569 research outputs found
Reducing variability in along-tract analysis with diffusion profile realignment
Diffusion weighted MRI (dMRI) provides a non invasive virtual reconstruction
of the brain's white matter structures through tractography. Analyzing dMRI
measures along the trajectory of white matter bundles can provide a more
specific investigation than considering a region of interest or tract-averaged
measurements. However, performing group analyses with this along-tract strategy
requires correspondence between points of tract pathways across subjects. This
is usually achieved by creating a new common space where the representative
streamlines from every subject are resampled to the same number of points. If
the underlying anatomy of some subjects was altered due to, e.g. disease or
developmental changes, such information might be lost by resampling to a fixed
number of points. In this work, we propose to address the issue of possible
misalignment, which might be present even after resampling, by realigning the
representative streamline of each subject in this 1D space with a new method,
coined diffusion profile realignment (DPR). Experiments on synthetic datasets
show that DPR reduces the coefficient of variation for the mean diffusivity,
fractional anisotropy and apparent fiber density when compared to the unaligned
case. Using 100 in vivo datasets from the HCP, we simulated changes in mean
diffusivity, fractional anisotropy and apparent fiber density. Pairwise
Student's t-tests between these altered subjects and the original subjects
indicate that regional changes are identified after realignment with the DPR
algorithm, while preserving differences previously detected in the unaligned
case. This new correction strategy contributes to revealing effects of interest
which might be hidden by misalignment and has the potential to improve the
specificity in longitudinal population studies beyond the traditional region of
interest based analysis and along-tract analysis workflows.Comment: v4: peer-reviewed round 2 v3 : deleted some old text from before
peer-review which was mistakenly included v2 : peer-reviewed version v1:
preprint as submitted to journal NeuroImag
On the sensitivity of the diffusion MRI signal to brain activity in response to a motor cortex paradigm
Diffusion functional MRI (dfMRI) is a promising technique to map functional
activations by acquiring diffusion-weighed spin-echo images. In previous
studies, dfMRI showed higher spatial accuracy at activation mapping compared to
classic functional MRI approaches. However, it remains unclear whether dfMRI
measures result from changes in the intra-/extracellular environment, perfusion
and/or T2 values. We designed an acquisition/quantification scheme to
disentangle such effects in the motor cortex during a finger tapping paradigm.
dfMRI was acquired at specific diffusion weightings to selectively suppress
perfusion and free-water diffusion, then times series of the apparent diffusion
coefficient (ADC-fMRI) and of the perfusion signal fraction (IVIM-fMRI) were
derived. ADC-fMRI provided ADC estimates sensitive to changes in perfusion and
free-water volume, but not to T2/T2* values. With IVIM-fMRI we isolated the
perfusion contribution to ADC, while suppressing T2 effects. Compared to
conventional gradient-echo BOLD fMRI, activation maps obtained with dfMRI and
ADC-fMRI had smaller clusters, and the spatial overlap between the three
techniques was below 50%. Increases of perfusion fractions were observed during
task in both dfMRI and ADC-fMRI activations. Perfusion effects were more
prominent with ADC-fMRI than with dfMRI but were significant in less than 25%
of activation ROIs. Taken together, our results suggest that the sensitivity to
task of dfMRI derives from a decrease of hindered diffusion and an increase of
the pseudo-diffusion signal fraction, leading to different, more confined
spatial activation patterns compared to classic functional MRI.Comment: Submitted to peer-reviewed journa
Isotropic non-white matter partial volume effects in constrained spherical deconvolution
Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a non-invasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. Significant partial volume effects (PVEs) are present in the DVV signal due to relatively large voxel sizes. These PVEs can be caused by both non-WM tissue, such as gray matter (GM) and cerebrospinal fluid (CSF), and by multiple non-parallel WM fiber populations. High angular resolution diffusion imaging (HARDI) methods have been developed to correctly characterize complex WM fiber configurations, but to date, many of the HARDI methods do not account for non-WM PVEs. In this work, we investigated the isotropic PVEs caused by non-WM tissue in WM voxels on fiber orientations extracted with constrained spherical deconvolution (CSD). Experiments were performed on simulated and real DW-MRI data. In particular, simulations were performed to demonstrate the effects of varying the diffusion weightings, signal-to-noise ratios (SNRs), fiber configurations, and tissue fractions. Our results show that the presence of non-WM tissue signal causes a decrease in the precision of the detected fiber orientations and an increase in the detection of false peaks in CSD. We estimated 35-50% of WM voxels to be affected by non-WM PVEs. For HARDI sequences, which typically have a relatively high degree of diffusion weighting, these adverse effects are most pronounced in voxels with GM PVEs. The non-WM PVEs become severe with 50% GM volume for maximum spherical harmonics orders of 8 and below, and already with 25% GM volume for higher orders. In addition, a low diffusion weighting or SNR increases the effects. The non-WM PVEs may cause problems in connectomics, where reliable fiber tracking at the WM G M interface is especially important. We suggest acquiring data with high diffusion-weighting 2500-3000 s/mm(2), reasonable SNR (similar to 30) and using lower SH orders in GM contaminated regions to minimize the non-WM PVEs in CSD
Cortical Network for Gaze Control in Humans Revealed Using Multimodal MRI
Functional magnetic resonance imaging (fMRI) techniques allow definition of cortical nodes that are presumed to be components of large-scale distributed brain networks involved in cognitive processes. However, very few investigations examine whether such functionally defined areas are in fact structurally connected. Here, we used combined fMRI and diffusion MRI-based tractography to define the cortical network involved in saccadic eye movement control in humans. The results of this multimodal imaging approach demonstrate white matter pathways connecting the frontal eye fields and supplementary eye fields, consistent with the known connectivity of these regions in macaque monkeys. Importantly, however, these connections appeared to be more prominent in the right hemisphere of humans. In addition, there was evidence of a dorsal frontoparietal pathway connecting the frontal eye field and the inferior parietal lobe, also right hemisphere dominant, consistent with specialization of the right hemisphere for directed attention in humans. These findings demonstrate the utility and potential of using multimodal imaging techniques to define large-scale distributed brain networks, including those that demonstrate known hemispheric asymmetries in human
Automated characterization of noise distributions in diffusion MRI data
Knowledge of the noise distribution in diffusion MRI is the centerpiece to
quantify uncertainties arising from the acquisition process. Accurate
estimation beyond textbook distributions often requires information about the
acquisition process, which is usually not available. We introduce two new
automated methods using the moments and maximum likelihood equations of the
Gamma distribution to estimate all unknown parameters using only the magnitude
data. A rejection step is used to make the framework automatic and robust to
artifacts. Simulations were created for two diffusion weightings with parallel
imaging. Furthermore, MRI data of a water phantom with different combinations
of parallel imaging were acquired. Finally, experiments on freely available
datasets are used to assess reproducibility when limited information about the
acquisition protocol is available. Additionally, we demonstrated the
applicability of the proposed methods for a bias correction and denoising task
on an in vivo dataset. A generalized version of the bias correction framework
for non integer degrees of freedom is also introduced. The proposed framework
is compared with three other algorithms with datasets from three vendors,
employing different reconstruction methods. Simulations showed that assuming a
Rician distribution can lead to misestimation of the noise distribution in
parallel imaging. Results showed that signal leakage in multiband can also lead
to a misestimation of the noise distribution. Repeated acquisitions of in vivo
datasets show that the estimated parameters are stable and have lower
variability than compared methods. Results show that the proposed methods
reduce the appearance of noise at high b-value. The proposed algorithms herein
can estimate both parameters of the noise distribution automatically, are
robust to signal leakage artifacts and perform best when used on acquired noise
maps.Comment: v3: Peer reviewed version v2: Manuscript as submitted to Medical
image analysis v1: Manuscript as submitted to Magnetic resonance in medicin
Association between motor planning and the frontoparietal network in children: An exploratory multimodal study
Objective: Evidence from adult literature shows the involvement of cortical grey matter areas of the frontoparietal lobe and the white matter bundle, the superior longitudinal fasciculus (SLF) in motor planning. This is yet to be confirmed in children.
Method: A multimodal study was designed to probe the neurostructural basis of childhood motor planning. Behavioural (motor planning), magnetic resonance imaging (MRI) and diffusion weighted imaging (DWI) data were acquired from 19 boys aged 8–11 years. Motor planning was assessed using the one and two colour sequences of the octagon task. The MRI data were preprocessed and analysed using FreeSurfer 6.0. Cortical thickness and cortical surface area were extracted from the caudal middle frontal gyrus (MFG), superior frontal gyrus (SFG), precentral gyrus (PcG), supramarginal gyrus (SMG), superior parietal lobe (SPL) and the inferior parietal lobe (IPL) using the Desikan– Killiany atlas. The DWI data were preprocessed and analysed using ExploreDTI 4.8.6 and the white matter tract, the SLF was reconstructed.
Results: Motor planning of the two colour sequence was associated with cortical thickness of the bilateral MFG and left SFG, PcG, IPL and SPL. The right SLF was related to motor planning for the two colour sequence as well as with the left cortical thickness of the SFG.
Conclusion: Altogether, morphology within frontodorsal circuity, and the white matter bundles that support communication between them, may be associated with individual differences in childhood motor planning
The importance of correcting for signal drift in diffusion MRI
Purpose
To investigate previously unreported effects of signal drift as a result of temporal scanner instability on diffusion MRI data analysis and to propose a method to correct this signal drift.
Methods
We investigated the signal magnitude of non-diffusion-weighted EPI volumes in a series of diffusion-weighted imaging experiments to determine whether signal magnitude changes over time. Different scan protocols and scanners from multiple vendors were used to verify this on phantom data, and the effects on diffusion kurtosis tensor estimation in phantom and in vivo data were quantified. Scalar metrics (eigenvalues, fractional anisotropy, mean diffusivity, mean kurtosis) and directional information (first eigenvectors and tractography) were investigated.
Results
Signal drift, a global signal decrease with subsequently acquired images in the scan, was observed in phantom data on all three scanners, with varying magnitudes up to 5% in a 15-min scan. The signal drift has a noticeable effect on the estimation of diffusion parameters. All investigated quantitative parameters as well as tractography were affected by this artifactual signal decrease during the scan.
Conclusion
By interspersing the non-diffusion-weighted images throughout the session, the signal decrease can be estimated and compensated for before data analysis; minimizing the detrimental effects on subsequent MRI analyses. Magn Reson Med 77:285–299, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine
Variability in diffusion kurtosis imaging: Impact on study design, statistical power and interpretation.
Diffusion kurtosis imaging (DKI) is an emerging technique with the potential to quantify properties of tissue microstructure that may not be observable using diffusion tensor imaging (DTI). In order to help design DKI studies and improve interpretation of DKI results, we employed statistical power analysis to characterize three aspects of variability in four DKI parameters; the mean diffusivity, fractional anisotropy, mean kurtosis, and radial kurtosis. First, we quantified the variability in terms of the group size required to obtain a statistical power of 0.9. Second, we investigated the relative contribution of imaging and post-processing noise to the total variance, in order to estimate the benefits of longer scan times versus the inclusion of more subjects. Third, we evaluated the potential benefit of including additional covariates such as the size of the structure when testing for differences in group means. The analysis was performed in three major white matter structures of the brain: the superior cingulum, the corticospinal tract, and the mid-sagittal corpus callosum, extracted using diffusion tensor tractography and DKI data acquired in a healthy cohort. The results showed heterogeneous variability across and within the white matter structures. Thus, the statistical power varies depending on parameter and location, which is important to consider if a pathogenesis pattern is inferred from DKI data. In the data presented, inter-subject differences contributed more than imaging noise to the total variability, making it more efficient to include more subjects rather than extending the scan-time per subject. Finally, strong correlations between DKI parameters and the structure size were found for the cingulum and corpus callosum. Structure size should thus be considered when quantifying DKI parameters, either to control for its potentially confounding effect, or as a means of reducing unexplained variance
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