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FBO DAILY - FEDBIZOPPS ISSUE OF AUGUST 20, 2015 FBO #5018
MODIFICATION

R -- Development of a Task-related and Resting State Realistic fMRI Simulator for Benchmarking fMRI Data

Notice Date

8/18/2015
 

Notice Type

Modification/Amendment
 

NAICS

541712 — Research and Development in the Physical, Engineering, and Life Sciences (except Biotechnology)
 

Contracting Office

Department of Health and Human Services, National Institutes of Health, National Library of Medicine, 6707 Democracy Blvd., Suite 105, Bethesda, Maryland, 20894, United States
 

ZIP Code

20894
 

Solicitation Number

NIHLM2015604
 

Archive Date

8/20/2015
 

Point of Contact

Suet Vu, Phone: 301-496-6546
 

E-Mail Address

vus@mail.nih.gov
(vus@mail.nih.gov)
 

Small Business Set-Aside

N/A
 

Description

GENERAL INFORMATION INTRODUCTION: This is a Small Business Sources Sought notice. This is NOT a solicitation for proposals, proposal abstracts, or quotations. The purpose of this notice is to obtain information regarding: (1) the availability and capability of qualified small business sources; (2) whether they are small businesses; HUBZone small businesses; service-disabled, veteran-owned small businesses; 8(a) small businesses; veteran-owned small businesses; woman-owned small businesses; or small disadvantaged businesses; and (3) their size classification relative to the North American Industry Classification System (NAICS) code for the proposed acquisition. Your responses to the information requested will assist the Government in determining the appropriate acquisition method, including whether a set-aside is possible. An organization that is not considered a small business under the applicable NAICS code should not submit a response to this notice. The National Institutes of Health (NIH), National Library of Medicine (NLM) is conducting a market survey to help determine the availability and technical capability of qualified small businesses, HUBZone small businesses; service- disabled, veteran-owned small businesses; 8(a) small businesses; veteran-owned small businesses; woman-owned small businesses; or small disadvantaged businesses capable of serving the needs identified below. Background: Multidisciplinary research to understand functional brain connections and circuits and their anatomical correlations continues to expand, especially in view of the NIH Brain Initiative. The understanding of the biological underpinning for human behavior and experience, including cognitive states, emotion, perception, communication, and motor control, is expected to play a major role in future disease treatment (including diseases of cognitive decline and psychological disorders), accelerated brain/machine interface development, and perhaps enhancements to sensory perception, memory, and learning. Toward this goal research and development is required to develop and evaluate tools that contribute to the analysis of anatomical and functional data of the brain by supporting researchers in the discovery and validation of connectivities and circuits within functional neuroimaging data (e.g., fMRI) and in correlating these functional characteristics with the underlying brain anatomy. Recent methods with possible application to brain segmentation have been developed, but not extensively evaluated, namely the Metamorphs/Active Volume Models [9,35] methods for generic volume segmentation and the SOAX [37] method, which is expected to have application for DTI data segmentation. Also, the application of pattern recognition/classification methods to the analysis of fMRI data has become a topic of increasing research interest [1-6], including, recently, the application of deep learning methods [7]. Finally, a growing body of research literature is concerned with the fusion of anatomical and functional data. (See [8], for example, for a survey of DTI and fMRI fusion methods.) Proposed Work The contractor will acquire functional and anatomical brain data from open source repositories (e.g., the Human Connectome Project) or as otherwise negotiated with the government. The anatomical data will include both conventional MRI anatomical data and Diffusion Tensor Imaging (DTI) data. The functional brain data will include fMRI data. The contractor will implement and evaluate methods to segment the anatomical brain data using previously-developed algorithms for deformable shape and appearance volume models and stretching open active contours (for the DTI data) and quantitatively compare these methods with methods from the published literature. The contractor will investigate state-of-the-art methods for analysis of fMRI data, and will implement and evaluate algorithms with the goal of activation regions and connectivities in both task-related and resting state fMRI data. In particular, the contractor will investigate pattern recognition and classification techniques, and their application to fMRI data analysis, with particular emphasis on machine learning methods; these will include, specifically, deep learning and its use in fMRI. REQUIRED TASKS Task Description: Base Period Task 1: Acquire human brain MRI and DTI anatomical data, and fMRI functional brain data from an open source repository, or as otherwise negotiated with the government. Investigate the application of active volume models, MetaMorphs, and SOAX for segmentation of brain anatomy; implement and provide initial experimental evaluations of at least one of these methods. Investigate the application of pattern recognition/classification methods including, specifically, deep learning, to the analysis of fMRI data. As negotiated with the government, and considering the available fMRI data, implement a deep learning method to identify anatomical activation sites, connectivities, and/or classification of brain states (for example, in task-related data collections where the subject is shown various classes of objects). Provide initial experimental results. Task 2: Implement and provide refined experimental evaluations for each of the active volume models, MetaMorphs, and SOAX methods for segmentation of brain anatomy. Provide refined experimental results for the deep learning method application of Task 1 and a comparative analysis of state-of-the-art pattern recognition/classification methods as applied to the analysis of fMRI data. Notice of Government Unlimited Rights to Work First Produced Under This Contract Government rights to work Ufirst produced under this contractU are established by Federal law including, but not limited to, this specific reference: FAR 42.227-14, Rights in Data - General, (b) (1). Requirement to Notify Government of Proprietary Work Dependencies It is the intent of the Government that all algorithms and code developed under this contract be for public use and benefit, including, but not limited to, internal use by government institutions, use by researchers or other parties collaborating in government work, and public domain release, at the option of the government. The Contractor is required to notify the Government in writing of any dependencies of the deliverables under this contract on proprietary, copyrighted, or patented work that potentially inhibits, restricts, or requires permission for the dissemination of the deliverables to the public, other governmental agencies or research groups, or to any other parties whatsoever. ANTICIPATED PERIOD OF PERFORMANCE: It is anticipated that the period of performance shall be for a 12 month base year with two (2) 12 month option periods. An award is anticipated to be made on or around September 2015. It is anticipated that the contract will be a Firmed-Fixed price type. Interested firms responding to this Sources Sought Notice must adhere to the following: (a) Provide a capability statement demonstrating relevant experience, skills, and ability to fulfill the Government's requirement. The capability statement should be complete and contain sufficient detail for the Government to make an informed decision regarding capabilities; however, the statement should not exceed 10 pages. (b) The capability statement must identify the responder's business type and size; DUNS number; NAICS code, and technical and administrative points of contact, including names, titles, addresses, telephone and fax numbers, and e-mail addresses. (c) The National Library of Medicine (NLM) requires proposals to be submitted via eCPS.: 1) Electronic copy via the NLM electronic Contract Proposal Submission (eCPS) website at https://ecps.nih.gov/nlm. All submissions must be submitted by 1:00pm, Local Prevailing Time, on August 18, 2015. For directions on using eCPS, go to https://ecps.nih.gov/nlm/home/howto and click on "How to Submit." NOTE: To submit your electronic proposal using eCPS, all offerors must have a valid NIH External Directory Account, which provides authentication and serves as a vehicle for secure transmission of documents and communication with the NLM. The NIH External Directory Account registration process may take up to 24 hours to become active. Submission of proposals by facsimile or e-mail is not accepted. EMAILS AND FACSIMILES WILL NOT BE ACCEPTABLE. Disclaimer and Important Notes: This notice does not obligate the Government to award a contract or otherwise pay for the information provided in response. The Government reserves the right to use information provided by respondents for any purpose deemed necessary and legally appropriate. Any organization responding to this notice should ensure that its response is complete and sufficiently detailed to allow the Government to determine the organization's qualifications to perform the work. Respondents are advised that the Government is under no obligation to acknowledge receipt of the information received or provide feedback to respondents with respect to any information submitted. After a review of the responses received, a pre-solicitation synopsis and solicitation may be published in Federal Business Opportunities. However, responses to this notice will not be considered adequate responses to a solicitation. Confidentiality. No proprietary, classified, confidential, or sensitive information should be included in your response. The Government reserves the right to use any non-proprietary technical information in any resultant solicitation(s).
 

Web Link

FBO.gov Permalink
(https://www.fbo.gov/spg/HHS/NIH/OAM/NIHLM2015604/listing.html)
 

Place of Performance

Address: Bethesda, Maryland, 20894, United States

Zip Code: 20894
 

Record

SN03844135-W 20150820/150819000407-5135a6ba3f780ead5692f07c3edccd65 (fbodaily.com)
 

Source

FedBizOpps Link to This Notice
(may not be valid after Archive Date)

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