In diffusion MRI (dMRI), microscopic diffusion anisotropy can be obscured by orientation dispersion. Separation of these properties is of high importance, since it could allow dMRI to non-invasively probe elongated structures such as neurites (axons and dendrites). However, conventional dMRI, based on single diffusion encoding (SDE), entangles microscopic anisotropy and orientation dispersion with intra-voxel variance in isotropic diffusivity. SDE-based methods for estimating microscopic anisotropy, such as the neurite orientation dispersion and density imaging (NODDI) method, must thus rely on model assumptions to disentangle these features. An alternative approach is to directly quantify microscopic anisotropy by the use of variable shape of the b-tensor. Along those lines, we here present the 'constrained diffusional variance decomposition' (CODIVIDE) method, which jointly analyzes data acquired with diffusion encoding applied in a single direction at a time (linear tensor encoding, LTE) and in all directions (spherical tensor encoding, STE). We then contrast the two approaches by comparing neurite density estimated using NODDI with microscopic anisotropy estimated using CODIVIDE. Data were acquired in healthy volunteers and in glioma patients. NODDI and CODIVIDE differed the most in gray matter and in gliomas, where NODDI detected a neurite fraction higher than expected from the level of microscopic diffusion anisotropy found with CODIVIDE. The discrepancies could be explained by the NODDI tortuosity assumption, which enforces a connection between the neurite density and the mean diffusivity of tissue. Our results suggest that this assumption is invalid, which leads to a NODDI neurite density that is inconsistent between LTE and STE data. Using simulations, we demonstrate that the NODDI assumptions result in parameter bias that precludes the use of NODDI to map neurite density. With CODIVIDE, we found high levels of microscopic anisotropy in white matter, intermediate levels in structures such as the thalamus and the putamen, and low levels in the cortex and in gliomas. We conclude that accurate mapping of microscopic anisotropy requires data acquired with variable shape of the b-tensor.
a b s t r a c tRecent research using magnetic resonance imaging has documented changes in the adult human brain's grey matter structure induced by alterations in experiential demands. We review this research and relate it to models of brain plasticity from related strands of research, such as work on animal models. This allows us to generate recommendations and predictions for future research that may advance the understanding of the function, sequential progression, and microstructural nature of experience-dependent changes in regional brain volumes. Informed by recent evidence on adult age differences in structural brain plasticity, we show how understanding learning-related changes in human brain structure can expand our knowledge about adult development and aging. We hope that this review will promote research on the mechanisms regulating experience-dependent structural plasticity of the adult human brain.
Hippocampal volume has been shown to be sensitive to variations in estrogen and progesterone levels across rodents' estrous cycle. However, little is known about the covariation of hormone levels and brain structure in the course of the human menstrual cycle. Here, we examine this covariation with a multi-method approach that includes several brain imaging methods and hormonal assessments. We acquired structural and functional scans from 21 naturally cycling women on four time points during their cycles (early follicular phase, late follicular phase, ovulation and luteal phase). Hormone blood concentrations and cognitive performance in different domains were assessed on each of the measurement occasions. Structural MRI images were processed by means of whole-brain voxel-based morphometry and FreeSurfer. With either method, bilateral increases in hippocampal volume were found in the late follicular phase relative to the early follicular phase. The gray matter probability in regions of hippocampal volume increase was associated with lower mean diffusivity in the same region. In addition, we observed higher functional connectivity between the hippocampi and the bilateral superior parietal lobe in the late follicular phase. We did not find any reliable cycle-related performance variations on the cognitive tasks. The present results show that hormonal fluctuations covary with hippocampal structure and function in the course of the human menstrual cycle.
In vivo mapping of the neurite density with diffusion MRI (dMRI) is a high but challenging aim. First, it is unknown whether all neurites exhibit completely anisotropic (“stick‐like”) diffusion. Second, the “density” of tissue components may be confounded by non‐diffusion properties such as T2 relaxation. Third, the domain of validity for the estimated parameters to serve as indices of neurite density is incompletely explored. We investigated these challenges by acquiring data with “b‐tensor encoding” and multiple echo times in brain regions with low orientation coherence and in white matter lesions. Results showed that microscopic anisotropy from b‐tensor data is associated with myelinated axons but not with dendrites. Furthermore, b‐tensor data together with data acquired for multiple echo times showed that unbiased density estimates in white matter lesions require data‐driven estimates of compartment‐specific T2 values. Finally, the “stick” fractions of different biophysical models could generally not serve as neurite density indices across the healthy brain and white matter lesions, where outcomes of comparisons depended on the choice of constraints. In particular, constraining compartment‐specific T2 values was ambiguous in the healthy brain and had a large impact on estimated values. In summary, estimating neurite density generally requires accounting for different diffusion and/or T2 properties between axons and dendrites. Constrained “index” parameters could be valid within limited domains that should be delineated by future studies.
We compared hippocampal volume measures obtained by manual tracing to automatic segmentation with FreeSurfer in 44 younger (20-30 years) and 47 older (60-70 years) adults, each measured with magnetic resonance imaging (MRI) over three successive time points, separated by four months. Retest correlations over time were very high for both manual and FreeSurfer segmentations. With FreeSurfer, correlations over time were significantly lower in the older than in the younger age group, which was not the case with manual segmentation. Pearson correlations between manual and FreeSurfer estimates were sufficiently high, numerically even higher in the younger group, whereas intra-class correlation coefficient (ICC) estimates were lower in the younger than in the older group. FreeSurfer yielded higher volume estimates than manual segmentation, particularly in the younger age group. Importantly, FreeSurfer consistently overestimated hippocampal volumes independently of manually assessed volume in the younger age group, but overestimated larger volumes in the older age group to a less extent, introducing a systematic age bias in the data. Age differences in hippocampal volumes were significant with FreeSurfer, but not with manual tracing. Manual tracing resulted in a significant difference between left and right hippocampus (right > left), whereas this asymmetry Additional Supporting Information may be found in the online version of this article.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. Purpose:To optimize diffusion-relaxation MRI with tensor-valued diffusion encoding for precise estimation of compartment-specific fractions, diffusivities, and T 2 values within a two-compartment model of white matter, and to explore the approach in vivo. Methods: Sampling protocols featuring different b-values (b), b-tensor shapes (b Δ ), and echo times (TE) were optimized using Cramér-Rao lower bounds (CRLB).Whole-brain data were acquired in children, adults, and elderly with white matter lesions. Compartment fractions, diffusivities, and T 2 values were estimated in a model featuring two microstructural compartments represented by a "stick" and a "zeppelin." Results: Precise parameter estimates were enabled by sampling protocols featuring seven or more "shells" with unique b/b Δ /TE-combinations. Acquisition times were approximately 15 minutes. In white matter of adults, the "stick" compartment had a fraction of approximately 0.5 and, compared with the "zeppelin" compartment, featured lower isotropic diffusivities (0.6 vs. 1.3 μm 2 /ms) but higher T 2 values (85 vs. 65 ms). Children featured lower "stick" fractions (0.4). White matter lesions exhibited high "zeppelin" isotropic diffusivities (1.7 μm 2 /ms) and T 2 values (150 ms). Conclusions: Diffusion-relaxation MRI with tensor-valued diffusion encoding expands the set of microstructure parameters that can be precisely estimated and therefore increases their specificity to biological quantities. K E Y W O R D Sbrain microstructure, diffusion-relaxation MRI, Fisher information, tensor-valued diffusion encoding 1606 | LAMPINEN Et AL. S (u) = (K ⊛ P) (u) = ∫ |n|=1 K(u ⋅ n) P(n) dn,(2) K(u ⋅ n) = S 0 J
A widespread network involving cortical and subcortical brain structures forms the neural substrate of human spatial navigation. Most studies investigating plasticity of this network have focused on the hippocampus. Here, we investigate age differences in cortical thickness changes evoked by four months of spatial navigation training in 91 men aged 20-30 or 60-70 years. Cortical thickness was automatically measured before, immediately after, and four months after termination of training. Younger as well as older navigators evidenced large improvements in navigation performance that were partly maintained after termination of training. Importantly, training-related cortical thickening in left precuneus and paracentral lobule were observed in young navigators only. Thus, spatial navigation training appears to affect cortical brain structure of young adults, but there is reduced potential for experience-dependent cortical alterations in old age.
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