Diffusion tensor fiber tracking of human brain connectivity: aquisition methods, reliability analysis and biological results

Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods

The corticospinal tract (CST) is a critically important white matter fiber tract in the human brain that enables control of voluntary movements of the body. The CST exhibits a somatotopic organization, which means that the motor neurons that control specific body parts are arranged in order within the CST. Diffusion magnetic resonance imaging (MRI) tractography is increasingly used to study the anatomy of the CST. However, despite many advances in tractography algorithms over the past decade, modern, state-of-the-art methods still face challenges. In this study, we compare the performance of six widely used tractography methods for reconstructing the CST and its somatotopic organization. These methods include constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD-Stream) methods, unscented Kalman filter (UKF) tractography methods including multi-fiber (UKF2T) and single-fiber (UKF1T) models, the generalized q-sampling imaging (GQI) based deterministic tractography method, and the TractSeg method. We investigate CST somatotopy by dividing the CST into four subdivisions per hemisphere that originate in the leg, trunk, hand, and face areas of the primary motor cortex. A quantitative and visual comparison is performed using diffusion MRI data (N = 100 subjects) from the Human Connectome Project. Quantitative evaluations include the reconstruction rate of the eight anatomical subdivisions, the percentage of streamlines in each subdivision, and the coverage of the white matter-gray matter (WM-GM) interface. CST somatotopy is further evaluated by comparing the percentage of streamlines in each subdivision to the cortical volumes for the leg, trunk, hand, and face areas. Overall, UKF2T has the highest reconstruction rate and cortical coverage. It is the only method with a significant positive correlation between the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex. However, our experimental results show that all compared tractography methods are biased toward generating many trunk streamlines (ranging from 35.10% to 71.66% of total streamlines across methods). Furthermore, the coverage of the WM-GM interface in the largest motor area (face) is generally low (under 40%) for all compared tractography methods. Different tractography methods give conflicting results regarding the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex, indicating that there is generally no clear relationship, and that reconstruction of CST somatotopy is still a large challenge. Overall, we conclude that while current tractography methods have made progress toward the well-known challenge of improving the reconstruction of the lateral projections of the CST, the overall problem of performing a comprehensive CST reconstruction, including clinically important projections in the lateral (hand and face areas) and medial portions (leg area), remains an important challenge for diffusion MRI tractography.

Towards reliable reconstruction of the mouse brain corticothalamic connectivity using diffusion MRI

Diffusion magnetic resonance imaging (dMRI) tractography has yielded intriguing insights into brain circuits and their relationship to behavior in response to gene mutations or neurological diseases across a number of species. Still, existing tractography approaches suffer from limited sensitivity and specificity, leading to uncertain interpretation of the reconstructed connections. Hence, in this study, we aimed to optimize the imaging and computational pipeline to achieve the best possible spatial overlaps between the tractography and tracer-based axonal projection maps within the mouse brain corticothalamic network. We developed a dMRI-based atlas of the mouse forebrain with structural labels imported from the Allen Mouse Brain Atlas (AMBA). Using the atlas and dMRI tractography, we first reconstructed detailed node-to-node mouse brain corticothalamic structural connectivity matrices using different imaging and tractography parameters. We then investigated the effects of each condition for accurate reconstruction of the corticothalamic projections by quantifying the similarities between the tractography and the tracer data from the Allen Mouse Brain Connectivity Atlas (AMBCA). Our results suggest that these parameters significantly affect tractography outcomes and our atlas can be used to investigate macroscopic structural connectivity in the mouse brain. Furthermore, tractography in mouse brain gray matter still face challenges and need improved imaging and tractography methods.

Diffusion-Weighted Imaging in Mild Traumatic Brain Injury: A Systematic Review of the Literature

There is evidence that diffusion-weighted imaging (DWI) is able to detect tissue alterations following mild traumatic brain injury (mTBI) that may not be observed on conventional neuroimaging; however, findings are often inconsistent between studies. This systematic review assesses patterns of differences in DWI metrics between those with and without a history of mTBI. A PubMed literature search was performed using relevant indexing terms for articles published prior to May 14, 2020. Findings were limited to human studies using DWI in mTBI. Articles were excluded if they were not full-length, did not contain original data, if they were case studies, pertained to military populations, had inadequate injury severity classification, or did not report post-injury interval. Findings were reported independently for four subgroups: acute/subacute pediatric mTBI, acute/subacute adult mTBI, chronic adult mTBI, and sport-related concussion, and all DWI acquisition and analysis methods used were included. Patterns of findings between studies were reported, along with strengths and weaknesses of the current state of the literature. Although heterogeneity of sample characteristics and study methods limited the consistency of findings, alterations in DWI metrics were most commonly reported in the corpus callosum, corona radiata, internal capsule, and long association pathways. Many acute/subacute pediatric studies reported higher FA and lower ADC or MD in various regions. In contrast, acute/subacute adult studies most commonly indicate lower FA within the context of higher MD and RD. In the chronic phase of recovery, FA may remain low, possibly indicating overall demyelination or Wallerian degeneration over time. Longitudinal studies, though limited, generally indicate at least a partial normalization of DWI metrics over time, which is often associated with functional improvement. We conclude that DWI is able to detect structural mTBI-related abnormalities that may persist over time, although future DWI research will benefit from larger samples, improved data analysis methods, standardized reporting, and increasing transparency.

Advanced Magnetic Resonance Imaging in Pediatric Glioblastomas

The shortly upcoming 5th edition of the World Health Organization Classification of Tumors of the Central Nervous System is bringing extensive changes in the terminology of diffuse high-grade gliomas (DHGGs). Previously "glioblastoma," as a descriptive entity, could have been applied to classify some tumors from the family of pediatric or adult DHGGs. However, now the term "glioblastoma" has been divested and is no longer applied to tumors in the family of pediatric types of DHGGs. As an entity, glioblastoma remains, however, in the family of adult types of diffuse gliomas under the insignia of "glioblastoma, IDH-wildtype." Of note, glioblastomas still can be detected in children when glioblastoma, IDH-wildtype is found in this population, despite being much more common in adults. Despite the separation from the family of pediatric types of DHGGs, what was previously labeled as "pediatric glioblastomas" still remains with novel labels and as new entities. As a result of advances in molecular biology, most of the previously called "pediatric glioblastomas" are now classified in one of the four family members of pediatric types of DHGGs. In this review, the term glioblastoma is still apocryphally employed mainly due to its historical relevance and the paucity of recent literature dealing with the recently described new entities. Therefore, "glioblastoma" is used here as an umbrella term in the attempt to encompass multiple entities such as astrocytoma, IDH-mutant (grade 4); glioblastoma, IDH-wildtype; diffuse hemispheric glioma, H3 G34-mutant; diffuse pediatric-type high-grade glioma, H3-wildtype and IDH-wildtype; and high grade infant-type hemispheric glioma. Glioblastomas are highly aggressive neoplasms. They may arise anywhere in the developing central nervous system, including the spinal cord. Signs and symptoms are non-specific, typically of short duration, and usually derived from increased intracranial pressure or seizure. Localized symptoms may also occur. The standard of care of "pediatric glioblastomas" is not well-established, typically composed of surgery with maximal safe tumor resection. Subsequent chemoradiation is recommended if the patient is older than 3 years. If younger than 3 years, surgery is followed by chemotherapy. In general, "pediatric glioblastomas" also have a poor prognosis despite surgery and adjuvant therapy. Magnetic resonance imaging (MRI) is the imaging modality of choice for the evaluation of glioblastomas. In addition to the typical conventional MRI features, i.e., highly heterogeneous invasive masses with indistinct borders, mass effect on surrounding structures, and a variable degree of enhancement, the lesions may show restricted diffusion in the solid components, hemorrhage, and increased perfusion, reflecting increased vascularity and angiogenesis. In addition, magnetic resonance spectroscopy has proven helpful in pre- and postsurgical evaluation. Lastly, we will refer to new MRI techniques, which have already been applied in evaluating adult glioblastomas, with promising results, yet not widely utilized in children.

Fiber tractography bundle segmentation depends on scanner effects, vendor effects, acquisition resolution, diffusion sampling scheme, diffusion sensitization, and bundle segmentation workflow

When investigating connectivity and microstructure of white matter pathways of the brain using diffusion tractography bundle segmentation, it is important to understand potential confounds and sources of variation in the process. While cross-scanner and cross-protocol effects on diffusion microstructure measures are well described (in particular fractional anisotropy and mean diffusivity), it is unknown how potential sources of variation effect bundle segmentation results, which features of the bundle are most affected, where variability occurs, nor how these sources of variation depend upon the method used to reconstruct and segment bundles. In this study, we investigate six potential sources of variation, or confounds, for bundle segmentation: variation (1) across scan repeats, (2) across scanners, (3) across vendors (4) across acquisition resolution, (5) across diffusion schemes, and (6) across diffusion sensitization. We employ four different bundle segmentation workflows on two benchmark multi-subject cross-scanner and cross-protocol databases, and investigate reproducibility and biases in volume overlap, shape geometry features of fiber pathways, and microstructure features within the pathways. We find that the effects of acquisition protocol, in particular acquisition resolution, result in the lowest reproducibility of tractography and largest variation of features, followed by vendor-effects, scanner-effects, and finally diffusion scheme and b-value effects which had similar reproducibility as scan-rescan variation. However, confounds varied both across pathways and across segmentation workflows, with some bundle segmentation workflows more (or less) robust to sources of variation. Despite variability, bundle dissection is consistently able to recover the same location of pathways in the deep white matter, with variation at the gray matter/ white matter interface. Next, we show that differences due to the choice of bundle segmentation workflows are larger than any other studied confound, with low-to-moderate overlap of the same intended pathway when segmented using different methods. Finally, quantifying microstructure features within a pathway, we show that tractography adds variability over-and-above that which exists due to noise, scanner effects, and acquisition effects. Overall, these confounds need to be considered when harmonizing diffusion datasets, interpreting or combining data across sites, and when attempting to understand the successes and limitations of different methodologies in the design and development of new tractography or bundle segmentation methods.

Physical and digital phantoms for validating tractography and assessing artifacts

Fiber tractography is widely used to non-invasively map white-matter bundles in vivo using diffusion-weighted magnetic resonance imaging (dMRI). As it is the case for all scientific methods, proper validation is a key prerequisite for the successful application of fiber tractography, be it in the area of basic neuroscience or in a clinical setting. It is well-known that the indirect estimation of the fiber tracts from the local diffusion signal is highly ambiguous and extremely challenging. Furthermore, the validation of fiber tractography methods is hampered by the lack of a real ground truth, which is caused by the extremely complex brain microstructure that is not directly observable non-invasively and that is the basis of the huge network of long-range fiber connections in the brain that are the actual target of fiber tractography methods. As a substitute for in vivo data with a real ground truth that could be used for validation, a widely and successfully employed approach is the use of synthetic phantoms. In this work, we are providing an overview of the state-of-the-art in the area of physical and digital phantoms, answering the following guiding questions: "What are dMRI phantoms and what are they good for?", "What would the ideal phantom for validation fiber tractography look like?" and "What phantoms, phantom datasets and tools used for their creation are available to the research community?". We will further discuss the limitations and opportunities that come with the use of dMRI phantoms, and what future direction this field of research might take.

Fluorescence recovery after photobleaching: direct measurement of diffusion anisotropy

Fluorescence recovery after photobleaching (FRAP) is a widely used technique for studying diffusion in biological tissues. Most of the existing approaches for the analysis of FRAP experiments assume isotropic diffusion, while only a few account for anisotropic diffusion. In fibrous tissues, such as articular cartilage, tendons and ligaments, diffusion, the main mechanism for molecular transport, is anisotropic and depends on the fibre alignment. In this work, we solve the general diffusion equation governing a FRAP test, assuming an anisotropic diffusivity tensor and using a general initial condition for the case of an elliptical (thereby including the case of a circular) bleaching profile. We introduce a closed-form solution in the spatial coordinates, which can be applied directly to FRAP tests to extract the diffusivity tensor. We validate the approach by measuring the diffusivity tensor of [Formula: see text] FITC-Dextran in porcine medial collateral ligaments. The measured diffusion anisotropy was [Formula: see text] (SE), which is in agreement with that reported in the literature. The limitations of the approach, such as the size of the bleached region and the intensity of the bleaching, are studied using COMSOL simulations.

Individual variations of the human corticospinal tract and its hand-related motor fibers using diffusion MRI tractography

The corticospinal tract (CST) is one of the most well studied tracts in human neuroanatomy. Its clinical significance can be demonstrated in many notable traumatic conditions and diseases such as stroke, spinal cord injury (SCI) or amyotrophic lateral sclerosis (ALS). With the advent of diffusion MRI and tractography the computational representation of the human CST in a 3D model became available. However, the representation of the entire CST and, specifically, the hand motor area has remained elusive. In this paper we propose a novel method, using manually drawn ROIs based on robustly identifiable neuroanatomic structures to delineate the entire CST and isolate its hand motor representation as well as to estimate their variability and generate a database of their volume, length and biophysical parameters. Using 37 healthy human subjects we performed a qualitative and quantitative analysis of the CST and the hand-related motor fiber tracts (HMFTs). Finally, we have created variability heat maps from 37 subjects for both the aforementioned tracts, which could be utilized as a reference for future studies with clinical focus to explore neuropathology in both trauma and disease states.

Denoise magnitude diffusion magnetic resonance images via variance-stabilizing transformation and optimal singular-value manipulation

Although shown to have a great utility for a wide range of neuroscientific and clinical applications, diffusion-weighted magnetic resonance imaging (dMRI) faces a major challenge of low signal-to-noise ratio (SNR), especially when pushing the spatial resolution for improved delineation of brain's fine structure or increasing the diffusion weighting for increased angular contrast or both. Here, we introduce a comprehensive denoising framework for denoising magnitude dMRI. The framework synergistically combines the variance stabilizing transform (VST) with optimal singular value manipulation. The purpose of VST is to transform the Rician data to Gaussian-like data so that an asymptotically optimal singular value manipulation strategy tailored for Gaussian data can be used. The output of the framework is the estimated underlying diffusion signal for each voxel in the image domain. The usefulness of the proposed framework for denoising magnitude dMRI is demonstrated using both simulation and real-data experiments. Our results show that the proposed denoising framework can significantly improve SNR across the entire brain, leading to substantially enhanced performances for estimating diffusion tensor related indices and for resolving crossing fibers when compared to another competing method. More encouragingly, the proposed method when used to denoise a single average of 7 ​Tesla Human Connectome Project-style diffusion acquisition provided comparable performances relative to those achievable with ten averages for resolving multiple fiber populations across the brain. As such, the proposed denoising method is expected to have a great utility for high-quality, high-resolution whole-brain dMRI, desirable for many neuroscientific and clinical applications.

Diffusion tensor imaging in schizophrenia

Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that is increasingly being used for the non-invasive evaluation of brain white matter abnormalities. In this review, we discuss the basic principles of DTI, its roots and the contribution of European centres in its development, and we review the findings from DTI studies in schizophrenia. We searched EMBASE, PubMed, PsychInfo, and Medline from February 1998 to December 2006 using as keywords ‘schizophrenia’, ‘diffusion’, ‘tensor’, and ‘DTI’. Forty studies fulfilling the inclusion criteria of this review were included and systematically reviewed. White matter abnormalities in many diverse brain regions were identified in schizophrenia. Although the findings are not completely consistent, frontal and temporal white matter seems to be more commonly affected. Limitations and future directions of this method are discussed.

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.

Diffusion MRI fiber tractography of the brain

The ability of fiber tractography to delineate non-invasively the white matter fiber pathways of the brain raises possibilities for clinical applications and offers enormous potential for neuroscience. In the last decade, fiber tracking has become the method of choice to investigate quantitative MRI parameters in specific bundles of white matter. For neurosurgeons, it is quickly becoming an invaluable tool for the planning of surgery, allowing for visualization and localization of important white matter pathways before and even during surgery. Fiber tracking has also claimed a central role in the field of "connectomics," a technique that builds and studies comprehensive maps of the complex network of connections within the brain, and to which significant resources have been allocated worldwide. Despite its unique abilities and exciting applications, fiber tracking is not without controversy, in particular when it comes to its interpretation. As neuroscientists are eager to study the brain's connectivity, the quantification of tractography-derived "connection strengths" between distant brain regions is becoming increasingly popular. However, this practice is often frowned upon by fiber-tracking experts. In light of this controversy, this paper provides an overview of the key concepts of tractography, the technical considerations at play, and the different types of tractography algorithm, as well as the common misconceptions and mistakes that surround them. We also highlight the ongoing challenges related to fiber tracking. While recent methodological developments have vastly increased the biological accuracy of fiber tractograms, one should be aware that, even with state-of-the-art techniques, many issues that severely bias the resulting structural "connectomes" remain unresolved.

Early bilateral and massive compromise of the frontal lobes

The frontal lobes are one of the most complex brain structures involved in both domain-general and specific functions. The goal of this work was to assess the anatomical and cognitive affectations from a unique case with massive bilateral frontal affectation. We report the case of GC, an eight-year old child with nearly complete affectation of bilateral frontal structures and spared temporal, parietal, occipital, and cerebellar regions. We performed behavioral, neuropsychological, and imaging (MRI, DTI, fMRI) evaluations. Neurological and neuropsychological examinations revealed a mixed pattern of affected (executive control/abstraction capacity) and considerably preserved (consciousness, language, memory, spatial orientation, and socio-emotional) functions. Both structural (DTI) and functional (fMRI) connectivity evidenced abnormal anterior connections of the amygdala and parietal networks. In addition, brain structural connectivity analysis revealed almost complete loss of frontal connections, with atypical temporo-posterior pathways. Similarly, functional connectivity showed an aberrant frontoparietal network and relative preservation of the posterior part of the default mode network and the visual network. We discuss this multilevel pattern of behavioral, structural, and functional connectivity results. With its unique pattern of compromised and preserved structures and functions, this exceptional case offers new constraints and challenges for neurocognitive theories.

Magnetic Resonance Imaging in Huntington's Disease

Magnetic resonance imaging (MRI) is a noninvasive technique used routinely to image the body in both clinical and research settings. Through the manipulation of radio waves and static field gradients, MRI uses the principle of nuclear magnetic resonance to produce images with high spatial resolution, appropriate for the investigation of brain structure and function.

Mapping temporo-parietal and temporo-occipital cortico-cortical connections of the human middle longitudinal fascicle in subject-specific, probabilistic, and stereotaxic Talairach spaces

Originally, the middle longitudinal fascicle (MdLF) was defined as a long association fiber tract connecting the superior temporal gyrus and temporal pole with the angular gyrus. More recently its description has been expanded to include all long postrolandic cortico-cortical association connections of the superior temporal gyrus and dorsal temporal pole with the parietal and occipital lobes. Despite its location and size, which makes MdLF one of the most prominent cerebral association fiber tracts, its discovery in humans is recent. Given the absence of a gold standard in humans for this fiber tract, its precise and complete connectivity remains to be determined with certainty. In this study using high angular resolution diffusion MRI (HARDI), we delineated for the first time, six major fiber connections of the human MdLF, four of which are temporo-parietal and two temporo-occipital, by examining morphology, topography, cortical connections, biophysical measures, volume and length in seventy brains. Considering the cortical affiliations of the different connections of MdLF we suggested that this fiber tract may be related to language, attention and integrative higher level visual and auditory processing associated functions. Furthermore, given the extensive connectivity provided to superior temporal gyrus and temporal pole with the parietal and occipital lobes, MdLF may be involved in several neurological and psychiatric conditions such as primary progressive aphasia and other aphasic syndromes, some forms of behavioral variant of frontotemporal dementia, atypical forms of Alzheimer's disease, corticobasal degeneration, schizophrenia as well as attention-deficit/hyperactivity Disorder and neglect disorders.

Cognitive reserve moderates the relationship between neuropsychological performance and white matter fiber bundle length in healthy older adults

Recent work using novel neuroimaging methods has revealed shorter white matter fiber bundle length (FBL) in older compared to younger adults. Shorter FBL also corresponds to poorer performance on cognitive measures sensitive to advanced age. However, it is unclear if individual factors such as cognitive reserve (CR) effectively moderate the relationship between FBL and cognitive performance. This study examined CR as a potential moderator of cognitive performance and brain integrity as defined by FBL. Sixty-three healthy adults underwent neuropsychological evaluation and 3T brain magnetic resonance imaging. Cognitive performance was measured using the Repeatable Battery of Assessment of Neuropsychological Status (RBANS). FBL was quantified from tractography tracings of white matter fiber bundles, derived from the diffusion tensor imaging. CR was determined by estimated premorbid IQ. Analyses revealed that lower scores on the RBANS were associated with shorter whole brain FBL (p = 0.04) and lower CR (p = 0.01) CR moderated the relationship between whole brain FBL and RBANS score (p < 0.01). Tract-specific analyses revealed that CR also moderated the association between FBL in the hippocampal segment of the cingulum and RBANS performance (p = 0.03). These results demonstrate that lower cognitive performance on the RBANS is more common with low CR and short FBL. On the contrary, when individuals have high CR, the relationship between FBL and cognitive performance is attenuated. Overall, CR protects older adults against lower cognitive performance despite age-associated reductions in FBL.

Processing Time Reduction: an Application in Living Human High-Resolution Diffusion Magnetic Resonance Imaging Data

High Angular Resolution Diffusion Imaging (HARDI) is a type of brain imaging that collects a very large amount of data, and if many subjects are considered then it amounts to a big data framework (e.g., the human connectome project has 20 Terabytes of data). HARDI is also becoming increasingly relevant for clinical settings (e.g., detecting early cerebral ischemic changes in acute stroke, and in pre-clinical assessment of white matter-WM anatomy using tractography). Thus, this method is becoming a routine assessment in clinical settings. In such settings, the computation time is critical, and finding forms of reducing the processing time in high computation processes such as Diffusion Spectrum Imaging (DSI), a form of HARDI data, is very relevant to increase data-processing speed. Here we analyze a method for reducing the computation time of the dMRI-based axonal orientation distribution function h by using Monte Carlo sampling-based methods for voxel selection. Results evidenced a robust reduction in required data sampling of about 50 % without losing signal's quality. Moreover, we show that the convergence to the correct value in this type of Monte Carlo HARDI/DSI data-processing has a linear improvement in data-processing speed of the ODF determination. Although further improvements are needed, our results represent a promissory step for future processing time reduction in big data.

A Simplified Method of Accurate Postprocessing of Diffusion Tensor Imaging for Use in Brain Tumor Resection

BACKGROUND: Use of diffusion tensor imaging (DTI) in brain tumor resection has been limited in part by a perceived difficulty in implementing the techniques into neurosurgical practice.

OBJECTIVE: To demonstrate a simple DTI postprocessing method performed without a neuroscientist and to share results in preserving patient function while aggressively resecting tumors.

METHODS: DTI data are obtained in all patients with tumors located within presumed eloquent cortices. Relevant white matter tracts are mapped and integrated with neuronavigation by a nonexpert in &lt; 20 minutes. We report operative results in 43 consecutive awake craniotomy patients from January 2014 to December 2014 undergoing resection of intracranial lesions. We compare DTI-expected findings with stimulation mapping results for the corticospinal tract, superior longitudinal fasciculus, and inferior fronto-occipital fasciculus.

RESULTS: Twenty-eight patients (65%) underwent surgery for high-grade gliomas and 11 patients (26%) for low-grade gliomas. Seventeen patients had posterior temporal lesions; 10 had posterior frontal lesions; 8 had parietal-temporal-occipital junction lesions; and 8 had insular lesions. With DTI-defined tracts used as a guide, a combined 65 positive maps and 60 negative maps were found via stimulation mapping. Overall sensitivity and specificity of DTI were 98% and 95%, respectively. Permanent speech worsening occurred in 1 patient (2%), and permanent weakness occurred in 3 patients (7%). Greater than 90% resection was achieved in 32 cases (74%).

CONCLUSION: Accurate DTI is easily obtained, postprocessed, and implemented into neuronavigation within routine neurosurgical workflow. This information aids in resecting tumors while preserving eloquent cortices and subcortical networks.

The Distinct Role of the Amygdala, Superior Colliculus and Pulvinar in Processing of Central and Peripheral Snakes

Introduction

Visual processing of ecologically relevant stimuli involves a central bias for stimuli demanding detailed processing (e.g., faces), whereas peripheral object processing is based on coarse identification. Fast detection of animal shapes holding a significant phylogenetic value, such as snakes, may benefit from peripheral vision. The amygdala together with the pulvinar and the superior colliculus are implicated in an ongoing debate regarding their role in automatic and deliberate spatial processing of threat signals.

Methods

Here we tested twenty healthy participants in an fMRI task, and investigated the role of spatial demands (the main effect of central vs. peripheral vision) in the processing of fear-relevant ecological features. We controlled for stimulus dependence using true or false snakes; snake shapes or snake faces and for task constraints (implicit or explicit). The main idea justifying this double task is that amygdala and superior colliculus are involved in both automatic and controlled processes. Moreover the explicit/implicit instruction in the task with respect to emotion is not necessarily equivalent to explicit vs. implicit in the sense of endogenous vs. exogenous attention, or controlled vs. automatic processes.

Results

We found that stimulus-driven processing led to increased amygdala responses specifically to true snake shapes presented in the centre or in the peripheral left hemifield (right hemisphere). Importantly, the superior colliculus showed significantly biased and explicit central responses to snake-related stimuli. Moreover, the pulvinar, which also contains foveal representations, also showed strong central responses, extending the results of a recent single cell pulvinar study in monkeys. Similar hemispheric specialization was found across structures: increased amygdala responses occurred to true snake shapes presented to the right hemisphere, with this pattern being closely followed by the superior colliculus and the pulvinar.

Conclusion

These results show that subcortical structures containing foveal representations such as the amygdala, pulvinar and superior colliculus play distinct roles in the central and peripheral processing of snake shapes. Our findings suggest multiple phylogenetic fingerprints in the responses of subcortical structures to fear-relevant stimuli.

The Ionic DTI Model (iDTI) of Dynamic Diffusion Tensor Imaging (dDTI)

Measurements of water molecule diffusion along fiber tracts in CNS by diffusion tensor imaging (DTI) provides a static map of neural connections between brain centers, but does not capture the electrical activity along axons for these fiber tracts. Here, a modification of the DTI method is presented to enable the mapping of active fibers. It is termed dynamic diffusion tensor imaging (dDTI) and is based on a hypothesized “anisotropy reduction due to axonal excitation” (“AREX”). The potential changes in water mobility accompanying the movement of ions during the propagation of action potentials along axonal tracts are taken into account. Specifically, the proposed model, termed “ionic DTI model”, was formulated as follows.•

First, based on theoretical calculations, we calculated the molecular water flow accompanying the ionic flow perpendicular to the principal axis of fiber tracts produced by electrical conduction along excited myelinated and non-myelinated axons.

•

Based on the changes in molecular water flow we estimated the signal changes as well as the changes in fractional anisotropy of axonal tracts while performing a functional task.

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The variation of fractional anisotropy in axonal tracts could allow mapping the active fiber tracts during a functional task.

Although technological advances are necessary to enable the robust and routine measurement of this electrical activity-dependent movement of water molecules perpendicular to axons, the proposed model of dDTI defines the vectorial parameters that will need to be measured to bring this much needed technique to fruition.

Diffusion tensor tractography of normal facial and vestibulocochlear nerves

PURPOSE

Diffusion tensor tractography (DTT) is not adequately reliable for prediction of facial and vestibulocochlear (VII-VIII) nerve locations, especially relative to a vestibular schwannoma (VS). Furthermore, it is often not possible to visualize normal VII-VIII nerves by DTT (visualization rates were 12.5-63.6%). Therefore, DTT post-processing was optimized for normal VII-VIII nerve visualization with and without manual noise elimination.

METHODS

DTT examinations of ten patients were evaluated to assess the improvement in performance by modifying seed region of interest (ROI) and fractional anisotropy (FA) threshold. Seed ROI was placed at the porus of the internal auditory meatus, and FA threshold values were either fixed or variable for each patient. DTT visualization of cranial nerves VII-VIII was evaluated and the noise effect was measured.

RESULTS

Cranial nerves VII-VIII were visualized in 90% of patients without using manual noise elimination by modifying the seed ROI and FA threshold. The visualization rate with FA threshold of the upper limit in each patient (100%) was significantly higher than that with FA threshold of 0.1 (75%) (p = 0.02). The incidence rate of noise with FA threshold of the upper limit (10%) was not significantly different than the FA threshold of 0.1 (20%) (p = 0.66).

CONCLUSION

Seed ROI modification and FA thresholding can improve the visualization of cranial nerve VII-VIII locations in DTT. This technique is promising for its potential to determine the relationship of cranial nerves VII-VIII to VS.

Improving the accuracy of PGSE DTI experiments using the spatial distribution of b matrix

A novel method for improving the accuracy of diffusion tensor imaging (DTI) is proposed. It takes into account the b matrix spatial variations, which can be easily determined using a simple anisotropic diffusion phantom. In opposite to standard numerical procedure of the b matrix calculation that requires the exact knowledge of amplitudes, shapes and time dependencies of diffusion gradients, the new method, which we call BSD-DTI (B-matrix spatial distribution in DTI), relies on direct measurements of its space-dependent components. The proposed technique was demonstrated on the Bruker Biospec 94/20USR system, using the spin echo diffusion sequence to image an isotropic water phantom and an anisotropic capillary phantom. The accuracy of the diffusion tensor determination was improved by an overall factor of about 8 for the isotropic water phantom.

White matter changes with age utilizing quantitative diffusion MRI

OBJECTIVE

To investigate the relationship between older age and mean cerebral white matter fiber bundle lengths (FBLs) in specific white matter tracts in the brain using quantified diffusion MRI.

METHODS

Sixty-three healthy adults older than 50 years underwent diffusion tensor imaging. Tractography tracings of cerebral white matter fiber bundles were derived from the diffusion tensor imaging data.

RESULTS

Results revealed significantly shorter FBLs in the anterior thalamic radiation for every 1-year increase over the age of 50 years.

CONCLUSIONS

We investigated the effects of age on FBL in specific white matter tracts in the brains of healthy older individuals utilizing quantified diffusion MRI. The results revealed a significant inverse relationship between age and FBL. Longitudinal studies of FBL across a lifespan are needed to examine the specific changes to the integrity of white matter.

Knowledge-based automated reconstruction of human brain white matter tracts using a path-finding approach with dynamic programming

It has been shown that the anatomy of major white matter tracts can be delineated using diffusion tensor imaging (DTI) data. Tract reconstruction, however, often suffers from a large number of false-negative results when a simple line propagation algorithm is used. This limits the application of this technique to only the core of prominent white matter tracts. By employing probabilistic path-generation algorithms, connectivity between a larger number of anatomical regions can be studied, but an increase in the number of false-positive results is inevitable. One of the causes of the inaccuracy is the complex axonal anatomy within a voxel; however, high-angular resolution (HAR) methods have been proposed to ameliorate this limitation. However, HAR data are relatively rare due to the long scan times required and the low signal-to-noise ratio. In this study, we tested a probabilistic path-finding method in which two anatomical regions with known connectivity were pre-defined and a path that maximized agreement with the DTI data was searched. To increase the accuracy of the trajectories, knowledge-based anatomical constraints were applied. The reconstruction protocols were tested using DTI data from 19 normal subjects to examine test-retest reproducibility and cross-subject variability. Fifty-two tracts were found to be reliably reconstructed using this approach, which can be viewed on our website.

Advanced MRI strategies for assessing spinal cord injury

Advanced magnetic resonance (MR) approaches permit the noninvasive quantification of macromolecular, functional, and physiological properties of biological tissues. In this chapter, we review the application of advanced MR techniques to the spinal cord. Macromolecular properties of the spinal cord can be studied using magnetization transfer (MT) MR, diffusion tensor imaging (DTI), Q-space diffusion spectroscopy, and selective detection of myelin water. The functional and metabolic status of the spinal cord can be studied using functional MRI (fMRI), perfusion imaging, and magnetic resonance spectroscopy (MRS). Finally, we consider the outlook for advanced MR studies in persons in whom metal hardware has been implanted to stabilize the cord. In spite of the spinal cord's diminutive size, its location deep within the body, and constant motion, recent work shows that the spinal cord can be studied using these advanced MR approaches.

Diffusion tensor magnetic resonance imaging in the analysis of neurodegenerative diseases

Diffusion tensor imaging (DTI) techniques provide information on the microstructural processes of the cerebral white matter (WM) in vivo. The present applications are designed to investigate differences of WM involvement patterns in different brain diseases, especially neurodegenerative disorders, by use of different DTI analyses in comparison with matched controls. DTI data analysis is performed in a variate fashion, i.e. voxelwise comparison of regional diffusion direction-based metrics such as fractional anisotropy (FA), together with fiber tracking (FT) accompanied by tractwise fractional anisotropy statistics (TFAS) at the group level in order to identify differences in FA along WM structures, aiming at the definition of regional patterns of WM alterations at the group level. Transformation into a stereotaxic standard space is a prerequisite for group studies and requires thorough data processing to preserve directional inter-dependencies. The present applications show optimized technical approaches for this preservation of quantitative and directional information during spatial normalization in data analyses at the group level. On this basis, FT techniques can be applied to group averaged data in order to quantify metrics information as defined by FT. Additionally, application of DTI methods, i.e. differences in FA-maps after stereotaxic alignment, in a longitudinal analysis at an individual subject basis reveal information about the progression of neurological disorders. Further quality improvement of DTI based results can be obtained during preprocessing by application of a controlled elimination of gradient directions with high noise levels. In summary, DTI is used to define a distinct WM pathoanatomy of different brain diseases by the combination of whole brain-based and tract-based DTI analysis.

Human middle longitudinal fascicle: variations in patterns of anatomical connections

Based on high-resolution diffusion tensor magnetic resonance imaging (DTI) tractographic analyses in 39 healthy adult subjects, we derived patterns of connections and measures of volume and biophysical parameters, such as fractional anisotropy (FA) for the human middle longitudinal fascicle (MdLF). Compared to previous studies, we found that the cortical connections of the MdLF in humans appear to go beyond the superior temporal (STG) and angular (AG) gyri, extending to the temporal pole (TP), superior parietal lobule (SPL), supramarginal gyrus, precuneus and the occipital lobe (including the cuneus and lateral occipital areas). Importantly, the MdLF showed a striking lateralized pattern with predominant connections between the TP, STG and AG on the left and TP, STG and SPL on the right hemisphere. In light of the results of the present study, and of the known functional role of the cortical areas interconnected by the MdLF, we suggested that this fiber pathway might be related to language, high order auditory association, visuospatial and attention functions.

Emerging structure-function relations in the developing face processing system

To evaluate emerging structure-function relations in a neural circuit that mediates complex behavior, we investigated age-related differences among cortical regions that support face recognition behavior and the fiber tracts through which they transmit and receive signals using functional neuroimaging and diffusion tensor imaging. In a large sample of human participants (aged 6-23 years), we derived the microstructural and volumetric properties of the inferior longitudinal fasciculus (ILF), the inferior fronto-occipital fasciculus, and control tracts, using independently defined anatomical markers. We also determined the functional characteristics of core face- and place-selective regions that are distributed along the trajectory of the pathways of interest. We observed disproportionately large age-related differences in the volume, fractional anisotropy, and mean and radial, but not axial, diffusivities of the ILF. Critically, these differences in the structural properties of the ILF were tightly and specifically linked with an age-related increase in the size of a key face-selective functional region, the fusiform face area. This dynamic association between emerging structural and functional architecture in the developing brain may provide important clues about the mechanisms by which neural circuits become organized and optimized in the human cortex.

In vivo high-resolution diffusion tensor imaging of the mouse brain

Diffusion tensor imaging (DTI) of the laboratory mouse brain provides important macroscopic information for anatomical characterization of mouse models in basic research. Currently, in vivo DTI of the mouse brain is often limited by the available resolution. In this study, we demonstrate in vivo high-resolution DTI of the mouse brain using a cryogenic probe and a modified diffusion-weighted gradient and spin echo (GRASE) imaging sequence at 11.7 T. Three-dimensional (3D) DTI of the entire mouse brain at 0.125 mm isotropic resolution could be obtained in approximately 2 h. The high spatial resolution, which was previously only available with ex vivo imaging, enabled non-invasive examination of small structures in the adult and neonatal mouse brains. Based on data acquired from eight adult mice, a group-averaged DTI atlas of the in vivo adult mouse brain with 60 structure segmentations was developed. Comparisons between in vivo and ex vivo mouse brain DTI data showed significant differences in brain morphology and tissue contrasts, which indicate the importance of the in vivo DTI-based mouse brain atlas.

Robust tract skeleton extraction of cingulum based on active contour model from diffusion tensor MR imaging

Cingulum is widely studied in healthy and psychiatric subjects. For cingulum analysis from diffusion tensor MR imaging, tractography and tract of interest method have been adopted for tract-based analysis. Because tractography performs fiber tracking according to local diffusion measures, they can be sensitive to noise and tracking errors can be accumulated along the fiber. For more accurate localization of cingulum, we attempt to define it by skeleton extraction using the tensors' information throughout the tract of cingulum simultaneously, which is quite different from the idea of tractography. In this study, we introduce an approach to extract the skeleton of cingulum using active contour model, which allows us to optimize the location of cingulum in a global sense based on the diffusion measurements along the entire tract and contour regularity. Validation of this method on synthetic and experimental data proved that our approach is able to reduce the influence of noise and partial volume effect, and extract the skeleton of cingulum robustly and reliably. Our proposed method provides an approach to localize cingulum robustly, which is a very important feature for tract-based analysis and can be of important practical utility.

[Structural connectivity and diffusion tensor imaging in autism spectrum disorders]

Over the past years, diffusion tensor imaging (DTI) has become an important brain-imaging technique in neuropsychiatric research. DTI allows noninvasive visualization of white matter tracts. In addition, with DTI it is possible to quantify the structural integrity of the investigated fiber tracts. In child and adolescent psychiatry, DTI has become an increasingly important research tool, especially for conditions like autism spectrum disorders (ASD). Yet, correct interpretation of DTI findings can be challenging, especially for clinicians. Thus, the present review article explains the basic principles of this frequently used imaging technique as well as essential indices, like fractional anisotropy, radial, mean, and axial diffusivity and its two main applications, fibertracking and whole brain analysis. The strengths and weaknesses as well as future perspectives are discussed in light of DTI studies in children and adolescents with ASD.

Model-based reconstruction of undersampled diffusion tensor k-space data

The practical utility of diffusion tensor imaging, especially for 3D high-resolution spin warp experiments of ex vivo specimens, has been hampered by long acquisition times. To accelerate the acquisition, a compressed sensing framework that uses a model-based formulation to reconstruct diffusion tensor fields from undersampled k-space data was presented and evaluated. Accuracies in brain specimen white matter fiber orientation, fractional anisotropy, and mean diffusivity mapping were compared with alternative methods achievable using the same scan time via reduced image resolution, fewer diffusion encoding directions, standard compressed sensing, or asymmetrical sampling reconstruction. The efficiency of the proposed approach was also compared with fully sampled cases across a range of the number of diffusion encoding directions. In general, the proposed approach was found to reduce the image blurring and noise and to provide more accurate fiber orientation, fractional anisotropy, and mean diffusivity measurements compared with the alternative methods. Moreover, depending on the degree of undersampling used and the diffusion tensor imaging parameter examined, the measurement accuracy of the proposed scheme was equivalent to fully sampled diffusion tensor imaging datasets that consist of 33-67% more encoding directions and require proportionally longer scan times. The findings show model-based compressed sensing to be promising for improving the resolution, accuracy, or scan time of diffusion tensor imaging.

White matter integrity, fiber count, and other fallacies: the do's and don'ts of diffusion MRI

Diffusion-weighted MRI (DW-MRI) has been increasingly used in imaging neuroscience over the last decade. An early form of this technique, diffusion tensor imaging (DTI) was rapidly implemented by major MRI scanner companies as a scanner selling point. Due to the ease of use of such implementations, and the plausibility of some of their results, DTI was leapt on by imaging neuroscientists who saw it as a powerful and unique new tool for exploring the structural connectivity of human brain. However, DTI is a rather approximate technique, and its results have frequently been given implausible interpretations that have escaped proper critique and have appeared misleadingly in journals of high reputation. In order to encourage the use of improved DW-MRI methods, which have a better chance of characterizing the actual fiber structure of white matter, and to warn against the misuse and misinterpretation of DTI, we review the physics of DW-MRI, indicate currently preferred methodology, and explain the limits of interpretation of its results. We conclude with a list of 'Do's and Don'ts' which define good practice in this expanding area of imaging neuroscience.

Age-related changes in parahippocampal white matter integrity: a diffusion tensor imaging study

The axons in the parahippocampal white matter (PWM) region that includes the perforant pathway relay multimodal sensory information, important for memory function, from the entorhinal cortex to the hippocampus. Previous work suggests that the integrity of the PWM shows changes in individuals with amnestic mild cognitive impairment and is further compromised as Alzheimer's disease progresses. The present study was undertaken to determine the effects of healthy aging on macro- and micro-structural alterations in the PWM. The study characterized in vivo white matter changes in the parahippocampal region that includes the perforant pathway in cognitively healthy young (YNG, n=21) compared to cognitively healthy older (OLD, n=21) individuals using volumetry, diffusion tensor imaging (DTI) and tractography. Results demonstrated a significant reduction in PWM volume in old participants, with further indications of reduced integrity of remaining white matter fibers. In logistic regressions, PWM volume, memory performance and DTI indices of PWM integrity were significant indicator variables for differentiating the young and old participants. Taken together, these findings suggest that age-related alterations do occur in the PWM region and may contribute to the normal decline in memory function seen in healthy aging by degrading information flow to the hippocampus.

Fasciculography: robust prior-free real-time normalized volumetric neural tract parcellation

Fiber tracking in diffusion tensor magnetic resonance images (DTIs) reveals 3-D structural connectivity of the brain conveniently and thus is a viable tool for investigating neural differences. Unfortunately, local noise, image artifacts and numerical tracking errors during integration-based techniques are cumulative. Prematurely terminated fibers and under-sampled fiber bundles result in incomplete reconstruction of white matter fiber tracts and hence incorrect anatomical measurements. Quantitative cross-subject tract analysis, which is critical for abnormality detection, is complicated by inefficient and inaccurate tract reconstruction and normalization from fiber bundles. Because of the above problems, we propose a parcellation method that aims for lower sensitivity to initialization and local orientation error by directly segmenting full white matter tracts (Fasciculography), rather than reconstructing individual curves, from diffusion tensor fields. A fast, robust volumetric, and intrinsically normalized solution is achieved by noise-filtering using a generic parametrized tract model to prevent premature tract termination. At the same time, orientation information reduces the search space, significantly speeding up the tract parcellation process with less human intervention. Detailed comparisons against streamline tracking, shortest-path tracking, and nonrigid registration using synthetic and real DTIs confirmed the superior properties of Fasciculography. Since a normalized tract can be delineated interactively in a just few seconds using the proposed method, accurate high volume tract comparisons become feasible.

Characterization of cerebral white matter properties using quantitative magnetic resonance imaging stains

The image contrast in magnetic resonance imaging (MRI) is highly sensitive to several mechanisms that are modulated by the properties of the tissue environment. The degree and type of contrast weighting may be viewed as image filters that accentuate specific tissue properties. Maps of quantitative measures of these mechanisms, akin to microstructural/environmental-specific tissue stains, may be generated to characterize the MRI and physiological properties of biological tissues. In this article, three quantitative MRI (qMRI) methods for characterizing white matter (WM) microstructural properties are reviewed. All of these measures measure complementary aspects of how water interacts with the tissue environment. Diffusion MRI, including diffusion tensor imaging, characterizes the diffusion of water in the tissues and is sensitive to the microstructural density, spacing, and orientational organization of tissue membranes, including myelin. Magnetization transfer imaging characterizes the amount and degree of magnetization exchange between free water and macromolecules like proteins found in the myelin bilayers. Relaxometry measures the MRI relaxation constants T1 and T2, which in WM have a component associated with the water trapped in the myelin bilayers. The conduction of signals between distant brain regions occurs primarily through myelinated WM tracts; thus, these methods are potential indicators of pathology and structural connectivity in the brain. This article provides an overview of the qMRI stain mechanisms, acquisition and analysis strategies, and applications for these qMRI stains.

Advanced MRI strategies for assessing spinal cord injury

Advanced magnetic resonance (MR) approaches permit the noninvasive quantification of macromolecular, functional, and physiological properties of biological tissues. In this chapter, we review the application of advanced MR techniques to the spinal cord. Macromolecular properties of the spinal cord can be studied using magnetization transfer (MT) MR, diffusion tensor imaging (DTI), Q-space diffusion spectroscopy, and selective detection of myelin water. The functional and metabolic status of the spinal cord can be studied using functional MRI (fMRI), perfusion imaging, and magnetic resonance spectroscopy (MRS). Finally, we consider the outlook for advanced MR studies in persons in whom metal hardware has been implanted to stabilize the cord. In spite of the spinal cord's diminutive size, its location deep within the body, and constant motion, recent work shows that the spinal cord can be studied using these advanced MR approaches.

Automated discrimination of brain pathological state attending to complex structural brain network properties: the shiverer mutant mouse case

Neuroimaging classification procedures between normal and pathological subjects are sparse and highly dependent of an expert's clinical criterion. Here, we aimed to investigate whether possible brain structural network differences in the shiverer mouse mutant, a relevant animal model of myelin related diseases, can reflect intrinsic individual brain properties that allow the automatic discrimination between the shiverer and normal subjects. Common structural networks properties between shiverer (C3Fe.SWV Mbp(shi)/Mbp(shi), n = 6) and background control (C3HeB.FeJ, n = 6) mice are estimated and compared by means of three diffusion weighted MRI (DW-MRI) fiber tractography algorithms and a graph framework. Firstly, we found that brain networks of control group are significantly more clustered, modularized, efficient and optimized than those of the shiverer group, which presented significantly increased characteristic path length. These results are in line with previous structural/functional complex brain networks analysis that have revealed topologic differences and brain network randomization associated to specific states of human brain pathology. In addition, by means of network measures spatial representations and discrimination analysis, we show that it is possible to classify with high accuracy to which group each subject belongs, providing also a probability value of being a normal or shiverer subject as an individual anatomical classifier. The obtained correct predictions (e.g., around 91.6-100%) and clear spatial subdivisions between control and shiverer mice, suggest that there might exist specific network subspaces corresponding to specific brain disorders, supporting also the point of view that complex brain network analyses constitutes promising tools in the future creation of interpretable imaging biomarkers.

Diffusion tensor microscopy in human nervous tissue with quantitative correlation based on direct histological comparison

Thanks to its proven utility in both clinical and research applications, diffusion tensor tractography (DTT) is regularly employed as a means of delineating white-matter tracts. While successful efforts have been made to validate tractographic predictions, comparative methods which would permit the validation of such predictions at microscopic resolutions in complex biological tissues have remained elusive. In a previous study, we attempted to validate for the first time such predictions at microscopic resolutions in rat and pig spinal cords using a semi-quantitative analysis method. In the current study, we report improved quantitative analysis methods that can be used to determine the accuracy of DTT through comparative histology and apply these techniques for the first time to human tissue (spinal cord) samples. Histological images are down-sampled to resolutions equivalent to our magnetic resonance microscopy (MRM) and converted to binary maps using an automated thresholding tool. These maps (n=3) are co-registered to the MRM allowing us to quantify the agreement based on the number of pixels which contain tracts common to both imaging datasets. In our experiments, we find that-on average-89% of imaging pixels predicted by DTT to contain in-plane white-matter tract structure correspond to physical tracts identified by histology. In addition, angular analysis comparing the orientation of fiber tracts measured in histology to their corresponding in-plane primary eigenvector components is presented. Thus, as well as demonstrating feasibility in human tissue, we report a robust agreement between imaging datasets taken at microscopic resolution and confirm the primary eigenvector's role as a fundamental parameter with clear physical correlates in the microscopic regime.

Integrated parcellation and normalization using DTI fasciculography

Existing methods for fiber tracking, interactive bundling and editing from Diffusion Magnetic Resonance Images (DMRI) reconstruct white matter fascicles using groups of virtual pathways. Classical numerical fibers suffer from image noise and cumulative tracking errors. 3D visualization of bundles of fibers reveals structural connectivity of the brain; however, extensive human intervention, tracking variations and errors in fiber sampling make quantitative fascicle comparison difficult. To simplify the process and offer standardized white matter samples for analysis, we propose a new integrated fascicle parcellation and normalization method that combines a generic parametrized volumetric tract model with orientation information from diffusion images. The new technique offers a tract-derived spatial parameter for each voxel within the model. Cross-subject statistics of tract data can be compared easily based on these parameters. Our implementation demonstrated interactive speed and is available to the public in a packaged application.

Segmentation of fiber tracts based on an accuracy analysis on diffusion tensor software phantoms

Due to its unique sensitivity to tissue microstructure, one of the primary applications of diffusion-weighted magnetic resonance imaging is the reconstruction of neural fiber pathways by means of fiber-tracking algorithms. In this work, we make use of realistic diffusion-tensor software phantoms in order to carry out an analysis of the precision of streamline tractography by systematically varying certain properties of the simulated image data (noise, tensor anisotropy, and image resolution) as well as certain fiber-tracking parameters (number of seed points and step length). Building upon the gained knowledge about the precision of the analyzed fiber-tracking algorithm, we proceed by suggesting a fuzzy segmentation algorithm for diffusion tensor images which better estimates the precise spatial extent of a tracked fiber bundle. The presented segmentation algorithm utilizes information given by the estimated main diffusion direction in a voxel and the respective uncertainty, and its validity is confirmed by both qualitative and quantitative analyses.

An improved Bayesian tensor regularization and sampling algorithm to track neuronal fiber pathways in the language circuit

PURPOSE

The purpose of this work is to design a neuronal fiber tracking algorithm, which will be more suitable for reconstruction of fibers associated with functionally important regions in the human brain. The functional activations in the brain normally occur in the gray matter regions. Hence the fibers bordering these regions are weakly myelinated, resulting in poor performance of conventional tractography methods to trace the fiber links between them. A lower fractional anisotropy in this region makes it even difficult to track the fibers in the presence of noise. In this work, the authors focused on a stochastic approach to reconstruct these fiber pathways based on a Bayesian regularization framework.

METHODS

To estimate the true fiber direction (propagation vector), the a priori and conditional probability density functions are calculated in advance and are modeled as multivariate normal. The variance of the estimated tensor element vector is associated with the uncertainty due to noise and partial volume averaging (PVA). An adaptive and multiple sampling of the estimated tensor element vector, which is a function of the pre-estimated variance, overcomes the effect of noise and PVA in this work.

RESULTS

The algorithm has been rigorously tested using a variety of synthetic data sets. The quantitative comparison of the results to standard algorithms motivated the authors to implement it for in vivo DTI data analysis. The algorithm has been implemented to delineate fibers in two major language pathways (Broca's to SMA and Broca's to Wernicke's) across 12 healthy subjects. Though the mean of standard deviation was marginally bigger than conventional (Euler's) approach [P. J. Basser et al., "In vivo fiber tractography using DT-MRI data," Magn. Reson. Med. 44(4), 625-632 (2000)], the number of extracted fibers in this approach was significantly higher. The authors also compared the performance of the proposed method to Lu's method [Y. Lu et al., "Improved fiber tractography with Bayesian tensor regularization," Neuroimage 31(3), 1061-1074 (2006)] and Friman's stochastic approach [O. Friman et al., "A Bayesian approach for stochastic white matter tractography," IEEE Trans. Med. Imaging 25(8), 965-978 (2006)]. Overall performance of the approach is found to be superior to above two methods, particularly when the signal-to-noise ratio was low.

CONCLUSIONS

The authors observed that an adaptive sampling of the tensor element vectors, estimated as a function of the variance in a Bayesian framework, can effectively delineate neuronal fibers to analyze the structure-function relationship in human brain. The simulated and in vivo results are in good agreement with the theoretical aspects of the algorithm.