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Strangman GE, Zhang Q, Zeffiro T - Front Neuroinform (2009)

Bottom Line: Python, a modular and computationally efficient development language, can support functional neuroimaging studies of diverse design and implementation.In particular, Python's easily readable syntax and modular architecture allow swift prototyping followed by efficient transition to stable production systems.Source code for the software discussed here will be made available at www.nmr.mgh.harvard.edu/Neural_SystemsGroup/software.html.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychiatry, Harvard Medical School Charlestown, MA, USA.

ABSTRACT
There has been substantial recent growth in the use of non-invasive optical brain imaging in studies of human brain function in health and disease. Near-infrared neuroimaging (NIN) is one of the most promising of these techniques and, although NIN hardware continues to evolve at a rapid pace, software tools supporting optical data acquisition, image processing, statistical modeling, and visualization remain less refined. Python, a modular and computationally efficient development language, can support functional neuroimaging studies of diverse design and implementation. In particular, Python's easily readable syntax and modular architecture allow swift prototyping followed by efficient transition to stable production systems. As an introduction to our ongoing efforts to develop Python software tools for structural and functional neuroimaging, we discuss: (i) the role of non-invasive diffuse optical imaging in measuring brain function, (ii) the key computational requirements to support NIN experiments, (iii) our collection of software tools to support NIN, called NinPy, and (iv) future extensions of these tools that will allow integration of optical with other structural and functional neuroimaging data sources. Source code for the software discussed here will be made available at www.nmr.mgh.harvard.edu/Neural_SystemsGroup/software.html.

No MeSH data available.


NinSTATS code fragment to perform statistical analysis with functional NIN data as a predictor of outcome.
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C2: NinSTATS code fragment to perform statistical analysis with functional NIN data as a predictor of outcome.

Mentions: A particular advantage of using R is that an extremely broad range of models can be applied to the data, since all input variables are treated equally. In particular, the neuroimaging data can be used either as an outcome variable, a predictor, or a covariate. This assignment flexibility is in contrast to that found in the most commonly used neuroimaging software packages, including SPM, FSL, AFNI, FSFast. These packages require the neuroimaging variable to be the outcome variable, which significantly restricts the types of scientific questions that can be addressed. For example, one question that is receiving growing interest concerns identification of brain regions that might provide predictive information about treatment response. This determination requires the neuroimaging data to act as a predictor and the therapeutic response measure to serve as a dependent or outcome variable. Implementing these models using existing neuroimaging packages requires extracting the data from each potential brain region of interest, exporting the data series, and then performing the statistical analysis using an external program (Strangman et al., 2008). By directly interfacing with R, one can fit predictive models as easily as those utilizing the image data as the dependent variable. Code Fragment 2 provides an example of a NinSTATS implementation of predictive modeling. Importantly, R includes a large, and continually growing, collection of heavily tested and more sophisticated models, including robust covariance and generalized linear models, as well as a wealth of post-hoc testing capabilities.


[Not Available].

Strangman GE, Zhang Q, Zeffiro T - Front Neuroinform (2009)

NinSTATS code fragment to perform statistical analysis with functional NIN data as a predictor of outcome.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2698776&req=5

C2: NinSTATS code fragment to perform statistical analysis with functional NIN data as a predictor of outcome.
Mentions: A particular advantage of using R is that an extremely broad range of models can be applied to the data, since all input variables are treated equally. In particular, the neuroimaging data can be used either as an outcome variable, a predictor, or a covariate. This assignment flexibility is in contrast to that found in the most commonly used neuroimaging software packages, including SPM, FSL, AFNI, FSFast. These packages require the neuroimaging variable to be the outcome variable, which significantly restricts the types of scientific questions that can be addressed. For example, one question that is receiving growing interest concerns identification of brain regions that might provide predictive information about treatment response. This determination requires the neuroimaging data to act as a predictor and the therapeutic response measure to serve as a dependent or outcome variable. Implementing these models using existing neuroimaging packages requires extracting the data from each potential brain region of interest, exporting the data series, and then performing the statistical analysis using an external program (Strangman et al., 2008). By directly interfacing with R, one can fit predictive models as easily as those utilizing the image data as the dependent variable. Code Fragment 2 provides an example of a NinSTATS implementation of predictive modeling. Importantly, R includes a large, and continually growing, collection of heavily tested and more sophisticated models, including robust covariance and generalized linear models, as well as a wealth of post-hoc testing capabilities.

Bottom Line: Python, a modular and computationally efficient development language, can support functional neuroimaging studies of diverse design and implementation.In particular, Python's easily readable syntax and modular architecture allow swift prototyping followed by efficient transition to stable production systems.Source code for the software discussed here will be made available at www.nmr.mgh.harvard.edu/Neural_SystemsGroup/software.html.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychiatry, Harvard Medical School Charlestown, MA, USA.

ABSTRACT
There has been substantial recent growth in the use of non-invasive optical brain imaging in studies of human brain function in health and disease. Near-infrared neuroimaging (NIN) is one of the most promising of these techniques and, although NIN hardware continues to evolve at a rapid pace, software tools supporting optical data acquisition, image processing, statistical modeling, and visualization remain less refined. Python, a modular and computationally efficient development language, can support functional neuroimaging studies of diverse design and implementation. In particular, Python's easily readable syntax and modular architecture allow swift prototyping followed by efficient transition to stable production systems. As an introduction to our ongoing efforts to develop Python software tools for structural and functional neuroimaging, we discuss: (i) the role of non-invasive diffuse optical imaging in measuring brain function, (ii) the key computational requirements to support NIN experiments, (iii) our collection of software tools to support NIN, called NinPy, and (iv) future extensions of these tools that will allow integration of optical with other structural and functional neuroimaging data sources. Source code for the software discussed here will be made available at www.nmr.mgh.harvard.edu/Neural_SystemsGroup/software.html.

No MeSH data available.