Multiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging

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.

Toward reliable characterization of functional homogeneity in the human brain: preprocessing, scan duration, imaging resolution and computational space

While researchers have extensively characterized functional connectivity between brain regions, the characterization of functional homogeneity within a region of the brain connectome is in early stages of development. Several functional homogeneity measures were proposed previously, among which regional homogeneity (ReHo) was most widely used as a measure to characterize functional homogeneity of resting state fMRI (R-fMRI) signals within a small region (Zang et al., 2004). Despite a burgeoning literature on ReHo in the field of neuroimaging brain disorders, its test-retest (TRT) reliability remains unestablished. Using two sets of public R-fMRI TRT data, we systematically evaluated the ReHo's TRT reliability and further investigated the various factors influencing its reliability and found: 1) nuisance (head motion, white matter, and cerebrospinal fluid) correction of R-fMRI time series can significantly improve the TRT reliability of ReHo while additional removal of global brain signal reduces its reliability, 2) spatial smoothing of R-fMRI time series artificially enhances ReHo intensity and influences its reliability, 3) surface-based R-fMRI computation largely improves the TRT reliability of ReHo, 4) a scan duration of 5 min can achieve reliable estimates of ReHo, and 5) fast sampling rates of R-fMRI dramatically increase the reliability of ReHo. Inspired by these findings and seeking a highly reliable approach to exploratory analysis of the human functional connectome, we established an R-fMRI pipeline to conduct ReHo computations in both 3-dimensions (volume) and 2-dimensions (surface).

The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry

The National Institute of Mental Health strategic plan for advancing psychiatric neuroscience calls for an acceleration of discovery and the delineation of developmental trajectories for risk and resilience across the lifespan. To attain these objectives, sufficiently powered datasets with broad and deep phenotypic characterization, state-of-the-art neuroimaging, and genetic samples must be generated and made openly available to the scientific community. The enhanced Nathan Kline Institute-Rockland Sample (NKI-RS) is a response to this need. NKI-RS is an ongoing, institutionally centered endeavor aimed at creating a large-scale (N > 1000), deeply phenotyped, community-ascertained, lifespan sample (ages 6-85 years old) with advanced neuroimaging and genetics. These data will be publically shared, openly, and prospectively (i.e., on a weekly basis). Herein, we describe the conceptual basis of the NKI-RS, including study design, sampling considerations, and steps to synchronize phenotypic and neuroimaging assessment. Additionally, we describe our process for sharing the data with the scientific community while protecting participant confidentiality, maintaining an adequate database, and certifying data integrity. The pilot phase of the NKI-RS, including challenges in recruiting, characterizing, imaging, and sharing data, is discussed while also explaining how this experience informed the final design of the enhanced NKI-RS. It is our hope that familiarity with the conceptual underpinnings of the enhanced NKI-RS will facilitate harmonization with future data collection efforts aimed at advancing psychiatric neuroscience and nosology.

An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data

Several recent reports in large, independent samples have demonstrated the influence of motion artifact on resting-state functional connectivity MRI (rsfc-MRI). Standard rsfc-MRI preprocessing typically includes regression of confounding signals and band-pass filtering. However, substantial heterogeneity exists in how these techniques are implemented across studies, and no prior study has examined the effect of differing approaches for the control of motion-induced artifacts. To better understand how in-scanner head motion affects rsfc-MRI data, we describe the spatial, temporal, and spectral characteristics of motion artifacts in a sample of 348 adolescents. Analyses utilize a novel approach for describing head motion on a voxelwise basis. Next, we systematically evaluate the efficacy of a range of confound regression and filtering techniques for the control of motion-induced artifacts. Results reveal that the effectiveness of preprocessing procedures on the control of motion is heterogeneous, and that improved preprocessing provides a substantial benefit beyond typical procedures. These results demonstrate that the effect of motion on rsfc-MRI can be substantially attenuated through improved preprocessing procedures, but not completely removed.

Signal fluctuations in fMRI data acquired with 2D-EPI and 3D-EPI at 7 Tesla

Segmented three-dimensional echo planar imaging (3D-EPI) provides higher image signal-to-noise ratio (SNR) than standard single-shot two-dimensional echo planar imaging (2D-EPI), but is more sensitive to physiological noise. The aim of this study was to compare physiological noise removal efficiency in single-shot 2D-EPI and segmented 3D-EPI acquired at 7 Tesla. Two approaches were investigated based either on physiological regressors (PR) derived from cardiac and respiratory phases, or on principal component analysis (PCA) using additional resting-state data. Results show that, prior to physiological noise removal, 2D-EPI data had higher temporal SNR (tSNR), while spatial SNR was higher in 3D-EPI. Blood oxygen level dependent (BOLD) sensitivity was similar for both methods. The PR-based approach allowed characterization of relative contributions from different noise sources, confirming significant increases in physiological noise from 2D to 3D prior to correction. Both physiological noise removal approaches produced significant increases in tSNR and BOLD sensitivity, and these increases were larger for 3D-EPI, resulting in higher BOLD sensitivity in the 3D-EPI than in the 2D-EPI data. The PCA-based approach was the most effective correction method, yielding higher tSNR values for 3D-EPI than for 2D-EPI postcorrection.

The future of the human connectome

The opportunity to explore the human connectome using cutting-edge neuroimaging methods has elicited widespread interest. How far will the field be able to progress in deciphering long-distance connectivity patterns and in relating differences in connectivity to phenotypic characteristics in health and disease? We discuss the daunting nature of this challenge in relation to specific complexities of brain circuitry and known limitations of in vivo imaging methods. We also discuss the excellent prospects for continuing improvements in data acquisition and analysis. Accordingly, we are optimistic that major insights will emerge from human connectomics in the coming decade.

The future of the human connectome

The opportunity to explore the human connectome using cutting-edge neuroimaging methods has elicited widespread interest. How far will the field be able to progress in deciphering long-distance connectivity patterns and in relating differences in connectivity to phenotypic characteristics in health and disease? We discuss the daunting nature of this challenge in relation to specific complexities of brain circuitry and known limitations of in vivo imaging methods. We also discuss the excellent prospects for continuing improvements in data acquisition and analysis. Accordingly, we are optimistic that major insights will emerge from human connectomics in the coming decade.

Ultrafast inverse imaging techniques for fMRI

Inverse imaging (InI) supercharges the sampling rate of traditional functional MRI 10-100 fold at a cost of a moderate reduction in spatial resolution. The technique is inspired by similarities between multi-sensor magnetoencephalography (MEG) and highly parallel radio-frequency (RF) MRI detector arrays. Using presently available 32-channel head coils at 3T, InI can be sampled at 10 Hz and provides about 5-mm cortical spatial resolution with whole-brain coverage. Here we discuss the present applications of InI, as well as potential future challenges and opportunities in further improving its spatiotemporal resolution and sensitivity. InI may become a helpful tool for clinicians and neuroscientists for revealing the complex dynamics of brain functions during task-related and resting states.

The rapid development of high speed, resolution and precision in fMRI

MRI pulse sequences designed to increase the speed and spatial resolution of fMRI have always been a hot topic. Here, we review and chronicle the history behind some of the pulse sequence ideas that have contributed not only to the enhancement of fMRI acquisition but also to diffusion imaging. (i) Partial Fourier EPI allows lengthening echo trains for higher spatial resolution while maintaining optimal TE and BOLD sensitivity. (ii) Inner-volume EPI renamed zoomed-EPI, achieves extremely high spatial resolution and has been applied to fMRI at 7Tesla to resolve cortical layer activity and columnar level fMRI. (iii) An early non-BOLD approach while unsuccessful for fMRI created a diffusion sequence of bipolar pulses called 'twice refocused spin echo' now widely used for high-resolution DTI and HARDI neuronal fiber track imaging. (iv) Multiplexed EPI shortens TR to a few hundred milliseconds, increasing sampling rates and statistical power in fMRI.

A 64-channel 3T array coil for accelerated brain MRI

A 64-channel brain array coil was developed and compared to a 32-channel array constructed with the same coil former geometry to precisely isolate the benefit of the 2-fold increase in array coil elements. The constructed coils were developed for a standard clinical 3T MRI scanner and used a contoured head-shaped curved former around the occipital pole and tapered in at the neck to both improve sensitivity and patient comfort. Additionally, the design is a compact, split-former design intended for robust daily use. Signal-to-noise ratio and noise amplification (G-factor) for parallel imaging were quantitatively evaluated in human imaging and compared to a size and shape-matched 32-channel array coil. For unaccelerated imaging, the 64-channel array provided similar signal-to-noise ratio in the brain center to the 32-channel array and 1.3-fold more signal-to-noise ratio in the brain cortex. Reduced noise amplification during highly parallel imaging of the 64-channel array provided the ability to accelerate at approximately one unit higher at a given noise amplification compared to the sized-matched 32-channel array. For example, with a 4-fold acceleration rate, the central brain and cortical signal-to-noise ratio of the 64-channel array was 1.2- and 1.4-fold higher, respectively, compared to the 32-channel array. The characteristics of the coil are demonstrated in accelerated brain imaging.

Noninvasive mapping of water diffusional exchange in the human brain using filter-exchange imaging

We present the first in vivo application of the filter-exchange imaging protocol for diffusion MRI. The protocol allows noninvasive mapping of the rate of water exchange between microenvironments with different self-diffusivities, such as the intracellular and extracellular spaces in tissue. Since diffusional water exchange across the cell membrane is a fundamental process in human physiology and pathophysiology, clinically feasible and noninvasive imaging of the water exchange rate would offer new means to diagnose disease and monitor treatment response in conditions such as cancer and edema. The in vivo use of filter-exchange imaging was demonstrated by studying the brain of five healthy volunteers and one intracranial tumor (meningioma). Apparent exchange rates in white matter range from 0.8±0.08 s(-1) in the internal capsule, to 1.6±0.11 s(-1) for frontal white matter, indicating that low values are associated with high myelination. Solid tumor displayed values of up to 2.9±0.8 s(-1). In white matter, the apparent exchange rate values suggest intra-axonal exchange times in the order of seconds, confirming the slow exchange assumption in the analysis of diffusion MRI data. We propose that filter-exchange imaging could be used clinically to map the water exchange rate in pathologies. Filter-exchange imaging may also be valuable for evaluating novel therapies targeting the function of aquaporins.

High-resolution functional MRI at 3 T: 3D/2D echo-planar imaging with optimized physiological noise correction

High-resolution functional MRI (fMRI) offers unique possibilities for studying human functional neuroanatomy. Although high-resolution fMRI has proven its potential at 7 T, most fMRI studies are still performed at rather low spatial resolution at 3 T. We optimized and compared single-shot two-dimensional echo-planar imaging (EPI) and multishot three-dimensional EPI high-resolution fMRI protocols. We extended image-based physiological noise correction from two-dimensional EPI to multishot three-dimensional EPI. The functional sensitivity of both acquisition schemes was assessed in a visual fMRI experiment. The physiological noise correction increased the sensitivity significantly, can be easily applied, and requires simple recordings of pulse and respiration only. The combination of three-dimensional EPI with physiological noise correction provides exceptional sensitivity for 1.5 mm high-resolution fMRI at 3 T, increasing the temporal signal-to-noise ratio by more than 25% compared to two-dimensional EPI. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.

Improving diffusion MRI using simultaneous multi-slice echo planar imaging

In diffusion MRI, simultaneous multi-slice single-shot EPI acquisitions have the potential to increase the number of diffusion directions obtained per unit time, allowing more diffusion encoding in high angular resolution diffusion imaging (HARDI) acquisitions. Nonetheless, unaliasing simultaneously acquired, closely spaced slices with parallel imaging methods can be difficult, leading to high g-factor penalties (i.e., lower SNR). The CAIPIRINHA technique was developed to reduce the g-factor in simultaneous multi-slice acquisitions by introducing inter-slice image shifts and thus increase the distance between aliased voxels. Because the CAIPIRINHA technique achieved this by controlling the phase of the RF excitations for each line of k-space, it is not directly applicable to single-shot EPI employed in conventional diffusion imaging. We adopt a recent gradient encoding method, which we termed "blipped-CAIPI", to create the image shifts needed to apply CAIPIRINHA to EPI. Here, we use pseudo-multiple replica SNR and bootstrapping metrics to assess the performance of the blipped-CAIPI method in 3× simultaneous multi-slice diffusion studies. Further, we introduce a novel image reconstruction method to reduce detrimental ghosting artifacts in these acquisitions. We show that data acquisition times for Q-ball and diffusion spectrum imaging (DSI) can be reduced 3-fold with a minor loss in SNR and with similar diffusion results compared to conventional acquisitions.

Whole brain, high resolution spin-echo resting state fMRI using PINS multiplexing at 7 T

This article demonstrates the application of spin-echo EPI for resting state fMRI at 7 T. A short repetition time of 1860 ms was made possible by the use of slice multiplexing which permitted whole brain coverage at high spatial resolution (84 slices of 1.6 mm thickness). Radiofrequency power deposition was kept within regulatory limits by use of the power independent of number of slices (PINS) technique. A high in-plane spatial resolution of 1.5 mm was obtained, while image distortion was ameliorated by the use of in-plane parallel imaging techniques. Data from six subjects were obtained with a measurement time of just over 15 min per subject. A group level independent component (IC) analysis revealed 24 non-artefactual resting state networks, including those commonly found in standard acquisitions, as well as plausible networks for a broad range of regions. Signal was measured from regions commonly rendered inaccessible due to signal voids in gradient echo acquisitions. Dual regression was used to obtain spatial IC maps at the single subject level revealing exquisite localisation to grey matter that is consistent with a high degree of T(2)-weighting in the acquisition sequence. This technique hence holds great promise for both resting state and activation studies at 7 T.

Functional MRI: A confluence of fortunate circumstances

Functional MRI has existed for about twenty years and by almost all measures has been incredibly successful. What are the reasons behind this success? In this review, eight extremely fortunate circumstances came together to produce BOLD based fMRI as we know it today. They are as follows: 1. The MRI signal, 2. The MRI relaxation rates, 3. The oxygen-dependent magnetic susceptibility of blood, 4. Neuronal-hemodynamic coupling, 5. The spatial scale of brain activation, 6. The prevalence of scanners able to perform echo planar imaging (EPI), 7. The parallel development of computing power, and 8. The very large group of neuroscientists who, pre-1991, were perfectly poised, willing, and able to exploit the capability of fMRI. These circumstances are discussed in detail. The desired goal of this review is primarily to convey the field of fMRI from the perspective of what was critically important before, during and after its inception and how things might have been if these circumstances would have been different. While there are many instances where circumstances could have been better, it is clear that they worked out extremely well, as the field of fMRI, a major aspect of functional neuroimaging today, is thriving.

An introduction to model-independent diffusion magnetic resonance imaging

q-Space-based techniques such as diffusion spectrum imaging, q-ball imaging, and their variations have been used extensively in research for their desired capability to delineate complex neuronal architectures such as multiple fiber crossings in each of the image voxels. The purpose of this article was to provide an introduction to the q-space formalism and the principles of basic q-space techniques together with the discussion on the advantages as well as challenges in translating these techniques into the clinical environment. A review of the currently used q-space-based protocols in clinical research is also provided.

Twenty years of functional MRI: the science and the stories

Since its inception over twenty years ago, the field of functional magnetic resonance imaging (fMRI) has grown in usage, sophistication, range of applications, and impact. After twenty years, it's useful to briefly look back as well as forward - to size up just how far we have come and speculate just how far we may go. This is an introduction to the special issue of "Twenty years of fMRI: the science and the stories." The one-hundred and three papers in this special issue highlight the major methodological developments and controversies of fMRI from a first person perspective over the past twenty years. The growth of this field is not just fascinating from a science and technology perspective, but also from a human perspective. Most who were fortunate enough to be part of this effort at the beginning, as well as those who jumped in along the way have their fair share of interesting stories consisting of top rate science as well as intense thought and effort, good or bad fortune, and some claim to a contribution. These stories are in the following papers, written by the current leaders in the field and the innovators throughout the twenty year history. The categories, designed to cover every aspect of the emergence and development of fMRI, include: pre-fMRI; the first BOLD brain activation results; developments in pulse sequences, imaging methods, and hardware for fMRI; methodological developments, issues, and mechanisms; new paradigm designs; education; and the future. Within this issue, we have a collage of overlapping, complementary, yet sometimes contradictory accounts of what happened during the breathtakingly diverse and intense development of this still growing field over the past twenty years.

Single-shot echo-planar imaging with Nyquist ghost compensation: interleaved dual echo with acceleration (IDEA) echo-planar imaging (EPI)

Echo planar imaging (EPI) is most commonly used for blood oxygen level-dependent fMRI, owing to its sensitivity and acquisition speed. A major problem with EPI is Nyquist (N/2) ghosting, most notably at high field. EPI data are acquired under an oscillating readout gradient and hence vulnerable to gradient imperfections such as eddy current delays and off-resonance effects, as these cause inconsistencies between odd and even k-space lines after time reversal. We propose a straightforward and pragmatic method herein termed "interleaved dual echo with acceleration (IDEA) EPI": two k-spaces (echoes) are acquired under the positive and negative readout lobes, respectively, by performing phase encoding blips only before alternate readout gradients. From these two k-spaces, two almost entirely ghost free images per shot can be constructed, without need for phase correction. The doubled echo train length can be compensated by parallel imaging and/or partial Fourier acquisition. The two k-spaces can either be complex averaged during reconstruction, which results in near-perfect cancellation of residual phase errors, or reconstructed into separate images. We demonstrate the efficacy of IDEA EPI and show phantom and in vivo images at both 3 T and 7 T.

The future of acquisition speed, coverage, sensitivity, and resolution

Two decades of technology development has continually improved the image quality, spatial-temporal resolution, and sensitivity of the fMRI acquisition. In this article, I assess our current acquisition needs, briefly examine the technological breakthroughs that have benefited fMRI in the past, and look at some promising technologies that are currently under development to try to envision what the fMRI acquisition protocol of the future might look like.

Enhancement of temporal resolution and BOLD sensitivity in real-time fMRI using multi-slab echo-volumar imaging

In this study, a new approach to high-speed fMRI using multi-slab echo-volumar imaging (EVI) is developed that minimizes geometrical image distortion and spatial blurring, and enables nonaliased sampling of physiological signal fluctuation to increase BOLD sensitivity compared to conventional echo-planar imaging (EPI). Real-time fMRI using whole brain 4-slab EVI with 286 ms temporal resolution (4mm isotropic voxel size) and partial brain 2-slab EVI with 136 ms temporal resolution (4×4×6 mm(3) voxel size) was performed on a clinical 3 Tesla MRI scanner equipped with 12-channel head coil. Four-slab EVI of visual and motor tasks significantly increased mean (visual: 96%, motor: 66%) and maximum t-score (visual: 263%, motor: 124%) and mean (visual: 59%, motor: 131%) and maximum (visual: 29%, motor: 67%) BOLD signal amplitude compared with EPI. Time domain moving average filtering (2s width) to suppress physiological noise from cardiac and respiratory fluctuations further improved mean (visual: 196%, motor: 140%) and maximum (visual: 384%, motor: 200%) t-scores and increased extents of activation (visual: 73%, motor: 70%) compared to EPI. Similar sensitivity enhancement, which is attributed to high sampling rate at only moderately reduced temporal signal-to-noise ratio (mean: -52%) and longer sampling of the BOLD effect in the echo-time domain compared to EPI, was measured in auditory cortex. Two-slab EVI further improved temporal resolution for measuring task-related activation and enabled mapping of five major resting state networks (RSNs) in individual subjects in 5 min scans. The bilateral sensorimotor, the default mode and the occipital RSNs were detectable in time frames as short as 75 s. In conclusion, the high sampling rate of real-time multi-slab EVI significantly improves sensitivity for studying the temporal dynamics of hemodynamic responses and for characterizing functional networks at high field strength in short measurement times.

Within-subject joint independent component analysis of simultaneous fMRI/ERP in an auditory oddball paradigm

The integration of event-related potential (ERP) and functional magnetic resonance imaging (fMRI) can contribute to characterizing neural networks with high temporal and spatial resolution. This research aimed to determine the sensitivity and limitations of applying joint independent component analysis (jICA) within-subjects, for ERP and fMRI data collected simultaneously in a parametric auditory frequency oddball paradigm. In a group of 20 subjects, an increase in ERP peak amplitude ranging 1-8 μV in the time window of the P300 (350-700 ms), and a correlated increase in fMRI signal in a network of regions including the right superior temporal and supramarginal gyri, was observed with the increase in deviant frequency difference. JICA of the same ERP and fMRI group data revealed activity in a similar network, albeit with stronger amplitude and larger extent. In addition, activity in the left pre- and post-central gyri, likely associated with right hand somato-motor response, was observed only with the jICA approach. Within-subject, the jICA approach revealed significantly stronger and more extensive activity in the brain regions associated with the auditory P300 than the P300 linear regression analysis. The results suggest that with the incorporation of spatial and temporal information from both imaging modalities, jICA may be a more sensitive method for extracting common sources of activity between ERP and fMRI.

The Human Connectome Project: a data acquisition perspective

The Human Connectome Project (HCP) is an ambitious 5-year effort to characterize brain connectivity and function and their variability in healthy adults. This review summarizes the data acquisition plans being implemented by a consortium of HCP investigators who will study a population of 1200 subjects (twins and their non-twin siblings) using multiple imaging modalities along with extensive behavioral and genetic data. The imaging modalities will include diffusion imaging (dMRI), resting-state fMRI (R-fMRI), task-evoked fMRI (T-fMRI), T1- and T2-weighted MRI for structural and myelin mapping, plus combined magnetoencephalography and electroencephalography (MEG/EEG). Given the importance of obtaining the best possible data quality, we discuss the efforts underway during the first two years of the grant (Phase I) to refine and optimize many aspects of HCP data acquisition, including a new 7T scanner, a customized 3T scanner, and improved MR pulse sequences.

The road to functional imaging and ultrahigh fields

The Center for Magnetic Resonance (CMRR) at the University of Minnesota was one of the laboratories where the work that simultaneously and independently introduced functional magnetic resonance imaging (fMRI) of human brain activity was carried out. However, unlike other laboratories pursuing fMRI at the time, our work was performed at 4T magnetic field and coincided with the effort to push human magnetic resonance imaging to field strength significantly beyond 1.5T which was the high-end standard of the time. The human fMRI experiments performed in CMRR were planned between two colleagues who had known each other and had worked together previously in Bell Laboratories, namely Seiji Ogawa and myself, immediately after the Blood Oxygenation Level Dependent (BOLD) contrast was developed by Seiji. We were waiting for our first human system, a 4T system, to arrive in order to attempt at imaging brain activity in the human brain and these were the first experiments we performed on the 4T instrument in CMRR when it became marginally operational. This was a prelude to a subsequent systematic push we initiated for exploiting higher magnetic fields to improve the accuracy and sensitivity of fMRI maps, first going to 9.4T for animal model studies and subsequently developing a 7T human system for the first time. Steady improvements in high field instrumentation and ever expanding armamentarium of image acquisition and engineering solutions to challenges posed by ultrahigh fields have brought fMRI to submillimeter resolution in the whole brain at 7T, the scale necessary to reach cortical columns and laminar differentiation in the whole brain. The solutions that emerged in response to technological challenges posed by 7T also propagated and continues to propagate to lower field clinical systems, a major advantage of the ultrahigh fields effort that is underappreciated. Further improvements at 7T are inevitable. Further translation of these improvements to lower field clinical systems to achieve new capabilities and to magnetic fields significantly higher than 7T to enable human imaging is inescapable.

Ultrahigh field systems and applications at 7 T and beyond: progress, pitfalls, and potential

About 150 researchers around the world convened at the Chateau Lake Louise on February 20-23, 2011 to present and discuss the latest research in human and animal imaging and spectroscopy at field strengths of 7 T or above (termed ultrahigh field) at the third ISMRM-sponsored high field workshop. The clear overall message from the workshop presentations and discussion is that ultrahigh field imaging is gaining momentum with regard to new clinically relevant findings, anatomic and functional MRI results, susceptibility contrast advancements, solutions to high field-related image quality challenges, and to generally push the limits of resolution and speed of high field imaging. This meeting report is organized in a manner reflecting the meeting organization itself, covering the seven sessions that were approximately titled: (1) high field overview from head to body to spectroscopy; (2) susceptibility imaging; (3) proffered session on susceptibility, ultrafast imaging, unique contrast at 7 T, and angiography; (4) neuroscience applications; (5) proffered session on coils, shimming, parallel imaging, diffusion tensor imaging, and MRI-PET fusion; (6) high field animal imaging and spectroscopy, as well as a vendor overview, and (7) Cutting edge technology at 7 T.

The future of FMRI connectivity

"FMRI connectivity" encompasses many areas of research, including resting-state networks, biophysical modelling of task-FMRI data and bottom-up simulation of multiple individual neurons interacting with each other. In this brief paper I discuss several outstanding areas that I believe will see exciting developments in the next few years, in particular concentrating on how I think the currently separate approaches will increasingly need to take advantage of each others' respective complementarities.

Changes in regional cerebral blood flow and functional connectivity in the cholinergic pathway associated with cognitive performance in subjects with mild Alzheimer's disease after 12-week donepezil treatment

Acetylcholinesterase inhibitors (AChEIs), such as donepezil, have been shown to improve cognition in mild to moderate Alzheimer's disease (AD) patients. In this paper, our goal is to determine the relationship between altered cerebral blood flow (CBF) and intrinsic functional network connectivity changes in mild AD patients before and after 12-week donepezil treatment. An integrative neuroimaging approach was employed by combining pseudocontinuous arterial spin labeling (pCASL) MRI and resting-state functional MRI (R-fMRI) methods to determine the changes in CBF and functional connectivity (FC) in the cholinergic pathway. Linear regression analyses determined the correlations of the regional CBF alterations and functional connectivity changes with cognitive responses. These were measured with the Mini-Mental Status Examination (MMSE) scores and Alzheimer's disease Assessment Scale-Cognitive subscale (ADAS-cog) scores. Our results show that the regional CBF in mild AD subjects after donepezil treatment was significantly increased in the middle cingulate cortex (MCC) and posterior cingulate cortex (PCC), which are the neural substrates of the medial cholinergic pathway. In both brain regions, the baseline CBF and its changes after treatment were significantly correlated with the behavioral changes in ADAS-cog scores. The intrinsic FC was significantly enhanced in the medial cholinergic pathway network in the brain areas of the parahippocampal, temporal, parietal and prefrontal cortices. Finally, the FC changes in the medial prefrontal areas demonstrated an association with the CBF level in the MCC and the PCC, and also were correlated with ADAS-cog score changes. These findings indicate that regional CBF and FC network changes in the medial cholinergic pathway were associated with cognitive performance. It also is suggested that the combined pCASL-MRI and R-fMRI methods could be used to detect regional CBF and FC changes when using drug treatments in mild AD subjects.

A review of the development of Vascular-Space-Occupancy (VASO) fMRI

Vascular-Space-Occupancy (VASO) fMRI is a non-invasive technique to detect brain activation based on changes in Cerebral Blood Volume (CBV), as opposed to conventional BOLD fMRI, which is based on changes in blood oxygenation. This technique takes advantage of the T1 difference between blood and surrounding tissue, and uses an inversion recovery pulse sequence to null blood signal while maintaining part of the tissue signal. The VASO signal intensity can thus be considered proportional to 1-CBV. When neural activation causes CBV to increase, the VASO signal will show a decrease, allowing the detection of activated regions in the brain. Activation-induced changes in VASO signal, ∆S/S, are in the order of -1%. Absolute quantification of ∆CBV requires additional assumptions on baseline CBV and water contents of the parenchyma and blood. The first VASO experiment was conducted approximately 10 years ago. The original goal of nulling the blood signal was to isolate and measure extravascular BOLD effects, thus a long TE of 50 ms was used in the inversion recovery experiment. Instead of a positive signal change, a slight decrease in signal was observed, which became more pronounced when TE was shortened to 10 ms. These findings led to the hypothesis of a CBV signal mechanism and the development of VASO fMRI. Since its discovery, VASO has been validated by comparison with MION-CBV studies in animals and has been used in humans and animals to understand metabolic and hemodynamic changes during brain activation and physiologic challenges. With recent development of more sensitive VASO acquisitions, the availability of arterial-based VASO sequences, and improvement in spatial coverage, this technique is finding its place in neuroscience and clinical studies.

Resting-state FMRI single subject cortical parcellation based on region growing

We propose a new method to parcellate the cerebral cortex based on spatial dependancy in the fluctuations observed with functional Magnetic Resonance Imaging (fMRI) during rest. Our surface-based approach uses a region growing method. In contrast to previous methods, locally stable seed points are identified on the cortical surface and these are grown into a (relatively large 1000 to 5000) number of spatially contiguous regions on both hemispheres. Spatially constrained hierarchical clustering is then used to further combine these regions in a hierarchical tree. Using short-TR resting state fMRI data, this approach allows a subject specific parcellation of the cortex into anatomically plausible subregions, identified with high scan-to-scan reproducibility and with borders that delineate clear changes in functional connectivity.

Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI

A central challenge in the fMRI based study of functional connectivity is distinguishing neuronally related signal fluctuations from the effects of motion, physiology, and other nuisance sources. Conventional techniques for removing nuisance effects include modeling of noise time courses based on external measurements followed by temporal filtering. These techniques have limited effectiveness. Previous studies have shown using multi-echo fMRI that neuronally related fluctuations are Blood Oxygen Level Dependent (BOLD) signals that can be characterized in terms of changes in R(2)* and initial signal intensity (S(0)) based on the analysis of echo-time (TE) dependence. We hypothesized that if TE-dependence could be used to differentiate BOLD and non-BOLD signals, non-BOLD signal could be removed to denoise data without conventional noise modeling. To test this hypothesis, whole brain multi-echo data were acquired at 3 TEs and decomposed with Independent Components Analysis (ICA) after spatially concatenating data across space and TE. Components were analyzed for the degree to which their signal changes fit models for R(2)* and S(0) change, and summary scores were developed to characterize each component as BOLD-like or not BOLD-like. These scores clearly differentiated BOLD-like "functional network" components from non BOLD-like components related to motion, pulsatility, and other nuisance effects. Using non BOLD-like component time courses as noise regressors dramatically improved seed-based correlation mapping by reducing the effects of high and low frequency non-BOLD fluctuations. A comparison with seed-based correlation mapping using conventional noise regressors demonstrated the superiority of the proposed technique for both individual and group level seed-based connectivity analysis, especially in mapping subcortical-cortical connectivity. The differentiation of BOLD and non-BOLD components based on TE-dependence was highly robust, which allowed for the identification of BOLD-like components and the removal of non BOLD-like components to be implemented as a fully automated procedure.

Single shot concentric shells trajectories for ultra fast fMRI

MR-encephalography is a technique that allows real-time observation of functional changes in the brain with a time-resolution of 100 ms. The high sampling rate is enabled by the use of undersampled image acquisition with regularized reconstruction. The article describes a novel imaging method for fast three-dimensional-MR-encephalography whole brain coverage based on undersampled, single-shot concentric shells trajectories and the use of multiple small receiver coils. The technique allows the observation of changes in blood oxygenation level dependent signal as a measure of brain physiology at very high temporal resolution.

Rapid full-brain fMRI with an accelerated multi shot 3D EPI sequence using both UNFOLD and GRAPPA

The desire to understand complex mental processes using functional MRI drives development of imaging techniques that scan the whole human brain at a high spatial and temporal resolution. In this work, an accelerated multishot three-dimensional echo-planar imaging sequence is proposed to increase the temporal resolution of these studies. A combination of two modern acceleration techniques, UNFOLD and GRAPPA is used in the secondary phase encoding direction to reduce the scan time effectively. The sequence (repetition time of 1.02 s) was compared with standard two-dimensional echo-planar imaging (3 s) and multishot three-dimensional echo-planar imaging (3 s) sequences with both block design and event-related functional MRI paradigms. With the same experimental setup and imaging time, the temporal resolution improvement with our sequence yields similar activation regions in the block design functional MRI paradigm with slightly increased t-scores. Moreover, additional information on the timing of rapid dynamic changes was extracted from accelerated images for the case of the event related complex mental paradigm.

High-field FMRI for human applications: an overview of spatial resolution and signal specificity

In the last decade, dozens of 7 Tesla scanners have been purchased or installed around the world, while 3 Tesla systems have become a standard. This increased interest in higher field strengths is driven by a demonstrated advantage of high fields for available signal-to-noise ratio (SNR) in the magnetic resonance signal. Functional imaging studies have additional advantages of increases in both the contrast and the spatial specificity of the susceptibility based BOLD signal. One use of this resultant increase in the contrast to noise ratio (CNR) for functional MRI studies at high field is increased image resolution. However, there are many factors to consider in predicting exactly what kind of resolution gains might be made at high fields, and what the opportunity costs might be. The first part of this article discusses both hardware and image quality considerations for higher resolution functional imaging. The second part draws distinctions between image resolution, spatial specificity, and functional specificity of the fMRI signals that can be acquired at high fields, suggesting practical limitations for attainable resolutions of fMRI experiments at a given field, given the current state of the art in imaging techniques. Finally, practical resolution limitations and pulse sequence options for studies in human subjects are considered.

Advances in High-Field BOLD fMRI

This review article examines the current state of BOLD fMRI at a high magnetic field strength of 7 Tesla. The following aspects are covered: a short description of the BOLD contrast, spatial and temporal resolution, BOLD sensitivity, localization and spatial specificity, technical challenges as well as an outlook on future developments are given. It is shown that the main technical challenges of performing BOLD fMRI at high magnetic field strengths-namely development of array coils, imaging sequences and parallel imaging reconstruction-have been solved successfully. The combination of these developments has lead to the availability of high-resolution BOLD fMRI protocols that are able to cover the whole brain with a repetition time (TR) shorter than 3 s. The structural information available from these high-resolution fMRI images itself is already very detailed, which helps to co-localize structure and function. Potential future applications include whole-brain connectivity analysis on a laminar resolution and single subject examinations.

Multi-echo acquisition

The rapid development of fMRI was paralleled early on by the adaptation of MR spectroscopic imaging (MRSI) methods to quantify water relaxation changes during brain activation. This review describes the evolution of multi-echo acquisition from high-speed MRSI to multi-echo EPI and beyond. It highlights milestones in the development of multi-echo acquisition methods, such as the discovery of considerable gains in fMRI sensitivity when combining echo images, advances in quantification of the BOLD effect using analytical biophysical modeling and interleaved multi-region shimming. The review conveys the insight gained from combining fMRI and MRSI methods and concludes with recent trends in ultra-fast fMRI, which will significantly increase temporal resolution of multi-echo acquisition.

Reconstructing visual experiences from brain activity evoked by natural movies

Quantitative modeling of human brain activity can provide crucial insights about cortical representations [1, 2] and can form the basis for brain decoding devices [3-5]. Recent functional magnetic resonance imaging (fMRI) studies have modeled brain activity elicited by static visual patterns and have reconstructed these patterns from brain activity [6-8]. However, blood oxygen level-dependent (BOLD) signals measured via fMRI are very slow [9], so it has been difficult to model brain activity elicited by dynamic stimuli such as natural movies. Here we present a new motion-energy [10, 11] encoding model that largely overcomes this limitation. The model describes fast visual information and slow hemodynamics by separate components. We recorded BOLD signals in occipitotemporal visual cortex of human subjects who watched natural movies and fit the model separately to individual voxels. Visualization of the fit models reveals how early visual areas represent the information in movies. To demonstrate the power of our approach, we also constructed a Bayesian decoder [8] by combining estimated encoding models with a sampled natural movie prior. The decoder provides remarkable reconstructions of the viewed movies. These results demonstrate that dynamic brain activity measured under naturalistic conditions can be decoded using current fMRI technology.

Human connectomics

Recent advances in non-invasive neuroimaging have enabled the measurement of connections between distant regions in the living human brain, thus opening up a new field of research: Human connectomics. Different imaging modalities allow the mapping of structural connections (axonal fibre tracts) as well as functional connections (correlations in time series), and individual variations in these connections may be related to individual variations in behaviour and cognition. Connectivity analysis has already led to a number of new insights about brain organization. For example, segregated brain regions may be identified by their unique patterns of connectivity, structural and functional connectivity may be compared to elucidate how dynamic interactions arise from the anatomical substrate, and the architecture of large-scale networks connecting sets of brain regions may be analysed in detail. The combined analysis of structural and functional networks has begun to reveal components or modules with distinct patterns of connections that become engaged in different cognitive tasks. Collectively, advances in human connectomics open up the possibility of studying how brain connections mediate regional brain function and thence behaviour.

Tractography: where do we go from here?

Diffusion tractography offers enormous potential for the study of human brain anatomy. However, as a method to study brain connectivity, tractography suffers from limitations, as it is indirect, inaccurate, and difficult to quantify. Despite these limitations, appropriate use of tractography can be a powerful means to address certain questions. In addition, while some of tractography's limitations are fundamental, others could be alleviated by methodological and technological advances. This article provides an overview of diffusion magnetic resonance tractography methods with a focus on how future advances might address challenges in measuring brain connectivity. Parts of this review are somewhat provocative, in the hope that they may trigger discussions possibly lacking in a field where the apparent simplicity of the methods (compared to their functional magnetic resonance imaging counterparts) can hide some fundamental issues that ultimately hinder the interpretation of findings, and cast doubt as to what tractography can really teach us about human brain anatomy.

Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty

Simultaneous multislice Echo Planar Imaging (EPI) acquisition using parallel imaging can decrease the acquisition time for diffusion imaging and allow full-brain, high-resolution functional MRI (fMRI) acquisitions at a reduced repetition time (TR). However, the unaliasing of simultaneously acquired, closely spaced slices can be difficult, leading to a high g-factor penalty. We introduce a method to create interslice image shifts in the phase encoding direction to increase the distance between aliasing pixels. The shift between the slices is induced using sign- and amplitude-modulated slice-select gradient blips simultaneous with the EPI phase encoding blips. This achieves the desired shifts but avoids an undesired "tilted voxel" blurring artifact associated with previous methods. We validate the method in 3× slice-accelerated spin-echo and gradient-echo EPI at 3 T and 7 T using 32-channel radio frequency (RF) coil brain arrays. The Monte-Carlo simulated average g-factor penalty of the 3-fold slice-accelerated acquisition with interslice shifts is <1% at 3 T (compared with 32% without slice shift). Combining 3× slice acceleration with 2× inplane acceleration, the g-factor penalty becomes 19% at 3 T and 10% at 7 T (compared with 41% and 23% without slice shift). We demonstrate the potential of the method for accelerating diffusion imaging by comparing the fiber orientation uncertainty, where the 3-fold faster acquisition showed no noticeable degradation.

Prospective and retrospective motion correction in diffusion magnetic resonance imaging of the human brain

Diffusion-weighting in magnetic resonance imaging (MRI) increases the sensitivity to molecular Brownian motion, providing insight in the micro-environment of the underlying tissue types and structures. At the same time, the diffusion weighting renders the scans sensitive to other motion, including bulk patient motion. Typically, several image volumes are needed to extract diffusion information, inducing also inter-volume motion susceptibility. Bulk motion is more likely during long acquisitions, as they appear in diffusion tensor, diffusion spectrum and q-ball imaging. Image registration methods are successfully used to correct for bulk motion in other MRI time series, but their performance in diffusion-weighted MRI is limited since diffusion weighting introduces strong signal and contrast changes between serial image volumes. In this work, we combine the capability of free induction decay (FID) navigators, providing information on object motion, with image registration methodology to prospectively--or optionally retrospectively--correct for motion in diffusion imaging of the human brain. Eight healthy subjects were instructed to perform small-scale voluntary head motion during clinical diffusion tensor imaging acquisitions. The implemented motion detection based on FID navigator signals is processed in real-time and provided an excellent detection performance of voluntary motion patterns even at a sub-millimetre scale (sensitivity≥92%, specificity>98%). Motion detection triggered an additional image volume acquisition with b=0 s/mm2 which was subsequently co-registered to a reference volume. In the prospective correction scenario, the calculated motion-parameters were applied to perform a real-time update of the gradient coordinate system to correct for the head movement. Quantitative analysis revealed that the motion correction implementation is capable to correct head motion in diffusion-weighted MRI to a level comparable to scans without voluntary head motion. The results indicate the potential of this method to improve image quality in diffusion-weighted MRI, a concept that can also be applied when highest diffusion weightings are performed.

Fractal analysis of spontaneous fluctuations of the BOLD signal in rat brain

Analysis of task-evoked fMRI data ignores low frequency fluctuations (LFF) of the resting-state the BOLD signal, yet LFF of the spontaneous BOLD signal is crucial for analysis of resting-state connectivity maps. We characterized the LFF of resting-state BOLD signal at 11.7T in α-chloralose and domitor anesthetized rat brain and modeled the spontaneous signal as a scale-free (i.e., fractal) distribution of amplitude power (|A|²) across a frequency range (f) compatible with an |A(f)|² ∝ 1/f(β) model where β is the scaling exponent (or spectral index). We compared β values from somatosensory forelimb area (S1FL), cingulate cortex (CG), and caudate putamen (CPu). With α-chloralose, S1FL and CG β values dropped from ~0.7 at in vivo to ~0.1 at post mortem (p<0.0002), whereas CPu β values dropped from ~0.3 at in vivo to ~0.1 at post mortem (p<0.002). With domitor, cortical (S1FL, CG) β values were slightly higher than with α-chloralose, while subcortical (CPu) β values were similar with α-chloralose. Although cortical and subcortical β values with both anesthetics were significantly different in vivo (p<0.002), at post mortem β values in these regions were not significantly different and approached zero (i.e., range of -0.1 to 0.2). Since a water phantom devoid of susceptibility gradients had a β value of zero (i.e., random), we conclude that deoxyhemoglobin present in voxels post-sacrifice still impacts tissue water diffusion. These results suggest that in the anesthetized rat brain the LFF of BOLD signal at 11.7T follow a general 1/f(β) model of fractality where β is a variable responding to physiology. We describe typical experimental pitfalls which may elude detection of fractality in the resting-state BOLD signal.

Informatics and data mining tools and strategies for the human connectome project

The Human Connectome Project (HCP) is a major endeavor that will acquire and analyze connectivity data plus other neuroimaging, behavioral, and genetic data from 1,200 healthy adults. It will serve as a key resource for the neuroscience research community, enabling discoveries of how the brain is wired and how it functions in different individuals. To fulfill its potential, the HCP consortium is developing an informatics platform that will handle: (1) storage of primary and processed data, (2) systematic processing and analysis of the data, (3) open-access data-sharing, and (4) mining and exploration of the data. This informatics platform will include two primary components. ConnectomeDB will provide database services for storing and distributing the data, as well as data analysis pipelines. Connectome Workbench will provide visualization and exploration capabilities. The platform will be based on standard data formats and provide an open set of application programming interfaces (APIs) that will facilitate broad utilization of the data and integration of HCP services into a variety of external applications. Primary and processed data generated by the HCP will be openly shared with the scientific community, and the informatics platform will be available under an open source license. This paper describes the HCP informatics platform as currently envisioned and places it into the context of the overall HCP vision and agenda.