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

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.

Transcranial direct current stimulation improves motor function in rats with 6-hydroxydopamine-induced Parkinsonism

Transcranial direct current stimulation (tDCS) is increasingly being used for Parkinson's disease (PD); however, the evaluation of its clinical impact remains complex owing to the heterogeneity of patients and treatments. Therefore, we used a unilateral 6-hydroxydopamine-induced PD rat model to investigate whether anodal tDCS of the primary motor cortex (M1) alleviates PD motor deficits. Before tDCS treatment, unilateral PD rats preferentially used the forelimb ipsilateral to the lesion in the exploratory cylinder test and showed reduced locomotor activity in the open field test. In addition, PD-related clumsy forelimb movements during treadmill walking were detected using deep learning-based video analysis (DeepLabCut). When the 5-day tDCS treatment began, the forelimb-use asymmetry was ameliorated gradually, and locomotor activity increased to pre-lesion levels. tDCS treatment also normalized unnatural forelimb movement during walking and restored a balanced gait. However, these therapeutic effects were rapidly lost or gradually disappeared when the tDCS treatment was terminated. Histological analysis at the end of the experiment revealed that the animals had moderately advanced PD, with 40-50% of dopamine neurons and fibers preserved on the injured side compared with those on the intact side. Although it remains a challenge to elucidate the neural mechanisms of the transient improvement in motor function induced by tDCS, the results of this study provide evidence that tDCS of the M1 produces positive behavioral outcomes in PD animals and provides the basis for further clinical research examining the application of tDCS in patients with PD.

Smartphone video nystagmography using convolutional neural networks: ConVNG

Background

Eye movement abnormalities are commonplace in neurological disorders. However, unaided eye movement assessments lack granularity. Although videooculography (VOG) improves diagnostic accuracy, resource intensiveness precludes its broad use. To bridge this care gap, we here validate a framework for smartphone video-based nystagmography capitalizing on recent computer vision advances.

Methods

A convolutional neural network was fine-tuned for pupil tracking using > 550 annotated frames: ConVNG. In a cross-sectional approach, slow-phase velocity of optokinetic nystagmus was calculated in 10 subjects using ConVNG and VOG. Equivalence of accuracy and precision was assessed using the “two one-sample t-test” (TOST) and Bayesian interval-null approaches. ConVNG was systematically compared to OpenFace and MediaPipe as computer vision (CV) benchmarks for gaze estimation.

Results

ConVNG tracking accuracy reached 9–15% of an average pupil diameter. In a fully independent clinical video dataset, ConVNG robustly detected pupil keypoints (median prediction confidence 0.85). SPV measurement accuracy was equivalent to VOG (TOST p < 0.017; Bayes factors (BF) > 24). ConVNG, but not MediaPipe, achieved equivalence to VOG in all SPV calculations. Median precision was 0.30°/s for ConVNG, 0.7°/s for MediaPipe and 0.12°/s for VOG. ConVNG precision was significantly higher than MediaPipe in vertical planes, but both algorithms’ precision was inferior to VOG.

Conclusions

ConVNG enables offline smartphone video nystagmography with an accuracy comparable to VOG and significantly higher precision than MediaPipe, a benchmark computer vision application for gaze estimation. This serves as a blueprint for highly accessible tools with potential to accelerate progress toward precise and personalized Medicine.