal species. It then provides quantitative measurements of each behavior, which quantify the behavior intensity and the body kinematics during the behavior. LabGym requires neither any intermediate step for processing features that causes information loss nor programming knowledge from users for post-hoc analysis. It tracks multiple animals simultaneously in various experimental settings for high-throughput and versatile analysis. It also provides users a way to generate visualizable behavioral datasets that are valuable resources for the research community. We demonstrate its efficacy in capturing subtle behavioral changes in animals ranging from soft-bodied invertebrates to mammals.

animal behavior and cognition↗