Automated quantitative gait analysis in animal models of movement disorders

<p>Abstract</p> <p>Background</p> <p>Accurate and reproducible behavioral tests in animal models are of major importance in the development and evaluation of new therapies for central nervous system disease. In this study we investigated for the first time gait parameters of rat models for Parkinson's disease (PD), Huntington's disease (HD) and stroke using the Catwalk method, a novel automated gait analysis test. Static and dynamic gait parameters were measured in all animal models, and these data were compared to readouts of established behavioral tests, such as the cylinder test in the PD and stroke rats and the rotarod tests for the HD group.</p> <p>Results</p> <p>Hemiparkinsonian rats were generated by unilateral injection of the neurotoxin 6-hydroxydopamine in the striatum or in the medial forebrain bundle. For Huntington's disease, a transgenic rat model expressing a truncated huntingtin fragment with multiple CAG repeats was used. Thirdly, a stroke model was generated by a photothrombotic induced infarct in the right sensorimotor cortex. We found that multiple gait parameters were significantly altered in all three disease models compared to their respective controls. Behavioural deficits could be efficiently measured using the cylinder test in the PD and stroke animals, and in the case of the PD model, the deficits in gait essentially confirmed results obtained by the cylinder test. However, in the HD model and the stroke model the Catwalk analysis proved more sensitive than the rotarod test and also added new and more detailed information on specific gait parameters.</p> <p>Conclusion</p> <p>The automated quantitative gait analysis test may be a useful tool to study both motor impairment and recovery associated with various neurological motor disorders.</p

Bioluminescence imaging of stroke-induced endogenous neural stem cell response

Brain injury following stroke affects neurogenesis in the adult mammalian brain. However, a complete under¬standing of the origin and fate of the endogenous neural stem cells (eNSCs) in vivo is missing. Tools and technol¬ogy that allow non-invasive imaging and tracking of eNSCs in living animals will help to overcome this hurdle. In this study, we aimed to monitor eNSCs in a photothrombotic (PT) stroke model using in vivo bioluminescence imaging (BLI). In a first strategy, inducible transgenic mice expressing firefly luciferase (Fluc) in the eNSCs were generated. In animals that received stroke, an increased BLI signal originating from the infarct region was ob¬served. However, due to histological limitations, the identity and exact origin of cells contributing to the in¬creased BLI signal could not be revealed. To overcome this limitation, we developed an alternative strategy employing stereotactic injection of conditional lentiviral vectors (Cre-Flex LVs) encoding Fluc and eGFP in the subventricular zone (SVZ) of Nestin-Cre transgenic mice, thereby specifically labeling the eNSCs. Upon induction of stroke, increased eNSC proliferation resulted in a significant increase in BLI signal between 2 days and 2 weeks after stroke, decreasing after 3 months. Additionally, the BLI signal relocalized from the SVZ towards the infarct region during the 2 weeks following stroke. Histological analysis at 90 days post stroke showed that in the peri-infarct area, 36% of labeled eNSC progeny differentiated into astrocytes, while 21% differentiated into mature neu¬rons. In conclusion, we developed and validated a novel imaging technique that unequivocally demonstrates that nestin+ eNSCs originating from the SVZ respond to stroke injury by increased proliferation, migration towards the infarct region and differentiation into both astrocytes and neurons. In addition, this new approach allows non-invasive and specific monitoring of eNSCs overtime, opening perspectives for preclinical evaluation of can¬didate stroke therapeutics

Offline phase analysis and optimization for multi-configuration processors

Energy consumption has become a major issue for modem microprocessors. In previous work, several techniques were presented to reduce the overall energy consumption by dynamically adapting various hardware structures. Most approaches however lack the ability to deal efficiently with the huge amount of possible hardware configurations in case of multiple adaptive structures. In this paper, we present a framework that is able to deal with this huge configuration space problem. We first identify phases through profiling and determine the optimal hardware configuration per phase using an efficient offline search algorithm. During program execution, we inspect the phase behavior and adapt the hardware on a per-phase basis. This paper also proposes a new phase classification scheme as well as a phase correspondence metric to quantify the phase similarity between different runs of a program. Using SPEC2000 benchmarks, we show that our adaptive processing framework achieves an energy reduction of 40% on average with an average performance degradation of only 2%

A detailed study on phase predictors

Most programs are repetitive, meaning that some parts of a program are executed more than once. As a result, a number of phases can be extracted in which each phase exhibits similar behavior. These phases can then be exploited for various purposes such as hardware adaptation for energy efficiency. Temporal phase classification schemes divide the execution of a program into consecutive (fixed-length) intervals. Intervals showing similar behavior are grouped into a phase. When a temporal scheme is used in an on-line system, phase predictors are necessary to predict when the next phase transition will occur and what the next phase will be. In this paper, we analyze and compare a number of existing state-of-the-art phase predictors using the SPEC CPU2000 benchmarks. The design space we explore is huge. We conclude that the 2-level burst predictor with confidence and conditional update is today's most accurate phase predictor within reasonable hardware budgets