Towards real-time whisker tracking in rodents for studying sensorimotor disorders

Ma, Yang; Geethakumari, Prajith Ramakrishnan; Smaragdos, Georgios; Lindeman, Sander; Romano, Vincenzo; Negrello, Mario; Sourdis, Ioannis; Bosman, Laurens W. J.; Zeeuw, Chris I. De; Al-Ars, Zaid; Strydis, Christos

doi:10.1109/samos.2017.8344621

Cited by 4 publications

(7 citation statements)

References 14 publications

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“…The whisker movements were tracked as described previously ( Ma et al., 2017 ; Rahmati et al., 2014 ; Romano et al., 2018 ) using the BIOTACT Whisker Tracking Tool ( Perkon et al., 2011 ) in combination with an acceleration version of the BIOTACT code ( https://gitlab.com/neurocomputing-lab/whisker/bwtt-acceleration and https://github.com/elifesciences-publications/BWTT_PP ). Multiple acceleration strategies have been explored, resulting in speedups ranging from 2 × to 4,500 ×.…”

Section: Methodsmentioning

confidence: 99%

Functional Convergence of Autonomic and Sensorimotor Processing in the Lateral Cerebellum

et al. 2020

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Summary The cerebellum is involved in the control of voluntary and autonomic rhythmic behaviors, yet it is unclear to what extent it coordinates these in concert. We studied Purkinje cell activity during unperturbed and perturbed respiration in lobules simplex, crus 1, and crus 2. During unperturbed (eupneic) respiration, complex spike and simple spike activity encode the phase of ongoing sensorimotor processing. In contrast, when the respiratory cycle is perturbed by whisker stimulation, mice concomitantly protract their whiskers and advance their inspiration in a phase-dependent manner, preceded by increased simple spike activity. This phase advancement of respiration in response to whisker stimulation can be mimicked by optogenetic stimulation of Purkinje cells and prevented by cell-specific genetic modification of their AMPA receptors, hampering increased simple spike firing. Thus, the impact of Purkinje cell activity on respiratory control is context and phase dependent, highlighting a coordinating role for the cerebellar hemispheres in aligning autonomic and sensorimotor behaviors.

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Section: Methodsmentioning

confidence: 99%

Functional Convergence of Autonomic and Sensorimotor Processing in the Lateral Cerebellum

et al. 2020

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“…We compared our tracker with two widely used trackers: BWTT and Janelia Whisk, by processing four video fragments, each 100,000 frames in length. We used Ma's accelerated version of BWTT (Ma et al, 2017 ), and version v1.1.0d of Janelia Whisk, downloaded from their Wiki (Clack, 2011a ).…”

Section: Methodsmentioning

confidence: 99%

“…BWTT is, however, not designed to follow the trajectories of individual whiskers within the whisker field. To overcome this limitation, we have previously introduced post-hoc processing of data generated with BWTT (Rahmati et al, 2014 ; Ma et al, 2017 ; Romano et al, 2018 ). Post-hoc processing of the BWTT data in order to follow individual whiskers turned out to be relatively inaccurate in untrimmed mice.…”

Section: Introductionmentioning

confidence: 99%

“…From a computational perspective, processing videos with frequencies of 1,000 Hz in a reasonable amount of time is challenging. Recently, we described that the computational load of BWTT, which was implemented in MATLAB, grows exponentially with the number of detected whisker points (Ma et al, 2017 ). By porting the BWTT algorithm to OpenMP, making use of parallelism, we managed to achieve a speedup of 4,500x (Ma et al, 2017 ; Romano et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

“…Recently, we described that the computational load of BWTT, which was implemented in MATLAB, grows exponentially with the number of detected whisker points (Ma et al, 2017 ). By porting the BWTT algorithm to OpenMP, making use of parallelism, we managed to achieve a speedup of 4,500x (Ma et al, 2017 ; Romano et al, 2020 ). Similar to BWTT, on which it is partially based, WhiskEras was implemented in MATLAB, but - in contrast to the original version of BWTT - WhiskEras makes use of the multicore CPU as well as a GPU to achieve reasonable processing times.…”

Section: Introductionmentioning

confidence: 99%

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WhiskEras: A New Algorithm for Accurate Whisker Tracking

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Romano

Al-Ars

et al. 2020

Front. Cell. Neurosci.

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Rodents engage in active touch using their facial whiskers: they explore their environment by making rapid back-and-forth movements. The fast nature of whisker movements, during which whiskers often cross each other, makes it notoriously difficult to track individual whiskers of the intact whisker field. We present here a novel algorithm, WhiskEras, for tracking of whisker movements in high-speed videos of untrimmed mice, without requiring labeled data. WhiskEras consists of a pipeline of image-processing steps: first, the points that form the whisker centerlines are detected with sub-pixel accuracy. Then, these points are clustered in order to distinguish individual whiskers. Subsequently, the whiskers are parameterized so that a single whisker can be described by four parameters. The last step consists of tracking individual whiskers over time. We describe that WhiskEras performs better than other whisker-tracking algorithms on several metrics. On our four video segments, WhiskEras detected more whiskers per frame than the Biotact Whisker Tracking Tool. The signal-to-noise ratio of the output of WhiskEras was higher than that of Janelia Whisk. As a result, the correlation between reflexive whisker movements and cerebellar Purkinje cell activity appeared to be stronger than previously found using other tracking algorithms. We conclude that WhiskEras facilitates the study of sensorimotor integration by markedly improving the accuracy of whisker tracking in untrimmed mice.

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