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[Not Available].

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.


Abridged examples of the trial definition (.DEF) file format and the trial order (.ORD) file format. Each trial named in the .ORD file must be defined in the .DEF file. For the first trial (“Ready”), “timing = −1 keyboard” means wait indefinitely for a keypress (the spacebar is the only allowable key) while displaying the text “Ready …” at position (0,0.2) and height 0.15. The “Fixation” trial involves displaying the image file cross.jpg in the center of the screen for 15 s, with extra frames inserted or removed there if cumulative timing errors have accumulated. The “Left.04” stimulus displays the image file L4.jpg in the center of the screen for exactly 1.5 s.
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Figure 1: Abridged examples of the trial definition (.DEF) file format and the trial order (.ORD) file format. Each trial named in the .ORD file must be defined in the .DEF file. For the first trial (“Ready”), “timing = −1 keyboard” means wait indefinitely for a keypress (the spacebar is the only allowable key) while displaying the text “Ready …” at position (0,0.2) and height 0.15. The “Fixation” trial involves displaying the image file cross.jpg in the center of the screen for 15 s, with extra frames inserted or removed there if cumulative timing errors have accumulated. The “Left.04” stimulus displays the image file L4.jpg in the center of the screen for exactly 1.5 s.

Mentions: Accurate and reliable control of stimulus presentation is a critical aspect of any functional neuroimaging experiment. NinSTIM is a high-level stimulus and experimental design toolkit, designed for non-programmers, that generates stimulus sequences for display by the Pyglet interface1 to the PsychoPy package2 (Peirce, 2008). NinSTIM directs PsychoPy to sequentially present an ordered collection of “trials”, where a trial is a very general entity consisting of one or more temporal phases, each composed of one or more visual or auditory stimuli. For example, a trial could be: (i) a simple instruction screen presented while the program waits indefinitely for a key press, (ii) a visual fixation of predetermined duration, (iii) a stimulus followed by a mask, or (iv) any other ordered series of stimuli. An example complex trial with five separate phases might be: (i) a side-by-side pair of photos, followed by (ii) a brief whole-screen mask image, followed by (iii) a variable duration blank screen delay period, followed by (iv) a go cue, and finally (v) an inter-trial rest period. Each unique trial type is defined in a ASCII trial definition (.DEF) file, with required Python-style indentation, for editing and interactive debugging (Figure 1, left).


[Not Available].

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

Abridged examples of the trial definition (.DEF) file format and the trial order (.ORD) file format. Each trial named in the .ORD file must be defined in the .DEF file. For the first trial (“Ready”), “timing = −1 keyboard” means wait indefinitely for a keypress (the spacebar is the only allowable key) while displaying the text “Ready …” at position (0,0.2) and height 0.15. The “Fixation” trial involves displaying the image file cross.jpg in the center of the screen for 15 s, with extra frames inserted or removed there if cumulative timing errors have accumulated. The “Left.04” stimulus displays the image file L4.jpg in the center of the screen for exactly 1.5 s.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Abridged examples of the trial definition (.DEF) file format and the trial order (.ORD) file format. Each trial named in the .ORD file must be defined in the .DEF file. For the first trial (“Ready”), “timing = −1 keyboard” means wait indefinitely for a keypress (the spacebar is the only allowable key) while displaying the text “Ready …” at position (0,0.2) and height 0.15. The “Fixation” trial involves displaying the image file cross.jpg in the center of the screen for 15 s, with extra frames inserted or removed there if cumulative timing errors have accumulated. The “Left.04” stimulus displays the image file L4.jpg in the center of the screen for exactly 1.5 s.
Mentions: Accurate and reliable control of stimulus presentation is a critical aspect of any functional neuroimaging experiment. NinSTIM is a high-level stimulus and experimental design toolkit, designed for non-programmers, that generates stimulus sequences for display by the Pyglet interface1 to the PsychoPy package2 (Peirce, 2008). NinSTIM directs PsychoPy to sequentially present an ordered collection of “trials”, where a trial is a very general entity consisting of one or more temporal phases, each composed of one or more visual or auditory stimuli. For example, a trial could be: (i) a simple instruction screen presented while the program waits indefinitely for a key press, (ii) a visual fixation of predetermined duration, (iii) a stimulus followed by a mask, or (iv) any other ordered series of stimuli. An example complex trial with five separate phases might be: (i) a side-by-side pair of photos, followed by (ii) a brief whole-screen mask image, followed by (iii) a variable duration blank screen delay period, followed by (iv) a go cue, and finally (v) an inter-trial rest period. Each unique trial type is defined in a ASCII trial definition (.DEF) file, with required Python-style indentation, for editing and interactive debugging (Figure 1, left).

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.