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.
