Detecting and Quantifying Ataxia-Related Motor Impairments in Rodents Using Markerless Motion Tracking With Deep Neural Networks

Phenotypic analysis of ataxia in spinocerebellar ataxia type 6 mice using DeepLabCut

This study emphasizes the benefits of open-source software such as DeepLabCut (DLC) and R to automate, customize and enhance data analysis of motor behavior. We recorded 2 different spinocerebellar ataxia type 6 mouse models while performing the classic beamwalk test, tracked multiple body parts using the markerless pose-estimation software DLC and analyzed the tracked data using self-written scripts in the programming language R. The beamwalk analysis script (BAS) counts and classifies minor and major hindpaw slips with an 83% accuracy compared to manual scoring. Nose, belly and tail positions relative to the beam, as well as the angle at the tail base relative to the nose and tail tip were determined to characterize motor deficits in greater detail. Our results found distinct ataxic abnormalities such as an increase in major left hindpaw slips and a lower belly and tail position in both SCA6 ataxic mouse models compared to control mice at 18 months of age. Furthermore, a more detailed analysis of various body parts relative to the beam revealed an overall lower body position in the SCA684Q compared to the CT-longQ27PC mouse line at 18 months of age, indicating a more severe ataxic deficit in the SCA684Q group.

Automated procedure to detect subtle motor alterations in the balance beam test in a mouse model of early Parkinson's disease

Parkinson's disease (PD) is the most common motor neurodegenerative disorder, characterised by aggregated α-synuclein (α-syn) constituting Lewy bodies. We aimed to investigate temporal changes in motor impairments in a PD mouse model induced by overexpression of α-syn with the conventional manual analysis of the balance beam test and a novel approach using machine learning algorithms to automate behavioural analysis. We combined automated animal tracking using markerless pose estimation in DeepLabCut, with automated behavioural classification in Simple Behavior Analysis. Our automated procedure was able to detect subtle motor deficits in mouse performances in the balance beam test that the manual analysis approach could not assess. The automated model revealed time-course significant differences for the "walking" behaviour in the mean interval between each behavioural bout, the median event bout duration and the classifier probability of occurrence in male PD mice, even though no statistically significant loss of tyrosine hydroxylase in the nigrostriatal system was found in either sex. These findings are valuable for early detection of motor impairment in early PD animal models. We provide a user-friendly, step-by-step guide for automated assessment of mouse performances in the balance beam test, which aims to be replicable without any significant computational and programming knowledge.

A novel naïve Bayes approach to identifying grooming behaviors in the force-plate actometric platform

Background

Self-grooming behavior in rodents serves as a valuable model for investigating stereotyped and perseverative responses. Most current grooming analyses primarily rely on video observation, which lacks standardization, efficiency, and quantitative information about force. To address these limitations, we developed an automated paradigm to analyze grooming using a force-plate actometer.

New Method

Grooming behavior is quantified by calculating ratios of relevant movement power spectral bands. These ratios are then input into a naïve Bayes classifier, trained with manual video observations. To validate the effectiveness of this method, we applied it to the behavioral analysis of the early-life striatal cholinergic interneuron depletion (CIN-d) mouse, a model of tic pathophysiology recently developed in our laboratory, which exhibits prolonged grooming responses to acute stressors. Behavioral monitoring was simultaneously conducted on the force-place actometer and by video recording.

Results

The naïve Bayes approach achieved 93.7% accurate classification and an area under the receiver operating characteristic curve of 0.894. We confirmed that male CIN-d mice displayed significantly longer grooming durations compared to controls. However, this elevation was not correlated with increases in grooming force. Notably, haloperidol, a benchmark therapy for tic disorders, reduced both grooming force and duration.

Comparison with Existing Methods

In contrast to observation-based approaches, our method affords rapid, unbiased, and automated assessment of grooming duration, frequency, and force.

Conclusions

Our novel approach enables fast and accurate automated detection of grooming behaviors. This method holds promise for high-throughput assessments of grooming stereotypies in animal models of tic disorders and other psychiatric conditions.

Slc9a6 mutation causes Purkinje cell loss and ataxia in the shaker rat

The shaker rat carries a naturally occurring mutation leading to progressive ataxia characterized by Purkinje cell (PC) loss. We previously reported on fine-mapping the shaker locus to the long arm of the rat X chromosome. In this work, we sought to identify the mutated gene underlying the shaker phenotype and confirm its identity by functional complementation. We fine-mapped the candidate region and analyzed cerebellar transcriptomes, identifying a XM_217630.9 (Slc9a6):c.[191_195delinsA] variant in the Slc9a6 gene that segregated with disease. We generated an adeno-associated virus (AAV) targeting Slc9a6 expression to PCs using the mouse L7-6 (L7) promoter. We administered the AAV prior to the onset of PC degeneration through intracerebroventricular injection and found that it reduced the shaker motor, molecular and cellular phenotypes. Therefore, Slc9a6 is mutated in shaker and AAV-based gene therapy may be a viable therapeutic strategy for Christianson syndrome, also caused by Slc9a6 mutation.

Automatic extraction of upper-limb kinematic activity using deep learning-based markerless tracking during deep brain stimulation implantation for Parkinson's disease: A proof of concept study

Optimal placement of deep brain stimulation (DBS) therapy for treating movement disorders routinely relies on intraoperative motor testing for target determination. However, in current practice, motor testing relies on subjective interpretation and correlation of motor and neural information. Recent advances in computer vision could improve assessment accuracy. We describe our application of deep learning-based computer vision to conduct markerless tracking for measuring motor behaviors of patients undergoing DBS surgery for the treatment of Parkinson's disease. Video recordings were acquired during intraoperative kinematic testing (N = 5 patients), as part of standard of care for accurate implantation of the DBS electrode. Kinematic data were extracted from videos post-hoc using the Python-based computer vision suite DeepLabCut. Both manual and automated (80.00% accuracy) approaches were used to extract kinematic episodes from threshold derived kinematic fluctuations. Active motor epochs were compressed by modeling upper limb deflections with a parabolic fit. A semi-supervised classification model, support vector machine (SVM), trained on the parameters defined by the parabolic fit reliably predicted movement type. Across all cases, tracking was well calibrated (i.e., reprojection pixel errors 0.016-0.041; accuracies >95%). SVM predicted classification demonstrated high accuracy (85.70%) including for two common upper limb movements, arm chain pulls (92.30%) and hand clenches (76.20%), with accuracy validated using a leave-one-out process for each patient. These results demonstrate successful capture and categorization of motor behaviors critical for assessing the optimal brain target for DBS surgery. Conventional motor testing procedures have proven informative and contributory to targeting but have largely remained subjective and inaccessible to non-Western and rural DBS centers with limited resources. This approach could automate the process and improve accuracy for neuro-motor mapping, to improve surgical targeting, optimize DBS therapy, provide accessible avenues for neuro-motor mapping and DBS implantation, and advance our understanding of the function of different brain areas.

A Novel SCA3 Knock-in Mouse Model Mimics the Human SCA3 Disease Phenotype Including Neuropathological, Behavioral, and Transcriptional Abnormalities Especially in Oligodendrocytes

Spinocerebellar ataxia type 3 is the most common autosomal dominant inherited ataxia worldwide, caused by a CAG repeat expansion in the Ataxin-3 gene resulting in a polyglutamine (polyQ)-expansion in the corresponding protein. The disease is characterized by neuropathological, phenotypical, and specific transcriptional changes in affected brain regions. So far, there is no mouse model available representing all the different aspects of the disease, yet highly needed for a better understanding of the disease pathomechanisms. Here, we characterized a novel Ataxin-3 knock-in mouse model, expressing a heterozygous or homozygous expansion of 304 CAACAGs in the murine Ataxin-3 locus using biochemical, behavioral, and transcriptomic approaches. We compared neuropathological, and behavioral features of the new knock-in model with the in SCA3 research mostly used YAC84Q mouse model. Further, we compared transcriptional changes found in cerebellar samples of the SCA3 knock-in mice and post-mortem human SCA3 patients. The novel knock-in mouse is characterized by the expression of a polyQ-expansion in the murine Ataxin-3 protein, leading to aggregate formation, especially in brain regions known to be vulnerable in SCA3 patients, and impairment of Purkinje cells. Along these neuropathological changes, the mice showed a reduction in body weight accompanied by gait and balance instability. Transcriptomic analysis of cerebellar tissue revealed age-dependent differential expression, enriched for genes attributed to myelinating oligodendrocytes. Comparing these changes with those found in cerebellar tissue of SCA3 patients, we discovered an overlap of differentially expressed genes pointing towards similar gene expression perturbances in several genes linked to myelin sheaths and myelinating oligodendrocytes.