Initiative Coordinators: Howard Poizner, Scott Makeig, Javier Movellan, and Emo Todorov.
 
Creating a new science requires new technologies for measuring and manipulating the dynamics that the brain controls. These include such phenomena as local field potentials, whole-brain activity, muscle activations, limb and body configurations, facial expressions, and student-teacher interactions. The quality and quantity of the resulting data require unique facilities for a large-scale system for storing, synchronizing, sharing, and analyzing that data. Such technological capabilities are beyond the reach of any individual lab and can only be realized in the center mode of funding. These capabilities will enable a number of cross-cutting research collaborations which would otherwise be technologically impossible. Our plan includes the further development of three such infrastructure facilities: Brain Dynamics, Motion Capture, and Data Sharing with others to be added depending upon demand. Given the inherent strength in measuring brain dynamics combined with motion capture, the Brain Dynamics Facility and the Motion Capture Facility are conjoined, offering our Center members the power of combined technology.
 
The Brain Dynamics Facility enables accurate measurement and analysis of whole-brain activity, by using a novel approach to combining the excellent temporal resolution of EEG along with the advanced data analysis and software tools developed by center participants. This facility is continually being advanced in collaboration with UCSD’s Swartz Center for Computational Neuroscience. These capabilities will be complemented by the Motion Capture Facility, which together with the Brain Dynamics facility will enable simultaneous recording on brain activity and complex motor behavior. This emerging integrated technology holds great promise in terms of understanding the spatio-temporal changes in brain dynamics that underlie the process of learning. The system will be housed in UCSD's Institute for Neural Computing. On the software side we will continue to develop and refine open-source appropriate to the new equipment including new analytic methods appropriate to the study of the temporal dynamics of learning.
 
The Motion Capture Facility is being developed in collaboration with the Institute for Neural Computation at UCSD. The facility will provide a range of devices for tracking behavior, including hand movements, eye movements, full body movements, facial expressions and inter-personal interactions, as well as to present stimuli that are tightly coupled with the observed behaviors (e.g. via Virtual Reality or mechanically via robotic devices). The goal is to provide researchers with the tools to manipulate time and timing and to investigate its role in learning and in the development of adaptive behavior. The facility will feature state-of-the-art equipment for marker-based motion capture, high-speed video recording, eye tracking, hand tracking, muscle recording. The facility will also be integrated with the Brain Dynamics Facility through the addition of a high-density EEG recording system for measuring brain dynamics. Complementing the hardware facilities, the Motion Capture Facility will provide a suite of software tools for data analysis and simulation, including a system for automated recognition of facial expressions, hand gestures and gaze directions; a system for probabilistic inference of joint angle trajectories, skeletal parameters and marker attachments from noisy data; and a modeling environment for simulation and visualization of musculo-skeletal dynamics. 
 
The Data Sharing Facility is developed in collaboration with Data Intensive Cyber Environments group (DICE) of the School of Information and Library Sciences at the University of North Carolina Chapel Hill, the Advanced Computing Center for Research and Education (ACCRE) at Vanderbilt University, and other members of the TDLC. The TDLC includes laboratories from twelve universities across the United States, Canada, Australia, and the UK. A
significant challenge for collaborations among such geographically distributed scientists is sharing large quantities of data and stimuli quickly and easily. Traditional methods of “data sharing” such as email attachments can only handle small files of a few tens of megabytes. FTP servers are cumbersome for many users, transfer times can be exceedingly slow, and a glitch in an internet connection – which happens far too frequently– can often mean restarting a transfer from scratch. CDs and DVDs of data sent through the mail are no solution when there are hundreds of gigabytes of data to share. Some researchers have resorted to mailing hard disk drives with data, but that approach engenders significant limits on accessing and annotating data in real time and brings significant challenges for keeping data, analyses, and annotations synchronized across multiple laboratories. Sharing neurophysiological data, motion-capture data, fMRI and electrophysiology data, or high-quality images and video demands a system for easy,
efficient, fault-tolerant transfer of hundreds of gigabytes, terabytes, and one day perhaps petabytes, of data on a regular basis. Moreover, collaborators need to be assured that shared data are only seen by those who should see the data, and sharing data collected from humans and animals demands strict access control as dictated by human IRB and animal IACUC protocols and regulations. In addition to being fast, efficient, and fault-tolerant, a distributed data sharing system needs sophisticated access control policies.  The Data Sharing Facility will include data sharing, data analysis, and quality control. The data systems will assure that all of the shared data have, among other components, proper IRB approval, traceable informed consent, and authorized privilege control. Sharing will be based on the concept of a datagrid - which provides data "virtualization" and makes it possible to organize distributed files into a logical collection that appears locally accessible. In addition to raw data, researchers can upload results from multiple iterations of data analysis in a variety of file formats. In this way complex data can be analyzed in collaborative fashion, while at the same time providing the access control and version control mechanisms needed to avoid data corruption and desynchronization. Such datagrids have becoming increasingly important in a growing list of scientific disciplines, and the DICE group through its Storage Resource Broker (SRB) and next generation iRODS technology– arguably the software platform of choice for datagrid development – can efficiently provide these functions with a moderate degree of tailoring being required. The second function of the Data Sharing facility will be to provide software tools for administrative management and reporting management, data mining and innovative analysis of large and diverse datasets incorporating sensory stimuli, brain responses, and behavioral responses. We will utilize (and when necessary, develop) unsupervised learning methods for automated discovery of meaningful features and dimensions of the raw data. Additional funding for the development of this collaborative data analysis environment will be sought in the form of a data-net cyberinfrastructure grant that will be submitted in partnership with the DICE group and several other large-scale research ventures.

 

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