Home cage-based insights into motor learning and strategy adaptation in a Huntington disease mouse model
Fig 7
Inter-mouse influence in a group-housed automated home cage setting.
(A) diagrammatic representation of the interaction motif between pairs termed as follower-influencer, specifying the criteria for identifying these interactions. (B) For comparative analysis, control patterns devoid of an influencer presence were also examined, characterized by a solitary mouse performing two bouts separated by a 5–15 minute timeout. (C) The frequency of follower-influencer motifs identified throughout the study period revealed no significant difference in occurrence based on the genotype of the follower, indicating similar levels of this interactive behavior across genotypes (unpaired t-test, p = 0.850). (D) In cases when an influencer exhibited higher success rates than the follower’s baseline (good influencer), WT but not zQ175 followers displayed a significant increase in success rate (one-sample t-test, theoretical mean 0.0, WT p = 0.014, zQ175 p = 0.366), although no genotype differences were noted when comparing these improvements (unpaired t-test, WT vs. zQ175 p = 0.290). (E) Conversely, when influencers had the same or lower success rates compared to the follower’s baseline (bad influencer), both WT and zQ175 followers showed no significant deviation from their initial success rates (one-sample t-test, theoretical mean 0.0, WT p = 0.766, zQ175 p = 0.695; unpaired t-test, WT vs. zQ175 p = 0.615). (F) The control scenarios, lacking an influencer and merely documenting the variance in success rates across two separated bouts, mirrored the outcomes observed with bad influencers, with no significant changes in success rates for either genotype (one-sample t-test, theoretical mean 0.0, WT p = 0.561, zQ175 p = 0.957; unpaired t-test, WT vs. zQ175 p = 0.851). Data presented as mean±SEM.