Controls for SCCRIP | To establish a well characterized cohort for pediatric patients living with sickle cell disease | 136/11185 | Secondary Analysis | Private |
Working Title: Differentiating Core Autism Symptomatology | Autism spectrum disorder (ASD) is characterized by persistent deficits in social communication and social interaction, social-emotional reciprocity, and repetitive behavior or restricted interest (American Psychiatric Association [APA], 2013). This study extends the existing literature by clarifying the extent to which mental health disorder symptoms differentially converge with autism symptoms related to social communication and restricted and repetitive behavior, as well as the extent to which mental health symptoms are empirically differentiated from the core autism symptom domains. Although there is a well-documented correlation between the severity of core ASD symptoms and the presence of mental health disorder symptoms, such as anxiety and irritability, the nature of this linkage remains poorly understood. In this project, the National Database for Autism Research (NDAR) and Research Domain Criteria Database (RDoCdb) were used to observe continuous symptom measures such as the Social Responsiveness Scale (SRS) and the Child Behavior Checklist (CBCL) to examine correlation matrices as well as factor structure models to examine these patterns of association. The SRS “social communication” and “repetitive restricted” subscales were correlated with the CBCL externalizing, internalizing, attention, conduct, aggression, psychosomatic, and withdrawn subscales. We hypothesized that “repetitive and restricted” behaviors would be more correlated with the CBCL scales than would the “social communication” scale. These results were also interpreted according to age and IQ. In conclusion, this study may elucidate ongoing questions about the centrality of mental health symptoms like anxiety to aspects of ASD taxonomy. | 65/11144 | Secondary Analysis | Private |
Characterizing Auditory Hyperreactivity in Autism | Objective: To answer the following research questions: 1) What is the prevalence of auditory hyper-reactivity in ASD? 2) Does auditory hyper-reactivity severity change with age? and 3) What are the most common auditory stimuli reported to be bothersome?
Research Design: Primarily descriptive secondary data analysis.
Methods: Type of data: Questionnaire items regarding auditory hyper-reactivity will be filtered from: Autism Diagnostic Interview-Revised, Sensory Profile (all forms), Sensory Over-Responsivity Scale, and Sensory Experiences Questionnaire in addition to demographics (i.e., age, race, ethnicity, diagnoses).
Analysis Plan: Descriptive statistics, tables and figures will be used to summarize the prevalence and severity of auditory hyper-reactivity by age. Linear regression modeling will be used to evaluate changes in auditory hyper-reactivity by age. If data is available for control subjects, statistical analyses will be conducted for means comparison (ASD vs. non-ASD).
| 101/7001 | Secondary Analysis | Private |
Investigating autism etiology and heterogeneity by decision tree algorithm | Autism spectrum disorder (ASD) is a neurodevelopmental disorder that causes deficits in cognition, communication and social skills. ASD, however, is a highly heterogeneous disorder. This heterogeneity has made identifying the etiology of ASD a particularly difficult challenge, as patients exhibit a wide spectrum of symptoms without any unifying genetic or environmental factors to account for the disorder. For better understanding of ASD, it is paramount to identify potential genetic and environmental risk factors that are comorbid with it. Identifying such factors is of great importance to determine potential causes for the disorder, and understand its heterogeneity. Existing large-scale datasets offer an opportunity for computer scientists to undertake this task by utilizing machine learning to reliably and efficiently obtain insight about potential ASD risk factors, which would in turn assist in guiding research in the field. In this study, decision tree algorithms were utilized to analyze related factors in datasets obtained from the National Database for Autism Research (NDAR) consisting of nearly 3000 individuals. We were able to identify 15 medical conditions that were highly associated with ASD diagnoses in patients; furthermore, we extended our analysis to the family medical history of patients and we report six potentially hereditary medical conditions associated with ASD. Associations reported had a 90% accuracy. Meanwhile, gender comparisons highlighted conditions that were unique to each gender and others that overlapped. Those findings were validated by the academic literature, thus opening the way for new directions for the use of decision tree algorithms to further understand the etiology of autism.
| 65/3382 | Secondary Analysis | Shared |
Autism Sensory Research Consortium Cross-lab Integrative Data Analysis | Since 2013, when sensory features were officially added to the diagnostic criteria for autism, research into the sensory manifestations of the condition has increased dramatically. However, the majority of this research has primarily been conducted using small laboratory-based samples of children on the autism spectrum, substantially limiting the hypotheses that can be tested in any one dataset and the generalizability of results to the wider autistic population. The Autism Sensory Research Consortium (ASRC), funded by the Nancy Lurie Marks Family Foundation, represents the first major international collaboration of over a dozen research groups that study sensory functioning in autism. As a major thrust of this collaboration, the ASRC has begun a data sharing initiative, in which all participating labs can contribute existing data from their past and present research studies to a centralized database. These “Big Data” can then be systematically examined using powerful large-sample statistical techniques such as structural equation modeling and item response theory, which will allow researchers to test more complex hypotheses regarding the nature of sensory differences in autism and their relationships with sociodemographic and non-sensory clinical features.
Once data from all sites has been pooled, it will be analyzed using a method called integrative data analysis, which is specially designed to derive insights from large and heterogeneous samples. One major advantage of this methodology is the ability to construct and test measurement models of sensory symptoms, determining the most appropriate set of questions for assessing each construct and making sure that the scales do not produce biased comparisons when they are examined across diagnostic groups or subsets of the autistic population. Furthermore, measurement models can be constructed to bridge multiple questionnaires, allowing for the calculation of robust composite scores that can be compared between studies that only administered items from one of the contributing questionnaires. These models can further facilitate pooling of data across studies, allowing us to amass even larger datasets to answer questions about sensory function in the autistic population. Furthermore, moving forward, the composite sensory measures from the integrative data analysis can be employed in other studies, providing investigators in sensory autism research with a suite of reliable and valid behavioral measures that can be used as outcomes in trials of interventions targeting these symptoms.
In the long term, this project has the potential to help us better understand the nature of sensory function in persons on the spectrum, as well as how sensory alterations relate to broader features of the condition—specifically, for whom and/or at what point in development sensory features are most predictive of core autism behaviors or other meaningful clinical outcomes such as language acquisition and adaptive behavior. Incorporation of neuroscientific data collected within the ASRC can also possibly shed some light on the neural basis of sensory disruptions in the autistic population. All of this will help to lay a foundation for future work testing the efficacy of candidate interventions aimed at improving sensory function and more distal skills in autistic individuals. | 13/2110 | Secondary Analysis | Private |
Imbalanced social-communicative and restricted repetitive behavior subtypes in autism spectrum disorder exhibit different neural circuitry | Social-communication (SC) and restricted repetitive behaviors (RRB) are autism diagnostic symptom domains. SC and RRB severity can markedly differ within and between individuals and may be underpinned by different neural circuitry and genetic mechanisms. Modeling SC-RRB balance could help identify how neural circuitry and genetic mechanisms map onto such phenotypic heterogeneity. Here, we developed a phenotypic stratification model that makes highly accurate (97–99%) out-of-sample SC = RRB, SC > RRB, and RRB > SC subtype predictions. Applying this model to resting state fMRI data from the EU-AIMS LEAP dataset (n = 509), we find that while the phenotypic subtypes share many commonalities in terms of intrinsic functional connectivity, they also show replicable differences within some networks compared to a typically-developing group (TD). Specifically, the somatomotor network is hypoconnected with perisylvian circuitry in SC > RRB and visual association circuitry in SC = RRB. The SC = RRB subtype show hyperconnectivity between medial motor and anterior salience circuitry. Genes that are highly expressed within these networks show a differential enrichment pattern with known autism-associated genes, indicating that such circuits are affected by differing autism-associated genomic mechanisms. These results suggest that SC-RRB imbalance subtypes share many commonalities, but also express subtle differences in functional neural circuitry and the genomic underpinnings behind such circuitry. | 77/1708 | Secondary Analysis | Shared |
Investigating possible biomarkers of autism in resting EEG | There are no clinically useful biomarkers of autism spectrum disorder (ASD). Electroencephalogram (EEG) can measure ongoing brain dynamics using cheap and widely available technology and is minimally invasive. As such, any measurement drived from EEG that is capable of serving as a biomarker for ASD would be hugely beneficial. Previous research has been conflicting and a large list of EEG measures have been suggested. | 5/771 | Secondary Analysis | Shared |
Development of EEG dynamics throughout the lifespan | Combining data from across several datasets available on the NIMH data repository, multiple metrics of EEG dynamics were examined in a large cross sectional sample of healthy participants from across the lifespan. The goal was to examine changes in brain dynamics that occur across development. | 5/551 | Secondary Analysis | Shared |
comparing EEG metrics during eyes closed versus eyes open rest in autism | Understanding the complex relationship between brain dynamics and mental disorders has proved difficult. Sample sizes have often been small, and brain dynamics have often been evaluated in only one state. Here, data obtained from the NIMH data archive were used to create a sample of 395 individuals with both eyes open and eyes closed resting state EEG data. All data were submitted to a standard pipeline to extract power spectra, peak alpha frequency, the slope of the 1/f curve, multi scale sample entropy, phase amplitude coupling, and intersite phase clustering. These data along with the survey data collected at the time of data collection form a valuable resource for interogating the relationship between brain state changes and autism diagnosis. | 12/336 | Secondary Analysis | Shared |
Neural Correlates of Restricted Repetitive Behavior in Autism Spectrum Disorder | The objective of this study is to investigate relationships between restricted, repetitive behavior and neural circuitry alterations in autism spectrum disorder (ASD). The neural circuitry mediating restricted, repetitive behaviors is largely unknown, and consequently effective treatments are lacking. In order to perform these investigations we will use the National Database for Autism Research (NDAR) to access questionnaires and assessments related to repetitive behavior as well as MRI, fMRI, and DTI scans from subjects with autism spectrum disorder (ASD) and typically developing controls. We will perform between-group morphological comparisons, as well as assessments of brain connectivity (e.g. diffusion tractography). For subjects with ASD, we will also correlate neuroimaging data to repetitive behavior scores. This study will help to better understand the link between restricted, repetitive behavior and specific brain alterations | 3/192 | Secondary Analysis | Private |
Critical test items to differentiate individuals with SPCD from those with ASD and typical controls | Social (pragmatic) communication disorder (SPCD) is a new category in the DSM-5. This study used IRT modelling to analyze archive data of item responses to the Social Communication Question-Lifetime (SCQ) from the National Database of Autism Research (NDAR), to select critical test items that could efficiently differentiate SPCD from ASD and TD.
Methods: The SCQ records were downloaded from the NDAR. The item difficulty values and participants ability in the social communication and repetitive behavior and restricted interests were estimated through Winsteps. The items with difficulty values mostly matching the participants ability at the cut-off zones among three groups were selected.
Result: The eight test items were identified for screening SPCD with 75% sensitivity. The specificity for differentiating SPCD from TD and ASD is 86.27% and 68.9% respectively.
Conclusion: This study provides a short list of critical items that could be used to screen SPCD from TD and ASD.
| 3/151 | Secondary Analysis | Private |