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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.


NIN data motion artifact reduction using NinPROC and adaptive filtering. Time courses are: (A) raw NIN data; (B) simultaneously acquired raw piezoelectric motion sensor data; (C) adaptively filtered NIN data, using (A) as the target and (B) as the reference signal; (D) signal in (C) plus a second-order Butterworth high-pass filter using scipy.lfilter() (cutoff = 0.05 Hz); (E) signal in (D) plus a sixth-order Butterworth low-pass filter using scipy.lfilter() (cutoff = 2 Hz).
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Figure 4: NIN data motion artifact reduction using NinPROC and adaptive filtering. Time courses are: (A) raw NIN data; (B) simultaneously acquired raw piezoelectric motion sensor data; (C) adaptively filtered NIN data, using (A) as the target and (B) as the reference signal; (D) signal in (C) plus a second-order Butterworth high-pass filter using scipy.lfilter() (cutoff = 0.05 Hz); (E) signal in (D) plus a sixth-order Butterworth low-pass filter using scipy.lfilter() (cutoff = 2 Hz).

Mentions: As with all neuroimaging data, NIN time series can contain physiological motion artifacts. When head motion occurs, the resulting signal modulations can be substantial and therefore must be identified and either excluded or otherwise mitigated. Exclusion of a motion contaminated time series segment is a less than ideal solution, so effective mitigation is an important tool. One approach, which is particularly well-suited to real-time applications, is adaptive filtering. In previous work, we have demonstrated the efficacy of adaptive filtering to identify and reduce global physiological interference in NIN signals, including signal modulations resulting from cardiac or respiratory oscillations (Zhang et al., 2007a,b). We have recently added a least mean squares-based adaptive filter for motion artifact reduction to NinPy called ninproc.lms() (Figure 4). Adaptive filtering has shown considerable promise in real-time reduction of physiological motion artifacts without the bandwidth loss associated with using a low-pass filter with a low cutoff frequency. Other published approaches to dealing with NIN motion artifacts include the use of principle component analysis or independent component analysis to identify and separate signal from motion waveforms (Morren et al., 2004; Zhang et al., 2005), solutions that could be incorporated using the Python-based Modular toolkit for Data Processing (Berkes et al., 2008) via mdp.pca() or mdp.fastica().


[Not Available].

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

NIN data motion artifact reduction using NinPROC and adaptive filtering. Time courses are: (A) raw NIN data; (B) simultaneously acquired raw piezoelectric motion sensor data; (C) adaptively filtered NIN data, using (A) as the target and (B) as the reference signal; (D) signal in (C) plus a second-order Butterworth high-pass filter using scipy.lfilter() (cutoff = 0.05 Hz); (E) signal in (D) plus a sixth-order Butterworth low-pass filter using scipy.lfilter() (cutoff = 2 Hz).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: NIN data motion artifact reduction using NinPROC and adaptive filtering. Time courses are: (A) raw NIN data; (B) simultaneously acquired raw piezoelectric motion sensor data; (C) adaptively filtered NIN data, using (A) as the target and (B) as the reference signal; (D) signal in (C) plus a second-order Butterworth high-pass filter using scipy.lfilter() (cutoff = 0.05 Hz); (E) signal in (D) plus a sixth-order Butterworth low-pass filter using scipy.lfilter() (cutoff = 2 Hz).
Mentions: As with all neuroimaging data, NIN time series can contain physiological motion artifacts. When head motion occurs, the resulting signal modulations can be substantial and therefore must be identified and either excluded or otherwise mitigated. Exclusion of a motion contaminated time series segment is a less than ideal solution, so effective mitigation is an important tool. One approach, which is particularly well-suited to real-time applications, is adaptive filtering. In previous work, we have demonstrated the efficacy of adaptive filtering to identify and reduce global physiological interference in NIN signals, including signal modulations resulting from cardiac or respiratory oscillations (Zhang et al., 2007a,b). We have recently added a least mean squares-based adaptive filter for motion artifact reduction to NinPy called ninproc.lms() (Figure 4). Adaptive filtering has shown considerable promise in real-time reduction of physiological motion artifacts without the bandwidth loss associated with using a low-pass filter with a low cutoff frequency. Other published approaches to dealing with NIN motion artifacts include the use of principle component analysis or independent component analysis to identify and separate signal from motion waveforms (Morren et al., 2004; Zhang et al., 2005), solutions that could be incorporated using the Python-based Modular toolkit for Data Processing (Berkes et al., 2008) via mdp.pca() or mdp.fastica().

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.