Different Purkinje cell pathologies cause specific patterns of progressive gait ataxia in mice.

Gait ataxia is one of the most common and impactful consequences of cerebellar dysfunction. Purkinje cells, the sole output neurons of the cerebellar cortex, are often involved in the underlying pathology, but their specific functions during locomotor control in health and disease remain obfuscated. We aimed to describe the effect of gradual adult-onset Purkinje cell degeneration on gaiting patterns in mice, and to determine whether two different mechanisms that both lead to Purkinje cell degeneration cause different patterns in the development of gait ataxia. Using the ErasmusLadder together with a newly developed limb detection algorithm and machine learning-based classification, we subjected mice to a challenging locomotor task with detailed analysis of single limb parameters, intralimb coordination and whole-body movement. We tested two Purkinje cell-specific mouse models, one involving stochastic cell death due to impaired DNA repair mechanisms (Pcp2-Ercc1-/-), the other carrying the mutation that causes spinocerebellar ataxia type 1 (Pcp2-ATXN1[82Q]). Both mouse models showed progressive gaiting deficits, but the sequence with which gaiting parameters deteriorated was different between mouse lines. Our longitudinal approach revealed that gradual loss of Purkinje cell function can lead to a complex pattern of loss of function over time, and that this pattern depends on the specifics of the pathological mechanisms involved. We hypothesize that this variability will also be present in disease progression in patients, and that our findings will facilitate the study of therapeutic interventions in mice, as subtle changes in locomotor abilities can be quantified by our methods.

Four-dimensional computational ultrasound imaging of brain hemodynamics.

Four-dimensional ultrasound imaging of complex biological systems such as the brain is technically challenging because of the spatiotemporal sampling requirements. We present computational ultrasound imaging (cUSi), an imaging method that uses complex ultrasound fields that can be generated with simple hardware and a physical wave prediction model to alleviate the sampling constraints. cUSi allows for high-resolution four-dimensional imaging of brain hemodynamics in awake and anesthetized mice.