Steen Moeller

Multiplexed Echo Planar Imaging for Sub-Second Whole Brain FMRI and Fast Diffusion Imaging

Echo planar imaging (EPI) is an MRI technique of particular value to neuroscience, with its use for virtually all functional MRI (fMRI) and diffusion imaging of fiber connections in the human brain. EPI generates a single 2D image in a fraction of a second; however, it requires 2–3 seconds to acquire multi-slice whole brain coverage for fMRI and even longer for diffusion imaging. Here we report on a large reduction in EPI whole brain scan time at 3 and 7 Tesla, without significantly sacrificing spatial resolution, and while gaining functional sensitivity. The multiplexed-EPI (M-EPI) pulse sequence combines two forms of multiplexing: temporal multiplexing (m) utilizing simultaneous echo refocused (SIR) EPI and spatial multiplexing (n) with multibanded RF pulses (MB) to achieve m×n images in an EPI echo train instead of the normal single image. This resulted in an unprecedented reduction in EPI scan time for whole brain fMRI performed at 3 Tesla, permitting TRs of 400 ms and 800 ms compared to a more conventional 2.5 sec TR, and 2–4 times reductions in scan time for HARDI imaging of neuronal fibertracks. The simultaneous SE refocusing of SIR imaging at 7 Tesla advantageously reduced SAR by using fewer RF refocusing pulses and by shifting fat signal out of the image plane so that fat suppression pulses were not required. In preliminary studies of resting state functional networks identified through independent component analysis, the 6-fold higher sampling rate increased the peak functional sensitivity by 60%. The novel M-EPI pulse sequence resulted in a significantly increased temporal resolution for whole brain fMRI, and as such, this new methodology can be used for studying non-stationarity in networks and generally for expanding and enriching the functional information.

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.

Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI†

Parallel imaging in the form of multiband radiofrequency excitation, together with reduced k‐space coverage in the phase‐encode direction, was applied to human gradient echo functional MRI at 7 T for increased volumetric coverage and concurrent high spatial and temporal resolution. Echo planar imaging with simultaneous acquisition of four coronal slices separated by 44mm and simultaneous 4‐fold phase‐encoding undersampling, resulting in 16‐fold acceleration and up to 16‐fold maximal aliasing, was investigated. Task/stimulus‐induced signal changes and temporal signal behavior under basal conditions were comparable for multiband and standard single‐band excitation and longer pulse repetition times. Robust, whole‐brain functional mapping at 7 T, with 2 × 2 × 2mm3 (pulse repetition time 1.25 sec) and 1 × 1 × 2mm3 (pulse repetition time 1.5 sec) resolutions, covering fields of view of 256 × 256 × 176mm3 and 192 × 172 × 176mm3, respectively, was demonstrated with current gradient performance. Magn Reson Med 63:1144–1153, 2010.

Temporally-independent functional modes of spontaneous brain activity

Resting-state functional magnetic resonance imaging has become a powerful tool for the study of functional networks in the brain. Even “at rest,” the brains different functional networks spontaneously fluctuate in their activity level; each networks spatial extent can therefore be mapped by finding temporal correlations between its different subregions. Current correlation-based approaches measure the average functional connectivity between regions, but this average is less meaningful for regions that are part of multiple networks; one ideally wants a network model that explicitly allows overlap, for example, allowing a regions activity pattern to reflect one networks activity some of the time, and another networks activity at other times. However, even those approaches that do allow overlap have often maximized mutual spatial independence, which may be suboptimal if distinct networks have significant overlap. In this work, we identify functionally distinct networks by virtue of their temporal independence, taking advantage of the additional temporal richness available via improvements in functional magnetic resonance imaging sampling rate. We identify multiple “temporal functional modes,” including several that subdivide the default-mode network (and the regions anticorrelated with it) into several functionally distinct, spatially overlapping, networks, each with its own pattern of correlations and anticorrelations. These functionally distinct modes of spontaneous brain activity are, in general, quite different from resting-state networks previously reported, and may have greater biological interpretability.

Resting-state fMRI in the Human Connectome Project

Resting-state functional magnetic resonance imaging (rfMRI) allows one to study functional connectivity in the brain by acquiring fMRI data while subjects lie inactive in the MRI scanner, and taking advantage of the fact that functionally related brain regions spontaneously co-activate. rfMRI is one of the two primary data modalities being acquired for the Human Connectome Project (the other being diffusion MRI). A key objective is to generate a detailed in vivo mapping of functional connectivity in a large cohort of healthy adults (over 1000 subjects), and to make these datasets freely available for use by the neuroimaging community. In each subject we acquire a total of 1h of whole-brain rfMRI data at 3 T, with a spatial resolution of 2×2×2 mm and a temporal resolution of 0.7s, capitalizing on recent developments in slice-accelerated echo-planar imaging. We will also scan a subset of the cohort at higher field strength and resolution. In this paper we outline the work behind, and rationale for, decisions taken regarding the rfMRI data acquisition protocol and pre-processing pipelines, and present some initial results showing data quality and example functional connectivity analyses.

B1 destructive interferences and spatial phase patterns at 7 T with a head transceiver array coil

RF behavior in the human head becomes complex at ultrahigh magnetic fields. A bright center and a weak periphery are observed in images obtained with volume coils, while surface coils provide strong signal in the periphery. Intensity patterns reported with volume coils are often loosely referred to as “dielectric resonances,” while modeling studies ascribe them to superposition of traveling waves greatly dampened in lossy brain tissues, raising questions regarding the usage of this term. Here we address this question experimentally, taking full advantage of a transceiver coil array that was used in volume transmit mode, multiple receiver mode, or single transmit surface coil mode. We demonstrate with an appropriately conductive sphere phantom that destructive interferences are responsible for a weak B1 in the periphery, without a significant standing wave pattern. The relative spatial phase of receive and transmit B1 proved remarkably similar for the different coil elements, although with opposite rotational direction. Additional simulation data closely matched our phantom results. In the human brain the phase patterns were more complex but still exhibited similarities between coil elements. Our results suggest that measuring spatial B1 phase could help, within an MR session, to perform RF shimming in order to obtain more homogeneous B1 in user‐defined areas of the brain. Magn Reson Med, 2005.

Advances in diffusion MRI acquisition and processing in the Human Connectome Project

The Human Connectome Project (HCP) is a collaborative 5-year effort to map human brain connections and their variability in healthy adults. A consortium of HCP investigators will study a population of 1200 healthy adults using multiple imaging modalities, along with extensive behavioral and genetic data. In this overview, we focus on diffusion MRI (dMRI) and the structural connectivity aspect of the project. We present recent advances in acquisition and processing that allow us to obtain very high-quality in-vivo MRI data, whilst enabling scanning of a very large number of subjects. These advances result from 2 years of intensive efforts in optimising many aspects of data acquisition and processing during the piloting phase of the project. The data quality and methods described here are representative of the datasets and processing pipelines that will be made freely available to the community at quarterly intervals, beginning in 2013.

Transmit and receive transmission line arrays for 7 Tesla parallel imaging.

Transceive array coils, capable of RF transmission and independent signal reception, were developed for parallel, 1H imaging applications in the human head at 7 T (300 MHz). The coils combine the advantages of high‐frequency properties of transmission lines with classic MR coil design. Because of the short wavelength at the 1H frequency at 300 MHz, these coils were straightforward to build and decouple. The sensitivity profiles of individual coils were highly asymmetric, as expected at this high frequency; however, the summed images from all coils were relatively uniform over the whole brain. Data were obtained with four‐ and eight‐channel transceive arrays built using a loop configuration and compared to arrays built from straight stripline transmission lines. With both the four‐ and the eight‐channel arrays, parallel imaging with sensitivity encoding with high reduction numbers was feasible at 7 T in the human head. A one‐dimensional reduction factor of 4 was robustly achieved with an average g value of 1.25 with the eight‐channel transmit/receive coils. Magn Reson Med 53:434–445, 2005.

ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging

The identification of resting state networks (RSNs) and the quantification of their functional connectivity in resting-state fMRI (rfMRI) are seriously hindered by the presence of artefacts, many of which overlap spatially or spectrally with RSNs. Moreover, recent developments in fMRI acquisition yield data with higher spatial and temporal resolutions, but may increase artefacts both spatially and/or temporally. Hence the correct identification and removal of non-neural fluctuations is crucial, especially in accelerated acquisitions. In this paper we investigate the effectiveness of three data-driven cleaning procedures, compare standard against higher (spatial and temporal) resolution accelerated fMRI acquisitions, and investigate the combined effect of different acquisitions and different cleanup approaches. We applied single-subject independent component analysis (ICA), followed by automatic component classification with FMRIBs ICA-based X-noiseifier (FIX) to identify artefactual components. We then compared two first-level (within-subject) cleaning approaches for removing those artefacts and motion-related fluctuations from the data. The effectiveness of the cleaning procedures was assessed using time series (amplitude and spectra), network matrix and spatial map analyses. For time series and network analyses we also tested the effect of a second-level cleaning (informed by group-level analysis). Comparing these approaches, the preferable balance between noise removal and signal loss was achieved by regressing out of the data the full space of motion-related fluctuations and only the unique variance of the artefactual ICA components. Using similar analyses, we also investigated the effects of different cleaning approaches on data from different acquisition sequences. With the optimal cleaning procedures, functional connectivity results from accelerated data were statistically comparable or significantly better than the standard (unaccelerated) acquisition, and, crucially, with higher spatial and temporal resolution. Moreover, we were able to perform higher dimensionality ICA decompositions with the accelerated data, which is very valuable for detailed network analyses.

A geometrically adjustable 16-channel transmit/receive transmission line array for improved RF efficiency and parallel imaging performance at 7 Tesla.

A novel geometrically adjustable transceiver array system is presented. A key feature of the geometrically adjustable array was the introduction of decoupling capacitors that allow for automatic change in capacitance dependent on neighboring resonant element distance. The 16‐element head array version of such an adjustable coil based on transmission line technology was compared to fixed geometry transmission line arrays (TLAs) of various sizes at 7T. The focus of this comparison was on parallel imaging performance, RF transmit efficiency, and signal‐to‐noise ratio (SNR). Significant gains in parallel imaging performance and SNR were observed for the new coil and attributed to its adjustability and to the design of the individual elements with a three‐sided ground plane. Magn Reson Med, 2008.