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

Abstract

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, 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).. CC-BY-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/149369 doi: bioRxiv preprint first posted online Jun. 13, 2017; PURPOSE OF DATA COLLECTIONPsychiatric and learning disorders are among the most common and debilitating illnesses across the lifespan. Epidemiologic studies indicate that 75% of all diagnosable psychiatric disorders begin prior to age 241 . This underscores the need for increased focus on studies of the developing brain 2 . Beyond improving our understanding of the pathophysiology that underlies the emergence of psychiatric illness throughout development, such research has the potential to identify clinically useful markers of illness that can improve the early detection of pathology and guide interventions. Although the use of neuroimaging, neuropsychology, neurophysiology and genetics has made significant strides in revealing biological correlates for a broad array of illnesses, findings have been lacking in specificity 3 . Consequently, progress in finding clinically useful brain-based biomarkers has been disappointing 4,5 .Given the slow pace in biomarker identification, investigators have been prompted to rethink research paradigms and practices. Most notably, the emphasis on mapping diagnostic labels from a clinically defined nosology (e.g., the Diagnostic and Statistical Manual of Mental Disorders (DSM) or the International Classification of Diseases) to varying biological indices has proven to be problematic, as it assumes consistent biological relationships with broad constellations of signs and symptoms 6,7 . Epidemiologists, psychopathologists, geneticists and neuroscientists are reconsidering the relevance of diagnostic boundaries due to the lack of specif...

Test–retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering

“…In our study, we showed a highly reproducible white matter parcellation using the fiber clustering method; thus, we suggested this method could be reproducibly applied for clinical applications and large dataset analysis. These applications can include white matter parcellation for neurosurgical planning (O'Donnell et al, ), parcellation of scans from different time points to track white matter changes, and analysis of very large diffusion MRI datasets that will soon become available (Alexander et al, ; Casey et al, ; Thompson et al, ).…”

“…A low relative difference (RD) value represents a high test-retest reproducibility. The tracts with significantly lower mean relative difference scores using the fiber clustering method are annotated with a red asterisk, while the tracts with significantly lower mean relative difference scores using the cortical-parcellation-based method are annotated with a blue X [Color figure can be viewed at wileyonlinelibrary.com] white matter parcellation for neurosurgical planning , parcellation of scans from different time points to track white matter changes, and analysis of very large diffusion MRI datasets that will soon become available (Alexander et al, 2017;Casey et al, 2018;Thompson et al, 2017).…”

“…White matter parcellation is also important for identifying anatomical fiber tracts for clinical visualization (Golby et al, ; Gong et al, ; Nimsky, Ganslandt, Dorit, Gregory Sorensen, & Fahlbusch, ; O'Donnell et al, ) or hypothesis‐driven research (Alexander et al, ; Shany et al, ; Wu et al, , ; Yeo, Jang, & Son, ). Automated and robust white matter parcellation can enable the analysis of new, large dMRI datasets that are being acquired to study complex neural systems across the lifespan and across brain disorders (Alexander et al, ; Casey et al, ; Thompson et al, ).…”

Measuring strengths and weaknesses in dimensional psychiatry

“…Child Mind Institute -Healthy Brain Network (CMI-HBN). Data were collected from 523 participants (ages 6.0-17.0 year old, mean age 10.3; M:F 309:214) of the CMI-HBN (Alexander et al, 2017), which is designed to recruit a sample of 10,000 children and adolescents from the New York City area, collected using a community self-referred model that recruits participants based on the presence of behavioral concerns. Testing correspondence between E-SWAN and traditional scales.…”

Longitudinally consistent estimates of intrinsic functional networks

“…Abnormalities in such functional development have been linked to neurological disorders or psychiatric diseases (Chase, ; Park et al, ). To further understanding of the maturation of functional networks, an increasing number of neuroimaging studies are acquiring longitudinal rs‐fMRI (Alexander et al, ; Kooijman et al, ; Nora et al, ). For example, the multi‐site National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) study (Brown et al, ; Müller‐Oehring et al, ; Pfefferbaum et al, ; Sullivan et al, ) is investigating adolescent neurodevelopment and its deviation due to alcohol use by acquiring rs‐fMRI of each study participant before initiating appreciable drinking and following them annually.…”