KineWheel-DeepLabCut Automated Paw Annotation Using Alternating Stroboscopic UV and White Light Illumination.

Uncovering the relationships between neural circuits, behavior, and neural dysfunction may require rodent pose tracking. While open-source toolkits such as DeepLabCut have revolutionized markerless pose estimation using deep neural networks, the training process still requires human intervention for annotating key points of interest in video data. To further reduce human labor for neural network training, we developed a method that automatically generates annotated image datasets of rodent paw placement in a laboratory setting. It uses invisible but fluorescent markers that become temporarily visible under UV light. Through stroboscopic alternating illumination, adjacent video frames taken at 720 Hz are either UV or white light illuminated. After color filtering the UV-exposed video frames, the UV markings are identified and the paw locations are deterministically mapped. This paw information is then transferred to automatically annotate paw positions in the next white light-exposed frame that is later used for training the neural network. We demonstrate the effectiveness of our method using a KineWheel-DeepLabCut setup for the markerless tracking of the four paws of a harness-fixed mouse running on top of the transparent wheel with mirror. Our automated approach, made available open-source, achieves high-quality position annotations and significantly reduces the need for human involvement in the neural network training process, paving the way for more efficient and streamlined rodent pose tracking in neuroscience research.

Cognitive training of mice attenuates age-related decline in associative learning and behavioral flexibility.

Identifying factors that influence age-related cognitive decline is crucial, given its severe personal and societal impacts. However, studying aging in human or animal models is challenging due to the significant variability in aging processes among individuals. Additionally, longitudinal and cross-sectional studies often produce differing results. In this context, home-cage-based behavioral analysis over lifespans has emerged as a significant method in recent years. This study aimed to explore how prior experience affects cognitive performance in mice of various age groups (4, 12, and 22 months) using a home-cage-based touchscreen test battery. In this automated system, group-housed, ID-chipped mice primarily obtain their food during task performance throughout the day, motivated by their own initiative, without being subjected to food deprivation. Spatial working memory and attention were evaluated using the trial unique non-matching to location (TUNL) and the five-choice serial reaction time task (5-CSRTT), respectively. The same set of mice learned both of these demanding tasks. While signs of cognitive decline were already apparent in middle-aged mice, older mice exhibited poorer performance in both tasks. Mice at both 12 and 22 months displayed an increase in perseverance and a decrease in the percentage of correct responses in the TUNL test compared to the 4-month-old mice. Furthermore, during the 5-CSRTT, they exhibited higher rates of omissions and premature responses compared to their younger counterparts. Additionally, the correct response rate in 22-month-old mice was lower than that of the 4-month-old ones. However, mice that had undergone cognitive training at 4 months maintained high-performance levels when re-tested at 12 months, showing an increase in correct responses during TUNL testing compared to their untrained controls. In the 5-CSRTT, previously trained mice demonstrated higher correct response rates, fewer omissions, and reduced premature responses compared to naive control mice. Notably, even when assessed on a visual discrimination and behavioral flexibility task at 22 months, experienced mice outperformed naive 4-month-old mice. These findings highlight the advantages of early-life cognitive training and suggest that its benefits extend beyond the cognitive domains primarily targeted during early training. The success of this study was significantly aided by the fully automated home-cage-based testing system, which allows for high throughput with minimal human intervention.

LabNet hardware control software for the Raspberry Pi.

Single-board computers such as the Raspberry Pi make it easy to control hardware setups for laboratory experiments. GPIOs and expansion boards (HATs) give access to a whole range of sensor and control hardware. However, controlling such hardware can be challenging, when many experimental setups run in parallel and the time component is critical. LabNet is a C++ optimized control layer software to give access to the Raspberry Pi connected hardware over a simple network protocol. LabNet was developed to be suitable for time-critical operations, and to be simple to expand. It leverages the actor model to simplify multithreading programming and to increase modularity. The message protocol is implemented in Protobuf and offers performance, small message size, and supports a large number of programming languages on the client side. It shows good performance compared to locally executed tools like Bpod, pyControl, or Autopilot and reaches sub-millisecond range in network communication latencies. LabNet can monitor and react simultaneously to up to 14 pairs of digital inputs, without increasing latencies. LabNet itself does not provide support for the design of experimental tasks. This is left to the client. LabNet can be used for general automation in experimental laboratories with its control PC located at some distance. LabNet is open source and under continuing development.

Spatial cognition in a virtual reality home-cage extension for freely moving rodents.

Virtual reality (VR) environments are a powerful tool to investigate brain mechanisms involved in the behavior of animals. With this technique, animals are usually head fixed or secured in a harness, and training for cognitively more complex VR paradigms is time consuming. A VR apparatus allowing free animal movement and the constant operator-independent training of tasks would enable many new applications. Key prospective usages include brain imaging of animal behavior when carrying a miniaturized mobile device such as a fluorescence microscope or an optetrode. Here, we introduce the Servoball, a spherical VR treadmill based on the closed-loop tracking of a freely moving animal and feedback counterrotation of the ball. Furthermore, we present the complete integration of this experimental system with the animals' group home cage, from which single individuals can voluntarily enter through a tunnel with radio-frequency identification (RFID)-automated access control and commence experiments. This automated animal sorter functions as a mechanical replacement of the experimenter. We automatically trained rats using visual or acoustic cues to solve spatial cognitive tasks and recorded spatially modulated entorhinal cells. When electrophysiological extracellular recordings from awake behaving rats were performed, head fixation can dramatically alter results, so that any complex behavior that requires head movement is impossible to achieve. We circumvented this problem with the use of the Servoball in open-field scenarios, as it allows the combination of open-field behavior with the recording of nerve cells, along with all the flexibility that a virtual environment brings. This integrated home cage with a VR arena experimental system permits highly efficient experimentation for complex cognitive experiments.NEW & NOTEWORTHY Virtual reality (VR) environments are a powerful tool for the investigation of brain mechanisms. We introduce the Servoball, a VR treadmill for freely moving rodents. The Servoball is integrated with the animals' group home cage. Single individuals voluntarily enter using automated access control. Training is highly time-efficient, even for cognitively complex VR paradigms.

A circle-monitor for computerised assessment of visual neglect in peripersonal space.

UNLABELLED: Current assessment of visual neglect involves paper-and-pencil tests or computer-based tasks. Both have been criticised because of their lack of ecological validity as target stimuli can only be presented in a restricted visual range. This study examined the user-friendliness and diagnostic strength of a new "Circle-Monitor" (CM), which enlarges the range of the peripersonal space, in comparison to a standard paper-and-pencil test (Neglect-Test, NET). METHODS: Ten stroke patients with neglect and ten age-matched healthy controls were examined by the NET and the CM test comprising of four subtests (Star Cancellation, Line Bisection, Dice Task, and Puzzle Test). RESULTS: The acceptance of the CM in elderly controls and neglect patients was high. Participants rated the examination by CM as clear, safe and more enjoyable than NET. Healthy controls performed at ceiling on all subtests, without any systematic differences between the visual fields. Both NET and CM revealed significant differences between controls and patients in Line Bisection, Star Cancellation and visuo-constructive tasks (NET: Figure Copying, CM: Puzzle Test). Discriminant analyses revealed cross-validated assignment of patients and controls to groups was more precise when based on the CM (hit rate 90%) as compared to the NET (hit rate 70%). CONCLUSION: The CM proved to be a sensitive novel tool to diagnose visual neglect symptoms quickly and accurately with superior diagnostic validity compared to a standard neglect test while being well accepted by patients. Due to its upgradable functions the system may also be a valuable tool not only to test for non-visual neglect symptoms, but also to provide treatment and assess its outcome.