Regularization of diffusion-based direction maps for the tracking of brain white matter fascicles

Diffusion tensor imaging tractography in the one-humped camel (Camelus dromedarius) brain

Introduction

Tractography is a technique used to trace the pathways of the brain using noninvasive diffusion tensor imaging (DTI) data. It is becoming increasingly popular for investigating the brains of domestic mammals and other animals with myelinated fibers but the principle of DTI can also apply for those with unmyelinated fibers. In the case of camels, DTI tractography is a promising method for enhancing current knowledge of the brain's structural connectivity and identifying white-matter tract changes potentially linked to neurodegenerative pathologies. The present study was therefore designed to describe representative white-matter tracts in the one-humped camel DTI tractography.

Methods

Post mortem DTI was used to obtain images of two one-humped camel brains using a 3 Tesla system. T2-weighted images were also acquired to identify regions of interest for each fiber tract and a fiber dissection technique was used to complement the DT images. The main association, commissural, and projection fibers were reconstructed and superimposed on T2-weighted images or fractional anisotropy maps.

Results

The results of the present study show the reconstruction of the most representative tracts, ie the cingulum, the corpus callosum and the internal capsule, in the one-humped camel brain using DTI data acquired post mortem. These DTI results were compared to those from fiber dissection.

Discussion

Anatomy of the cingulum, corpus callosum and internal capsule correlates well with the description in anatomical textbooks and appears to be similar to fibers describe in large animals. Further research will be required to improve and validate these findings and to generate a tractography atlas based on MRI and histological data, as such an atlas would be a valuable resource for future neuroimaging research.

BOLD fMRI and DTI fiber tracking for preoperative mapping of eloquent cerebral regions in brain tumor patients: impact on surgical approach and outcome

PURPOSE

Task-based BOLD fMRI and DTI-fiber tracking have become part of the routine presurgical work-up of brain tumor patients in many institutions. However, their potential impact on both surgical treatment and neurologic outcome remains unclear, in despite of the high costs and complex implementation.

METHODS

We retrospectively investigated whether performing fMRI and DTI-ft preoperatively substantially impacted surgical planning and patient outcome in a series of brain tumor patients. We assessed (i) the quality of fMRI and DTI-ft results, by using a scale of 0-2 (0 = failed mapping; 1 = intermediate confidence; 2 = good confidence), (ii) whether functional planning substantially contributed to defining the surgical strategy to be undertaken (i.e., no surgery, biopsy, or resection, with or without ESM), the surgical entry point and extent of resection, and (iii) the incidence of neurological deficits post-operatively.

RESULTS

Twenty-seven patients constituted the study population. The mean confidence rating was 1.9/2 for fMRI localization of the eloquent cortex and lateralization of the language function and 1.7/2 for DTI-ft results. Treatment strategy was altered in 33% (9/27) of cases. Surgical entry point was modified in 8% (2/25) of cases. The extent of resection was modified in 40% (10/25). One patient (1/25, 4%) developed one new functional deficit post-operatively.

CONCLUSION

Functional MR mapping - which must not be considered an alternative to ESM - has a critical role preoperatively, potentially modifying treatment strategy or increasing the neurosurgeons' confidence in the surgical approach hypothesized based on conventional imaging.

A novel male Japanese quail structural connectivity atlas using ultra-high field diffusion MRI at 11.7 T

The structural connectivity of animal brains can be revealed using post-mortem diffusion-weighted magnetic resonance imaging (MRI). Despite the existence of several structural atlases of avian brains, few of them address the bird’s structural connectivity. In this study, a novel atlas of the structural connectivity is proposed for the male Japanese quail (Coturnix japonica), aiming at investigating two lines divergent on their emotionality trait: the short tonic immobility (STI) and the long tonic immobility (LTI) lines. The STI line presents a low emotionality trait, while the LTI line expresses a high emotionality trait. 21 male Japanese quail brains from both lines were scanned post-mortem for this study, using a preclinical Bruker 11.7 T MRI scanner. Diffusion-weighted MRI was performed using a 3D segmented echo planar imaging (EPI) pulsed gradient spin-echo (PGSE) sequence with a 200 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}μm isotropic resolution, 75 diffusion-encoding directions and a b-value fixed at 4500 s/mm². Anatomical MRI was likewise performed using a 2D anatomical T₂-weighted spin-echo (SE) sequence with a 150 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}μm isotropic resolution. This very first anatomical connectivity atlas of the male Japanese quail reveals 34 labeled fiber tracts and the existence of structural differences between the connectivity patterns characterizing the two lines. Thus, the link between the male Japanese quail’s connectivity and its underlying anatomical structures has reached a better understanding.

Reducing the Complexity of Model-Based MRI Reconstructions via Sparsification

Model-based reconstruction methods have emerged as a powerful alternative to classical Fourier-based MRI techniques, largely because of their ability to explicitly model (and therefore, potentially overcome) moderate field inhomogeneities, streamline reconstruction from non-Cartesian sampling, and even allow for the use of custom designed non-Fourier encoding methods. Their application in such scenarios, however, often comes with a substantial increase in computational cost, owing to the fact that the corresponding forward model in such settings no longer possesses a direct Fourier Transform based implementation. This paper introduces an algorithmic framework designed to reduce the computational burden associated with model-based MRI reconstruction tasks. The key innovation is the strategic sparsification of the corresponding forward operators for these models, giving rise to approximations of the forward models (and their adjoints) that admit low computational complexity application. This enables overall a reduced computational complexity application of popular iterative first-order reconstruction methods for these reconstruction tasks. Computational results obtained on both synthetic and experimental data illustrate the viability and efficiency of the approach.

Network Neuroscience and the Adapted Mind: Rethinking the Role of Network Theories in Evolutionary Psychology

Evolutionary psychology is the comprehensive study of cognition and behavior in the light of evolutionary theory, a unifying paradigm integrating a huge diversity of findings across different levels of analysis. Since natural selection shaped the brain into a functionally organized system of interconnected neural structures rather than an aggregate of separate neural organs, the network-based account of anatomo-functional architecture is bound to yield the best mechanistic explanation for how the brain mediates the onset of evolved cognition and adaptive behaviors. While this view of a flexible and highly distributed organization of the brain is more than a century old, it was largely ignored up until recently. Technological advances are only now allowing this approach to find its rightful place in the scientific landscape. Historically, early network theories mostly relied on lesion studies and investigations on white matter circuitry, subject areas that still provide great empirical findings to this day. Thanks to new neuroimaging techniques, the traditional localizationist framework, in which any given cognitive process is thought to be carried out by its dedicated brain structure, is slowly being abandoned in favor of a network-based approach. We argue that there is a special place for network neuroscience in the upcoming quest for the biological basis of information-processing systems identified by evolutionary psychologists. By reviewing history of network theories, and by addressing several theoretical and methodological implications of this view for evolutionary psychologists, we describe the current state of knowledge about human neuroanatomy for those who wish to be mindful of both evolutionary and network neuroscience paradigms.

Data-Driven Identification of the Regions of Interest for Fiber Tracking in Patients with Brain Tumors

BACKGROUND

We investigated the added value of combining information from direction-encoded color (DEC) maps with high-resolution structural magnetic resonance imaging scans (T1-weighted images [T1WIs]) to improve the identification of regions of interest (ROIs) for fiber tracking during preoperative planning for patients with brain tumors.

METHODS

The dataset included 42 patients with gliomas and 10 healthy subjects from the Human Connectome Project. For identification of the ROIs, we combined the structural information from high-resolution T1WIs and the directional information from DEC maps. To test our hypothesis, we examined the interrater and intrarater agreement.

RESULTS

We identified specific ROIs to extract the main white matter bundles. The directional information from the DEC maps combined with the T1WIs (T1WI-DEC maps) had significantly facilitated ROI identification in patients with brain tumors, especially patients in whom the tracts had been displaced by the mass effect of the tumor. Fiber tracking using the combined T1WI-DEC maps showed significantly greater inter- and intrarater agreement compared with using either T1WI or DEC maps alone.

CONCLUSION

Combining the information from diffusion-derived color-encoded maps with high-resolution anatomical details from structural imaging (T1WI-DEC map), especially in patients with brain tumors, could be useful for accurate identification of the ROIs.

How Human Is Human Connectional Neuroanatomy?

The structure of the human brain has been studied extensively. Despite all the knowledge accrued, direct information about connections, from origin to termination, in the human brain is extremely limited. Yet there is a widespread misperception that human connectional neuroanatomy is well-established and validated. In this article, we consider what is known directly about human structural and connectional neuroanatomy. Information on neuroanatomical connections in the human brain is derived largely from studies in non-human experimental models in which the entire connectional pathway, including origins, course, and terminations, is directly visualized. Techniques to examine structural connectivity in the human brain are progressing rapidly; nevertheless, our present understanding of such connectivity is limited largely to data derived from homological comparisons, particularly with non-human primates. We take the position that an in-depth and more precise understanding of human connectional neuroanatomy will be obtained by a systematic application of this homological approach.

Using GPUs to accelerate computational diffusion MRI: From microstructure estimation to tractography and connectomes

The great potential of computational diffusion MRI (dMRI) relies on indirect inference of tissue microstructure and brain connections, since modelling and tractography frameworks map diffusion measurements to neuroanatomical features. This mapping however can be computationally highly expensive, particularly given the trend of increasing dataset sizes and the complexity in biophysical modelling. Limitations on computing resources can restrict data exploration and methodology development. A step forward is to take advantage of the computational power offered by recent parallel computing architectures, especially Graphics Processing Units (GPUs). GPUs are massive parallel processors that offer trillions of floating point operations per second, and have made possible the solution of computationally-intensive scientific problems that were intractable before. However, they are not inherently suited for all problems. Here, we present two different frameworks for accelerating dMRI computations using GPUs that cover the most typical dMRI applications: a framework for performing biophysical modelling and microstructure estimation, and a second framework for performing tractography and long-range connectivity estimation. The former provides a front-end and automatically generates a GPU executable file from a user-specified biophysical model, allowing accelerated non-linear model fitting in both deterministic and stochastic ways (Bayesian inference). The latter performs probabilistic tractography, can generate whole-brain connectomes and supports new functionality for imposing anatomical constraints, such as inherent consideration of surface meshes (GIFTI files) along with volumetric images. We validate the frameworks against well-established CPU-based implementations and we show that despite the very different challenges for parallelising these problems, a single GPU achieves better performance than 200 CPU cores thanks to our parallel designs.

Functional Informed Fiber Tracking Using Combination of Diffusion and Functional MRI

Fiber tractography using diffusion weighted MRI (DWI) is a primary tool for mapping structural connectivity in the human brain in vivo. However, this method suffers from a number of inherent limitations that have a significant impact on its capability in faithfully constructing fiber bundles for specific function. In this paper, a novel tractography algorithm combining DWI and functional MRI (fMRI) was proposed. Specifically, a spatio-temporal correlation tensor that characterizes the anisotropy of fMRI signals in white matter was introduced to complement the estimation of fiber orientation density function from DWI. The proposed method has been demonstrated to identify functional pathways implicated in fMRI task. It can effectively follow tracts in the genu of the corpus callosum that connects to the frontal lobe cortex, obtain connections between the thalamus and the anterior insula under sensory simulation, and reconstruct optic radiations in the visual circuit under visual stimulation. Taken together, the method we proposed in this work may benefit our understanding of structure-function relations in the human brain.

Modelfree global tractography

Tractography based on diffusion-weighted MRI investigates the large scale arrangement of the neurite fibers in brain white matter. It is usually assumed that the signal is a convolution of a fiber specific response function (FRF) with a fiber orientation distribution (FOD). The FOD is the focus of tractography. While in the past the FRF was estimated beforehand and was usually assumed to be fix, more recent approaches estimate the response function during tractography. This work proposes a novel objective function independent of the FRF, just aiming for FOD reconstruction. The objective is integrated into global tractography showing promising results.

Visualization of Central Nervous System Nerve Communications Using Diffusion Tensor Imaging

Diffusion tensor imaging (DTI) is a magnetic resonance (MR) technique used to analyze diffusion anisotropy of the central nervous system (CNS) and can demonstrate subtle white matter anatomy. In particular, tractography is expected to be a unique, non-invasive tool to provide more pertinent insights into brain structure and orientation not accessible with conventional MRI. Data collection was performed in a normal volunteer on a 1.5-T MRI system using several techniques including six axis single-shot echo planar imaging (EPI), over six axis EPI, and periodically rotated overlapping parallel lines with enhanced reconstruction techniques. Tractography was generated by a continuous tracking method with our original software, Volume-One (for viewing volumetric image data) and VizDT-II (for analysis of DTI data). Using these data, estimated tracts were generated in arcuate fibers of cerebrum, cingulum, superior longitudinal fasciculus, uncinate fasciculus, inferior longitudinal fasciculus, corpus callosum, fornix, anterior thalamic radiation, central thalamic radiation, thalamo-parietal fibers, optic radiation, superior cerebellar peduncle, middle cerebellar peduncle, inferior cerebellar peduncle and intrinsic commissure paths of the hipoccampous. DTI including tractography allows differentiation between complex white matter tracts. The information regarding the detailed relationship may be useful for diagnosis of the location and extent of brain lesions, and preoperative planning.

Diffusion-Weighted and Diffusion Tensor Magnetic Resonance Brain Imaging: Principles and Applications

Diffusion Weighted Imaging (DWI) is one of the most recent products of Magnetic Resonance (MR) technology evolution. DWI has been proposed as a noninvasive tool for evaluating structural and physiologic states in biologic tissues as hyperacute ischemic changes within brain tissue. Recently, its more complex and detailed evolution, Diffusion Tensor Imaging (DTI), has been introduced and its clinical applications are the evaluation of anatomical structures and pathologic processes in white matter. White matter quantitative maps that indicate the integrity of brain tissue, color map, and tractography that identifies macroscopic three-dimensional architecture of fiber tracts (e.g., projections and association pathways) can be obtained with DTI.

Diffusion weighted imaging visualization techniques (ADC and Trace) are applied for the study of stroke, in the differential diagnosis of expansive lesions (e.g. epidermoid vs. arachnoid cyst) and in detecting traumatic and other lesions associated with restricted diffusion (e.g. MS plaques). On the other hand, DTI provides the identification of abnormalities in the otherwise normal appearing white matter with the understanding of the organization of the fibers, both in tumors and in other cortical or white matter diseases (including stroke, dementias, demyelinating-dismyelinating diseases, epilepsy, schizophrenia). Furthermore, in combination with functional MR, DTI might contribute to the comprehension of brain development, aging and connectivity, thus having a significant impact on brain functional studies.

Semiparametric Bayesian models for human brain mapping

Functional magnetic resonance imaging (fMRI) has led to enormous progress in human brain mapping. Adequate analysis of the massive spatiotemporal data sets generated by this imaging technique, combining parametric and non-parametric components, imposes challenging problems in statistical modelling. Complex hierarchical Bayesian models in combination with computer-intensive Markov chain Monte Carlo inference are promising tools.

The purpose of this paper is twofold. First, it provides a review of general semiparametric Bayesian models for the analysis of fMRI data. Most approaches focus on important but separate temporal or spatial aspects of the overall problem, or they proceed by stepwise procedures. Therefore, as a second aim, we suggest a complete spatiotemporal model for analysing fMRI data within a unified semiparametric Bayesian framework. An application to data from a visual stimulation experiment illustrates our approach and demonstrates its computational feasibility.

Kernel regression estimation of fiber orientation mixtures in diffusion MRI

We present and evaluate a method for kernel regression estimation of fiber orientations and associated volume fractions for diffusion MR tractography and population-based atlas construction in clinical imaging studies of brain white matter. This is a model-based image processing technique in which representative fiber models are estimated from collections of component fiber models in model-valued image data. This extends prior work in nonparametric image processing and multi-compartment processing to provide computational tools for image interpolation, smoothing, and fusion with fiber orientation mixtures. In contrast to related work on multi-compartment processing, this approach is based on directional measures of divergence and includes data-adaptive extensions for model selection and bilateral filtering. This is useful for reconstructing complex anatomical features in clinical datasets analyzed with the ball-and-sticks model, and our framework's data-adaptive extensions are potentially useful for general multi-compartment image processing. We experimentally evaluate our approach with both synthetic data from computational phantoms and in vivo clinical data from human subjects. With synthetic data experiments, we evaluate performance based on errors in fiber orientation, volume fraction, compartment count, and tractography-based connectivity. With in vivo data experiments, we first show improved scan-rescan reproducibility and reliability of quantitative fiber bundle metrics, including mean length, volume, streamline count, and mean volume fraction. We then demonstrate the creation of a multi-fiber tractography atlas from a population of 80 human subjects. In comparison to single tensor atlasing, our multi-fiber atlas shows more complete features of known fiber bundles and includes reconstructions of the lateral projections of the corpus callosum and complex fronto-parietal connections of the superior longitudinal fasciculus I, II, and III.

Ensemble Tractography

Tractography uses diffusion MRI to estimate the trajectory and cortical projection zones of white matter fascicles in the living human brain. There are many different tractography algorithms and each requires the user to set several parameters, such as curvature threshold. Choosing a single algorithm with specific parameters poses two challenges. First, different algorithms and parameter values produce different results. Second, the optimal choice of algorithm and parameter value may differ between different white matter regions or different fascicles, subjects, and acquisition parameters. We propose using ensemble methods to reduce algorithm and parameter dependencies. To do so we separate the processes of fascicle generation and evaluation. Specifically, we analyze the value of creating optimized connectomes by systematically combining candidate streamlines from an ensemble of algorithms (deterministic and probabilistic) and systematically varying parameters (curvature and stopping criterion). The ensemble approach leads to optimized connectomes that provide better cross-validated prediction error of the diffusion MRI data than optimized connectomes generated using a single-algorithm or parameter set. Furthermore, the ensemble approach produces connectomes that contain both short- and long-range fascicles, whereas single-parameter connectomes are biased towards one or the other. In summary, a systematic ensemble tractography approach can produce connectomes that are superior to standard single parameter estimates both for predicting the diffusion measurements and estimating white matter fascicles.

Diffusion MRI and tractography opened a new avenue for studying white matter fascicles and their tissue properties in the living human brain. There are many different tractography methods, and each requires the user to set several parameters. A limitation of tractography is that the results depend on the selection of algorithms and parameters. Here, we analyze an ensemble method, Ensemble Tractography (ET), that reduces the effect of algorithm and parameter selection. ET creates a large set of candidate streamlines using an ensemble of algorithms and parameter values and then selects the streamlines with strong support from the data using a global fascicle evaluation method. Compared to single parameter connectomes, ET connectomes predict diffusion MRI signals better and cover a wider range of white matter volume. Importantly, ET connectomes include both short- and long-association fascicles, which are not typically found together in single-parameter connectomes.

Magnetic resonance imaging of the subthalamic nucleus for deep brain stimulation

The subthalamic nucleus (STN) is one of the most important stereotactic targets in neurosurgery, and its accurate imaging is crucial. With improving MRI sequences there is impetus for direct targeting of the STN. High-quality, distortion-free images are paramount. Image reconstruction techniques appear to show the greatest promise in balancing the issue of geometrical distortion and STN edge detection. Existing spin echo- and susceptibility-based MRI sequences are compared with new image reconstruction methods. Quantitative susceptibility mapping is the most promising technique for stereotactic imaging of the STN.

Tract Orientation and Angular Dispersion Deviation Indicator (TOADDI): A framework for single-subject analysis in diffusion tensor imaging

The purpose of this work is to develop a framework for single-subject analysis of diffusion tensor imaging (DTI) data. This framework is termed Tract Orientation and Angular Dispersion Deviation Indicator (TOADDI) because it is capable of testing whether an individual tract as represented by the major eigenvector of the diffusion tensor and its corresponding angular dispersion are significantly different from a group of tracts on a voxel-by-voxel basis. This work develops two complementary statistical tests based on the elliptical cone of uncertainty, which is a model of uncertainty or dispersion of the major eigenvector of the diffusion tensor. The orientation deviation test examines whether the major eigenvector from a single subject is within the average elliptical cone of uncertainty formed by a collection of elliptical cones of uncertainty. The shape deviation test is based on the two-tailed Wilcoxon-Mann-Whitney two-sample test between the normalized shape measures (area and circumference) of the elliptical cones of uncertainty of the single subject against a group of controls. The False Discovery Rate (FDR) and False Non-discovery Rate (FNR) were incorporated in the orientation deviation test. The shape deviation test uses FDR only. TOADDI was found to be numerically accurate and statistically effective. Clinical data from two Traumatic Brain Injury (TBI) patients and one non-TBI subject were tested against the data obtained from a group of 45 non-TBI controls to illustrate the application of the proposed framework in single-subject analysis. The frontal portion of the superior longitudinal fasciculus seemed to be implicated in both tests (orientation and shape) as significantly different from that of the control group. The TBI patients and the single non-TBI subject were well separated under the shape deviation test at the chosen FDR level of 0.0005. TOADDI is a simple but novel geometrically based statistical framework for analyzing DTI data. TOADDI may be found useful in single-subject, graph-theoretic and group analyses of DTI data or DTI-based tractography techniques.

Improving Fiber Alignment in HARDI by Combining Contextual PDE Flow with Constrained Spherical Deconvolution

We propose two strategies to improve the quality of tractography results computed from diffusion weighted magnetic resonance imaging (DW-MRI) data. Both methods are based on the same PDE framework, defined in the coupled space of positions and orientations, associated with a stochastic process describing the enhancement of elongated structures while preserving crossing structures. In the first method we use the enhancement PDE for contextual regularization of a fiber orientation distribution (FOD) that is obtained on individual voxels from high angular resolution diffusion imaging (HARDI) data via constrained spherical deconvolution (CSD). Thereby we improve the FOD as input for subsequent tractography. Secondly, we introduce the fiber to bundle coherence (FBC), a measure for quantification of fiber alignment. The FBC is computed from a tractography result using the same PDE framework and provides a criterion for removing the spurious fibers. We validate the proposed combination of CSD and enhancement on phantom data and on human data, acquired with different scanning protocols. On the phantom data we find that PDE enhancements improve both local metrics and global metrics of tractography results, compared to CSD without enhancements. On the human data we show that the enhancements allow for a better reconstruction of crossing fiber bundles and they reduce the variability of the tractography output with respect to the acquisition parameters. Finally, we show that both the enhancement of the FODs and the use of the FBC measure on the tractography improve the stability with respect to different stochastic realizations of probabilistic tractography. This is shown in a clinical application: the reconstruction of the optic radiation for epilepsy surgery planning.

Diffusion Magnetic Resonance Imaging: What Water Tells Us about Biological Tissues

Since its introduction in the mid-1980s, diffusion magnetic resonance imaging (MRI), which measures the random motion of water molecules in tissues, revealing their microarchitecture, has become a pillar of modern neuroimaging. Its main clinical domain has been the diagnosis of acute brain stroke and neurogical disorders, but it is also used in the body for the detection and management of cancer lesions. It can also produce stunning maps of white matter tracks in the brain, with the potential to aid in the understanding of some psychiatric disorders. However, in order to exploit fully the potential of this method, a deeper understanding of the mechanisms that govern the diffusion of water in tissues is needed.

A review on functional and structural brain connectivity in numerical cognition

Only recently has the complex anatomo-functional system underlying numerical cognition become accessible to evaluation in the living brain. We identified 27 studies investigating brain connectivity in numerical cognition. Despite considerable heterogeneity regarding methodological approaches, populations investigated, and assessment procedures implemented, the results provided largely converging evidence regarding the underlying brain connectivity involved in numerical cognition. Analyses of both functional/effective as well as structural connectivity have consistently corroborated the assumption that numerical cognition is subserved by a fronto-parietal network including (intra)parietal as well as (pre)frontal cortex sites. Evaluation of structural connectivity has indicated the involvement of fronto-parietal association fibers encompassing the superior longitudinal fasciculus dorsally and the external capsule/extreme capsule system ventrally. Additionally, commissural fibers seem to connect the bilateral intraparietal sulci when number magnitude information is processed. Finally, the identification of projection fibers such as the superior corona radiata indicates connections between cortex and basal ganglia as well as the thalamus in numerical cognition. Studies on functional/effective connectivity further indicated a specific role of the hippocampus. These specifications of brain connectivity augment the triple-code model of number processing and calculation with respect to how gray matter areas associated with specific number-related representations may work together.

Prediction of the stress relaxation property of diene rubber composites by artificial neural network approaches

An artificial neural network was established to predict the stress relaxation property of diene rubber composites during ozone aging.

Does diffusion MRI tell us anything about the white matter? An overview of methods and pitfalls

One key pitfall in diffusion magnetic resonance imaging (dMRI) clinical neuroimaging research is the challenge of understanding and interpreting the results of a complex analysis pipeline. The sophisticated algorithms employed by the analysis software, combined with the relatively non-specific nature of many diffusion measurements, lead to challenges in interpretation of the results. This paper is aimed at an intended audience of clinical researchers who are learning about dMRI or trying to interpret dMRI results, and who may be wondering "Does dMRI tell us anything about the white matter?" We present a critical review of dMRI methods and measures used in clinical neuroimaging research, focusing on the most commonly used analysis methods and the most commonly reported measures. We describe important pitfalls in every section, and provide extensive references for the reader interested in more detail.

Spatial mapping of structural and connectional imaging data for the developing human brain with diffusion tensor imaging

During human brain development from fetal stage to adulthood, the white matter (WM) tracts undergo dramatic changes. Diffusion tensor imaging (DTI), a widely used magnetic resonance imaging (MRI) modality, offers insight into the dynamic changes of WM fibers as these fibers can be noninvasively traced and three-dimensionally (3D) reconstructed with DTI tractography. The DTI and conventional T1 weighted MRI images also provide sufficient cortical anatomical details for mapping the cortical regions of interests (ROIs). In this paper, we described basic concepts and methods of DTI techniques that can be used to trace major WM tracts noninvasively from fetal brain of 14 postconceptional weeks (pcw) to adult brain. We applied these techniques to acquire DTI data and trace, reconstruct and visualize major WM tracts during development. After categorizing major WM fiber bundles into five unique functional tract groups, namely limbic, brain stem, projection, commissural and association tracts, we revealed formation and maturation of these 3D reconstructed WM tracts of the developing human brain. The structural and connectional imaging data offered by DTI provides the anatomical backbone of transcriptional atlas of the developing human brain.

Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited

Tractography based on diffusion-weighted MRI (DWI) is widely used for mapping the structural connections of the human brain. Its accuracy is known to be limited by technical factors affecting in vivo data acquisition, such as noise, artifacts, and data undersampling resulting from scan time constraints. It generally is assumed that improvements in data quality and implementation of sophisticated tractography methods will lead to increasingly accurate maps of human anatomical connections. However, assessing the anatomical accuracy of DWI tractography is difficult because of the lack of independent knowledge of the true anatomical connections in humans. Here we investigate the future prospects of DWI-based connectional imaging by applying advanced tractography methods to an ex vivo DWI dataset of the macaque brain. The results of different tractography methods were compared with maps of known axonal projections from previous tracer studies in the macaque. Despite the exceptional quality of the DWI data, none of the methods demonstrated high anatomical accuracy. The methods that showed the highest sensitivity showed the lowest specificity, and vice versa. Additionally, anatomical accuracy was highly dependent upon parameters of the tractography algorithm, with different optimal values for mapping different pathways. These results suggest that there is an inherent limitation in determining long-range anatomical projections based on voxel-averaged estimates of local fiber orientation obtained from DWI data that is unlikely to be overcome by improvements in data acquisition and analysis alone.

DTI of the visual pathway - white matter tracts and cerebral lesions

DTI is a technique that identifies white matter tracts (WMT) non-invasively in healthy and non-healthy patients using diffusion measurements. Similar to visual pathways (VP), WMT are not visible with classical MRI or intra-operatively with microscope. DIT will help neurosurgeons to prevent destruction of the VP while removing lesions adjacent to this WMT. We have performed DTI on fifty patients before and after surgery between March 2012 to January 2014. To navigate we used a 3DT1-weighted sequence. Additionally, we performed a T2-weighted and DTI-sequences. The parameters used were, FOV: 200 x 200 mm, slice thickness: 2 mm, and acquisition matrix: 96 x 96 yielding nearly isotropic voxels of 2 x 2 x 2 mm. Axial MRI was carried out using a 32 gradient direction and one b0-image. We used Echo-Planar-Imaging (EPI) and ASSET parallel imaging with an acceleration factor of 2 and b-value of 800 s/mm². The scanning time was less than 9 min. The DTI-data obtained were processed using a FDA approved surgical navigation system program which uses a straightforward fiber-tracking approach known as fiber assignment by continuous tracking (FACT). This is based on the propagation of lines between regions of interest (ROI) which is defined by a physician. A maximum angle of 50, FA start value of 0.10 and ADC stop value of 0.20 mm²/s were the parameters used for tractography. There are some limitations to this technique. The limited acquisition time frame enforces trade-offs in the image quality. Another important point not to be neglected is the brain shift during surgery. As for the latter intra-operative MRI might be helpful. Furthermore the risk of false positive or false negative tracts needs to be taken into account which might compromise the final results.

The value of novel MRI techniques in Parkinson-plus syndromes: diffusion tensor imaging and anatomical connectivity studies

Conventional MRI is a well-described, highly useful tool for the differential diagnosis of degenerative parkinsonian syndromes. Nevertheless, the observed abnormalities may only appear in late-stage disease. Diffusion tensor imaging (DTI) can identify microstructural changes in brain tissue integrity and connectivity. The technique has proven value in the differential diagnosis of multiple system atrophy (MSA), progressive supranuclear palsy (PSP) and Parkinson's disease (PD). Here, we performed a systematic review of the literature on the main corticosubcortical DTI abnormalities identified to date in the context of the diagnosis of MSA and PSP with diffusion-weighted imaging, diffusion tensor imaging and anatomical connectivity studies. In good agreement with the histological data, increased diffusivity in the putamen (in MSA and PSP), in the middle cerebellar peduncles (in MSA) and in the upper cerebellar peduncles (in PSP) has been reported. Motor pathway involvement is characterized by low fraction anisotropy (FA) in the primary motor cortex in MSA-P and PSP, a high apparent diffusion coefficient (ADC) and low FA in the supplementary motor area in PSP. We then outline the value of these techniques in differential diagnosis (especially with respect to PD). Anatomical connectivity studies have revealed a lower number of fibers in the corticospinal tract in MSA and PSP (relative to PD and controls) and fewer tracked cortical projection fibers in patients with PSP or late-stage MSA (relative to patients with early MSA or PD and controls). Lastly, we report the main literature data concerning the value of DTI parameters in monitoring disease progression. The observed correlations between DTI parameters on one hand and clinical scores and/or disease duration on the other constitute strong evidence of the value of DTI in monitoring disease progression. In MSA, the ataxia score was correlated with ADC values in the pons and the upper cerebellar peduncles, whereas both the motor score and the disease duration were correlated with putaminal ADC values. In conclusion, DTI and connectivity studies constitute promising tools for differentiating between "Parkinson-plus" syndromes.

Gray matter parcellation constrained full brain fiber bundling with diffusion tensor imaging

Purpose:

Studying white matter fibers from diffusion tensor imaging (DTI) often requires them to be grouped into bundles that correspond to coherent anatomic structures, particularly bundles that connect cortical/subcortical basic units. However, traditional fiber clustering algorithms usually generate bundles with poor anatomic correspondence as they do not incorporate brain anatomic information into the clustering process. On the other hand, image registration‐based bundling methods segment fiber bundles by referring to a coregistered atlas or template with prelabeled anatomic information, but these approaches suffer from the uncertainties introduced from misregistration and fiber tracking errors and thus the resulting bundles usually have poor coherence. In this work, a bundling algorithm is proposed to overcome the above issues.

Methods:

The proposed algorithm combines clustering‐ and registration‐based approaches so that the bundle coherence and the consistency with brain anatomy are simultaneously achieved. Moreover, based on this framework, a groupwise fiber bundling method is further proposed to leverage a group of DTI data for reducing the effect of the uncertainties in a single DTI data set and improving cross‐subject bundle consistency.

Results:

Using the Montreal Neurological Institute template, the proposed methods are applied to building a full brain bundle network that connects cortical/subcortical basic units. Based on several proposed metrics, the resulting bundles show promising bundle coherence and anatomic consistency as well as improved cross‐subject consistency for the groupwise bundling.

Conclusions:

A fiber bundling algorithm has been proposed in this paper to cluster a set of whole brain fibers into coherent bundles that are consistent to the brain anatomy.

Diffusion tensor imaging and tractography of the human language pathways: moving into the clinical realm

The functional correlates of anatomical derangements are of interest to the neurological clinician. Diffusion tensor tractography (DTT) is a relatively new tool in the arsenal of functional neuroimaging, by which to assess white matter tracts in the brain. While much import has been given to tracking corticospinal tracts in neurological disease, studying language pathway interconnections using DTT has largely remained in the research realm. Hardware and software advances have allowed this tool to ease into clinical practice, with several radiologists, neurologists, and neurosurgeons now familiar with its applications. DTT images, although visually appealing, are founded in mathematical equations and assumptions, and require a more than basic understanding of principles and limitations before they can be integrated into routine clinical practice. Cognitive pathways like that of language, that are normally hard to assess and especially more so when pathologically affected, have been at the receiving end of several opposing and often controversial hypotheses, and the past decade has seen the clarification, validation or rejection of several of these by the in vivo charting of functional connectivity using DTT. The focus of this review is to illustrate DTT of the language pathways with emphasis on practical considerations, clinical applications, and limitations.

Diffusion microscopist simulator: a general Monte Carlo simulation system for diffusion magnetic resonance imaging

This article describes the development and application of an integrated, generalized, and efficient Monte Carlo simulation system for diffusion magnetic resonance imaging (dMRI), named Diffusion Microscopist Simulator (DMS). DMS comprises a random walk Monte Carlo simulator and an MR image synthesizer. The former has the capacity to perform large-scale simulations of Brownian dynamics in the virtual environments of neural tissues at various levels of complexity, and the latter is flexible enough to synthesize dMRI datasets from a variety of simulated MRI pulse sequences. The aims of DMS are to give insights into the link between the fundamental diffusion process in biological tissues and the features observed in dMRI, as well as to provide appropriate ground-truth information for the development, optimization, and validation of dMRI acquisition schemes for different applications. The validity, efficiency, and potential applications of DMS are evaluated through four benchmark experiments, including the simulated dMRI of white matter fibers, the multiple scattering diffusion imaging, the biophysical modeling of polar cell membranes, and the high angular resolution diffusion imaging and fiber tractography of complex fiber configurations. We expect that this novel software tool would be substantially advantageous to clarify the interrelationship between dMRI and the microscopic characteristics of brain tissues, and to advance the biophysical modeling and the dMRI methodologies.

Validation of DTI tractography-based measures of primary motor area connectivity in the squirrel monkey brain

Diffusion tensor imaging (DTI) tractography provides noninvasive measures of structural cortico-cortical connectivity of the brain. However, the agreement between DTI-tractography-based measures and histological 'ground truth' has not been quantified. In this study, we reconstructed the 3D density distribution maps (DDM) of fibers labeled with an anatomical tracer, biotinylated dextran amine (BDA), as well as DTI tractography-derived streamlines connecting the primary motor (M1) cortex to other cortical regions in the squirrel monkey brain. We evaluated the agreement in M1-cortical connectivity between the fibers labeled in the brain tissue and DTI streamlines on a regional and voxel-by-voxel basis. We found that DTI tractography is capable of providing inter-regional connectivity comparable to the neuroanatomical connectivity, but is less reliable measuring voxel-to-voxel variations within regions.

Cortico-cortical, cortico-striatal, and cortico-thalamic white matter fiber tracts generated in the macaque brain via dynamic programming

Probabilistic methods have the potential to generate multiple and complex white matter fiber tracts in diffusion tensor imaging (DTI). Here, a method based on dynamic programming (DP) is introduced to reconstruct fibers pathways whose complex anatomical structures cannot be resolved beyond the resolution of standard DTI data. DP is based on optimizing a sequentially additive cost function derived from a Gaussian diffusion model whose covariance is defined by the diffusion tensor. DP is used to determine the optimal path between initial and terminal nodes by efficiently searching over all paths, connecting the nodes, and choosing the path in which the total probability is maximized. An ex vivo high-resolution scan of a macaque hemi-brain is used to demonstrate the advantages and limitations of DP. DP can generate fiber bundles between distant cortical areas (superior longitudinal fasciculi, arcuate fasciculus, uncinate fasciculus, and fronto-occipital fasciculus), neighboring cortical areas (dorsal and ventral banks of the principal sulcus), as well as cortical projections to the hippocampal formation (cingulum bundle), neostriatum (motor cortical projections to the putamen), thalamus (subcortical bundle), and hippocampal formation projections to the mammillary bodies via the fornix. Validation is established either by comparison with in vivo intracellular transport of horseradish peroxidase in another macaque monkey or by comparison with atlases. DP is able to generate known pathways, including crossing and kissing tracts. Thus, DP has the potential to enhance neuroimaging studies of cortical connectivity.

Time-optimized high-resolution readout-segmented diffusion tensor imaging

Readout-segmented echo planar imaging with 2D navigator-based reacquisition is an uprising technique enabling the sampling of high-resolution diffusion images with reduced susceptibility artifacts. However, low signal from the small voxels and long scan times hamper the clinical applicability. Therefore, we introduce a regularization algorithm based on total variation that is applied directly on the entire diffusion tensor. The spatially varying regularization parameter is determined automatically dependent on spatial variations in signal-to-noise ratio thus, avoiding over- or under-regularization. Information about the noise distribution in the diffusion tensor is extracted from the diffusion weighted images by means of complex independent component analysis. Moreover, the combination of those features enables processing of the diffusion data absolutely user independent. Tractography from in vivo data and from a software phantom demonstrate the advantage of the spatially varying regularization compared to un-regularized data with respect to parameters relevant for fiber-tracking such as Mean Fiber Length, Track Count, Volume and Voxel Count. Specifically, for in vivo data findings suggest that tractography results from the regularized diffusion tensor based on one measurement (16 min) generates results comparable to the un-regularized data with three averages (48 min). This significant reduction in scan time renders high resolution (1×1×2.5 mm³) diffusion tensor imaging of the entire brain applicable in a clinical context.

Diffusional kurtosis imaging of cingulate fibers in Parkinson disease: comparison with conventional diffusion tensor imaging

OBJECTIVE

The pathological changes in Parkinson disease begin in the brainstem; reach the limbic system and ultimately spread to the cerebral cortex. In Parkinson disease (PD) patients, we evaluated the alteration of cingulate fibers, which comprise part of the limbic system, by using diffusional kurtosis imaging (DKI).

METHODS

Seventeen patients with PD and 15 age-matched healthy controls underwent DKI with a 3-T MR imager. Diffusion tensor tractography images of the anterior and posterior cingulum were generated. The mean kurtosis (MK) and conventional diffusion tensor parameters measured along the images in the anterior and posterior cingulum were compared between the groups. Receiver operating characteristic (ROC) analysis was also performed to compare the diagnostic abilities of the MK and conventional diffusion tensor parameters.

RESULTS

The MK and fractional anisotropy (FA) in the anterior cingulum were significantly lower in PD patients than in healthy controls. The area under the ROC curve was 0.912 for MK and 0.747 for FA in the anterior cingulum. MK in the anterior cingulum had the best diagnostic performance (mean cutoff, 0.967; sensitivity, 0.87; specificity, 0.94).

CONCLUSIONS

DKI can detect alterations of the anterior cingulum in PD patients more sensitively than can conventional diffusion tensor imaging. Use of DKI can be expected to improve the ability to diagnose PD.

Gaining insight of fetal brain development with diffusion MRI and histology

Human brain is extraordinarily complex and yet its origin is a simple tubular structure. Its development during the fetal period is characterized by a series of accurately organized events which underlie the mechanisms of dramatic structural changes during fetal development. Revealing detailed anatomy at different stages of human fetal brain development provides insight on understanding not only this highly ordered process, but also the neurobiological foundations of cognitive brain disorders such as mental retardation, autism, schizophrenia, bipolar and language impairment. Diffusion tensor imaging (DTI) and histology are complementary tools which are capable of delineating the fetal brain structures at both macroscopic and microscopic levels. In this review, the structural development of the fetal brains has been characterized with DTI and histology. Major components of the fetal brain, including cortical plate, fetal white matter and cerebral wall layer between the ventricle and subplate, have been delineated with DTI and histology. Anisotropic metrics derived from DTI were used to quantify the microstructural changes during the dynamic process of human fetal cortical development and prenatal development of other animal models. Fetal white matter pathways have been traced with DTI-based tractography to reveal growth patterns of individual white matter tracts and corticocortical connectivity. These detailed anatomical accounts of the structural changes during fetal period may provide the clues of detecting developmental and cognitive brain disorders at their early stages. The anatomical information from DTI and histology may also provide reference standards for diagnostic radiology of premature newborns.

Reconstruction of white matter tracts via repeated deterministic streamline tracking--initial experience

Diffusion Tensor Imaging (DTI) and fiber tractography are established methods to reconstruct major white matter tracts in the human brain in-vivo. Particularly in the context of neurosurgical procedures, reliable information about the course of fiber bundles is important to minimize postoperative deficits while maximizing the tumor resection volume. Since routinely used deterministic streamline tractography approaches often underestimate the spatial extent of white matter tracts, a novel approach to improve fiber segmentation is presented here, considering clinical time constraints. Therefore, fiber tracking visualization is enhanced with statistical information from multiple tracking applications to determine uncertainty in reconstruction based on clinical DTI data. After initial deterministic fiber tracking and centerline calculation, new seed regions are generated along the result’s midline. Tracking is applied to all new seed regions afterwards, varying in number and applied offset. The number of fibers passing each voxel is computed to model different levels of fiber bundle membership. Experimental results using an artificial data set of an anatomical software phantom are presented, using the Dice Similarity Coefficient (DSC) as a measure of segmentation quality. Different parameter combinations were classified to be superior to others providing significantly improved results with DSCs of 81.02%±4.12%, 81.32%±4.22% and 80.99%±3.81% for different levels of added noise in comparison to the deterministic fiber tracking procedure using the two-ROI approach with average DSCs of 65.08%±5.31%, 64.73%±6.02% and 65.91%±6.42%. Whole brain tractography based on the seed volume generated by the calculated seeds delivers average DSCs of 67.12%±0.86%, 75.10%±0.28% and 72.91%±0.15%, original whole brain tractography delivers DSCs of 67.16%, 75.03% and 75.54%, using initial ROIs as combined include regions, which is clearly improved by the repeated fiber tractography method.

3D stochastic completion fields for mapping connectivity in diffusion MRI

The 2D stochastic completion field algorithm, introduced by Williams and Jacobs [1], [2], uses a directional random walk to model the prior probability of completion curves in the plane. This construct has had a powerful impact in computer vision, where it has been used to compute the shapes of likely completion curves between edge fragments in visual imagery. Motivated by these developments, we extend the algorithm to 3D, using a spherical harmonics basis to achieve a rotation invariant computational solution to the Fokker-Planck equation describing the evolution of the probability density function underlying the model. This provides a principled way to compute 3D completion patterns and to derive connectivity measures for orientation data in 3D, as arises in 3D tracking, motion capture, and medical imaging. We demonstrate the utility of the approach for the particular case of diffusion magnetic resonance imaging, where we derive connectivity maps for synthetic data, on a physical phantom and on an in vivo high angular resolution diffusion image of a human brain.

Robust detection of traumatic axonal injury in individual mild traumatic brain injury patients: intersubject variation, change over time and bidirectional changes in anisotropy

To identify and characterize otherwise occult inter-individual spatial variation of white matter abnormalities across mild traumatic brain injury (mTBI) patients. After informed consent and in compliance with Health Insurance Portability and Accountability Act (HIPAA), Diffusion tensor imaging (DTI) was performed on a 3.0 T MR scanner in 34 mTBI patients (19 women; 19-64 years old) and 30 healthy control subjects. The patients were imaged within 2 weeks of injury, 3 months after injury, and 6 months after injury. Fractional anisotropy (FA) images were analyzed in each patient. To examine white matter diffusion abnormalities across the entire brain of individual patients, we applied Enhanced Z-score Microstructural Assessment for Pathology (EZ-MAP), a voxelwise analysis optimized for the assessment of individual subjects. Our analysis revealed areas of abnormally low or high FA (voxel-wise P-value < 0.05, cluster-wise P-value < 0.01(corrected for multiple comparisons)). The spatial pattern of white matter FA abnormalities varied among patients. Areas of low FA were consistent with known patterns of traumatic axonal injury. Areas of high FA were most frequently detected in the deep and subcortical white matter of the frontal, parietal, and temporal lobes, and in the anterior portions of the corpus callosum. The number of both abnormally low and high FA voxels changed during follow up. Individual subject assessments reveal unique spatial patterns of white matter abnormalities in each patient, attributable to inter-individual differences in anatomy, vulnerability to injury and mechanism of injury. Implications of high FA remain unclear, but may evidence a compensatory mechanism or plasticity in response to injury, rather than a direct manifestation of brain injury.

Magnetic resonance imaging of the substantia nigra in Parkinson's disease

Until recently, conventional magnetic resonance imaging (MRI) was most often negative in Parkinson's disease or showed nonspecific findings. Recent developments in structural MRI, including relaxometry, magnetization transfer, and neuromelanin imaging, have demonstrated improved contrast and enabled more accurate visualization of deep brain nuclei, in particular, the substantia nigra. Meanwhile, diffusion imaging has provided useful biomarkers of substantia nigra degeneration, showing reduced anisotropy and anatomical connectivity with the striatum and thalamus. These advances in structural imaging are complemented by findings of magnetic resonance spectroscopy on brain metabolism and resting-state functional MRI on functional connectivity. This article presents an overview of these new structural, metabolic, and resting-state functional MRI techniques and their implications for Parkinson's disease. The techniques are reviewed in the context of their potential for better understanding the disease in terms of diagnosis and pathophysiology and as biomarkers of its progression.

Differences in Metabolism of Fiber Tract Alterations in Gliomas

BACKGROUND:

Gliomas propagate diffusely throughout and along white matter structures. Glioma-related changes in structural integrity and metabolism are not detectable by standard magnetic resonance (MR) imaging.

OBJECTIVE:

To investigate differences in the metabolism of fiber tract alterations between gliomas grade II to IV by correlation of fiber density values with metabolite concentrations measured by fiber density mapping and MR spectroscopic imaging.

METHODS:

Fiber density mapping and MR spectroscopic imaging were performed in 48 patients with gliomas WHO grade II to IV. Fiber density mapping data were used to define fiber tracts in tumoral and peritumoral areas. Structural integrity of fiber tracts was assessed as fiber density ipsilateral-to-contralateral ratio (FD ICR). Metabolite concentrations for choline-containing compounds and N-acetyl-aspartate were computed and correlated to FD ICR values after coregistration with anatomic MR imaging.

RESULTS:

In tumoral areas, choline-containing compound concentrations of altered fiber tracts were significantly different between low- and high-grade glioma and showed different courses for the correlations of FD ICR and choline-containing czeompounds. In high-grade glioma, increasing fiber destruction was associated with a massive progression in cell membrane proliferation. Peritumoral fiber structures showed significantly decreased N-acetyl-aspartate concentrations for all patients, but only patients with glioblastoma multiforme had significantly decreased fiber density compared with the contralateral side. Glioma grades II and III had significantly higher peritumoral FD ICR than glioblastoma multiforme.

CONCLUSION:

A multiparametric MR imaging strategy providing information about both structural integrity and metabolism of the tumor is required for detailed assessment of glioma-related fiber tract alterations, which in turn is essential for treatment planning.

A qualitative and quantitative review of diffusion tensor imaging studies in reading and dyslexia

In this review paper we address whether deficits in reading (i.e. developmental dyslexia) are rooted in neurobiological anomalies in white matter tracts. Diffusion tensor imaging (DTI) offers an index of the connections between brain regions (via tractography) and of the white matter properties of these connections (via fractional anisotropy, FA). The reported studies generally show that lower FA values in left temporoparietal and frontal areas are indicative of poorer reading ability or dyslexia. Second, most studies have indicated that these regions coincide with the left arcuate fasciculus and corona radiata, with fewer studies suggesting a role for the posterior part of the corpus callosum or for more ventral tracts such as the inferior longitudinal fasciculus or the inferior fronto-occipital fasciculus. Finally, a quantitative activation likelihood estimation (ALE) meta-analysis on all reported studies that used a voxel-based approach reveals a cluster located close to the left temporoparietal region (x=-29, y=-17, z=26). Fibertracking through this cluster demonstrates that this region hosts both the left arcuate fasciculus and the left corona radiata.

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.

Diffusion tensor imaging of freezing of gait in patients with white matter changes

BACKGROUND

Freezing of gait is a common and disabling symptom of parkinsonism. However, the corresponding anatomic structures have yet to be clearly elucidated.

METHODS

We performed diffusion tensor imaging on 40 subjects with white matter changes. We compared apparent diffusion coefficient values and fraction anisotropy values of 7 candidate anatomic structures between 14 patients with freezing of gait (freezing of gait group) and 26 without freezing of gait (control group).

RESULTS

Fraction anisotropy values of the bilateral pedunculopontine nucleus, bilateral superior premotor cortex, right orbitofrontal area, and left supplement motor area were significantly lower in the freezing of gait group than in the control group. In contrast, there were no significant differences in apparent diffusion coefficient values between freezing of gait and control groups.

CONCLUSIONS

Our findings suggest that the bilateral pedunculopontine nucleus, bilateral superior premotor cortex, right orbitofrontal area, and left supplement motor area are closely related to freezing of gait.