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A system for automated tracking of motor components in neurophysiological research

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Author

Summary, in English

In the study of motor systems it is often necessary to track the movements of an experimental animal in great detail to allow for interpretation of recorded brain signals corresponding to different control signals. This task becomes increasingly difficult when analyzing complex compound movements in freely moving animals. One example of a complex motor behavior that can be studied in rodents is the skilled reaching test where animals are trained to use their forepaws to grasp small food objects, in many ways similar to human hand use. To fully exploit this model in neurophysiological research it is desirable to describe the kinematics at the level of movements around individual joints in 3D space since this permits analyses of how neuronal control signals relate to complex movement patterns. To this end, we have developed an automated system that estimates the

paw pose using an anatomical paw model and recorded video images from six different image planes in rats chronically implanted with recording electrodes in neuronal circuits involved in selection and execution of forelimb movements. The kinematic description provided by the system allowed for a decomposition of reaching movements into a subset of motor components. Interestingly, firing rates of individual neurons were found to be modulated in relation to the actuation of these motor components suggesting that sets of motor primitives may constitute building blocks for the encoding of movement commands in motor circuits. The designed system will, thus, enable a more detailed analytical approach in neurophysiological studies of motor systems.

Publishing year

2012

Language

English

Pages

334-344

Publication/Series

Journal of Neuroscience Methods

Volume

205

Issue

2

Document type

Journal article

Publisher

Elsevier

Topic

  • Neurosciences

Keywords

  • Corticostriatal
  • Hand pose estimation
  • Reaching
  • 3D-model
  • Motor control

Status

Published

Research group

  • Neuronano Research Center (NRC)
  • Neurophysiology

ISBN/ISSN/Other

  • ISSN: 1872-678X