Controls for SCCRIP | To establish a well characterized cohort for pediatric patients living with sickle cell disease | 126/11185 | 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were correlated with the CBCL externalizing, internalizing, attention, conduct, aggression, psychosomatic, and withdrawn subscales.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. | 2/11144 | 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).
| 17/7001 | Secondary Analysis | Private |
The Frequency of Symptom-Based Phenotypes of Mental Disorders is Long-Tailed | The heterogeneity of symptoms among individuals diagnosed with the same mental disorder has been blamed to hinder research in mental health and the development of effective treatments. Although widely acknowledged as problematic, the characteristics of this heterogeneity are largely unknown. We assessed the frequency of symptom phenotypes across a variety of clinical and non-clinical populations and found a consistent, long-tailed distribution. This distribution represents a mixture of a few very commonly expressed phenotypes and the sum of many, each only rarely displayed ones. As a consequence, the non-normality of this distribution induces a systematic bias, affecting all research and treatments relying on a symptom-based definition of mental disorders. | 86/5743 | Secondary Analysis | Shared |
Trajectories of Adult Aging | EEG Trajectories of Aging. | 159/3461 | 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.
| 121/3382 | Secondary Analysis | Shared |
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. | 1/1708 | 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. | 12/771 | 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. | 39/551 | Secondary Analysis | Shared |
Face-processing performance is an independent predictor of social affect as measured by the Autism Diagnostic Observation Schedule across large-scale datasets | Face-processing deficits, while not required for the diagnosis of Autism Spectrum Disorder (ASD), have been associated with impaired social skills—a core feature of ASD; however, the strength and prevalence of this relationship remains unclear. Across 445 participants from the NIMH Data Archive, we examined the relationship between Benton Face Recognition Test (BFRT) performance and Autism Diagnostic Observation Schedule-Social Affect (ADOS-SA) scores. Lower BFRT scores (worse face-processing performance) were associated with higher ADOS-SA scores (higher ASD severity)–a relationship that held after controlling for other factors associated with face processing, i.e., age, sex, and IQ. These findings underscore the utility of face discrimination, not just recognition of facial emotion, as a key covariate for the severity of symptoms that characterize ASD. | 1/445 | 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. | 1/336 | Secondary Analysis | Shared |
Aging trajectories in adults with ASD. | Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by lifelong social communication impairments (American Psychiatric Association. 2013). The CDC estimates that 5.4 million adults in the U.S. currently have an ASD (Dietz et al. 2020), and that one in four of these adults will be over 65 years by 2035 (Bureau n.d.). Several lines of evidence demonstrate that individuals with ASD are more vulnerable to age-related health risks (Hand et al. 2020), including cognitive decline (Lever and Geurts 2016; Powell et al. 2017). Recent studies also show that neurophysiological brain aging markers decline atypically in adults with ASD (Koolschijn et al. 2017; Walsh et al. 2019). Here we examine if slowing of oscillatory brain activity, a key sign of neural aging, occurs at a faster rate in ASD compared to NT controls.
Oscillatory slowing will be quantified using the peak frequency of alpha oscillations (7-13Hz). Peak alpha frequency will be extracted from the resting EEG data of adults with an ASD diagnosis, and age-matched controls. Associations between peak alpha frequency and age will be assessed and compared between groups.
| 157/269 | Secondary Analysis | Private |
Measuring theory of mind in schizophrenia research: Cross-cultural validation. | Theory of mind (ToM) is the ability to understand mental states of others and it is crucial for building sensitivity to other persons or events. Measuring ToM is important for understanding and rehabilitating social cognitive impairments in persons with schizophrenia. The Social Attribution Task-Multiple Choice (SAT-MC) has been successfully employed to measure ToM between individuals with schizophrenia (SZ) and healthy controls (HC) in North America. Given that the SAT-MC uses geometric shapes, is nonverbal and less culturally loaded than other social cognition measures, it may serve for measuring ToM in schizophrenia across cultures. A total of 120 participants (30 per group; Korean SZ; Korean HC; North American SZ; North American HC) were selected from existing databases to examine the reliability and validity of the SAT-MC. Internal consistency, factor structure, measurement invariance, discriminant validity, and convergent/divergent validity were examined. The SAT-MC had good internal consistency regardless of the clinical and cultural group as evidence by Cronbach's α ≥ 0.78 in all groups. Confirmatory factor analysis confirmed the one-factor model with a good model fit (χ2 = 188.122, TLI = 0.958, CFI = 0.963, RMSEA = 0.045). The SAT-MC was sensitive to detect individual differences in ToM of SZ and HC, regardless of culture (p < 0.001), and significantly correlated with other social cognition tasks (Hinting and Reading the Mind in the Eyes Test) among Korean and North American patients. The SAT-MC is a reliable measure for evaluating ToM in both Koreans and North Americans with or without schizophrenia, supporting its potential utility in diverse language and cultures for schizophrenia research. | 36/36 | Secondary Analysis | Shared |