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).

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...

Persons living with HIV (PLWH) are living longer but experiencing more adverse symptoms associated with the disease and its treatment. This study aimed to examine the impact of a mHealth application (app) comprised of evidence-based self-care strategies on the symptom experience of PLWH. We conducted a 12-week feasibility study with 80 PLWH who were randomized (1:1) to a mHealth app, mobile Video Information Provider (mVIP), with self-care strategies for improving 13 commonly experienced symptoms in PLWH or to a control app. Intervention group participants showed a significantly greater improvement than the control group in 5 symptoms: anxiety (p = 0.001), depression (p = 0.001), neuropathy (p = 0.002), fever/chills/sweat (p = 0.037), and weight loss/wasting (p = 0.020). Participants in the intervention group showed greater improvement in adherence to their antiretroviral medications (p = 0.017) as compared to those in the control group. In this 12-week trial, mVIP was associated with improved symptom burden and increased medication adherence in PLWH.

In adult obesity low-grade systemic inflammation is considered an important step in the pathogenesis of insulin resistance (IR). The association between obesity and inflammation is less well established in adolescents. Here, we ascertain the importance of inflammation in IR among obese adolescents by utilizing either Random Forest (RF) classification or mediation analysis approaches. The inflammation balance score, composed of 8 pro- and anti-inflammatory makers, as well as most of the individual inflammatory markers differed significantly between lean and overweight/obese. In contrast, adiponectin was the only individual marker selected as a predictor of IR by RF, and the balance score only revealed a medium to low importance score. Neither adiponectin nor the inflammation balance score were found to mediate the relationship between obesity and IR. These findings do not support the premise that low grade systemic inflammation is a key for the expression of IR in the human. Prospective longitudinal studies should confirm these findings.

The rise in the rate of adolescent obesity has led to a concurrent rise in the rate of metabolic syndrome (MetS) among young people. In addition to diabetes and cardiovascular disease, MetS has also been linked to cognitive dysfunction. The goal of this study was to assess whether cognitive differences exist between minority urban adolescents carrying excess weight who meet criteria for MetS as compared to their peers without MetS. Two hundred and ninety-six urban adolescents, predominantly Hispanic and carrying excess weight as defined by a BMI above 25 kg/m, were screened for MetS and divided into MetS and no MetS groups. All participants completed the CNS Vital Signs (CNS-VS) computerized neurocognitive battery that assesses cognitive domains of Memory, Processing Speed, Reaction Time, Executive Function, Complex Attention, and Cognitive Flexibility. The MetS group (29.2%, n = 84) performed significantly lower on 2 of the 7 cognitive domains: Executive Function (EF) and Cognitive Flexibility. Additionally, waist circumference was determined to be a significant predictor of both these domains. These findings suggest EF is negatively impacted in adolescents with MetS, despite there being no statistical differences between MetS groups on most other measured cognitive domains. Due to the interrelated nature of obesity, waist circumference, and MetS, these findings have larger implications for the obesity epidemic as well.