2,340 research outputs found
Axon diameters and myelin content modulate microscopic fractional anisotropy at short diffusion times in fixed rat spinal cord
Mapping tissue microstructure accurately and noninvasively is one of the
frontiers of biomedical imaging. Diffusion Magnetic Resonance Imaging (MRI) is
at the forefront of such efforts, as it is capable of reporting on microscopic
structures orders of magnitude smaller than the voxel size by probing
restricted diffusion. Double Diffusion Encoding (DDE) and Double Oscillating
Diffusion Encoding (DODE) in particular, are highly promising for their ability
to report on microscopic fractional anisotropy ({\mu}FA), a measure of the pore
anisotropy in its own eigenframe, irrespective of orientation distribution.
However, the underlying correlates of {\mu}FA have insofar not been studied.
Here, we extract {\mu}FA from DDE and DODE measurements at ultrahigh magnetic
field of 16.4T in the aim to probe fixed rat spinal cord microstructure. We
further endeavor to correlate {\mu}FA with Myelin Water Fraction (MWF) derived
from multiexponential T2 relaxometry, as well as with literature-based
spatially varying axonal diameters. In addition, a simple new method is
presented for extracting unbiased {\mu}FA from three measurements at different
b-values. Our findings reveal strong anticorrelations between {\mu}FA (derived
from DODE) and axon diameter in the distinct spinal cord tracts; a moderate
correlation was also observed between {\mu}FA derived from DODE and MWF. These
findings suggest that axonal membranes strongly modulate {\mu}FA, which - owing
to its robustness towards orientation dispersion effects - reflects axon
diameter much better than its typical FA counterpart. The {\mu}FA exhibited
modulations when measured via oscillating or blocked gradients, suggesting
selective probing of different parallel path lengths and providing insight into
how those modulate {\mu}FA metrics. Our findings thus shed light into the
underlying microstructural correlates of {\mu}FA and are (...
Neurite imaging reveals microstructural variations in human cerebral cortical gray matter
We present distinct patterns of neurite distribution in the human cerebral cortex using diffusion magnetic resonance imaging (MRI). We analyzed both high-resolution structural (T1w and T2w images) and diffusion MRI data in 505 subjects from the Human Connectome Project. Neurite distributions were evaluated using the neurite orientation dispersion and density imaging (NODDI) model, optimized for gray matter, and mapped onto the cortical surface using a method weighted towards the cortical mid-thickness to reduce partial volume effects. The estimated neurite density was high in both somatosensory and motor areas, early visual and auditory areas, and middle temporal area (MT), showing a strikingly similar distribution to myelin maps estimated from the T1w/T2w ratio. The estimated neurite orientation dispersion was particularly high in early sensory areas, which are known for dense tangential fibers and are classified as granular cortex by classical anatomists. Spatial gradients of these cortical neurite properties revealed transitions that colocalize with some areal boundaries in a recent multi-modal parcellation of the human cerebral cortex, providing mutually supportive evidence. Our findings indicate that analyzing the cortical gray matter neurite morphology using diffusion MRI and NODDI provides valuable information regarding cortical microstructure that is related to but complementary to myeloarchitecture
Modeling high resolution MRI: Statistical issues with low SNR
Noise is a common issue for all Magnetic Resonance Imaging (MRI)
techniques and obviously leads to variability of the estimates in any model
describing the data. A number of special MR sequences as well as increasing
spatial resolution in MR experiments further diminish the signal-to-noise
ratio (SNR). However, with low SNR the expected signal deviates from its
theoretical value. Common modeling approaches therefore lead to a bias in
estimated model parameters. Adjustments require an analysis of the data
generating process and a characterization of the resulting distribution of
the imaging data. We provide an adequate quasi-likelihood approach that
employs these characteristics. We elaborate on the effects of typical data
preprocessing and analyze the bias effects related to low SNR for the example
of the diffusion tensor model in diffusion MRI. We then demonstrate that the
problem is relevant even for data from the Human Connectome Project, one of
the highest quality diffusion MRI data available so far
Modeling high resolution MRI: Statistical issues with low SNR
Noise is a common issue for all Magnetic Resonance Imaging (MRI) techniques and obviously leads to variability of the estimates in any model describing the data. A number of special MR sequences as well as increasing spatial resolution in MR experiments further diminish the signal-to-noise ratio (SNR). However, with low SNR the expected signal deviates from its theoretical value. Common modeling approaches therefore lead to a bias in estimated model parameters. Adjustments require an analysis of the data generating process and a characterization of the resulting distribution of the imaging data. We provide an adequate quasi-likelihood approach that employs these characteristics. We elaborate on the effects of typical data preprocessing and analyze the bias effects related to low SNR for the example of the diffusion tensor model in diffusion MRI. We then demonstrate that the problem is relevant even for data from the Human Connectome Project, one of the highest quality diffusion MRI data available so far
Diffusion tensor model links to neurite orientation dispersion and density imaging at high b-value in cerebral cortical gray matter
Diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) are widely used models to infer microstructural features in the brain from diffusion-weighted MRI. Several studies have recently applied both models to increase sensitivity to biological changes, however, it remains uncertain how these measures are associated. Here we show that cortical distributions of DTI and NODDI are associated depending on the choice of b-value, a factor reflecting strength of diffusion weighting gradient. We analyzed a combination of high, intermediate and low b-value data of multi-shell diffusion-weighted MRI (dMRI) in healthy 456 subjects of the Human Connectome Project using NODDI, DTI and a mathematical conversion from DTI to NODDI. Cortical distributions of DTI and DTI-derived NODDI metrics were remarkably associated with those in NODDI, particularly when applied highly diffusion-weighted data (b-value = 3000 sec/mm2). This was supported by simulation analysis, which revealed that DTI-derived parameters with lower b-value datasets suffered from errors due to heterogeneity of cerebrospinal fluid fraction and partial volume. These findings suggest that high b-value DTI redundantly parallels with NODDI-based cortical neurite measures, but the conventional low b-value DTI is hard to reasonably characterize cortical microarchitecture
Lagged and instantaneous dynamical influences related to brain structural connectivity
Contemporary neuroimaging methods can shed light on the basis of human neural
and cognitive specializations, with important implications for neuroscience and
medicine. Different MRI acquisitions provide different brain networks at the
macroscale; whilst diffusion-weighted MRI (dMRI) provides a structural
connectivity (SC) coincident with the bundles of parallel fibers between brain
areas, functional MRI (fMRI) accounts for the variations in the
blood-oxygenation-level-dependent T2* signal, providing functional connectivity
(FC).Understanding the precise relation between FC and SC, that is, between
brain dynamics and structure, is still a challenge for neuroscience. To
investigate this problem, we acquired data at rest and built the corresponding
SC (with matrix elements corresponding to the fiber number between brain areas)
to be compared with FC connectivity matrices obtained by 3 different methods:
directed dependencies by an exploratory version of structural equation modeling
(eSEM), linear correlations (C) and partial correlations (PC). We also
considered the possibility of using lagged correlations in time series; so, we
compared a lagged version of eSEM and Granger causality (GC). Our results were
two-fold: firstly, eSEM performance in correlating with SC was comparable to
those obtained from C and PC, but eSEM (not C nor PC) provides information
about directionality of the functional interactions. Second, interactions on a
time scale much smaller than the sampling time, captured by instantaneous
connectivity methods, are much more related to SC than slow directed influences
captured by the lagged analysis. Indeed the performance in correlating with SC
was much worse for GC and for the lagged version of eSEM. We expect these
results to supply further insights to the interplay between SC and functional
patterns, an important issue in the study of brain physiology and function.Comment: Accepted and published in Frontiers in Psychology in its current
form. 27 pages, 1 table, 5 figures, 2 suppl. figure
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