Single shot concentric shells trajectories for ultra fast fMRI

Resting-State Functional Brain Connectivity

Resting-state functional near-infrared spectroscopy (R-fNIRS) is an active area of interest and is currently attracting considerable attention as a new imaging tool for the study of resting-state brain function. Using variations in hemodynamic concentration signals, R-fNIRS measures the brain’s low-frequency spontaneous neural activity, combining the advantages of portability, low-cost, high temporal sampling rate and less physical burden to participants. The temporal synchronization of spontaneous neuronal activity in anatomically separated regions is referred to as resting-state functional connectivity (RSFC). In the past several years, an increasing body of R-fNIRS RSFC studies has led to many important findings about functional integration among local or whole-brain regions by measuring inter-regional temporal synchronization. Here, we summarize recent advances made in the R-fNIRS RSFC methodologies, from the detection of RSFC (e.g., seed-based correlation analysis, independent component analysis, whole-brain correlation analysis, and graph-theoretical topological analysis), to the assessment of RSFC performance (e.g., reliability, repeatability, and validity), to the application of RSFC in studying normal development and brain disorders. The literature reviewed here suggests that RSFC analyses based on R-fNIRS data are valid and reliable for the study of brain function in healthy and diseased populations, thus providing a promising imaging tool for cognitive science and clinics.

Investigating effective brain connectivity from fMRI data: past findings and current issues with reference to Granger causality analysis

Interactions between brain regions have been recognized as a critical ingredient required to understand brain function. Two modes of interactions have held prominence-synchronization and causal influence. Efforts to ascertain causal influence from functional magnetic resonance imaging (fMRI) data have relied primarily on confirmatory model-driven approaches, such as dynamic causal modeling and structural equation modeling, and exploratory data-driven approaches such as Granger causality analysis. A slew of recent articles have focused on the relative merits and caveats of these approaches. The relevant studies can be classified into simulations, theoretical developments, and experimental results. In the first part of this review, we will consider each of these themes and critically evaluate their arguments, with regard to Granger causality analysis. Specifically, we argue that simulations are bounded by the assumptions and simplifications made by the simulator, and hence must be regarded only as a guide to experimental design and should not be viewed as the final word. On the theoretical front, we reason that each of the improvements to existing, yet disparate, methods brings them closer to each other with the hope of eventually leading to a unified framework specifically designed for fMRI. We then review latest experimental results that demonstrate the utility and validity of Granger causality analysis under certain experimental conditions. In the second part, we will consider current issues in causal connectivity analysis-hemodynamic variability, sampling, instantaneous versus causal relationship, and task versus resting states. We highlight some of our own work regarding these issues showing the effect of hemodynamic variability and sampling on Granger causality. Further, we discuss recent techniques such as the cubature Kalman filtering, which can perform blind deconvolution of the hemodynamic response robustly well, and hence enabling wider application of Granger causality analysis. Finally, we discuss our previous work on the less-appreciated interactions between instantaneous and causal relationships and the utility and interpretation of Granger causality results obtained from task versus resting state (e.g., ability of causal relationships to provide a mode of connectivity between regions that are instantaneously dissociated in resting state). We conclude by discussing future directions in this area.

Noninvasive functional imaging of cerebral blood volume with vascular-space-occupancy (VASO) MRI

Functional MRI (fMRI) based on changes in cerebral blood volume (CBV) can probe directly vasodilatation and vasoconstriction during brain activation or physiologic challenges, and can provide important insights into the mechanism of blood oxygenation level-dependent (BOLD) signal changes. At present, the most widely used CBV fMRI technique in humans is called vascular-space-occupancy (VASO) MRI, and this article provides a technical review of this method. VASO MRI utilizes T1 differences between blood and tissue to distinguish between these two compartments within a voxel, and employs a blood-nulling inversion recovery sequence to yield an MR signal proportional to 1 - CBV. As such, vasodilatation will result in a VASO signal decrease and vasoconstriction will have the reverse effect. The VASO technique can be performed dynamically with a temporal resolution comparable with several other fMRI methods, such as BOLD or arterial spin labeling (ASL), and is particularly powerful when conducted in conjunction with these complementary techniques. The pulse sequence and imaging parameters of VASO can be optimized such that the signal change is predominantly of CBV origin, but careful considerations should be taken to minimize other contributions, such as those from the BOLD effect, cerebral blood flow (CBF) and cerebrospinal fluid (CSF). The sensitivity of the VASO technique is the primary disadvantage when compared with BOLD, but this technique is increasingly demonstrating its utility in neuroscientific and clinical applications.

Three-dimensional Fourier encoding of simultaneously excited slices: generalized acquisition and reconstruction framework

PURPOSE

Simultaneous multislice (SMS) acquisitions have recently received much attention as a means of increasing single-shot imaging speed. SMS acquisitions combine the advantages of single-shot sampling and acceleration along the slice dimension which was previously limited to three-dimensional (3D) volumetric acquisitions. A two-dimensional description of SMS sampling and reconstruction has become established in the literature. Here, we present a more general 3D Fourier encoding and reconstruction formalism for SMS acquisitions that can easily be applied to non-Cartesian SMS acquisitions.

THEORY AND METHODS

An "SMS 3D" k-space is defined in which the field of view along the slice select direction is equal to the number of excited slices times their separation. In this picture, SMS acceleration can be viewed as an undersampling of SMS 3D k-space that can be freely distributed between the in-plane and slice directions as both are effective phase-encoding directions.

RESULTS

Use of the SMS 3D k-space picture is demonstrated in phantom and in vivo brain acquisitions including data obtained with blipped-controlled aliasing in parallel imaging sampling. SMS sensitivity encoding reconstruction is demonstrated as well as non-Cartesian SMS imaging using blipped spiral trajectories.

CONCLUSIONS

The full framework of reconstruction methods can be applied to SMS acquisitions by employing a 3D k-space approach. The blipped-controlled aliasing in parallel imaging method can be viewed as a special case of undersampling an SMS 3D k-space. The extension of SMS methods to non-Cartesian 3D sampling and reconstruction is straightforward.

An Investigation of RSN Frequency Spectra Using Ultra-Fast Generalized Inverse Imaging

With the advancements in MRI hardware, pulse sequences and reconstruction techniques, many low TR sequences are becoming more and more popular within the functional MRI (fMRI) community. In this study, we have investigated the spectral characteristics of resting state networks (RSNs) with a newly introduced ultra fast fMRI technique, called generalized inverse imaging (GIN). The high temporal resolution of GIN (TR = 50 ms) enables to sample cardiac signals without aliasing into a separate frequency band from the BOLD fluctuations. Respiration related signal changes are, on the other hand, removed from the data without the need for external physiological recordings. We have observed that the variance over the subjects is higher than the variance over RSNs.

Ultra-fast MRI of the human brain with simultaneous multi-slice imaging

The recent advancement of simultaneous multi-slice imaging using multiband excitation has dramatically reduced the scan time of the brain. The evolution of this parallel imaging technique began over a decade ago and through recent sequence improvements has reduced the acquisition time of multi-slice EPI by over ten fold. This technique has recently become extremely useful for (i) functional MRI studies improving the statistical definition of neuronal networks, and (ii) diffusion based fiber tractography to visualize structural connections in the human brain. Several applications and evaluations are underway which show promise for this family of fast imaging sequences.

Single shot whole brain imaging using spherical stack of spirals trajectories

MR-encephalography allows the observation of functional signal in the brain at a frequency of 10 Hz, permitting filtering of physiological "noise" and the detection of single event activations. High temporal resolution is achieved by the use of undersampled non-Cartesian trajectories, parallel imaging and regularized image reconstruction. MR-encephalography is based on 3D-encoding, allowing undersampling in two dimensions and providing advantages in terms of signal to noise ratio. Long readout times, which are necessary for single shot whole brain imaging (up to 75 ms), cause off-resonance artifacts. To meet this issue, a spherical stack of spirals trajectory is proposed in this work. By examining the trajectories in local k-space, it is shown that in areas of strong susceptibility gradients spatial information is fundamentally lost, making a meaningful image reconstruction impossible in the affected areas. It is shown that the loss of spatial information is reduced when using a stack of spirals trajectory compared to concentric shells.

Generalized INverse imaging (GIN): ultrafast fMRI with physiological noise correction

An ultrafast functional magnetic resonance imaging (fMRI) technique, called generalized inverse imaging (GIN), is proposed, which combines inverse imaging with a phase constraint-leading to a less underdetermined reconstruction-and physiological noise correction. A single 3D echo planar imaging (EPI) prescan is sufficient to obtain the necessary coil sensitivity information and reference images that are used to reconstruct standard images, so that standard analysis methods are applicable. A moving dots stimulus paradigm was chosen to assess the performance of GIN. We find that the spatial localization of activation for GIN is comparable to an EPI protocol and that maximum z-scores increase significantly. The high temporal resolution of GIN (50 ms) and the acquisition of the phase information enable unaliased sampling and regression of physiological signals. Using the phase time courses obtained from the 32 channels of the receiver coils as nuisance regressors in a general linear model results in significant improvement of the functional activation, rendering the acquisition of external physiological signals unnecessary. The proposed physiological noise correction can in principle be used for other fMRI protocols, such as simultaneous multislice acquisitions, which acquire the phase information sufficiently fast and sample physiological signals unaliased.