Basic NeuroscienceA system for automated tracking of motor components in neurophysiological research

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.

► We developed an automated system that estimates paw poses using a 3D paw model. ► The paw pose is described by 19 parameters. ► The full model can be easily reduced to heuristically defined motor components. ► The model allows for detailed correlation of kinematic data to neural signals.