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...

These findings underscore the importance of monitoring the neurocognitive late effects in survivors of pediatric brain tumors, and the need for more consistent consideration of demographic, diagnostic, and treatment-related variables in future research to allow for examination of factors that may moderate these deficits.

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Cognitive decline and accompanying neurological changes associated with non-CNS cancer diagnosis and treatment have been increasingly identified in a subset of patients. Initially believed to be due to neurotoxic effects of chemotherapy exposure, observation of cognitive decline in patients not treated with chemotherapy, cancer-diagnosed individuals prior to treatment, and patients receiving alternative treatment modalities (surgery, endocrine therapy, radiation), has led to investigation of additional potential etiologies and moderating factors. Stressful experiences have long been posited as a contributor to these cognitive changes. Through reciprocal connectivity with peripheral systems, the brain maintains a dynamic circuitry to adapt to stress (allostasis). However, overuse of this system leads to dysregulation and contributes to pathophysiology (allostatic load). At this time, little research has been conducted to systematically examine the role of allostatic load in cancer-related cognitive dysfunction. Here we integrate theories of stress biology, neuropsychology, and coping and propose a model through which individuals with a high level of allostatic load at diagnosis may be particularly vulnerable to the neurocognitive effects of cancer. Opportunities for future research to test and extend proposed mechanisms are discussed in addition to points of prevention and intervention based on individual variation in stress reactivity and coping skills.

The current study examined the relations of measures of cognitive reappraisal and secondary control coping with working memory abilities, positive and negative affect, and symptoms of anxiety and depression in young adults (N=124). Results indicate significant relations between working memory abilities and reports of secondary control coping and between reports of secondary control coping and cognitive reappraisal. Associations were also found between measures of secondary control coping and cognitive reappraisal and positive and negative affect and symptoms of depression and anxiety. Further, the findings suggest that reports of cognitive reappraisal may be more strongly predictive of positive affect whereas secondary control coping may be more strongly predictive of negative affect and symptoms of depression and anxiety. Overall, the results suggest that current measures of secondary control coping and cognitive reappraisal capture related but distinct constructs and suggest that the assessment of working memory may be more strongly related to secondary control coping in predicting individual differences in distress.

Although the college years represent a high-risk period for depressive symptoms and insomnia, little research has explored their prevalence, comorbidities and risk factors within this developmental period. Two studies were conducted; the first evaluated the prevalence and comorbidity of depressive symptoms and insomnia in 1338 students (ages 18-23 years) from a large Southwestern University. Mild depressive symptoms were endorsed by 19% of students and 14.5% reported moderate to severe symptoms. Forty-seven percent of students reported mild insomnia and 22.5% endorsed moderate to severe insomnia severity. A second study investigated perceived stress as a potential mediator of the relation between self-reported childhood adversity and concurrent depressive symptoms and insomnia. Undergraduates (N = 447) from a Southwestern and Southeastern University reported prior childhood adversity, current perceived stress, insomnia and depressive symptoms. Self-reported childhood adversity predicted higher levels of depressive symptoms and insomnia severity, partially mediated by perceived stress. Results support the high prevalence of depressive symptoms and insomnia among undergraduates. The risk for depressive and insomnia symptoms may be increased among students who experienced greater levels of childhood adversity.