Justin Cort Sanchez - US grants

Two related DDDAS application areas considered in this project are (1) cognitive brain modeling from experiments with live subjects and (2) the design of brain-inspired assistive systems to help human beings with severe motor behavior limitations (e.g. paraplegics) through brain-machine interfaces (BMIs). Simply stated, a BMI uses brain signals to directly control devices such as computers and robots. Today's BMI designs are extremely primitive and are a far cry from the seamless interface between brain and body in animals. In a healthy animal, the brain constantly learns and adapts to the needs of new physical movement, in addition to providing perfectly timed signals to the motor system. In this process, the brain receives and uses sensory feedback to both learn and generate the signals that lead to purposeful motion. In order to inch closer to BMI designs that are of use to humans, better models of brain motor control and movement planning are needed along with the necessary adaptive algorithms and computational architecture needed for their execution in real time. In light of such goals, this project's activities aim to significantly advance the state of the art of BMI research by developing the models, algorithms and computational architecture of dynamically-data-driven BMIs (DDDBMIs) that implement recently proposed advanced brain models of motor control. Achieving this goal in the proposed approaches will also allow to address a chief problem in current BMI research: The fact that paraplegics cannot train their own network models because they cannot move their limbs.

The research on DDDBMI systems conducted under this project is a drastic departure of the conventional BMI paradigm. The control interface architecture is distributed and borrowed from recent models of neurophysiology of movement, which will enable better overall performance. Learning occurs simultaneously for the subject and the control models in a synergistic manner, which requires more powerful adaptation schemes. Selective use of many computational models is the reason why a dynamically data-driven system is needed to provide the computational needs of a DDDBMI. The project interdisciplinary activities are closely intertwined around the development and integration of the DDDBMI components into a platform for BMI research. Research on middleware addresses the need for dynamic aggregation of Grid-resources with Quality-of-Service guarantees, and support for dynamic computation steering. Research on adaptive algorithms focuses on new data models and learning algorithms. Research on brain modeling concentrates on cognitive models of motor control and advancing our understanding of the neurobiology of movement. In the long run, the BMI experimental research platform will have a dual role: it will help validate the brain models under investigation and it will provide insights on to how to design BMIs for use by paraplegic patients.

Proposal #: CNS 08-21622
PI(s): Fortes, Jose A.
McIntyre, Lauren M.; Moroz, Leonid L.; Principe, Jose C.; Sanchez, Justin C.
Institution: University of Florida
Gainesville, FL 32611-2002
Title: MRI/Acq.: Instrumentation for Coupled Experimental-Computational Neuroscience and Biology Research

Project Proposed:
This project, acquiring a virtualized multicomputer instrument with shared-memory subsystems and storage capacity, intends to use this instrument as a shared instrument whose resources can be virtualized, reserved, and configured on demand for different research activities related to neuroscience, computational biology, and cyberinfrastructure. Its configurations can also be dedicated and tightly coupled to in vivo experiments using network connections to in situ instrumentation used for experiments. It can simultaneously support multiple research activities because of its unique capability to support real-time computer-in-the-loop experiments, its ability to run many concurrent computation threads, its shared memory and storage subsystems, and its use of virtualization technology to manage the coexistence of multiple computing environments. To develop and validate autonomic computing nodes and techniques for multiuser virtualized computational systems, the instrument provides traces of performance and other needed monitored data. Research activities in brain-machine interfaces, neurogenesis, genomics, bioinformatics, signal processing, cyberinfrastructure, autonomic computing and other areas include:
- Brain-machine interfaces where cortex models for motor control are dynamically learned and applied in real-time,
- Experimental drug discovery through real-time analysis of large amounts of genetic data and many thousands of compounds,
- Analysis of Terabytes of genetic data captured in real-time as a neuron grows, learns, and remembers, and
- Online learning algorithms using dynamic filter topologies with online computation requirements that increase over time.
The activities have transformative goals, including the introduction of real-time, and address the high performance computation into closed-loop experiments and/or systems whose behavior is driven by complex processing of sensed data. Another goal involves providing off-line computing capabilities to match the unprecedented rate and volume of genetic data produced by massively parallel DNA sequencing technology.

DESCRIPTION (provided by applicant): Tourette syndrome (TS) is in a class of neuropsychiatric disorders referred to as "tic disorders" which are characterized by involuntary, often repetitive behaviors that can be disruptive, inappropriate, and self-injurious. While recent work in the pathophysiology [1] and functional imaging [2] has provided new knowledge into the neural circuitry of TS, there remains a formidable knowledge gap in understanding the activity of single neurons, neuronal populations, and local field potentials that may be related to human tic generation. The goal of this project is to accelerate of the characterization of human physiology in patients with TS through the utilization of microelectrode based physiological techniques that can be coupled to time-synchronized recordings of tic phenomenology/appearance. This procedure of coupling the high-resolution neuronal recordings with behavior is not common in TS because of both the lack of availability to intracranial recordings in TS patients and also expertise to perform such work. Accessing Deep Brain Stimulation (DBS) patients through this grant will offer a unique opportunity to quantify the neural representation of tics. Our interdisciplinary team has demonstrated feasibility and has particular expertise in neural coding, novel neural/behavioral experimental design, neurology of TS, and DBS surgery for TS (currently the only center in the US with a FDA IDE to perform TS DBS).