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).

Figure 1. HBN Protocol Timeline.

Here we depict the month in which each assessment was added (and in some cases removed). Dark gray boxes indicate inclusion of the assessment in the protocol for a given month, while white boxes indicate the measure was not included.

Figure 2. Distribution of IQ measures and CBCL Scores.

Participant IQ was measured using the WISC, with the exception of: (1) early participants for whom the more abbreviated WASI was administered, (2) individuals with limited verbal skills and/or known IQ less than 70, or (3) children under age 6. For these latter two cases, the KBIT was administered. These figures include overall performance IQ, verbal IQ, and full-scale IQ measures from all three tests.

Figure 3. Correlation Matrix of HBN Phenotypic Measures.

Heatmap depicts significant correlations between a broad sampling of HBN behavioral, cognitive, and physical measures after multiple comparisons correction (false discovery rate; q<0.05). The associations revealed are in general alignment with the broader psychiatric literature.

Figure 4. Median Framewise Displacement Measures.

The upper left panel plots Median Framewise Displacement (Median FD) versus.

Figure 5. Preprocessed Connectome Project Quality Assurance Measures for functional and morphometric MRI.

Shown here are PCP QA results for morphometry (upper panel) and functional (lower panel) MRI data quality for each data acquisition phase—Staten Island (SI; 1.5 Tesla Siemens Avanto) and Rutgers (RU; 3.0 T Siemens Tim Trio).

Figure 6. Correlation Between Phenotypic Measures and QAP measures.

Here we depict significant Pearson correlations (after false discovery rate correction for multiple comparisons) between phenotypic measures and key QA indices for morphometry MRI (left panel), as well as each of the functional MRI scan types (resting state fMRI, naturalistic viewing fMRI: ‘Despicable Me’, naturalistic viewing fMRI: ‘The Present’) (right panel). To facilitate visualization, significance values are depicted as −log10(p).

Figure 7. EEG quality assessment.

Shown here are the number of rejected EEG channels for each of the paradigms.

Figure 8. Age and Sex Distribution of HBN Participants.

Figure 9. Diagnostic Breakdown of HBN Participants.

This figure shows the frequency of diagnoses given to HBN participants. Data for this figure comes from the final consensus diagnosis given by the lead clinician at the end of participation. Diagnoses are grouped by category.