2023 Innovation Fund Awardees

A Spoken Language Analysis App for Automated Dyslexia Screening

Gopala Anumanchipalli (UC Berkeley)

Fluent reading requires thoroughly integrated pronunciation, spelling and semantic representations in the brain. It is known that multi-modal representational convergence is not innate but is a result of sustained learning. Large variance exists in different individuals’ learning strategies, making it hard to create a one-size fits all solution to teach fluent reading and spoken language fluency. More importantly, neurological conditions may predispose individuals to difficulties in internalizing the complex phonological, phonetic, morphological computations that underlie spoken language. In this work, we will use Artificial intelligence (AI) methods to create a comprehensive spoken language analysis application to analyze phonetic, phonological and morphological processes from produced speech. Specifically, we will develop approaches for automatic analyses of spoken language from children that we will elicit using a series of speaking and reading tasks. We believe automated analyses of speech will be the first step to early diagnosis of speech disorders like dyslexia.

Spatiotemporal Profiling of GABAergic Interneuron in the Infant Temporal Cortex and its Implication with Developing Dyslexia

Jaeyeon Kim (UCSF), Mercedes Paredes (UCSF)

Linguistic disabilities are prevalent conditions across many neurodevelopmental disorders (NDDs). These clinical symptoms, which currently have limited treatment options, are closely associated with malfunction within the Temporal Cortex (TC). Yet, little is known about the development of human TC for multisensory integration of auditory with visual information underlying the acquisition of linguistic abilities. Here, we will characterize the developmental process of GABAergic interneurons in the infant TC, a neuronal population shown to be critical for neuronal network balance; their disruption has been linked to the neuronal noise seen in several NDDs. This application will create spatiotemporal profiling of GABAergic-interneuron populations in the human TC. Through a single-cell proteome of GABAergic interneurons migrating into TC subregions, we will identify TC-specific-interneuron migration machinery. These results will reveal the GABAergic interneurons features unique to human TC, providing clues to understanding the neuronal noises in patients with linguistic disabilities.

Brain, Behavioral and Developmental Correlates of Reading and Language Dysfunction in Critical Congenital Heart Disease and Developmental Dyslexia

Patrick McQuillen (UCSF), Christa Pereira (UCSF), and Sarah Inkelis (UCSF)

Children with history of early surgery or neonatal brain injury (e.g., critical congenital heart disease [CCHD]) are unique cohorts who have experienced brain insult during language development. These children struggle with language/reading and represent a group at the nexus of developmental and acquired dyslexia. Additionally, one causal theory of developmental dyslexia (DD) includes perinatal neuronal migration anomalies. However, most children with DD never receive brain imaging and some may have had a “silent” peri-natal insult that corresponds to their language-based learning difficulties. This project will leverage existing data to 1) compare neurocognitive profiles of children with known risk for anomalous neurodevelopment (e.g., CCHD) to children with DD without an acquired component; and 2) examine relationships between neuroimaging and language outcomes. This will allow us to look for predictors of dyslexia risk and clarify neurocognitive mechanisms that connect perinatal brain anomalies to specific neurocognitive differences in school age children.

A Wind of Change: Leveraging Infographic-Based Interventions to Promote Literacy and Reduce Stigma Related to ADHD and Dyslexia

Phuc Nguyen (UC Berkeley), Shari Aronson (UC Berkeley)

Individuals with diagnostic labels, such as ADHD and specific learning disorders (e.g., dyslexia), are stigmatized across various settings and populations (Haft et al., 2022; Lebowitz, 2016). Many such individuals report barriers to accessing treatment, including stereotypes, social distance, and resulting discrimination (Kooij et al., 2019). Yet little has been done to promote accessible public knowledge and thereby reduce stigma. For this pilot study, we propose to develop and disseminate comprehensible, targeted, infographics to college students and teachers to evaluate the potential for and effectiveness of infographic-based teaching methods for increasing awareness and knowledge of ADHD and dyslexia as well as decreasing associated social distance and stigma. The hypothesized implications are three-fold: to (a) increase ADHD- and dyslexia-related mental health literacy through intentional teaching via accessible infographics, (b) bridge gaps between research and practice via enhancing community-based access to and understanding of science, and (c) reduce ADHD- and dyslexia-related stigma.

Assessing and Improving Emotional Intelligence in Individuals with Autism Spectrum Disorder and Dyslexia

Jefferson Ortega (UC Berkeley), David Whitney (UC Berkeley)

The ability to quickly and accurately infer the emotions of others is essential for having successful social interactions in our daily lives. Social communication deficits have been found in both Autism Spectrum Disorder (ASD) and dyslexia, suggesting that there may be a link between the two disorders (Egilsdóttir 2015; Simon Baron-Cohen et al. 2001). Currently, there is a lack of studies that have investigated co-occurring psychopathology, let alone the co-occurrence of ASD and dyslexia. In Aim 1, we will investigate the co-occurrence of ASD and dyslexia with the use of a recently developed context-based emotion recognition task, called Inferential Emotion Tracking (IET), in a large and diverse subject pool. In Aim 2, we will attempt to replicate our original findings and investigate whether repeated practice of the IET task leads to improved emotion recognition ability and emotional intelligence for individuals who score high on an ASD and dyslexia self-report measure.

The Neurocognitive Mechanisms Underlying the Co-Occurrence of Dyslexia and Dyscalculia

Pedro Pinheiro-Chagas (UCSF), Steven Piantadosi (UC Berkeley)

The project aims to characterize the neurocognitive mechanisms of the co-occurrence of developmental dyslexia and dyscalculia in children. To do so, we are going to leverage existing data from the UCSF Dyslexia Center and estimate the co-occurrence of specific language and math difficulties in dyslexic children. Next, we are going to test the hypothesis that a common mechanism explaining this co-occurrence is a deficit in the procedural memory system. For that, we are going develop a novel sequence learning paradigm informed by computational models of language and math concepts acquisition. Finally, we are going to build a machine learning platform to characterize the neural correlates of this co-occurrence in large datasets, with the goal of increasing reproducibility of brain-wide associations. We expect that the outcomes of this project will help us better understand the development of symbolic reasoning in children with the overarching goal of informing pedagogical interventions.

Family Conversations About Learning Differences: Exploring Parent-Child Attributions of Dyslexia in Immigrant Families

Aya Williams (UCSF)

Children with dyslexia in immigrant families have faced cultural and linguistic barriers to fully access and benefit from intervention. To address such gap, the proposed research will characterize attributions (i.e., how to interpret, evaluate, and make meaning) of children’s dyslexia and related learning differences in immigrant families using mixed-method approach. We will recruit a diverse sample of 20 school-age children in first- and second-generation immigrant families. In Aim 1, we will characterize cultural variations in family attributions of dyslexia across sociodemographic factors (e.g., acculturation, race, SES, bilingual language use) and stages of treatment engagement. Qualitative analyses and automated speech analyses will be conducted on family conversations collected through semi-structured interviews. In Aim 2, we will investigate associations between attributions, family functioning, and quality of parent-child relationship. Results will serve as an important, preliminary step for viewing the child-in-context and developing family intervention tools to support children with learning differences.