Search for: All records

Preclinical models of neurodevelopmental disorders typically use single inbred mouse strains, which fail to capture the genetic diversity and symptom heterogeneity that is common clinically. We tested whether modeling genetic background diversity in mouse genetic reference panels would recapitulate population and individual differences in responses to a syndromic mutation in the high-confidence autism risk gene, CHD8. We measured clinically relevant phenotypes in >1,000 mice from 33 strains, including brain and body weights and cognition, activity, anxiety, and social behaviors, using 5 behavioral assays: cued fear conditioning, open field tests in dark and bright light, direct social interaction, and social dominance. Trait disruptions mimicked those seen clinically, with robust strain and sex differences. Some strains exhibited large effect-size trait disruptions, sometimes in opposite directions, and-remarkably-others expressed resilience. Therefore, systematically introducing genetic diversity into models of neurodevelopmental disorders provides a better framework for discovering individual differences in symptom etiologies. 

Abstract Mutations inCHD8are one of the highest genetic risk factors for autism spectrum disorder. Studies in mice that investigate underlying mechanisms have shownChd8haploinsufficient mice display some trait disruptions that mimic clinical phenotypes, although inconsistencies have been reported in some traits across different models on the same strain background. One source of variation across studies may be the impact ofChd8haploinsufficiency on maternal‐offspring interactions. While differences in maternal care as a function ofChd8genotype have not been studied directly, a previous study showed that pup survival was reduced when reared byChd8heterozygous dams compared with wild‐type (WT) dams, suggesting altered maternal care as a function ofChd8genotype. Through systematic observation of the C57BL/6 strain, we first determined the impact ofChd8haploinsufficiency in the offspring on WT maternal care frequencies across preweaning development. We next determined the impact of maternalChd8haploinsufficiency on pup care. Compared with litters with all WT offspring, WT dams exhibited less frequent maternal behaviors toward litters consisting of offspring with mixedChd8genotypes, particularly during postnatal week 1. DamChd8haploinsufficiency decreased litter survival and increased active maternal care also during postnatal week 1. Determining the impact ofChd8haploinsufficiency on early life experiences provides an important foundation for interpreting offspring outcomes and determining mechanisms that underlie heterogeneous phenotypes. 

Observing how infants and mothers coordinate their behaviors can highlight meaningful patterns in early communication and infant development. While dyads often differ in the modalities they use to communicate, especially in the first year of life, it remains unclear how to capture coordination across multiple types of behaviors using existing computational models of interpersonal synchrony. This paper explores Dynamic Mode Decomposition with control (DMDc) as a method of integrating multiple signals from each communicating partner into a model of multimodal behavioral coordination. We used an existing video dataset to track the head pose, arm pose, and vocal fundamental frequency of infants and mothers during the Face-to-Face Still-Face (FFSF) procedure, a validated 3-stage interaction paradigm. For each recorded interaction, we fit both unimodal and multimodal DMDc models to the extracted pose data. The resulting dynamic characteristics of the models were analyzed to evaluate trends in individual behaviors and dyadic processes across infant age and stages of the interactions. Results demonstrate that observed trends in interaction dynamics across stages of the FFSF protocol were stronger and more significant when models incorporated both head and arm pose data, rather than a single behavior modality. Model output showed significant trends across age, identifying changes in infant movement and in the relationship between infant and mother behaviors. Models that included mothers’ audio data demonstrated similar results to those evaluated with pose data, confirming that DMDc can leverage different sets of behavioral signals from each interacting partner. Taken together, our results demonstrate the potential of DMDc toward integrating multiple behavioral signals into the measurement of multimodal interpersonal coordination.