Structural and functional connectivity markers of developmental speech and language disorders

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ABSTRACT Developmental speech and language disorders affect an estimated 15% of children and have lifelong impacts on social and emotional development and employment. Two common neurodevelopmental disorders are developmental language disorder (DLD; also called specific language impairment) and developmental stuttering, affecting 7% and 5% of children respectively. Despite their prevalence and immense impact, little is known of the neural causes, correlates, and consequences of these common neurodevelopmental disorders; thus, effective treatment remains elusive. In the proposed project, we will study the neural underpinnings of these disorders using magnetic resonance imaging (MRI) to study structural and functional neural connectivity. Previous studies of connectivity in these populations are limited and show little consensus, likely due in part to small sample sizes. Theoretical accounts of both disorders implicate dysfunctional neural circuits through the basal ganglia. In the current proposal, we will test and compare the structural and functional integrity of neural pathways in large cohorts of people with DLD (N=80) and people who stutter (PWS; N=80) and compare them with similar data obtained in age- and sex-matched control groups of people with typical development (N=160). First, we will assess connectivity in speech/language-specific networks, using diffusion data to assess structural connectivity and resting-state data to assess functional connectivity. Results will indicate abnormalities in connectivity in large cohorts of PWS and people with DLD. In each disorder, we will also determine connectivity contributions to individual differences in behavior. This will reveal how different connectivity patterns are correlated to differences in severity along relevant dimensions (e.g., fluency, language measures), ideally resulting in neural correlates of the disorders. Finally, we will evaluate whole-brain functional connectivity differences between each disorder group and its matched control group using data-driven machine learning approaches. Results will indicate patterns of neural activity that differentiate these disorders from controls. The outcome of this proposal will be the characterization of underlying network differences in these populations, which will ideally lead to the development of targeted behavioral and neuro-modulatory treatments of these multifaceted and pervasive disorders. Research and training will take place at the University of Oxford, an ideal environment in which to pursue this line of research. The applicant will be mentored by world-leading researchers with the knowledge needed to guide him in this work, including expertise in the neural bases of developmental speech and language disorders, cutting-edge methodology in neuroimaging, and machine learning. Achieving these aims will illuminate the neural correlates of these speech and language disorders as well as prepare the applicant for an independent research career in this area.