Functional ultrasound (fUS) using a 1-D-array transducer normally is insufficient to capture volumetric functional activity due to being restricted to imaging a single brain slice at a time. Typically, for volumetric fUS, functional recordings are repeated many times as the transducer is moved to a new location after each recording, resulting in a nonunique average mapping of the brain response and long scan times. Our objective was to perform volumetric 3-D fUS in an efficient and cost-effective manner. This was achieved by mounting a 1-D-array transducer to a high-precision motorized linear stage and continuously translating over the mouse brain in a sweeping manner. We show how the speed at which the 1-D-array is translated over the brain affects the sampling of the hemodynamic response (HR) during visual stimulation as well as the quality of the resulting power Doppler image (PDI). Functional activation maps were compared between stationary recordings, where only one functional slice is obtained for every recording, and our swept-3-D method, where volumetric fUS was achieved in a single functional recording. The results show that the activation maps obtained with our method closely resemble those obtained during a stationary recording for that same location, while our method is not restricted to functional imaging of a single slice. Lastly, a mouse brain subvolume of 6 mm is scanned at a volume rate of 1.5 s per volume, with a functional PDI reconstructed every 200\mu \text{m} , highlighting swept-3-D's potential for volumetric fUS. Our method provides an affordable alternative to volumetric fUS using 2-D-matrix transducers, with a high SNR due to using a fully sampled 1-D-array transducer, and without the need to repeat functional measurements for every 2-D slice, as is most often the case when using a 1-D-array. This places our swept-3-D method as a potentially valuable addition to conventional 2-D fUS, especially when investigating whole-brain functional connectivity, or when shorter recording durations are desired.

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Mice and rats can rapidly move their whiskers when exploring the environment. Accurate description of these movements is important for behavioral studies in neuroscience. Whisker tracking is, however, a notoriously difficult task due to the fast movements and frequent crossings and juxtapositionings among whiskers. We have recently developed WhiskEras, a computer-vision-based algorithm for whisker tracking in untrimmed, head-restrained mice. Although WhiskEras excels in tracking the movements of individual unmarked whiskers over time based on high-speed videos, the initial version of WhiskEras still had two issues preventing its widespread use: it involved tuning a great number of parameters manually to adjust for different experimental setups, and it was slow, processing less than 1 frame per second. To overcome these problems, we present here WhiskEras 2.0, in which the unwieldy stages of the initial algorithm were improved. The enhanced algorithm is more robust, not requiring intense parameter tuning. Furthermore, it was accelerated by first porting the code from MATLAB to C++ and then using advanced parallelization techniques with CUDA and OpenMP to achieve a speedup of at least 75x when processing a challenging whisker video. The improved WhiskEras 2.0 is made publicly available and is ready for processing high-speed videos, thus propelling behavioral research in neuroscience, in particular on sensorimotor integration.

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Romano et al. show that cerebellar Purkinje cell activity follows the respiratory rhythm during rest. Triggered by sensory input, Purkinje cells can alter their activity and thereby accelerate the timing of the next inspiration. Concomitantly, they also augment whisker movements, highlighting a coordinating role in aligning autonomic and sensorimotor behaviors.

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Automated whisker tracking is important for researching active touch in rodents. Earlier efforts to detect whiskers and represent them in a small set of parameters were either not accurate enough to enable tracking over time, or computationally expensive. In this article we propose an algorithm to cluster whisker centerline points, detected through a curvilinear structure algorithm, using the shape of smaller clusters to form bigger clusters of centerline points. After that, a least-squares approach is used to define each whisker by a set of four parameters. We implemented the algorithm in MATLAB in a parallelized fashion, and found that the processing time per frame is reasonable in MATLAB, and is likely to be short when ported to a lower-level language. When tested on a 33,634-frame segment, 89.2% of the whiskers could be represented in an abstract fashion by four parameters with a mean-squares fitting error of lower than 10 pixels, and visual inspection shows that crossing whiskers are detected and parameterized in an accurate way.

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The rodent whisker system is a prominent experimental subject for the study of sensorimotor integration and active sensing. As a result of improved video-recording technology and progressively better neurophysiological methods, there is now the prospect of precisely analyzing the intact vibrissal sensorimotor
system. The vibrissae and snout analyzer (ViSA), a widely used algorithm based on computer vision and image processing, has been proven successful for tracking and quantifying rodent sensorimotor behavior, but at a great cost in processing time. In order to accelerate this offline algorithm and eventually
employ it for online whisker tracking (less than 1 ms/frame latency), we have explored various optimizations and acceleration platforms, including OpenMP multithreading, NVidia GPUs and Maxeler Dataflow Engines. Our experimental results indicate that the optimal solution for an offline implementation of ViSA is
currently the OpenMP-based CPU execution. By using 16 CPU threads, we achieve more than 4,500x speedup compared to the original Matlab serial version, resulting in an average processing latency of 1.2 ms/frame, which is a solid step towards real-time (and online) tracking. Analysis shows that running the algorithm on a 32-thread-enabled machine can reduce this number to
0.72 ms/frame, thereby enabling real-time performance. This will allow direct interaction with the whisker system during behavioral experiments. In conclusion, our approach shows that a combination of software optimizations and the careful selection of hardware platform yields the best performance increase.@en

Synaptic and intrinsic processing in Purkinje cells, interneurons and granule cells of the cerebellar cortex have been shown to underlie various relatively simple, single-joint, reflex types of motor learning, including eyeblink conditioning and adaptation of the vestibulo-ocular reflex. However, to what extent these processes contribute to more complex, multi-joint motor behaviors, such as locomotion performance and adaptation during obstacle crossing, is not well understood. Here, we investigated these functions using the Erasmus Ladder in cell-specific mouse mutant lines that suffer from impaired Purkinje cell output (Pcd), Purkinje cell potentiation (L7-Pp2b), molecular layer interneuron output (L7-Δγ2), and granule cell output (α6-Cacna1a). We found that locomotion performance was severely impaired with small steps and long step times in Pcd and L7-Pp2b mice, whereas it was mildly altered in L7-Δγ2 and not significantly affected in α6-Cacna1a mice. Locomotion adaptation triggered by pairing obstacle appearances with preceding tones at fixed time intervals was impaired in all four mouse lines, in that they all showed inaccurate and inconsistent adaptive walking patterns. Furthermore, all mutants exhibited altered front–hind and left–right interlimb coordination during both performance and adaptation, and inconsistent walking stepping patterns while crossing obstacles. Instead, motivation and avoidance behavior were not compromised in any of the mutants during the Erasmus Ladder task. Our findings indicate that cell type-specific abnormalities in cerebellar microcircuitry can translate into pronounced impairments in locomotion performance and adaptation as well as interlimb coordination, highlighting the general role of the cerebellar cortex in spatiotemporal control of complex multi-joint movements.@en