The Healthy Brain Network Serial Scanning Initiative: a resource for evaluating inter-individual differences and their reliabilities across scan conditions and sessions

Background: Although typically measured during the resting state, a growing literature is illustrating the ability to map intrinsic connectivity with functional MRI during task and naturalistic viewing conditions. These paradigms are drawing excitement due to their greater tolerability in clinical and developing populations and because they enable a wider range of analyses (e.g., inter-subject correlations). To be clinically useful, the test-retest reliability of connectivity measured during these paradigms needs to be established. This resource provides data for evaluating test-retest reliability for full-brain connectivity patterns detected during each of four scan conditions that differ with respect to level of engagement (rest, abstract animations, movie clips, flanker task). Data are provided for 13 participants, each scanned in 12 sessions with 10 minutes for each scan of the four conditions. Diffusion kurtosis imaging data was also obtained at each session. Findings: Technical validation and demonstrative reliability analyses were carried out at the connection-level using the Intraclass Correlation Coefficient and at network-level representations of the data using the Image Intraclass Correlation Coefficient. Variation in intrinsic functional connectivity across sessions was generally found to be greater than that attributable to scan condition. Between-condition reliability was generally high, particularly for the frontoparietal and default networks. Between-session reliabilities obtained separately for the different scan conditions were comparable, though notably lower than between-condition reliabilities

Data Descriptor: An open resource for transdiagnostic research in pediatric mental health and learning disorders

Technological and methodological innovations are equipping researchers with unprecedented capabilities for detecting and characterizing pathologic processes in the developing human brain. As a result, ambitions to achieve clinically useful tools to assist in the diagnosis and management of mental health and learning disorders are gaining momentum. To this end, it is critical to accrue large-scale multimodal datasets that capture a broad range of commonly encountered clinical psychopathology. The Child Mind Institute has launched the Healthy Brain Network (HBN), an ongoing initiative focused on creating and sharing a biobank of data from 10,000 New York area participants (ages 5–21). The HBN Biobank houses data about psychiatric, behavioral, cognitive, and lifestyle phenotypes, as well as multimodal brain imaging (resting and naturalistic viewing fMRI, diffusion MRI, morphometric MRI), electroencephalography, eyetracking, voice and video recordings, genetics and actigraphy. Here, we present the rationale, design and implementation of HBN protocols. We describe the first data release (n =664) and the potential of the biobank to advance related areas (e.g., biophysical modeling, voice analysis

The Healthy Brain Network Biobank: An open resource for transdiagnostic research in pediatric mental health and learning disorders

Innovations in methods and technologies are equipping researchers with unprecedented capabilities for detecting and characterizing pathologic processes in the developing human brain. As a result, there is growing enthusiasm about the prospect of achieving clinically useful tools that can assist in the diagnosis and management of mental health and learning disorders. For these ambitions to be realized, it is critical to accrue large-scale multimodal datasets that capture a broad range of commonly encountered clinical psychopathology. To this end, the Child Mind Institute has launched the Healthy Brain Network (HBN), an ongoing initiative focused on creating and sharing a biobank comprised of data from 10,000 New York City area children and adolescents (ages 5-21). The HBN has adopted a community-referred recruitment model. Specifically, study advertisements seek the participation of families who have concerns about one or more psychiatric symptoms in their child. The HBN Biobank houses data about psychiatric, behavioral, cognitive, and lifestyle (e.g., fitness, diet) phenotypes, as well as multimodal brain imaging, electroencephalography, digital voice and video recordings, genetics, and actigraphy. In this paper, we present the motivation, rationale and design for the HBN along with the initial implementation and evolution of the HBN protocols. We describe the first major open data release (n = 664) containing descriptive, electroencephalography, and multimodal brain imaging data (resting state and naturalistic viewing functional MRI, diffusion MRI and morphometric MRI). Beyond accelerating transdiagnostic research, we discuss the potential of the HBN Biobank to advance related areas, such as biophysical modeling, voice and speech analysis, natural viewing fMRI and EEG, and methods optimization

Association between immigration status and anxiety, depression, and use of anxiolytic and antidepressant medications in the Hispanic Community Health Study/Study of Latinos

The purpose of this study was to investigate the association between undocumented immigration status and anxiety, depression, and use of anxiolytic or antidepressant medications in the Hispanic Community Health Study/Study of Latinos. Cross-sectional analysis of data collected between 2014 and 2017. Participants were categorized as U.S.-born citizens, naturalized citizens, documented noncitizens, or undocumented noncitizens. We calculated prevalence and prevalence ratios for anxiety, depression, and use of anxiolytic or antidepressant medication, by immigration status. Of 9257 participants, 1403 (15%) were undocumented noncitizens, 2872 (31%) were documented noncitizens, 3766 (41%) were naturalized citizens, and 1216 (13%) were U.S.-born citizens. Prevalence of anxiety was lower among undocumented than documented noncitizens (9 vs. 15%, P < .0001) but not significantly different in adjusted analyses. Prevalence of depression was similar among undocumented and documented noncitizens (20 vs. 24%, P = .07) and not significantly different in adjusted analyses. Among participants with depression, 7% of undocumented and 27% of documented noncitizens reported use of antidepressants (adjusted prevalence ratio 0.49, 95% CI 0.27–0.87). Undocumented noncitizens had similar likelihood of anxiety and depression, but lower likelihood of antidepressant use, compared with documented noncitizens. These results may reflect the resilience of an undocumented population facing multiple stressors but suggest that this group may be undertreated for depression

An open resource for transdiagnostic research in pediatric mental health and learning disorders

AbstractTechnological and methodological innovations are equipping researchers with unprecedented capabilities for detecting and characterizing pathologic processes in the developing human brain. As a result, ambitions to achieve clinically useful tools to assist in the diagnosis and management of mental health and learning disorders are gaining momentum. To this end, it is critical to accrue large-scale multimodal datasets that capture a broad range of commonly encountered clinical psychopathology. The Child Mind Institute has launched the Healthy Brain Network (HBN), an ongoing initiative focused on creating and sharing a biobank of data from 10,000 New York area participants (ages 5–21). The HBN Biobank houses data about psychiatric, behavioral, cognitive, and lifestyle phenotypes, as well as multimodal brain imaging (resting and naturalistic viewing fMRI, diffusion MRI, morphometric MRI), electroencephalography, eye-tracking, voice and video recordings, genetics and actigraphy. Here, we present the rationale, design and implementation of HBN protocols. We describe the first data release (n=664) and the potential of the biobank to advance related areas (e.g., biophysical modeling, voice analysis).</jats:p