Regular rhythmic primes improve sentence repetition in children with developmental language disorder.

Recently reported links between rhythm and grammar processing have opened new perspectives for using rhythm in clinical interventions for children with developmental language disorder (DLD). Previous research using the rhythmic priming paradigm has shown improved performance on language tasks after regular rhythmic primes compared to control conditions. However, this research has been limited to effects of rhythmic priming on grammaticality judgments. The current study investigated whether regular rhythmic primes could also benefit sentence repetition, a task requiring proficiency in complex syntax-an area of difficultly for children with DLD. Regular rhythmic primes improved sentence repetition performance compared to irregular rhythmic primes in children with DLD and with typical development-an effect that did not occur with a non-linguistic control task. These findings suggest processing overlap for musical rhythm and linguistic syntax, with implications for the use of rhythmic stimulation for treatment of children with DLD in clinical research and practice.

Exploring individual differences in musical rhythm and grammar skills in school-aged children with typically developing language.

A growing number of studies have shown a connection between rhythmic processing and language skill. It has been proposed that domain-general rhythm abilities might help children to tap into the rhythm of speech (prosody), cueing them to prosodic markers of grammatical (syntactic) information during language acquisition, thus underlying the observed correlations between rhythm and language. Working memory processes common to task demands for musical rhythm discrimination and spoken language paradigms are another possible source of individual variance observed in musical rhythm and language abilities. To investigate the nature of the relationship between musical rhythm and expressive grammar skills, we adopted an individual differences approach in N = 132 elementary school-aged children ages 5-7, with typical language development, and investigated prosodic perception and working memory skills as possible mediators. Aligning with the literature, musical rhythm was correlated with expressive grammar performance (r = 0.41, p < 0.001). Moreover, musical rhythm predicted mastery of complex syntax items (r = 0.26, p = 0.003), suggesting a privileged role of hierarchical processing shared between musical rhythm processing and children's acquisition of complex syntactic structures. These relationships between rhythm and grammatical skills were not mediated by prosodic perception, working memory, or non-verbal IQ; instead, we uncovered a robust direct effect of musical rhythm perception on grammatical task performance. Future work should focus on possible biological endophenotypes and genetic influences underlying this relationship.

Exploring the genetics of rhythmic perception and musical engagement in the Vanderbilt Online Musicality Study.

Uncovering the genetic underpinnings of musical ability and engagement is a foundational step for exploring their wide-ranging associations with cognition, health, and neurodevelopment. Prior studies have focused on using twin and family designs, demonstrating moderate heritability of musical phenotypes. The current study used genome-wide complex trait analysis and polygenic score (PGS) approaches utilizing genotype data to examine genetic influences on two musicality traits (rhythmic perception and music engagement) in N = 1792 unrelated adults in the Vanderbilt Online Musicality Study. Meta-analyzed heritability estimates (including a replication sample of Swedish individuals) were 31% for rhythmic perception and 12% for self-reported music engagement. A PGS derived from a recent study on beat synchronization ability predicted both rhythmic perception (ß = 0.11) and music engagement (ß = 0.19) in our sample, suggesting that genetic influences underlying self-reported beat synchronization ability also influence individuals' rhythmic discrimination aptitude and the degree to which they engage in music. Cross-trait analyses revealed a modest contribution of PGSs from several nonmusical traits (from the cognitive, personality, and circadian chronotype domains) to individual differences in musicality (ß = -0.06 to 0.07). This work sheds light on the complex relationship between the genetic architecture of musical rhythm processing, beat synchronization, music engagement, and other nonmusical traits.

Automated Phenotyping Tool for Identifying Developmental Language Disorder Cases in Health Systems Data (APT-DLD): A New Research Algorithm for Deployment in Large-Scale Electronic Health Record Systems.

Purpose Data mining algorithms using electronic health records (EHRs) are useful in large-scale population-wide studies to classify etiology and comorbidities (Casey et al., 2016). Here, we apply this approach to developmental language disorder (DLD), a prevalent communication disorder whose risk factors and epidemiology remain largely undiscovered. Method We first created a reliable system for manually identifying DLD in EHRs based on speech-language pathologist (SLP) diagnostic expertise. We then developed and validated an automated algorithmic procedure, called, Automated Phenotyping Tool for identifying DLD cases in health systems data (APT-DLD), that classifies a DLD status for patients within EHRs on the basis of ICD (International Statistical Classification of Diseases and Related Health Problems) codes. APT-DLD was validated in a discovery sample (N = 973) using expert SLP manual phenotype coding as a gold-standard comparison and then applied and further validated in a replication sample of N = 13,652 EHRs. Results In the discovery sample, the APT-DLD algorithm correctly classified 98% (concordance) of DLD cases in concordance with manually coded records in the training set, indicating that APT-DLD successfully mimics a comprehensive chart review. The output of APT-DLD was also validated in relation to independently conducted SLP clinician coding in a subset of records, with a positive predictive value of 95% of cases correctly classified as DLD. We also applied APT-DLD to the replication sample, where it achieved a positive predictive value of 90% in relation to SLP clinician classification of DLD. Conclusions APT-DLD is a reliable, valid, and scalable tool for identifying DLD cohorts in EHRs. This new method has promising public health implications for future large-scale epidemiological investigations of DLD and may inform EHR data mining algorithms for other communication disorders. Supplemental Material https://doi.org/10.23641/asha.12753578.