Human Diffusion Tensor Imaging Research Articles

Fractal basins as a mechanism for the nimble brain

An interesting feature of the brain is its ability to respond to disparate sensory signals from the environment in unique ways depending on the environmental context or current brain state. In dynamical systems, this is an example of multi-stability, the ability to switch between multiple stable states corresponding to specific patterns of brain activity/connectivity. In this article, we describe chimera states, which are patterns consisting of mixed synchrony and incoherence, in a brain-inspired dynamical systems model composed of a network with weak individual interactions and chaotic/periodic local dynamics. We illustrate the mechanism using synthetic time series interacting on a realistic anatomical brain network derived from human diffusion tensor imaging. We introduce the so-called vector pattern state (VPS) as an efficient way of identifying chimera states and mapping basin structures. Clustering similar VPSs for different initial conditions, we show that coexisting attractors of such states reveal intricately “mingled” fractal basin boundaries that are immediately reachable. This could explain the nimble brain’s ability to rapidly switch patterns between coexisting attractors.

White Matter Plasticity Keeps the Brain in Tune: Axons Conduct While Glia Wrap.

Precise timing of neuronal inputs is crucial for brain circuit function and development, where it contributes critically to experience-dependent plasticity. Myelination therefore provides an important adaptation mechanism for vertebrate circuits. Despite its importance to circuit activity, the interplay between neuronal activity and myelination has yet to be fully elucidated. In recent years, significant attention has been devoted to uncovering and explaining the phenomenon of white matter (WM) plasticity. Here, we summarize some of the critical evidence for modulation of the WM by neuronal activity, ranging from human diffusion tensor imaging (DTI) studies to experiments in animal models. These experiments reveal activity-dependent changes in the differentiation and proliferation of the oligodendrocyte lineage, and in the critical properties of the myelin sheaths. We discuss the implications of such changes for synaptic function and plasticity, and present the underlying mechanisms of neuron–glia communication, with a focus on glutamatergic signaling and the axomyelinic synapse. Finally, we examine evidence that myelin plasticity may be subject to critical periods. Taken together, the present review aims to provide insights into myelination in the context of brain circuit formation and function, emphasizing the bidirectional interplay between neurons and myelinating glial cells to better inform future investigations of nervous system plasticity.

Improvement of Partial Volume Segmentation for Brain Tissue on Diffusion Tensor Images Using Multiple-Tensor Estimation

To improve evaluations of cortical and subcortical diffusivity in neurological diseases, it is necessary to improve the accuracy of brain diffusion tensor imaging (DTI) data segmentation. The conventional partial volume segmentation method fails to classify voxels with multiple white matter (WM) fiber orientations such as fiber-crossing regions. Our purpose was to improve the performance of segmentation by taking into account the partial volume effects due to both multiple tissue types and multiple WM fiber orientations. We quantitatively evaluated the overall performance of the proposed method using digital DTI phantom data. Moreover, we applied our method to human DTI data, and compared our results with those of a conventional method. In the phantom experiments, the conventional method and proposed method yielded almost the same root mean square error (RMSE) for gray matter (GM) and cerebrospinal fluid (CSF), while the RMSE in the proposed method was smaller than that in the conventional method for WM. The volume overlap measures between our segmentation results and the ground truth of the digital phantom were more than 0.8 in all three tissue types, and were greater than those in the conventional method. In visual comparisons for human data, the WM/GM/CSF regions obtained using our method were in better agreement with the corresponding regions depicted in the structural image than those obtained using the conventional method. The results of the digital phantom experiment and human data demonstrated that our method improved accuracy in the segmentation of brain tissue data on DTI compared to the conventional method.

The acute phase of Wallerian degeneration: Longitudinal diffusion tensor imaging of the fornix following temporal lobe surgery

Numerous animal studies have shown the applicability of diffusion tensor imaging (DTI) to track Wallerian degeneration that occurs after injury to the neural fiber. Non-invasive biomarkers that may differentiate the early axonal breakdown and later myelin degradation have been attributed to either reduced parallel and elevated perpendicular diffusivity, respectively. While several human DTI studies have shown this potential at subacute and chronic time points, the diffusion changes that occur within the first week are unknown. Anterior temporal lobectomy (i.e. resection of hippocampus) is the standard surgical treatment of medically refractory temporal lobe epilepsy. The concomitant transection of the fimbria-fornix serves as a unique opportunity to examine the process of Wallerian degeneration since the timing is known. Six temporal lobe epilepsy patients underwent brain DTI before the surgery, three to four times within the first week post-operatively, and at one to four months following surgery. Both parallel and perpendicular diffusivities decreased markedly by a similar amount in the ipsilateral fornix within the first two days post-surgery. Approaching the end of the first week, perpendicular (but not parallel) diffusivity pseudo-recovered towards its pre-surgical value, but then increased dramatically months later. Fractional anisotropy, as a result of the combined action of the parallel and perpendicular diffusivities, stayed relatively stable within the first week and only reduced drastically at the chronic stage. DTI demonstrated acute water diffusion changes within days of transection that are not just limited to parallel diffusivity. While the chronic diffusion changes in the fornix are compatible with myelin degradation, the acute changes may reflect beading and swelling of axolemma, granular disintegration of the axonal neurofilaments, ischemia induced cytotoxic edema, and/or changes in the extra-axonal space including inflammatory changes and gliosis.

High‐resolution human diffusion tensor imaging using 2‐D navigated multishot SENSE EPI at 7 T

The combination of parallel imaging with partial Fourier acquisition has greatly improved the performance of diffusion-weighted single-shot EPI and is the preferred method for acquisitions at low to medium magnetic field strength such as 1.5 or 3 T. Increased off-resonance effects and reduced transverse relaxation times at 7 T, however, generate more significant artifacts than at lower magnetic field strength and limit data acquisition. Additional acceleration of k-space traversal using a multishot approach, which acquires a subset of k-space data after each excitation, reduces these artifacts relative to conventional single-shot acquisitions. However, corrections for motion-induced phase errors are not straightforward in accelerated, diffusion-weighted multishot EPI because of phase aliasing. In this study, we introduce a simple acquisition and corresponding reconstruction method for diffusion-weighted multishot EPI with parallel imaging suitable for use at high field. The reconstruction uses a simple modification of the standard sensitivity-encoding (SENSE) algorithm to account for shot-to-shot phase errors; the method is called image reconstruction using image-space sampling function (IRIS). Using this approach, reconstruction from highly aliased in vivo image data using 2-D navigator phase information is demonstrated for human diffusion-weighted imaging studies at 7 T. The final reconstructed images show submillimeter in-plane resolution with no ghosts and much reduced blurring and off-resonance artifacts.

Globally optimized fiber tracking and hierarchical clustering — a unified framework

Structural connectivity between cortical regions of the human brain can be characterized noninvasively with diffusion tensor imaging (DTI)-based fiber tractography. In this paper, a novel fiber tractography technique, globally optimized fiber tracking and hierarchical fiber clustering, is presented. The proposed technique uses k-means clustering in conjunction with modified Hubert statistic to partition fiber pathways, which are evaluated with simultaneous consideration of consistency with underlying DTI data and smoothness of fiber courses in the sense of global optimality, into individual anatomically coherent fiber bundles. In each resulting bundle, fibers are sampled, perturbed and clustered iteratively to approach the optimal solution. The global optimality allows the proposed technique to resist local image artifacts and to possess inherent capabilities of handling complex fiber structures and tracking fibers between gray matter regions. The embedded hierarchical clustering allows multiple fiber bundles between a pair of seed regions to be naturally reconstructed and partitioned. The integration of globally optimized tracking and hierarchical clustering greatly benefits applications of DTI-based fiber tractography to clinical studies, particularly to studies of structure–function relations of the complex neural network of the human. Experiments with synthetic and in vivo human DTI data have demonstrated the effectiveness of the proposed technique in tracking complex fiber structures, thus proving its significant advantages over traditionally used streamline fiber tractography.

The effect of local perturbation fields on human DTI: Characterisation, measurement and correction

Indices derived from diffusion tensor imaging (DTI) data, including the mean diffusivity (MD) and fractional anisotropy (FA), are often used to better understand the microstructure of the brain. DTI, however, is susceptible to imaging artefacts, which can bias these indices. The most important sources of artefacts in DTI include eddy currents, nonuniformity and mis-calibration of gradients. We modelled these and other artefacts using a local perturbation field (LPF) approach. LPFs during the diffusion-weighting period describe the local mismatches between the effective and the expected diffusion gradients resulting in a spatially varying error in the diffusion weighting B matrix and diffusion tensor estimation. We introduced a model that makes use of phantom measurements to provide a robust estimation of the LPF in DTI without requiring any scanner-hardware-specific information or special MRI sequences. We derived an approximation of the perturbed diffusion tensor in the isotropic-diffusion limit that can be used to identify regions in any DTI index map that are affected by LPFs. Using these models, we simulated and measured LPFs and characterised their effect on human DTI for three different clinical scanners. The small FA values found in grey matter were biased towards greater anisotropy leading to lower grey-to-white matter contrast (up to 10%). Differences in head position due to e.g. repositioning produced errors of up to 10% in the MD, reducing comparability in multi-centre or longitudinal studies. We demonstrate the importance of the proposed correction by showing improved consistency across scanners, different head positions and an increased FA contrast between grey and white matter.

Genetic white matter fiber tractography with global optimization

Diffusion tensor imaging (DTI) tractography is a novel technique that can delineate the trajectories between cortical region of the human brain non-invasively. In this paper, a novel DTI based white matter fiber tractography using genetic algorithm is presented. Adapting the concepts from evolutionary biology which include selection, recombination and mutation, globally optimized fiber pathways are generated iteratively. Global optimality of the fiber tracts is evaluated using Bayes decision rule, which simultaneously considers both the fiber geometric smoothness and consistency with the tensor field. This global optimality assigns the tracking fibers great immunity to random image noise and other local image artifacts, thus avoiding the detrimental effects of cumulative noise on fiber tracking. Experiments with synthetic and in vivo human DTI data have demonstrated the feasibility and robustness of this new fiber tracking technique, and an improved performance over commonly used probabilistic fiber tracking.

Evaluation of measurement uncertainties in human diffusion tensor imaging (DTI)‐derived parameters and optimization of clinical DTI protocols with a wild bootstrap analysis

To quantify measurement uncertainties of fractional anisotropy, mean diffusivity, and principal eigenvector orientations in human diffusion tensor imaging (DTI) data acquired with common clinical protocols using a wild bootstrap analysis, and to establish optimal scan protocols for clinical DTI acquisitions. A group of 13 healthy volunteers were scanned using three commonly used DTI protocols with similar total scan times. Two important parameters-the number of unique diffusion gradient directions (NUDG) and the ratio of the total number of diffusion-weighted (DW) images to the total number of non-DW images (DTIR)-were analyzed in order to investigate their combined effects on uncertainties of DTI-derived parameters, using results from both the Monte Carlo simulation and the wild bootstrap analysis of uncertainties in human DTI data. The wild bootstrap analysis showed that uncertainties in human DTI data are significantly affected by both NUDG and DTIR in many brain regions. These results agree with previous predictions based on error-propagations as well as results from simulations. Our results demonstrate that within a clinically feasible DTI scan time of about 10 minutes, a protocol with number of diffusion gradient directions close to 30 provides nearly optimal measurement results when combined with a ratio of the total number of DW images over non-DW images equal to six. Wild bootstrap can serve as a useful tool to quantify the measurement uncertainty from human DTI data.

Using the wild bootstrap to quantify uncertainty in diffusion tensor imaging

Estimation of noise-induced variability in diffusion tensor imaging (DTI) is needed to objectively follow disease progression in therapeutic monitoring and to provide consistent readouts of pathophysiology. The noise variability of nonlinear quantities of the diffusion tensor (e.g., fractional anisotropy, fiber orientation, etc.) have been quantified using the bootstrap, in which the data are resampled from the experimental averages, yet this approach is only applicable to DTI scans that contain multiple averages from the same sampling direction. It has been shown that DTI acquisitions with a modest to large number of directions, in which each direction is only sampled once, outperform the multiple averages approach. These acquisitions resist the traditional (regular) bootstrap analysis though. In contrast to the regular bootstrap, the wild bootstrap method can be applied to such protocols in which there is only one observation per direction. Here, we compare and contrast the wild bootstrap with the regular bootstrap using Monte Carlo numerical simulations for a number of diffusion scenarios. The regular and wild bootstrap methods are applied to human DTI data and empirical distributions are obtained for fractional anisotropy and the diffusion tensor eigensystem. Spatial maps of the estimated variability in the diffusion tensor principal eigenvector are provided. The wild bootstrap method can provide empirical distributions for tensor-derived quantities, such as fractional anisotropy and principal eigenvector direction, even when the exact distributions are not easily derived.

White matter tractography by anisotropic wavefront evolution and diffusion tensor imaging

Determination of axonal pathways provides an invaluable means to study the connectivity of the human brain and its functional network. Diffusion tensor imaging (DTI) is unique in its ability to capture the restricted diffusion of water molecules which can be used to infer the directionality of tissue components. In this paper, we introduce a white matter tractography method based on anisotropic wavefront propagation in diffusion tensor images. A front propagates in the white matter with a speed profile governed by the isocontour of the diffusion tensor ellipsoid. By using the ellipsoid, we avoid possible misclassification of the principal eigenvector in oblate regions. The wavefront evolution is described by an anisotropic version of the static Hamilton–Jacobi equation, which is solved by a sweeping method in order to obtain correct arrival times. Pathways of connection are determined by tracing minimum-cost trajectories using the characteristic vector field of the resulting partial differential equation. A validity index is described to rate the goodness of the resulting pathways with respect to the directionality of the tensor field. Connectivity results using normal human DTI brain images are illustrated and discussed. We also compared our method with a similar level set-based tractography technique, and found that the anisotropic evolution increased the validity index of the obtained pathways by 18%.