An Investigation of Skill Acquisition under Conditions of Augmented Reality

Augmented reality is a virtual environment that integrates rendered content with the experience of the real world. There is evidence suggesting that augmented reality provides for important spatial constancy of objects relative to the real world coordinate system and that this quality contributes to rapid skill acquisition. The qualities of simulation, through the use of augmented reality, may be incorporated into actual job activities to produce a condition of just-in-time learning. This may make possible the rapid acquisition of information and reliable completion of novel or infrequently performed tasks by individuals possessing a basic skill-set. The purpose of this research has been to investigate the degree to which the acquisition of a skill is enhanced through the use of an augmented reality training device

Toward open sharing of task-based fMRI data: the OpenfMRI project

The large-scale sharing of task-based functional neuroimaging data has the potential to allow novel insights into the organization of mental function in the brain, but the field of neuroimaging has lagged behind other areas of bioscience in the development of data sharing resources. This paper describes the OpenFMRI project (accessible online at http://www.openfmri.org), which aims to provide the neuroimaging community with a resource to support open sharing of task-based fMRI studies. We describe the motivation behind the project, focusing particularly on how this project addresses some of the well-known challenges to sharing of task-based fMRI data. Results from a preliminary analysis of the current database are presented, which demonstrate the ability to classify between task contrasts with high generalization accuracy across subjects, and the ability to identify individual subjects from their activation maps with moderately high accuracy. Clustering analyses show that the similarity relations between statistical maps have a somewhat orderly relation to the mental functions engaged by the relevant tasks. These results highlight the potential of the project to support large-scale multivariate analyses of the relation between mental processes and brain function

Best practices in data analysis and sharing in neuroimaging using MRI

Given concerns about the reproducibility of scientific findings, neuroimaging must define best practices for data analysis, results reporting, and algorithm and data sharing to promote transparency, reliability and collaboration. We describe insights from developing a set of recommendations on behalf of the Organization for Human Brain Mapping, and identify barriers that impede these practices, including how the discipline must change to fully exploit the potential of the world’s neuroimaging data

Best practices in data analysis and sharing in neuroimaging using MRI

Given concerns about the reproducibility of scientific findings, neuroimaging must define best practices for data analysis, results reporting, and algorithm and data sharing to promote transparency, reliability and collaboration. We describe insights from developing a set of recommendations on behalf of the Organization for Human Brain Mapping, and identify barriers that impede these practices, including how the discipline must change to fully exploit the potential of the world’s neuroimaging data

Data Descriptor: An open resource for transdiagnostic research in pediatric mental health and learning disorders

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, eyetracking, 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

SOBP 2014 Poster: Connectome-Wide Signatures of Dimensional Psychiatric Symptomatology

<p>Presented May 9th, 2014 at the Society for Biological Psychiatry Annual Meeting in New York, NY</p