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This paper introduces a model for longitudinal functional data analysis that accounts for pointwise skewness. The proposed procedure decouples the marginal pointwise variation from the complex longitudinal and functional dependence using copula methodology. Pointwise variation is described through parametric distribution functions that capture varying skewness and change smoothly both in time and over the functional argument. Joint dependence is quantified through a Gaussian copula with a low-rank approximation-based covariance. The introduced class of models provides a unifying platform for both pointwise quantile estimation and prediction of complete trajectories at new times. We investigate the methods numerically in simulations and discuss their application to a diffusion tensor imaging study of multiple sclerosis patients. This approach is implemented in the R package sLFDA that is publicly available on GitHub., (© The Author(s) 2024. Published by Oxford University Press on behalf of The International Biometric Society.)

Aim: To evaluate the role of diffusion tensor imaging of the auditory pathway in patients with Crigler Najjar syndrome type I and its relation to auditory brainstem response., Methods: Prospective study was done including 12 patients with Crigler Najjar syndrome type I and 10 age- and sex-matched controls that underwent diffusion tensor imaging of brain. Mean diffusivity and fractional anisotropy at 4 regions of the brain and brainstem on each side were measured and correlated with the results of auditory brainstem response for patients., Results: There was significantly higher mean diffusivity of cochlear nucleus, superior olivary nucleus, inferior colliculus, and auditory cortex of patients versus controls on both sides for all regions ( P = .001). The fractional anisotropy of cochlear nucleus, superior olivary nucleus, inferior colliculus, and auditory cortex of patients versus controls was significantly lower, with P values of, respectively, .001, .001, .003, and .001 on the right side and .001, .001, .003, and .001 on left side, respectively. Also, a negative correlation was found between the maximum bilirubin level and fractional anisotropy of the left superior olivary nucleus and inferior colliculus of both sides. A positive correlation was found between the mean diffusivity and auditory brainstem response wave latency of the right inferior colliculus and left cochlear nucleus. The fractional anisotropy and auditory brainstem response wave latency of the right superior olivary nucleus, left cochlear nucleus, and inferior colliculus of both sides were negatively correlated., Conclusion: Diffusion tensor imaging can detect microstructural changes in the auditory pathway in Crigler Najjar syndrome type I that can be correlated with auditory brainstem response.

The white matter contains long-range connections between different brain regions and the organization of these connections holds important implications for brain function in health and disease. Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) to quantify tissue properties along the trajectories of these connections. Statistical inference from tractometry usually either averages these quantities along the length of each fiber bundle or computes regression models separately for each point along every one of the bundles. These approaches are limited in their sensitivity, in the former case, or in their statistical power, in the latter. We developed a method based on the sparse group lasso (SGL) that takes into account tissue properties along all of the bundles and selects informative features by enforcing both global and bundle-level sparsity. We demonstrate the performance of the method in two settings: i) in a classification setting, patients with amyotrophic lateral sclerosis (ALS) are accurately distinguished from matched controls. Furthermore, SGL identifies the corticospinal tract as important for this classification, correctly finding the parts of the white matter known to be affected by the disease. ii) In a regression setting, SGL accurately predicts "brain age." In this case, the weights are distributed throughout the white matter indicating that many different regions of the white matter change over the lifespan. Thus, SGL leverages the multivariate relationships between diffusion properties in multiple bundles to make accurate phenotypic predictions while simultaneously discovering the most relevant features of the white matter., Competing Interests: The authors have declared that no competing interests exist.

The role of white matter in reading has been established by diffusion tensor imaging (DTI), but DTI cannot identify specific microstructural features driving these relationships. Neurite orientation dispersion and density imaging (NODDI), inhomogeneous magnetization transfer (ihMT) and multicomponent driven equilibrium single-pulse observation of T1/T2 (mcDESPOT) can be used to link more specific aspects of white matter microstructure and reading due to their sensitivity to axonal packing and fiber coherence (NODDI) and myelin (ihMT and mcDESPOT). We applied principal component analysis (PCA) to combine DTI, NODDI, ihMT and mcDESPOT measures (10 in total), identify major features of white matter structure, and link these features to both reading and age. Analysis was performed for nine reading-related tracts in 46 neurotypical 6-16 year olds. We identified three principal components (PCs) which explained 79.5% of variance in our dataset. PC1 probed tissue complexity, PC2 described myelin and axonal packing, while PC3 was related to axonal diameter. Mixed effects regression models did not identify any significant relationships between principal components and reading skill. Bayes factor analysis revealed that the absence of relationships was not due to low power. Increasing PC1 in the left arcuate fasciculus with age suggest increases in tissue complexity, while increases of PC2 in the bilateral arcuate, inferior longitudinal, inferior fronto-occipital fasciculi, and splenium suggest increases in myelin and axonal packing with age. Multimodal white matter imaging and PCA provide microstructurally informative, powerful principal components which can be used by future studies of development and cognition. Our findings suggest major features of white matter undergo development during childhood and adolescence, but changes are not linked to reading during this period in our typically-developing sample., Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: Author RML is an employee of GE Healthcare. The funder provided support in the form of salaries for author RML, but played no other role in this study. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Objective: This study aimed to define the prevalence and predictors of non-right-handedness and its link to long-term neurodevelopmental outcome and early neuroimaging in a cohort of children born extremely preterm (<28 weeks gestation)., Methods: 179 children born extremely preterm admitted to the Neonatal Intensive Care Unit of our tertiary centre from 2006-2013 were included in a prospective longitudinal cohort study. Collected data included perinatal data, demographic characteristics, neurodevelopmental outcome measured by the Bayley Scales of Infant and Toddler Development at 2 years and the Movement Assessment Battery for Children at 5 years, and handedness measured at school age (4-8 years). Magnetic resonance imaging performed at term-equivalent age was used to study overt brain injury. Diffusion tensor imaging scans were analysed using tract-based spatial statistics to assess white matter microstructure in relation to handedness and neurodevelopmental outcome., Results: The prevalence of non-right-handedness in our cohort was 22.9%, compared to 12% in the general population. Weaker fine motor skills at 2 years and paternal non-right-handedness were significantly associated with non-right-handedness. Both overt brain injury and fractional anisotropy of white matter structures on diffusion tensor images were not related to handedness. Fractional anisotropy measurements showed significant associations with neurodevelopmental outcome., Conclusions: Our data show that non-right-handedness in children born extremely preterm occurs almost twice as frequently as in the general population. In the studied population, non-right-handedness is associated with weaker fine motor skills and paternal non-right-handedness, but not with overt brain injury or microstructural brain development on early magnetic resonance imaging., Competing Interests: The authors have declared that no competing interests exist.

The field of neuroimaging dedicated to mapping connections in the brain is increasingly being recognized as key for understanding neurodevelopment and pathology. Networks of these connections are quantitatively represented using complex structures, including matrices, functions, and graphs, which require specialized statistical techniques for estimation and inference about developmental and disorder-related changes. Unfortunately, classical statistical testing procedures are not well suited to high-dimensional testing problems. In the context of global or regional tests for differences in neuroimaging data, traditional analysis of variance (ANOVA) is not directly applicable without first summarizing the data into univariate or low-dimensional features, a process that might mask the salient features of high-dimensional distributions. In this work, we consider a general framework for two-sample testing of complex structures by studying generalized within-group and between-group variances based on distances between complex and potentially high-dimensional observations. We derive an asymptotic approximation to the null distribution of the ANOVA test statistic, and conduct simulation studies with scalar and graph outcomes to study finite sample properties of the test. Finally, we apply our test to our motivating study of structural connectivity in autism spectrum disorder., (© 2019 The International Biometric Society.)

Introduction: Graph theory is a promising mathematical tool to study the connectome. However, little research has been undertaken to correlate graph metrics to functional properties of the brain. In this study, we report a unique association between the strength of cortical regions and their function., Methods: Eight structural graphs were constructed within DSI Studio using publicly available imaging data derived from the Human Connectome Project. Whole-brain fiber tractography was performed to quantify the strength of each cortical region comprising our atlas., Results: Rank-order analysis revealed 27 distinct areas with high average strength, several of which are associated with eloquent cortical functions. Area 4 localizes to the primary motor cortex and is important for fine motor control. Areas 2, 3a and 3b localize to the primary sensory cortex and are involved in primary sensory processing. Areas V1-V4 in the occipital pole are involved in primary visual processing. Several language areas, including area 44, were also found to have high average strength., Conclusions: Regions of average high strength tend to localize to eloquent areas of the brain, such as the primary sensorimotor cortex, primary visual cortex, and Broca's area. Future studies will examine the dynamic effects of neurologic disease on this metric., (Copyright © 2019. Published by Elsevier B.V.)

Compared with whites, blacks develop hypertension earlier in life, progress from prehypertension to hypertension at an accelerated rate, and exhibit greater hypertension-mediated organ damage (e.g., kidney disease, stroke). In this paper, we tested whether the longitudinal associations between elevated systolic blood pressure and disruption of brain white matter structural integrity differ as a function of race. A community sample of 100 middle-aged adults with prehypertension underwent diffusion imaging to quantify indirect metrics of white matter structural integrity, including fractional anisotropy. Blood pressure and diffusion imaging measurements were collected at baseline and at a 2-year follow-up. Regression analyses showed that higher systolic blood pressure at baseline was associated with a decrease in fractional anisotropy over 2 years in blacks only (β = -0.51 [95% CI = -0.85, -0.16], t = -2.93, p = 0.004, ΔR 2  = 0.09). These findings suggest that blacks are more susceptible to the impact of systolic prehypertension on white matter structural integrity.

Background: Diffusion-tensor fractional anisotropy (FA) is an index of neural-fiber damage in patients following stroke. To better characterize FA, tract-based spatial statistics (TBSS) is frequently used, which involves spatial transformation into the standard brain space. Despite its utility, this technique is susceptible to space-occupying hematoma in patients with intracerebral hemorrhage. To correct this, "lesion making" has been proposed. Here, FA values from TBSS without lesion masking and TBSS with lesion masking were compared, and the clinical utility was evaluated., Methods: Forty patients from our previously published work were entered into the study. Diffusion-tensor imagings were acquired 14-21 days after onset and FA maps were generated. Lesion masks were produced in reference with nondiffusion (b = 0) brain images. Two types (with or without lesion masking) of TBSS were then performed. For both types, using individual data we extracted mean FA values within for the corticospinal tract (CST) and the superior longitudinal fasciculus (SLF). FA ratio (rFA) between the lesioned hemisphere and the unaffected hemisphere was then calculated. The two sets of the data were then compared by assessing residuals of mean root sum square error (RMSE)., Results: Although rFA obtained from TBSS with lesion masking tended to be slightly smaller, the estimated RMSE was .025 for both the CST and the SLF., Conclusions: The estimated FA differences between the two sets of TBSS were very small. Considering the time for manual labor for producing lesion masks, regular TBSS without lesion masking may be sufficient in terms of clinical utility., (Copyright © 2019 Elsevier Inc. All rights reserved.)

This study presents a modelling framework in which information on muscle fiber direction and orientation during contraction is derived from diffusion tensor imaging (DTI) and incorporated in a computational model of the surface electromyographic (EMG) signal. The proposed model makes use of the principle of reciprocity to simultaneously calculate the electric potentials produced at the recording electrode by charges distributed along an arbitrary number of muscle fibers within the muscle, allowing for a computationally efficient evaluation of extracellular motor unit action potentials. The approach is applied to the complex architecture of the first dorsal interosseous (FDI) muscle of the hand to simulate EMG during index finger flexion and abduction. Using diffusion tensor imaging methods, the results show how muscle fiber orientation and curvature in this intrinsic hand muscle change during flexion and abduction. Incorporation of anatomically accurate muscle architecture and other hand tissue morphologies enables the model to capture variations in extracellular action potential waveform shape across the motor unit population and to predict experimentally observed differences in EMG signal features when switching from index finger abduction to flexion. The simulation results illustrate how structural and electrical properties of the tissues comprising the volume conductor, in combination with fiber direction and curvature, shape the detected action potentials. Using the model, the relative contribution of motor units of different sizes located throughout the muscle under both conditions is examined, yielding a prediction of the detection profile of the surface EMG electrode array over the muscle cross-section., Competing Interests: The authors have declared that no competing interests exist.

We describe a recent framework for statistical shape analysis of curves and show its applicability to various biological datasets. The presented methods are based on a functional representation of shape called the square-root velocity function and a closely related elastic metric. The main benefit of this approach is its invariance to reparameterization (in addition to the standard shape-preserving transformations of translation, rotation and scale), and ability to compute optimal registrations (point correspondences) across objects. Building upon the defined distance between shapes, we additionally describe tools for computing sample statistics including the mean and covariance. Based on the covariance structure, one can also explore variability in shape samples via principal component analysis. Finally, the estimated mean and covariance can be used to define Wrapped Gaussian models on the shape space, which are easy to sample from. We present multiple case studies on various biological datasets including (1) leaf outlines, (2) internal carotid arteries, (3) Diffusion Tensor Magnetic Resonance Imaging fiber tracts, (4) Glioblastoma Multiforme tumors, and (5) vertebrae in mice. We additionally provide a MATLAB package that can be used to produce the results given in this manuscript.

The relationship between the left arcuate fasciculus (AF) and stroke-related aphasia is unclear. In this retrospective study, we aimed to investigate the role of subcomponents of the left AF in predicting prognosis of aphasia after stroke. Twenty stroke patients with aphasia were recruited and received language assessment as well as diffusion tensor tractography scanning at admission. According to injury of the left AF, the participants were classified into four groups: group A (4 cases), the AF preserved intactly; group B (6 cases), the anterior segment injured; group C (4 cases), the posterior segment injured; and group D (6 cases), completely injured. After a consecutive speech therapy, language assessment was performed again. Changes of language functions among the groups were compared and the relation between these changes with segments injury of the AF was analyzed. After therapy, relatively high increase score percentage changes in terms of all the subcategories of language assessment were observed both in group A and C; by contrast, only naming in group B, and spontaneous speech in group D. Although no statistical difference was demonstrated among the four groups. In addition, there was no significant correlation between improvement of language function with segments injury of the AF. The predictive role of subcomponents of the left AF in prognosis of aphasia is obscure in our study. Nevertheless, it indicates the importance of integrity of the left AF for recovery of aphasia, namely that preservation of the left AF on diffusion tensor tractography could mean recovery potential of aphasia after stroke.

Purpose of this work was to assess feasibility of cardiac diffusion tensor imaging (cDTI) at 7 T in a set of healthy, unfixed, porcine hearts using various parallel imaging acceleration factors and to compare SNR and derived cDTI metrics to a reference measured at 3 T. Magnetic resonance imaging was performed on 7T and 3T whole body systems using a spin echo diffusion encoding sequence with echo planar imaging readout. Five reference (b = 0 s/mm2) images and 30 diffusion directions (b = 700 s/mm2) were acquired at both 7 T and 3 T using a GRAPPA acceleration factor R = 1. Scans at 7 T were repeated using R = 2, R = 3, and R = 4. SNR evaluation was based on 30 reference (b = 0 s/mm2) images of 30 slices of the left ventricle and cardiac DTI metrics were compared within AHA segmentation. The number of hearts scanned at 7 T and 3 T was n = 11. No statistically significant differences were found for evaluated helix angle, secondary eigenvector angle, fractional anisotropy and apparent diffusion coefficient at the different field strengths, given sufficiently high SNR and geometrically undistorted images. R≥3 was needed to reduce susceptibility induced geometric distortions to an acceptable amount. On average SNR in myocardium of the left ventricle was increased from 29±3 to 44±6 in the reference image (b = 0 s/mm2) when switching from 3 T to 7 T. Our study demonstrates that high resolution, ex vivo cDTI is feasible at 7 T using commercial hardware., Competing Interests: The authors have declared that no competing interests exist.

Introduction: The habenula (Hb) is a small midbrain structure that signals negative events and may play a major role in the etiology of psychiatric disorders including depression. The lateral Hb has three major efferent connections: serotonergic raphe nuclei, noradrenergic locus coeruleus, and dopaminergic ventral tegmental area/substantia nigra compacta. We wanted to test whether Hb connectivity may be important to predict treatment outcomes in depression patients., Methods: We studied whether habenular connectivity at admission into a psychiatric clinic can predict treatment response. We used an inpatient sample (N = 175) to assess habenular connectivity (diffusion tensor imaging and resting state functional connectivity (RSFC) between the Hb and its targets) close to admission. In addition, we obtained the Patient Health Questionnaire-depression module (PHQ-9) close to admission and at discharge. Inpatients in the study entered the clinic with at least moderately severe depression (score 15 and up). Inpatients considered treatment resistant had scores of 9 or more at discharge., Results: Compared to responders, treatment non-responders had lower fractional anisotropy in the right Hb afferent fibers and lower RSFC between right Hb and median raphe, but higher RSFC between left Hb and locus coeruleus. A logistic regression model was significantly different from chance, and explained 27.7% of the variance in treatment resistance (sensitivity = 75%; specificity = 71.9%)., Discussion: The anatomical and functional connectivity of the Hb may be a predictor of treatment success in psychiatric populations. Limitations include the Hb small size and the limited time (5 min) of resting state data obtained., (Copyright © 2018. Published by Elsevier B.V.)

Methods of estimating the local false discovery rate (LFDR) have been applied to different types of datasets such as high-throughput biological data, diffusion tensor imaging (DTI), and genome-wide association (GWA) studies. We present a model for LFDR estimation that incorporates a covariate into each test. Incorporating the covariates may improve the performance of testing procedures, because it contains additional information based on the biological context of the corresponding test. This method provides different estimates depending on a tuning parameter. We estimate the optimal value of that parameter by choosing the one that minimizes the estimated LFDR resulting from the bias and variance in a bootstrap approach. This estimation method is called an adaptive reference class (ARC) method. In this study, we consider the performance of ARC method under certain assumptions on the prior probability of each hypothesis test as a function of the covariate. We prove that, under these assumptions, the ARC method has a mean squared error asymptotically no greater than that of the other method where the entire set of hypotheses is used and assuming a large covariate effect. In addition, we conduct a simulation study to evaluate the performance of estimator associated with the ARC method for a finite number of hypotheses. Here, we apply the proposed method to coronary artery disease (CAD) data taken from a GWA study and diffusion tensor imaging (DTI) data., Competing Interests: The authors have declared that no competing interests exist.

Objectives: We describe measurement of skeletal muscle kinetics with multiple echo diffusion tensor imaging (MEDITI). This approach allows characterization of the microstructural dynamics in healthy and pathologic muscle., Materials and Methods: In a Siemens 3-T Skyra scanner, MEDITI was used to collect dynamic DTI with a combination of rapid diffusion encoding, radial imaging, and compressed sensing reconstruction in a multi-compartment agarose gel rotation phantom and within in vivo calf muscle. An MR-compatible ergometer (Ergospect Trispect) was employed to enable in-scanner plantar flexion exercise. In a HIPAA-compliant study with written informed consent, post-exercise recovery of DTI metrics was quantified in eight volunteers. Exercise response of DTI metrics was compared with that of T2-weighted imaging and characterized by a gamma variate model., Results: Phantom results show quantification of diffusivities in each compartment over its full dynamic rotation. In vivo calf imaging results indicate larger radial than axial exercise response and recovery in the plantar flexion-challenged gastrocnemius medialis (fractional response: nT2w = 0.385 ± 0.244, nMD = 0.163 ± 0.130, nλ 1  = 0.110 ± 0.093, nλ rad  = 0.303 ± 0.185). Diffusion and T2-weighted response magnitudes were correlated (e.g., r = 0.792, p = 0.019 for nMD vs. nT2w)., Conclusion: We have demonstrated the feasibility of MEDITI for capturing spatially resolved diffusion tensor data in dynamic systems including post-exercise skeletal muscle recovery following in-scanner plantar flexion.

Background: The cortico-ponto-cerebellar tract (CPCT) is the largest projection pathway, which synapses at the pons. Remote effects of supratentorial brain tumors have not been evaluated along the infratentorial course of the CPCT., Aim: The purpose of this study is to evaluate the possible lateralization of the diffusion tensor metrics of the affected CPCT in patients with supratentorial brain tumor., Methods and Results: We included 39 patients with 29 left-sided tumors (LST) and 10 right-sided tumors, retrospectively. We measured the magnitude of changes of the fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values of the CPCT prior to the brain surgery at the level of crus cerebri and middle cerebellar peduncle. Regions of interest (ROIs) were placed on the lateral side of crus cerebri, and ROI-1 (anterior 1/3), ROI-2 (middle 1/3), ROI-3 (posterior 1/3), and ROI-4 were placed at the level of middle cerebellar peduncle. We hypothesized that there would be decreased FA and increased ADC values of the ipsilesional CPCT compared with contralesional CPCT. Ipsilesional FA values were decreased with simultaneous increased ADC value along the CPCT compared with contralesional CPCT in following ROIs, ROI-1 (LST FA: P = .005, ADC: P = .037) and ROI-3 (LST FA: P = .049, ADC: P = .049), respectively. Affected ROI-4 in LST cases also showed lower FA values, although not statistically significant., Conclusion: We observed a statistically significant FA value decrease and ADC increase along the left ROI-1 and ROI-3 as well as the nonstatistically significant FA decrease of the left ROI-4 at the second neuron level when there was a related supratentorial tumor. These findings are suggestive of presynaptic and postsynaptic microstructural changes of these tracts following the presynaptic involvement by a primary supratentorial brain tumor., (© 2018 Wiley Periodicals, Inc.)

Gliomas are primary brain tumours arising from the glial cells of the nervous system. The diffuse nature of spread, coupled with proximity to critical brain structures, makes treatment a challenge. Pathological analysis confirms that the extent of glioma spread exceeds the extent of the grossly visible mass, seen on conventional magnetic resonance imaging (MRI) scans. Gliomas show faster spread along white matter tracts than in grey matter, leading to irregular patterns of spread. We propose a mathematical model based on Diffusion Tensor Imaging, a new MRI imaging technique that offers a methodology to delineate the major white matter tracts in the brain. We apply the anisotropic diffusion model of Painter and Hillen (J Thoer Biol 323:25-39, 2013) to data from 10 patients with gliomas. Moreover, we compare the anisotropic model to the state-of-the-art Proliferation-Infiltration (PI) model of Swanson et al. (Cell Prolif 33:317-329, 2000). We find that the anisotropic model offers a slight improvement over the standard PI model. For tumours with low anisotropy, the predictions of the two models are virtually identical, but for patients whose tumours show higher anisotropy, the results differ. We also suggest using the data from the contralateral hemisphere to further improve the model fit. Finally, we discuss the potential use of this model in clinical treatment planning.

Objective: To determine the frequency of incidental acute or subacute cerebral infarction (CI) in a population-based study., Patients and Methods: We identified 2095 participants aged 50 to 98 years in the population-based Mayo Clinic Study of Aging from October 23, 2009, to October 5, 2016, with a usable diffusion tensor imaging (DTI) sequence (total scans=3230). Acute and subacute infarcts were identified by neuroradiologists. For each participant, vascular risk factors, medications, clinical symptoms, and neurological examination near the time of the CI were abstracted from the medical record. The probable etiologic mechanism for the CI was determined., Results: Nine CIs were identified with a frequency of 0.28% among individual magnetic resonance imaging (MRI) scans and 0.43% among unique individuals. Infarctions were detected in 0.097% of scans from participants younger than 70 years and in 0.36% of scans of those 70 years or older. Six CIs were acute, and 3 were subacute. Most participants with infarcts were men (78%), with a mean age of 76.9±6.74 years. All were asymptomatic at the time of CI detection. The probable mechanisms of CI were small vessel (n=6), cardioembolic (n=2), and cryptogenic (n=1)., Conclusion: Acute and subacute cerebral infarcts occur as incidental findings in approximately 1 in 230 people aged 50 to 98 years, particularly in elderly men and those with vascular risk factors. As brain MRI becomes more widely used, incidentally detected acute or subacute infarcts will provide an opportunity to improve stroke prevention., (Copyright © 2017 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.)

Mild cognitive impairment (MCI) gained a lot of interest recently, especially that the conversion rate to Alzheimer Disease (AD) in the amnestic subtype (aMCI) is higher than in the non-amnestic subtype (naMCI). We aimed to determine whether and how diffusion-weighted MRI (DWI) using the diffusion tensor model (DTI) can differentiate MCI subtypes from healthy subjects. High resolution 3D T1W and DWI images of patients (aMCI, n = 18; naMCI, n = 20; according to Petersen criteria) and controls (n = 27) were acquired at 3T and processed using ExploreDTI and SPM. Voxel-wise and region of interest (ROI) analyses of fractional anisotropy (FA) and mean diffusivity (MD) were performed with ANCOVA; MD was higher in aMCI compared to controls or naMCI in several grey and white matter (GM, WM) regions (especially in the temporal pole and the inferior temporal lobes), while FA was lower in WM ROI-s (e.g. left Cingulum). Moreover, significant correlations were identified between verbal fluency, visual and verbal memory performance and DTI metrics. Logistic regression showed that measuring FA of the crus of fornix along GM volumetry improves the discrimination of aMCI from naMCI. Additional information from DWI/DTI aids preclinical detection of AD and may help detecting early non-Alzheimer type dementia, too., (Copyright © 2017 Elsevier B.V. All rights reserved.)

OBJECTIVE The depiction of cranial nerves (CNs) using diffusion tensor imaging (DTI) is of great interest in skull base tumor surgery and DTI used with deterministic tracking methods has been reported previously. However, there are still no good methods usable for the elimination of noise from the resulting depictions. The authors have hypothesized that probabilistic tracking could lead to more accurate results, because it more efficiently extracts information from the underlying data. Moreover, the authors have adapted a previously described technique for noise elimination using gradual threshold increases to probabilistic tracking. To evaluate the utility of this new approach, a comparison is provided with this work between the gradual threshold increase method in probabilistic and deterministic tracking of CNs. METHODS Both tracking methods were used to depict CNs II, III, V, and the VII+VIII bundle. Depiction of 240 CNs was attempted with each of the above methods in 30 healthy subjects, which were obtained from 2 public databases: the Kirby repository (KR) and Human Connectome Project (HCP). Elimination of erroneous fibers was attempted by gradually increasing the respective thresholds (fractional anisotropy [FA] and probabilistic index of connectivity [PICo]). The results were compared with predefined ground truth images based on corresponding anatomical scans. Two label overlap measures (false-positive error and Dice similarity coefficient) were used to evaluate the success of both methods in depicting the CN. Moreover, the differences between these parameters obtained from the KR and HCP (with higher angular resolution) databases were evaluated. Additionally, visualization of 10 CNs in 5 clinical cases was attempted with both methods and evaluated by comparing the depictions with intraoperative findings. RESULTS Maximum Dice similarity coefficients were significantly higher with probabilistic tracking (p < 0.001; Wilcoxon signed-rank test). The false-positive error of the last obtained depiction was also significantly lower in probabilistic than in deterministic tracking (p < 0.001). The HCP data yielded significantly better results in terms of the Dice coefficient in probabilistic tracking (p < 0.001, Mann-Whitney U-test) and in deterministic tracking (p = 0.02). The false-positive errors were smaller in HCP data in deterministic tracking (p < 0.001) and showed a strong trend toward significance in probabilistic tracking (p = 0.06). In the clinical cases, the probabilistic method visualized 7 of 10 attempted CNs accurately, compared with 3 correct depictions with deterministic tracking. CONCLUSIONS High angular resolution DTI scans are preferable for the DTI-based depiction of the cranial nerves. Probabilistic tracking with a gradual PICo threshold increase is more effective for this task than the previously described deterministic tracking with a gradual FA threshold increase and might represent a method that is useful for depicting cranial nerves with DTI since it eliminates the erroneous fibers without manual intervention.

A whole-brain streamlines data-set (so-called tractogram) generated from diffusion MRI provides a wealth of information regarding structural connectivity in the brain. Besides visualisation strategies, a number of post-processing approaches have been proposed to extract more detailed information from the tractogram. One such approach is based on exploiting the information contained in the tractogram to generate track-weighted (TW) images. In the track-weighted imaging (TWI) approach, a very large number of streamlines are often generated throughout the brain, and an image is then computed based on properties of the streamlines themselves (e.g. based on the number of streamlines in each voxel, or their average length), or based on the values of an associated image (e.g. a diffusion anisotropy map, a T 2 map) measured at the coordinates of the streamlines. This review article describes various approaches used to generate TW images and discusses the flexible formalism that TWI provides to generate a range of images with very different contrast, as well as the super-resolution properties of the resulting images. It also explains how this approach provides a powerful means to study structural and functional connectivity simultaneously. Finally, a number of key issues for its practical implementation are discussed.

Objectives: To compare the sensitivity and early temporal changes of diffusion parameters obtained from diffusion tensor imaging (DTI), diffusional kurtosis imaging (DKI), q-space analysis (QSA) and bi-exponential modelling in hyperacute stroke patients., Materials and Methods: A single investigational acquisition allowing the four diffusion analyses was performed on seven hyperacute stroke patients with a 3T system. The percentage change between ipsi- and contralateral regions were compared at admission and 24 h later. Two out of the seven patients were imaged every 6 h during this period., Results: Kurtoses from both DKI and QSA were the most sensitive of the tested diffusion parameters in the few hours following ischemia. An early increase-maximum-decrease pattern of evolution was highlighted during the 24-h period for all parameters proportional to diffusion coefficients. A similar pattern was observed for both kurtoses in only one of two patients., Conclusion: Our comparison was performed using identical diffusion encoding timings and on patients in the same stage of their condition. Although preliminary, our findings confirm those of previous studies that showed enhanced sensitivity of kurtosis. A fine time mapping of diffusion metrics in hyperacute stroke patients was presented which advocates for further investigations on larger animal or human cohorts.

Background: Diffusion tensor imaging (DTI) is increasingly investigated as a potential diagnostic and prognostic tool for symptomatic degenerative cervical pathology; however, it is yet to be validated for this purpose., Objective: To investigate the association of preoperative DTI signal changes and postoperative outcomes in patients with cervical spondylotic myelopathy (CSM)., Methods: We performed a systematic literature review using PubMed for clinical studies using DTI in adults undergoing operative management for CSM. Data on preoperative clinical status, preoperative DTI metrics, and postoperative clinical outcomes were abstracted. Preoperative DTI parameters were correlated with preoperative severity and postoperative outcomes and pooled across studies., Results: Nine studies met inclusion criteria for 238 patients who underwent operative management with mean follow-up time 310 days. Higher preoperative fractional anisotropy (FA) at the level of maximal compression correlates strongly with a higher preoperative modified Japanese Orthopaedic Association (mJOA) score (n = 192 patients, rho = 0.62, P < 0.001). Higher preoperative FA is associated with less postoperative mJOA change (n = 27, rho = -0.42, P = 0.02) but a greater recovery rate (n = 93, rho = 0.32, P < 0.001). Preoperative FA correlated with lower Neck Disability Index (n = 15, rho = -0.61, P = 0.04). Preoperative fiber tract ratio had a large positive correlation with a postoperative recovery rate (n = 20, rho = 0.61, P = 0.005). When reported, an apparent diffusion coefficient showed an inverse correlation compared with FA., Conclusion: DTI is associated with preoperative severity and postoperative outcomes in CSM patients, suggesting that DTI may become useful in identifying those most likely to benefit from operative intervention (Level 3 Evidence). Prospective trials with standardized DTI acquisition techniques and patient selection are required for higher-level evidence., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Purpose: To evaluate the diagnostic performances of the diffusion tensor imaging (DTI) parameters in the diagnosis of breast cancer and to investigate the variations in DTI parameters according to the breast cancer biomarkers., Materials and Methods: At 3.0 Tesla (T), DTI was performed in 85 patients with 92 enhancing breast lesions. λ 1 , λ 2 , λ 3 , mean diffusivity (MD), radial diffusivity (RD), fractional anisotropy (FA), relative anisotropy (RA), and geodesic anisotropy (GA) were studied and compared with diffusion-weighted imaging-derived apparent diffusion coefficient. Lesions were analyzed according to BIRADS lexicon. Logistic regression models were constructed to determine the contribution of DTI to the specificity and the accuracy of DCE-MRI. Breast cancer biomarkers; estrogen receptor (ER), HER-2 status, and Ki-67 were correlated with DTI in malignant cases., Results: Malignant lesions exhibited significantly lower MD, RD, λ 1 , λ 2 , λ 3 and higher FA, RA, GA values (P <  0.001). Logistic regression models showed that MD, RD, λ 1 , λ 2 , λ 3 , FA, and RA increase the specificity of the DCE-MRI (from 83.0% to 89.4-93.6%; P <  0.05). Higher RD, λ 2 , λ 3 and lower FA, RA, and GA values were observed in ER-negative breast cancer (P <  0.05). Ki-67 showed significant, negative correlation with FA, RA, GA, λ 1 -λ 3 and λ 1 -λ 2 (r = -0.336 to -0.435; P <  0.05)., Conclusion: Besides its ability to differentiate malignant breast lesions, DTI improves the specificity of conventional 3.0T breast MRI and shows correlation with biomarkers ER and Ki-67., Level of Evidence: 1 J. Magn. Reson. Imaging 2017;45:660-672., (© 2016 International Society for Magnetic Resonance in Medicine.)

Background: Preoperative diffusion tensor tractography (DTT) has recently been used to aid in the mapping of functional pathways to limit damage associated with resection of low-grade gliomas (LGGs)., Objective: To assess the predictive capacity of DTT as a biomarker of postoperative motor outcomes in patients with LGGs involving the corticospinal tract (CST). CST parameters obtained using a quantitative fiber tracking approach were used to investigate the reliability of the DTT biomarker by comparing their values in the tumoral (Tcst) and healthy (Hcst) hemispheres., Methods: Thirty-seven patients with LGGs involving the CST were enrolled. Quantification of structural differences between the Tcst and Hcst were analyzed according to the novel biomarker (NF index), defined as follows: (Hcst NF - Tcst NF)/Hcst NF, where NF represents the number of fibers in each region. Logistic regression analysis was used to examine associations among clinical postoperative outcomes and NF index values, tumoral patterns, and premotor/motor evoked potentials., Results: NF values significantly differed between the Tcst and Hcst. Analysis of the NF index showed that patients with a preoperative NF index <0.22 had a significantly lower risk of developing transient postoperative deficits (area under the curve, 0.92; 95% binomial confidence interval, 0.834-1). Patients with less pronounced differences in NF between the Tcst and Hcst also experienced better clinical outcomes., Conclusions: The NF index may be a useful biomarker for predicting clinical outcomes in patients with LGGs. Furthermore, the NF index may provide a preoperative estimate of the patient's potential for recovery from possible postsurgical neurologic deficits., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Objective: To determine if spinal cord diffusion tensor imaging indexes correlate with short-term clinical outcome in patients undergoing elective cervical spine surgery for cervical spondylotic myelopathy (CSM)., Methods: A prospective consecutive cohort study was performed in patients undergoing elective cervical spine surgery for CSM. After obtaining informed consent, patients with CSM underwent preoperative T2-weighted magnetic resonance imaging and diffusion tensor imaging of the cervical spine. Fractional anisotropy (FA) values at the level of maximum cord compression and at the noncompressed C1-2 level were calculated on axial images. We recorded the modified Japanese Orthopaedic Association (mJOA) scale, Neck Disability Index, and Short Form-36 physical functioning subscale scores for all patients preoperatively and 3 months postoperatively. Statistical analysis was performed to identify correlations between FA and clinical outcome scores., Results: The study included 27 patients (mean age 54.5 years ± 1.9, 12 men). The mean postoperative changes in mJOA scale, Neck Disability Index, and Short Form-36 physical functioning subscale scores were 0.9 ± 0.3, -6.0 ± 1.9, and 3.4 ± 1.9. The mean FA at the level of maximum compression was significantly lower than the mean FA at the C1-2 level (0.5 vs. 0.55, P = 0.01). FA was significantly correlated with change in mJOA scale score (Pearson r = -0.42, P = 0.02). FA was significantly correlated with the preoperative mJOA scale score (Pearson r = 0.65, P < 0.001)., Conclusions: Preoperative FA at the level of maximum cord compression significantly correlates with the 3-month change in mJOA scale score among patients with CSM. FA was also significantly associated with preoperative mJOA scale score and is a potential biomarker for spinal cord dysfunction in CSM., (Published by Elsevier Inc.)

Purpose: To study the between-scanner variation and the between-visit reproducibility of brain apparent diffusion coefficient (ADC) measurements in the setting of a multi-center chemotherapy clinical trial for glioblastoma multiforme., Methods and Materials: ADC maps of 52 patients at six sites were calculated in-house from diffusion-weighted images obtained by seven individual scanner models of two vendors. The median and coefficient of variation (CV) of normal brain white matter ADC values from a defined region of interest were used to evaluate the differences among scanner models, vendors, magnetic fields, as well as successive visits. All patients participating in this study signed institutional review board approved informed consent. Data acquisition was performed in compliance with all applicable Health Insurance Portability and Accountability Act regulations. The study spanned from August 1, 2006, to January 29, 2008., Results: For baseline median ADC, no difference was observed between the different scanner models, different vendors, and different magnetic field strength. For baseline ADC CV, a significant difference was found between different scanner models (p = 0.0002). No between-scanner difference was observed in ADC changes between two visits. For between-visit reproducibility, significant difference was seen between the ADC values measured at two successive visits for the whole patient group., Conclusion: The CVs varied significantly between scanners, presumably due to image noise. Consistent scanner parameter setup can improve reproducibility of the ADC measurements between visits.

Introduction: Early assessment of the pyramidal tracts is important for intracerebral hemorrhage (ICH) patients in order to decide the optimal treatment or to assess appropriate rehabilitation strategies, and management of patient expectations and goals. The purpose of this study was to systematically review and summarize the current available literature on the value of Fractional Anisotropy (FA) parameter of the diffusion tensor imaging (DTI) in predicting upper extremity (UE) motor recovery after subacute ICH., Methods: PubMed, EMBASE, MEDLINE, Google Scholar, and Cochrane CENTRAL searches were conducted from 1 January 1950 to 31 March 2016 which were supplemented with relevant articles identified in the references. Pooled estimate using correlation between DTI parameter FA and UE motor recovery was done using comprehensive meta-analysis software., Results: Out of 97 citations, only eight studies met the criteria for inclusion in the systematic review and six studies were included in the meta-analysis. A random effects model revealed that DTI parameter FA is a significant predictor for UE motor recovery after subacute ICH (correlation coefficient = 0.56; 95 % confidence interval 0.44 to 0.65, P value <0.001). However, moderate heterogeneity was observed between the studies (Tau-squared = 0.28, I-squared = 70.3)., Conclusion: The studies reported so far on correlation between FA parameter of DTI and UE motor recovery in ICH patients are few with small sample sizes. This meta-analysis suggests a strong correlation between DTI parameter FA and UE motor recovery in ICH patients. Further well-designed prospective studies embedded with larger sample size are needed to confirm these findings.

Background: White matter (WM) integrity may represent a shared biomarker for emotional disorders (ED). Aims: To identify transdiagnostic biomarkers of reduced WM by meta-analysis of findings across multiple EDs., Method: Web of Science was searched systematically for studies of whole brain analysis of fractional anisotropy (FA) in adults with major depressive disorder, bipolar disorder, social anxiety disorder, obsessive-compulsive disorder or posttraumatic stress disorder compared with a healthy control (HC) group. Peak MNI coordinates were extracted from 37 studies of voxel-based analysis (892 HC and 962 with ED) and meta-analyzed using seed-based d Mapping (SDM) Version 4.31. Separate meta-analyses were also conducted for each disorder., Results: In the transdiagnostic meta-analysis, reduced FA was identified in ED studies compared to HCs in the left inferior fronto-occipital fasciculus, forceps minor, uncinate fasciculus, anterior thalamic radiation, superior corona radiata, bilateral superior longitudinal fasciculi, and cerebellum. Disorder-specific meta-analyses revealed the OCD group had the most similarities in reduced FA to other EDs, with every cluster of reduced FA overlapping with at least one other diagnosis. The PTSD group was the most distinct, with no clusters of reduced FA overlapping with any other diagnosis. The BD group were the only disorder to show increased FA in any region, and showed a more bilateral pattern of WM changes, compared to the other groups which tended to demonstrate a left lateralized pattern of FA reductions., Conclusions: Distinct diagnostic categories of ED show commonalities in WM tracts with reduced FA when compared to HC, which links brain networks involved in cognitive and affective processing. This meta-analysis facilitates an increased understanding of the biological markers that are shared by these ED.

Objective: Temporo-occipital junction arteriovenous malformations (TOJ-AVMs) do not often involve eloquent brain cortex, but rather exist beside functional fiber tracts. The objective of this study was to determine the outcomes after surgical treatment in patients with TOJ-AVMs and to identify risk factors associated with postoperative neurologic deficits., Methods: We retrospectively studied 41 patients with TOJ-AVMs. All patients underwent preoperative diffusion tensor imaging. Every potentially involved function (visual field and language) was studied as an independent function object (FO). The function-related optic radiation or arcuate fasciculus was tracked. Lesion-to-eloquent fiber distances (LFDs) were analyzed in regard to postoperative neurologic deficits. The areas under the receiver operating characteristic curves were compared., Results: There were 58 FOs analyzed. Of these, 26 (44.8%) FOs led to short-term neurologic deficits, and 21 (36.2%) FOs resulted in long-term neurologic deficits. LFD was a significant predictor of short-term (P = 0.002) and long-term (P = 0.007) neurologic deficits. The Spetzler-Martin (S-M) score was associated with short-term neurologic deficits (P = 0.045), but it was not associated with long-term neurologic deficits. The area under the receiver operating characteristic curve of LFD was higher than that of the S-M score in predicting short-term neurologic deficits (0.89 vs. 0.72, P = 0.04) and long-term neurologic deficits (0.90 vs. 0.62, P = 0.002). The cutoff point for LFD in predicting long-term neurologic deficits was 3.10 mm., Conclusions: Patients with TOJ-AVMs have a high risk of surgical morbidity, although they often have relatively low S-M scores; LFD is a crucial risk factor associated with postoperative neurologic deficits in patients with TOJ-AVMs., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Adrenomyeloneuropathy is the late-onset form of X-linked adrenoleukodystrophy, and is considered the most frequent metabolic hereditary spastic paraplegia. In adrenomyeloneuropathy the spinal cord is the main site of pathology. Differently from quantitative magnetic resonance imaging of the brain, little is known about the feasibility and utility of advanced neuroimaging in quantifying the spinal cord abnormalities in hereditary diseases. Moreover, little is known about the subtle pathological changes that can characterize the brain of adrenomyeloneuropathy subjects in the early stages of the disease. We performed a cross-sectional study on 13 patients with adrenomyeloneuropathy and 12 age-matched healthy control subjects who underwent quantitative magnetic resonance imaging to assess the structural changes of the upper spinal cord and brain. Total cord areas from C2-3 to T2-3 level were measured, and diffusion tensor imaging metrics, i.e. fractional anisotropy, mean, axial and radial diffusivity values were calculated in both grey and white matter of spinal cord. In the brain, grey matter regions were parcellated with Freesurfer and average volume and thickness, and mean diffusivity and fractional anisotropy from co-registered diffusion maps were calculated in each region. Brain white matter diffusion tensor imaging metrics were assessed using whole-brain tract-based spatial statistics, and tractography-based analysis on corticospinal tracts. Correlations among clinical, structural and diffusion tensor imaging measures were calculated. In patients total cord area was reduced by 26.3% to 40.2% at all tested levels (P < 0.0001). A mean 16% reduction of spinal cord white matter fractional anisotropy (P ≤ 0.0003) with a concomitant 9.7% axial diffusivity reduction (P < 0.009) and 34.5% radial diffusivity increase (P < 0.009) was observed, suggesting co-presence of axonal degeneration and demyelination. Brain tract-based spatial statistics showed a marked reduction of fractional anisotropy, increase of radial diffusivity (P < 0.001) and no axial diffusivity changes in several white matter tracts, including corticospinal tracts and optic radiations, indicating predominant demyelination. Tractography-based analysis confirmed the results within corticospinal tracts. No significant cortical volume and thickness reduction or grey matter diffusion tensor imaging values alterations were observed in patients. A correlation between radial diffusivity and disease duration along the corticospinal tracts (r = 0.806, P < 0.01) was found. In conclusion, in adrenomyeloneuropathy patients quantitative magnetic resonance imaging-derived measures identify and quantify structural changes in the upper spinal cord and brain which agree with the expected histopathology, and suggest that the disease could be primarily caused by a demyelination rather than a primitive axonal damage. The results of this study may also encourage the employment of quantitative magnetic resonance imaging in other hereditary diseases with spinal cord involvement., (© The Author (2016). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

This study examined the reliability of high angular resolution diffusion tensor imaging (HARDI) data collected on a single individual across several sessions using the same scanner. HARDI data was acquired for one healthy adult male at the same time of day on ten separate days across a one-month period. Environmental factors (e.g. temperature) were controlled across scanning sessions. Tract Based Spatial Statistics (TBSS) was used to assess session-to-session variability in measures of diffusion, fractional anisotropy (FA) and mean diffusivity (MD). To address reliability within specific structures of the medial temporal lobe (MTL; the focus of an ongoing investigation), probabilistic tractography segmented the Entorhinal cortex (ERc) based on connections with Hippocampus (HC), Perirhinal (PRc) and Parahippocampal (PHc) cortices. Streamline tractography generated edge weight (EW) metrics for the aforementioned ERc connections and, as comparison regions, connections between left and right rostral and caudal anterior cingulate cortex (ACC). Coefficients of variation (CoV) were derived for the surface area and volumes of these ERc connectivity-defined regions (CDR) and for EW across all ten scans, expecting that scan-to-scan reliability would yield low CoVs. TBSS revealed no significant variation in FA or MD across scanning sessions. Probabilistic tractography successfully reproduced histologically-verified adjacent medial temporal lobe circuits. Tractography-derived metrics displayed larger ranges of scanner-to-scanner variability. Connections involving HC displayed greater variability than metrics of connection between other investigated regions. By confirming the test retest reliability of HARDI data acquisition, support for the validity of significant results derived from diffusion data can be obtained.

Orientation distribution functions (ODFs) are widely used to resolve fiber crossing problems in high angular resolution diffusion imaging (HARDI). The characteristics of the ODFs are often assessed using a visual criterion, although the use of objective criteria is also reported, which are directly borrowed from classic signal and image processing theory because they are intuitive and simple to compute. However, they are not always pertinent for the characterization of ODFs. We propose a more general paradigm for assessing the characteristics of ODFs. The idea consists in regarding an ODF as a three-dimensional (3D) point cloud, projecting the 3D point cloud onto an angle-distance map, constructing an angle-distance matrix, and calculating metrics such as length ratio, separability, and uncertainty. The results from both simulated and real data show that the proposed metrics allow for the assessment of the characteristics of ODFs in a quantitative and relatively complete manner.

Graph matching-aligning a pair of graphs to minimize their edge disagreements-has received wide-spread attention from both theoretical and applied communities over the past several decades, including combinatorics, computer vision, and connectomics. Its attention can be partially attributed to its computational difficulty. Although many heuristics have previously been proposed in the literature to approximately solve graph matching, very few have any theoretical support for their performance. A common technique is to relax the discrete problem to a continuous problem, therefore enabling practitioners to bring gradient-descent-type algorithms to bear. We prove that an indefinite relaxation (when solved exactly) almost always discovers the optimal permutation, while a common convex relaxation almost always fails to discover the optimal permutation. These theoretical results suggest that initializing the indefinite algorithm with the convex optimum might yield improved practical performance. Indeed, experimental results illuminate and corroborate these theoretical findings, demonstrating that excellent results are achieved in both benchmark and real data problems by amalgamating the two approaches.

Objectives: To explore the utility of diffusion tensor imaging (DTI) and acoustic radiation force impulse (ARFI) imaging in the diagnosis of parotid tumours., Methods: 51 patients with parotid tumours were examined with DTI on 3.0-T MRI and ARFI imaging on an ultrasound scanner before surgery. Values of apparent diffusion coefficient (ADC), fractional anisotropy (FA) and shear-wave velocity (SWV) were calculated and analyzed with independent samples Wilcoxon-Mann-Whitney test. Cut-off values, sensitivity and specificity were calculated with receiver-operating characteristic (ROC) curve analysis. The value of combination was calculated through parallel test for the cut-off value of ADC, FA and SWV (combination = 0 or 1); then, ROC analysis was performed with pathological results as the gold standard to calculate the sensitivity and specificity for the combination of the three parameters distinguishing benign and malignant parotid tumours. Pathological diagnosis for every patient was made post-operatively from the tumour tissue taken during operation., Results: There was a significant difference between benign and malignant tumours in the values of ADC, FA and SWV (p = 0.032, p = 0.011 and p < 0.0001); a significant difference in the values was also found between pleomorphic adenoma and malignant tumour (p = 0.0012, p < 0.0001 and p = 0.0002). The diagnosis cut-off points between benign and malignant tumours for ADC, FA and SWV were 1.02 × 10(-3) mm(2) s(-1), 0.24 and 2.76 m s(-1), respectively; the sensitivity for ADC, FA and SWV was 87.50, 62.50 and 68.75%; the specificity was 45.71, 82.86 and 97.14%. Analysis of the combination of the three parameters increased the sensitivity, specificity, Youden index and area under the ROC curve compared with analysis of each parameter alone for distinguishing benign and malignant tumours., Conclusions: The diagnostic value of the combination of the three parameters for distinguishing benign and malignant parotid tumours is the best; SWV is the preferred indicator. Parameters of DTI and ARFI may reflect the histological characteristics of parotid tumours and predict benignancy and malignancy and could provide quantitative information about the tumour. Combination of DTI with ARFI imaging had obvious advantage for the diagnosis of parotid tumours than each alone.

Gliomas are a class of rarely curable tumors arising from abnormal glia cells in the human brain. The understanding of glioma spreading patterns is essential for both radiological therapy as well as surgical treatment. Diffusion tensor imaging (DTI) allows to infer the white matter fibre structure of the brain in a noninvasive way. Painter and Hillen (J Theor Biol 323:25-39, 2013) used a kinetic partial differential equation to include DTI data into a class of anisotropic diffusion models for glioma spread. Here we extend this model to explicitly include adhesion mechanisms between glioma cells and the extracellular matrix components which are associated to white matter tracts. The mathematical modelling follows the multiscale approach proposed by Kelkel and Surulescu (Math Models Methods Appl Sci 23(3), 2012). We use scaling arguments to deduce a macroscopic advection-diffusion model for this process. The tumor diffusion tensor and the tumor drift velocity depend on both, the directions of the white matter tracts as well as the binding dynamics of the adhesion molecules. The advanced computational platform DUNE enables us to accurately solve our macroscopic model. It turns out that the inclusion of cell binding dynamics on the microlevel is an important factor to explain finger-like spread of glioma.

Background: To study early neurodevelopment in preterm infants, evaluation of brain maturation and injury is increasingly performed using diffusion tensor imaging, for which the reliability of underlying data is paramount., Objective: To review the literature to evaluate acquisition and processing methodology in diffusion tensor imaging studies of preterm infants., Materials and Methods: We searched the Embase, Medline, Web of Science and Cochrane databases for relevant papers published between 2003 and 2013. The following keywords were included in our search: prematurity, neuroimaging, brain, and diffusion tensor imaging., Results: We found 74 diffusion tensor imaging studies in preterm infants meeting our inclusion criteria. There was wide variation in acquisition and processing methodology, and we found incomplete reporting of these settings. Nineteen studies (26%) reported the use of neonatal hardware. Data quality assessment was not reported in 13 (18%) studies. Artefacts-correction and data-exclusion was not reported in 33 (45%) and 18 (24%) studies, respectively. Tensor estimation algorithms were reported in 56 (76%) studies but were often suboptimal., Conclusion: Diffusion tensor imaging acquisition and processing settings are incompletely described in current literature, vary considerably, and frequently do not meet the highest standards.

We present an example-based multi-atlas approach for classifying white matter (WM) tracts into anatomic bundles. Our approach exploits expert-provided example data to automatically classify the WM tracts of a subject. Multiple atlases are constructed to model the example data from multiple subjects in order to reflect the individual variability of bundle shapes and trajectories over subjects. For each example subject, an atlas is maintained to allow the example data of a subject to be added or deleted flexibly. A voting scheme is proposed to facilitate the multi-atlas exploitation of example data. For conceptual simplicity, we adopt the same metrics in both example data construction and WM tract labeling. Due to the huge number of WM tracts in a subject, it is time-consuming to label each WM tract individually. Thus, the WM tracts are grouped according to their shape similarity, and WM tracts within each group are labeled simultaneously. To further enhance the computational efficiency, we implemented our approach on the graphics processing unit (GPU). Through nested cross-validation we demonstrated that our approach yielded high classification performance. The average sensitivities for bundles in the left and right hemispheres were 89.5% and 91.0%, respectively, and their average false discovery rates were 14.9% and 14.2%, respectively.

Brain parenchymal lesions are frequently observed on conventional magnetic resonance imaging (MRI) scans of patients with neuromyelitis optica (NMO) spectrum disorder, but the specific morphological and temporal patterns distinguishing them unequivocally from lesions caused by other disorders have not been identified. This literature review summarizes the literature on advanced quantitative imaging measures reported for patients with NMO spectrum disorder, including proton MR spectroscopy, diffusion tensor imaging, magnetization transfer imaging, quantitative MR volumetry, and ultrahigh-field strength MRI. It was undertaken to consider the advanced MRI techniques used for patients with NMO by different specialists in the field. Although quantitative measures such as proton MR spectroscopy or magnetization transfer imaging have not reproducibly revealed diffuse brain injury, preliminary data from diffusion-weighted imaging and brain tissue volumetry indicate greater white matter than gray matter degradation. These findings could be confirmed by ultrahigh-field MRI. The use of nonconventional MRI techniques may further our understanding of the pathogenic processes in NMO spectrum disorders and may help us identify the distinct radiographic features corresponding to specific phenotypic manifestations of this disease.

We introduce a class of covariate-adjusted skewed functional models (cSFM) designed for functional data exhibiting location-dependent marginal distributions. We propose a semi-parametric copula model for the pointwise marginal distributions, which are allowed to depend on covariates, and the functional dependence, which is assumed covariate invariant. The proposed cSFM framework provides a unifying platform for pointwise quantile estimation and trajectory prediction. We consider a computationally feasible procedure that handles densely as well as sparsely observed functional data. The methods are examined numerically using simulations and is applied to a new tractography study of multiple sclerosis. Furthermore, the methodology is implemented in the R package cSFM, which is publicly available on CRAN., (© The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

In vivo tractography based on diffusion magnetic resonance imaging (dMRI) has opened new doors to study structure-function relationships in the human brain. Initially developed to map the trajectory of major white matter tracts, dMRI is used increasingly to infer long-range anatomical connections of the cortex. Because axonal projections originate and terminate in the gray matter but travel mainly through the deep white matter, the success of tractography hinges on the capacity to follow fibers across this transition. Here we demonstrate that the complex arrangement of white matter fibers residing just under the cortical sheet poses severe challenges for long-range tractography over roughly half of the brain. We investigate this issue by comparing dMRI from very-high-resolution ex vivo macaque brain specimens with histological analysis of the same tissue. Using probabilistic tracking from pure gray and white matter seeds, we found that ∼50% of the cortical surface was effectively inaccessible for long-range diffusion tracking because of dense white matter zones just beneath the infragranular layers of the cortex. Analysis of the corresponding myelin-stained sections revealed that these zones colocalized with dense and uniform sheets of axons running mostly parallel to the cortical surface, most often in sulcal regions but also in many gyral crowns. Tracer injection into the sulcal cortex demonstrated that at least some axonal fibers pass directly through these fiber systems. Current and future high-resolution dMRI studies of the human brain will need to develop methods to overcome the challenges posed by superficial white matter systems to determine long-range anatomical connections accurately.

Diffusional kurtosis imaging (DKI) measures the diffusion and kurtosis tensors to quantify restricted, non-Gaussian diffusion that occurs in biological tissue. By estimating the kurtosis tensor, DKI accounts for higher order diffusion dynamics, when compared with diffusion tensor imaging (DTI), and consequently can describe more complex diffusion profiles. Here, we compare several measures of diffusional anisotropy which incorporate information from the kurtosis tensor, including kurtosis fractional anisotropy (KFA) and generalized fractional anisotropy (GFA), with the diffusion tensor-derived fractional anisotropy (FA). KFA and GFA demonstrate a net enhancement relative to FA when multiple white matter fiber bundle orientations are present in both simulated and human data. In addition, KFA shows net enhancement in deep brain structures, such as the thalamus and the lenticular nucleus, where FA indicates low anisotropy. Thus, KFA and GFA provide additional information relative to FA with regard to diffusional anisotropy, and may be particularly advantageous for the assessment of diffusion in complex tissue environments., (Copyright © 2015 John Wiley & Sons, Ltd.)

Background and Purpose: Studies on Whites and Asians have shown contradictory results on the association between an incomplete circle of Willis (CoW) and white matter disease (WMD). We evaluated such relationship in Ecuadorian Mestizos., Methods: Ecuadorian Mestizos aged ≥40 years were evaluated with MRI and MRA. WMD was graded according to the modified Fazekas' scale, and the CoW was classified as complete or incomplete according to the presence or absence of one A1 segment of the anterior cerebral artery or one or both P1 segments of posterior cerebral arteries., Results: A total of 315 persons (mean age 64.7 ± 13.4 years, 56.5% women) were prospectively enrolled over 6 months. MRAs revealed a complete CoW in 65.1%, and MRIs showed WMD in 62.5% of cases. Irrespective of age and sex, incompleteness of the CoW was not associated with a higher prevalence or severity of WMD. In contrast, age was an independent risk factor for the occurrence of WMD., Conclusions: In the Ecuadorian Mestizo population, WMD occurrence and severity do not seem to be related to incompleteness of the CoW., (Copyright © 2014 by the American Society of Neuroimaging.)

Purpose: To delineate microstructural changes in transected white matter tracts after corpus callosotomy in relation to seizure recurrence using tract-based spatial statistics of diffusion tensor imaging (DTI-TBSS)., Methods: We retrospectively included 12 total corpus callosotomy patients who had undergone serial pre- and postoperative DTI studies. The first postoperative DTI was performed within 6 months after callosotomy. The second postoperative DTI was performed in five patients with seizure recurrence (symptomatic group) and in seven patients without seizure recurrence (asymptomatic group) after 1 year following surgery. Group comparisons of fractional anisotropy (FA) with age- and sex-matched controls were performed in a whole brain voxel-wise manner using DTI-TBSS., Results: The first postoperative DTI-TBSS showed a significant FA decrease in the entire corpus callosum in all patients. The second postoperative DTI-TBSS showed that a significant FA decrease remained in the entire corpus callosum in the asymptomatic group. However, in the symptomatic group, no significant decrease of FA was observed in some parts of the posterior body and splenium of the corpus callosum, although there was still a significant FA decrease in the genu of the corpus callosum., Conclusions: Using DTI-TBSS analysis, we characterized and visualized microstructural white matter changes over time in relation to seizure recurrence in callosotomy patients, suggesting that reorganization of some transected white matter tracts may be related to seizure recurrence. DTI-TBSS analysis can provide reliable and useful information about the state of white matter bundles affected by corpus callosotomy in a noninvasive manner.

Background: The purpose of the present study was to investigate the existence of microstructure abnormalities in the white matter circuit in stroke patients and its relationship to depressive episodes. To target the prevention of depression, we also investigated the relationship between lymphocyte subsets and cerebral abnormalities in patients., Methods: Participants included 18 patients with acute ischemic stroke and 22 healthy control subjects. Diffusion tensor imaging was performed. Whole-brain voxel-based analysis was used to compare fractional anisotropy (FA) between groups. Blood samples were obtained, and the lymphocyte subsets were evaluated using flow cytometry. Follow-up examinations were conducted on 12 patients at 6 months., Results: FA was decreased in the bilateral anterior limb of the internal capsule in stroke patients. At the 6-month follow-up examination, there was a significant increase in FA, which was associated with a lower depression scale score. Patients showed a decreased percentage of circulated regulatory T lymphocytes, and the degree of reduction was related to the decrease in the FA value in the internal capsule., Conclusions: FA reductions in the anterior limb of the internal capsule cause abnormality in the frontal-subcortical circuits and confer a biological vulnerability, which in combination with environmental stressors results in the onset of depression. Our findings also demonstrated the possibility of preventing post-stroke depression by targeting the role of regulatory T lymphocytes in brain tissue repair and regeneration after stroke., (© 2014 The Authors. Psychogeriatrics © 2014 Japanese Psychogeriatric Society.)

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

Diffusion kurtosis imaging (DKI) is a new model in magnetic resonance imaging (MRI) characterizing restricted diffusion of water molecules in living tissues. We propose a method for fast estimation of the DKI parameters. These parameters - apparent diffusion coefficient (ADC) and apparent kurtosis coefficient (AKC) - are evaluated using an alternative iteration schema (AIS). This schema first roughly estimates a pair of ADC and AKC values from a subset of the DKI data acquired at 3 b-values. It then iteratively and alternately updates the ADC and AKC until they are converged. This approach employs the technique of linear least square fitting to minimize estimation error in each iteration. In addition to the common physical and biological constrains that set the upper and lower boundaries of the ADC and AKC values, we use a smoothing procedure to ensure that estimation is robust. Quantitative comparisons between our AIS methods and the conventional methods of unconstrained nonlinear least square (UNLS) using both synthetic and real data showed that our unconstrained AIS method can significantly accelerate the estimation procedure without compromising its accuracy, with the computational time for a DKI dataset successfully reduced to only 1 or 2min. Moreover, the incorporation of the smoothing procedure using one of our AIS methods can significantly enhance the contrast of AKC maps and greatly improve the visibility of details in fine structures., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Disconnectivity between the Default Mode Network (DMN) nodes can cause clinical symptoms and cognitive deficits in Alzheimer׳s disease (AD). We aimed to examine the structural connectivity between DMN nodes, to verify the extent in which white matter disconnection affects cognitive performance. MRI data of 76 subjects (25 mild AD, 21 amnestic Mild Cognitive Impairment subjects and 30 controls) were acquired on a 3.0T scanner. ExploreDTI software (fractional Anisotropy threshold=0.25 and the angular threshold=60°) calculated axial, radial, and mean diffusivities, fractional anisotropy and streamline count. AD patients showed lower fractional anisotropy (P=0.01) and streamline count (P=0.029), and higher radial diffusivity (P=0.014) than controls in the cingulum. After correction for white matter atrophy, only fractional anisotropy and radial diffusivity remained significantly lower in AD compared to controls (P=0.003 and P=0.05). In the parahippocampal bundle, AD patients had lower mean and radial diffusivities (P=0.048 and P=0.013) compared to controls, from which only radial diffusivity survived for white matter adjustment (P=0.05). Regression models revealed that cognitive performance is also accounted for by white matter microstructural values. Structural connectivity within the DMN is important to the execution of high-complexity tasks, probably due to its relevant role in the integration of the network., (Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.)

The aim of the study was to investigate the existence of microstructural abnormalities in the white matter of the brain in stroke patients, as well as the relationship between these microstructural abnormalities and changes in depressive symptoms over 6 months. Participants were 29 acute ischemic stroke patients and 37 healthy control subjects. Depressive symptoms were assessed in all subjects using the Hamilton Rating Scale for Depression and the Zung Self-rating Depression Scale. Whole brain voxel-based analysis was used to compare diffusion tensor imaging measures of Fractional Anisotropy (FA) between the groups. Six-month follow-up examinations were conducted. Patients showed significantly lower white matter FA values in the left and right anterior limbs of the internal capsule, and 6 months after the stroke they showed significantly increased FA values in these regions. We found a significant negative correlation between the increased ratio of the FA values and the change in depression scale scores at 6-month follow-up. Regional white matter damage may reflect abnormalities in neuroanatomical pathways related to the pathophysiology of depression., (Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.)