Visualization of latent components assessed in O*Net occupations (VOLCANO): A robust method for standardized conversion of occupational labels to ratio scale format.

Occupations are typically characterized in nominal form, a format that limits options for hypothesis testing and data analysis. We drew upon ratings of knowledge, skills, and abilities for 966 occupations listed in the US Department of Labor's Occupational Classification Network (O*NET) database to create an accessible, standardized multidimensional space in which occupations can be quantitatively localized and compared. Principal component analysis revealed that the occupation space comprises three main dimensions that correspond to (1) the required amount of education and training, (2) the degree to which an occupation falls within a science, technology, engineering, and mathematics (STEM) discipline versus social sciences and humanities, and (3) whether occupations are more mathematically or health related. Additional occupational spaces reflecting cognitive versus labor-oriented categories were created for finer-grained characterization of dimensions within occupational sets defined by higher or lower required educational preparation. Data-driven groupings of related occupations were obtained with hierarchical cluster analysis (HCA). Proof-of-principle was demonstrated with a real-world dataset (470 participants from the Nathan Kline Institute - Rockland Sample; NKI-RS), whereby verbal and non-verbal abilities-as assessed by standardized testing-were related to the STEM versus social sciences and humanities dimension. Visualization of Latent Components Assessed in O*Net Occupations (VOLCANO) is provided to the research community as a freely accessible tool, along with a Shiny app for users to extract quantitative scores along the relevant dimensions. VOLCANO brings much-needed standardization to unwieldy occupational data. Moreover, it can be used to create new occupational spaces customized to specific research domains.

Infants who develop autism show smaller inventories of deictic and symbolic gestures at 12 months of age.

Gestures are an important social communication skill that infants and toddlers use to convey their thoughts, ideas, and intentions. Research suggests that early gesture use has important downstream impacts on developmental processes, such as language learning. However, autistic children are more likely to have challenges in their gestural development. The current study expands upon previous literature on the differences in gesture use between young autistic and non-autistic toddlers by collecting data using a parent-report questionnaire called the MCDI-Words and Gestures at three time points, 12, 18, and 24 months of age. Results (N = 467) showed that high-likelihood infants who later met diagnostic criteria for ASD (n = 73 HL-ASD) have attenuated gesture growth from 12 to 24 months for both deictic gestures and symbolic gestures when compared to high-likelihood infants who later did not meet criteria for ASD (n = 249 HL-Neg) and low-likelihood infants who did not meet criteria for ASD (n = 145 LL-Neg). Other social communicative skills, like play behaviors and imitation, were also found to be impacted in young autistic children when compared to their non-autistic peers. Understanding early differences in social communication growth before a formal autism diagnosis can provide important insights for early intervention.

Language exposure during infancy is negatively associated with white matter microstructure in the arcuate fasciculus.

Decades of research have established that the home language environment, especially quality of caregiver speech, supports language acquisition during infancy. However, the neural mechanisms behind this phenomenon remain under studied. In the current study, we examined associations between the home language environment and structural coherence of white matter tracts in 52 typically developing infants from English speaking homes in a western society. Infants participated in at least one MRI brain scan when they were 3, 6, 12, and/or 24 months old. Home language recordings were collected when infants were 9 and/or 15 months old. General linear regression models indicated that infants who heard the most adult words and participated in the most conversational turns at 9 months of age also had the lowest fractional anisotropy in the left posterior parieto-temporal arcuate fasciculus at 24 months. Similarly, infants who vocalized the most at 9 months also had the lowest fractional anisotropy in the same tract at 6 months of age. This is one of the first studies to report significant associations between caregiver speech collected in the home and white matter structural coherence in the infant brain. The results are in line with prior work showing that protracted white matter development during infancy confers a cognitive advantage.