Precision fMRI and cluster-failure in the individual brain.

Advances in neuroimaging acquisition protocols and denoising techniques, along with increasing magnetic field strengths, have dramatically improved the temporal signal-to-noise ratio (tSNR) in functional magnetic resonance imaging (fMRI). This permits spatial resolution with submillimeter voxel sizes and ultrahigh temporal resolution and opens a route toward performing precision fMRI in the brains of individuals. Yet ultrahigh spatial and temporal resolution comes at a cost: it reduces tSNR and, therefore, the sensitivity to the blood oxygen level-dependent (BOLD) effect and other functional contrasts across the brain.

Investigating apparent differences between standard DKI and axisymmetric DKI and its consequences for biophysical parameter estimates.

Purpose: The purpose of the study is to identify differences between axisymmetric diffusion kurtosis imaging (DKI) and standard DKI, their consequences for biophysical parameter estimates, and the protocol choice influence on parameter estimation.

Methods: Noise-free and noisy, synthetic diffusion MRI human brain data is simulated using standard DKI for a standard and the fast "199" acquisition protocol. First the noise-free "baseline" difference between both DKI models is estimated and the influence of fiber complexity is investigated.

Axisymmetric diffusion kurtosis imaging with Rician bias correction: A simulation study.

Purpose: To compare the estimation accuracy of axisymmetric diffusion kurtosis imaging (DKI) and standard DKI in combination with Rician bias correction (RBC).

Methods: Axisymmetric DKI is more robust against noise-induced variation in the measured signal than standard DKI because of its reduced parameter space. However, its susceptibility to Rician noise bias at low signal-to-noise ratios (SNR) is unknown.

Error quantification in multi-parameter mapping facilitates robust estimation and enhanced group level sensitivity.

Multi-Parameter Mapping (MPM) is a comprehensive quantitative neuroimaging protocol that enables estimation of four physical parameters (longitudinal and effective transverse relaxation rates R and R, proton density PD, and magnetization transfer saturation MT) that are sensitive to microstructural tissue properties such as iron and myelin content. Their capability to reveal microstructural brain differences, however, is tightly bound to controlling random noise and artefacts (e.g.

Denoising for Improved Parametric MRI of the Kidney: Protocol for Nonlocal Means Filtering.

In order to tackle the challenges caused by the variability in estimated MRI parameters (e.g., T* and T) due to low SNR a number of strategies can be followed.

Improving Accuracy and Temporal Resolution of Learning Curve Estimation for within- and across-Session Analysis.

Estimation of learning curves is ubiquitously based on proportions of correct responses within moving trial windows. Thereby, it is tacitly assumed that learning performance is constant within the moving windows, which, however, is often not the case. In the present study we demonstrate that violations of this assumption lead to systematic errors in the analysis of learning curves, and we explored the dependency of these errors on window size, different statistical models, and learning phase.

Magnetic resonance advection imaging of cerebrovascular pulse dynamics.

We analyze the pulsatile signal component of dynamic echo planar imaging data from the brain by modeling the dependence between local temporal and spatial signal variability. The resulting magnetic resonance advection imaging maps depict the location of major arteries. Color direction maps allow for visualization of the direction of blood vessels.

More Specific Signal Detection in Functional Magnetic Resonance Imaging by False Discovery Rate Control for Hierarchically Structured Systems of Hypotheses.

Signal detection in functional magnetic resonance imaging (fMRI) inherently involves the problem of testing a large number of hypotheses. A popular strategy to address this multiplicity is the control of the false discovery rate (FDR). In this work we consider the case where prior knowledge is available to partition the set of all hypotheses into disjoint subsets or families, e.

Recovery of thalamic microstructural damage after Shiga toxin 2-associated hemolytic-uremic syndrome.

Introduction: The underlying pathophysiology of neurological complications in patients with hemolytic-uremic syndrome (HUS) remains unclear. It was recently attributed to a direct cytotoxic effect of Shiga toxin 2 (Stx2) in the thalamus. Conventional MRI of patients with Stx2-caused HUS revealed - despite severe neurological symptoms - only mild alterations if any, mostly in the thalamus.

Evidence for early, non-lesional cerebellar damage in patients with multiple sclerosis: DTI measures correlate with disability, atrophy, and disease duration.

Background: Common symptoms of multiple sclerosis (MS) such as gait ataxia, poor coordination of the hands, and intention tremor are usually the result of dysfunctionality in the cerebellum. Magnetic resonance imaging (MRI) has frequently failed to detect cerebellar damage in the form of inflammatory lesions in patients presenting with symptoms of cerebellar dysfunction.

Objective: To detect microstructural cerebellar tissue alterations in early MS patients with a "normal appearing" cerebellum using diffusion tensor imaging (DTI).

High-resolution diffusion kurtosis imaging at 3T enabled by advanced post-processing.

Diffusion Kurtosis Imaging (DKI) is more sensitive to microstructural differences and can be related to more specific micro-scale metrics (e.g., intra-axonal volume fraction) than diffusion tensor imaging (DTI), offering exceptional potential for clinical diagnosis and research into the white and gray matter.

Local estimation of the noise level in MRI using structural adaptation.

We present a method for local estimation of the signal-dependent noise level in magnetic resonance images. The procedure uses a multi-scale approach to adaptively infer on local neighborhoods with similar data distribution. It exploits a maximum-likelihood estimator for the local noise level.

POAS4SPM: a toolbox for SPM to denoise diffusion MRI data.

We present an implementation of a recently developed noise reduction algorithm for dMRI data, called multi-shell position orientation adaptive smoothing (msPOAS), as a toolbox for SPM. The method intrinsically adapts to the structures of different size and shape in dMRI and hence avoids blurring typically observed in non-adaptive smoothing. We give examples for the usage of the toolbox and explain the determination of experiment-dependent parameters for an optimal performance of msPOAS.

Adaptive smoothing of multi-shell diffusion weighted magnetic resonance data by msPOAS.

We present a novel multi-shell position-orientation adaptive smoothing (msPOAS) method for diffusion weighted magnetic resonance data. Smoothing in voxel and diffusion gradient space is embedded in an iterative adaptive multiscale approach. The adaptive character avoids blurring of the inherent structures and preserves discontinuities.

Adaptive smoothing as inference strategy: more specificity for unequally sized or neighbouring regions.

Although spatial smoothing of fMRI data can serve multiple purposes, increasing the sensitivity of activation detection is probably its greatest benefit. However, this increased detection power comes with a loss of specificity when non-adaptive smoothing (i.e.

Position-orientation adaptive smoothing of diffusion weighted magnetic resonance data (POAS).

We introduce an algorithm for diffusion weighted magnetic resonance imaging data enhancement based on structural adaptive smoothing in both voxel space and diffusion-gradient space. The method, called POAS, does not refer to a specific model for the data, like the diffusion tensor or higher order models. It works by embedding the measurement space into a space with defined metric, in this case the Lie group of three-dimensional Euclidean motion SE(3).

Modeling the orientation distribution function by mixtures of angular central Gaussian distributions.

In this paper we develop a tensor mixture model for diffusion weighted imaging data using an automatic model order selection criterion for the number of tensor components in a voxel. We show that the weighted orientation distribution function for this model can be expanded into a mixture of angular central Gaussian distributions. We investigate properties of this model in extensive simulations and in a high angular resolution scan of a human brain.

Dissociations between behavioural and functional magnetic resonance imaging-based evaluations of cognitive function after brain injury.

Functional neuroimaging methods hold promise for the identification of cognitive function and communication capacity in some severely brain-injured patients who may not retain sufficient motor function to demonstrate their abilities. We studied seven severely brain-injured patients and a control group of 14 subjects using a novel hierarchical functional magnetic resonance imaging assessment utilizing mental imagery responses. Whereas the control group showed consistent and accurate (for communication) blood-oxygen-level-dependent responses without exception, the brain-injured subjects showed a wide variation in the correlation of blood-oxygen-level-dependent responses and overt behavioural responses.

Image analysis and statistical inference in neuroimaging with R.

R is a language and environment for statistical computing and graphics. It can be considered an alternative implementation of the S language developed in the 1970s and 1980s for data analysis and graphics (Becker and Chambers, 1984; Becker et al., 1988).

Structural adaptive segmentation for statistical parametric mapping.

Functional Magnetic Resonance Imaging inherently involves noisy measurements and a severe multiple test problem. Smoothing is usually used to reduce the effective number of multiple comparisons and to locally integrate the signal and hence increase the signal-to-noise ratio. Here, we provide a new structural adaptive segmentation algorithm (AS) that naturally combines the signal detection with noise reduction in one procedure.

High-resolution fMRI: overcoming the signal-to-noise problem.

Increasing the spatial resolution in functional Magnetic Resonance Imaging (fMRI) inherently lowers the signal-to-noise ratio (SNR). In order to still detect functionally significant activations in high-resolution images, spatial smoothing of the data is required. However, conventional non-adaptive smoothing comes with a reduced effective resolution, foiling the benefit of the higher acquisition resolution.

Accurate localization of brain activity in presurgical FMRI by structure adaptive smoothing.

An important problem of the analysis of functional magnetic resonance imaging (fMRI) experiments is to achieve some noise reduction of the data without blurring the shape of the activation areas. As a novel solution to this problem, recently the propagation-separation (PS) approach has been proposed. PS is a structure adaptive smoothing method that adapts to different shapes of activation areas.

Diffusion tensor imaging: structural adaptive smoothing.

Diffusion Tensor Imaging (DTI) data is characterized by a high noise level. Thus, estimation errors of quantities like anisotropy indices or the main diffusion direction used for fiber tracking are relatively large and may significantly confound the accuracy of DTI in clinical or neuroscience applications. Besides pulse sequence optimization, noise reduction by smoothing the data can be pursued as a complementary approach to increase the accuracy of DTI.