Autism & Development Disabilites

Data Driven Brain Shape Analysis Framework and Explainable AI 2022 Award: $66,691 Specific shape aspects are associated with individual differences in cognitive function in children and adults as well as abnormality in many neuropsychiatric disorders. In this project, we will investigate brain shape and reveal specific characteristics for different neuropsychiatric disorders. Our approach is based on multi-view and neural network interpretability techniques. Need/Problem: The overarching goal of this proposal is to develop deep learning techniques for brain shape analysis. We need to investigate, identify, and implement better approaches that will help us make predictions about health outcomes as well as improve early diagnosis of mental health disorders. Grant Summary: Brain shape analysis has been researched extensively and we know that specific shape aspects of the White Matter (WM) are associated with individual differences in cognitive function in children and [...]

Circuit-level investigation of global hypoconnectivity in the sensory neocortex of a mouse model for autism 2022 Award: $40,092 In addition to the well-known social communication difficulties, autistic patients frequently suffer from sensory processing problems, such as sensory overload. In this project, we will investigate neural circuit alterations that affect sensory responses, using a preclinical mouse model for autism. Findings in the simple mouse brain should provide a first step toward understanding circuit mechanisms underlying broad sensory symptoms in autistic patients. Need/Problem: Atypical behavioral responses to sensory inputs, such as sensory overload and hypersensitivity, are observed in around 90% of autism spectrum disorder (ASD) patients, and developing treatments for this is critically needed to reduce the burden on patients and their families. Grant Summary: To identify the circuit alterations causing sensory processing problems in ASD, we will focus on a preclinical [...]

Biobehavioral Markers of Suicide Risk in Female Adolescents on the Autism Spectrum 2021 Award: $39,992 Suicide is a leading cause of premature death for individuals on the autism spectrum. Females on the spectrum are at a particularly elevated risk of experiencing suicidal thoughts and behavior. However, very few studies have examined risk factors for suicidal thoughts and behaviors in females on the spectrum. Our project is the first prospective study of potential biological vulnerabilities for suicidal thoughts and behaviors in females on the spectrum. Study findings will set the stage for a program of research focused on identifying females on the spectrum at risk for suicide and developing tailored suicide prevention programs to improve outcomes across the lifespan. Need/Problem: Suicide is a leading cause of premature death for autistic* individuals (*note that we use identity-first language to respect the [...]

A Pilot Study of Gender Differences in Neuroinflammation in Autism 2020 Award: $50,000 Autism spectrum disorder (ASD) impacts the functioning of brain regions that process social information, but the reasons for this are unclear. In this project we will examine whether inflammation in brain regions that process social information contributes to social impairments in ASD and whether there are sex differences in brain inflammation in ASD. Results will indicate whether treatments that impact inflammation may be beneficial for those with ASD. Need/Problem: Autism spectrum disorder (ASD) is an etiologically heterogeneous disorder that affects 1 in 54 individuals and confers a lifelong burden to most affected individuals and their families. The brain basis of ASD remains poorly understood. Grant Summary: We will use combined positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) brain imaging to examine whether ASD [...]

Quantifying the Brain Developmental Trajectory of Autism-Associated Brain Overgrowth Using 3D Cellular Resolution Imaging 2019 Award: $39,069 Need/Problem: Alterations in brain development are associated with a variety of neuropsychiatric disorders, including autism spectrum disorder (ASD). Many studies demonstrate that mouse models re-capitulate a core human phenotype, brain overgrowth, associated with an increase in the neural progenitor pool and that this brain overgrowth shows a regionally specific pattern. However, we do not know the cell-types that are driving the effect. And due to the limited temporal resolution of previous immunohistochemistry, we do not know the full developmental trajectory of cell-types leading to brain overgrowth. Grant Summary: We aim to develop high throughput computational tools for 3D light-sheet microscopy images to quantify the brain developmental trajectory of autism-associated brain overgrowth. The computational tools developed in this project will be used to study [...]

Cellular and Molecular Mechanisms Governing Cortical Surface Area Overgrowth in iPSC-derived Neural Cells from Longitudinally Characterized Autism Individuals 2018 Award: $49,720 Many individuals with autism spectrum disorder exhibit larger brain volumes early in life. By mimicking and studying brain cells from subjects with ASD and subjects without ASD, Dr. Hazlett and her team are working to determine the underlying pathologies of brain overgrowth. These results will help identify biological markers which will inform more personalized early treatments for ASD. Need/Problem: Autism spectrum disorder (ASD) is an etiologically heterogeneous disorder that affects 1 in 68 individuals, and confers a lifelong burden to most affected individuals and their families. ASD has been consistently characterized by early brain overgrowth in neuroimaging studies. Grant Summary: We will generate and study neural cells from individuals with ASD and matched controls who have been profiled [...]

Predicting Early Brain Development via Deep Learning of DNA 2018 Award: $39,742 The state-of-art data science with the most advanced machine learning algorithm such as deep learning provides us new opportunity to uncover the genetic code underlying in millions of genetic markers across the human genome. Our research project will build the most advanced deep convolutional neural network utilizing DNA markers to predict cognitive function of early age. We will also adapt this deep learning framework to predict other psychiatric disorders such as autism, schizophrenia, and bipolar disorder so that the DNA code of human genome about mental disorders can be decoded. Need/Problem: Innovative methodological strategies are urgently needed to predict short-term and long-term cognitive outcome at early life so that early intervention can be implemented before any cognitive delay appears. Grant Summary: This grant is to develop deep [...]