Nicole Landi, MA, BS, PhD - US grants

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The purpose of this study is to address the differences in brain activation patterns between reading narrative texts that are inconsistent across longer spans and narrative texts that are consistent across longer spans. Behavioral research has shown that readers'reaction times slow down when they encounter such an inconsistency in a text (O'Brien et al., 1993). Converging evidence from fMRI methodology would aide in understanding how narrative texts are processed by locating which brain regions are activated during the inconsistent versus consistent information. Specifically, we expect greater activation in the prefrontal cortex in the inconsistent version if participants are trying to resolve the inconsistency in the text, which would explain the reading time difference in the O'Brien et al. (1993) study. Alternatively, if there is not greater activation in the prefrontal cortex, then the regions involved when reading inconsistent information across longer spans of text would differ from the regions involved when reading inconsistent information across shorter spans of text (Sieborger, Ferstl, &von Crammon, 2007). In addition to the prefrontal cortex, we expect to examine differences in activation between the consistent and inconsistent texts in the dorsal fronto-medial cortex (Ferstl et al., 2005), left lateral frontal cortex (Kiehl, Laurens, &Liddle, 2002), left posterior fusiform gyrus (Kiehl, Laurens, &Liddle, 2002) and precuneous regions (based on our pilot data).

DESCRIPTION (provided by applicant): Prenatal cocaine exposure (PCE) can have broad effects on neural development and cognition, with language skills routinely being affected. Extant findings have revealed multiple deficits/delays in language function associated with PCE including: 1) delayed age for language acquisition 2) poorer receptive language 3) poorer expressive language (fewer words spoken) and 4) abnormal response to auditory stimuli. The specific pattern of findings observed indicates language processing deficits at multiple levels from low-level perceptual deficits (e.g., speech processing) to more metacognitive deficits (e.g., semantics, syntax, comprehension). What is unknown is whether these deficits stem from the same or different underlying neurobiological anomalies. Moreover, because extant studies have looked at infants and young children, the extent to which these profiles of language dysfunction change as system demands change, e.g., from phonological development (early childhood) to the understanding of complex semantic and syntactic relationships (late childhood and adolescence), is unknown. It is likely, given the teratogenic effect of PCE, that multiple systems are affected, but also that the observed impairments may differ as a function of changes in system demands associated with development and/or academic pressures. We hypothesize that the perceptual deficits observed are a result of anomalous development of auditory perceptual systems in the temporal lobe and that the higher-level language deficits observed are due to a more diffuse effect on prefrontal lobe function. Additionally, we predict a change in the relative weighting of the affected systems associated with development, which will result in an increase in metacognitve impairment for older children and adolescents. The proposed research will partner with an ongoing longitudinal study of PCE to examine the underlying neurobiology of language processing deficits in a well-characterized behavioral sample of PCE adolescents. Specifically, the goals of the proposed research are 1) to use event related potentials (ERP) to assess the neurocognitive language profiles of PCE adolescents and of control children who were not exposed to cocaine (NCE);2) to examine the relationship between earlier behavioral performance (measured by standardized tests of language and cognitive skills) and these neurocognitive language profiles;and c) to link these brain-behavior patterns to polymorphisms in candidate genes that have been associated with perceptual or metacognitive skills (COMT and BDNF), in order to establish an endophenotype associated with poor language performance in PCE children. The research outlined in this proposal will have a direct impact our understanding of the relationship between prenatal cocaine exposure and the underlying neurobiology associated with impairments in critical language skills that are important for academic and social success. PUBLIC HEALTH RELEVANCE: Prenatal cocaine exposure (PCE) can alter normal neural development and affect the infant's cognitive and behavioral function throughout its lifespan, thus making it an important public health concern. A significant body of research has now identified poorer performance in PCE relative to non-exposed children across a number of cognitive tasks, with language skills routinely affected. The research outlined in this proposal aims to refine our understanding of the language impairments associated with PCE by identifying the neurobiological locus of these impairments and exploring the cognitive- developmental trajectories associated with language skills in PCE.

Project Summary/Abstract. Reading Disability (RD) is the most common learning disability, affecting 10 ? 15% of school age children. It incurs major functional impairments at all stages of life, with a wealth of data documenting lifelong disadvantages in educational and occupational attainment. Therefore, identifying effective and affordable treatments for RD is a high priority for reading researchers, clinicians and educators. Problematically, current evidence-based reading interventions largely rely on services by trained specialists, either in well-resourced classrooms or clinical settings. As such, under-resourced schools (or countries) often are unable to provide reading interventions for their students. In recent years, technology-based reading interventions have been proposed as a means of overcoming these challenges, as they can be administered in the home, without direct expert supervision - thereby minimizing resource demands. In the area of reading-focused EdTech, GraphoLearn has emerged as a leader, with the largest evidence-base to date. However, the vast majority of studies to date have been conducted in highly controlled settings, rather than the home environment it was intended for ? leaving open questions about effectiveness. Additionally, similar to any intervention, not all children with RD benefit equally from treatment; however little attention has been given to identifying predictors of treatment response. Here we propose to evaluate the effectiveness of home-administered GraphoLearn through the implementation of a large-scale, randomized controlled trial (RCT) in 450 reading disabled children (boys and girls, ages 6.0- 10.0). To accomplish this goal rapidly and with minimal cost, we will recruit participants from the Healthy Brain Network [HBN], an ongoing study of mental health and learning disorders in children, ages 5.0-21.0, whose family have concerns about behavior and/or learning (target n = 10,000; current n = 3000+). The availability of comprehensive characterizations (e.g., demographic, cognitive, mental health, EEG, multimodal MRI) for all HBN participants makes the sample optimal for exploring an extensive set of participant and environmental factors that may affect treatment outcomes (i.e., demographic, cognitive, emotional, neurobiological, environmental). Specific aims of the proposed work are to: 1) Evaluate the effectiveness of GraphoLearn in a large sample of children with RD, and 2) Identify participant-related and environment-level factors that are significantly associated with GraphoLearn outcomes. To accomplish this latter aim, sophisticated machine learning approaches (Random Forest Regression models) will be employed.