Nonrigid coregistration of diffusion tensor images using a viscous fluid model and mutual information

A novel data-driven method for the analysis and reconstruction of cardiac cine MRI

Cardiac cine magnetic resonance imaging (MRI) can be considered the optimal criterion for measuring cardiac function. This imaging technique can provide us with detailed information about cardiac structure, tissue composition and even blood flow, which makes it highly used in medical science. But due to the image time acquisition and several other factors the MRI sequences can easily get corrupted, causing radiologists to misdiagnose 40 million people worldwide each and every single year. Hence, the urge to decrease these numbers, researchers from different fields have been introducing novel tools and methods in the medical field. Aiming to the same target, we consider in this work the application of the higher order dynamic mode decomposition (HODMD) technique. The HODMD algorithm is a linear method, which was originally introduced in the fluid dynamics domain, for the analysis of complex systems. Nevertheless, the proposed method has extended its applicability to numerous domains, including medicine. In this work, HODMD in used to analyze sets of MR images of a heart, with the ultimate goal of identifying the main patterns and frequencies driving the heart dynamics. Furthermore, a novel interpolation algorithm based on singular value decomposition combined with HODMD is introduced, providing a three-dimensional reconstruction of the heart. This algorithm is applied (i) to reconstruct corrupted or missing images, and (ii) to build a reduced order model of the heart dynamics.

DTI Atlases Evaluations

The cerebral atlas of diffusion tensor magnetic resonance image (DT-MRI, shorted as DTI) is one of the key issues in neuroimaging research. It is crucial for comparisons of neuronal structural integrity and connectivity across populations. Usually, the atlas is constructed by iteratively averaging the registered individual image. In tradition, the fuzzy group average image is easily generated in the initial stage, which is harmful to providing clear guidance for subsequent registration, to the performance of the final atlas. To solve this problem, an improved unbiased DTI atlas construction algorithm based on adaptive weights is proposed in this paper. The adaptive weighted strategy based on diffeomorphic deformable tensor registration is introduced. At the same time, the distance measure for tensors is used as a constraint condition, which ensures the unbiasedness of the atlas. Then, using 77 DTIs from the dataset in http://www.brain-development.org , three study-specific atlases, i.e. the constructed atlases of the proposed algorithm and two open-sourced algorithms (DTIAtlasBuilder and DTI-TK), are compared with two standardized atlases (IIT v. 4.1 and NTU-DSI-122-DTI). The performances of the atlases were evaluated in spatial normalization way with six region-based criteria (including Euclidean distances between diffusion tensors, Euclidean distances of the deviatoric tensors, standard deviation, overlaps of eigenvalue-eigenvector, cross-correlations and three sets angles of eigenvalue-eigenvector pairs between diffusion tensors) and three fiber-based criteria (including distances between fiber bundles, angles between fiber bundles and fiber property profile-based criteria). The experimental results showed that the overall performances of the study-specific atlases are better than those of the standardized atlases for specific datasets, and the comprehensive performance of the improved algorithm proposed in this paper is the best.

Development of brain atlases for early-to-middle adolescent collision-sport athletes

Human brains develop across the life span and largely vary in morphology. Adolescent collision-sport athletes undergo repetitive head impacts over years of practices and competitions, and therefore may exhibit a neuroanatomical trajectory different from healthy adolescents in general. However, an unbiased brain atlas targeting these individuals does not exist. Although standardized brain atlases facilitate spatial normalization and voxel-wise analysis at the group level, when the underlying neuroanatomy does not represent the study population, greater biases and errors can be introduced during spatial normalization, confounding subsequent voxel-wise analysis and statistical findings. In this work, targeting early-to-middle adolescent (EMA, ages 13-19) collision-sport athletes, we developed population-specific brain atlases that include templates (T1-weighted and diffusion tensor magnetic resonance imaging) and semantic labels (cortical and white matter parcellations). Compared to standardized adult or age-appropriate templates, our templates better characterized the neuroanatomy of the EMA collision-sport athletes, reduced biases introduced during spatial normalization, and exhibited higher sensitivity in diffusion tensor imaging analysis. In summary, these results suggest the population-specific brain atlases are more appropriate towards reproducible and meaningful statistical results, which better clarify mechanisms of traumatic brain injury and monitor brain health for EMA collision-sport athletes.

Secondary Degeneration of White Matter After Focal Sensorimotor Cortical Ischemic Stroke in Rats

Ischemic lesions could lead to secondary degeneration in remote regions of the brain. However, the spatial distribution of secondary degeneration along with its role in functional deficits is not well understood. In this study, we explored the spatial and connectivity properties of white matter (WM) secondary degeneration in a focal unilateral sensorimotor cortical ischemia rat model, using advanced microstructure imaging on a 14 T MRI system. Significant axonal degeneration was observed in the ipsilateral external capsule and even remote regions including the contralesional external capsule and corpus callosum. Further fiber tractography analysis revealed that only fibers having direct axonal connections with the primary lesion exhibited a significant degeneration. These results suggest that focal ischemic lesions may induce remote WM degeneration, but limited to fibers tied to the primary lesion. These “direct” fibers mainly represent perilesional, interhemispheric, and subcortical axonal connections. At last, we found that primary lesion volume might be the determining factor of motor function deficits.

Diffusion Tensor Imaging Biomarkers to Predict Motor Outcomes in Stroke: A Narrative Review

Stroke is a leading cause of disability worldwide. Motor impairments occur in most of the patients with stroke in the acute phase and contribute substantially to disability. Diffusion tensor imaging (DTI) biomarkers such as fractional anisotropy (FA) measured at an early phase after stroke have emerged as potential predictors of motor recovery. In this narrative review, we: (1) review key concepts of diffusion MRI (dMRI); (2) present an overview of state-of-art methodological aspects of data collection, analysis and reporting; and (3) critically review challenges of DTI in stroke as well as results of studies that investigated the correlation between DTI metrics within the corticospinal tract and motor outcomes at different stages after stroke. We reviewed studies published between January, 2008 and December, 2018, that reported correlations between DTI metrics collected within the first 24 h (hyperacute), 2-7 days (acute), and >7-90 days (early subacute) after stroke. Nineteen studies were included. Our review shows that there is no consensus about gold standards for DTI data collection or processing. We found great methodological differences across studies that evaluated DTI metrics within the corticospinal tract. Despite heterogeneity in stroke lesions and analysis approaches, the majority of studies reported significant correlations between DTI biomarkers and motor impairments. It remains to be determined whether DTI results could enhance the predictive value of motor disability models based on clinical and neurophysiological variables.

Advances in computational and statistical diffusion MRI

Computational methods are crucial for the analysis of diffusion magnetic resonance imaging (MRI) of the brain. Computational diffusion MRI can provide rich information at many size scales, including local microstructure measures such as diffusion anisotropies or apparent axon diameters, whole-brain connectivity information that describes the brain's wiring diagram and population-based studies in health and disease. Many of the diffusion MRI analyses performed today were not possible five, ten or twenty years ago, due to the requirements for large amounts of computer memory or processor time. In addition, mathematical frameworks had to be developed or adapted from other fields to create new ways to analyze diffusion MRI data. The purpose of this review is to highlight recent computational and statistical advances in diffusion MRI and to put these advances into context by comparison with the more traditional computational methods that are in popular clinical and scientific use. We aim to provide a high-level overview of interest to diffusion MRI researchers, with a more in-depth treatment to illustrate selected computational advances.

A combined diffusion-weighted and electroencephalography study on age-related differences in connectivity in the motor network during bimanual performance

We studied the relationship between age-related differences in inter- and intra-hemispheric structural and functional connectivity in the bilateral motor network. Our focus was on the correlation between connectivity and declined motor performance in older adults. Structural and functional connectivity were estimated using diffusion weighted imaging and resting-state electro-encephalography, respectively. A total of 48 young and older healthy participants were measured. In addition, motor performances were assessed using bimanual coordination tasks. To pre-select regions-of-interest (ROIs), a neural model was adopted that accounts for intra-hemispheric functional connectivity between dorsal premotor area (PMd) and primary motor cortex (M1) and inter-hemispheric connections between left and right M1 (M1_L and M1_R ). Functional connectivity was determined via the weighted phase-lag index (wPLI) in the source-reconstructed beta activity during rest. We quantified structural connectivity using kurtosis anisotropy (KA) values of tracts derived from diffusion tensor-based fiber tractography between the aforementioned areas. In the group of older adults, wPLI values between M1_L -M1_R were negatively associated with the quality of bimanual motor performance. The additional association between wPLI values of PMd_L --M1_L and PMd_R -M1_L supports that functional connectivity with the left hemisphere mediated (bimanual) motor control in older adults. The correlational analysis between the selected structural and functional connections revealed a strong association between wPLI values in the left intra-hemispheric PMd_L -M1_L pathway and KA values in M1_L -M1_R and PMd_R -M1_L pathways in the group of older adults. This suggests that weaker structural connections in older adults correlate with stronger functional connectivity and, hence, poorer motor performance.

Characterisation of brain volume and microstructure at term-equivalent age in infants born across the gestational age spectrum

BACKGROUND

Risk of morbidity differs between very preterm (VP; <32 weeks' gestational age (GA)), moderate preterm (MP; 32-33 weeks' GA), late preterm (LP; 34-36 weeks' GA), and full-term (FT; ≥37 weeks' GA) infants. However, brain structure at term-equivalent age (TEA; 38-44 weeks) remains to be characterised in all clinically important GA groups. We aimed to compare global and regional brain volumes, and regional white matter microstructure, between VP, MP, LP and FT groups at TEA, in order to establish the magnitude and anatomical locations of between-group differences.

METHODS

Structural images from 328 infants (91 VP, 63 MP, 104 LP and 70 FT) were segmented into white matter, cortical grey matter, cerebrospinal fluid (CSF), subcortical grey matter, brainstem and cerebellum. Global tissue volumes were analysed, and additionally, cortical grey matter and white matter volumes were analysed at the regional level using voxel-based morphometry. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) images from 361 infants (92 VP, 69 MP, 120 LP and 80 FT) were analysed using Tract-Based Spatial Statistics. Statistical analyses involved examining the overall effect of GA group on global volumes (using linear regressions) and regional volumes and microstructure (using non-parametric permutation testing), as well performing post-hoc comparisons between the GA sub-groups.

RESULTS

On global analysis, cerebrospinal fluid (CSF) volume was larger in all preterm sub-groups compared with the FT group. On regional analysis, volume was smaller in parts of the temporal cortical grey matter, and parts of the temporal white matter and corpus callosum, in all preterm sub-groups compared with the FT group. FA was lower, and RD and MD were higher in voxels located in much of the white matter in all preterm sub-groups compared with the FT group. The anatomical locations of group differences were similar for each preterm vs. FT comparison, but the magnitude and spatial extent of group differences was largest for the VP, followed by the MP, and then the LP comparison. Comparing within the preterm groups, the VP sub-group had smaller frontal and temporal grey and white matter volume, and lower FA and higher MD and RD within voxels in the approximate location of the corpus callosum compared with the MP sub-group. There were few volume and microstructural differences between the MP and LP sub-groups.

CONCLUSION

All preterm sub-groups had atypical brain volume and microstructure at TEA when compared with a FT group, particularly for the CSF, temporal grey and white matter, and corpus callosum. In general, the groups followed a gradient, where the differences were most pronounced for the VP group, less pronounced for the MP group, and least pronounced for the LP group. The VP sub-group was particularly vulnerable compared with the MP and LP sub-groups.

The effect of feature image on sensitivity of the statistical analysis in the pipeline of a tractography atlas-based analysis

Tractography atlas-based analysis (TABS) is a new diffusion tensor image (DTI) statistical analysis method for detecting and understanding voxel-wise white matter properties along a fiber tract. An important requisite for accurate and sensitive TABS is the availability of a deformation field that is able to register DTI in native space to standard space. Here, three different feature images including the fractional anisotropy (FA) image, T1 weighted image, and the maximum eigenvalue of the Hessian of the FA (hFA) image were used to calculate the deformation fields between individual space and population space. Our results showed that when the FA image was a feature image, the tensor template had the highest consistency with each subject for scalar and vector information. Additionally, to demonstrate the sensitivity and specificity of the TABS method with different feature images, we detected a gender difference along the corpus callosum. A significant difference between the male and female group in diffusion measurement appeared predominantly in the right corpus callosum only when FA was the feature image. Our results demonstrated that the FA image as a feature image was more accurate with respect to the underlying tensor information and had more accurate analysis results with the TABS method.

Complex regional pain syndrome: The matter of white matter?

INTRODUCTION

Many central pathophysiological aspects of complex regional pain syndrome (CRPS) are still unknown. Although brain-imaging studies are increasingly supporting the contribution of the central nervous system to the generation and maintenance of the CRPS pain, the brain's white-matter alterations are seldom investigated.

METHODS

In this study, we used diffusion tensor imaging to explore white-matter changes in twelve CRPS-type-1 female patients suffering from chronic right upper-limb pain compared with twelve healthy control subjects.

RESULTS

Tract-based spatial-statistics analysis revealed significantly higher mean diffusivity, axial diffusivity, and radial diffusivity in the CRPS patients, suggesting that the structural connectivity is altered in CRPS. All these measures were altered in the genu, body, and splenium of corpus callosum, as well as in the left anterior and posterior and the right superior parts of the corona radiata. Axial diffusivity was significantly correlated with clinical motor symptoms at whole-brain level, supporting the physiological significance of the observed white-matter abnormalities.

CONCLUSIONS

Altogether, our findings further corroborate the involvement of the central nervous system in CRPS.

A tract-specific approach to assessing white matter in preterm infants

Diffusion-weighted imaging (DWI) is becoming an increasingly important tool for studying brain development. DWI analyses relying on manually-drawn regions of interest and tractography using manually-placed waypoints are considered to provide the most accurate characterisation of the underlying brain structure. However, these methods are labour-intensive and become impractical for studies with large cohorts and numerous white matter (WM) tracts. Tract-specific analysis (TSA) is an alternative WM analysis method applicable to large-scale studies that offers potential benefits. TSA produces a skeleton representation of WM tracts and projects the group's diffusion data onto the skeleton for statistical analysis. In this work we evaluate the performance of TSA in analysing preterm infant data against results obtained from native space tractography and tract-based spatial statistics. We evaluate TSA's registration accuracy of WM tracts and assess the agreement between native space data and template space data projected onto WM skeletons, in 12 tracts across 48 preterm neonates. We show that TSA registration provides better WM tract alignment than a previous protocol optimised for neonatal spatial normalisation, and that TSA projects FA values that match well with values derived from native space tractography. We apply TSA for the first time to a preterm neonatal population to study the effects of age at scan on WM tracts around term equivalent age. We demonstrate the effects of age at scan on DTI metrics in commissural, projection and association fibres. We demonstrate the potential of TSA for WM analysis and its suitability for infant studies involving multiple tracts.

•

Evaluation of tract-specific analysis (TSA) for white matter studies in infants.

•

TSA improves white matter tract alignment over scalar-based registration.

•

TSA closely approximates native space tractography DTI values.

•

The first application of TSA to a neonatal population.

White matter microstructure and volitional motor activity in schizophrenia: A diffusion kurtosis imaging study

Avolition is a core feature of schizophrenia and may arise from altered brain connectivity. Here we used diffusion kurtosis imaging (DKI) to investigate the association between white matter (WM) microstructure and volitional motor activity. Multi-shell diffusion MRI and 24-h actigraphy data were obtained from 20 right-handed patients with schizophrenia and 16 right-handed age and gender matched healthy controls. We examined correlations between fractional anisotropy (FA), mean diffusivity (MD), mean kurtosis (MK), and motor activity level, as well as group differences in these measures. In the patient group, increasing motor activity level was positively correlated with MK in the inferior, medial and superior longitudinal fasciculus, the corpus callosum, the posterior fronto-occipital fasciculus and the posterior cingulum. This association was not found in control subjects or in DTI measures. These results show that a lack of volitional motor activity in schizophrenia is associated with potentially altered WM microstructure in posterior brain regions associated with cognitive function and motivation. This could reflect both illness related dysconnectivity which through altered cognition, manifests as reduced volitional motor activity, and/or the effects of reduced physical activity on brain WM.

Evaluations of diffusion tensor image registration based on fiber tractography

Background

Diffusion Tensor Magnetic Resonance Imaging (DT-MRI, also known as DTI) measures the diffusion properties of water molecules in tissues and to date is one of the main techniques that can effectively study the microstructures of the brain in vivo. Presently, evaluation of DTI registration techniques is still in an initial stage of development.

Methods and results

In this paper, six well-known open source DTI registration algorithms: Elastic, Rigid, Affine, DTI-TK, FSL and SyN were applied on 11 subjects from an open-access dataset, among which one was randomly chosen as the template. Eight different fiber bundles of 10 subjects and the template were obtained by drawing regions of interest (ROIs) around various structures using deterministic streamline tractography. The performances of the registration algorithms were evaluated by computing the distances and intersection angles between fiber tracts, as well as the fractional anisotropy (FA) profiles along the fiber tracts. Also, the mean squared error (MSE) and the residual MSE (RMSE) of fibers originating from the registered subjects and the template were calculated to assess the registration algorithm. Twenty-seven different fiber bundles of the 10 subjects and template were obtained by drawing ROIs around various structures using probabilistic tractography. The performances of registration algorithms on this second tractography method were evaluated by computing the spatial correlation similarity of the fibers between subjects as well as between each subject and the template.

Conclusion

All experimental results indicated that DTI-TK performed the best under the study conditions, and SyN ranked just behind it.

Alterations in brain white matter contributing to age-related slowing of task switching performance: The role of radial diffusivity and magnetization transfer ratio

Successfully switching between tasks is critical in many daily activities. Age-related slowing of this switching behavior has been documented extensively, but the underlying neural mechanisms remain unclear. Here, we investigated the contribution of brain white matter changes associated with myelin alterations to age-related slowing of switching performance. Diffusion tensor imaging derived radial diffusivity (RD) and magnetization transfer imaging derived magnetization transfer ratio (MTR) were selected as myelin sensitive measures. These metrics were studied in relation to mixing cost (i.e., the increase in reaction time during task blocks that require task switching) on a local-global switching task in young (n = 24) and older (n = 22) adults. Results showed that higher age was associated with widespread increases in RD and decreases in MTR, indicative of white matter deterioration, possibly due to demyelination. Older adults also showed a higher mixing cost, implying slowing of switching performance. Finally, mediation analyses demonstrated that decreases in MTR of the bilateral superior corona radiata contributed to the observed slowing of switching performance with increasing age. These findings provide evidence for a role of cortico-subcortical white matter changes in task switching performance deterioration with healthy aging. Hum Brain Mapp 37:4084-4098, 2016. © 2016 Wiley Periodicals, Inc.

DTI Image Registration under Probabilistic Fiber Bundles Tractography Learning

Diffusion Tensor Imaging (DTI) image registration is an essential step for diffusion tensor image analysis. Most of the fiber bundle based registration algorithms use deterministic fiber tracking technique to get the white matter fiber bundles, which will be affected by the noise and volume. In order to overcome the above problem, we proposed a Diffusion Tensor Imaging image registration method under probabilistic fiber bundles tractography learning. Probabilistic tractography technique can more reasonably trace to the structure of the nerve fibers. The residual error estimation step in active sample selection learning is improved by modifying the residual error model using finite sample set. The calculated deformation field is then registered on the DTI images. The results of our proposed registration method are compared with 6 state-of-the-art DTI image registration methods under visualization and 3 quantitative evaluation standards. The experimental results show that our proposed method has a good comprehensive performance.

Diffusion Kurtosis Imaging: A Possible MRI Biomarker for AD Diagnosis?

The purpose of this explorative study was to investigate whether diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) parameter changes are reliable measures of white matter integrity changes in Alzheimer's disease (AD) patients using a whole brain voxel-based analysis (VBA). Therefore, age- and gender-matched patients with mild cognitive impairment (MCI) due to AD (n = 18), dementia due to AD (n = 19), and age-matched cognitively healthy controls (n = 14) were prospectively included. The magnetic resonance imaging protocol included routine structural brain imaging and DKI. Datasets were transformed to a population-specific atlas space. Groups were compared using VBA. Differences in diffusion and mean kurtosis measures between MCI and AD patients and controls were shown, and were mainly found in the splenium of the corpus callosum and the corona radiata. Hence, DTI and DKI parameter changes are suggestive of white matter changes in AD.

Axon density and axon orientation dispersion in children born preterm

BACKGROUND

Very preterm birth (VPT, <32 weeks' gestation) is associated with altered white matter fractional anisotropy (FA), the biological basis of which is uncertain but may relate to changes in axon density and/or dispersion, which can be measured using Neurite Orientation Dispersion and Density Imaging (NODDI). This study aimed to compare whole brain white matter FA, axon dispersion, and axon density between VPT children and controls (born ≥37 weeks' gestation), and to investigate associations with perinatal factors and neurodevelopmental outcomes.

METHODS

FA, neurite dispersion, and neurite density were estimated from multishell diffusion magnetic resonance images for 145 VPT and 33 control 7-year-olds. Diffusion values were compared between groups and correlated with perinatal factors (gestational age, birthweight, and neonatal brain abnormalities) and neurodevelopmental outcomes (IQ, motor, academic, and behavioral outcomes) using Tract-Based Spatial Statistics.

RESULTS

Compared with controls, VPT children had lower FA and higher axon dispersion within many major white matter fiber tracts. Neonatal brain abnormalities predicted lower FA and higher axon dispersion in many major tracts in VPT children. Lower FA, higher axon dispersion, and lower axon density in various tracts correlated with poorer neurodevelopmental outcomes in VPT children.

CONCLUSIONS

FA and NODDI measures distinguished VPT children from controls and were associated with neonatal brain abnormalities and neurodevelopmental outcomes. This study provides a more detailed and biologically meaningful interpretation of white matter microstructure changes associated with prematurity. Hum Brain Mapp 37:3080-3102, 2016. © 2016 Wiley Periodicals, Inc.

Techniques and applications of skeletal muscle diffusion tensor imaging: A review

Diffusion tensor imaging (DTI) is increasingly applied to study skeletal muscle physiology, anatomy, and pathology. The reason for this growing interest is that DTI offers unique, noninvasive, and potentially diagnostically relevant imaging readouts of skeletal muscle structure that are difficult or impossible to obtain otherwise. DTI has been shown to be feasible within most skeletal muscles. DTI parameters are highly sensitive to patient-specific properties such as age, body mass index (BMI), and gender, but also to more transient factors such as exercise, rest, pressure, temperature, and relative joint position. However, when designing a DTI study one should not only be aware of sensitivity to the above-mentioned factors but also the fact that the DTI parameters are dependent on several acquisition parameters such as echo time, b-value, and diffusion mixing time. The purpose of this review is to provide an overview of DTI studies covering the technical, demographic, and clinical aspects of DTI in skeletal muscles. First we will focus on the critical aspects of the acquisition protocol. Second, we will cover the reported normal variance in skeletal muscle diffusion parameters, and finally we provide an overview of clinical studies and reported parameter changes due to several (patho-)physiological conditions.

Microstructural white matter tissue characteristics are modulated by homocysteine: a diffusion tensor imaging study

Homocysteine level can lead to adverse effects on the brain white matter through endothelial dysfunction, microstructural inflammation, and neurotoxin effects. Despite previously observed associations between elevated homocysteine and macroscopic structural brain changes, it is still unknown whether microstructural associations of homocysteine on brain tissue properties can be observed in healthy subjects with routine MRI. To this end, we investigated potential relationships between homocysteine levels and microstructural measures computed with diffusion tensor imaging (DTI) in a cohort of 338 healthy participants. Significant positive correlations were observed between homocysteine levels and diffusivity measures in the bilateral temporal WM, the brainstem, and the bilateral cerebellar peduncle. This is the first study demonstrating that DTI is sufficiently sensitive to relate microstructural WM properties to homocysteine levels in healthy subjects.

Training-induced improvements in postural control are accompanied by alterations in cerebellar white matter in brain injured patients

We investigated whether balance control in young TBI patients can be promoted by an 8-week balance training program and whether this is associated with neuroplastic alterations in brain structure. The cerebellum and cerebellar peduncles were selected as regions of interest because of their importance in postural control as well as their vulnerability to brain injury. Young patients with moderate to severe TBI and typically developing (TD) subjects participated in balance training using PC-based portable balancers with storage of training data and real-time visual feedback. An additional control group of TD subjects did not attend balance training. Mean diffusivity and fractional anisotropy were determined with diffusion MRI scans and were acquired before, during (4 weeks) and at completion of training (8 weeks) together with balance assessments on the EquiTest® System (NeuroCom) which included the Sensory Organization Test, Rhythmic Weight Shift and Limits of Stability protocols. Following training, TBI patients showed significant improvements on all EquiTest protocols, as well as a significant increase in mean diffusivity in the inferior cerebellar peduncle. Moreover, in both training groups, diffusion metrics in the cerebellum and/or cerebellar peduncles at baseline were predictive of the amount of performance increase after training. Finally, amount of training-induced improvement on the Rhythmic Weight Shift test in TBI patients was positively correlated with amount of change in fractional anisotropy in the inferior cerebellar peduncle. This suggests that training-induced plastic changes in balance control are associated with alterations in the cerebellar white matter microstructure in TBI patients.

•

Brain injury patients and healthy subjects attended 8-weeks of balance training.

•

Diffusion MRI and postural tests were acquired before, during and after training.

•

Cerebellum and cerebellar peduncles were selected as regions of interest.

•

Training-induced changes shown in postural control and inferior cerebellar peduncle

•

Correlations between change in balance and change in white matter microstructure

A diffusion tensor imaging family study of the fornix in schizophrenia

Diffusion tensor imaging (DTI) studies suggest abnormalities in the white matter microstructure of the fornix in schizophrenia patients. Research evaluating schizophrenia patient and relatives also suggests that the white matter microstructure of the fornix is heritable. However, previous studies have been hindered by limited DTI methodology. Therefore, the goal of this study was to assess whether fornix abnormalities were related to the genetic liability for schizophrenia using the novel methodological approach of assessing multiple metrics of along-tract measurements, in addition to whole-tract means. Twenty-five schizophrenia patients, 24 adult non-psychotic first-degree biological relatives, and 27 community controls underwent neuroimaging. No group differences were found for any of the DTI metrics using the classical whole-tract measures of the fornix. Along-tract analysis detected local increases in fractional anisotropy (FA) in the right fimbria of the fornix for relatives compared to patients and controls corrected for false discovery rate. No significant associations were found between symptoms, global functioning, or IQ and whole-tract FA means in schizophrenia patients or relatives. Increased FA in non-psychotic relatives could represent a compensatory mechanism to guard against psychosis or an abnormality associated with the genetic liability for the disorder. These findings underscore the importance of obtaining along-tract measurements, in addition to whole-tract measurements to fully understand white matter abnormalities in schizophrenia.

Methodological considerations on tract-based spatial statistics (TBSS)

Having gained a tremendous amount of popularity since its introduction in 2006, tract-based spatial statistics (TBSS) can now be considered as the standard approach for voxel-based analysis (VBA) of diffusion tensor imaging (DTI) data. Aiming to improve the sensitivity, objectivity, and interpretability of multi-subject DTI studies, TBSS includes a skeletonization step that alleviates residual image misalignment and obviates the need for data smoothing. Although TBSS represents an elegant and user-friendly framework that tackles numerous concerns existing in conventional VBA methods, it has limitations of its own, some of which have already been detailed in recent literature. In this work, we present general methodological considerations on TBSS and report on pitfalls that have not been described previously. In particular, we have identified specific assumptions of TBSS that may not be satisfied under typical conditions. Moreover, we demonstrate that the existence of such violations can severely affect the reliability of TBSS results. With TBSS being used increasingly, it is of paramount importance to acquaint TBSS users with these concerns, such that a well-informed decision can be made as to whether and how to pursue a TBSS analysis. Finally, in addition to raising awareness by providing our new insights, we provide constructive suggestions that could improve the validity and increase the impact of TBSS drastically.

Diffusion tensor MRI of chemotherapy-induced cognitive impairment in non-CNS cancer patients: a review

Patients with non-central nervous system cancers often experience subtle cognitive deficits after treatment with cytotoxic agents. Therapy-induced structural changes to the brain could be one of the possible causes underlying these reported cognitive deficits. In this review, we evaluate the use of diffusion tensor imaging (DTI) for assessing possible therapy-induced changes in the microstructure of the cerebral white matter (WM) and provide a critical overview of the published DTI research on therapy-induced cognitive impairment. Both cross-sectional and longitudinal DTI studies have demonstrated abnormal microstructural properties in WM regions involved in cognition. These findings correlated with cognitive performance, suggesting that there is a link between reduced "WM integrity" and chemotherapy-induced impaired cognition. In this paper, we will also introduce the basics of diffusion tensor imaging and how it can be applied to evaluate effects of therapy on structural changes in cerebral WM. The review concludes with considerations and discussion regarding DTI data interpretation and possible future directions for investigating therapy-induced WM changes in cancer patients. This review article is part of a Special Issue entitled: Neuroimaging Studies of Cancer and Cancer Treatment.

White matter microstructural abnormalities in families multiply affected with bipolar I disorder: a diffusion tensor tractography study

BACKGROUND

White matter (WM) abnormalities are proposed as potential endophenotypic markers of bipolar disorder (BD). In a diffusion tensor imaging (DTI) voxel-based analysis (VBA) study of families multiply affected with BD, we previously reported that widespread abnormalities of fractional anisotropy (FA) are associated with both BD and genetic liability for illness. In the present study, we further investigated the endophenotypic potential of WM abnormalities by applying DTI tractography to specifically investigate tracts implicated in the pathophysiology of BD.

METHOD

Diffusion magnetic resonance imaging (MRI) data were acquired from 19 patients with BD type I from multiply affected families, 21 of their unaffected first-degree relatives and 18 healthy volunteers. DTI tractography was used to identify the cingulum, uncinate fasciculus (UF), arcuate portion of the superior longitudinal fasciculus (SLF), inferior longitudinal fasciculus (ILF), corpus callosum, and the anterior limb of the internal capsule (ALIC). Regression analyses were conducted to investigate the effect of participant group and genetic liability on FA and radial diffusivity (RD) in each tract.

RESULTS

We detected a significant effect of group on both FA and RD in the cingulum, SLF, callosal splenium and ILF driven by reduced FA and increased RD in patients compared to controls and relatives. Increasing genetic liability was associated with decreased FA and increased RD in the UF, and decreased FA in the SLF, among patients.

CONCLUSIONS

WM microstructural abnormalities in limbic, temporal and callosal pathways represent microstructural abnormalities associated with BD whereas alterations in the SLF and UF may represent potential markers of endophenotypic risk.

Diffusion tensor image registration using hybrid connectivity and tensor features

Most existing diffusion tensor imaging (DTI) registration methods estimate structural correspondences based on voxelwise matching of tensors. The rich connectivity information that is given by DTI, however, is often neglected. In this article, we propose to integrate complementary information given by connectivity features and tensor features for improved registration accuracy. To utilize connectivity information, we place multiple anchors representing different brain anatomies in the image space, and define the connectivity features for each voxel as the geodesic distances from all anchors to the voxel under consideration. The geodesic distance, which is computed in relation to the tensor field, encapsulates information of brain connectivity. We also extract tensor features for every voxel to reflect the local statistics of tensors in its neighborhood. We then combine both connectivity features and tensor features for registration of tensor images. From the images, landmarks are selected automatically and their correspondences are determined based on their connectivity and tensor feature vectors. The deformation field that deforms one tensor image to the other is iteratively estimated and optimized according to the landmarks and their associated correspondences. Experimental results show that, by using connectivity features and tensor features simultaneously, registration accuracy is increased substantially compared with the cases using either type of features alone.

Diffusion tensor image registration using polynomial expansion

In this paper, we present a deformable registration framework for the diffusion tensor image (DTI) using polynomial expansion. The use of polynomial expansion in image registration has previously been shown to be beneficial due to fast convergence and high accuracy. However, earlier work was developed only for 3D scalar medical image registration. In this work, it is shown how polynomial expansion can be applied to DTI registration. A new measurement is proposed for DTI registration evaluation, which seems to be robust and sensitive in evaluating the result of DTI registration. We present the algorithms for DTI registration using polynomial expansion by the fractional anisotropy image, and an explicit tensor reorientation strategy is inherent to the registration process. Analytic transforms with high accuracy are derived from polynomial expansion and used for transforming the tensor's orientation. Three measurements for DTI registration evaluation are presented and compared in experimental results. The experiments for algorithm validation are designed from simple affine deformation to nonlinear deformation cases, and the algorithms using polynomial expansion give a good performance in both cases. Inter-subject DTI registration results are presented showing the utility of the proposed method.

White matter differences in euthymic bipolar I disorder: a combined magnetic resonance imaging and diffusion tensor imaging voxel-based study

OBJECTIVES

A broad range of subtle and markedly heterogenous neuroanatomical abnormalities of grey matter and white matter have been reported in bipolar disorder. Euthymic bipolar disorder patients represent a clinically homogenous group in which to identify trait-based biomarkers of bipolar disorder. In this study, we sought to clarify the nature and extent of neuroanatomical differences in a large, clinically homogeneous group of euthymic bipolar disorder patients.

METHODS

Structural magnetic resonance imaging (sMRI) was obtained for 60 patients with prospectively confirmed euthymic bipolar I disorder and 60 individually age- and gender-matched healthy volunteers. High angular resolution diffusion tensor imaging (DTI) scans were obtained for a subset of this sample comprising 35 patients and 43 controls. Voxel-based analysis of both sMRI and DTI data sets was performed.

RESULTS

Bipolar disorder patients displayed global reductions in white matter volume and fractional anisotropy reductions in the corpus callosum, posterior cingulum, and prefrontal white matter compared with controls. There were corresponding increases in radial diffusivity in the callosal splenium in patients compared with controls. No significant group differences were detected in grey matter. In patients, lithium was associated with a bilateral increase in grey matter volume in the temporal lobes, but not with any DTI parameter.

CONCLUSIONS

Euthymic bipolar I disorder is characterized by both diffuse global white matter deficits and potential regional disorganization in interhemispheric and longitudinal tracts, while grey matter appears to be preserved.

Improving alignment in Tract-based spatial statistics: evaluation and optimization of image registration

Anatomical alignment in neuroimaging studies is of such importance that considerable effort is put into improving the registration used to establish spatial correspondence. Tract-based spatial statistics (TBSS) is a popular method for comparing diffusion characteristics across subjects. TBSS establishes spatial correspondence using a combination of nonlinear registration and a “skeleton projection” that may break topological consistency of the transformed brain images. We therefore investigated feasibility of replacing the two-stage registration-projection procedure in TBSS with a single, regularized, high-dimensional registration.

To optimize registration parameters and to evaluate registration performance in diffusion MRI, we designed an evaluation framework that uses native space probabilistic tractography for 23 white matter tracts, and quantifies tract similarity across subjects in standard space. We optimized parameters for two registration algorithms on two diffusion datasets of different quality. We investigated reproducibility of the evaluation framework, and of the optimized registration algorithms. Next, we compared registration performance of the regularized registration methods and TBSS. Finally, feasibility and effect of incorporating the improved registration in TBSS were evaluated in an example study.

The evaluation framework was highly reproducible for both algorithms (R² 0.993; 0.931). The optimal registration parameters depended on the quality of the dataset in a graded and predictable manner. At optimal parameters, both algorithms outperformed the registration of TBSS, showing feasibility of adopting such approaches in TBSS. This was further confirmed in the example experiment.

Acquisition guidelines and quality assessment tools for analyzing neonatal diffusion tensor MRI data

Diffusion tensor imaging is a valuable measure in clinical settings to assess diagnosis and prognosis of neonatal brain development. However, obtaining reliable images is not straightforward because of the tissue characteristics of the neonatal brain and the high likelihood of motion artifacts. In this review, we present guidelines on how to acquire DTI data of the neonatal brain and recommend high-quality data acquisition and processing as an essential means to obtain accurate and robust parametric maps. Sudden head movements are problematic for DTI in neonates, and these may lead to incorrect values. We describe strategies to minimize the corrupting effects both in terms of acquisition (eg, more gradient directions) and postprocessing (eg, tensor estimation methods). In addition, tools are described that can help assess whether a dataset is of sufficient quality for further assessment.

Limbic and callosal white matter changes in euthymic bipolar I disorder: an advanced diffusion magnetic resonance imaging tractography study

BACKGROUND

White matter microstructural changes detected using diffusion tensor imaging have been reported in bipolar disorder. However, findings are heterogeneous, which may be related to the use of analysis techniques that cannot adequately model crossing fibers in the brain. We therefore sought to identify altered diffusion anisotropy and diffusivity changes using an improved high angular resolution fiber-tracking technique.

METHODS

Diffusion magnetic resonance imaging data was obtained from 35 prospectively confirmed euthymic bipolar disorder type 1 patients (age 22-59) and 43 control subjects (age 22-59) drawn from a sample of 120 age- and gender-matched demographically similar case-control pairs. Tractography using a constrained spherical deconvolution approach to account for crossing fibers was implemented. Changes in fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity between patient and control groups in subdivisions of the corpus callosum, cingulum, and fornix were measured as indicators of trait differences in white matter microstructural organization in bipolar disorder.

RESULTS

Patients had significantly reduced fractional anisotropy and increased mean diffusivity and radial diffusivity in all divisions of the corpus callosum, left fornix, and subgenual cingulum compared with control subjects. Axial diffusivity was increased in the fornix bilaterally and right dorsal-anterior cingulum.

CONCLUSIONS

By using an improved fiber-tracking method in a clinically homogeneous population, we were able to localize trait diffusivity changes to specific subdivisions of limbic fiber pathways, including the fornix. Our findings extend previous reports of altered limbic system microstructural disorganization as a trait feature of bipolar disorder.

Diffusion tensor imaging metrics of the corpus callosum in relation to bimanual coordination: effect of task complexity and sensory feedback

When manipulating objects with both hands, the corpus callosum (CC) is of paramount importance for interhemispheric information exchange. Hence, CC damage results in impaired bimanual performance. Here, healthy young adults performed a complex bimanual dial rotation task with or without augmented visual feedback and according to five interhand frequency ratios (1:1, 1:3, 2:3, 3:1, 3:2). The relation between bimanual task performance and microstructural properties of seven CC subregions (i.e., prefrontal, premotor/supplementary motor, primary motor, primary sensory, occipital, parietal, and temporal) was studied by means of diffusion tensor imaging (DTI). Findings revealed that bimanual coordination deteriorated in the absence as compared to the presence of augmented visual feedback. Simple frequency ratios (1:1) were performed better than the multifrequency ratios (non 1:1). Moreover, performance was more accurate when the preferred hand (1:3-2:3) as compared to the nonpreferred hand (3:1-3:2) moved faster and during noninteger (2:3-3:2) as compared to integer frequency ratios (1:3-3:1). DTI findings demonstrated that bimanual task performance in the absence of augmented visual feedback was significantly related to the microstructural properties of the primary motor and occipital region of the CC, suggesting that white matter microstructure is associated with the ability to perform bimanual coordination patterns in young adults.

Diffusion tensor imaging provides an insight into the microstructure of meningiomas, high-grade gliomas, and peritumoral edema

OBJECTIVE

Fractional anisotropy (FA) is a measure for the degree of microstructural organization. Several studies have used FA values to assess microstructural organization of brain tumors and peritumoral edema. The purpose of our study was to validate FA and apparent diffusion constant (ADC) values in the diagnosis of meningiomas versus high-grade glial tumors, with the focus on the ability of diffusion tensor imaging (DTI) to reveal tumor ultrastructure. Our hypothesis was that FA and ADC values significantly differ between high-grade gliomas and meningiomas, and in the peritumoral edema.

METHODS

Diffusion tensor imaging values were obtained from 20 patients with meningiomas (21 tumors) and 15 patients with high-grade gliomas. Regions of interest were outlined in FA and ADC maps for solid-enhancing tumor tissue and peritumoral edema. Fractional anisotropy and ADC values were normalized by comparison to normal-appearing white matter (NAWM) in the contralateral hemisphere. Differences between meningiomas and high-grade gliomas were statistically analyzed.

RESULTS

Meningiomas showed a significantly higher FA tumor/FA NAWM ratio (P = 0.0001) and lower ADC tumor/ADC NAWM ratio (P = 0.0008) compared to high-grade gliomas. On average, meningiomas also showed higher FA values in peritumoral edema than high-grade gliomas (P = 0.016). Apparent diffusion constant values of peritumoral edema for the 2 tumor groups did not differ significantly (P = 0.5).

CONCLUSIONS

Diffusion tensor imaging can be used to reveal microstructural differences between meningiomas and high-grade gliomas and may contribute toward predicting the histopathology of intracranial tumors. We advocate that diffusion tensor imaging should be included in the standard imaging protocol for patients with intracranial tumors.

Automated approaches for analysis of multimodal MRI acquisitions in a study of cognitive aging

In this work we describe an integrated and automated workflow for a comprehensive and robust analysis of multimodal MR images from a cohort of more than hundred subjects. Image examinations are done three years apart and consist of 3D high-resolution anatomical images, low resolution tensor-valued DTI recordings and 4D resting state fMRI time series. The integrated analysis of the data requires robust tools for segmentation, registration and fiber tracking, which we combine in an automated manner. Our automated workflow is strongly desired due to the large number of subjects. Especially, we introduce the use of histogram segmentation to processed fMRI data to obtain functionally important seed and target regions for fiber tracking between them. This enables analysis of individually important resting state networks. We also discuss various approaches for the assessment of white matter integrity parameters along tracts, and in particular we introduce the use of functional data analysis (FDA) for this task.

Altered diffusion tensor imaging measurements in aged transgenic Huntington disease rats

Rodent models of Huntington disease (HD) are valuable tools for investigating HD pathophysiology and evaluating new therapeutic approaches. Non-invasive characterization of HD-related phenotype changes is important for monitoring progression of pathological processes and possible effects of interventions. The first transgenic rat model for HD exhibits progressive late-onset affective, cognitive, and motor impairments, as well as neuropathological features reflecting observations from HD patients. In this report, we contribute to the anatomical phenotyping of this model by comparing high-resolution ex vivo DTI measurements obtained in aged transgenic HD rats and wild-type controls. By region of interest analysis supplemented by voxel-based statistics, we find little evidence of atrophy in basal ganglia regions, but demonstrate altered DTI measurements in the dorsal and ventral striatum, globus pallidus, entopeduncular nucleus, substantia nigra, and hippocampus. These changes are largely compatible with DTI findings in preclinical and clinical HD patients. We confirm earlier reports that HD rats express a moderate neuropathological phenotype, and provide evidence of altered DTI measures in specific HD-related brain regions, in the absence of pronounced morphometric changes.

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

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

Measures for Validation of DTI Tractography

The evaluation of analysis methods for diffusion tensor imaging (DTI) remains challenging due to the lack of gold standards and validation frameworks. Significant work remains in developing metrics for comparing fiber bundles generated from streamline tractography. We propose a set of volumetric and tract oriented measures for evaluating tract differences. The different methods developed for this assessment work are: an overlap measurement, a point cloud distance and a quantification of the diffusion properties at similar locations between fiber bundles. The application of the measures in this paper is a comparison of atlas generated tractography to tractography generated in individual images. For the validation we used a database of 37 subject DTIs, and applied the measurements on five specific fiber bundles: uncinate, cingulum (left and right for both bundles) and genu. Each measurments is interesting for specific use: the overlap measure presents a simple and comprehensive metric but is sensitive to partial voluming and does not give consistent values depending on the bundle geometry. The point cloud distance associated with a quantile interpretation of the distribution gives a good intuition of how close and similar the bundles are. Finally, the functional difference is useful for a comparison of the diffusion properties since it is the focus of many DTI analysis to compare scalar invariants. The comparison demonstrated reasonable similarity of results. The tract difference measures are also applicable to comparison of tractography algorithms, quality control, reproducibility studies, and other validation problems.

DTI analyses and clinical applications in Alzheimer's disease

DTI is one of the most effective MR tools for the investigation of the brain anatomy. In addition to the gray matter, histopathological studies indicate that white matter is also a good target for both the early diagnosis of AD and for monitoring disease progression, which motivates us to use DTI to study AD patients in vivo. There are already a large amount of studies reporting significant differences between AD patients and controls, as well as to predict progression of disease in symptomatic non-demented individuals. Application of these findings in clinical practice remains to be demonstrated.

Diffusion tensor image registration with combined tract and tensor features

Registration of diffusion tensor (DT) images is indispensible, especially in white-matter studies involving a significant amount of data. This task is however faced with challenging issues such as the generally low SNR of diffusion-weighted images and the relatively high complexity of tensor representation. To improve the accuracy of DT image registration, we design an attribute vector that encapsulates both tract and tensor information to serve as a voxel morphological signature for effective correspondence matching. The attribute vector captures complementary information from both the global connectivity structure given by the fiber tracts and the local anatomical architecture given by the tensor regional descriptors. We incorporate this attribute vector into a multi-scale registration framework where the moving image is warped to the space of the fixed image under the guidance of tract information at a more global level (coarse scales), followed by alignment refinement using regional tensor distribution features at a more local level (fine scales). Experimental results indicate that this framework yields marked improvement over DT image registration using volumetric information alone.

Population-averaged diffusion tensor imaging atlas of the Sprague Dawley rat brain

Rats are widely used in experimental neurobiological research, and rat brain atlases are important resources for identifying brain regions in the context of experimental microsurgery, tissue sampling, and neuroimaging, as well as comparison of findings across experiments. Currently, most available rat brain atlases are constructed from histological material derived from single specimens, and provide two-dimensional or three-dimensional (3D) outlines of diverse brain regions and fiber tracts. Important limitations of such atlases are that they represent individual specimens, and that finer details of tissue architecture are lacking. Access to more detailed 3D brain atlases representative of a population of animals is needed. Diffusion tensor imaging (DTI) is a unique neuroimaging modality that provides sensitive information about orientation structure in tissues, and is widely applied in basic and clinical neuroscience investigations. To facilitate analysis and assignment of location in rat brain neuroimaging investigations, we have developed a population-averaged three-dimensional DTI atlas of the normal adult Sprague Dawley rat brain. The atlas is constructed from high resolution ex vivo DTI images, which were nonlinearly warped into a population-averaged in vivo brain template. The atlas currently comprises a selection of manually delineated brain regions, the caudate-putamen complex, globus pallidus, entopeduncular nucleus, substantia nigra, external capsule, corpus callosum, internal capsule, cerebral peduncle, fimbria of the hippocampus, fornix, anterior commisure, optic tract, and stria terminalis. The atlas is freely distributed and potentially useful for several purposes, including automated and manual delineation of rat brain structural and functional imaging data.