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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.


Graphical depiction of channel by channel SNR, computed as mean signal intensity divided by the SD of signal intensity over time (S = source position, D = detector position). Source–detector pairs with SNR > 50 are connected with green lines, while those with lower SNRs are connected with progressively darker lines. Sources or detectors with few or only bad connections (e.g., S16, D25) could be candidates for pruning. Regions of red colors indicate reduced sensitivity relative to other regions, as seen in the vicinity of sources S4 and S6.
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Figure 3: Graphical depiction of channel by channel SNR, computed as mean signal intensity divided by the SD of signal intensity over time (S = source position, D = detector position). Source–detector pairs with SNR > 50 are connected with green lines, while those with lower SNRs are connected with progressively darker lines. Sources or detectors with few or only bad connections (e.g., S16, D25) could be candidates for pruning. Regions of red colors indicate reduced sensitivity relative to other regions, as seen in the vicinity of sources S4 and S6.

Mentions: To identify and remove the sorts of signal artifacts specific to NIN data, we have included algorithms in NinPROC for semi-automated signal pruning. For a variety of reasons, not all source–detector pairs will provide useful information in all experiments. Data from some source–detector pairs not of primary interest may have been recorded during the experiment, some source–detector pairs may have been too far apart to provide reliable signals, or a detector may have lost contact with the head, thereby generating large signal artifacts. Within the preprocessing component NinPROC, the ninproc.prune() function is available to remove particular sources, detectors, or channels based on the known source–detector separations. In addition, low overall signal intensity can result in unreliable information, and high overall signal intensity can indicate light leakage from source to detector. Hence, facilities for displaying and pruning based on absolute signal intensity and signal-to-noise ratio (SNR) are also provided as options (Figure 3). In addition, the ninproc.lowpass(), ninproc.highpass(), and ninproc.notch() functions provide simple, zero-phase filtering to reduce 1/f physiological, instrument, or electrical interference noise components.


[Not Available].

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

Graphical depiction of channel by channel SNR, computed as mean signal intensity divided by the SD of signal intensity over time (S = source position, D = detector position). Source–detector pairs with SNR > 50 are connected with green lines, while those with lower SNRs are connected with progressively darker lines. Sources or detectors with few or only bad connections (e.g., S16, D25) could be candidates for pruning. Regions of red colors indicate reduced sensitivity relative to other regions, as seen in the vicinity of sources S4 and S6.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Graphical depiction of channel by channel SNR, computed as mean signal intensity divided by the SD of signal intensity over time (S = source position, D = detector position). Source–detector pairs with SNR > 50 are connected with green lines, while those with lower SNRs are connected with progressively darker lines. Sources or detectors with few or only bad connections (e.g., S16, D25) could be candidates for pruning. Regions of red colors indicate reduced sensitivity relative to other regions, as seen in the vicinity of sources S4 and S6.
Mentions: To identify and remove the sorts of signal artifacts specific to NIN data, we have included algorithms in NinPROC for semi-automated signal pruning. For a variety of reasons, not all source–detector pairs will provide useful information in all experiments. Data from some source–detector pairs not of primary interest may have been recorded during the experiment, some source–detector pairs may have been too far apart to provide reliable signals, or a detector may have lost contact with the head, thereby generating large signal artifacts. Within the preprocessing component NinPROC, the ninproc.prune() function is available to remove particular sources, detectors, or channels based on the known source–detector separations. In addition, low overall signal intensity can result in unreliable information, and high overall signal intensity can indicate light leakage from source to detector. Hence, facilities for displaying and pruning based on absolute signal intensity and signal-to-noise ratio (SNR) are also provided as options (Figure 3). In addition, the ninproc.lowpass(), ninproc.highpass(), and ninproc.notch() functions provide simple, zero-phase filtering to reduce 1/f physiological, instrument, or electrical interference noise components.

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.