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NIRS-SPM

Revision History : Ver. 4_r1 (PDF)

NIRS-SPM is a SPM (http://www.fil.ion.ucl.ac.uk/spm/) and MATLAB-based software package for statistical analysis of near-infrared spectroscopy (NIRS) signals, developed at the Bio Imaging Signal Processing (BISP) lab. at KAIST in Korea.




Based on the general linear model (GLM), and Sun's tube formula / Lipschitz-Killing curvature (LKC) based expected Euler characteristics, NIRS-SPM not only provides activation maps of oxy-, deoxy-, and total- hemoglobin, but also allows for super-resolution activation localization. More details are described in Ye et al., 2009 and Li et al., 2012.
To remove the unknown global trends due to breathing, cardiac, vaso-motion, or other experimental errors, NIRS-SPM provides a wavelet-minimum description length (MDL) detrending algorithm (Jang et al., 2009).
In addition, we have developed a method to estimate cerebral metabolic rate of oxygen (CMRO2) without hypercapnia by using simultaneous measurements of NIRS and fMRI (Tak et al., 2010). Using the optimization framework, many assumed parameters such as baseline hemoglobin concentration and hypercapnia can be readily estimated, which promise more accurate estimation of cerebral blood flow (CBF) and CMRO2.

Some specific features of the NIRS-SPM software package are as follows:

NIRS file format

NIRS-SPM was initially developed for the analysis of optical data from the continuous wave 24-channel NIRS system (OXYMON MKIII, Artinis). NIRS-SPM has been recently updated for analyzing the optical density data from the other systems including the ETG 4000 (Hitachi Medical Systems), the ImagentTM (ISS, Champaign, Illinois), the NIRO 200 (Hamamatsu Photonics), DYNOT-232 (NIRx Medical Technologies, LLC.), spectratech OEG-16, FOIRE-3000 (Shimadzu OMM), fNIR (BIOPAC Systems, Inc.), and CW6 (Techen Inc.). Furthermore, NIRS-SPM allows for the optical density changes or HbO, HbR concentration changes as for the manual input of HbO and HbR. To read other NIRS data formats from other venders, please send a data set and file format to shtak@kaist.ac.kr. We will update NIRS-SPM packages to include the data formats.

Spatial registration of NIRS channel locations

NIRS-fMRI alignment
NIRS channel positions in real coordinates obtained from a 3D digitizer are localized onto the cerebral cortex of an anatomical MR image using Horn's algorithm (Horn, 1987). At least three measured real coordinates of reference positions, such as a marker capsule, nasion, and inion, are necessary to elicit the relationship between the MR coordinates and real coordinates in the 3-D digitizer. Real coordinates of reference positions and optodes should be saved in a Microsoft Excel 97-2003 file format (.xls) or a text file format (.txt). Specifically, column indexes are x, y, and z coordinates, respectively. (Please refer to the sample Excel or text file.)
NIRS-SPM provides several channel configurations such as:
1) OXYMON MKIII 4x4 (1set), 2) ETG 4000 3x11 (1set), 3) ETG 4000 4x4 (2sets), 4) ETG 4000 3x5 (2sets).

Stand-alone NIRS
The spatial registration of NIRS channels to MNI space with MNI coordinate input is available. NIRS-SPM provides two methods for receiving MNI coordinates: 1) manual input of MNI coordinate, 2) choice of MNI coordinates from SPM template images. Also, NIRS-SPM allows the spatial registration of NIRS channels to MNI space without MRI using NFRI' fNIRS tools (Singh et al., 2005).

Wavelet-MDL detrending

Wavelet-MDL detrending algorithm effectively removes an unknown global trend due to breathing, cardiac, vaso-motion, or other experimental errors. Specifically, the wavelet transform is applied to decompose NIRS measurements into global trends, hemodynamic signals and uncorrelated noise components as distinct scales. The minimum description length (MDL) principle thereupon plays an important role in preventing over- or under- fitting and facilitates optimal model order selection for the global trend estimate. More details are described in Jang et al., 2009.

In estimating the temporal correlations, NIRS-SPM provides both precoloring and prewhitening method. In our data set, we showed that precoloring is more appropriate for estimating temporal correlation of NIRS data than the prewhitening method. Hence, we recommend using the precoloring method (See Ye et al., 2009). In addition, channel-residual covariance estimation was modified to consider channel-wise least square residual correlation (See Li et al., 2012).

In making inference of brain activation, NIRS-SPM provides Sun's tube formula and Lipschitz-Killing curvature based expected Euler characteristics for p-value correction. In the case of Sun's tube formula correction, p-values are calculated as the excursion probability of an inhomogeneous random field on a representation manifold that is dependent on the structure of the error covariance matrix and the interpolating kernels. However, Sun's tube formula cannot be used for general random fields such as F-statistics from either individual or group analysis. To overcome these difficulties, we also provide the expected Euler characteristic approach based on Lipschitz-Killing curvature to control the family-wise error rate.

CMRO2 estimation without hypercapnia: Estimation of the CMRO2 and CBF is important to investigate the neurovascular coupling and physiological components in blood oxygenation level dependent (BOLD) signals quantitatively. Using an optimization framework that minimizes the differences between two-forms of relative CMRO2-CBF coupling ratio from BOLD and NIRS biophysical models, unknown model parameters including hypercapnia and baseline hemoglobin concentrations are readily optimized. CMRO2 and CBF relative to its baseline are then estimated accurately (See Tak et al., 2010).

fMRI-BOLD activation map that has been analyzed using SPM5 can be simultaneously visualized and compared with the NIRS activation map.


Software Requirements

MATLAB (Mathworks, Natick, MA, http://www.mathworks.com). The Image Processing Toolbox is required.
SPM5 or SPM8 (Welcome Department of Cognitive Neurology in London). It can be freely downloaded from: http://www.fil.ion.ucl.ac.uk/spm/software/


Hardware Requirement

NIRS-SPM has been developed and tested on Intel(R) Pentium(R) 4 CPU 3.00 GHz, 2.00 GB RAM. However, NIRS-SPM will work on any computers with MATLAB 7 with approximately 2.0 GB RAM. Note that the process for estimating temporal correlations requires large amount of memory. Depending on total recording time, more than 2.0 GB RAM will be required.


Sample Datasets

NIRS data files: Several sample files from OXYMON MKIII, ETG4000 (1set, 2set), ImagentTM system, DYNOT-232, Spectratech OEG-16, and FOIRE-3000 are provided.
Spatial registration files: Real coordinates of reference positions/optodes and MNI coordinates of 12 optodes and corresponding 14 channels (for stand-alone NIRS) are provided.
Ch_config folder: Folder that contains several channel configurations.
T1 MRimage folder: Folder that contains anatomical (normalized) MR images and segmented gray/white matter images.
fMRI result folder: Folder that contains fMRI-SPM result files. fMRI data was simultaneously measured with NIRS data.
CMRO2_Est folder: Folder that contains sample dataset for estimating the CMRO2 without hypercapnia from simultaneous fMRI and NIRS measurements.

Acknowledgement

This research is supported by the IT R&D program of MKE/IITA [2008-F-021-01]. This research is also supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korea government(MEST) (No. 2009-0081089).


Updates

Although we have endeavored to develop NIRS-SPM as accurate and high quality software, there remains the possibility of some bugs. Please assist us by reporting any bugs to shtak@kaist.ac.kr. Reported bugs will be fixed in the next version of the NIRS-SPM.


User's Guide

NIRS-SPM User's Guide, December 16th, 2011 (PDF).


Download and Installation Instructions

In order to download NIRS-SPM software and sample data sets, registration is required. After uncompressing NIRS_SPM_v4_r1.zip, 'NIRS_SPM_v4_r1', 'batch_file', 'nfri_functions', 'Sample_data', and 'Documentation' directories will be created. Then, add 1) NIRS-SPM_v4_r1 directories with sub-folders, 2) spm5 directories with sub-folders in the MATLAB path.


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Copyright (c) 2012, Sungho Tak and Jong Chul Ye, KAIST

NIRS-SPM is free software; you can distribute it and/ or modify it under the terms of the GNU General Public License (GPL) as publishing by the Free Software Foundation, either version 3 of the License, or any later version. For the copy of the GNU General Public License, see (http://www.gnu.org/licenses/).

Additional Note from Authors

You can get the software freely - including source code - by downloading it here. We appreciate if you cite the following papers in producing the results using NIRS-SPM toolbox.

[1] Ye, J.C., Tak, S., Jang, K.E., Jung, J.W., Jang, J.D., 2009. NIRS-SPM: Statistical parametric mapping for near-infrared spectroscopy. NeuroImage 44, 428-447.

[2] Jang, K.E., Tak, S., Jung, J.W., Jang, J.D., Jeong, Y., and Ye, J.C., 2009. Wavelet-MDL detrending for near infrared spectroscopy (NIRS). J. Biomed. Opt. 14, 1-13.

[3] Tak, S., Jang, J., Lee. K., and Ye, J.C., 2010. Quantification of CMRO2 without hypercapnia using simultaneous near-infrared spectroscopy and fMRI measurements. Phys. Med. Biol. 55, 3249-3269.

[4] Tak, S., Yoon, S. J., Jang, J., Yoo, K., Jeong, Y., and Ye, J.C., 2011. Quantitative analysis of hemodynamic and metabolic changes in subcortical vascular dementia using simultaneous near-infrared spectroscopy and fMRI measurements. NeuroImage 55, 176-184.

[5] Li, H., Tak, S., and Ye, J.C., 2012. Lipschitz-Killing curvature based expected Euler characteristics for p-value correction in fNIRS. J. Neurosci. Meth. 204, 61-67.


NIRS-SPM course information

The NIRS-SPM short course was held in Japan on 18 February 2010. Detailed information (PDF)
The morning workshop for NIRS-SPM was held in Human Brain Mapping's 16th Annual Meeting (Barcelona, June 6-10, 2010). Detailed information (PDF)




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