Cameron Craddock

The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry

The National Institute of Mental Health strategic plan for advancing psychiatric neuroscience calls for an acceleration of discovery and the delineation of developmental trajectories for risk and resilience across the lifespan. To attain these objectives, sufficiently powered datasets with broad and deep phenotypic characterization, state-of-the-art neuroimaging, and genetic samples must be generated and made openly available to the scientific community. The enhanced Nathan Kline Institute-Rockland Sample (NKI-RS) is a response to this need. NKI-RS is an ongoing, institutionally centered endeavor aimed at creating a large-scale (N > 1000), deeply phenotyped, community-ascertained, lifespan sample (ages 6–85 years old) with advanced neuroimaging and genetics. These data will be publically shared, openly, and prospectively (i.e., on a weekly basis). Herein, we describe the conceptual basis of the NKI-RS, including study design, sampling considerations, and steps to synchronize phenotypic and neuroimaging assessment. Additionally, we describe our process for sharing the data with the scientific community while protecting participant confidentiality, maintaining an adequate database, and certifying data integrity. The pilot phase of the NKI-RS, including challenges in recruiting, characterizing, imaging, and sharing data, is discussed while also explaining how this experience informed the final design of the enhanced NKI-RS. It is our hope that familiarity with the conceptual underpinnings of the enhanced NKI-RS will facilitate harmonization with future data collection efforts aimed at advancing psychiatric neuroscience and nosology.

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.

Assessment of the impact of shared data on the scientific literature

Data sharing is increasingly recommended as a means of accelerating science by facilitating collaboration, transparency, and reproducibility. While few oppose data sharing philosophically, a range of barriers deter most researchers from implementing it in practice (e.g., workforce and infrastructural demands, sociocultural and privacy concerns, lack of standardization). To justify the significant effort required for sharing data (e.g., organization, curation, distribution), funding agencies, institutions, and investigators need clear evidence of benefit. Here, using the International Neuroimaging Data-sharing Initiative, we present a brain imaging case study that provides direct evidence of the impact of open sharing on data use and resulting publications over a seven-year period (2010-2017). We dispel the myth that scientific findings using shared data cannot be published in high-impact journals and demonstrate rapid growth in the publication of such journal articles, scholarly theses, and conference proceedings. In contrast to commonly used ‘pay to play’ models, we demonstrate that openly shared data can increase the scale (i.e., sample size) of scientific studies conducted by data contributors, and can recruit scientists from a broader range of disciplines. These findings suggest the transformative power of data sharing for accelerating science and underscore the need for the scientific ecosystem to embrace the challenge of implementing data sharing universally.

Fcp-Indi/C-Pac: Cpac Version 1.0.1B Beta

The updates to CPAC in this new version include: CPAC now offers De-Spiking as an option in nuisance regression, which regresses out the impact of motion-induced artifacts from the functional timeseries from volumes exhibiting motion greater than a specified threshold, without removing those volumes. Users can now select which Framewise Displacement (FD) calculation to use (Jenkinsons or Powers) when applying the motion threshold for either Scrubbing or De-Spiking. CPAC can now automatically select a Framewise Displacement (FD) threshold based on a percentage value provided in the pipeline configuration. For example, if provided 5%, CPAC will select a cut-off derived from the top 5% of highest-motion volumes. See the User Guide for more information. Scrubbing has been moved to the Nuisance Regression tab in the GUIs pipeline configuration editor. The pipeline configuration YAML keys have changed for scrubbing settings. See the User Guide Nuisance Regression page and the sample pipeline configuration file for more details. Re-introduced the ability to stop pipeline runs easily from the GUI. Fixed a bug in the data configuration (subject list) builder that would cause non-NIfTI files to be included if the user did not explicitly define the file extension in the file template. Fixed a bug in the data configuration (subject list) builder where some fields would not get populated when re-loading the settings in the GUI. Added better error-catching and messages in nuisance regression which warn the user if nuisance parameters are too stringent for the regression to complete properly. Updated user documentation for this release can be found here: http://fcp-indi.github.io/docs/user/index.html And as always, you can contact us here for user support and discussion: https://groups.google.com/forum/#!forum/cpax_forum Regards, The CPAC development team.