Towards substitution of invasive telemetry: An integrated home cage concept for unobtrusive monitoring of objective physiological parameters in rodents.

This study presents a novel concept for a smart home cage design, tools, and software used to monitor the physiological parameters of mice and rats in animal-based experiments. The proposed system focuses on monitoring key clinical parameters, including heart rate, respiratory rate, and body temperature, and can also assess activity and circadian rhythm. As the basis of the smart home cage system, an in-depth analysis of the requirements was performed, including camera positioning, imaging system types, resolution, frame rates, external illumination, video acquisition, data storage, and synchronization. Two different camera perspectives were considered, and specific camera models, including two near-infrared and two thermal cameras, were selected to meet the requirements. The developed specifications, hardware models, and software are freely available via GitHub. During the first testing phase, the system demonstrated the potential of extracting vital parameters such as respiratory and heart rate. This technology has the potential to reduce the need for implantable sensors while providing reliable and accurate physiological data, leading to refinement and improvement in laboratory animal care.

Camera-Based Respiration Monitoring of Unconstrained Rodents.

Animal research has always been crucial for various medical and scientific breakthroughs, providing information on disease mechanisms, genetic predisposition to diseases, and pharmacological treatment. However, the use of animals in medical research is a source of great controversy and ongoing debate in modern science. To ensure a high level of bioethics, new guidelines have been adopted by the EU, implementing the 3R principles to replace animal testing wherever possible, reduce the number of animals per experiment, and refine procedures to minimize stress and pain. Supporting these guidelines, this article proposes an improved approach for unobtrusive, continuous, and automated monitoring of the respiratory rate of laboratory rats. It uses the cyclical expansion and contraction of the rats' thorax/abdominal region to determine this physiological parameter. In contrast to previous work, the focus is on unconstrained animals, which requires the algorithms to be especially robust to motion artifacts. To test the feasibility of the proposed approach, video material of multiple rats was recorded and evaluated. High agreement was obtained between RGB imaging and the reference method (respiratory rate derived from electrocardiography), which was reflected in a relative error of 5.46%. The current work shows that camera-based technologies are promising and relevant alternatives for monitoring the respiratory rate of unconstrained rats, contributing to the development of new alternatives for a continuous and objective assessment of animal welfare, and hereby guiding the way to modern and bioethical research.

A Proof-of-Concept Preclinical Study Using a Novel Thermal Insulation Device in a Porcine Kidney Auto-Transplantation Model.

Ischemia-reperfusion injury remains a fundamental problem during organ transplantation logistics. One key technical factor is the rapid allograft rewarming during the time of vascular reconstruction in the recipient. In this pilot study, a new thermal insulation bag (TIB) for organ transplantation was used. Insulation capacity, tissue compatibility, and usability were tested initially ex vivo on porcine kidneys (n = 24) followed by the first in vivo usage. Fourteen female German landrace pigs underwent kidney auto-transplantation after 24 h cold storage (4 °C). During the implantation process the kidney was either insulated with the new TIB, or it was not thermo-protected at all, which represents the clinical standard. In this proof-of-concept study, the usability (knife-to-skin-time) and the general thermal capacity (30 min warm storage at 38 °C ex vivo p < 0.001) was shown. The clinical outcome showed significant differences in the determination of CRP and pi-GST levels. Syndecan-1 Antibody staining showed clear significant higher counts in the control group (p < 0.01) indicating epithelial damage. However, the effect on renal outcomes in not severely pre-damaged kidneys does not appear to be conclusively significant. A close follow-up study is warranted, especially in the context of marginal organs or in cases where anastomosis-times are prolonged due to surgical complexity (e.g., multiple vessels and complex reconstructions).

A Modular System for Detection, Tracking and Analysis of Human Faces in Thermal Infrared Recordings.

We present a system that utilizes a range of image processing algorithms to allow fully automated thermal face analysis under both laboratory and real-world conditions. We implement methods for face detection, facial landmark detection, face frontalization and analysis, combining all of these into a fully automated workflow. The system is fully modular and allows implementing own additional algorithms for improved performance or specialized tasks. Our suggested pipeline contains a histogtam of oriented gradients support vector machine (HOG-SVM) based face detector and different landmark detecion methods implemented using feature-based active appearance models, deep alignment networks and a deep shape regression network. Face frontalization is achieved by utilizing piecewise affine transformations. For the final analysis, we present an emotion recognition system that utilizes HOG features and a random forest classifier and a respiratory rate analysis module that computes average temperatures from an automatically detected region of interest. Results show that our combined system achieves a performance which is comparable to current stand-alone state-of-the-art methods for thermal face and landmark datection and a classification accuracy of 65.75% for four basic emotions.

Ocular and cardiac artifact rejection for real-time analysis in MEG.

BACKGROUND: Recently, magnetoencephalography (MEG) based real-time brain computing interfaces (BCI) have been developed to enable novel and promising methods for neuroscience research. It is well known that artifact rejection prior to source localization largely enhances the localization accuracy. However, many BCI approaches neglect real-time artifact removal due to its time consuming process. NEW METHOD: The method (referred to as ocular and cardiac artifact rejection for real-time analysis, OCARTA) is based on constrained independent component analysis (cICA), where a priori information of the underlying source signals is used to optimize and accelerate signal decomposition. Thereby, prior information is incorporated by using the subject's individual cardiac and ocular activity. The algorithm automatically uses different separation strategies depending on the underlying source activity. RESULTS: OCARTA was tested and applied to data from three different but most commonly used MEG systems (4D-Neuroimaging, VSM MedTech Inc. and Elekta Neuromag). Ocular and cardiac artifacts were effectively reduced within one iteration at a time delay of 1ms performed on a standard PC (Intel Core i5-2410M). COMPARISON WITH EXISTING METHODS: The artifact rejection results achieved with OCARTA are in line with the results reported for offline ICA-based artifact rejection methods. CONCLUSION: Due to the fast and subject-specific signal decomposition the new approach introduced here is capable of real-time ocular and cardiac artifact rejection.

Methods for pulse artefact reduction: experiences with EEG data recorded at 9.4 T static magnetic field.

BACKGROUND: The feasibility of recording electroencephalography (EEG) at ultra-high static magnetic fields up to 9.4 T was recently demonstrated and is expected to be incorporated into functional magnetic resonance imaging (fMRI) studies at 9.4 T. Correction of the pulse artefact (PA) is a significant challenge since its amplitude is proportional to the strength of the magnetic field in which EEG is recorded. NEW METHOD: We conducted a study in which different PA correction methods were applied to EEG data recorded inside a 9.4 T scanner in order to retrieve visual P100 and auditory P300 evoked potentials. We explored different PA reduction methods, including the optimal basis set (OBS) method as well as objective and subjective component rejection using independent component analysis (ICA). RESULTS: ICA followed by objective rejection of components is optimal for retrieving visual P100 and auditory P300 from EEG data recorded inside the scanner. COMPARISON WITH EXISTING METHODS: Previous studies suggest that OBS or OBS followed by ICA are optimal for retrieving evoked potentials at 3T. In our EEG data recorded at 9.4 T OBS performed alone was not fully optimal for the identification of evoked potentials. OBS followed by ICA was partially effective. CONCLUSIONS: In this study ICA has been shown to be an important tool for correcting the PA in EEG data recorded at 9.4 T, particularly when automated rejection of components is performed.

A constrained ICA approach for real-time cardiac artifact rejection in magnetoencephalography.

Recently, magnetoencephalography (MEG)-based real-time brain computing interfaces (BCI) have been developed to enable novel and promising methods of neuroscience research and therapy. Artifact rejection prior to source localization largely enhances the localization accuracy. However, many BCI approaches neglect real-time artifact removal due to its time consuming processing. With cardiac artifact rejection for real-time analysis (CARTA), we introduce a novel algorithm capable of real-time cardiac artifact (CA) rejection. The method is based on constrained independent component analysis (ICA), where a priori information of the underlying source signal is used to optimize and accelerate signal decomposition. In CARTA, this is performed by estimating the subject's individual density distribution of the cardiac activity, which leads to a subject-specific signal decomposition algorithm. We show that the new method is capable of effectively reducing CAs within one iteration and a time delay of 1 ms. In contrast, Infomax and Extended Infomax ICA converged not until seven iterations, while FastICA needs at least ten iterations. CARTA was tested and applied to data from three different but most common MEG systems (4-D-Neuroimaging, VSM MedTech Inc., and Elekta Neuromag). Therefore, the new method contributes to reliable signal analysis utilizing BCI approaches.

A new constrained ICA approach for optimal signal decomposition in polarized light imaging.

BACKGROUND: Polarized light imaging (PLI) has evolved into a powerful neuroimaging tool to analyze fiber tracts with submillimeter resolution in microtome sections of postmortem human brain tissue. In PLI polarized light changes its polarization state while passing through birefringent tissue, i.e., myelinated axons, which results in sinusoidal signals that characterize different fiber orientations. Noise, light scatter and filter inhomogeneities of the polarimeter interfere with the original sinusoidal PLI signals, which have direct effects on the accuracy of subsequent fiber modeling. New method: In our recent publications we have shown that the sinusoidal signal at each pixel location in PLI images can be restored utilizing independent component analysis (ICA). We now have further improved the signal separation quality by introducing a new constrained ICA algorithm (cICAP) where the component selection is directly included. In cICAP an analytical expression of the expected signal of interest is implemented as a priori information. RESULTS: The algorithm precisely decomposes the deteriorated PLI signals into its underlying source signals. As such, the approach enhances sinusoidal basis functions and is therefore optimal for the extraction of independent spatial maps from PLI images. Comparison with existing methods: The new algorithm performs better and is faster compared to other well-known ICA algorithms. CONCLUSION: The decomposition in cICAP is optimal with respect to separation and identification of the sinusoidal nature of the PLI signal. In this way the identification of the relevant components is automatically included and does not require any further component selection tool.

Automatic identification of gray and white matter components in polarized light imaging.

Polarized light imaging (PLI) enables the visualization of fiber tracts with high spatial resolution in microtome sections of postmortem brains. Vectors of the fiber orientation defined by inclination and direction angles can directly be derived from the optical signals employed by PLI analysis. The polarization state of light propagating through a rotating polarimeter is varied in such a way that the detected signal of each spatial unit describes a sinusoidal signal. Noise, light scatter and filter inhomogeneities, however, interfere with the original sinusoidal PLI signals, which in turn have direct impact on the accuracy of subsequent fiber tracking. Recently we showed that the primary sinusoidal signals can effectively be restored after noise and artifact rejection utilizing independent component analysis (ICA). In particular, regions with weak intensities are greatly enhanced after ICA based artifact rejection and signal restoration. Here, we propose a user independent way of identifying the components of interest after decomposition; i.e., components that are related to gray and white matter. Depending on the size of the postmortem brain and the section thickness, the number of independent component maps can easily be in the range of a few ten thousand components for one brain. Therefore, we developed an automatic and, more importantly, user independent way of extracting the signal of interest. The automatic identification of gray and white matter components is based on the evaluation of the statistical properties of the so-called feature vectors of each individual component map, which, in the ideal case, shows a sinusoidal waveform. Our method enables large-scale analysis (i.e., the analysis of thousands of whole brain sections) of nerve fiber orientations in the human brain using polarized light imaging.