Tensor Magnetic Resonance Imaging Data Research Articles

A Transmural Path Model Improves the Definition of the Orthotropic Tissue Structure in Heart Simulations

In the past decades, the structure of the heart, human as well as other species, has been explored in a detailed way, e.g., via histological studies or diffusion tensor magnetic resonance imaging. Nevertheless, the assignment of the characteristic orthotropic structure in a patient-specific finite element model remains a challenging task. Various types of rule-based models, which define the local fiber and sheet orientation depending on the transmural depth, have been developed. However, the correct assessment of the transmural depth is not trivial. Its accuracy has a substantial influence on the overall mechanical and electrical properties in rule-based models. The main purpose of this study is the development of a finite element-based approach to accurately determine the transmural depth on a general unstructured grid. Instead of directly using the solution of the Laplace problem as the transmural depth, we make use of a well-established model for the assessment of the transmural thickness. It is based on two hyperbolic first-order partial differential equations for the definition of a transmural path, whereby the transmural thickness is defined as the arc length of this path. Subsequently, the transmural depth is determined based on the position on the transmural path. Originally, the partial differential equations were solved via finite differences on structured grids. In order to circumvent the need of two grids and mapping between the structured (to determine the transmural depth) and unstructured (electromechanical heart simulation) grids, we solve the equations directly on the same unstructured tetrahedral mesh. We propose a finite-element-based discontinuous Galerkin approach. Based on the accurate transmural depth, we assign the local material orientation of the orthotropic tissue structure in a usual fashion. We show that this approach leads to a more accurate definition of the transmural depth. Furthermore, for the left ventricle, we propose functions for the transmural fiber and sheet orientation by fitting them to literature-based diffusion tensor magnetic resonance imaging data. The proposed functions provide a distinct improvement compared to existing rules from the literature.

Development of Helical Myofiber Tracts in the Human Fetal Heart: Analysis of Myocardial Fiber Formation in the Left Ventricle From the Late Human Embryonic Period Using Diffusion Tensor Magnetic Resonance Imaging

BackgroundDetection of the fiber orientation pattern of the myocardium using diffusion tensor magnetic resonance imaging lags ≈12 weeks of gestational age (WGA) behind fetal myocardial remodeling with invasion by the developing coronary vasculature (8 WGA). We aimed to use diffusion tensor magnetic resonance imaging tractography to characterize the evolution of fiber architecture in the developing human heart from the later embryonic period.Methods and ResultsTwenty human specimens (8–24 WGA) from the Kyoto Collection of Human Embryos and Fetuses, including specimens from the embryonic period (Carnegie stages 20–23), were used. Diffusion tensor magnetic resonance imaging data were acquired with a 7T magnetic resonance system. Fractional anisotropy and helix angle were calculated using standard definitions. In all samples, the fibers ran helically in an organized pattern in both the left and right ventricles. A smooth transmural change in helix angle values (from positive to negative) was detected in all 16 directions of the ventricles. This feature was observed in almost all small (Carnegie stage 23) and large samples. A higher fractional anisotropy value was detected at the outer side of the anterior wall and septum at Carnegie stage 20 to 22, which spread around the ventricular wall at Carnegie stage 23 and in the early fetal samples (11–12 WGA). The fractional anisotropy value of the left ventricular walls decreased in samples with ≥13 WGA, which remained low (≈0.09) in larger samples.ConclusionsFrom the human late embryonic period (from 8 WGA), the helix angle arrangement of the myocardium is comparable to that of the adult, indicating that the myocardial structure blueprint, organization, and integrity are already formed.

Altered neonatal white and gray matter microstructure is associated with neurodevelopmental impairments in very preterm infants with high-grade brain injury.

Background:This study examines relationships between neonatal white and gray matter microstructure and neurodevelopment in very preterm infants (VPT; ≤30 weeks gestation) with high-grade brain injury (BI).Methods:Term-equivalent diffusion tensor magnetic resonance imaging (DTI) data were obtained in 32 VPT infants with high-grade BI spanning grade III/IV intraventricular hemorrhage, post-hemorrhagic hydrocephalus (PHH), and cystic periventricular leukomalacia (BI group); 69 VPT infants without high-grade injury (VPT group); and 55 term-born infants. The Bayley-III assessed neurodevelopmental outcomes at age 2-years.Results:BI infants had lower fractional anisotropy (FA) in the posterior limb of the internal capsule (PLIC), cingulum, and corpus callosum; and higher mean diffusivity (MD) in the optic radiations and cingulum than VPT infants. PHH was associated with higher MD in the optic radiations and left PLIC; and higher FA in the right caudate. For BI infants, higher MD in the right optic radiation and lower FA in the right cingulum, PLIC, and corpus callosum were related to motor impairments.Conclusions:BI infants demonstrated altered white and gray matter microstructure in regions affected by injury in a manner dependent upon injury type. PHH infants demonstrated the greatest impairments. Aberrant white matter microstructure was related to motor impairment in BI infants.

Low Smoking Exposure, the Adolescent Brain, and the Modulating Role of CHRNA5 Polymorphisms

BackgroundStudying the neural consequences of tobacco smoking during adolescence, including those associated with early light use, may help expose the mechanisms that underlie the transition from initial use to nicotine dependence in adulthood. However, only a few studies in adolescents exist, and they include small samples. In addition, the neural mechanism, if one exists, that links nicotinic receptor genes to smoking behavior in adolescents is still unknown. MethodsStructural and diffusion tensor magnetic resonance imaging data were acquired from a large sample of 14-year-old adolescents who completed an extensive battery of neuropsychological, clinical, personality, and drug-use assessments. Additional assessments were conducted at 16 years of age. ResultsExposure to smoking in adolescents, even at low doses, is linked to volume changes in the ventromedial prefrontal cortex and to altered neuronal connectivity in the corpus callosum. The longitudinal analyses strongly suggest that these effects are not preexisting conditions in those who progress to smoking. There was a genetic contribution wherein the volume reduction effects were magnified in smokers who were carriers of the high-risk genotype of the alpha 5 nicotinic receptor subunit gene, rs16969968. ConclusionsThese findings give insight into a mechanism involving genes, brain structure, and connectivity underlying why some adolescents find nicotine especially addictive.

Brain structural basis of individual variability in dream recall frequency.

Recent neuroimaging studies have indicated that inter-individual variability in dream recall frequency (DRF) is associated with both resting-state regional cerebral blood flow and task-induced brain activations. However, the brain structure underpinning this inter-individual variability in DRF remains unclear. The aim of the current study is to investigate the relationship between brain structural characteristics and DRF. We collected both T1-weighted and diffusion tensor magnetic resonance imaging data from 43 healthy volunteers. DRF was obtained from a two-week sleep diary with a subjective report of dream recall upon waking every morning. General linear model analysis was used to evaluate the relationship between brain structural characteristics (cortical volume and white matter integrity) and DRF. Not only the cortical volume of the medial portion of the right fusiform gyrus and parahippocampal gyrus but also the fractional anisotropy of white matter fibers connected to these regions were significantly negatively correlated with DRF, and these relationships were not modulated by a regular sleep. These findings provide direct evidence that brain structural characteristics are associated with inter-individual variability in DRF and may help us to better understand the structural mechanisms in the brain underlying dream recall.

Role of 3-Dimensional Architecture of Scar and Surviving Tissue in Ventricular Tachycardia: Insights From High-Resolution Ex Vivo Porcine Models.

An improved knowledge of the spatial organization of infarct structure and its contribution to ventricular tachycardia (VT) is important for designing optimal treatments. This study explores the relationship between the 3-dimensional structure of the healed infarct and the VT reentrant pathways in high-resolution models of infarcted porcine hearts. Structurally detailed models of infarcted ventricles were reconstructed from ex vivo late gadolinium enhancement and diffusion tensor magnetic resonance imaging data of 8 chronically infarcted porcine hearts at submillimeter resolution (0.25×0.25×0.5 mm3). To characterize the 3-dimensional structure of surviving tissue in the zone of infarct, a novel scar-mapped thickness metric was introduced. Further, using the ventricular models, electrophysiological simulations were conducted to determine and analyze the 3-dimensional VT pathways that were established in each of the complex infarct morphologies. The scar-mapped thickness metric revealed the heterogeneous organization of infarct and enabled us to systematically characterize the distribution of surviving tissue thickness in 8 hearts. Simulation results demonstrated the involvement of a subendocardial tissue layer of varying thickness in the majority of VT pathways. Importantly, they revealed that VT pathways are most frequently established within thin surviving tissue structures of thickness ≤2.2 mm (90th percentile) surrounding the scar. The combination of high-resolution imaging data and ventricular simulations revealed the 3-dimensional distribution of surviving tissue surrounding the scar and demonstrated its involvement in VT pathways. The new knowledge obtained in this study contributes toward a better understanding of infarct-related VT.

Regional white matter development in very preterm infants: perinatal predictors and early developmental outcomes.

BackgroundPreterm infants are at risk for white matter injury and adverse neurodevelopmental outcomes.MethodsSerial diffusion tensor MRI data were obtained from very preterm infants (N=78) born <30 weeks gestation imaged up to four times from 26-42 weeks postmenstrual age. Slopes were calculated for fractional anisotropy (FA) and mean diffusivity (MD) within regions of interest for infants with ≥2 scans (N=50). Sixty-five children underwent neurodevelopmental testing at age two years.ResultsFA slope for the posterior limb of the internal capsule was greater than other regions. The anterior limb of the internal capsule (ALIC), corpus callosum and optic radiations demonstrated greater FA slope with increasing gestational age. Infants with PDA had lower FA slope in the ALIC. MD slope was lower with prolonged ventilation or lack of antenatal steroids. At age 2 years, lower motor scores were associated with lower FA in the left but higher FA in the right inferior temporal lobe at term-equivalent. Better social-emotional competence was related to lower FA in the left cingulum bundle.ConclusionThis study demonstrates regional variability in the susceptibility/sensitivity of white matter maturation to perinatal factors and relationships between altered diffusion measures and developmental outcomes in preterm neonates.

An empirical comparison of different approaches for combining multimodal neuroimaging data with support vector machine.

In the pursuit of clinical utility, neuroimaging researchers of psychiatric and neurological illness are increasingly using analyses, such as support vector machine, that allow inference at the single-subject level. Recent studies employing single-modality data, however, suggest that classification accuracies must be improved for such utility to be realized. One possible solution is to integrate different data types to provide a single combined output classification; either by generating a single decision function based on an integrated kernel matrix, or, by creating an ensemble of multiple single modality classifiers and integrating their predictions. Here, we describe four integrative approaches: (1) an un-weighted sum of kernels, (2) multi-kernel learning, (3) prediction averaging, and (4) majority voting, and compare their ability to enhance classification accuracy relative to the best single-modality classification accuracy. We achieve this by integrating structural, functional, and diffusion tensor magnetic resonance imaging data, in order to compare ultra-high risk (n = 19), first episode psychosis (n = 19) and healthy control subjects (n = 23). Our results show that (i) whilst integration can enhance classification accuracy by up to 13%, the frequency of such instances may be limited, (ii) where classification can be enhanced, simple methods may yield greater increases relative to more computationally complex alternatives, and, (iii) the potential for classification enhancement is highly influenced by the specific diagnostic comparison under consideration. In conclusion, our findings suggest that for moderately sized clinical neuroimaging datasets, combining different imaging modalities in a data-driven manner is no “magic bullet” for increasing classification accuracy. However, it remains possible that this conclusion is dependent on the use of neuroimaging modalities that had little, or no, complementary information to offer one another, and that the integration of more diverse types of data would have produced greater classification enhancement. We suggest that future studies ideally examine a greater variety of data types (e.g., genetic, cognitive, and neuroimaging) in order to identify the data types and combinations optimally suited to the classification of early stage psychosis.

Optimization of the Parameters for Diffusion Tensor Magnetic Resonance Imaging Data Acquisition for Breast Fiber Tractography at 1.5 T

IntroductionDiffusion tensor MRI has emerged as a promising tool for the analysis of the microscopic properties of tissues. Optimizing image acquisition parameters is essential for producing high-quality DTI. This study aimed to optimize the parameters for DTI data acquisition for breast fiber tractography at 1.5 T. Patients and MethodsA total of 21 healthy volunteers received breast DTI scanning using an ASSET-based EPI technique operated under different parameters including b value, the number of diffusion gradient directions, and spatial resolution. The images were analyzed for signal-to-noise, signal intensity ratio, mean number and length of reconstructive fiber tracts, and fractional anisotropy value. ResultsThe optimal acquisition parameters at 1.5 T for breast DT-MRI fiber tractography were determined as follows: axial 31 direction, b = 600 seconds per mm2, matrix 128 × 128 with slice thickness of 3 mm. ConclusionThe optimization of data acquisition parameters could improve the quality of breast DT-MRI images and assist fiber tractography at 1.5 T.

FMEM: Functional mixed effects modeling for the analysis of longitudinal white matter Tract data

Many longitudinal imaging studies have collected repeated diffusion tensor magnetic resonance imaging data to understand white matter maturation and structural connectivity pattern in normal controls and diseased subjects. There is an urgent demand for the development of statistical methods for the analysis of diffusion properties along fiber tracts and clinical data obtained from longitudinal studies. Jointly analyzing repeated fiber-tract diffusion properties and covariates (e.g., age or gender) raises several major challenges including (i) infinite-dimensional functional response data, (ii) complex spatial–temporal correlation structure, and (iii) complex spatial smoothness. To address these challenges, this article is to develop a functional mixed effects modeling (FMEM) framework to delineate the dynamic changes of diffusion properties along major fiber tracts and their association with a set of covariates of interest and the structure of the variability of these white matter tract properties in various longitudinal studies. Our FMEM consists of a functional mixed effects model for addressing all three challenges, an efficient method for spatially smoothing varying coefficient functions, an estimation method for estimating the spatial–temporal correlation structure, a test procedure with local and global test statistics for testing hypotheses of interest associated with functional response, and a simultaneous confidence band for quantifying the uncertainty in the estimated coefficient functions. Simulated data are used to evaluate the finite sample performance of FMEM and to demonstrate that FMEM significantly outperforms the standard pointwise mixed effects modeling approach. We apply FMEM to study the spatial–temporal dynamics of white-matter fiber tracts in a clinical study of neurodevelopment.

Executive deficits, not processing speed relates to abnormalities in distinct prefrontal tracts in amyotrophic lateral sclerosis

Cognitive impairment in amyotrophic lateral sclerosis is characterized by deficits on tests of executive function; however, the contribution of abnormal processing speed is unknown. Methods are confounded by tasks that depend on motor speed in patients with physical disability. Structural and functional magnetic resonance imaging studies have revealed multi-system cerebral involvement, with evidence of reduced white matter volume and integrity in predominant frontotemporal regions. The current study has two aims. First, to investigate whether cognitive impairments in amyotrophic lateral sclerosis are due to executive dysfunction or slowed processing speed using methodology that accommodates motor disability. This is achieved using a dual-task paradigm and tasks that manipulate stimulus presentation times and do not rely on response motor speed. Second, to identify relationships between specific cognitive impairments and the integrity of distinct white matter tracts. Thirty patients with amyotrophic lateral sclerosis and 30 age- and education-matched control subjects were administered an experimental dual-task procedure that combined a visual inspection time task and digit recall. In addition, measures of executive function (including letter fluency) and processing speed (visual inspection time and rapid serial letter identification) were administered. Integrity of white matter tracts was determined using region of interest analyses of diffusion tensor magnetic resonance imaging data. Patients with amyotrophic lateral sclerosis did not show impairments on tests of processing speed, but executive deficits were revealed once visual inspection time was combined with digit recall (dual-task) and in letter fluency. In addition to the corticospinal tracts, significant differences in fractional anisotropy and mean diffusivity were found between groups in a number of prefrontal and temporal white matter tracts including the anterior cingulate, anterior thalamic radiation, uncinate fasciculus and hippocampal portion of the cingulum bundles. Significant differences also emerged in the anterior corona radiata as well as in white matter underlying the superior, medial and inferior frontal gyri and the temporal gyri. Dual-task performance significantly correlated with fractional anisotropy measures in the middle frontal gyrus white matter and anterior corona radiata. Letter fluency indices significantly correlated with fractional anisotropy measures of the inferior frontal gyrus white matter and corpus callosum in addition to the corticospinal tracts and mean diffusivity measures in the white matter of the superior frontal gyrus. The current study demonstrates that cognitive impairment in amyotrophic lateral sclerosis is not due to generic slowing of processing speed. Moreover, different executive deficits are related to distinct prefrontal tract involvement in amyotrophic lateral sclerosis with dual-task impairment associating with dorsolateral prefrontal dysfunction and letter fluency showing greater dependence on inferolateral prefrontal dysfunction.

Cingulum White Matter in Young Women at Risk of Depression: The Effect of Family History and Anhedonia

Altered white matter microstructure in tracts integral to mood regulation networks could underlie vulnerability to major depressive disorder (MDD). Guided by functional magnetic resonance studies, we explored whether a positive family history of MDD (FH+) and anhedonia (reduced capacity for pleasure) were associated with altered white matter microstructure in the cingulum bundles and uncinate fasciculi. Diffusion tensor magnetic resonance imaging data were acquired on 34 healthy female student volunteers (mean age 22 years). Exclusion criteria included other current or previous psychiatric disorder, current depression, and current psychotropic medication. Family history was determined using established criteria. Fiber tractography was performed for each individual for a priori tracts of interest and a comparison tract. Mean fractional anisotropy (FA), an index of microstructure, was calculated for each tract. Tracts were reconstructed in 18 FH+ individuals and 15 FH- individuals, who did not differ by age or subclinical depressive symptoms. FH+ subjects had 3% to 5% lower FA in the right and left cingulum bundles than FH- individuals (p = .012, p = .059, respectively). Post hoc analysis demonstrated 8% lower FA in the left subgenual cingulate (p = .007). Hedonic tone correlated positively with FA in the right and left cingulum bundles (r = .342, p = .052; r = .477, p = .005, respectively), and the left subgenual cingulum (r = .500, p = .003). Both family history of MDD and subclinical anhedonia are associated with reduced FA in the bilateral cingulum bundles, particularly in the left subgenual cingulum. Altered cingulum white matter architecture is implicated in the etiology of MDD.

Pilot multimodal twin imaging study of generalized anxiety disorder.

Generalized anxiety disorder (GAD) is a common chronic condition that is relatively understudied compared to other psychiatric syndromes. Neuroimaging studies have begun to implicate particular neural structures and circuitry in its pathophysiology; however, no genetically informative research has examined the potential sources of reported brain differences. We acquired spectroscopic, volumetric, and diffusion tensor magnetic resonance imaging data from a pilot study of 34 female subjects selected from monozygotic twin pairs based upon their affection status for GAD, and examined brain regions previously implicated in fear and anxiety for their relationship with affection status and genetic risk. Lifetime GAD associated with increased creatine levels in the amygdala, smaller left hippocampal volume, and lower fractional anisotropy in the uncinate fasciculus which connects amygdala and frontal cortex. In addition, GAD genetic risk predicted increases in myo-inositol in the amygdala and, possibly, glutamate/glutamine/GABA alterations in the hippocampus. The association of lifetime GAD with smaller hippocampal volume was independent of major depression and might represent a common genetic risk marker for internalizing disorders. These preliminary data suggest that GAD and its genetic risk factors are likely correlated with volumetric and spectroscopic changes in fear-related limbic structures and their connections with the frontal cortex.

Frontal white matter abnormalities following chronic ketamine use: a diffusion tensor imaging study

Ketamine abuse has been shown to have a deleterious impact on brain function. However, the precise mechanisms of ketamine dependence-induced pathological change remain poorly understood. Although there is evidence for white matter changes in drug abuse, the presence of white matter abnormalities in chronic ketamine users has not been studied. White matter volumes were measured using in vivo diffusion tensor magnetic resonance imaging data in 41 ketamine-dependent subjects and 44 drug-free healthy volunteers. White matter changes associated with chronic ketamine use were found in bilateral frontal and left temporoparietal cortices. There was also evidence that frontal white matter fractional anisotropy correlated with the severity of drug use (as measured by estimated total ketamine consumption). We provide direct evidence for dose-dependent abnormalities of white matter in bilateral frontal and left temporoparietal regions following chronic ketamine use. The findings suggest a microstructural basis for the changes in cognition and experience observed with prolonged ketamine use. Moreover, the similarities of these changes to those observed in chronic schizophrenia have implications for the glutamate model of this illness.

Computational methods for predicting drug transport in anisotropic and heterogeneous brain tissue

Effective drug delivery for many neurodegenerative diseases or tumors of the central nervous system is challenging. Targeted invasive delivery of large macromolecules such as trophic factors to desired locations inside the brain is difficult due to anisotropy and heterogeneity of the brain tissue. Despite much experimental research, prediction of bio-transport phenomena inside the brain remains unreliable. This article proposes a rigorous computational approach for accurately predicting the fate of infused therapeutic agents inside the brain. Geometric and physiological properties of anisotropic and heterogeneous brain tissue affecting drug transport are accounted for by in-vivo diffusion tensor magnetic resonance imaging data. The three-dimensional brain anatomy is reconstructed accurately from subject-specific medical images. Tissue anisotropy and heterogeneity are quantified with the help of diffusion tensor imaging (DTI). Rigorous first principles physical transport phenomena are applied to predict the fate of a high molecular weight trophic factor infused into the midbrain. Computer prediction of drug distribution in humans accounting for heterogeneous and anisotropic brain tissue properties have not been adequately researched in open literature before.

Reconstruction and Visualization of Fiber and Laminar Structure in the Normal Human Heart from Ex Vivo Diffusion Tensor Magnetic Resonance Imaging (DTMRI) Data

The human heart is composed of a helical network of muscle fibers organized to form sheets that are separated by cleavage planes responsible for the orthotropic mechanical properties of cardiac muscle. The purpose of this study is the reconstruction and visualization of these structures in 3 dimensions. Anisotropic least square filtering followed by fiber and sheet tracking techniques were applied to diffusion tensor magnetic resonance imaging data of the excised human heart. Fibers were reconstructed using the first eigenvectors of the diffusion tensors. The sheets were reconstructed using the second and third eigenvectors and visualized as surfaces. The fibers are shown to lie in sheets that have transmural structure, which correspond to histologic studies published in the literature. Quantitative measurements show that the sheets as appose to the fibers are organized into laminar orientations without dominant populations. A visualization algorithm was developed to demonstrate the complex 3-dimensional orientation of the fibers and sheets in human myocardium.

A Diffusion Tensor Imaging Study of White Matter in Early-Onset Schizophrenia

Voxel-based analysis of diffusion tensor magnetic resonance imaging (DTI) data was used to examine white matter integrity in adolescents with early-onset schizophrenia (EOS), defined as schizophrenia beginning before the 18th birthday. Nineteen patients with EOS, aged 13 to 19, were compared with 20 healthy volunteers matched for age, gender, and parental socioeconomic status. Diffusion tensor magnetic resonance imaging data were acquired on a GE Signa NVi 1.5 Tesla system (General Electric, Milwaukee, Wisconsin). Maps of fractional anisotropy (FA) were registered into standard space, and group differences were examined using a nonparametric statistical approach. In comparison with healthy participants, EOS patients had significantly lower FA in the white matter of the parietal association cortex bilaterally and in the left middle cerebellar penduncle. No areas with significantly higher FA in patients were identified. Parietal and cerebellar white matter abnormalities may contribute to the emergence of psychotic symptoms in adolescence.

Perisylvian language networks of the human brain

Early anatomically based models of language consisted of an arcuate tract connecting Broca's speech and Wernicke's comprehension centers; a lesion of the tract resulted in conduction aphasia. However, the heterogeneous clinical presentations of conduction aphasia suggest a greater complexity of perisylvian anatomical connections than allowed for in the classical anatomical model. This article re-explores perisylvian language connectivity using in vivo diffusion tensor magnetic resonance imaging tractography. Diffusion tensor magnetic resonance imaging data from 11 right-handed healthy male subjects were averaged, and the arcuate fasciculus of the left hemisphere reconstructed from this data using an interactive dissection technique. Beyond the classical arcuate pathway connecting Broca's and Wernicke's areas directly, we show a previously undescribed, indirect pathway passing through inferior parietal cortex. The indirect pathway runs parallel and lateral to the classical arcuate fasciculus and is composed of an anterior segment connecting Broca's territory with the inferior parietal lobe and a posterior segment connecting the inferior parietal lobe to Wernicke's territory. This model of two parallel pathways helps explain the diverse clinical presentations of conduction aphasia. The anatomical findings are also relevant to the evolution of language, provide a framework for Lichtheim's symptom-based neurological model of aphasia, and constrain, anatomically, contemporary connectionist accounts of language.