Fabio PasqualettiProfessorMechanical Engineering Dept fabiopas@ucr.edu(951) 827-2327
CRCNS: Collaborative Research: Mapping and Control of Large-Scale Neural Dynamics
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AWARD NUMBER
006975-002
FUND NUMBER
21291
STATUS
Closed
AWARD TYPE
3-Grant
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AWARD EXECUTION DATE
8/9/2014
BEGIN DATE
9/1/2014
END DATE
8/31/2017
AWARD AMOUNT
$343,931
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Sponsor Information
SPONSOR AWARD NUMBER
SPONSOR
SPONSOR TYPE
FUNCTION
Organized Research
PROGRAM NAME
Proposal Information
PROPOSAL NUMBER
14070671
PROPOSAL TYPE
New
ACTIVITY TYPE
Basic Research
PI Information
PI
Pasqualetti, Fabio
PI TITLE
Other
PI DEPTARTMENT
Mechanical Engineering
PI COLLEGE/SCHOOL
Bourns College of Engineering
CO PIs
Project Information
ABSTRACT
Brain stimulation is currently being used to treat a variety of cognitive, neurological, and psychiatric disorders, including depression, Parkinson's disease, and schizophrenia. Despite this broad use, how brain stimulation works remains largely a mystery. This project aims to address this gap in understanding by studying and modeling how neural processes change with brain stimulation. The investigators aim to develop criteria by which to evaluate and optimize stimulation-based treatments of neurological and psychiatric disorders. To accompany the scientific advances, the investigators will engage in educational efforts to bring the research to the classroom and to enhance cross-institutional opportunities for students. The investigators will place special emphasis on mentoring and encouraging women and minorities on the academic path in science and engineering. In addition, the investigators are combining their efforts in the Skirkanich Internship in Network Visualization, which hosts undergraduate art students each summer.
The investigators propose a new set of mathematical techniques to describe and predict neural processes and how they change with brain stimulation. Investigators at the University of California at Riverside (one of the most ethnically diverse research-intensive institutions in the U.S.) and the University of Pennsylvania will collaborate on theoretical, computational, and experimental research with three main research initiatives: (1) static and dynamic modeling of complex neural dynamics, identification of multi-resolution regions of interest in the human brain, the nature of their interconnections, and their function in observed cognitive dynamics; (2) analysis of network-wide dynamic properties of neural systems, characterization of brain regions based on controllability metrics, and design of non-disruptive control algorithms for the modulation of complex neural processes; (3) validation of models and control strategies in a wide variety of empirical settings to engineer and predict the outcomes of clinical interventions in neurological and psychiatric disorders. The success of this project will enable a deepened understanding of complex neurobiological systems, construct novel maps of the human brain and its dynamic processes, and develop non-disruptive control techniques for therapeutic brain stimulation protocols.(Abstract from NSF)
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