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The DEPARTMENT OF HEALTH AND HUMAN SERVICES has issued a Tender notice for the procurement of a Development of a task-related in the USA. This Tender notice was published on 13 Aug 2015 and is scheduled to close on 18 Aug 2015, with an estimated Tender value of Refer Document. Interested bidders can access detailed Tender information, eligibility criteria, and complete bidding documents by referencing TOT Ref No. 3449051, while the tender notice number is NIHLM2015600 and Registering on the platform.
Procurement Summary
Country: USA
Summary: Development of a task-related
Deadline: 18 Aug 2015
Posting Date: 13 Aug 2015
Other Information
Notice Type: Tender
TOT Ref.No.: 3449051
Document Ref. No.: NIHLM2015600
Financier: Self Financed
Purchaser Ownership: -
Tender Value: Refer Document
Purchaser's Detail
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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:
there is a particular need today to be able to generate a synthetic yet realistically modeled "ground truth" for various fmri benchmarking. Due to the massive amount of noisy and complex data collected in a single fmri study, the statistical analysis of fmri data is challenging but essential for understanding and obtaining an accurate interpretation of the data (lindquist, 2008). Many fmri analysis techniques are ad hoc and without a solid basis or any ground truth; however, sufficiently modeled ground truth data is needed to validate the assumptions and the statistical methodology used to analyze the data, as well as to access statistical measures such as specificity and sensitivity.
Such a ground truth could be estimated from intracranial eeg (ieeg) observations; however these are unobtainable for most human subjects. Recent research suggests that dynamic functional connectivity (fc) patterns may reflect intrinsic characteristics of neural brain functions; however, this research area is still exploratory and evolving (huchison et al., 2013).
Therefore, development of a truly realistic fmri simulator requiring full knowledge of inherent signal as well as noise characteristics is not quite possible at this time. Development of any realistically modeled fmri simulator must allow iterative processes for upgrading the model as more information becomes available.
Currently there are four well-known fmri simulators, two in-house and two cross-platform applications; these are possum (drobnjak et. Al., 2006), neurosim (welvaert et al., 2011), dcm (marreiros et al., 2008), and simtb (erhardt et al. 2011). However, each of the above four simulators has its own limitations.
The in-house simulator for fsl (fmrib s software library) is called possum (physics-oriented simulated scanner for understanding mri), which is more of an fmri sequence simulator that takes t2* 4d data, typically back-calculated from data, as input. Possum requires a gradient echo pulse sequence, a segmented object with known tissue parameters, and a motion sequence; making it possible to simulate spin history effects, motion during the readout periods and interactions that these have with b0 inhomogeneities (drobnjak, 2006). However, the current limitations of possum are not including physiological noise, inability to incorporate any general pulse sequences, and not being friendly to general users such as psychologists and clinicians.
The in-house fmri simulator in spm is a dcm (dynamical causal modelling) connectivity simulator used to infer directed connectivity among brain regions (marreiros et al., 2008). The matlab toolbox, simtb, is useful for simulating the ica components for group functional connectivity studies, but only operates on a 2d "cartoon" brain and lacks much of mr physics (erhardt et al. 2011).
The r-based neurosim fmri data simulator is still a work in progress and was created to allow for the experimental fmri design to be specified, activation regions defined, and various statistical properties and noise simulated (welvaert et al., 2011).
Despite the creation of these four fmri simulators, a comprehensive fmri data generation program that can be used to fully optimize experimental fmri data collection and provide a realistically modeled "ground truth" is still lacking (welvaert, 2014). There are no current fmri simulators that simultaneously employ realistic characterizations of the underlying mr physics and assumptions about the spatial and temporal frequencies of neural activity, including resting state networks (rsns). Rsns are observed in many task-related fmri studies (raichle et al. 2001; frannson et al. 2005), and it is essential to benchmark resting state fmri (rfmri) before any task-related fmri time series can be properly simulated; also, rfmri can be a study by itself (biswall et al. 2001; deco et al. 2011; smith et al. 2013).
Because of this lack of an accessible simulator, neuroscientists often only optimize parts of their experimental protocol, such as stimulus presentation (dale et al., 1999), without considering fine-tuning of the image acquisition parameters or subsequent analysis for specific research goals, or their power and false-positive rates (e.g., mumford et al. 2014). In addition, all of the above four simulators are mostly used by researchers, for specific research applications, and not by more "general" users (such as psychologists) who collect fmri data, but without any validation of the collected data or design protocol used.
The fmri simulator proposed here provides a user friendly environment (currently matlab based with a final gui) with capability of generating synthetic fmri activation and time-series through bloch equations solver (figure 1) based on the known signal and noise characteristics available at this time, but having capability of being iteratively upgraded as more realistic modeling of signal and noise in fmri are available. This would allow not only the researchers in neuroscience to validate protocol selection for their experimental goals tailored to hypothesized bold signals and functional connectivity, which are themselves indirectly related to the underlying neuronal activities and interactions (hutchison et al. 2013), but also provide a modeled ground truth for validation of fmri data, including in potential clinical applications in the future.
Finally, recent work (liu, nutter, ao, mitra, 2011) in alternative coding of collected mri signals, specifically wavelet encoding, suggests that significant efficiencies in data collection time may be achievable. A realistic fmri simulator would provide a platform for further exploring and evaluating this technology.
Proposed work
the proposed work is to create an fmri simulator with more realistic signal modeling, as compared to current simulators, a user-friendly interface for non-specialists in mri/fmri technology and physics, and capability to model wavelet-encoded signal acquisition.
A description of the proposed methodology and validation methods are given below.
Methodology
first, a baseline brain model will be constructed from accurate proton density, t1 and t2* parameter maps, as well a fuzzy mask of the cortex to properly localize neural activity (see flowchart below). One requirement for an fmri simulator are realistic 4d t2* maps. All changes in the magnetic field, I'b, can be added to the baseline by the formula (edelman et al., 2006):
1/(t_2^* (t))=1/(t_2 (t))+1/2 I
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