The large spatial inhomogeneity in transmit B 1 field (B 1 + ) observable in human MR images at high static magnetic fields (B 0 ) severely impairs image quality. To overcome this effect in brain T 1 -weighted images, the MPRAGE sequence was modified to generate two different images at different inversion times, MP2RAGE. By combining the two images in a novel fashion, it was possible to create T 1 -weigthed images where the result image was free of proton density contrast, T 2 ⁎ contrast, reception bias field, and, to first order, transmit field inhomogeneity. MP2RAGE sequence parameters were optimized using Bloch equations to maximize contrast-to-noise ratio per unit of time between brain tissues and minimize the effect of B 1 + variations through space. Images of high anatomical quality and excellent brain tissue differentiation suitable for applications such as segmentation and voxel-based morphometry were obtained at 3 and 7 T. From such T 1 -weighted images, acquired within 12 min, high-resolution 3D T 1 maps were routinely calculated at 7 T with sub-millimeter voxel resolution (0.65-0.85 mm isotropic). T 1 maps were validated in phantom experiments. In humans, the T 1 values obtained at 7 T were 1.15 ± 0.06 s for white matter (WM) and 1.92 ± 0.16 s for grey matter (GM), in good agreement with literature values obtained at lower spatial resolution. At 3 T, where whole-brain acquisitions with 1 mm isotropic voxels were acquired in 8 min, the T 1 values obtained (0.81 ± 0.03 s for WM and 1.35 ± 0.05 for GM) were once again found to be in very good agreement with values in the literature. © 2009 Elsevier Inc. All rights reserved. IntroductionIn the past decade, the magnetization-prepared rapid gradient echo, MPRAGE (Mugler and Brookeman, 1990), sequence has become one of the most commonly used sequences to obtain T 1 -weighted anatomical images of the human brain, in particular at high magnetic field. MPRAGE images are routinely used as anatomical reference for fMRI or for brain tissue classification in voxel-based morphometry (Ashburner and Friston, 2000). However, at high static magnetic fields (≥ 3 T), the increased inhomogeneity of the transmit B 1 + and receive B 1 − fields creates intensity variations throughout the image (bias field). Bias fields not only render segmentation and quantitative analysis difficult but also severely affect image quality at ultra-high fields (≥7 T). The use of adiabatic pulses to perform the inversion in the MPRAGE is only partially able to mitigate the effects of inhomogeneous B 1 . A number of strategies have been proposed to minimize or to correct bias fields generated by the inhomogeneity of the B 1 fields. Most correction strategies aim at correcting the combined (transmit and receive) bias field via post-processing techniques. This can be done either by low-pass filtering (Cohen et al., 2000;Wald et al., 1995) or by fitting slowly varying functions such as Gaussians or low order polynomials (Styner et al., 2000). The result from these low pass filters or fits is then su...

Visual words that are masked and presented so briefly that they cannot be seen may nevertheless facilitate the subsequent processing of related words, a phenomenon called masked priming. It has been debated whether masked primes can activate cognitive processes without gaining access to consciousness. Here we use a combination of behavioural and brain-imaging techniques to estimate the depth of processing of masked numerical primes. Our results indicate that masked stimuli have a measurable influence on electrical and haemodynamic measures of brain activity. When subjects engage in an overt semantic comparison task with a clearly visible target numeral, measures of covert motor activity indicate that they also unconsciously apply the task instructions to an unseen masked numeral. A stream of perceptual, semantic and motor processes can therefore occur without awareness.

The human connectome project (HCP) relies primarily on three complementary magnetic resonance (MR) methods. These are: 1) resting state functional MR imaging (rfMRI) which uses correlations in the temporal fluctuations in an fMRI time series to deduce ‘functional connectivity’; 2) diffusion imaging (dMRI), which provides the input for tractography algorithms used for the reconstruction of the complex axonal fiber architecture; and 3) task based fMRI (tfMRI), which is employed to identify functional parcellation in the human brain in order to assist analyses of data obtained with the first two methods. We describe technical improvements and optimization of these methods as well as instrumental choices that impact speed of acquisition of fMRI and dMRI images at 3 Tesla, leading to whole brain coverage with 2 mm isotropic resolution in 0.7 second for fMRI, and 1.25 mm isotropic resolution dMRI data for tractography analysis with three-fold reduction in total data acquisition time. Ongoing technical developments and optimization for acquisition of similar data at 7 Tesla magnetic field are also presented, targeting higher resolution, specificity of functional imaging signals, mitigation of the inhomogeneous radio frequency (RF) fields and power deposition. Results demonstrate that overall, these approaches represent a significant advance in MR imaging of the human brain to investigate brain function and structure.

In this study, we used functional MRI (fMRI) at high field (3T) to track the time course of activation in the entire basal ganglia circuitry, as well as other motor-related structures, during the explicit learning of a sequence of finger movements over a month of training. Fourteen right-handed healthy volunteers had to practice 15 min daily a sequence of eight moves using the left hand. MRI sessions were performed on days 1, 14 and 28. In both putamen, activation decreased with practice in rostrodorsal (associative) regions. In contrast, there was a significant signal increase in more caudoventral (sensorimotor) regions of the putamen. Subsequent correlation analyses between signal variations and behavioral variables showed that the error rate (movement accuracy) was positively correlated with signal changes in areas activated during early learning, whereas reaction time (movement speed) was negatively correlated with signal changes in areas activated during advanced learning stages, including the sensorimotor putamen and globus pallidus. These results suggest the possibility that motor representations shift from the associative to the sensorimotor territories of the striato-pallidal complex during the explicit learning of motor sequences, suggesting that motor skills are stored in the sensorimotor territory of the basal ganglia that supports a speedy performance.functional MRI ͉ human ͉ subthalamic nucleus T here is now ample evidence from a number of sources that indicates that the basal ganglia are implicated in the formation of motor skills (1). In human studies using brain-imaging techniques, for example, changes of activity have been observed in the basal ganglia at different stages of the acquisition of motor abilities. Decreases of activity in the basal ganglia were reported during the early phase of trial-and-error learning of sequential movements (2). Using a similar task, Toni et al. (3) also reported a decrease of activity in the caudate nucleus, pre-supplementary motor area (SMA), and prefrontal cortex during the first hour of the acquisition process (3). By contrast, studies of implicit motor learning showed that the striatum was more active when subjects reached asymptotic performance, thus suggesting that this structure may be critical for the long-term storage of motor sequences (4-7). However, other investigations (8, 9) failed to document the presence of a greater increase in activity during the execution of well-learned sequential movements compared with the early learning phase.Although still conjectural, differences in the patterns of activation described above may reflect the involvement of different cortico-basal ganglia circuits during the acquisition process, because it is now known that cortical areas project to the striatum in separate associative, premotor, and sensorimotor circuits (10, 11). Indeed, previous imaging works have shown that rostral striatal areas are activated during learning of new motor sequences (2, 3) whereas the execution of well learned sequential movements invo...