A system for automated tracking of motor components in neurophysiological research

Abstract

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.

Systems-level neurophysiological state characteristics for drug evaluation in an animal model of levodopa-induced dyskinesia

“…If more detailed behavioral assessments had been carried out with quantitative assessment scales that were adapted to include a wider range of motor symptoms, it is probable that the behavioral state descriptions would be better correlated to the neurophysiological activity states recorded in these motor circuits. Notably, however, such behavioral assessments are technically very challenging to carry out and will likely require more advanced automated procedures (e.g., Palmér et al 2012;Santana et al 2015). In addition, it is well-known that PD also includes nonmotor symptoms, and in several other disorders few overt signs, if any, may be associated with a specific pathological condition.…”

A Within-Animal Comparison of Skilled Forelimb Assessments in Rats

“…Kinematic analysis of forelimb movement may reveal lasting forelimb dysfunction on all tasks that is not observed in the performance measures reported in this study. Kinematic analysis of rodent reaching behavior; however, has not historically been amenable to high-throughput analysis, although several semiautomated methods have been developed [ 19 , 20 ]. While compensation is likely reflected in measures of forelimb performance in the tasks employed in this study, the larger effect size seen with the isometric pull task suggests that the task may reduce compensation and better reveal true deficits in performance.…”

Motor compensation and its effects on neural reorganization after stroke

“…One obstacle is that it is currently very challenging to precisely quantify movement patterns in manual skill tasks in rodents, which are the most popular model species for chronic stroke (BOX 2). Approaches that are in development for automated measurement of such movement patterns 140,141 hold promise for remedying this situation.…”

“…This includes grid-walking, ladder-walking and beam-walking tests, skilled reaching tests and rotarod tests, as they are normally performed. The gold standard for distinguishing motor recovery from compensation is to perform kinematic (geometry of movement) analyses 65,68,140 , but this is currently challenging to do routinely (because the skin markers that are commonly used in kinematic systems to automatically track joint positions do not work well on rodent forelimbs). Automated measures of rodent movement that are in development 140,141 may help to overcome this obstacle.…”