Repetitive motor behavior: further characterization of development and temporal dynamics

Abstract

Repetitive behaviors are diagnostic for autism spectrum disorders, common in related neurodevelopmental disorders, and normative in typical development. In order to identify factors that mediate repetitive behavior development, it is necessary to characterize the expression of these behaviors from an early age. Extending previous findings, we characterized further the ontogeny of stereotyped motor behavior both in terms of frequency and temporal organization in deer mice. A three group trajectory model provided a good fit to the frequencies of stereotyped behavior across eight developmental time points. Group based trajectory analysis using a measure of temporal organization of stereotyped behavior also resulted in a three group solution. Additionally, as the frequency of stereotyped behavior increased with age, the temporal distribution of stereotyped responses became increasingly regular or organized indicating a strong association between these measures. Classification tree and principal components analysis showed that accurate classification of trajectory group could be done with fewer observations. This ability to identify trajectory group membership earlier in development allows for examination of a wide range of variables, both experiential and biological, to determine their impact on altering the expected trajectory of repetitive behavior across development. Such studies would have important implications for treatment efforts in neurodevelopmental disorders such as autism.

Keywords: autism; clustering; deer mice; developmental trajectory; group trajectory modeling; neurodevelopmental disorders; stereotypy.

Fig. 1

Successive difference score plots showing increasingly temporally organized repeitive behavior (decreasing Y-scores) across three developmental time points for the same mouse.

Figure 2

a: Mean stereotypy counts at each of eight developmental time points for each of three trajectory groups. Figure 2b: Mean log transformed stereotypy counts from the estimated mean (solid line) and one from the fitted quadratic (dashed line) for three trajectory groups as provided by Proc Traj. Error bars are ± SEM.

Figure 3

a: Mean stereotypy counts at each of eight developmental time points for each of four trajectory groups. Figure 3b: Mean log transformed stereotypy counts from the estimated mean (solid line) and one from the fitted quadratic (dashed line) for four trajectory groups as provided by Proc Traj. Error bars are ± SEM.

Figure 4

Mean Y-scores (solid line) and fitted quadratic (dashed line) at each of eight developmental time points for each of three trajectory groups. Error bars are ± SEM.

Figure 5

Mean Y-scores (solid line) and fitted quadratic (dashed line) at each of eight developmental time points for each of four trajectory groups. Error bars are ± SEM.

Figure 6

Hierarchical clustering dendogram based on log-frequencies of stereotypy counts for each time point. The dendrogram shows cluster formation using Ward's distance. Larger vertical distances between mergers indicate that the groups being combined are more dissimilar.

Figure 7

A classification tree based on stereotypy counts at each of eight developmental time points and predicted trajectory. The tree represents binary splits (nodes) that separate the mice into final groupings (end nodes) which are optimally homogenous in regards to predicted trajectory. Restrictions are put on the splits to prevent overly small end nodes and guard against overfitting. The tree shows that time points PND 28, PND 35, and PND 22 were most informative in determining mouse trajectories (h, m, l or high frequency, mid frequency, low frequency).