Data-driven analyses of motor impairments in animal models of neurological disorders
Fig 5
The clustering of the network feature space revealed movement elements specific only to the stroke or the control condition.
(Top insert) Blue and red ellipses outline the distribution of points in feature space of the network before and after the stroke, respectively (the same as in Fig 4A). Black ellipses outline subclusters corresponding to individual movement subcomponents. For visualization clarity, only 10 subclusters out of 40 are shown. Dashed ellipses indicate clusters most selective for the stroke and the control categories and arrows point to sample frames from those clusters. Note that clustering was done using the first seven PCs of the network features; thus, subclusters appear to overlap in this 2D projection. (Main panel) Each point represents cluster selectivity by expressing the fraction of frames from stroke versus control rats in each subcluster (see Results). Labels below denote the movement category assigned to subclusters, and images above show representative frames from corresponding subclusters. Points in black denote a “not clear” clusters category. The bottom insert shows the average cluster selectivity index (“avr clust select. index”) for each movement category. Error bars denote standard deviation. The sample network and data on which this figure is based are available at github.com/hardeepsryait/behaviour_net. adv, advance; PC, principal component; pron, pronation; sup, supination.