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Recent studies of functional connectivity MR imaging have revealed that the default-mode network activity is disrupted in diseases such as Alzheimer's 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 Alzheimer's 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 Alzheimer's disease.

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 Sun's 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.

Compressed sensing (CS) may be useful for accelerating data acquisitions in high-resolution fMRI. However, due to the inherent slow temporal dynamics of the hemodynamic signals and concerns of potential statistical power loss, the CS approach for fMRI (CS–fMRI) has not been extensively investigated. To evaluate the utility of CS in fMRI application, we systematically investigated the properties of CS–fMRI using computer simulations and in vivo experiments of rat forepaw sensory and odor stimulations with gradient-recalled echo (GRE) and echo planar imaging (EPI) sequences. Various undersampling patterns along the phase-encoding direction were studied and k–t FOCUSS was used as the CS reconstruction algorithm, which exploits the temporal redundancy of images. Functional sensitivity, specificity, and time courses were compared between fully-sampled and CS–fMRI with reduction factors of 2 and 4. CS–fMRI with GRE, but not with EPI, improves the statistical sensitivity for activation detection over the fully sampled data when the ratio of the fMRI signal change to noise is low. CS improves the temporal resolution and reduces temporal noise correlations. While CS reduces the functional response amplitudes, the noise variance is also reduced to make the overall activation detection more sensitive. Consequently, CS is a valuable fMRI acceleration approach, especially for GRE fMRI studies.

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.