Natalia M. Kleinhans - US grants

[unreadable] DESCRIPTION (provided by applicant): Several lines of evidence provide support for a model of abnormal connectivity to explain the underlying neuropathology of autism. However, no study has provided converging evidence of both functional and structural connectivity abnormalities in individuals with autism spectrum disorders (ASD). In this proposal, functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) will be used to investigate abnormalities in neural connectivity in ASD. We will study two domains of impairment (face perception and response to social information) and determine how neural connectivity relates to symptom severity. Twenty-five individuals with high functioning ASD (FSIQ > 80) and 25 typically developing (TD) individuals will participate in the study. The TD group will be matched to the ASD group according to age, gender, and FSIQ. White matter structural abnormalities (reduced fractional anisotropy) are hypothesized to be correlated to abnormal functional connectivity in ASD. The degree of abnormality in measures of structural and functional connectivity is expected to be mediated by clinical severity. A Career Development Award is requested in order to develop the skills necessary to conduct multimodal imaging research. The applicant will obtain training in DTI and develop methods for combining and statistically analyzing imaging data from different imaging modalities. Consultation and training from experts in the fields of neuroimaging and autism spectrum disorders will be augmented by coursework and seminars. The implementation of this training plan will yield a cohesive and thorough assessment of the integrity of white matter connections that underlie specific impairments in ASD. [unreadable] [unreadable]

The objective of this award is to develop a computational framework for identifying the critical network topology of brain connectivity in neuroimaging data, specifically functional magnetic resonance imaging (fMRI). In this framework, network optimization modeling and mathematical programming algorithms will be employed to characterize connectivity patterns in fMRI data from different brain regions. Machine learning techniques will be employed to construct a pattern recognition model used to detect biomarkers and predict the brain disease conditions (i.e., abnormals vs. controls). An information-theoretic approach will be used to select the most informative brain regions to improve the generalizability and to increase the accuracy of the diagnosis prediction model.

If successful, the results of this research will lead to improvements in efficiency and efficacy of brain functional connectivity modeling and new developments of optimization methods for handling large-scale spatio-temporal data. The developed computational framework will be extremely useful for neuroscientists and neurologists to identify abnormal functional connectivity in the brain and to gain a greater understanding of the brain function. The framework will be employed and tested as a novel biomarker for differential diagnoses of brain disorders. Alzheimer?s disease (AD), autism spectrum disorder (ASD), and Parkinson?s disease (PD) will be the case points in this project to test if our computational framework is a sensitive enough tool to detect alterations in brain connectivity associated with brain disorders. Accurate diagnosis can substantially extend a patient?s lifespan and some treatments have different outcomes at different disease stages. Additionally, the developed computational framework can be applied to other real-life large-scale spatio-temporal data that arise in other research areas such as manufacturing, medicine, bioinformatics, neuroscience, finance, and geosciences.

DESCRIPTION (provided by applicant): This project is designed to evaluate the efficacy of a novel imaging, biochemical, and behavioral approach for detecting autism spectrum disorder (ASD) and discovering mechanisms associated with ASD symptomology. Although considerable knowledge has been gained, the lack of reliable predictors during the first year of life remains a major impediment to implementing effective early interventions in children at-risk for ASD. The heterogeneity in ASD renders it unlikely that one specific biomarker will provide a pathognomonic sign of ASD. However, the combination of biomarkers and behavioral indicators being tested in this application has the potential to reveal a biosignature of ASD that can be identified in infancy. We are focusing on the amygdala and limbic system dysfunction in our application. Amygdala dysfunction has been proposed as a critical component of social impairment in ASD, the core symptom that differentiates ASD from other neurodevelopmental disorders. However, functional imaging biomarkers of amygdala dysfunction are yet to be discovered and validated. The current project combines two sensitive functional magnetic resonance imaging (fMRI) measures of amygdala dysfunction in ASD: rapid face detection and reduced amygdala habituation to faces into a new, robust, fMRI habituation paradigm. In addition, we developed a novel amygdala habituation measure using olfactory stimuli. First, we will confirm the sensitivity of our amygdala habituation measures (assayed using emotional faces and odors) for distinguishing children with ASD from typically developing controls. Second we address the mechanisms for atypical habituation, by testing whether reduced fMRI habituation in ASD is driven by alterations in levels of glutamate (excitatory) and/or gamma-amino butyric acid (GABA, inhibitory). Lastly, we are testing whether our battery of olfactory measures including odor detection, cyclic adenosine monophosphate (cAMP) levels (the primary signaling pathway used by olfactory sensory neurons), and fMRI alterations are sensitive and specific biomarkers of ASD. We propose that olfactory measures may be an effective proxy for socioemotional processing given the primacy of emotion in olfactory perception and its shared neuroanatomical substrates with limbic structures affected in ASD. To further investigate the specificity of olfactory measures, we will test the ability of our measures to discriminate between individuals with ASD, typically developing (TD) children and children with clinically significant sensory processing symptoms (SPD). The proposed research addresses Objective 1 of the NIMH Strategic Plan by integrating behavioral and biological markers and examining how neurobiological mechanisms - specifically GABA and glutamate levels - contribute to atypical brain habituation in ASD. Fifty children (8-12 years of age) with high functioning ASD (Full-scale IQ > 70), 50 children with clinically significant sensory processing symptoms (SPD) and 50 typically developing controls (TD) will participate in the study. The TD and SPD groups will be matched to the ASD group according to age, gender, and Full-scale IQ.

Summary Cannabis use during pregnancy has increased substantially, in conjunction with widespread decriminalization/legalization, changing public perceptions about harm, and evidence of cannabis's antiemetic properties. Prior outcomes research on prenatal marijuana exposure is narrow in scope and may have limited relevance to medicinal users, as these older studies included research participants with polysubstance use (e.g. tobacco, alcohol, illicit drugs). In addition, prior research also likely underestimated potential risks of cannabis use during pregnancy because modern strains are 3x more potent than they were 30 years ago. We propose to study brain development in infants exposed in utero to cannabis using state-of-the-art MRI and behavioral measures that we have developed in our studies of infants at high-risk for developing autism spectrum disorder. Cutting-edge neuroimaging techniques have been shown to identify brain changes and subtle behavioral differences before outward symptoms are visible. By focusing on infancy, we aim to characterize cannabis-induced brain and behavioral changes while minimizing environmental effects that contribute to outcomes at older ages. To test our hypotheses, we will recruit 35 pregnant women who are using cannabis to alleviate morning sickness and 35 pregnant women who are using prescribed medication for morning sickness. When infants reach 6 months of age, they will receive extensive neuropsychological assessment and multi-modal imaging (fMRI odor task, resting state fMRI, magnetic resonance spectroscopy, and diffusion tensor imaging) under natural sleep.

Project Summary/Abstract The ongoing goal of the Autism Biomarkers Consortium for Clinical Trials (ABC-CT) is to establish electroencephalography (EEG) and eye-tracking (ET) biomarkers that can be used for stratification and/or as sensitive and reliable objective assays related to social function in autism spectrum disorder (ASD) clinical trials. This renewal application seeks to further validate promising measures through three studies designed to enhance and extend the original ABC-CT study: (1) a confirmation study of the original findings in a new cohort using similar design (T1: Baseline, T2: 6 weeks post baseline, T3: 24 weeks post baseline) and sample size/characteristics (200 with ASD, 200 with typical development (TD)); (2) a follow-up study of the original cohort (N=399) to re-administer the biomarker and clinical batteries 2.5-4 years after original ABC-CT enrollment; (3) a feasibility study of parallel EEG and ET biomarkers in preschool-aged (3-5-year-old) children (25 with ASD, 25 with TD). The biomarker and clinical batteries measure key facets of social-communication in ASD using well- validated paradigms appropriate for the intended developmental and cognitive range. The study will rely on the same leadership and five Collaborating Implementation Sites (?Sites?) from the first phase, all highly experienced in multi-site collaborative clinical research using the proposed clinical, EEG, and ET methodologies. The Data Coordinating Core (DCC) will provide a secure informatics infrastructure for communication and data integration across the consortium to ensure organized data management, quality control, and reliable upload to the National Database for Autism Research (NDAR) and NIH Data Repositories. The Data Acquisition and Analysis Core (DAAC) will oversee consistent use of scientific standards and methodological rigor for data acquisition, processing, and analytics. The Administrative Core, in coordination with federal partners in this cooperative agreement, will oversee the operations of the sites, DCC, and DAAC to ensure methodologically and ethically rigorous, efficient completion of study aims: 1) In the confirmation study with a new cohort, evaluate whether EEG and ET measures, individually or in combination, have utility as stratification biomarkers and/or sensitive, reliable measures of change in clinical trials; 2) In the follow-up study of the original ABC-CT cohort, assess long-term stability, sensitivity to change, and longitudinal predictive value of the markers; 3) In the feasibility study, determine the viability of parallel EEG and ET measures as potential biomarkers in 3-5-year-old children with ASD and TD. Blood (DNA) samples will be collected from participants with ASD and biological parents for future genomic analyses, and raw, processed, and analyzed data will be shared to create a community resource accessible for use by all qualified investigators. These objectives are designed to further develop promising biomarkers to advance qualification with the FDA Biomarker Qualification Program.