Whole brain MP2RAGE-based mapping of the longitudinal relaxation time at 9.4T.

Mapping of the longitudinal relaxation time (T1) with high accuracy and precision is central for neuroscientific and clinical research, since it opens up the possibility to obtain accurate brain tissue segmentation and gain myelin-related information. An ideal, quantitative method should enable whole brain coverage within a limited scan time yet allow for detailed sampling with sub-millimeter voxel sizes. The use of ultra-high magnetic fields is well suited for this purpose, however the inhomogeneous transmit field potentially hampers its use. In the present work, we conducted whole brain T1 mapping based on the MP2RAGE sequence at 9.4T and explored potential pitfalls for automated tissue classification compared with 3T. Data accuracy and T2-dependent variation of the adiabatic inversion efficiency were investigated by single slice T1 mapping with inversion recovery EPI measurements, quantitative T2 mapping using multi-echo techniques and simulations of the Bloch equations. We found that the prominent spatial variation of the transmit field at 9.4T (yielding flip angles between 20% and 180% of nominal values) profoundly affected the result of image segmentation and T1 mapping. These effects could be mitigated by correcting for both flip angle and inversion efficiency deviations. Based on the corrected T1 maps, new, 'flattened', MP2RAGE contrast images were generated, that were no longer affected by variations of the transmit field. Unlike the uncorrected MP2RAGE contrast images acquired at 9.4T, these flattened images yielded image segmentations comparable to 3T, making bias-field correction prior to image segmentation and tissue classification unnecessary. In terms of the T1 estimates at high field, the proposed correction methods resulted in an improved precision, with test-retest variability below 1% and a coefficient-of-variation across 25 subjects below 3%.

Quantification of gray matter changes in the cerebral cortex after isolated cerebellar damage: a voxel-based morphometry study.

There is growing evidence based on behavioral and functional imaging studies about the cerebellar involvement in the modulation of cognitive functions. However, it still remains to be clarified how the cerebellum interacts with brain regions sub-serving different cognitive domains. In this study we used magnetic resonance imaging (MRI) and voxel based morphometry (VBM) to investigate changes of cerebral gray matter (GM) density in 15 patients with a focal cerebellar damage (CD) compared to 15 healthy controls. T2-weighted scans and T1-weighted volumes were collected from each subject. With the exception of the cerebellar lesion, none of the patients showed any additional brain MRI abnormality. T1-volumes were analyzed by voxel-based morphometry. Consistent with their neuropsychological abnormalities, patients with right-CD compared to controls showed a reduction of GM density mainly involving the left frontal, parietal and temporal lobes. Conversely, patients with left-CD did not show any significant neuropsychological or cerebral GM abnormality. The present study indicates that specific GM changes may be detected in patients with isolated CD and cognitive dysfunction. We discuss the findings in terms of cerebellar influence on the neuronal networks involved in higher level functions of the association cortex.

Essential head tremor is associated with cerebellar vermis atrophy: a volumetric and voxel-based morphometry MR imaging study.

BACKGROUND AND PURPOSE: Our aim was to investigate the presence of brain gray matter (GM) abnormalities in patients with different forms of essential tremor (ET). MATERIALS AND METHODS: We used optimized voxel-based morphometry (VBM) and manually traced single region-of-interest analysis in 50 patients with familial ET and in 32 healthy subjects. Thirty patients with ET had tremor of the arms (a-ET), whereas the remaining 20 patients had both arm and head tremor (h-ET). RESULTS: VBM showed marked atrophy of the cerebellar vermis in the patients with h-ET with respect to healthy subjects (P(corrected) < .001). Patients with a-ET showed a trend toward a vermal GM volume loss that did not reach a significant difference with respect to healthy controls (P(uncorrected) < .01). The region-of-interest analysis showed a reduction of the cerebellar volume (CV) in the h-ET group (98.2 +/- 13.6 mm(3)) compared with healthy controls (110.5 +/- 15.5 mm(3), P < .012) as well as in the entire vermal area (790.3 +/- 94.5 mm(2), 898.6 +/- 170.6 mm(2), P < .04 in h-ET and control groups, respectively). CONCLUSIONS: Atrophy of the cerebellar vermis detected in patients with h-ET strongly supports the evidence for the involvement of the cerebellum in the pathophysiology of ET. The lack of a significant CV loss observed in patients with a-ET suggests that a-ET and h-ET might represent distinct subtypes of the same disease.

Realistic simulations of neuronal activity: a contribution to the debate on direct detection of neuronal currents by MRI.

Many efforts have been done in order to preview the properties of the magnetic resonance (MR) signals produced by the neuronal currents using simulations. In this paper, starting with a detailed calculation of the magnetic field produced by the neuronal currents propagating over single hippocampal CA1 pyramidal neurons placed inside a cubic MR voxel of length 1.2 mm, we proceeded on the estimation of the phase and magnitude MR signals. We then extended the results to layers of parallel and synchronous similar neurons and to ensembles of layers, considering different echo times, voxel volumes and neuronal densities. The descriptions of the neurons and of their electrical activity took into account the real neuronal morphologies and the physiology of the neuronal events. Our results concern: (a) the expected time course of the MR signals produced by the neuronal currents in the brain, based on physiological and anatomical properties; (b) the different contributions of post-synaptic potentials and of action potentials to the MR signals; (c) the estimation of the equivalent current dipole and the influence of its orientation with respect to the external magnetic field on the observable MR signal variations; (d) the size of the estimated neuronal current induced phase and magnitude MR signal changes with respect to the echo time, voxel-size and neuronal density. The inclusion of realistic neuronal properties into the simulation introduces new information that can be helpful for the design of MR sequences for the direct detection of neuronal current effects and the testing of bio-electromagnetic models.

Imaging nervous pathways with MR tractography.

This paper describes the state of the art of tractography, a technique which enables the virtual reconstruction of axon bundles of the central nervous system using diffusion-weighted magnetic resonance images. This technique has raised enthusiasm and expectations among specialists because it is the only non-invasive method for studying the three-dimensional architecture of axonal fibres in vivo. Tractography is a new technique used to assess the anatomy of the central nervous system, and it will be available for routine clinical use in the future. Understanding its potential applications and limitations is therefore important.

Turbo-Proton Echo Planar Spectroscopic Imaging (t-PEPSI) MR technique in the detection of diffuse axonal damage in brain injury. Comparison with Gradient-Recalled Echo (GRE) sequence.

PURPOSE: Diffuse axonal injury (DAI) is a common type of primary neuronal injury in patients with severe traumatic brain injury, and is frequently accompanied by tissue tear haemorrhage. The T2*-weighted gradient-recalled echo (GRE) sequences are more sensitive than T2-weighted spin-echo images for detection of haemorrhage. This study was undertaken to determine whether turbo-PEPSI, an extremely fast multi-echo-planar-imaging sequence, can be used as an alternative to the GRE sequence for detection of DAI. MATERIALS AND METHODS: Nineteen patients (mean age 24,5 year) with severe traumatic brain injury (TBI), occurred at least 3 months earlier, underwent a brain MRI study on a 1.5-Tesla scanner. A qualitative evaluation of the turbo-PEPSI sequences was performed by identifying the optimal echo time and in-plane resolution. The number and size of DAI lesions, as well as the signal intensity contrast ratio (SI CR), were computed for each set of GRE and turbo-PEPSI images, and divided according to their anatomic location into lobar and/or deep brain. RESULTS: There was no significant difference between GRE and turbo-PEPSI sequences in the total number of DAI lesions detected (283 vs 225 lesions, respectively). The GRE sequence identified a greater number of hypointense lesions in the temporal lobe compared to the t-PEPSI sequence (72 vs 35, p<0.003), while no significant differences were found for the other brain regions. The SI CR was significantly better (i.e. lower) for the turbo-PEPSI than for the GRE sequence (p<0.00001). CONCLUSIONS: Owing to its very short scan time and high sensitivity to the haemorrhage foci, the turbo-PEPSI sequence can be used as an alternative to the GRE to assess brain DAI in severe TBI patients, especially if uncooperative and medically unstable.

Evaluation of mixed effects in event-related fMRI studies: impact of first-level design and filtering.

With the introduction of event-related designs in fMRI, it has become crucial to optimize design efficiency and temporal filtering to detect activations at the 1st level with high sensitivity. We investigate the relevance of these issues for fMRI population studies, that is, 2nd-level analysis, for a set of event-related fMRI (er-fMRI) designs with different 1st-level efficiencies, adopting three distinct 1st-level filtering strategies as implemented in SPM99, SPM2, and FSL3.0. By theory, experiments, and simulations using physiological fMRI noise, we show that both design and filtering impact the outcome of the statistical analysis, not only at the 1st but also at the 2nd level. There are several reasons behind this finding. First, sensitivity is affected by both design and filtering, since the scan-to-scan variance, that is the fixed effect, is not negligible with respect to the between-subject variance, that is the random effect, in er-fMRI population studies. The impact of the fixed effects error on the sensitivity of the mixed effects analysis can be mitigated by an optimal choice of er-fMRI design and filtering. Moreover, the accuracy of the 1st- and 2nd-level parameter estimates also depend on design and filtering; especially, we show that inaccuracies caused by the presence of residual noise autocorrelations can be constrained by designs that have hemodynamic responses with a Gaussian distribution. In conclusion, designs with both good efficiency and decorrelating properties, for example, such as the geometric or Latin square probability distributions, combined with the "whitening" filters of SPM2 and FSL3.0, give the best result, both for 1st- and 2nd-level analysis of er-fMRI studies.

Intermolecular double quantum coherences (iDQc) and diffusion-weighted imaging (DWI) imaging of the human brain at 1.5 T.

To study the sensitivity of intermolecular double quantum coherences (iDQc) imaging contrast to brain microstructure and brain anisotropy, we investigated the iDQC contrast between differently structured areas of the brain according to the strength and the direction of the applied correlation gradient. Thus diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) maps have been obtained. This procedure, which consists of analyzing both iDQc and DWI images at different gradient strength and gradient direction, could be a promising tool for clinical brain investigations performed with higher than 1.5 T magnetic fields.

Real-time quantification of T(2)(*) changes using multiecho planar imaging and numerical methods.

Conventional approaches to quantify whole brain T(2)(*) maps use nonlinear regression with intensive computational requirements that therefore likely limit quantitative T(2)(*) mapping for real-time applications. To overcome these limitations an alternative method, NumART(2)(*) (NUMerical Algorithm for Real-time T(2)(*) mapping) that directly calculates T(2)(*) by a linear combination of images obtained at three or more different echo times was developed. NumART(2)(*), linear least-squares, and nonlinear regression techniques were applied to multiecho planar images of the human brain and to simulated data. Although NumART(2)(*) may overestimate T(2)(*), it yields comparable values to regression techniques in cortical and subcortical areas, with only moderate deviations for echo spacings between 18 and 40 ms. NumART(2)(*), like linear regression, requires 2% of the computational time needed for nonlinear regression and compares favorably with linear regression due to its higher precision. The use of NumART(2)(*) for continuous on-line T(2)(*) mapping in real time fMRI studies is shown.

Improved detection of event-related functional MRI signals using probability functions.

Selecting an optimal event distribution for experimental use in event-related fMRI studies can require the generation of large numbers of event sequences with characteristics hard to control. The use of known probability distributions offers the possibility to control event timing and constrain the search space for finding optimal event sequences. We investigated different probability distributions in terms of response estimation (estimation efficiency), detectability (detection power, parameter estimation efficiency, sensitivity to true positives), and false-positive activation. Numerous simulated event sequences were generated selecting interevent intervals (IEI) from the uniform, uniform permuted, Latin square, exponential, binomial, Poisson, chi(2), geometric, and bimodal probability distributions and fixed IEI. Event sequences from the bimodal distribution, like block designs, had the best performance for detection and the poorest for estimation, while high estimation and detectability occurred for the long-decay exponential distribution. The uniform distribution also yielded high estimation efficiency, but probability functions with a long tail toward higher IEI, such as the geometric and the chi(2) distributions, had superior detectability. The distributions with the best detection performance also had a relatively high incidence of false positives, in contrast to the ordered distributions (Latin square and uniform permuted). The predictions of improved sensitivities for distributions with long tails were confirmed with empirical data. Moreover, the Latin square design yielded detection of activated voxels similar to the chi(2) distribution. These results indicate that high detection and suitable behavioral designs have compatibility for application of functional MRI methods to experiments requiring complex designs.