Sungho Tak

NIRS-SPM: statistical parametric mapping for near-infrared spectroscopy.

Near infrared spectroscopy (NIRS) is a non-invasive method to measure brain activity via changes in the degree of hemoglobin oxygenation through the intact skull. As optically measured hemoglobin signals strongly correlate with BOLD signals, simultaneous measurement using NIRS and fMRI promises a significant mutual enhancement of temporal and spatial resolutions. Although there exists a powerful statistical parametric mapping tool in fMRI, current public domain statistical tools for NIRS have several limitations related to the quantitative analysis of simultaneous recording studies with fMRI. In this paper, a new public domain statistical toolbox known as NIRS-SPM is described. It enables the quantitative analysis of NIRS signal. More specifically, NIRS data are statistically analyzed based on the general linear model (GLM) and Suns tube formula. The 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. NIRS-SPM not only enables the calculation of activation maps of oxy-, deoxy-hemoglobin and total hemoglobin, but also allows for the super-resolution localization, which is not possible using conventional analysis tools. Extensive experimental results using finger tapping and memory tasks confirm the viability of the proposed method.

Wavelet minimum description length detrending for near-infrared spectroscopy

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.

A Data-Driven Sparse GLM for fMRI Analysis Using Sparse Dictionary Learning With MDL Criterion

We propose a novel statistical analysis method for functional magnetic resonance imaging (fMRI) to overcome the drawbacks of conventional data-driven methods such as the independent component analysis (ICA). Although ICA has been broadly applied to fMRI due to its capacity to separate spatially or temporally independent components, the assumption of independence has been challenged by recent studies showing that ICA does not guarantee independence of simultaneously occurring distinct activity patterns in the brain. Instead, sparsity of the signal has been shown to be more promising. This coincides with biological findings such as sparse coding in V1 simple cells, electrophysiological experiment results in the human medial temporal lobe, etc. The main contribution of this paper is, therefore, a new data driven fMRI analysis that is derived solely based upon the sparsity of the signals. A compressed sensing based data-driven sparse generalized linear model is proposed that enables estimation of spatially adaptive design matrix as well as sparse signal components that represent synchronous, functionally organized and integrated neural hemodynamics. Furthermore, a minimum description length (MDL)-based model order selection rule is shown to be essential in selecting unknown sparsity level for sparse dictionary learning. Using simulation and real fMRI experiments, we show that the proposed method can adapt individual variation better compared to the conventional ICA methods.

Projection reconstruction MR imaging using FOCUSS

The focal underdetermined system solver (FOCUSS) was originally designed to obtain sparse solutions by successively solving quadratic optimization problems. This article adapts FOCUSS for a projection reconstruction MR imaging problem to obtain high resolution reconstructions from angular under‐sampled radial k‐space data. We show that FOCUSS is effective for projection reconstruction MRI, since medical images are usually sparse in some sense and the center region of the undersampled radial k‐space samples still provides a low resolution, yet meaningful, image essential for the convergence of FOCUSS. The new algorithm is successfully applied for synthetic data as well as in vivo brain imaging obtained by under‐sampled radial spin echo sequence. Magn Reson Med 57:764–775, 2007.

NIRS-SPM: statistical parametric mapping for near infrared spectroscopy

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 Horns algorithm. These powerful tools allows us the super-resolution localization of the brain activation which is not possible using the conventional NIRS analysis tools.

Quantitative analysis of hemodynamic and metabolic changes in subcortical vascular dementia using simultaneous near-infrared spectroscopy and fMRI measurements.

Subcortical vascular dementia (SVD) is a form of vascular dementia from small vessel disease with white matter lesions and lacunes. We hypothesized that hemodynamic and metabolic changes in the cortex during a simple motor task may reflect the impaired neurovascular coupling in SVD. We used fMRI and near-infrared spectroscopy (NIRS) simultaneously, which together provided multiple hemodynamic responses as well as a robust estimation of the cerebral metabolic rate of oxygen (CMRO(2)). During the task periods, the oxy-hemoglobin, total-hemoglobin, blood oxygenation level-dependent (BOLD) response, cerebral blood flow (CBF), and CMRO(2) decreased statistically significantly in the primary motor and somatosensory cortices of SVD patients, whereas the oxygen extraction fraction increased when compared with controls. Notably, the flow-metabolism coupling ratio, n representing the ratio of oxygen supply to its utilization, showed a robust reduction in the SVD patient group (n(Control)=1.99 ± 0.23; n(SVD)=1.08 ± 0.24), which implies a loss of metabolic reserve. These results support the pathological small vessel compromise, including an increased vessel stiffness, impaired vascular reactivity, and impaired neurovascular coupling in SVD. In conclusion, simultaneous measurement by NIRS and fMRI can reveal various hemodynamic and metabolic changes and may be used for as an early detection or monitoring of SVD.

Quantification of CMRO2 without hypercapnia using simultaneous near-infrared spectroscopy and fMRI measurements

Estimation of the cerebral metabolic rate of oxygen (CMRO(2)) and cerebral blood flow (CBF) is important to investigate the neurovascular coupling and physiological components in blood oxygenation level-dependent (BOLD) signals quantitatively. Although there are methods that can determine CMRO(2) changes using functional MRI (fMRI) or near-infrared spectroscopy (NIRS), current approaches require a separate hypercapnia calibration process and have the potential to incur bias in many assumed model parameters. In this paper, a novel method to estimate CMRO(2) without hypercapnia is described using simultaneous measurements of NIRS and fMRI. Specifically, an optimization framework is proposed that minimizes the differences between the two forms of the relative CMRO(2)-CBF coupling ratio from BOLD and NIRS biophysical models, from which hypercapnia calibration and model parameters are readily estimated. Based on the new methods, we found that group average CBF, CMRO(2), cerebral blood volume (CBV), and BOLD changes within activation of the primary motor cortex during a finger tapping task increased by 39.5 +/- 21.4%, 18.4 +/- 8.7%, 12.9 +/- 6.7%, and 0.5 +/- 0.2%, respectively. The group average estimated flow-metabolism coupling ratio was 2.38 +/- 0.65 and the hypercapnia parameter was 7.7 +/- 1.7%. These values are within the range of values reported from other literatures. Furthermore, the activation maps from CBF and CMRO(2) were well localized on the primary motor cortex, which is the main target region of the finger tapping task.

Lipschitz-Killing curvature based expected Euler characteristics for p-value correction in fNIRS

Functional near-infrared spectroscopy (fNIRS) is a non-invasive imaging approach for measuring brain activities based on changes in the cerebral concentrations of hemoglobin. Recently, statistical analysis based on a general linear model (GLM) has become popular. Here, to impose statistical significance on the activation detected by fNIRS, family-wise error (FWE) rate control is important. However, unlike fMRI, in which measurements are densely sampled on a regular lattice and Gaussian smoothing makes the resulting random field homogeneous, the random fields from fNIRS are inhomogeneous due to the interpolation from sparsely and irregularly distributed optode locations. Thus, tube formula based correction has been proposed to address this issue. However, Suns tube formula cannot be used for general random fields such as F-statistics. To overcome these difficulties, we employ the expected Euler characteristic approach based on Lipschitz-Killing curvature (LKC) to control the family-wise error rate. We compared this correction method with Suns tube formula for t-statistics to confirm the existing method. Based on this comparison, we show that covariance estimation should be modified to consider channel-wise least-square residual correlation. These new results supplement the existing tool of statistical parameter mapping for fNIRS.

Dynamic causal modelling for functional near-infrared spectroscopy.

Functional near-infrared spectroscopy (fNIRS) is an emerging technique for measuring changes in cerebral hemoglobin concentration via optical absorption changes. Although there is great interest in using fNIRS to study brain connectivity, current methods are unable to infer the directionality of neuronal connections. In this paper, we apply Dynamic Causal Modelling (DCM) to fNIRS data. Specifically, we present a generative model of how observed fNIRS data are caused by interactions among hidden neuronal states. Inversion of this generative model, using an established Bayesian framework (variational Laplace), then enables inference about changes in directed connectivity at the neuronal level. Using experimental data acquired during motor imagery and motor execution tasks, we show that directed (i.e., effective) connectivity from the supplementary motor area to the primary motor cortex is negatively modulated by motor imagery, and this suppressive influence causes reduced activity in the primary motor cortex during motor imagery. These results are consistent with findings of previous functional magnetic resonance imaging (fMRI) studies, suggesting that the proposed method enables one to infer directed interactions in the brain mediated by neuronal dynamics from measurements of optical density changes.

Sparse SPM: Group Sparse-dictionary learning in SPM framework for resting-state functional connectivity MRI analysis.

Recent studies of functional connectivity MR imaging have revealed that the default-mode network activity is disrupted in diseases such as Alzheimers disease (AD). However, there is not yet a consensus on the preferred method for resting-state analysis. Because the brain is reported to have complex interconnected networks according to graph theoretical analysis, the independency assumption, as in the popular independent component analysis (ICA) approach, often does not hold. Here, rather than using the independency assumption, we present a new statistical parameter mapping (SPM)-type analysis method based on a sparse graph model where temporal dynamics at each voxel position are described as a sparse combination of global brain dynamics. In particular, a new concept of a spatially adaptive design matrix has been proposed to represent local connectivity that shares the same temporal dynamics. If we further assume that local network structures within a group are similar, the estimation problem of global and local dynamics can be solved using sparse dictionary learning for the concatenated temporal data across subjects. Moreover, under the homoscedasticity variance assumption across subjects and groups that is often used in SPM analysis, the aforementioned individual and group analyses using sparse dictionary learning can be accurately modeled by a mixed-effect model, which also facilitates a standard SPM-type group-level inference using summary statistics. Using an extensive resting fMRI data set obtained from normal, mild cognitive impairment (MCI), and Alzheimers disease patient groups, we demonstrated that the changes in the default mode network extracted by the proposed method are more closely correlated with the progression of Alzheimers disease.