Potential Pitfalls of Using Fractional Anisotropy, Axial Diffusivity, and Radial Diffusivity as Biomarkers of Cerebral White Matter Microstructure

Fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) are commonly used as MRI biomarkers of white matter microstructure in diffusion MRI studies of neurodevelopment, brain aging, and neurologic injury/disease. Some of the more frequent practices include performing voxel-wise or region-based analyses of these measures to cross-sectionally compare individuals or groups, longitudinally assess individuals or groups, and/or correlate with demographic, behavioral or clinical variables. However, it is now widely recognized that the majority of cerebral white matter voxels contain multiple fiber populations with different trajectories, which renders these metrics highly sensitive to the relative volume fractions of the various fiber populations, the microstructural integrity of each constituent fiber population, and the interaction between these factors. Many diffusion imaging experts are aware of these limitations and now generally avoid using FA, AD or RD (at least in isolation) to draw strong reverse inferences about white matter microstructure, but based on the continued application and interpretation of these metrics in the broader biomedical/neuroscience literature, it appears that this has perhaps not yet become common knowledge among diffusion imaging end-users. Therefore, this paper will briefly discuss the complex biophysical underpinnings of these measures in the context of crossing fibers, provide some intuitive “thought experiments” to highlight how conventional interpretations can lead to incorrect conclusions, and suggest that future studies refrain from using (over-interpreting) FA, AD, and RD values as standalone biomarkers of cerebral white matter microstructure.

Differential Developmental Associations of Material Hardship Exposure and Adolescent Amygdala–Prefrontal Cortex White Matter Connectivity

Accumulating literature has linked poverty to brain structure and function, particularly in affective neural regions; however, few studies have examined associations with structural connections or the importance of developmental timing of exposure. Moreover, prior neuroimaging studies have not used a proximal measure of poverty (i.e., material hardship, which assesses food, housing, and medical insecurity) to capture the lived experience of growing up in harsh economic conditions. The present investigation addressed these gaps collectively by examining the associations between material hardship (ages 1, 3, 5, 9, and 15 years) and white matter connectivity of frontolimbic structures (age of 15 years) in a low-income sample. We applied probabilistic tractography to diffusion imaging data collected from 194 adolescents. Results showed that material hardship related to amygdala–prefrontal, but not hippocampus–prefrontal or hippocampus–amygdala, white matter connectivity. Specifically, hardship during middle childhood (ages 5 and 9 years) was associated with greater connectivity between the amygdala and dorsomedial pFC, whereas hardship during adolescence (age of 15 years) was related to reduced amygdala–orbitofrontal (OFC) and greater amygdala–subgenual ACC connectivity. Growth curve analyses showed that greater increases of hardship across time were associated with both greater (amygdala–subgenual ACC) and reduced (amygdala–OFC) white matter connectivity. Furthermore, these effects remained above and beyond other types of adversity, and greater hardship and decreased amygdala–OFC connectivity were related to increased anxiety and depressive symptoms. Results demonstrate that the associations between material hardship and white matter connections differ across key prefrontal regions and developmental periods, providing support for potential windows of plasticity for structural circuits that support emotion processing.

Group-Level Ranking-Based Hubness Analysis of Human Brain Connectome Reveals Significant Interhemispheric Asymmetry and Intraparcel Heterogeneities

Objective: Graph theory applications are commonly used in connectomics research to better understand connectivity architecture and characterize its role in cognition, behavior and disease conditions. One of the numerous open questions in the field is how to represent inter-individual differences with graph theoretical methods to make inferences for the population. Here, we proposed and tested a simple intuitive method that is based on finding the correlation between the rank-ordering of nodes within each connectome with respect to a given metric to quantify the differences/similarities between different connectomes.Methods: We used the diffusion imaging data of the entire HCP-1065 dataset of the Human Connectome Project (HCP) (n = 1,065 subjects). A customized cortical subparcellation of HCP-MMP atlas (360 parcels) (yielding a total of 1,598 ROIs) was used to generate connectivity matrices. Six graph measures including degree, strength, coreness, betweenness, closeness, and an overall “hubness” measure combining all five were studied. Group-level ranking-based aggregation method (“measure-then-aggregate”) was used to investigate network properties on population level.Results: Measure-then-aggregate technique was shown to represent population better than commonly used aggregate-then-measure technique (overall rs: 0.7 vs 0.5). Hubness measure was shown to highly correlate with all five graph measures (rs: 0.88–0.99). Minimum sample size required for optimal representation of population was found to be 50 to 100 subjects. Network analysis revealed a widely distributed set of cortical hubs on both hemispheres. Although highly-connected hub clusters had similar distribution between two hemispheres, average ranking values of homologous parcels of two hemispheres were significantly different in 71% of all cortical parcels on group-level.Conclusion: In this study, we provided experimental evidence for the robustness, limits and applicability of a novel group-level ranking-based hubness analysis technique. Graph-based analysis of large HCP dataset using this new technique revealed striking hemispheric asymmetry and intraparcel heterogeneities in the structural connectivity of the human brain.

Diffusion-Weighted Imaging of the Macaque Brain Using Diffusion-Prepared Turbo Spin Echo in MRI

Magnetic resonance imaging (MRI) integrates a static magnetic field, a time-varying gradient magnetic field at kHz and a radio-frequency (RF) magnetic field for non-invasive and real-time imaging; meanwhile, diffusion MRI (dMRI) pushes a further and closer dimension to the scale of neural fibers through sensitizing the gradient field to recognize water molecular displacement over distances of 1~20 μm along fibers. Contemporary dMRI approaches face challenges of magnetic field inhomogeneity as well as sequence-associated distortion and signal loss, the common remedies of which are repeated scans and post-reconstruction algorithms. In this study, over an anesthetized macaque with a customized head coil on 3 T MRI, we have proposed and implemented a monopolar diffusion-prepared module for turbo spin echo sequence (DP-TSE) as an alternative to achieve distortion-free, high-resolution diffusion imaging with improved SNR. The results showed high image quality and SNR efficiency as compared with conventional dMRI methods at millimeter level, allowing us to pursue submillimeter-scale dMRI over non-human primates (NHPs) in a relatively short scan time and without repetitions or post-processing, which could merit and advance our understanding of the structure and organizations of the primate’s brain.

Tissue Optimisation Strategies for High Quality Ex Vivo Diffusion Imaging

Ex vivo diffusion imaging can be used to study healthy and pathological tissue microstructure in the rodent brain with microscopic resolution, providing a link between in vivo MRI and ex vivo microscopy techniques. A major challenge for the successful acquisition of ex vivo diffusion imaging data however are changes in the relaxivity and diffusivity of brain tissue following perfusion fixation. In this study we address this question by examining the combined effects of tissue preparation factors that influence image quality, including tissue rehydration time, fixative concentration and contrast agent concentration. We present an optimisation strategy combining these factors to manipulate the T1 and T2 of fixed tissue and maximise signal-to-noise ratio (SNR) efficiency. Applying this strategy in the rat brain resulted in a doubling of SNR and an increase in SNR per unit time by 135% in grey matter and 88% in white matter. This enabled the acquisition of excellent quality high-resolution (78 μm isotropic voxel size) diffusion data in less than 4 days, with a b-value of 4000 s/mm2, 30 diffusion directions and a field of view of 40 x 13 x 18 mm, using a 9.4 Tesla scanner with a standard 39 mm volume coil and a 660 mT/m 114 mm gradient insert. It was also possible to achieve comparable data quality for a standard resolution (150 μm) diffusion dataset in 21/4 hours. In conclusion, the optimisation strategy presented here may be used to improve signal quality, increase spatial resolution and/or allow faster acquisitions in preclinical ex vivo diffusion MRI experiments.

Myelin Water Fraction in Relation To Fractional Anisotropy and Reading in 10-Year-Old Children

Diffusion-weighted imaging studies have repeatedly shown that white matter correlates with reading throughout development. However, the neurobiological interpretation of this relationship is constrained by the limited microstructural specificity of diffusion imaging. A critical component of white matter microstructure is myelin, which can be investigated noninvasively using MRI. Here, diffusion-weighted as well as myelin water imaging were applied to examine the links of myelin water fraction (MWF) with fractional anisotropy (FA; a common diffusion index) and reading ability in 10-year-old children (n = 69). The results replicate previous reports on a positive relationship between FA and MWF, which is significant in dorsal but not ventral tracts. Moreover, our findings revealed a negative correlation between word reading and MWF in left reading-related white matter tracts. Altogether, this study contributes important insights into the role of myelin-related processes in the relationship between reading and white matter structure.

Application of 7 tesla magnetic resonance imaging for pediatric neurological disorders: Early clinical experience

Ultra-high field magnetic resonance imaging (MRI) has been introduced for use in pediatric developmental neurology. While higher magnetic fields have certain advantages, optimized techniques with specific considerations are required to ensure rational and safe use in children and those with pediatric neurological disorders (PNDs). Here, we summarize our initial experience with clinical translational studies that utilized 7 tesla (T)-MRI in the fields of developmental neurology. T2-reversed images and three-dimensional anisotropy contrast imaging enabled the depiction of targeted pathological brain structures with better spatial resolution. Diffusion imaging and susceptibility-weighted imaging enabled visualization of intracortical, subcortical, and intratumoral microstructures in vivo within highly limited scan times appropriate for patients with PNDs. 7T-MRI appears to have significant potential to enhance the depiction of the structural and functional properties of the brain, particularly those associated with atypical brain development.

DETERMINATION OF FIBER ORIENTATION IN HIGH ANGULAR RESOLUTION DIFFUSION IMAGING USING SPHERICAL HARMONICS AND PARTICLE SWARM OPTIMIZATION

The fiber directions in High Angular Resolution Diffusion Imaging (HARDI) with low fractional anisotropy or low Signal to Noise Ratio (SNR) cannot be estimated accurately. In this paper, the fiber directions are estimated using Particle Swarm Optimization and Spherical Deconvolution (PSO-SD). Fiber orientation is modeled as a Dirac delta function in [Formula: see text]. The Spherical Harmonic Coefficients (SHC) of the Dirac delta function in the [Formula: see text] direction are obtained using the rotational harmonic matrix and the SHC of the Dirac delta function in the [Formula: see text]-axis. The PSO-SD method is used to determine ([Formula: see text]). We generated noise-free synthetic data for isotropic regions (FA varied from 0.1 to 0.8) and synthetic data with two crossing fibers for anisotropic regions with SNRs of 20, 15, 10 and 5 (FA [Formula: see text] 0.78). In the noise-free signal (FA [Formula: see text] 0.3), the Success Ratio (SR) and Mean Difference Angle (MDA) of the PSO-SD method were 1∘ and 9.48∘, respectively. In the noisy signal (FA [Formula: see text] 0.78, SNR [Formula: see text] 10, crossing angle [Formula: see text] 40), the SR and MDA of PSO-SD (with [Formula: see text]) were 0.46∘ and 12.3∘, respectively. The PSO-SD method can estimate fiber directions in HARDI with low fractional anisotropy and low SNR. Moreover, it has a higher SR and lower MDA in comparison with those of the super-CSD method.

A large-scale diffusion imaging study of tinnitus and hearing loss

Subjective, chronic tinnitus, the perception of sound in the absence of an external source, commonly occurs with many comorbidities, making it a difficult condition to study. Hearing loss, often believed to be the driver for tinnitus, is perhaps one of the most significant comorbidities. In the present study, white matter correlates of tinnitus and hearing loss were examined. Diffusion imaging data were collected from 96 participants—43 with tinnitus and hearing loss (TINHL), 17 with tinnitus and normal hearing thresholds (TINNH), 17 controls with hearing loss (CONHL) and 19 controls with normal hearing (CONNH). Fractional anisotropy (FA), mean diffusivity and probabilistic tractography analyses were conducted on the diffusion imaging data. Analyses revealed differences in FA and structural connectivity specific to tinnitus, hearing loss, and both conditions when comorbid, suggesting the existence of tinnitus-specific neural networks. These findings also suggest that age plays an important role in neural plasticity, and thus may account for some of the variability of results in the literature. However, this effect is not seen in tractography results, where a sensitivity analysis revealed that age did not impact measures of network integration or segregation. Based on these results and previously reported findings, we propose an updated model of tinnitus, wherein the internal capsule and corpus callosum play important roles in the evaluation of, and neural plasticity in response to tinnitus.