Research has identified early appearing differences in gross and fine motor abilities in infants at heightened risk (HR) for autism spectrum disorder (ASD) because they are the younger siblings of children with ASD, and it suggests that such differences may be especially apparent among those HR infants themselves eventually diagnosed with ASD. The present study examined overall and item-level performance on the gross (GM) and fine motor (FM) subscales of the Mullen Scales of Early Learning (MSEL) administered at 6 months to a large, geographically diverse sample of HR infants with varying developmental outcomes (ASD, elevated ADOS without ASD, low ADOS without ASD) and to infants with low ASD risk (low risk [LR]). We also explored whether motor abilities assessed at 6 months predicted ASD symptom severity at 36 months. FM (but not GM) performance distinguished all 3 HR groups from LR infants with the weakest performance observed in the HR-Elevated ADOS children, who exhibited multiple differences from both LR and other HR infants in both gross and fine motor skills. Finally, 6-month FM (but not GM) scores significant predicted 36-month ADOS severity scores in the HR group; but no evidence was found of specific early appearing motor signs associated with a later ASD diagnosis. Vulnerabilities in infants' fine and gross motor skills may have significant consequences for later development not only in the motor domain but in other domains. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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).