Characterization of the Neurobiological Profiles of Young Adults with and without Developmental Language Disorder (DLD)

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ABSTRACT Approximately 7% of school-aged children have Developmental Language Disorder (DLD), making it one of the highest prevalence of the child language disorders. DLD places individuals at risk for academic failure, social isolation, anxiety, depression, poor emotional regulation, juvenile incarceration (65%), and repeat offending (70%). DLD persists into adulthood, with conservative estimates indicating that 12 million adults in the United States have DLD, but because the behavioral phenotype can overlap with that of typical individuals', they may no longer qualify for support services. Notably, although these young adults with DLD "appear" normal, their language abilities are linked to brain structure and cortical dynamics that differ qualitative from typical individuals, suggesting that neural signature of DLD may be a critical marker of the disorder. New machine learning methods have revolutionized the neuroscience of neurodevelopmental disorders and advances in registering the optical signal of functional near infrared spectroscopy (fNIRS) to neuroanatomical data now make capturing the spatial- temporal dynamics of spoken language processing feasible and cost-effective for speech, language, and hearing populations. Expertise in these two domains is critical for high impact speech-language research. The candidate is an established investigator with a strong record of research and extramural funding spanning more than 30 years in the area of DLD. The goal of the enhancement is to augment the candidate's current expertise in DLD by gaining advanced training in fNIRS neuroimaging and newer computational modeling techniques to keep the candidate's program of research in-step with emerging and evolving neuroimaging and computational modeling approaches. The goals of the enhancement are to: (1) advance the candidate's skills in cutting-edge fNIRS methods, (2) incorporate computational modeling into the candidate's program of research, and (3) catalyze new research collaborations with cognitive neuroscientists, optical imagers, and computational modelers. Didactic course work in applied machine learning and computational modeling (semester long courses), 1:1 meetings, scholarly travel and a small-scale research project will provide the enhanced experience to substantially augment the research skills of the candidate, seed new collaborations with scientists in other fields whose work is relevant to DLD. The research project will provide the hands-on opportunity for the candidate to acquire expertise under the mentorship of a superior team of young, up and coming mentors with expertise in optical imaging (fNIRS), and functional and structural brain imaging (fMRI, MRI), and a group of senior collaborators with expertise in computational modeling and cognitive neuroscience. A group of young adults ages 18;0 - 21;0 with/without DLD (n = 44) will complete standardized assessments, a structural MRI, and a series of fNIRS tasks, which will then be used as inputs to derive multidimensional models of DLD. Overall, the enhancement will significantly augment the current research trajectory of a well-established DLD researcher and provide the foundation for new NIH funding to develop multidimensional neurobiologically derived models of DLD.