Wavelet variance components in image space for spatiotemporal neuroimaging data

Double-wavelet transform for multi-subject resting state functional magnetic resonance imaging data

Conventional regions of interest (ROIs)-level resting state fMRI (functional magnetic resonance imaging) response analyses do not rigorously model the underlying spatial correlation within each ROI. This can result in misleading inference. Moreover, they tend to estimate the temporal covariance matrix with the assumption of stationary time series, which may not always be valid. To overcome these limitations, we propose a double-wavelet approach that simplifies temporal and spatial covariance structure because wavelet coefficients are approximately uncorrelated under mild regularity conditions. This property allows us to analyze much larger dimensions of spatial and temporal resting-state fMRI data with reasonable computational burden. Another advantage of our double-wavelet approach is that it does not require the stationarity assumption. Simulation studies show that our method reduced false positive and false negative rates by properly taking into account spatial and temporal correlations in data. We also demonstrate advantages of our method by using resting-state fMRI data to study the difference in resting-state functional connectivity between healthy subjects and patients with major depressive disorder.

Double-wavelet transform for multisubject task-induced functional magnetic resonance imaging data

The goal of this article is to model multisubject task-induced functional magnetic resonance imaging (fMRI) response among predefined regions of interest (ROIs) of the human brain. Conventional approaches to fMRI analysis only take into account temporal correlations, but do not rigorously model the underlying spatial correlation due to the complexity of estimating and inverting the high dimensional spatio-temporal covariance matrix. Other spatio-temporal model approaches estimate the covariance matrix with the assumption of stationary time series, which is not always feasible. To address these limitations, we propose a double-wavelet approach for modeling the spatio-temporal brain process. Working with wavelet coefficients simplifies temporal and spatial covariance structure because under regularity conditions, wavelet coefficients are approximately uncorrelated. Different wavelet functions were used to capture different correlation structures in the spatio-temporal model. The main advantages of the wavelet approach are that it is scalable and that it deals with nonstationarity in brain signals. Simulation studies showed that our method could reduce false-positive and false-negative rates by taking into account spatial and temporal correlations simultaneously. We also applied our method to fMRI data to study activation in prespecified ROIs in the prefontal cortex. Data analysis showed that the result using the double-wavelet approach was more consistent than the conventional approach when sample size decreased.

Pulmonary PET imaging confirms preferential lung target occupancy of an inhaled bronchodilator

BACKGROUND

Positron emission tomography (PET) is a non-invasive molecular imaging technique that traces the distribution of radiolabeled molecules in experimental animals and human subjects. We hypothesized that PET could be used to visualize the binding of the bronchodilator drug ipratropium to muscarinic receptors (MR) in the lungs of living non-human primates (NHP). The objectives of this study were two-fold: (i) to develop a methodology for quantitative imaging of muscarinic receptors in NHP lung and (ii) to estimate and compare ipratropium-induced MR occupancy following drug administration via intravenous injection and inhalation, respectively.

METHODS

A series of PET measurements (n = 18) was performed after intravenous injection of the selective muscarinic radioligand ¹¹C-VC-002 in NHP (n = 5). The lungs and pituitary gland (both rich in MR) were kept in the field of view. Each PET measurement was followed by a PET measurement preceded by treatment with ipratropium (intravenous or inhaled).

RESULTS

Radioligand binding was quantified using the Logan graphical analysis method providing the total volume of distribution (V_T). Ipratropium reduced the V_T in the lung and pituitary in a dose-dependent fashion. At similar plasma ipratropium concentrations, administration by inhalation produced larger reductions in V_T for the lungs. The plasma-derived apparent affinity for ipratropium binding in the lung was one order of magnitude higher after inhalation (K_iih = 1.01 nM) than after intravenous infusion (K_iiv = 10.84 nM).

CONCLUSION

Quantitative muscarinic receptor occupancy imaging by PET articulates and quantifies the therapeutic advantage of the inhaled route of delivery and provides a tool for future developments of improved inhaled drugs.

Anatomically-adapted graph wavelets for improved group-level fMRI activation mapping

A graph based framework for fMRI brain activation mapping is presented. The approach exploits the spectral graph wavelet transform (SGWT) for the purpose of defining an advanced multi-resolutional spatial transformation for fMRI data. The framework extends wavelet based SPM (WSPM), which is an alternative to the conventional approach of statistical parametric mapping (SPM), and is developed specifically for group-level analysis. We present a novel procedure for constructing brain graphs, with subgraphs that separately encode the structural connectivity of the cerebral and cerebellar gray matter (GM), and address the inter-subject GM variability by the use of template GM representations. Graph wavelets tailored to the convoluted boundaries of GM are then constructed as a means to implement a GM-based spatial transformation on fMRI data. The proposed approach is evaluated using real as well as semi-synthetic multi-subject data. Compared to SPM and WSPM using classical wavelets, the proposed approach shows superior type-I error control. The results on real data suggest a higher detection sensitivity as well as the capability to capture subtle, connected patterns of brain activity.

WSPM: Wavelet-based statistical parametric mapping

Recently, we have introduced an integrated framework that combines wavelet-based processing with statistical testing in the spatial domain. In this paper, we propose two important enhancements of the framework. First, we revisit the underlying paradigm; i.e., that the effect of the wavelet processing can be considered as an adaptive denoising step to "improve" the parameter map, followed by a statistical detection procedure that takes into account the non-linear processing of the data. With an appropriate modification of the framework, we show that it is possible to reduce the spatial bias of the method with respect to the best linear estimate, providing conservative results that are closer to the original data. Second, we propose an extension of our earlier technique that compensates for the lack of shift-invariance of the wavelet transform. We demonstrate experimentally that both enhancements have a positive effect on performance. In particular, we present a reproducibility study for multi-session data that compares WSPM against SPM with different amounts of smoothing. The full approach is available as a toolbox, named WSPM, for the SPM2 software; it takes advantage of multiple options and features of SPM such as the general linear model.

DWT-CEM: an algorithm for scale-temporal clustering in fMRI

The number of studies using functional magnetic resonance imaging (fMRI) has grown very rapidly since the first description of the technique in the early 1990s. Most published studies have utilized data analysis methods based on voxel-wise application of general linear models (GLM). On the other hand, temporal clustering analysis (TCA) focuses on the identification of relationships between cortical areas by measuring temporal common properties. In its most general form, TCA is sensitive to the low signal-to-noise ratio of BOLD and is dependent on subjective choices of filtering parameters. In this paper, we introduce a method for wavelet-based clustering of time-series data and show that it may be useful in data sets with low signal-to-noise ratios, allowing the automatic selection of the optimum number of clusters. We also provide examples of the technique applied to simulated and real fMRI datasets.

A systematic comparison of kinetic modelling methods generating parametric maps for [(11)C]-(R)-PK11195

[(11)C]-(R)-PK11195 is presently the most widely used radiotracer for the monitoring of microglia activity in the central nervous system (CNS). Microglia, the resident immune cells of the brain, play a critical role in acute and chronic diseases of the central nervous system and in host defence against neoplasia. The purpose of this investigation was to evaluate the reliability and sensitivity of five kinetic modelling methods for the formation of parametric maps from dynamic [(11)C]-(R)-PK11195 studies. The methods we tested were the simplified reference tissue model (SRTM), basis pursuit, a simple target-to-reference ratio, the Logan plot and a wavelet based Logan plot. For the reliability assessment, the test-retest data consisted of four Alzheimer's patients that were scanned twice at approximately a six-week interval. For the sensitivity assessment, comparison of [(11)C]-(R)-PK11195 binding in Huntington's disease (HD) patients and normal subjects was performed using a group contrast to localize significant increases in mean pixel volume of distribution (VD) in HD. In all instances, a reference region kinetic extracted by a supervised clustering technique was used as input function. Reliability was assessed by use of the intra-class correlation coefficient (ICC) across a wide set of anatomical regions and it was found that the wavelet-based Logan plot, basis pursuit and SRTM gave the highest ICC values on average. The same methods produced the highest z-scores resulting from increases in mean striatal VD in HD patients compared with controls. The reference-to-target ratio and the Logan graphical approach were significantly less reliable and less sensitive.

Bayesian fMRI data analysis with sparse spatial basis function priors

In previous work we have described a spatially regularised General Linear Model (GLM) for the analysis of brain functional Magnetic Resonance Imaging (fMRI) data where Posterior Probability Maps (PPMs) are used to characterise regionally specific effects. The spatial regularisation is defined over regression coefficients via a Laplacian kernel matrix and embodies prior knowledge that evoked responses are spatially contiguous and locally homogeneous. In this paper we propose to finesse this Bayesian framework by specifying spatial priors using Sparse Spatial Basis Functions (SSBFs). These are defined via a hierarchical probabilistic model which, when inverted, automatically selects an appropriate subset of basis functions. The method includes non-linear wavelet shrinkage as a special case. As compared to Laplacian spatial priors, SSBFs allow for spatial variations in signal smoothness, are more computationally efficient and are robust to heteroscedastic noise. Results are shown on synthetic data and on data from an event-related fMRI experiment.

Use of wavelet and fast Fourier transforms in pharmacodynamics

Progress has been made in the development and application of mechanism-based pharmacodynamic models for describing the drug-specific and physiological factors influencing the time course of responses to the diverse actions of drugs. However, the biological variability in biosignals and the complexity of pharmacological systems often complicate or preclude the direct application of traditional structural and nonstructural models. Mathematical transforms may be used to provide measures of drug effects, identify structural and temporal patterns, and visualize multidimensional data from analyses of biomedical signals and images. Fast Fourier transform (FFT) and wavelet analyses are two methodologies that have proven to be useful in this context. FFT converts a signal from the time domain to the frequency domain, whereas wavelet transforms colocalize in both domains and may be utilized effectively for nonstationary signals. Nonstationary drug effects are common but have not been well analyzed and characterized by other methods. In this review, we discuss specific applications of these transforms in pharmacodynamics and their potential role in ascertaining the dynamics of spatiotemporal properties of complex pharmacological systems.

Converging language streams in the human temporal lobe

There is general agreement that, after initial processing in unimodal sensory cortex, the processing pathways for spoken and written language converge to access verbal meaning. However, the existing literature provides conflicting accounts of the cortical location of this convergence. Most aphasic stroke studies localize verbal comprehension to posterior temporal and inferior parietal cortex (Wernicke's area), whereas evidence from focal cortical neurodegenerative syndromes instead implicates anterior temporal cortex. Previous functional imaging studies in normal subjects have failed to reconcile these opposing positions. Using a functional imaging paradigm in normal subjects that used spoken and written narratives and multiple baselines, we demonstrated common activation during implicit comprehension of spoken and written language in inferior and lateral regions of the left anterior temporal cortex and at the junction of temporal, occipital, and parietal cortex. These results indicate that verbal comprehension uses unimodal processing streams that converge in both anterior and posterior heteromodal cortical regions in the left temporal lobe.

HBM functional imaging analysis contest data analysis in wavelet space

An analysis of the Functional Imaging Analysis Contest (FIAC) data is presented using spatial wavelet processing. This technique allows the image to be filtered adaptively according to the data itself, rather than relying on a predetermined filter. This adaptive filtering leads to better estimation of the parameters and contrasts in terms of mean squared error. It will be shown that by introducing a slight bias into the estimation, a large reduction in the variance can be achieved, leading to better overall mean squared error estimates. As no single filter needs to be preselected, results containing many scales of information can be found. In the FIAC data, it is shown that both small-scale and large-scale (smoother, more dispersed) effects occur. The combination of small- and large-scale effects detected in the FIAC data would be easy to miss using conventional single filter analysis.