The human brain connectome weighted by the myelin content and total intra-axonal cross-sectional area of white matter tracts

A central goal in neuroscience is the development of a comprehensive mapping between structural and functional brain features, which facilitates mechanistic interpretation of brain function. However, the interpretability of structure-function brain models remains limited by a lack of biological detail. Here, we characterize human structural brain networks weighted by multiple white matter microstructural features including total intra-axonal cross-sectional area and myelin content. We report edge-weight-dependent spatial distributions, variance, small-worldness, rich club, hubs, as well as relationships with function, edge length, and myelin. Contrasting networks weighted by the total intra-axonal cross-sectional area and myelin content of white matter tracts, we find opposite relationships with functional connectivity, an edge-length-independent inverse relationship with each other, and the lack of a canonical rich club in myelin-weighted networks. When controlling for edge length, networks weighted by either fractional anisotropy, radial diffusivity, or neurite density show no relationship with whole-brain functional connectivity. We conclude that the co-utilization of structural networks weighted by total intra-axonal cross-sectional area and myelin content could improve our understanding of the mechanisms mediating the structure-function brain relationship.

Optimization of acquisition parameters for cortical inhomogeneous magnetization transfer (ihMT) imaging using a rapid gradient echo readout

PURPOSE

Imaging biomarkers with increased myelin specificity are needed to better understand the complex progression of neurological disorders. Inhomogeneous magnetization transfer (ihMT) imaging is an emergent technique that has a high degree of specificity for myelin content but suffers from low signal to-noise ratio (SNR). This study used simulations to determine optimal sequence parameters for ihMT imaging for use in high-resolution cortical mapping.

METHODS

MT-weighted cortical image intensity and ihMT SNR were simulated using modified Bloch equations for a range of sequence parameters. The acquisition time was limited to 4.5 min/volume. A custom MT-weighted RAGE sequence with center-out k-space encoding was used to enhance SNR at 3 T. Pulsed MT imaging was studied over a range of saturation parameters, and the impact of the turbo factor on the effective ihMT resolution was investigated. 1 mm isotropic ihMTsat maps were generated in 25 healthy adults.

RESULTS

Greater SNR was observed for larger number of bursts consisting of 6-8 saturation pulses each, combined with a high readout turbo factor. However, that protocol suffered from a point spread function that was more than twice the nominal resolution. For high-resolution cortical imaging, we selected a protocol with a higher effective resolution at the cost of a lower SNR. We present the first group-average ihMTsat whole-brain map at 1 mm isotropic resolution.

CONCLUSION

This study presents the impact of saturation and excitation parameters on ihMTsat SNR and resolution. We demonstrate the feasibility of high-resolution cortical myelin imaging using ihMTsat in less than 20 min.

A model-based framework for correcting B 1 + inhomogeneity effects in magnetization transfer saturation and inhomogeneous magnetization transfer saturation maps

PURPOSE

In this work, we propose that Δ B 1 + -induced errors in magnetization transfer (MT) saturation (MT_sat ) maps can be corrected with use of an R₁ and B 1 + map and through numerical simulations of the sequence.

THEORY AND METHODS

One healthy subject was scanned at 3.0T using a partial quantitative MT protocol to estimate the relationship between observed R₁ (R_1,obs ) and apparent bound pool size ( M 0 , a p p B ) in the brain. MT_sat values were simulated for a range of B 1 + , R_1,obs , and M 0 , a p p B . An equation was fit to the simulated MT_sat , then a linear relationship between R_1,obs and M 0 , a p p B was generated. These results were used to generate correction factor maps for the MT_sat acquired from single-point data. The proposed correction was compared to an empirical correction factor with different MT-preparation schemes.

RESULTS

M 0 , a p p B was highly correlated with R_1,obs (r > 0.96), permitting the use of R_1,obs to estimate M 0 , a p p B for B 1 + correction. All B 1 + corrected MT_sat maps displayed a decreased correlation with B 1 + compared to uncorrected MT_sat and MT_sat corrected with an empirical factor in the corpus callosum. There was good agreement between the proposed approach and the empirical correction with radiofrequency saturation at 2 kHz, with larger deviations seen when using saturation pulses further off-resonance and in inhomogeneous (ih) MT_sat maps.

CONCLUSION

The proposed correction decreases the dependence of MT_sat on B 1 + inhomogeneities. Furthermore, this flexible framework permits the use of different saturation protocols, making it useful for correcting B 1 + inhomogeneities in ihMT.

Efficient whole-brain tract-specific T1 mapping at 3T with slice-shuffled inversion-recovery diffusion-weighted imaging

PURPOSE

Most voxels in white matter contain multiple fiber populations with different orientations and levels of myelination. Conventional T₁ mapping measures 1 T₁ value per voxel, representing a weighted average of the multiple tract T₁ times. Inversion-recovery diffusion-weighted imaging (IR-DWI) allows the T₁ times of multiple tracts in a voxel to be disentangled, but the scan time is prohibitively long. Recently, slice-shuffled IR-DWI implementations have been proposed to significantly reduce scan time. In this work, we demonstrate that we can measure tract-specific T₁ values in the whole brain using simultaneous multi-slice slice-shuffled IR-DWI at 3T.

METHODS

We perform simulations to evaluate the accuracy and precision of our crossing fiber IR-DWI signal model for various fiber parameters. The proposed sequence and signal model are tested in a phantom consisting of crossing asparagus pieces doped with gadolinium to vary T₁ , and in 2 human subjects.

RESULTS

Our simulations show that tract-specific T₁ times can be estimated within 5% of the nominal fiber T₁ values. Tract-specific T₁ values were resolved in subvoxel 2 fiber crossings in the asparagus phantom. Tract-specific T₁ times were resolved in 2 different tract crossings in the human brain where myelination differences have previously been reported; the crossing of the cingulum and genu of the corpus callosum and the crossing of the corticospinal tract and pontine fibers.

CONCLUSION

Whole-brain tract-specific T₁ mapping is feasible using slice-shuffled IR-DWI at 3T. This technique has the potential to improve the microstructural characterization of specific tracts implicated in neurodevelopment, aging, and demyelinating disorders.

Promise and pitfalls of g-ratio estimation with MRI

The fiber g-ratio is the ratio of the inner to the outer diameter of the myelin sheath of a myelinated axon. It has a limited dynamic range in healthy white matter, as it is optimized for speed of signal conduction, cellular energetics, and spatial constraints. In vivo imaging of the g-ratio in health and disease would greatly increase our knowledge of the nervous system and our ability to diagnose, monitor, and treat disease. MRI based g-ratio imaging was first conceived in 2011, and expanded to be feasible in full brain white matter with preliminary results in 2013. This manuscript reviews the growing g-ratio imaging literature and speculates on future applications. It details the methodology for imaging the g-ratio with MRI, and describes the known pitfalls and challenges in doing so.

Quantitative analysis of the myelin g-ratio from electron microscopy images of the macaque corpus callosum

We provide a detailed morphometric analysis of eight transmission electron micrographs (TEMs) obtained from the corpus callosum of one cynomolgus macaque. The raw TEM images are included in the article, along with the distributions of the axon caliber and the myelin g-ratio in each image. The distributions are analyzed to determine the relationship between axon caliber and g-ratio, and compared against the aggregate metrics (myelin volume fraction, fiber volume fraction, and the aggregate g-ratio), as defined in the accompanying research article entitled 'In vivo histology of the myelin g-ratio with magnetic resonance imaging' (Stikov et al., NeuroImage, 2015).

Beyond crossing fibers: bootstrap probabilistic tractography using complex subvoxel fiber geometries

Diffusion magnetic resonance imaging fiber tractography is a powerful tool for investigating human white matter connectivity in vivo. However, it is prone to false positive and false negative results, making interpretation of the tractography result difficult. Optimal tractography must begin with an accurate description of the subvoxel white matter fiber structure, includes quantification of the uncertainty in the fiber directions obtained, and quantifies the confidence in each reconstructed fiber tract. This paper presents a novel and comprehensive pipeline for fiber tractography that meets the above requirements. The subvoxel fiber geometry is described in detail using a technique that allows not only for straight crossing fibers but for fibers that curve and splay. This technique is repeatedly performed within a residual bootstrap statistical process in order to efficiently quantify the uncertainty in the subvoxel geometries obtained. A robust connectivity index is defined to quantify the confidence in the reconstructed connections. The tractography pipeline is demonstrated in the human brain.

Potential and limitations of diffusion MRI tractography for the study of language

Diffusion magnetic resonance imaging (MRI) is a tremendously promising tool for imaging tissue microstructure, and for inferring large scale structural connectivity in vivo. However, the sensitivity of the technique is highly dependent on methodological details. Acquisition parameters, pre-processing steps, reconstruction models, and statistical analysis all affect the final sensitivity, specificity, and accuracy of a study. In the case of fiber pathway reconstruction in the central nervous system, termed tractography, false positive and false negative results abound, and interpretation of results must take into account the potential shortcomings of the techniques used. This article will review the strengths and limitations of different types of diffusion MRI tractography analysis, and highlight what one can realistically hope to learn from such imaging studies of the human brain.

Fronto-temporal disconnectivity and clinical short-term outcome in first episode psychosis: a DTI-tractography study

Determining reliable markers of clinical outcome for psychosis is essential to adjust intervention efforts. White matter alterations exist prior to psychosis onset but its association with clinical outcome in the very early phase of psychosis is currently unknown. In the present study, white matter was assessed by diffusion tensor imaging (DTI) in patients with first episode psychosis (FEP) and healthy controls. Forty-four FEP patients and 30 matched healthy controls completed a DTI scan. The patient group was split in poor (n = 24) and good (n = 20) outcome subgroups based on 6-month clinical data. DTI tractography was used to estimate fractional anisotropy (FA) in the three main tracts connecting frontal and temporal regions (i.e. the cingulum, the superior longitudinal fasciculus and the uncinate fasciculus). The analyses showed selective FA reductions in both the uncinate and the superior longitudinal fasciculi, but not in the cingulum, when comparing FEP patients to healthy controls. FEP subgroup analyses revealed greater white matter changes in these tracts in patients with poor outcome as compared to patients with good outcome. These findings confirm that abnormal fronto-temporal connectivity contributes to the physiopathology of FEP and constitutes an early marker of clinical short-term outcome.

Mathematical methods for diffusion MRI processing

In this article, we review recent mathematical models and computational methods for the processing of diffusion Magnetic Resonance Images, including state-of-the-art reconstruction of diffusion models, cerebral white matter connectivity analysis, and segmentation techniques. We focus on Diffusion Tensor Images (DTI) and Q-Ball Images (QBI).

Labeling of ambiguous subvoxel fibre bundle configurations in high angular resolution diffusion MRI

Whereas high angular resolution reconstruction methods for diffusion MRI can estimate multiple dominant fibre orientations within a single imaging voxel, they are fundamentally limited in certain cases of complex subvoxel fibre structures, resulting in ambiguous local orientation distribution functions. In this article we address the important problem of disambiguating such complex subvoxel fibre tract configurations, with the purpose of improving the performance of fibre tractography. We do so by extending a curve inference method to distinguish between the cases of curving and fanning fibre bundles using differential geometric estimates in a local neighbourhood. The key benefit of this method is the inference of curves, instead of only fibre orientations, to model the underlying fibre bundles. This in turn allows distinct fibre geometries that contain nearly identical sets of fibre orientations at a voxel, to be distinguished from one another. Experimental results demonstrate the ability of the method to successfully label voxels into one of the above categories and improve the performance of a fibre-tracking algorithm.

3D curve inference for diffusion MRI regularization and fibre tractography

We develop a differential geometric framework for regularizing diffusion MRI data. The key idea is to model white matter fibres as 3D space curves and to then extend Parent and Zucker's 2D curve inference approach [Parent, P., Zucker, S., 1989. Trace inference, curvature consistency, and curve detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 823-839] by using a notion of co-helicity to indicate compatibility between fibre orientations at each voxel with those in a local neighborhood. We argue that this provides several advantages over earlier regularization methods. We validate the approach quantitatively on a biological phantom and on synthetic data, and qualitatively on data acquired in vivo from a human brain. We also demonstrate the use of the technique to improve the performance of a fibre tracking algorithm.

3D curve inference for diffusion MRI regularization

We develop a differential geometric framework for regularizing diffusion MRI data. The key idea is to model white matter fibers as 3D space curves and to then extend Parent and Zucker's 2D curve inference approach [8] by using a notion of co-helicity to indicate compatibility between fibre orientation estimates at each voxel with those in a local neighborhood. We argue that this provides several advantages over earlier regularization methods. We validate the approach quantitatively on a biological phantom and on synthetic data, and qualitatively on data acquired in vivo from a human brain.

Flow-based fiber tracking with diffusion tensor and q-ball data: Validation and comparison to principal diffusion direction techniques

In this study, we evaluate the performance of a flow-based surface evolution fiber tracking algorithm by means of a physical anisotropic diffusion phantom with known connectivity. We introduce a novel speed function for surface evolution that is derived from either diffusion tensor (DT) data, high angular resolution diffusion (HARD) data, or a combined DT-HARD hybrid approach. We use the model-free q-ball imaging (QBI) approach for HARD reconstruction. The anisotropic diffusion phantom allows us to compare and evaluate the performance of different fiber tracking approaches in the presence of real imaging artifacts, noise, and subvoxel partial volume averaging of fiber directions. The surface evolution approach, using the full diffusion tensor as opposed to the principal diffusion direction (PDD) only, is compared to PDD-based line propagation fiber tracking. Additionally, DT reconstruction is compared to HARD reconstruction for fiber tracking, both using surface evolution. We show the potential for surface evolution using the full diffusion tensor to map connections in regions of subvoxel partial volume averaging of fiber directions, which can be difficult to map with PDD-based methods. We then show that the fiber tracking results can be improved by using high angular resolution reconstruction of the diffusion orientation distribution function in cases where the diffusion tensor model fits the data poorly.