The application of functional near-infrared spectroscopy (fNIRS) in the neurosciences has been expanding over the last 40 years. Today, it is addressing a wide range of applications within different populations and utilizes a great variety of experimental paradigms. With the rapid growth and the diversification of research methods, some inconsistencies are appearing in the way in which methods are presented, which can make the interpretation and replication of studies unnecessarily challenging. The Society for Functional Near-Infrared Spectroscopy has thus been motivated to organize a representative (but not exhaustive) group of leaders in the field to build a consensus on the best practices for describing the methods utilized in fNIRS studies.
Our paper has been designed to provide guidelines to help enhance the reliability, repeatability, and traceability of reported fNIRS studies and encourage best practices throughout the community. A checklist is provided to guide authors in the preparation of their manuscripts and to assist reviewers when evaluating fNIRS papers.
Near-infrared spectroscopy (NIRS) can be employed to investigate brain activities associated with regional changes of the oxy- and deoxyhemoglobin concentration by measuring the absorption of near-infrared light through the intact skull. NIRS is regarded as a promising neuroimaging modality thanks to its excellent temporal resolution and flexibility for routine monitoring. Recently, the general linear model (GLM), which is a standard method for functional MRI (fMRI) analysis, has been employed for quantitative analysis of NIRS data. However, the GLM often fails in NIRS when there exists an unknown global trend due to breathing, cardiac, vasomotion, or other experimental errors. We propose a wavelet minimum description length (Wavelet-MDL) detrending algorithm to overcome this problem. Specifically, the wavelet transform is applied to decompose NIRS measurements into global trends, hemodynamic signals, and uncorrelated noise components at distinct scales. The minimum description length (MDL) principle plays an important role in preventing over- or underfitting and facilitates optimal model order selection for the global trend estimate. Experimental results demonstrate that the new detrending algorithm outperforms the conventional approaches.
KEYWORDS: Near infrared spectroscopy, Data modeling, Brain, Wavelets, Functional magnetic resonance imaging, Positron emission tomography, Brain activation, Linear filtering, Neuroimaging, Magnetic resonance imaging
Near infrared spectroscopy (NIRS) is a relatively new non-invasive brain imaging method to measure brain
activities associated with regional changes of the oxy- and deoxy- hemoglobin concentration. Typically, functional
MRI or PET data are analyzed using the general linear model (GLM), in which measurements are modeled as a
linear combination of explanatory variables plus an error term. However, the GLM often fails in NIRS if there
exists an unknown global trend due to breathing, cardiac, vaso- motion and other experimental errors. In order
to overcome these problems, we propose a wavelet-MDL based detrending algorithm. Specifically, the wavelet
transform is applied to NIRS measurements to decompose them into global trends, signals and uncorrelated
noise components in distinct scales. In order to prevent the over-fitting the minimum length description (MDL)
principle is applied. Experimental results demonstrate that the new detrending algorithm outperforms the
conventional approaches.
KEYWORDS: Near infrared spectroscopy, Functional magnetic resonance imaging, Scanning probe microscopy, Statistical analysis, Brain mapping, Smoothing, Data modeling, Magnetic resonance imaging, Brain, 3D modeling
Even though there exists a powerful statistical parametric mapping (SPM) tool for fMRI, similar public domain
tools are not available for near infrared spectroscopy (NIRS). In this paper, we describe a new public domain
statistical toolbox called NIRS-SPM for quantitative analysis of NIRS signals. Specifically, NIRS-SPM statistically
analyzes the NIRS data using GLM and makes inference as the excursion probability which comes from
the random field that are interpolated from the sparse measurement. In order to obtain correct inference, NIRS-SPM
offers the pre-coloring and pre-whitening method for temporal correlation estimation. For simultaneous
recording NIRS signal with fMRI, the spatial mapping between fMRI image and real coordinate in 3-D digitizer
is estimated using Horn's algorithm. These powerful tools allows us the super-resolution localization of the brain
activation which is not possible using the conventional NIRS analysis tools.
KEYWORDS: Reconstruction algorithms, Magnetic resonance imaging, Image resolution, Medical imaging, Compressed sensing, Spatial resolution, Optimization (mathematics), Data acquisition, In vivo imaging, Computer simulations
This paper is concerned about high resolution reconstruction of projection reconstruction MR imaging from
angular under-sampled k-space data. A similar problem has been recently addressed in the framework of compressed
sensing theory. Unlike the existing algorithms used in compressed sensing theory, this paper employs
the FOCal Underdetermined System Solver(FOCUSS), which was originally designed for EEG and MEG source
localization to obtain sparse solution by successively solving quadratic optimization. We show that FOCUSS
is very effective for the projection reconstruction MRI, because the medical images are usually sparse in image
domain, and the center region of the under-sampled radial k-space data still provides a meaningful low resolution
image, which is essential for the convergence of FOCUSS. We applied FOCUSS for projection reconstruction MR
imaging using single coil. Extensive experiments confirms that high resolution reconstruction with virtually free
of angular aliasing artifacts can be obtained from severely under-sampled k-space data.
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