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...

We hypothesized that differences in cardiac baroreflex sensitivity (BRS) would be independently associated with aortic stiffness and augmentation index (AI), clinical biomarkers of cardiovascular disease (CVD) risk, among young sedentary and middle-aged/older sedentary and endurance-trained adults. A total of 36 healthy middle-aged/older (age 55-76 years, n=22 sedentary; n=14 endurance-trained) and 5 young sedentary (age 18-31 years) adults were included in a cross-sectional study. A subset of the middle-aged/older sedentary adults (n=12) completed an 8-week aerobic exercise intervention. Invasive brachial artery blood pressure waveforms were used to compute spontaneous cardiac BRS (via sequence technique) and estimated aortic pulse wave velocity (PWV) and AI (AI, via brachial-aortic transfer function and wave separation analysis). In the cross-sectional study, cardiac BRS was 71% lower in older compared with young sedentary adults (P<0.05), but only 40% lower in older adults who performed habitual endurance exercise (P=0.03). In a regression model that included age, sex, resting heart rate, mean arterial pressure (MAP), body mass index and maximal exercise oxygen uptake, estimated aortic PWV (β±SE = −5.76 ± 2.01, P=0.01) was the strongest predictor of BRS (Model R2=0.59, P<0.001). The 8 week exercise intervention improved BRS by 38% (P=0.04) and this change in BRS was associated with improved aortic PWV (r=−0.65, P=0.044, adjusted for changes in MAP). Age- and endurance exercise-related differences in cardiac BRS are independently associated with corresponding alterations in aortic PWV among healthy adults, consistent with a mechanistic link between variations in the sensitivity of the baroreflex and aortic stiffness with age and exercise.

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