Major White Matter Fiber Bundles Research Articles

Posttraumatic survivor guilt is associated with white matter microstructure alterations

BackgroundMilitary veterans with posttraumatic stress disorder (PTSD) commonly experience posttraumatic guilt. Guilt over commission or omission evolves when responsibility is assumed for an unfortunate outcome (e.g., the death of a fellow combatant). Survivor guilt is a state of intense emotional distress experienced by the weight of knowing that one survived while others did not. MethodsThis study of the Translational Research Center for TBI and Stress Disorders (TRACTS) analyzed structural and diffusion-weighted magnetic resonance imaging data from 132 male Iraq/Afghanistan veterans with PTSD. The Clinician-Administered PTSD Scale for DSM-IV (CAPS-IV) was employed to classify guilt. Thirty (22.7 %) veterans experienced guilt over acts of commission or omission, 34 (25.8 %) experienced survivor guilt, and 68 (51.5 %) had no posttraumatic guilt. White matter microstructure (fractional anisotropy, FA), cortical thickness, and cortical volume were compared between veterans with guilt over acts of commission or omission, veterans with survivor guilt, and veterans without guilt. ResultsVeterans with survivor guilt had significantly lower white matter FA compared to veterans who did not experience guilt (p < .001), affecting several regions of major white matter fiber bundles. There were no significant differences in white matter FA, cortical thickness, or volumes between veterans with guilt over acts of commission or omission and veterans without guilt (p > .050). LimitationsThis cross-sectional study with exclusively male veterans precludes inferences of causality between the studied variables and generalizability to the larger veteran population that includes women. ConclusionSurvivor guilt may be a particularly impactful form of posttraumatic guilt that requires specific treatment efforts targeting brain health.

From images to insights: a neuroradiologist's practical guide on white matter fiber tract anatomy and DTI patterns for pre-surgical planning.

Diffusion tensor imaging (DTI) is a valuable non-invasive imaging modality for mapping white matter tracts and assessing microstructural integrity, and can be used as a "biomarker" in diagnosis, differentiation, and therapeutic monitoring. Although it has gained clinical importance as a marker of neuropathology, limitations in its interpretation underscore the need for caution. This review provides an overview of the principles and clinical applicability of DTI. We focus on major white matter fiber bundles, detailing their normal anatomy and pathological DTI patterns, with emphasis on tracts routinely requested in our neurosurgical department in the preoperative context (uncinate fasciculus, arcuate fasciculus, pyramidal pathway, optic radiation, and dentatorubrothalamic tract). We guide neuroradiologists and neurosurgeons in defining volumes of interest for mapping individual tracts and demonstrating their 3D reconstructions. The intricate trajectories of white matter tracts pose a challenge for accurate fiber orientation recording, with each bundle exhibiting specific characteristics. Tracts adjacent to brain lesions are categorized as displaced, edematous, infiltrated, or disrupted, illustrated with clinical cases of brain neoplasms. To improve structured reporting, we propose a checklist of topics for inclusion in imaging evaluations and MRI reports. DTI is emerging as a powerful tool for assessing microstructural changes in brain disorders, despite some challenges in standardization and interpretation. This review serves an educational purpose by providing guidance for fiber monitoring and interpretation of pathological patterns observed in clinical cases, highlighting the importance and potential pitfalls of DTI in neuroradiology and surgical planning.

Test-Retest Reliability of Diffusion Measures Extracted Along White Matter Language Fiber Bundles Using HARDI-Based Tractography.

High angular resolution diffusion imaging (HARDI)-based tractography has been increasingly used in longitudinal studies on white matter macro- and micro-structural changes in the language network during language acquisition and in language impairments. However, test-retest reliability measurements are essential to ascertain that the longitudinal variations observed are not related to data processing. The aims of this study were to determine the reproducibility of the reconstruction of major white matter fiber bundles of the language network using anatomically constrained probabilistic tractography with constrained spherical deconvolution based on HARDI data, as well as to assess the test-retest reliability of diffusion measures extracted along them. Eighteen right-handed participants were scanned twice, one week apart. The arcuate, inferior longitudinal, inferior fronto-occipital, and uncinate fasciculi were reconstructed in the left and right hemispheres and the following diffusion measures were extracted along each tract: fractional anisotropy, mean, axial, and radial diffusivity, number of fiber orientations, mean length of streamlines, and volume. All fiber bundles showed good morphological overlap between the two scanning timepoints and the test-retest reliability of all diffusion measures in most fiber bundles was good to excellent. We thus propose a fairly simple, but robust, HARDI-based tractography pipeline reliable for the longitudinal study of white matter language fiber bundles, which increases its potential applicability to research on the neurobiological mechanisms supporting language.

Brain structural connectivity correlates with fluid intelligence in children: A DTI graph analysis

PurposeThe aim of this study is to better understand the neural substrate of intelligence in children, through the characterization of the brain's structural connectivity (graph measures), as well as the major white matter (WM) fibers microstructure. Materials and methodsFourty-three children (8 to 12 years) were include in this study. MRI acquisition included conventional and diffusion tensor imaging sequences. Children also underwent neuropsychological tests, providing the ten WISC-subtests. A factor analysis was performed to explore the inter-correlations of WISC-IV subtests and to extract intelligence domains as well as the general intelligence factor (g-score). Correlations between the intelligence domains and both graphs and diffusivity metrics were also explored. ResultsGlobal graph metrics revealed a strong relationship between high intelligence scores and brain network homogeneity (high density and low modularity), mainly in the temporal and parietal lobes. Furthermore, quantitative WM fiber-bundle analysis showed an increased axonal density in the major WM fiber-bundles that was associated to increased intelligence performances. ConclusionThese findings demonstrated that intelligence neural substrate is based on a strong WM microstructure of the major intra- and inter- hemispheric fiber-bundles and a well-balanced network organization between local and global scales.

Reduced White Matter Integrity in Antisocial Personality Disorder: A Diffusion Tensor Imaging Study

Emerging neuroimaging research suggests that antisocial personality disorder (ASPD) may be linked to abnormal brain anatomy, but little is known about possible impairments of white matter microstructure in ASPD, as well as their relationship with impulsivity or risky behaviors. In this study, we systematically investigated white matter abnormalities of ASPD using diffusion tensor imaging (DTI) measures: fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD). Then, we further investigated their correlations with the scores of impulsivity or risky behaviors. ASPD patients showed decreased FA in multiple major white matter fiber bundles, which connect the fronto-parietal control network and the fronto-temporal network. We also found AD/RD deficits in some additional white matter tracts that were not detected by FA. More interestingly, several regions were found correlated with impulsivity or risky behaviors in AD and RD values, although not in FA values, including the splenium of corpus callosum, left posterior corona radiate/posterior thalamic radiate, right superior longitudinal fasciculus, and left inferior longitudinal fasciculus. These regions can be the potential biomarkers, which would be of great interest in further understanding the pathomechanism of ASPD.

How do parcellation size and short-range connectivity affect dynamics in large-scale brain network models?

Recent efforts to model human brain activity on the scale of the whole brain rest on connectivity estimates of large-scale networks derived from diffusion magnetic resonance imaging (dMRI). This type of connectivity describes white matter fiber tracts. The number of short-range cortico-cortical white-matter connections is, however, underrepresented in such large-scale brain models. It is still unclear on the one hand, which scale of representation of white matter fibers is optimal to describe brain activity on a large-scale such as recorded with magneto- or electroencephalography (M/EEG) or functional magnetic resonance imaging (fMRI), and on the other hand, to which extent short-range connections that are typically local should be taken into account. In this article we quantified the effect of connectivity upon large-scale brain network dynamics by (i) systematically varying the number of brain regions before computing the connectivity matrix, and by (ii) adding generic short-range connections. We used dMRI data from the Human Connectome Project. We developed a suite of preprocessing modules called SCRIPTS to prepare these imaging data for The Virtual Brain, a neuroinformatics platform for large-scale brain modeling and simulations. We performed simulations under different connectivity conditions and quantified the spatiotemporal dynamics in terms of Shannon Entropy, dwell time and Principal Component Analysis. For the reconstructed connectivity, our results show that the major white matter fiber bundles play an important role in shaping slow dynamics in large-scale brain networks (e.g. in fMRI). Faster dynamics such as gamma oscillations (around 40 Hz) are sensitive to the short-range connectivity if transmission delays are considered.

Comparing free water imaging and magnetization transfer measurements in schizophrenia

Diffusion weighted imaging (DWI) has been extensively used to study the microarchitecture of white matter in schizophrenia. However, popular DWI-derived measures such as fractional anisotropy (FA) may be sensitive to many types of pathologies, and thus the interpretation of reported differences in these measures remains difficult. Combining DWI with magnetization transfer ratio (MTR) – a putative measure of white matter myelination – can help us reveal the underlying mechanisms. Previous findings hypothesized that MTR differences in schizophrenia are associated with free water concentrations, which also affect the DWIs. In this study we use a recently proposed DWI-derived method called free-water imaging to assess this hypothesis. We have reanalyzed data from a previous study by using a fiber-based analysis of free-water imaging, providing a free-water fraction, as well as mean diffusivity and FA corrected for free-water, in addition to MTR along twelve major white matter fiber bundles in 40 schizophrenia patients and 40 healthy controls. We tested for group differences in each fiber bundle and for each measure separately and computed correlations between the MTR and the DWI-derived measures separately for both groups. Significant higher average MTR values in patients were found for the right uncinate fasciculus, the right arcuate fasciculus and the right inferior-frontal occipital fasciculus. No significant results were found for the other measures. No significant differences in correlations were found between MTR and the DWI-derived measures. The results suggest that MTR and free-water imaging measures can be considered complementary, promoting the acquisition of MTR in addition to DWI to identify group differences, as well as to better understand the underlying mechanisms in schizophrenia.

Prenatal alcohol exposure reduces magnetic susceptibility contrast and anisotropy in the white matter of mouse brains

Prenatal alcohol exposure can result in long-term cognitive and behavioral deficits. Fetal alcohol spectrum disorder (FASD) refers to a range of permanent birth defects caused by prenatal alcohol exposure, and is the most common neurodevelopmental disorder in the US. Studies by autopsy and conventional structural MRI indicate that the midline structures of the brain are particularly vulnerable to prenatal alcohol exposure. Diffusion tensor imaging (DTI) has shown that abnormalities in brain white matter especially the corpus callosum are very common in FASD. Quantitative susceptibility mapping (QSM) is a novel technique that measures tissue's magnetic property. Such magnetic property is affected by tissue microstructure and molecular composition including that of myelin in the white matter. In this work, we studied three major white matter fiber bundles of a mouse model of FASD and compared it to control mice using both QSM and DTI. QSM revealed clear and significant abnormalities in anterior commissure, corpus callosum, and hippocampal commissure, which were likely due to reduced myelination. Our data also suggested that QSM may be even more sensitive than DTI for examining changes due to prenatal alcohol exposure. Although this is a preclinical study, the technique of QSM is readily translatable to human brain.

Comparison of Magnetic Susceptibility Tensor and Diffusion Tensor of the Brain.

Susceptibility tensor imaging (STI) provides a novel approach for noninvasive assessment of the white matter pathways of the brain. Using mouse brain ex vivo, we compared STI with diffusion tensor imaging (DTI), in terms of tensor values, principal tensor values, anisotropy values, and tensor orientations. Despite the completely different biophysical underpinnings, magnetic susceptibility tensors and diffusion tensors show many similarities in the tensor and principal tensor images, for example, the tensors perpendicular to the fiber direction have the highest gray-white matter contrast, and the largest principal tensor is along the fiber direction. Comparison to DTI fractional anisotropy, the susceptibility anisotropy provides much higher sensitivity to the chemical composition of the white matter, especially myelin. The high sensitivity can be further enhanced with the perfusion of ProHance, a gadolinium-based contrast agent. Regarding the tensor orientations, the direction of the largest principal susceptibility tensor agrees with that of diffusion tensors in major white matter fiber bundles. The STI fiber tractography can reconstruct the fiber pathways for the whole corpus callosum and for white matter fiber bundles that are in close contact but in different orientations. There are some differences between susceptibility and diffusion tensor orientations, which are likely due to the limitations in the current STI reconstruction. With the development of more accurate reconstruction methods, STI holds the promise for probing the white matter micro-architectures with more anatomical details and higher chemical sensitivity.

193 Imaging Tools to Assess the Development of Cortical Morphology and Connectivity

Studying how the brain develops and becomes functional is a prerequisite to understand the complexity of developmental pathologies. Thanks to Magnetic Resonance Imaging (MRI), it is now possible to image...

Altered white matter connectivity in never-medicated patients with schizophrenia

Numerous diffusion tensor imaging (DTI) studies have implicated white matter brain tissue abnormalities in schizophrenia. However, the vast majority of these studies included patient populations that use antipsychotic medication. Previous research showed that medication intake can affect brain morphology and the question therefore arises to what extent the reported white matter aberrations can be attributed to the disease rather than to the use of medication. In this study we included 16 medication-naïve patients with schizophrenia and compared them to 23 healthy controls to exclude antipsychotic medication use as a confounding factor. For each subject DTI scans and magnetization transfer imaging (MTI) scans were acquired. A new tract-based analysis was used that combines fractional anisoptropy (FA), mean diffusivity (MD) and magnetization transfer ratio (MTR) to examine group differences in 12 major white matter fiber bundles. Significant group differences in combined FA, MD, MTR values were found for the right uncinate fasciculus and the left arcuate fasciculus. Additional analysis revealed that the largest part of both tracts showed an increase in MTR in combination with an increase in MD for patients with schizophrenia. We interpret these group-related differences as disease-related axonal or glial aberrations that cannot be attributed to antipsychotic medication use.

Anatomy and functional role of the inferior longitudinal fasciculus: a search that has just begun

The inferior longitudinal fasciculus (ILF) was classically defined as a direct connection from the occipital cortex to the temporal lobe. But even after the advent of diffusion tensor imaging (DTI) this view was challenged by several authors.1, 2 Using DTI and tractography, which allows for a non-invasive assessment of the microstructural organization of white matter pathways, Catani et al.3 elegantly confirmed the direct pathway between the occipital cortex and temporal lobe in a group average, as well as in eleven individual normal controls. The ILF fiber bundle connects the occipital lobe with the anterior part of the temporal lobe, running laterally and inferiorly above optic radiation fibers. However, in addition to the ILF there is a second major fiber bundle connecting the occipital cortex to the frontal brain known as the inferior fronto-occipital fasciculus (IFOF) that runs medially and above the optic pathways and spatially overlaps with the ILF along part of their pathways. The IFOF is known as a direct pathway, one that connects the occipital, posterior temporal, and the orbito-frontal areas. The ILF, on the other hand, is considered to be an indirect pathway essentially connecting similar brain areas and anteriorly joins the uncinate fasciculus to relay information to the orbito-frontal brain. In a recent report, Wahl et al.4 studied whether specific patterns of correlation and overlap among the major brain fibers bundles exist, using DTI scalar parameters such as fractional anisotropy, and axial, radial, and mean diffusivity. The only significant overlaps they found were between the IFOF and the ILF, and between the IFOF and the uncinate fasciculus.4 Such overlap may indeed reflect functional similarity as well as spatially sharing overlapping paths. The ability to track white matter bundles and access detailed anatomical information about white matter broadens our investigative ability to uncover disease-related abnormalities. However, even though advances in DTI have enabled us to better study the anatomy of the ILF, the understanding of the functional role of the ILF is still poorly understood. Thus the study by Ortibus et al.5 is of great importance as it broadens our knowledge of ILF functionality. This study is the first to report compromised ILF integrity in children with visual-perceptual impairment and found correlations between decreased fractional anisotropy of the ILF with impaired object recognition. Although the authors clearly showed the relationship between ILF integrity and object recognition, based on strong correlations between the ILF and IFOF,4 it is likely that the IFOF also plays a role in the execution of such processes. As stated by Ortibus et al., when comparing mean fractional anisotropy values in impaired versus non-impaired patients, decreased fractional anisotropy in the ILF should not be considered a biomarker for visual perception, as the ILF has been shown to be involved in multiple functions and various diseases. For example, lower fractional anisotropy in the ILF was also reported in a group of adolescent patients with schizophrenia with visual hallucinations.6 A recent review by Chanraud et al.7 provides a comprehensive review of diseases with ILF involvement and other major white matter fiber bundles. As discussed by Chanraud et al., the ILF has functionally been correlated with thought disorders, visual emotion, cognitive impairments, and other symptoms. As a whole, our knowledge of the functional influences of white matter pathways continues to progress rapidly by virtue of the non-invasive nature of DTI and the insight it provides into the brain’s white matter microstructures. It is important, however, to bear in mind that abnormality in a specific fiber bundle may also be reflected in other fiber bundles integrating or sharing the functions of multiple affected cortical regions. Studies such as the one by Ortibus et al. have initiated a strong correlative beginning for white matter deficit and patient function. Future studies focusing on the brain’s cortical networks and connectivity between associated regions and their functionalities are needed to expand our understanding of ILF functionality. In particular, future tractography and cortical stimulation studies in patients with occipito-temporal stroke or disorders involving these tracts may help define the functional roles of the direct and indirect occipito-temporal pathways.

FADTTS: Functional analysis of diffusion tensor tract statistics

The aim of this paper is to present a functional analysis of a diffusion tensor tract statistics (FADTTS) pipeline for delineating the association between multiple diffusion properties along major white matter fiber bundles with a set of covariates of interest, such as age, diagnostic status and gender, and the structure of the variability of these white matter tract properties in various diffusion tensor imaging studies. The FADTTS integrates five statistical tools: (i) a multivariate varying coefficient model for allowing the varying coefficient functions in terms of arc length to characterize the varying associations between fiber bundle diffusion properties and a set of covariates, (ii) a weighted least squares estimation of the varying coefficient functions, (iii) a functional principal component analysis to delineate the structure of the variability in fiber bundle diffusion properties, (iv) a global test statistic to test hypotheses of interest, and (v) a simultaneous confidence band to quantify the uncertainty in the estimated coefficient functions. Simulated data are used to evaluate the finite sample performance of FADTTS. We apply FADTTS to investigate the development of white matter diffusivities along the splenium of the corpus callosum tract and the right internal capsule tract in a clinical study of neurodevelopment. FADTTS can be used to facilitate the understanding of normal brain development, the neural bases of neuropsychiatric disorders, and the joint effects of environmental and genetic factors on white matter fiber bundles. The advantages of FADTTS compared with the other existing approaches are that they are capable of modeling the structured inter-subject variability, testing the joint effects, and constructing their simultaneous confidence bands. However, FADTTS is not crucial for estimation and reduces to the functional analysis method for the single measure.

Coclustering for Cross-Subject Fiber Tract Analysis Through Diffusion Tensor Imaging

One of the fundamental goals of computational neuroscience is the study of anatomical features that reflect the functional organization of the brain. The study of physical associations between neuronal structures and the examination of brain activity in vivo have given rise to the concept of anatomical and functional connectivity, which has been invaluable for our understanding of brain mechanisms and their plasticity during development. However, at present, there is no robust and accurate computational framework for the quantitative assessment of cortical connectivity patterns. In this paper, we present a quantitative analysis and modeling tool that is able to characterize anatomical connectivity patterns based on a newly developed coclustering algorithm, termed the business model-based coclustering algorithm (BCA). We apply BCA to diffusion tensor imaging (DTI) data in order to provide an automated and reproducible assessment of the connectivity patterns between different cortical areas in human brains. BCA not only partitions the cortical mantel into well-defined clusters, but also maximizes the connectivity strength between these clusters. Moreover, BCA is computationally robust and allows both outlier detection as well as parameter-independent determination of the number of clusters. Our coclustering results have showed good performance of BCA in identifying major white matter fiber bundles in human brains and facilitate the detection of abnormal connectivity patterns in patients suffering from various neurological diseases.

On the contribution of deoxy-hemoglobin to MRI gray–white matter phase contrast at high field

High field (≥7 T) MRI studies based on signal phase have been used to improve visualization of the fine structure of the brain, most notably the major white matter fiber bundles, the gray–white matter subdivision, and the laminar cortical architecture. The observed contrast has been attributed in part to local variations in magnetic susceptibility arising from iron in storage proteins and tissue lipid. Another contribution could come from the paramagnetic blood constituent deoxy-hemoglobin, the tissue concentration of which may vary through local variations in vascular density. To investigate this possibility, we examined phase contrast between gray and white matter in rats after intravenous administration of a superparamagnetic contrast agent at various dosages. At the maximum dosage (3 mg Fe/kg), which resulted in an estimated paramagnetic susceptibility shift 4–8 times larger than deoxy-hemoglobin, we observed a negligible increase in phase contrast between gray and white matter. This result suggests that endogenous deoxy-hemoglobin has no significant contribution to phase contrast between gray and white matter.

Assessing atrophy of the major white matter fiber bundles of the brain from diffusion tensor MRI data

Brain atrophy is a typical feature of many neurological conditions. Therefore, quantitative evaluation and spatial characterization of atrophy are potentially useful for monitoring the evolution of central nervous system (CNS) disorders. In this study, a method for measuring atrophy of the major white matter (WM) fiber bundles in the brain using diffusion tensor (DT) MRI data is developed. To this end, an atlas was created from sets of diffusion anisotropy images from normal subjects, and the deformations necessary to match single subject anisotropy images to this atlas were then computed. Because diffusion anisotropy images were used, this approach should be sensitive to fiber bundle volume changes in the same way that using T1-weighted images allows gray matter volume changes to be measured. The Jacobian determinant of the deformation field for each subject was then used as a measure of contraction or expansion of the tissue at each image voxel. An overview of the nonlinear registration problem is given; then an optimization of the parameters for the chosen algorithm is performed and the method for producing the atlas is described. The effectiveness of the method was then tested on data from five patients with multiple sclerosis (MS) and two patients with amyotrophic lateral sclerosis (ALS).