Dynamic magnetic resonance inverse imaging of human brain function

Standardized hierarchical adaptive Lp regression for noise robust focal epilepsy source reconstructions

OBJECTIVE

To investigate the ability of standardization to reduce source localization errors and measurement noise uncertainties for hierarchical Bayesian algorithms with L1- and L2-norms as priors in electroencephalography and magnetoencephalography of focal epilepsy.

METHODS

Description of the standardization methodology relying on the Hierarchical Bayesian framework, referred to as the Standardized Hierarchical Adaptive Lp-norm Regularization (SHALpR). The performance was tested using real data from two focal epilepsy patients. Simulated data that resembled the available real data was constructed for further localization and noise robustness investigation.

RESULTS

The proposed algorithms were compared to their non-standardized counterparts, Standardized low-resolution brain electromagnetic tomography, Standardized Shrinking LORETA-FOCUSS, and Dynamic statistical parametric maps. Based on the simulations, the standardized Hierarchical adaptive algorithm using L2-norm was noise robust for 10 dB signal-to-noise ratio (SNR), whereas the L1-norm prior worked robustly also with 5 dB SNR. The accuracy of the standardized L1-normed methodology to localize focal activity was under 1 cm for both patients.

CONCLUSIONS

Numerical results of the proposed methodology display improved localization and noise robustness. The proposed methodology also outperformed the compared methods when dealing with real data.

SIGNIFICANCE

The proposed standardized methodology, especially when employing the L1-norm, could serve as a valuable assessment tool in surgical decision-making.

Technical Advancements in Abdominal Diffusion-weighted Imaging

Since its first observation in the 18th century, the diffusion phenomenon has been actively studied by many researchers. Diffusion-weighted imaging (DWI) is a technique to probe the diffusion of water molecules and create a MR image with contrast based on the local diffusion properties. The DWI pixel intensity is modulated by the hindrance the diffusing water molecules experience. This hindrance is caused by structures in the tissue and reflects the state of the tissue. This characteristic makes DWI a unique and effective tool to gain more insight into the tissue's pathophysiological condition. In the past decades, DWI has made dramatic technical progress, leading to greater acceptance in clinical practice. In the abdominal region, however, acquiring DWI with good quality is challenging because of several reasons, such as large imaging volume, respiratory and other types of motion, and difficulty in achieving homogeneous fat suppression. In this review, we discuss technical advancements from the past decades that help mitigate these problems common in abdominal imaging. We describe the use of scan acceleration techniques such as parallel imaging and compressed sensing to reduce image distortion in echo planar imaging. Then we compare techniques developed to mitigate issues due to respiratory motion, such as free-breathing, respiratory-triggering, and navigator-based approaches. Commonly used fat suppression techniques are also introduced, and their effectiveness is discussed. Additionally, the influence of the abovementioned techniques on image quality is demonstrated. Finally, we discuss the current and future clinical applications of abdominal DWI, such as whole-body DWI, simultaneous multiple-slice excitation, intravoxel incoherent motion, and the use of artificial intelligence. Abdominal DWI has the potential to develop further in the future, thanks to scan acceleration and image quality improvement driven by technological advancements. The accumulation of clinical proof will further drive clinical acceptance.

Trading off spatio-temporal properties in 3D high-speed fMRI using interleaved stack-of-spirals trajectories

PURPOSE

Highly undersampled acquisitions have been proposed to push the limits of temporal resolution in functional MRI. This contribution is aimed at identifying parameter sets that let the user trade-off between ultra-high temporal resolution and spatial signal quality by varying the sampling densities. The proposed method maintains the synergies of a temporal resolution that enables direct filtering of physiological artifacts for highest statistical power, and 3D read-outs with optimal use of encoding capabilities of multi-coil arrays for efficient sampling and high signal-to-noise ratio (SNR).

METHODS

One- to four-shot interleaved spherical stack-of-spiral trajectories with repetition times from 96 to 352 ms at a nominal resolution of 3 mm using different sampling densities were compared for image quality and temporal SNR (tSNR). The one- and three-shot trajectories were employed in a resting state study for functional characterization.

RESULTS

Compared to a previously described single-shot trajectory, denser sampled trajectories of the same type are shown to be less prone to blurring and off-resonance vulnerability that appear in addition to the variable density artifacts of the point spread function. While the multi-shot trajectories lead to a decrease in tSNR efficiency, the high SNR due to the 3D read-out, combined with notable increases in image quality, leads to superior overall results of the three-shot interleaved stack of spirals. A resting state analysis of 15 subjects shows significantly improved functional sensitivity in areas of high off-resonance gradients.

CONCLUSION

Mild variable-density sampling leads to excellent tSNR behavior and no increased off-resonance vulnerability, and is suggested unless maximum temporal resolution is sought.

Improving the sensitivity of spin-echo fMRI at 3T by highly accelerated acquisitions

PURPOSE

Spin-echo (SE) functional MRI (fMRI) can be highly advantageous compared to gradient-echo (GE) fMRI with respect to magnetic field-inhomogeneity artifacts. However, at 3T, the majority of blood oxygenation level-dependent (BOLD) fMRI experiments are performed using T 2 ∗ -weighted GE sequences because of their superior sensitivity compared to SE-fMRI. The presented SE implementation of a highly accelerated GE pulse sequence therefore aims to improve the sensitivity of SE-fMRI while profiting from a reduction of susceptibility-induced signal dropout.

METHODS

Spin-echo MR encephalography (SE-MREG) is compared with the more conventionally used spin-echo echo-planar imaging (SE-EPI) and spin-echo simultaneous multislice (SE-SMS) at 3T in terms of capability to detect neuronal activations and resting-state functional connectivity. For activation analysis, healthy subjects underwent consecutive SE-MREG (pulse repetition time [TR] = 0.25 seconds), SE-SMS (TR = 1.3 seconds), and SE-EPI (TR = 4.4 seconds) scans in pseudorandomized order applied to a visual block design paradigm for generation of t-statistics maps. For the investigation of functional connectivity, additional resting-state data were acquired for 5 minutes and a seed-based correlation analysis using Stanford's FIND (Functional Imaging in Neuropsychiatric Disorders) atlas was performed.

RESULTS

The increased sampling rate of SE-MREG relative to SE-SMS and SE-EPI improves the sensitivity to detect BOLD activation by 33% and 54%, respectively, and increases the capability to extract resting-state networks. Compared with a brain region that is not affected by magnetic field inhomogeneities, SE-MREG shows 2.5 times higher relative signal strength than GE-MREG in mesial temporal structures.

CONCLUSION

SE-MREG offers a viable possibility for whole-brain fMRI with consideration of brain regions that are affected by strong susceptibility-induced magnetic field gradients.

Integrated and segregated frequency architecture of the human brain network

The frequency of brain activity modulates the relationship between the brain and human behavior. Insufficient understanding of frequency-specific features may thus lead to inconsistent explanations of human behavior. However, to date, the frequency-specific features of the human brain functional network at the whole-brain level remain poorly understood. Here, we used resting-state fMRI data and graph-theory analyses to investigate the frequency-specific characteristics of fMRI signals in 12 frequency bands (frequency range 0.01-0.7 Hz) in 75 healthy participants. We found that brain regions with higher level and more complex functions had a more variable functional connectivity pattern but engaged less in higher frequency ranges. Moreover, brain regions that engaged in fewer frequency bands played more integrated roles (i.e., higher network participation coefficient and lower within-module degree) in the functional network, whereas regions that engaged in broader frequency ranges exhibited more segregated functions (i.e., lower network participation coefficient and higher within-module degree). Finally, behavioral analyses revealed that regional frequency variability was associated with a spectrum of behavioral functions from sensorimotor functions to complex cognitive and social functions. Taken together, our results showed that segregated functions are executed in wide frequency ranges, whereas integrated functions are executed mainly in lower frequency ranges. These frequency-specific features of brain networks provided crucial insights into the frequency mechanism of fMRI signals, suggesting that signals in higher frequency ranges should be considered for their relation to cognitive functions.

New acquisition techniques and their prospects for the achievable resolution of fMRI

This work reviews recent advances in technologies for functional magnetic resonance imaging (fMRI) of the human brain and highlights the push for higher functional specificity based on increased spatial resolution and specific MR contrasts to reveal previously undetectable functional properties of small-scale cortical structures. We discuss how the combination of MR hardware, advanced acquisition techniques and various MR contrast mechanisms have enabled recent progress in functional neuroimaging. However, these advanced fMRI practices have only been applied to a handful of neuroscience questions to date, with the majority of the neuroscience community still using conventional imaging techniques. We thus discuss upcoming challenges and possibilities for fMRI technology development in human neuroscience. We hope that readers interested in functional brain imaging acquire an understanding of current and novel developments and potential future applications, even if they don't have a background in MR physics or engineering. We summarize the capabilities of standard fMRI acquisition schemes with pointers to relevant literature and comprehensive reviews and introduce more recent developments.

15 Years MR-encephalography

Objective

This review article gives an account of the development of the MR-encephalography (MREG) method, which started as a mere ‘Gedankenexperiment’ in 2005 and gradually developed into a method for ultrafast measurement of physiological activities in the brain. After going through different approaches covering k-space with radial, rosette, and concentric shell trajectories we have settled on a stack-of-spiral trajectory, which allows full brain coverage with (nominal) 3 mm isotropic resolution in 100 ms. The very high acceleration factor is facilitated by the near-isotropic k-space coverage, which allows high acceleration in all three spatial dimensions.

Methods

The methodological section covers the basic sequence design as well as recent advances in image reconstruction including the targeted reconstruction, which allows real-time feedback applications, and—most recently—the time-domain principal component reconstruction (tPCR), which applies a principal component analysis of the acquired time domain data as a sparsifying transformation to improve reconstruction speed as well as quality.

Applications

Although the BOLD-response is rather slow, the high speed acquisition of MREG allows separation of BOLD-effects from cardiac and breathing related pulsatility. The increased sensitivity enables direct detection of the dynamic variability of resting state networks as well as localization of single interictal events in epilepsy patients. A separate and highly intriguing application is aimed at the investigation of the glymphatic system by assessment of the spatiotemporal patterns of cardiac and breathing related pulsatility.

Discussion

MREG has been developed to push the speed limits of fMRI. Compared to multiband-EPI this allows considerably faster acquisition at the cost of reduced image quality and spatial resolution.

Breath hold effect on cardiovascular brain pulsations - A multimodal magnetic resonance encephalography study

Ultra-fast functional magnetic resonance encephalography (MREG) enables separate assessment of cardiovascular, respiratory, and vasomotor waves from brain pulsations without temporal aliasing. We examined effects of breath hold- (BH) related changes on cardiovascular brain pulsations using MREG to study the physiological nature of cerebrovascular reactivity. We used alternating 32 s BH and 88 s resting normoventilation (NV) to change brain pulsations during MREG combined with simultaneously measured respiration, continuous non-invasive blood pressure, and cortical near-infrared spectroscopy (NIRS) in healthy volunteers. Changes in classical resting-state network BOLD-like signal and cortical blood oxygenation were reproduced based on MREG and NIRS signals. Cardiovascular pulsation amplitudes of MREG signal from anterior cerebral artery, oxygenated hemoglobin concentration in frontal cortex, and blood pressure decreased after BH. MREG cardiovascular pulse amplitudes in cortical areas and sagittal sinus increased, while cerebrospinal fluid and white matter remained unchanged. Respiratory centers in the brainstem - hypothalamus - thalamus - amygdala network showed strongest increases in cardiovascular pulsation amplitude. The spatial propagation of averaged cardiovascular impulses altered as a function of successive BH runs. The spread of cardiovascular pulse cycles exhibited a decreasing spatial similarity over time. MREG portrayed spatiotemporally accurate respiratory network activity and cardiovascular pulsation dynamics related to BH challenges at an unpreceded high temporal resolution.

Model-based physiological noise removal in fast fMRI

Recent improvements in the speed and sensitivity of fMRI acquisition techniques suggest that fast fMRI can be used to detect and precisely localize sub-second neural dynamics. This enhanced temporal resolution has enormous potential for neuroscientists. However, physiological noise poses a major challenge for the analysis of fast fMRI data. Physiological noise scales with sensitivity, and its autocorrelation structure is altered in rapidly sampled data, suggesting that new approaches are needed for physiological noise removal in fast fMRI. Existing strategies either rely on external physiological recordings, which can be noisy or difficult to collect, or employ data-driven approaches which make assumptions that may not hold true in fast fMRI. We created a statistical model of harmonic regression with autoregressive noise (HRAN) to estimate and remove cardiac and respiratory noise from the fMRI signal directly. This technique exploits the fact that cardiac and respiratory noise signals are fully sampled (rather than aliasing) when imaging at fast rates, allowing us to track and model physiology over time without requiring external physiological measurements. We then created a joint model of neural hemodynamics, and physiological and autocorrelated noise to more accurately remove noise. We first verified that HRAN accurately estimates cardiac and respiratory dynamics and that our model demonstrates goodness-of-fit in fast fMRI data. In task-driven data, we then demonstrated that HRAN is able to remove physiological noise while leaving the neural signal intact, thereby increasing detection of task-driven voxels. Finally, we established that in both simulations and fast fMRI data HRAN is able to improve statistical inferences as compared with gold-standard physiological noise removal techniques. In conclusion, we created a tool that harnesses the novel information in fast fMRI to remove physiological noise, enabling broader use of the technology to study human brain function.

3D Multi-Resolution Optical Flow Analysis of Cardiovascular Pulse Propagation in Human Brain

The brain is cleaned from waste by glymphatic clearance serving a similar purpose as the lymphatic system in the rest of the body. Impairment of the glymphatic brain clearance precedes protein accumulation and reduced cognitive function in Alzheimer's disease (AD). Cardiovascular pulsations are a primary driving force of the glymphatic brain clearance. We developed a method to quantify cardiovascular pulse propagation in the human brain with magnetic resonance encephalography (MREG). We extended a standard optical flow estimation method to three spatial dimensions, with a multi-resolution processing scheme. We added application-specific criteria for discarding inaccurate results. With the proposed method, it is now possible to estimate the propagation of cardiovascular pulse wavefronts from the whole brain MREG data sampled at 10 Hz. The results show that on average the cardiovascular pulse propagates from major arteries via cerebral spinal fluid spaces into all tissue compartments in the brain. We present an example, that cardiovascular pulsations are significantly altered in AD: coefficient of variation and sample entropy of the pulse propagation speed in the lateral ventricles change in AD. These changes are in line with the theory of glymphatic clearance impairment in AD. The proposed non-invasive method can assess a performance indicator related to the glymphatic clearance in the human brain.

Characterizing Rapid Fluctuations of Resting State Functional Connectivity in Demyelinating, Neurodegenerative, and Psychiatric Conditions: From Static to Time-Varying Analysis

Functional magnetic resonance imaging (fMRI) at resting state (RS) has been widely used to characterize the main brain networks. Functional connectivity (FC) has been mostly assessed assuming that FC is static across the whole fMRI examination. However, FC is highly variable at a very fast time-scale, as demonstrated by neurophysiological techniques. Time-varying functional connectivity (TVC) is a novel approach that allows capturing reoccurring patterns of interaction among functional brain networks. Aim of this review is to provide a description of the methods currently used to assess TVC on RS fMRI data, and to summarize the main results of studies applying TVC in healthy controls and patients with multiple sclerosis (MS). An overview of the main results obtained in neurodegenerative and psychiatric conditions is also provided. The most popular TVC approach is based on the so-called “sliding windows,” in which the RS fMRI acquisition is divided in small temporal segments (windows). A window of fixed length is shifted over RS fMRI time courses, and data within each window are used to calculate FC and its variability over time. Sliding windows can be combined with clustering techniques to identify recurring FC states or used to assess global TVC properties of large-scale functional networks or specific brain regions. TVC studies have used heterogeneous methodologies so far. Despite this, similar results have been obtained across investigations. In healthy subjects, the default-mode network (DMN) exhibited the highest degree of connectivity dynamism. In MS patients, abnormal global TVC properties and TVC strengths were found mainly in sensorimotor, DMN and salience networks, and were associated with more severe structural MRI damage and with more severe physical and cognitive disability. Conversely, abnormal TVC measures of the temporal network were correlated with better cognitive performances and less severe fatigue. In patients with neurodegenerative and psychiatric conditions, TVC abnormalities of the DMN, attention and executive networks were associated to more severe clinical manifestations. TVC helps to provide novel insights into fundamental properties of functional networks, and improves the understanding of brain reorganization mechanisms. Future technical advances might help to clarify TVC association with disease prognosis and response to treatment.

Olfactory fMRI: Implications of Stimulation Length and Repetition Time

Studying olfaction with functional magnetic resonance imaging (fMRI) poses various methodological challenges. This study aimed to investigate the effects of stimulation length and repetition time (TR) on the activation pattern of 4 olfactory brain regions: the anterior and the posterior piriform cortex, the orbitofrontal cortex, and the insula. Twenty-two healthy participants with normal olfaction were examined with fMRI, with 2 stimulation lengths (6 s and 15 s) and 2 TRs (0.901 s and 1.34 s). Data were analyzed using General Linear Model (GLM), Tensorial Independent Component Analysis (TICA), and by plotting the event-related time course of brain activation in the 4 olfactory regions of interest. The statistical analysis of the time courses revealed that short TR was associated with more pronounced signal increase and short stimulation was associated with shorter time to peak signal. Additionally, both long stimulation and short TR were associated with oscillatory time courses, whereas both short stimulation and short TR resulted in more typical time courses. GLM analysis showed that the combination of short stimulation and short TR could result in visually larger activation within these olfactory areas. TICA validated that the tested paradigm was spatially and temporally associated with a functionally connected network that included all 4 olfactory regions. In conclusion, the combination of short stimulation and short TR is associated with higher signal increase and shorter time to peak, making it more amenable to standard GLM-type analyses than long stimulation and long TR, and it should, thus, be preferable for olfactory fMRI.

On the analysis of rapidly sampled fMRI data

Recent advances in parallel imaging and simultaneous multi-slice techniques have permitted whole-brain fMRI acquisitions at sub-second sampling intervals, without significantly sacrificing the spatial coverage and resolution. Apart from probing brain function at finer temporal scales, faster sampling rates may potentially lead to enhanced functional sensitivity, owing possibly to both cleaner neural representations (due to less aliased physiological noise) and additional statistical benefits (due to more degrees of freedom for a fixed scan duration). Accompanying these intriguing aspects of fast acquisitions, however, confusion has also arisen regarding (1) how to preprocess/analyze these fast fMRI data, and (2) what exactly is the extent of benefits with fast acquisitions, i.e., how fast is fast enough for a specific research aim? The first question is motivated by the altered spectral distribution and noise characteristics at short sampling intervals, while the second question seeks to reconcile the complicated trade-offs between the functional contrast-to-noise ratio and the effective degrees of freedom. Although there have been recent efforts to empirically approach different aspects of these two questions, in this work we discuss, from a theoretical perspective accompanied by some illustrative, proof-of-concept experimental in vivo human fMRI data, a few considerations that are rarely mentioned, yet are important for both preprocessing and optimizing statistical inferences for studies that employ acquisitions with sub-second sampling intervals. Several summary recommendations include concerns regarding advisability of relying on low-pass filtering to de-noise physiological contributions, employment of statistical models with sufficient complexity to account for the substantially increased serial correlation, and cautions regarding using rapid sampling to enhance functional sensitivity given that different analysis models may associate with distinct trade-offs between contrast-to-noise ratios and the effective degrees of freedom. As an example, we demonstrate that as TR shortens, the intrinsic differences in how noise is accommodated in general linear models and Pearson correlation analyses (assuming Gaussian distributed stochastic signals and noise) can result in quite different outcomes, either gaining or losing statistical power.

Radiofrequency phase encoded half-pulses in simultaneous multislice ultrashort echo time imaging

PURPOSE

To describe a simultaneous multislice (SMS) ultrashort echo time (UTE) imaging method using radiofrequency phase encoded half-pulses in combination with power independent of number of slices (PINS) inversion recovery (IR) pulses to generate multiple-slice images with short T₂ * contrasts in less than 3 min with close to an eightfold acceleration compared with a standard 2D approach.

THEORY AND METHODS

Radiofrequency phase encoding is applied in an SMS (N_SMS = 4) excitation scheme using "sinc" half-pulses. With the use of coil sensitivity encoding (SENSE) and controlled aliasing in parallel imaging (CAIPI) in combination with a gradient echo 2D spiral readout trajectory and IR PINS pulses for contrast enhancement a fast UTE sequence is developed. Images are obtained using a model-based reconstruction method. Sequence details and performance tests on phantoms as well as the heads of healthy volunteers at 3T are presented.

RESULTS

An SMS UTE sequence with an undersampling factor of 4 is developed using radiofrequency phase encoded half-pulses and PINS IR pulses which enables the acquisition of 8 slices at 0.86² mm² resolution in less than 3-min scan time. UTE images of the head are obtained showing highlighted signal of cortical bone. Image quality and T₂ contrast are comparable to the one obtained by corresponding single slice acquisitions with only minor deviations.

CONCLUSIONS

The method combining radiofrequency phase encoded SMS half-pulses and PINS IR pulses presents a suitable approach to SMS UTE imaging. The usage of coil sensitivity information and increased sampling density by means of interleaved slice group acquisitions allows to reduce the total scan time by a factor close to 8.

Targeted partial reconstruction for real-time fMRI with arbitrary trajectories

PURPOSE

A partial image reconstruction formalism is introduced for the targeted extraction of real-time feedback from arbitrary trajectories when full image reconstruction in real time is computationally too demanding.

METHODS

Explicit calculation and storage of linear combinations of lines of the reconstruction matrix by an incomplete basis change in spatial coordinates lead to translation of the expensive full reconstruction from a frame-wise application to a region of interest (ROI)-wise application. This step is independent from signal data and can be executed before the experiment. Subsequently, the results of the sum over fully reconstructed voxels can be evaluated directly. Data from a high-speed fMRI acquisition was used to investigate the targeted partial reconstruction of a functional ROI atlas, incorporating an intravolume dephasing correction. The same data and ROIs were used for a comparison of the time series obtained with those obtained from already existing methods for compartment-wise reconstruction. To examine real-time feasibility, the reconstruction was implemented and tested for online reconstruction performance.

RESULTS

The reconstruction yields results that are virtually identical to the standard reconstruction (i.e., the magnitude sums over the ROIs), with negligible discrepancies even after termination of the conjugate gradient algorithm at a feasible number of iterations. Notably, more discrepancies arise with existing compartment-wise reconstructions. The online real-time implementation evaluated 1 ROI within 2.8 ms in the case of a highly parallel 3D whole brain acquisition.

CONCLUSION

The high reconstruction fidelity and speed are satisfying for the exemplary application of real-time functional feedback using a highly parallel 3D whole brain acquisition.

Improving the signal detection accuracy of functional Magnetic Resonance Imaging

A major drawback of functional Magnetic Resonance Imaging (fMRI) concerns the lack of detection accuracy of the measured signal. Although this limitation stems in part from the neuro-vascular nature of the fMRI signal, it also reflects particular methodological decisions in the fMRI data analysis pathway. Here we show that the signal detection accuracy of fMRI is affected by the specific way in which whole-brain volumes are created from individually acquired brain slices, and by the method of statistically extracting signals from the sampled data. To address these limitations, we propose a new framework for fMRI data analysis. The new framework creates whole-brain volumes from individual brain slices that are all acquired at the same point in time relative to a presented stimulus. These whole-brain volumes contain minimal temporal distortions, and are available at a high temporal resolution. In addition, statistical signal extraction occurred on the basis of a non-standard time point-by-time point approach. We evaluated the detection accuracy of the extracted signal in the standard and new framework with simulated and real-world fMRI data. The new slice-based data-analytic framework yields greatly improved signal detection accuracy of fMRI signals.

Simultaneous multi-slice inverse imaging of the human brain

Ultrafast functional magnetic resonance imaging (fMRI) can measure blood oxygen level dependent (BOLD) signals with high sensitivity and specificity. Here we propose a novel method: simultaneous multi-slice inverse imaging (SMS-InI) — a combination of simultaneous multi-slice excitation, simultaneous echo refocusing (SER), blipped controlled aliasing in parallel imaging echo-planar imaging (EPI), and regularized image reconstruction. Using a 32-channel head coil array on a 3 T scanner, SMS-InI achieves nominal isotropic 5-mm spatial resolution and 10 Hz sampling rate at the whole-brain level. Compared with traditional inverse imaging, we found that SMS-InI has higher spatial resolution with lower signal leakage and higher time-domain signal-to-noise ratio with the optimized regularization parameter in the reconstruction. SMS-InI achieved higher effective resolution and higher detection power in detecting visual cortex activity than InI. SMS-InI also detected subcortical fMRI signals with the similar sensitivity and localization accuracy like EPI. The spatiotemporal resolution of SMS-InI was used to reveal that presenting visual stimuli with 0.2 s latency between left and right visual hemifield led to 0.2 s relative hemodynamic response latency between the left and right visual cortices. Together, these results indicate that SMS-InI is a useful tool in measuring cortical and subcortical hemodynamic responses with high spatiotemporal resolution.

Improving temporal resolution in fMRI using a 3D spiral acquisition and low rank plus sparse (L+S) reconstruction

Rapid whole-brain dynamic Magnetic Resonance Imaging (MRI) is of particular interest in Blood Oxygen Level Dependent (BOLD) functional MRI (fMRI). Faster acquisitions with higher temporal sampling of the BOLD time-course provide several advantages including increased sensitivity in detecting functional activation, the possibility of filtering out physiological noise for improving temporal SNR, and freezing out head motion. Generally, faster acquisitions require undersampling of the data which results in aliasing artifacts in the object domain. A recently developed low-rank (L) plus sparse (S) matrix decomposition model (L+S) is one of the methods that has been introduced to reconstruct images from undersampled dynamic MRI data. The L+S approach assumes that the dynamic MRI data, represented as a space-time matrix M, is a linear superposition of L and S components, where L represents highly spatially and temporally correlated elements, such as the image background, while S captures dynamic information that is sparse in an appropriate transform domain. This suggests that L+S might be suited for undersampled task or slow event-related fMRI acquisitions because the periodic nature of the BOLD signal is sparse in the temporal Fourier transform domain and slowly varying low-rank brain background signals, such as physiological noise and drift, will be predominantly low-rank. In this work, as a proof of concept, we exploit the L+S method for accelerating block-design fMRI using a 3D stack of spirals (SoS) acquisition where undersampling is performed in the k_z-t domain. We examined the feasibility of the L+S method to accurately separate temporally correlated brain background information in the L component while capturing periodic BOLD signals in the S component. We present results acquired in control human volunteers at 3T for both retrospective and prospectively acquired fMRI data for a visual activation block-design task. We show that a SoS fMRI acquisition with an acceleration of four and L+S reconstruction can achieve a brain coverage of 40 slices at 2mm isotropic resolution and 64 x 64 matrix size every 500ms.

Relative latency and temporal variability of hemodynamic responses at the human primary visual cortex

The blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) signal is a robust surrogate for local neuronal activity. However, it has been shown to vary substantially across subjects, brain regions, and repetitive measurements. This variability represents a limit to the precision of the BOLD response and the ability to reliably discriminate brain hemodynamic responses elicited by external stimuli or behavior that are nearby in time. While the temporal variability of the BOLD signal at human visual cortex has been found in the range of a few hundreds of milliseconds, the spatial distributions of the average and standard deviation of this temporal variability have not been quantitatively characterized. Here we use fMRI measurements with a high sampling rate (10Hz) to map the latency, intra- and inter-subject variability of the evoked BOLD signal in human primary (V1) visual cortices using an event-related fMRI paradigm. The latency relative to the average BOLD signal evoked by 30 stimuli was estimated to be 0.03±0.20s. Within V1, the absolute value of the relative BOLD latency was found correlated to intra- and inter-subject temporal variability. After comparing these measures to retinotopic maps, we found that locations with V1 areas sensitive to smaller eccentricity have later responses and smaller inter-subject variabilities. These correlations were found from data with either short inter-stimulus interval (ISI; average 4s) or long ISI (average 30s). Maps of the relative latency as well as inter-/intra-subject variability were found visually asymmetric between hemispheres. Our results suggest that the latency and variability of regional BOLD signal measured with high spatiotemporal resolution may be used to detect regional differences in hemodynamics to inform fMRI studies. However, the physiological origins of timing index distributions and their hemispheric asymmetry remain to be investigated.

Increased fMRI Sensitivity at Equal Data Burden Using Averaged Shifted Echo Acquisition

There is growing evidence as to the benefits of collecting BOLD fMRI data with increased sampling rates. However, many of the newly developed acquisition techniques developed to collect BOLD data with ultra-short TRs require hardware, software, and non-standard analytic pipelines that may not be accessible to all researchers. We propose to incorporate the method of shifted echo into a standard multi-slice, gradient echo EPI sequence to achieve a higher sampling rate with a TR of <1 s with acceptable spatial resolution. We further propose to incorporate temporal averaging of consecutively acquired EPI volumes to both ameliorate the reduced temporal signal-to-noise inherent in ultra-fast EPI sequences and reduce the data burden. BOLD data were collected from 11 healthy subjects performing a simple, event-related visual-motor task with four different EPI sequences: (1) reference EPI sequence with TR = 1440 ms, (2) shifted echo EPI sequence with TR = 700 ms, (3) shifted echo EPI sequence with every two consecutively acquired EPI volumes averaged and effective TR = 1400 ms, and (4) shifted echo EPI sequence with every four consecutively acquired EPI volumes averaged and effective TR = 2800 ms. Both the temporally averaged sequences exhibited increased temporal signal-to-noise over the shifted echo EPI sequence. The shifted echo sequence with every two EPI volumes averaged also had significantly increased BOLD signal change compared with the other three sequences, while the shifted echo sequence with every four EPI volumes averaged had significantly decreased BOLD signal change compared with the other three sequences. The results indicated that incorporating the method of shifted echo into a standard multi-slice EPI sequence is a viable method for achieving increased sampling rate for collecting event-related BOLD data. Further, consecutively averaging every two consecutively acquired EPI volumes significantly increased the measured BOLD signal change and the subsequently calculated activation map statistics.

Enhanced subject-specific resting-state network detection and extraction with fast fMRI

Resting-state networks have become an important tool for the study of brain function. An ultra-fast imaging technique that allows to measure brain function, called Magnetic Resonance Encephalography (MREG), achieves an order of magnitude higher temporal resolution than standard echo-planar imaging (EPI). This new sequence helps to correct physiological artifacts and improves the sensitivity of the fMRI analysis. In this study, EPI is compared with MREG in terms of capability to extract resting-state networks. Healthy controls underwent two consecutive resting-state scans, one with EPI and the other with MREG. Subject-level independent component analyses (ICA) were performed separately for each of the two datasets. Using Stanford FIND atlas parcels as network templates, the presence of ICA maps corresponding to each network was quantified in each subject. The number of detected individual networks was significantly higher in the MREG data set than for EPI. Moreover, using short time segments of MREG data, such as 50 seconds, one can still detect and track consistent networks. Fast fMRI thus results in an increased capability to extract distinct functional regions at the individual subject level for the same scan times, and also allow the extraction of consistent networks within shorter time intervals than when using EPI, which is notably relevant for the analysis of dynamic functional connectivity fluctuations. Hum Brain Mapp 38:817-830, 2017. © 2016 Wiley Periodicals, Inc.

Subject-level reliability analysis of fast fMRI with application to epilepsy

PURPOSE

Recent studies have applied the new magnetic resonance encephalography (MREG) sequence to the study of interictal epileptic discharges (IEDs) in the electroencephalogram (EEG) of epileptic patients. However, there are no criteria to quantitatively evaluate different processing methods, to properly use the new sequence.

METHODS

We evaluated different processing steps of this new sequence under the common generalized linear model (GLM) framework by assessing the reliability of results. A bootstrap sampling technique was first used to generate multiple replicated data sets; a GLM with different processing steps was then applied to obtain activation maps, and the reliability of these maps was assessed.

RESULTS

We applied our analysis in an event-related GLM related to IEDs. A higher reliability was achieved by using a GLM with head motion confound regressor with 24 components rather than the usual 6, with an autoregressive model of order 5 and with a canonical hemodynamic response function (HRF) rather than variable latency or patient-specific HRFs. Comparison of activation with IED field also favored the canonical HRF, consistent with the reliability analysis.

CONCLUSION

The reliability analysis helps to optimize the processing methods for this fast fMRI sequence, in a context in which we do not know the ground truth of activation areas. Magn Reson Med 78:370-382, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

LORAKS makes better SENSE: Phase-constrained partial fourier SENSE reconstruction without phase calibration

PURPOSE

Parallel imaging and partial Fourier acquisition are two classical approaches for accelerated MRI. Methods that combine these approaches often rely on prior knowledge of the image phase, but the need to obtain this prior information can place practical restrictions on the data acquisition strategy. In this work, we propose and evaluate SENSE-LORAKS, which enables combined parallel imaging and partial Fourier reconstruction without requiring prior phase information.

THEORY AND METHODS

The proposed formulation is based on combining the classical SENSE model for parallel imaging data with the more recent LORAKS framework for MR image reconstruction using low-rank matrix modeling. Previous LORAKS-based methods have successfully enabled calibrationless partial Fourier parallel MRI reconstruction, but have been most successful with nonuniform sampling strategies that may be hard to implement for certain applications. By combining LORAKS with SENSE, we enable highly accelerated partial Fourier MRI reconstruction for a broader range of sampling trajectories, including widely used calibrationless uniformly undersampled trajectories.

RESULTS

Our empirical results with retrospectively undersampled datasets indicate that when SENSE-LORAKS reconstruction is combined with an appropriate k-space sampling trajectory, it can provide substantially better image quality at high-acceleration rates relative to existing state-of-the-art reconstruction approaches.

CONCLUSION

The SENSE-LORAKS framework provides promising new opportunities for highly accelerated MRI. Magn Reson Med 77:1021-1035, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

A 32-Channel Head Coil Array with Circularly Symmetric Geometry for Accelerated Human Brain Imaging

The goal of this study is to optimize a 32-channel head coil array for accelerated 3T human brain proton MRI using either a Cartesian or a radial k-space trajectory. Coils had curved trapezoidal shapes and were arranged in a circular symmetry (CS) geometry. Coils were optimally overlapped to reduce mutual inductance. Low-noise pre-amplifiers were used to further decouple between coils. The SNR and noise amplification in accelerated imaging were compared to results from a head coil array with a soccer-ball (SB) geometry. The maximal SNR in the CS array was about 120% (1070 vs. 892) and 62% (303 vs. 488) of the SB array at the periphery and the center of the FOV on a transverse plane, respectively. In one-dimensional 4-fold acceleration, the CS array has higher averaged SNR than the SB array across the whole FOV. Compared to the SB array, the CS array has a smaller g-factor at head periphery in all accelerated acquisitions. Reconstructed images using a radial k-space trajectory show that the CS array has a smaller error than the SB array in 2- to 5-fold accelerations.

k-t FASTER: Acceleration of functional MRI data acquisition using low rank constraints

PURPOSE

In functional MRI (fMRI), faster sampling of data can provide richer temporal information and increase temporal degrees of freedom. However, acceleration is generally performed on a volume-by-volume basis, without consideration of the intrinsic spatio-temporal data structure. We present a novel method for accelerating fMRI data acquisition, k-t FASTER (FMRI Accelerated in Space-time via Truncation of Effective Rank), which exploits the low-rank structure of fMRI data.

THEORY AND METHODS

Using matrix completion, 4.27× retrospectively and prospectively under-sampled data were reconstructed (coil-independently) using an iterative nonlinear algorithm, and compared with several different reconstruction strategies. Matrix reconstruction error was evaluated; a dual regression analysis was performed to determine fidelity of recovered fMRI resting state networks (RSNs).

RESULTS

The retrospective sampling data showed that k-t FASTER produced the lowest error, approximately 3-4%, and the highest quality RSNs. These results were validated in prospectively under-sampled experiments, with k-t FASTER producing better identification of RSNs than fully sampled acquisitions of the same duration.

CONCLUSION

With k-t FASTER, incoherently under-sampled fMRI data can be robustly recovered using only rank constraints. This technique can be used to improve the speed of fMRI sampling, particularly for multivariate analyses such as temporal independent component analysis.

Time-frequency dynamics of the sum of intra- and extracerebral hemodynamic functional connectivity during resting-state and respiratory challenges assessed by multimodal functional near-infrared spectroscopy

Monitoring respiratory processes is important for evaluating neuroimaging data, given their influence on time-frequency dynamics of intra- and extracerebral hemodynamics. Here we investigated the time-frequency dynamics of the sum of intra- and extracerebral hemodynamic functional connectivity states during hypo- and hypercapnia by using three different respiratory challenge tasks (i.e., hyperventilation, breath-holding, and rebreathing) compared to resting-state. The sum of intra- and extracerebral hemodynamic responses were assessed using functional near-infrared spectroscopy (fNIRS) within two regions of interest (i.e., the dorsolateral and the medial prefrontal cortex). Time-frequency fNIRS analysis was performed based on wavelet transform coherence to quantify functional connectivity in terms of positive and negative phase-coupling within each region of interest. Physiological measures were assessed in the form of partial end-tidal carbon dioxide, heart rate, arterial tissue oxygen saturation, and respiration rate. We found that the three respiration challenges modulated time-frequency dynamics differently with respect to resting-state: 1) Hyperventilation and breath-holding exhibited inverse patterns of positive and negative phase-coupling. 2) In contrast, rebreathing had no significant effect. 3) Low-frequency oscillations contributed to a greater extent to time-frequency dynamics compared to high-frequency oscillations. The results highlight that there exist distinct differences in time-frequency dynamics of the sum of intra- and extracerebral functional connectivity not only between hypo- (hyperventilation) and hypercapnia but also between different states of hypercapnia (breath-holding versus rebreathing). This suggests that a multimodal assessment of intra-/extracerebral and systemic physiological changes during respiratory challenges compared to resting-state may have potential use in the differentiation between physiological and pathological respiratory behavior accompanied by the psycho-physiological state of a human.

Rotary scanning acquisition in ultra-low-field MRI

PURPOSE

To develop a method of achieving large field of view (FOV) imaging with a smaller amount of data in ultra-low-field (ULF) MRI.

THEORY

In rotary scanning acquisition (RSA), data from the imaging object is acquired at multiple angles by rotating the object or the scanner. RSA is similar to radial-trajectory acquisition but simplifies the measurement and image reconstruction when concomitant fields are nonnegligible.

METHODS

RSA was implemented to achieve large FOV with only three localized superconductive quantum interference device (SQUID) sensors at the ULF-MRI field of 50 μT.

RESULTS

Simulations suggest benefits of RSA, including reduced concomitant field artifacts, large FOV imaging, and SNR improvement. Experimental data demonstrate the feasibility of reconstructing large FOV images using only three SQUID sensors with 33% of the amount of data collected using a Cartesian trajectory.

CONCLUSION

RSA can be useful in low-field, low-weight, or portable MRI to generate large FOV images with only a few sensors. Magn Reson Med 75:2255-2264, 2016. © 2015 Wiley Periodicals, Inc.

Advantages and disadvantages of a fast fMRI sequence in the context of EEG-fMRI investigation of epilepsy patients: A realistic simulation study

EEG-fMRI is an established technique to allow mapping BOLD changes in response to interictal discharges recorded in the EEG of epilepsy patients. Traditional fMRI experiments rely on an echo planar imaging (EPI) sequence with a time resolution given by its time-to-repetition (TR) of ~2 s. Recently, multiple fast fMRI sequences have been developed to get around the limited temporal resolution of the EPI sequence, and achieved a TR in the 100 ms range or lower. One such sequence is called Magnetic Resonance EncephaloGraphy (MREG). Its high temporal resolution should offer increased detection sensitivity and statistical power in the context of epilepsy studies and in fMRI experiments in general. The aim of this work was to investigate the advantages and disadvantages offered by MREG. This was done by superimposing artificial event-related BOLD responses on EPI and MREG background signals, from 5 epileptic patients, that were free of epileptic discharges (spikes) on simultaneously recorded EEG. These functional datasets simulated different spiking rates and hemodynamic response amplitudes, and were analyzed with the commonly used General Linear Model (GLM) with the canonical hemodynamic response function (HRF) as a fixed model of the response. Robustness to violation of the assumptions of the GLM was additionally assessed with similar simulations using variable spike-to-spike response amplitudes and 8 non-canonical HRFs. Consistent with previous work, MREG yields higher maximum statistical t-values than EPI, but our simulations showed these statistics to be inflated, as the false positive rate at a standard threshold was high. At thresholds set to appropriately control specificity, EPI showed better true positive rate and larger cluster size than MREG. However, the lack of an appropriate calibration of the amplitude of the responses across the sequences precludes definitive judgment on their relative sensitivity. In addition, we show that a mismatch between the assumed and actual HRF impairs more MREG detection performance, but that EPI is more affected by non-modeled spike-to-spike variations of response amplitude. Filtering-out physiological noise, which is not aliased at the fast sampling rate of MREG, and the modeling of temporal autocorrelation are advantageous in increasing the detection power of MREG. This simulation study 1) warrants care when interpreting statistical t-values from fast fMRI sequences, 2) proposes thresholds for valid inferences and processing methods for maximal sensitivities, and 3) demonstrates the relative robustness/susceptibility of MREG and EPI to violation of the GLM's assumptions.

Altered development and multifaceted band-specific abnormalities of resting state networks in autism

BACKGROUND

Extensive evidence indicates that cortical connectivity patterns are abnormal in autism spectrum disorders (ASD), showing both overconnectivity and underconnectivity. Since, however, studies to date have focused on either spatial or spectral dimensions, but not both simultaneously, much remains unknown about the nature of these abnormalities. In particular, it remains unknown whether abnormal connectivity patterns in ASD are driven by specific frequency bands, by spatial network properties, or by some combination of these factors.

METHODS

Magnetoencephalography recordings (15 ASD, 15 control subjects) mapped back onto cortical space were used to study resting state networks in ASD with both spatial and spectral specificity. The data were quantified using graph theoretic metrics.

RESULTS

The two major factors that drove the nature of connectivity abnormalities in ASD were the mediating frequency band and whether the network included frontal nodes. These factors determined whether clustering and integration were increased or decreased in cortical resting state networks in ASD. These measures also correlated with abnormalities in the developmental trajectory of resting state networks in ASD. Lastly, these measures correlated with ASD severity in some frequency bands and spatially specific subnetworks.

CONCLUSIONS

Our findings suggest that network abnormalities in ASD are widespread, are more likely in subnetworks that include the frontal lobe, and can be opposite in nature depending on the frequency band. These findings thus elucidate seemingly contradictory prior findings of both overconnectivity and underconnectivity in ASD.

BOLD fractional contribution to resting-state functional connectivity above 0.1 Hz

Blood oxygen level dependent (BOLD) spontaneous signals from resting-state (RS) brains have typically been characterized by low-pass filtered timeseries at frequencies ≤ 0.1 Hz, and studies of these low-frequency fluctuations have contributed exceptional understanding of the baseline functions of our brain. Very recently, emerging evidence has demonstrated that spontaneous activities may persist in higher frequency bands (even up to 0.8 Hz), while presenting less variable network patterns across the scan duration. However, as an indirect measure of neuronal activity, BOLD signal results from an inherently slow hemodynamic process, which in fact might be too slow to accommodate the observed high-frequency functional connectivity (FC). To examine whether the observed high-frequency spontaneous FC originates from BOLD contrast, we collected RS data as a function of echo time (TE). Here we focus on two specific resting state networks - the default-mode network (DMN) and executive control network (ECN), and the major findings are fourfold: (1) we observed BOLD-like linear TE-dependence in the spontaneous activity at frequency bands up to 0.5 Hz (the maximum frequency that can be resolved with TR=1s), supporting neural relevance of the RSFC at a higher frequency range; (2) conventional models of hemodynamic response functions must be modified to support resting state BOLD contrast, especially at higher frequencies; (3) there are increased fractions of non-BOLD-like contributions to the RSFC above the conventional 0.1 Hz (non-BOLD/BOLD contrast at 0.4-0.5 Hz is ~4 times that at <0.1 Hz); and (4) the spatial patterns of RSFC are frequency-dependent. Possible mechanisms underlying the present findings and technical concerns regarding RSFC above 0.1 Hz are discussed.

Negative BOLD in default-mode structures measured with EEG-MREG is larger in temporal than extra-temporal epileptic spikes

Introduction: EEG-fMRI detects BOLD changes associated with epileptic interictal discharges (IED) and can identify epileptogenic networks in epilepsy patients. Besides positive BOLD changes, negative BOLD changes have sometimes been observed in the default-mode network, particularly using group analysis. A new fast fMRI sequence called MREG (Magnetic Resonance Encephalography) shows increased sensitivity to detect IED-related BOLD changes compared to the conventional EPI sequence, including frequent occurrence of negative BOLD responses in the DMN. The present study quantifies the concordance between the DMN and negative BOLD related to IEDs of temporal and extra-temporal origin.

Methods: Focal epilepsy patients underwent simultaneous EEG-MREG. Areas of overlap were calculated between DMN regions, defined as precuneus, posterior cingulate, bilateral inferior parietal and mesial prefrontal cortices according to a standardized atlas, and significant negative BOLD changes revealed by an event-related analysis based on the timings of IED seen on EEG. Correlation between IED number/lobe of origin and the overlap were calculated.

Results: 15 patients were analyzed, some showing IED over more than one location resulting in 30 different IED types. The average overlap between negative BOLD and DMN was significantly larger in temporal (23.7 ± 19.6 cm³) than extra-temporal IEDs (7.4 ± 5.1 cm³, p = 0.008). There was no significant correlation between the number of IEDs and the overlap between DMN structures and negative BOLD areas.

Discussion: MREG results in an increased sensitivity to detect negative BOLD responses related to focal IED in single patients, with responses often occurring in DMN regions. In patients with high overlap with the DMN, this suggests that epileptic IEDs may be associated with a brief decrease in attention and cognitive ability. Interestingly this observation was not dependent on the frequency of IED but more common in IED of temporal origin.

Increasing fMRI sampling rate improves Granger causality estimates

Estimation of causal interactions between brain areas is necessary for elucidating large-scale functional brain networks underlying behavior and cognition. Granger causality analysis of time series data can quantitatively estimate directional information flow between brain regions. Here, we show that such estimates are significantly improved when the temporal sampling rate of functional magnetic resonance imaging (fMRI) is increased 20-fold. Specifically, healthy volunteers performed a simple visuomotor task during blood oxygenation level dependent (BOLD) contrast based whole-head inverse imaging (InI). Granger causality analysis based on raw InI BOLD data sampled at 100-ms resolution detected the expected causal relations, whereas when the data were downsampled to the temporal resolution of 2 s typically used in echo-planar fMRI, the causality could not be detected. An additional control analysis, in which we SINC interpolated additional data points to the downsampled time series at 0.1-s intervals, confirmed that the improvements achieved with the real InI data were not explainable by the increased time-series length alone. We therefore conclude that the high-temporal resolution of InI improves the Granger causality connectivity analysis of the human brain.

Error decomposition for parallel imaging reconstruction using modulation-domain representation of undersampled data

This paper presents a quantitative approach to evaluating and optimizing parallel imaging reconstruction for a clinical requirement. By introducing a "modulation domain representation" for undersampled data, the presented approach decomposes parallel imaging reconstruction error into multiple error components that can be grouped into three categories: image fidelity error, residue aliasing artifacts, and amplified noise. It is experimentally found that these error components have different image-space patterns that compromise imaging quality in different fashions. An error function may be defined as the weighted summation of these error components. By choosing a set of weighting coefficients that can quantify desirable image quality, parallel imaging may be optimized for a clinical requirement. It is found that error decomposition model may improve clinical utility of parallel imaging, providing an application-oriented approach to clinical parallel imaging.

Improving the spatial resolution of magnetic resonance inverse imaging via the blipped-CAIPI acquisition scheme

Using simultaneous acquisition from multiple channels of a radio-frequency (RF) coil array, magnetic resonance inverse imaging (InI) achieves functional MRI acquisitions at a rate of 100ms per whole-brain volume. InI accelerates the scan by leaving out partition encoding steps and reconstructs images by solving under-determined inverse problems using RF coil sensitivity information. Hence, the correlated spatial information available in the coil array causes spatial blurring in the InI reconstruction. Here, we propose a method that employs gradient blips in the partition encoding direction during the acquisition to provide extra spatial encoding in order to better differentiate signals from different partitions. According to our simulations, this blipped-InI (bInI) method can increase the average spatial resolution by 15.1% (1.3mm) across the whole brain and from 32.6% (4.2mm) in subcortical regions, as compared to the InI method. In a visual fMRI experiment, we demonstrate that, compared to InI, the spatial distribution of bInI BOLD response is more consistent with that of a conventional echo-planar imaging (EPI) at the level of individual subjects. With the improved spatial resolution, especially in subcortical regions, bInI can be a useful fMRI tool for obtaining high spatiotemporal information for clinical and cognitive neuroscience studies.

Fast fMRI provides high statistical power in the analysis of epileptic networks

EEG-fMRI is a unique method to combine the high temporal resolution of EEG with the high spatial resolution of MRI to study generators of intrinsic brain signals such as sleep grapho-elements or epileptic spikes. While the standard EPI sequence in fMRI experiments has a temporal resolution of around 2.5-3s a newly established fast fMRI sequence called MREG (Magnetic-Resonance-Encephalography) provides a temporal resolution of around 100ms. This technical novelty promises to improve statistics, facilitate correction of physiological artifacts and improve the understanding of epileptic networks in fMRI. The present study compares simultaneous EEG-EPI and EEG-MREG analyzing epileptic spikes to determine the yield of fast MRI in the analysis of intrinsic brain signals. Patients with frequent interictal spikes (>3/20min) underwent EEG-MREG and EEG-EPI (3T, 20min each, voxel size 3×3×3mm, EPI TR=2.61s, MREG TR=0.1s). Timings of the spikes were used in an event-related analysis to generate activation maps of t-statistics. (FMRISTAT, |t|>3.5, cluster size: 7 voxels, p<0.05 corrected). For both sequences, the amplitude and location of significant BOLD activations were compared with the spike topography. 13 patients were recorded and 33 different spike types could be analyzed. Peak T-values were significantly higher in MREG than in EPI (p<0.0001). Positive BOLD effects correlating with the spike topography were found in 8/29 spike types using the EPI and in 22/33 spikes types using the MREG sequence. Negative BOLD responses in the default mode network could be observed in 3/29 spike types with the EPI and in 19/33 with the MREG sequence. With the latter method, BOLD changes were observed even when few spikes occurred during the investigation. Simultaneous EEG-MREG thus is possible with good EEG quality and shows higher sensitivity in regard to the localization of spike-related BOLD responses than EEG-EPI. The development of new methods of analysis for this sequence such as modeling of physiological noise, temporal analysis of the BOLD signal and defining appropriate thresholds is required to fully profit from its high temporal resolution.

Dynamic functional connectivity: promise, issues, and interpretations

The brain must dynamically integrate, coordinate, and respond to internal and external stimuli across multiple time scales. Non-invasive measurements of brain activity with fMRI have greatly advanced our understanding of the large-scale functional organization supporting these fundamental features of brain function. Conclusions from previous resting-state fMRI investigations were based upon static descriptions of functional connectivity (FC), and only recently studies have begun to capitalize on the wealth of information contained within the temporal features of spontaneous BOLD FC. Emerging evidence suggests that dynamic FC metrics may index changes in macroscopic neural activity patterns underlying critical aspects of cognition and behavior, though limitations with regard to analysis and interpretation remain. Here, we review recent findings, methodological considerations, neural and behavioral correlates, and future directions in the emerging field of dynamic FC investigations.

Whole-head rapid fMRI acquisition using echo-shifted magnetic resonance inverse imaging

The acquisition time of BOLD contrast functional MRI (fMRI) data with whole-brain coverage typically requires a sampling rate of one volume in 1-3s. Although the volumetric sampling time of a few seconds is adequate for measuring the sluggish hemodynamic response (HDR) to neuronal activation, faster sampling of fMRI might allow for monitoring of rapid physiological fluctuations and detection of subtle neuronal activation timing information embedded in BOLD signals. Previous studies utilizing a highly accelerated volumetric MR inverse imaging (InI) technique have provided a sampling rate of one volume per 100 ms with 5mm spatial resolution. Here, we propose a novel modification of this technique, the echo-shifted InI, which allows TE to be longer than TR, to measure BOLD fMRI at an even faster sampling rate of one volume per 25 ms with whole-brain coverage. Compared with conventional EPI, echo-shifted InI provided an 80-fold speedup with similar spatial resolution and less than 2-fold temporal SNR loss. The capability of echo-shifted InI to detect HDR timing differences was tested empirically. At the group level (n=6), echo-spaced InI was able to detect statistically significant HDR timing differences of as low as 50 ms in visual stimulus presentation. At the level of individual subjects, significant differences in HDR timing were detected for 400 ms stimulus-onset differences. Our results also show that the temporal resolution of 25 ms is necessary for maintaining the temporal detecting capability at this level. With the capabilities of being able to distinguish the timing differences in the millisecond scale, echo-shifted InI could be a useful fMRI tool for obtaining temporal information at a time scale closer to that of neuronal dynamics.

High-speed real-time resting-state FMRI using multi-slab echo-volumar imaging

We recently demonstrated that ultra-high-speed real-time fMRI using multi-slab echo-volumar imaging (MEVI) significantly increases sensitivity for mapping task-related activation and resting-state networks (RSNs) compared to echo-planar imaging (Posse et al., 2012). In the present study we characterize the sensitivity of MEVI for mapping RSN connectivity dynamics, comparing independent component analysis (ICA) and a novel seed-based connectivity analysis (SBCA) that combines sliding-window correlation analysis with meta-statistics. This SBCA approach is shown to minimize the effects of confounds, such as movement, and CSF and white matter signal changes, and enables real-time monitoring of RSN dynamics at time scales of tens of seconds. We demonstrate highly sensitive mapping of eloquent cortex in the vicinity of brain tumors and arterio-venous malformations, and detection of abnormal resting-state connectivity in epilepsy. In patients with motor impairment, resting-state fMRI provided focal localization of sensorimotor cortex compared with more diffuse activation in task-based fMRI. The fast acquisition speed of MEVI enabled segregation of cardiac-related signal pulsation using ICA, which revealed distinct regional differences in pulsation amplitude and waveform, elevated signal pulsation in patients with arterio-venous malformations and a trend toward reduced pulsatility in gray matter of patients compared with healthy controls. Mapping cardiac pulsation in cortical gray matter may carry important functional information that distinguishes healthy from diseased tissue vasculature. This novel fMRI methodology is particularly promising for mapping eloquent cortex in patients with neurological disease, having variable degree of cooperation in task-based fMRI. In conclusion, ultra-high-real-time speed fMRI enhances the sensitivity of mapping the dynamics of resting-state connectivity and cerebro-vascular pulsatility for clinical and neuroscience research applications.

Highly accelerated projection imaging with coil sensitivity encoding for rapid MRI

PURPOSE

Rapid magnetic resonance imaging (MRI) acquisition is typically achieved by acquiring all or most lines of k-space after one radio frequency (RF) excitation. Parallel imaging techniques can further accelerate data acquisition by acquiring fewer phase-encoded lines and utilizing the spatial sensitivity information of the RF coil arrays. The goal of this study was to develop a new MRI data acquisition and reconstruction technique that is capable of reconstructing a two-dimensional (2D) image using highly undersampled k-space data without any special hardware. Such a technique would be very efficient, as it would significantly reduce the time wasted during multiple RF excitations or phase encoding and gradient switching periods.

METHODS

The essence of this new technique is to densely sample a small number of projections, which should be acquired at an angle other than 0° or multiples of 45°. This results in multiple rays passing through a voxel and provides new and independent measurements for each voxel. Then the images are reconstructed using the unique information coming from these projections combined with RF coil sensitivity profiles. The feasibility of this new technique was investigated with realistic simulations and experimental studies using a phantom and compared with conventional nonuniform fast Fourier transform technique. Eigenvalue analysis and error calculations were conducted to find optimal projection angles and minimum requirements for dense sampling.

RESULTS

Reconstruction of 64 × 64 images was done using a single projection from simulated data under different noise levels. Simulated reconstruction was also tested with two projections to assess the improvement. Experimental phantom images were reconstructed at higher resolution using 4, 8, and 16 projections. Cross-sectional profiles illustrate that the new technique resolved compartment boundaries clearly.

CONCLUSIONS

Simulations demonstrated that only a single k-space line might be sufficient to reconstruct a 2D image using this new technique. Experimental results showed that this is a promising new technique for fast imaging. Using the information from the simulations and fast imaging parameters published in the literature, it could be predicted that a two-dimensional image could be acquired in about 10 ms. One of the major advantages of this new technique is that it does not require any additional hardware and can be implemented on a conventional scanner with an eight-channel coil.

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.

Constrained source space imaging: application to fast, region-based functional MRI

A new technique called constrained source space imaging is introduced that holds promise for ultrafast acquisition of functional magnetic resonance imaging data. A sparse set of arbitrarily positioned, coarse voxels is first localized using radiofrequency selective excitation, from which magnetization signals are separated using only the spatial sensitivities of multichannel receiver coils, without the need for k-space encoding using imaging gradients. This method permits very fast acquisitions of targeted magnetization without complex or time-consuming image reconstruction techniques. Furthermore, because the data acquisition is performed without imaging gradients, T2* decays can be densely sampled and processed for contrast enhancement to improve functional magnetic resonance imaging data quality. Here, the constrained source space imaging technique is validated in proof-of-concept form, for a simple functional magnetic resonance imaging motor task using a prototype dual-band stimulated echo acquisition mode excitation to image four voxels at TR = 250 ms. Results demonstrate good voxel signal separation and good characterization of hemodynamic responses in primary motor cortices (M1) and supplementary motor areas through T2* fitting of the measured signals. With further refinement, the constrained source space imaging method has potential utility in a priori ROI-based functional magnetic resonance imaging experiments with TR values under 100 ms. Rapid, multivoxel measurements of other sources of MR signal contrast are also possible.

Correlation imaging for multiscan MRI with parallel data acquisition

A new approach to high-speed magnetic resonance imaging (MRI) that uses all the data acquired in a multiscan imaging session is presented. This approach accelerates MRI data acquisition by statistically estimating correlation functions from images with different contrast and/or resolution. In multiscan MRI with parallel data acquisition, the estimation of correlation functions is dynamically improved as imaging proceeds. This allows imaging acceleration factors to be increased in subsequent scans, thereby reducing the total time of a multiscan MRI protocol. Furthermore, the correlation function estimates bring information about both coil sensitivity and anatomical structure into image reconstruction, thereby offering the ability to speed up MRI beyond the parallel imaging acceleration limit posed by a coil array alone. In this study, the feasibility of correlation imaging is demonstrated experimentally using brain and spine imaging protocols. The ability of correlation imaging to achieve an aggregate acceleration factor in excess of the number of coil elements in the phase encoding direction is also demonstrated.

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.