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Real-Time Functional MRI with Automated Pattern Classification

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 1R41NS062474-01
Agency Tracking Number: NS062474
Amount: $152,667.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N/A
Solicitation Number: PHS2007-2
Solicitation Year: 2008
Award Year: 2008
Award Start Date (Proposal Award Date): N/A
Award End Date (Contract End Date): N/A
Small Business Information
2501 Yale Blvd. SE Suite 201
United States
DUNS: 183651723
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: Yes
Principal Investigator
 () -
Business Contact
Phone: (505) 798-2505
Research Institution

DESCRIPTION (provided by applicant): With the rapid development of functional MRI (fMRI) in neuroscience and the increasing number of studies using fMRI in clinical research, there is a need for automated methods to analyze and interpret the data. The high
sensitivity of fMRI enables online analysis, which calls for online interpretation of the results in terms of underlying sensory, motor and cognitive processes. This capability can help the user to maximize the information obtained from fMRI and to make d
ecisions on the data quality. The goals are to substantially improve the performance of multi-class pattern classification of very high- dimensional fMRI data with limited number of sample data sets, and to develop an integrated tool based on our custom- d
esigned real-time fMRI analysis platform (TurboFIRE) to perform pattern classification of dynamically changing activation patterns during an ongoing real-time fMRI scan. The aims are (a) Develop an integrated high-performance fMRI analysis chain for real-t
ime pattern recognition, (b) Develop novel sparsity-adaptive aggregation and PLS methods for pattern classification, and (c) Characterize the performance of the data analysis chain for classifying spatially distributed activation patterns by demonstrating
the methods on motor, visual, auditory and mental computation tasks. The successful demonstration of this automatic, real-time functional MRI methodology will provide a proof-of-concept of classifying dynamically changing brain activation patterns during t
he ongoing scan. This will provide a criterion for successfully performing a sensory and motor function in the clinical setting and facilitate the identification of higher cognitive processes in the research setting. Online display of the classification re
sult will also assist the user in adapting the paradigm to improve specificity and enables an interactive interview of the subject to further explore underlying brain processes. The long-term goal of this project is to develop an automated fMRI analysis to
ol that will have significant commercial potential for clinical and research use.

* Information listed above is at the time of submission. *

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