Data-driven analyses of motor impairments in animal models of neurological disorders
Fig 4
Extracting knowledge from the network to identify the movement elements most predictive of stroke severity.
(A) Representation of video frames transformed into the internal feature space of the network (see Methods). Each point represents a single video fame. Blue points represent video frames from a single rat during trials obtained on the day before stroke. Red points represent video frames from trials obtained after stroke for the same rat. Blue and red ellipses outline distributions of points before and after the stroke, respectively. Note the disparity between distributions. For example, eating with both hands (Aa) was only observed before the stroke, whereas reaching for the food pellet with the mouth (Ab) was only observed after the stroke. Panels Ac and Ad illustrate the parts of frames in Aa and Ab that the network evaluated as being most important for its scoring decision. (B) Ellipses outline the distribution of points before the stroke (blue) and on day after the stroke (red) for each rat. Close overlap of the red ellipses indicates that features predictive of stroke found by the network were consistent across rats. The sample network and data on which this figure is based are available at github.com/hardeepsryait/behaviour_net. PC, principal component.