Diffusion abnormalities in temporal lobes of children with temporal lobe epilepsy: a preliminary diffusional kurtosis imaging study and comparison with diffusion tensor imaging

Spatiotemporal Patterns of White Matter Maturation after Pre-Adolescence: A Diffusion Kurtosis Imaging Study

Diffusion tensor imaging (DTI) enables the assessment of changes in brain tissue microstructure during maturation and ageing. In general, patterns of cerebral maturation and decline render non-monotonic lifespan trajectories of DTI metrics with age, and, importantly, the rate of microstructural changes is heterochronous for various white matter fibres. Recent studies have demonstrated that diffusion kurtosis imaging (DKI) metrics are more sensitive to microstructural changes during ageing compared to those of DTI. In a previous work, we demonstrated that the Cohen's d of mean diffusional kurtosis (dMK) represents a useful biomarker for quantifying maturation heterochronicity. However, some inferences on the maturation grades of different fibre types, such as association, projection, and commissural, were of a preliminary nature due to the insufficient number of fibres considered. Hence, the purpose of this follow-up work was to further explore the heterochronicity of microstructural maturation between pre-adolescence and middle adulthood based on DTI and DKI metrics. Using the effect size of the between-group parametric changes and Cohen's d, we observed that all commissural fibres achieved the highest level of maturity, followed by the majority of projection fibres, while the majority of association fibres were the least matured. We also demonstrated that dMK strongly correlates with the maxima or minima of the lifespan curves of DTI metrics. Furthermore, our results provide substantial evidence for the existence of spatial gradients in the timing of white matter maturation. In conclusion, our data suggest that DKI provides useful biomarkers for the investigation of maturation spatial heterogeneity and heterochronicity.

Mapping early tumor response to radiotherapy using diffusion kurtosis imaging*

PURPOSE

In this pilot study, DKI measures of diffusivity and kurtosis were compared in active tumor regions and correlated to radiologic response to radiotherapy after completion of 2 weeks of treatment to derive potential early measures of tumor response.

METHODS

MRI and Magnetic Resonance Spectroscopic Imaging (MRSI) data were acquired before the beginning of RT (pre-RT) and 2 weeks after the initiation of treatment (during-RT) in 14 glioblastoma patients. The active tumor region was outlined as the union of the residual contrast-enhancing region and metabolically active tumor region. Average and standard deviation of mean, axial, and radial diffusivity (MD, AD, RD) and mean, axial, and radial kurtosis (MK, AK, RK) values were calculated for the active tumor VOI from images acquired pre-RT and during-RT and paired t-tests were executed to estimate pairwise differences. Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the predictive capabilities of changes in diffusion metrics for progression-free survival (PFS).

RESULTS

Analysis showed significant pairwise differences for AD (p = 0.035; Cohen's d of 0.659) and AK (p = 0.019; Cohen's d of 0.753) in diffusion measures after 2 weeks of RT. ROC curve analysis showed that percentage change differences in AD and AK between pre-RT and during-RT scans provided an Area Under the Curve (AUC) of 0.524 and 0.762, respectively, in discriminating responders (PFS>180 days) and non-responders (PFS<180 days).

CONCLUSION

This pilot study, although preliminary in nature, showed significant changes in AD and AK maps, with kurtosis derived AK maps showing an increased sensitivity in mapping early changes in the active tumor regions.

Applications of advanced diffusion MRI in early brain development: a comprehensive review

Brain development follows a protracted developmental timeline with foundational processes of neurodevelopment occurring from the third trimester of gestation into the first decade of life. Defining structural maturational patterns of early brain development is a critical step in detecting divergent developmental trajectories associated with neurodevelopmental and psychiatric disorders that arise later in life. While considerable advancements have already been made in diffusion magnetic resonance imaging (dMRI) for pediatric research over the past three decades, the field of neurodevelopment is still in its infancy with remarkable scientific and clinical potential. This comprehensive review evaluates the application, findings, and limitations of advanced dMRI methods beyond diffusion tensor imaging, including diffusion kurtosis imaging (DKI), constrained spherical deconvolution (CSD), neurite orientation dispersion and density imaging (NODDI) and composite hindered and restricted model of diffusion (CHARMED) to quantify the rapid and dynamic changes supporting the underlying microstructural architectural foundations of the brain in early life.

Sensitivity of Diffusion MRI to White Matter Pathology: Influence of Diffusion Protocol, Magnetic Field Strength, and Processing Pipeline in Systemic Lupus Erythematosus

There are many ways to acquire and process diffusion MRI (dMRI) data for group studies, but it is unknown which maximizes the sensitivity to white matter (WM) pathology. Inspired by this question, we analyzed data acquired for diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) at 3T (3T-DTI and 3T-DKI) and DTI at 7T in patients with systemic lupus erythematosus (SLE) and healthy controls (HC). Parameter estimates in 72 WM tracts were obtained using TractSeg. The impact on the sensitivity to WM pathology was evaluated for the diffusion protocol, the magnetic field strength, and the processing pipeline. Sensitivity was quantified in terms of Cohen's d for group comparison. Results showed that the choice of diffusion protocol had the largest impact on the effect size. The effect size in fractional anisotropy (FA) across all WM tracts was 0.26 higher when derived by DTI than by DKI and 0.20 higher in 3T compared with 7T. The difference due to the diffusion protocol was larger than the difference due to magnetic field strength for the majority of diffusion parameters. In contrast, the difference between including or excluding different processing steps was near negligible, except for the correction of distortions from eddy currents and motion which had a clearly positive impact. For example, effect sizes increased on average by 0.07 by including motion and eddy correction for FA derived from 3T-DTI. Effect sizes were slightly reduced by the incorporation of denoising and Gibbs-ringing removal (on average by 0.011 and 0.005, respectively). Smoothing prior to diffusion model fitting generally reduced effect sizes. In summary, 3T-DTI in combination with eddy current and motion correction yielded the highest sensitivity to WM pathology in patients with SLE. However, our results also indicated that the 3T-DKI and 7T-DTI protocols used here may be adjusted to increase effect sizes.

Evaluation of crossed cerebellar diaschisis after cerebral infarction in MCAO rats based on DKI

OBJECTIVE

To observe the expression of N-methyl-D-aspartate (NMDA), apoptosis and the effect on neurological function recovery in rat model with middle cerebral artery occlusion (MCAO). Diffusion kurtosis imaging (DKI) was used to evaluate crossed cerebellar diaschisis (CCD) and to provide experimental and theoretical basis for the clinical treatment.

MATERIALS AND METHODS

The MCAO models were established in rats. Eighty-four rats were randomly and evenly divided into 7 groups, including control group, 6-h group, 12-h group, 24-h group, 48-h group, 7-day group and 14-day group. The rats were scanned by MRI at the above time points. Then, rats were sacrificed for H&E staining, immunohistochemical staining and TUNEL staining to detect the expression of NMDA in the core infarct area and cerebellum. At the end, the discussion of relationships between molecular biology and MRI parameters (ADC derived from DWI, and MD, MK and FA derived from DKI) was performed.

RESULTS

The values of MD, ADC and FA in MCAO rats were all lower than those in the control group. All MRI parameters of the contralateral cerebellum were lower than those of the ipsilateral cerebellum (p < .05). The parameters reached the lowest value at 12 h, except that the MK reached the highest at 12 h. The expression of NMDA showed a fluctuation along time in the MCAO group. Overall, it is higher in the MCAO group than in the control group, reaching the maximum at 24 h (p < .05). At the same time, the expression of NMDA in the contralateral cerebellum was higher than in the ipsilateral cerebellum.

CONCLUSION

It is found that NMDA and DKI of CCD have the same changing trend, which indicates that the intervention of NMDA receptor apoptosis may become a new target for the treatment of cerebral infarction, and MRI parameters can predict the occurrence and development of CCD.

Comparative Evaluation of Diffusion Kurtosis Imaging and Diffusion Tensor Imaging in Detecting Cerebral Microstructural Changes in Alzheimer Disease

OBJECTIVE

Comparative evaluation of diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) using a whole-brain atlas to comprehensively evaluate microstructural changes in the brain of Alzheimer disease (AzD) patients.

METHODS

Twenty-seven AzD patients and 25 age-matched controls were included. MRI data was analyzed using a whole-brain atlas with inclusion of 98 region of interests. White matter (WM) microstructural changes were assessed by Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), Kurtosis fractional anisotropy (KFA), mean kurtosis (MK), axial kurtosis (AK) and radial kurtosis (RK). Gray matter (GM) integrity was evaluated using KFA, MK, RK, AK and MD. Comparison of the DKI and DTI metrics were done using student t-test (p ≤ 0.001).

RESULTS

In AzD patients widespread increase in MD, AD and RD were found in various WM and GM region of interests. The extent of abnormality for DKI parameters was more limited in both GM and WM regions and revealed reduced kurtosis values except in lentiform nuclei. Both DKI and DTI parameters were sensitive to detect abnormality in WM areas with coherent and complex fiber arrangement. Receiver operating characteristic curve analysis for hippocampal values revealed the highest specificity of 88% for AK <0.6965 and highest sensitivity of 95.2% for MD >1.2659.

CONCLUSION

AzD patients have microstructural changes in both WM and GM and are well-depicted by both DKI and DTI. The alterations in kurtosis parameters, however, are more limited and correlate with areas in the brain primarily involved in cognition.

Identifying epilepsy based on machine-learning technique with diffusion kurtosis tensor

Introduction

Epilepsy is a serious hazard to human health. Minimally invasive surgery is an extremely effective treatment to refractory epilepsy currently if the location of epileptic foci is given. However, it is challenging to locate the epileptic foci since a multitude of patients are MRI‐negative. It is well known that DKI (diffusion kurtosis imaging) can analyze the pathological changes of local tissues and other regions of epileptic foci at the molecular level. In this article, we propose a new localization way for epileptic foci based on machine‐learning method with kurtosis tensor in DKI.

Methods

We recruited 59 children with hippocampus epilepsy and 70 age‐ and sex‐matched normal controls; their T1‐weighted images and DKI were collected simultaneously. Then, the hippocampus in DKI is segmented based on a mask as a local brain region, and DKE is utilized to estimate the kurtosis tensor of each subject's hippocampus. Finally, the kurtosis tensor is fed into SVM (support vector machine) to identify epilepsy.

Results

The classifier produced 95.24% accuracy for patient versus normal controls, which is higher than that obtained with FA (fractional anisotropy) and MK (mean kurtosis). Experimental results show that the kurtosis tensor is a kind of remarkable feature to identify epilepsy, which indicates that DKI images can act as an important biomarker for epilepsy from the view of clinical diagnosis.

Conclusion

Although the classification task for epileptic patients and normal controls discussed in this article did not directly achieve the location of epileptic foci and only identified epilepsy on certain brain region, the epileptic foci can be located with the results of identifying results on other brain regions.

Cognitive and behavioral profiles of pediatric surgical candidates with frontal and temporal lobe epilepsy

BACKGROUND

We aimed to prospectively analyze memory and executive and social cognitive functioning in patients with drug-resistant frontal lobe epilepsy (FLE) and temporal lobe epilepsy (TLE) with focal lesions and isolate the impact of intellectual ability on specific deficits.

METHODS

A neuropsychological evaluation was performed in 23 children with FLE, 22 children with TLE, and 36 healthy pediatric controls (HCs). Patients in the epilepsy groups had a range of lesions, including low-grade epilepsy-associated tumors (LEAT), focal cortical dysplasia (FCD) type II, and mesial temporal sclerosis (MS).

RESULTS

There were no significant differences between children with FLE and TLE regarding memory, executive, or social cognitive functioning. General Ability Index (GAI) was a predictor of memory, executive function, and social cognition scores and was influenced by age at onset, duration of epilepsy, and number of antiepileptic drugs (AEDs) prescribed at the time of assessment. Working Memory Index scores of patients with TLE, which measure verbal mnesic processing, were significantly lower than those of HCs and patients with TLE. The greatest differences in both clinical groups compared to HCs were recorded in cognitive executive functions, and patients with FLE had lower scores in this domain. Regarding behavioral executive functions, patients with TLE presented impaired emotional control and impulse inhibition and patients with FLE exhibited decreased flexibility.

CONCLUSION

Consistent with previous research, our findings provide further detailed evidence of small differences in cognitive performance among children with FLE and TLE. These differences emerge on analysis of the factors with which deficits are associated.

A deep learning-based method for improving reliability of multicenter diffusion kurtosis imaging with varied acquisition protocols

Multicenter magnetic resonance imaging is gaining more popularity in large-sample projects. Since both varying hardware and software across different centers cause unavoidable data heterogeneity across centers, its impact on reliability in study outcomes has also drawn much attention recently. One fundamental issue arises in how to derive model parameters reliably from image data of varying quality. This issue is even more challenging for advanced diffusion methods such as diffusion kurtosis imaging (DKI). Recently, deep learning-based methods have been demonstrated with their potential for robust and efficient computation of diffusion-derived measures. Inspired by these approaches, the current study specifically designed a framework based on a three-dimensional hierarchical convolutional neural network, to jointly reconstruct and harmonize DKI measures from multicenter acquisition to reformulate these to a state-of-the-art hardware using data from traveling subjects. The results from the harmonized data acquired with different protocols show that: 1) the inter-scanner variation of DKI measures within white matter was reduced by 51.5% in mean kurtosis, 65.9% in axial kurtosis, 53.7% in radial kurtosis, and 61.5% in kurtosis fractional anisotropy, respectively; 2) data reliability of each single scanner was enhanced and brought to the level of the reference scanner; and 3) the harmonization network was able to reconstruct reliable DKI values from high data variability. Overall the results demonstrate the feasibility of the proposed deep learning-based method for DKI harmonization and help to simplify the protocol setup procedure for multicenter scanners with different hardware and software configurations.

Evaluating the Therapeutic Effect of Low-Intensity Transcranial Ultrasound on Traumatic Brain Injury With Diffusion Kurtosis Imaging

BACKGROUND

Low-intensity transcranial ultrasound (LITUS) has a therapeutic effect on traumatic brain injury (TBI). Diffusion kurtosis imaging (DKI) might be able to evaluate the effect changes of injured brain microstructure.

PURPOSE

To evaluate the therapeutic effect of LITUS in a moderate TBI rat model with DKI parameters.

STUDY TYPE

Prospective case-control animal study.

ANIMAL MODEL

Forty-five rats were randomly divided into sham control, TBI, and LITUS treatment groups (n = 15).

FIELD STRENGTH/SEQUENCE

Single-shot spin echo echo-planar imaging and fast T₂ WI sequences at 3.0T.

ASSESSMENT

DKI parameters were obtained on days 1, 7, 14, 21, 28, 35, and 42 after TBI.

STATISTICAL TESTS

For the mean kurtosis (MK), axial kurtosis (Ka), and radial kurtosis (Kr) values, groups were compared using a two-way analysis of variance (ANOVA).

RESULTS

LITUS inhibited TBI and caused MK values to increase significantly during the early stage (LITUS vs. TBI, day 7, adjusted P < 0.0001) and decrease during the late stage (LITUS vs. TBI, day 42, adjusted P = 0.0156) in the damaged cortex. In the thalamus, the MK value of the TBI group began to rise on day 7, with no change observed in the LITUS group. TBI increases Ka value during the early stage in the cortex and decreases during the late stage in the cortex and thalamus. LITUS inhibited these Ka changes (LITUS vs. TBI, day 7, adjusted P = 0.0014; LITUS vs. TBI, day 42, adjusted P = 0.0026 and 0.0478, respectively, for cortex and thalamus). The Kr value increased slightly during the early stage in the cortex (TBI vs. Sham, day 1, adjusted P = 0.0016).

DATA CONCLUSION

The DKI parameter, particularly the MK value, evaluates primary cortical injury as well as the secondary brain injury that could not be detected by conventional T₂ WI.

LEVEL OF EVIDENCE

1 Technical Efficacy Stage: 4 J. Magn. Reson. Imaging 2020;52:520-531.

Development of a novel method for visualizing restricted diffusion using subtraction of apparent diffusion coefficient values

In order to visualize restricted diffusion, the present study developed a novel method called 'apparent diffusion coefficient (ADC) subtraction method (ASM)' and compared it with diffusion kurtosis imaging (DKI). The diffusion-weighted images of physiological saline, in addtion to bio-phatoms of low cell density and the highest cell density were obtained using two sequences with different effective diffusion times. Then, the calculated ADC values were subtracted. The mean values and standard deviations (SD) of the ADC values of physiological saline, low cell density and the highest cell density phantoms were 2.95±0.08x10‑3, 1.90±0.35x10‑3 and 0.79±0.05x10‑3 mm2/sec, respectively. The mean kurtosis values and SD of DKI were 0.04±0.01, 0.44±0.13 and 1.27±0.03, respectively. The ASM and SD values were 0.25±0.20x104, 0.51±0.41x104 and 4.80±4.51x104 (sec/mm2)2, respectively. Using bio‑phantoms, the present study demonstrated that DKI exhibits restricted diffusion in the extracellular space. Similarly, ASM may reflect the extent of restricted diffusion in the extracellular space.

Behaviour outcomes in children with epilepsy 1 year after surgical resection of the ventromedial prefrontal cortex

Early damage to the ventromedial prefrontal cortex (VM) has been associated with impaired behavioural functioning in children without epilepsy, yet behaviour in children with epilepsy and VM lesions has not been investigated. The primary aim of this study was to examine behavioural outcomes in children with epilepsy emanating from the VM preoperatively and one year after epilepsy surgery compared to the general population and matched epilepsy controls. Behavioural outcomes were defined as comprising both problems and competencies (i.e. social, school and co-curricular performance). A secondary aim was to examine whether seizure outcome, number of antiepileptic drugs (AEDs), or age at surgery related to behavioural outcomes. Ratings on the Child Behavior Checklist were examined preoperatively and 1 year after surgery for 20 children with epilepsy who had undergone surgical resection of the VM (N = 10) or temporal lobe (TL, N = 10). VM and TL groups were comparable on Full Scale IQ (40-101), age of seizure onset (0.5-9.0 years), age at surgery (3.1-16.9 years), seizure laterality (5 left in each group), age at assessments, sex (3 female in VM group, 2 female in TL group) and seizure outcome (7 seizure free in VM group, 6 seizure free in TL group). The VM group had significantly elevated behaviour problems (i.e. withdrawn, thought, social and attention problems) and reduced competencies (i.e. social and school) compared to the general population before and after surgery. VM and TL cases did not differ on any behaviour problem scales pre or postoperatively and neither group showed significant change in functioning over time; however, VM patients had significantly lower total competence than TL patients postoperatively. A significant seizure outcome × time interaction was observed: children who were seizure free following surgery (collapsed across surgical site) showed an improvement in total behaviour problems and aggression at 1 year follow-up, whereas children with ongoing seizures showed a deterioration in these domains. In conclusion, VM lesions in children with epilepsy are associated with behavioural problems but their profile does not differ from that of children with temporal lobe epilepsy. These results are consistent with the concept that seizures arise from epileptogenic networks that may affect multiple cortical areas, even when onset is in a focal site.

Advances in microstructural diffusion neuroimaging for psychiatric disorders

Understanding the neuropathological underpinnings of mental disorders such as schizophrenia, major depression, and bipolar disorder is an essential step towards the development of targeted treatments. Diffusion MRI studies utilizing the diffusion tensor imaging (DTI) model have been extremely successful to date in identifying microstructural brain abnormalities in individuals suffering from mental illness, especially in regions of white matter, although identified abnormalities have been biologically non-specific. Building on DTI's success, in recent years more advanced diffusion MRI methods have been developed and applied to the study of psychiatric populations, with the aim of offering increased sensitivity to subtle neurological abnormalities, as well as improved specificity to candidate pathologies such as demyelination and neuroinflammation. These advanced methods, however, usually come at the cost of prolonged imaging sequences or reduced signal to noise, and they are more difficult to evaluate compared with the more simplified approach taken by the now common DTI model. To date, a limited number of advanced diffusion MRI methods have been employed to study schizophrenia, major depression and bipolar disorder populations. In this review we survey these studies, compare findings across diverse methods, discuss the main benefits and limitations of the different methods, and assess the extent to which the application of more advanced diffusion imaging approaches has led to novel and transformative information with regards to our ability to better understand the etiology and pathology of mental disorders.

Preliminary evaluation of accelerated microscopic diffusional kurtosis imaging (μDKI) in a rodent model of epilepsy

PURPOSE

Our study aimed to develop accelerated microscopic diffusional kurtosis imaging (μDKI) and preliminarily evaluated it in a rodent model of chronic epilepsy.

METHODS

We investigated two μDKI acceleration schemes of reduced sampling density and angular range in a phantom and wild-type rats, and further tested μDKI method in pilocarpine-induced epilepsy rats using a 4.7 Tesla MRI. Single slice average μD_app and μK_app maps were derived, and Nissl staining was obtained.

RESULTS

The kurtosis maps from two accelerated μDKI sampling schemes (sampling density and range) are very similar to that using fully sampled data (SSIM > 0.95). For the epileptic models, μDKI showed noticeably different contrast from those obtained with conventional DKI. Specifically, the average μK_app was significantly less than that of the average of K_app (0.15 ± 0.01 vs. 0.47 ± 0.02) in the ventricle.

CONCLUSIONS

Our study demonstrated the feasibility of accelerated in vivo μDKI. Our work revealed that μDKI provides complementary information to conventional DKI method, suggesting that advanced DKI sequences are promising to elucidate tissue microstructure in neurological diseases.

Effects of SYN1Q555X mutation on cortical gray matter microstructure

A new Q555X mutation on the SYN1 gene was recently found in several members of a family segregating dyslexia, epilepsy, and autism spectrum disorder. To describe the effects of this mutation on cortical gray matter microstructure, we performed a surface-based group study using novel diffusion and quantitative multiparametric imaging on 13 SYN1Q555X mutation carriers and 13 age- and sex-matched controls. Specifically, diffusion kurtosis imaging (DKI) and neurite orientation and dispersion and density imaging (NODDI) were used to analyze multi-shell diffusion data and obtain parametric maps sensitive to tissue structure, while quantitative metrics sensitive to tissue composition (T1, T2* and relative proton density [PD]) were obtained from a multi-echo variable flip angle FLASH acquisition. Results showed significant microstructural alterations in several regions usually involved in oral and written language as well as dyslexia. The most significant changes in these regions were lowered mean diffusivity and increased fractional anisotropy. This study is, to our knowledge, the first to successfully use diffusion imaging and multiparametric mapping to detect cortical anomalies in a group of subjects with a well-defined genotype linked to language impairments, epilepsy and autism spectrum disorder (ASD).

Diffusion kurtosis imaging with free water elimination: A bayesian estimation approach

PURPOSE

Diffusion kurtosis imaging (DKI) is an advanced magnetic resonance imaging modality that is known to be sensitive to changes in the underlying microstructure of the brain. Image voxels in diffusion weighted images, however, are typically relatively large making them susceptible to partial volume effects, especially when part of the voxel contains cerebrospinal fluid. In this work, we introduce the "Diffusion Kurtosis Imaging with Free Water Elimination" (DKI-FWE) model that separates the signal contributions of free water and tissue, where the latter is modeled using DKI.

THEORY AND METHODS

A theoretical study of the DKI-FWE model, including an optimal experiment design and an evaluation of the relative goodness of fit, is carried out. To stabilize the ill-conditioned estimation process, a Bayesian approach with a shrinkage prior (BSP) is proposed. In subsequent steps, the DKI-FWE model and the BSP estimation approach are evaluated in terms of estimation error, both in simulation and real data experiments.

RESULTS

Although it is shown that the DKI-FWE model parameter estimation problem is ill-conditioned, DKI-FWE was found to describe the data significantly better compared to the standard DKI model for a large range of free water fractions. The acquisition protocol was optimized in terms of the maximally attainable precision of the DKI-FWE model parameters. The BSP estimator is shown to provide reliable DKI-FWE model parameter estimates.

CONCLUSION

The combination of the DKI-FWE model with BSP is shown to be a feasible approach to estimate DKI parameters, while simultaneously eliminating free water partial volume effects. Magn Reson Med 80:802-813, 2018. © 2018 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Susceptibility-weighted and diffusion kurtosis imaging to evaluate encephalomalacia with epilepsy after traumatic brain injury

Objective

Encephalomalacia after traumatic brain injury (TBI) is one of the factors leading to epilepsy. In this study, magnetic resonance imaging (MRI) was used to explore the brain image features of epilepsy after traumatic encephalomalacia, and to provide objective evidence for predicting the possible occurrence of epilepsy after traumatic encephalomalacia.

Methods

Two‐hundred‐fifty‐two patients with traumatic encephalomalacia were prospectively enrolled in the study. All patients underwent MRI after discharge from the hospital. At the 1‐year follow‐up, participants were divided into epilepsy and nonepilepsy groups. All participants underwent MRI including conventional imaging, susceptibility‐weighted imaging (SWI), and diffusion kurtosis imaging (DKI). The lesion volume, iron deposition, mean diffusion (MD), and mean kurtosis (MK) around the lesions were calculated for each group and compared using t‐tests. P values < 0.05 were considered statistically significant.

Results

Sixty patients with epilepsy and 91 without epilepsy were reported. There were no significant differences in Glasgow Coma Scale (GCS), lesion volume, encephalomalacia, or MD values between the two groups. Iron deposition was significantly higher in the epilepsy group (P < 0.05). The MK values were significantly different (P < 0.05).

Interpretation

Advanced MRI is an important means of evaluating risk of developing epilepsy at 1 year due to encephalomalacia in patients with TBI. SWI and DKI could be used to assess the microstructural changes around the encephalomalacia, and therefore be used to evaluate risk of developing epilepsy at 1 year.

Efficient experimental designs for isotropic generalized diffusion tensor MRI (IGDTI)

PURPOSE

We propose a new generalized diffusion tensor imaging (GDTI) experimental design and analysis framework for efficiently measuring orientationally averaged diffusion-weighted images (DWIs), which remove bulk signal modulations attributed to diffusion anisotropy and quantify isotropic higher-order diffusion tensors (HOT). We illustrate how this framework accelerates the clinical measurement of rotation-invariant tissue microstructural parameters derived from HOT, such as the HOT-Trace and the mean t-kurtosis.

THEORY AND METHODS

For a large range of b-values, we compare orientationally averaged DWIs measured with high angular resolution diffusion imaging to those obtained with the proposed isotropic GDTI (IGDTI) experimental design. We compare rotation-invariant microstructural parameters measured with IGDTI to those derived from HOTs measured explicitly with GDTI.

RESULTS

In both fixed-brain microimaging and in vivo clinical experiments, IGDTI accurately quantifies mean apparent diffusion coefficient (mADC)-weighted DWIs over a wide range of b-values and allows efficient computation of HOT-derived scalar tissue parameters from a small number of DWIs.

CONCLUSIONS

IGDTI provides direct and accurate estimates of orientationally averaged tissue water mobilities over a wide range of b-values. This efficient method may enable new, sensitive, and quantitative assessments for clinical applications in which changes in mADC can be observe,d such as detecting and characterizing stroke, cancers, and neurodegenerative diseases. Magn Reson Med 79:180-194, 2018. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.

Histological and MRI markers of white matter damage in focal epilepsy

Growing evidence highlights the importance of white matter in the pathogenesis of focal epilepsy. Ex vivo and post-mortem studies show pathological changes in epileptic patients in white matter myelination, axonal integrity, and cellular composition. Diffusion-weighted MRI and its analytical extensions, particularly diffusion tensor imaging (DTI), have been the most widely used technique to image the white matter in vivo for the last two decades, and have shown microstructural alterations in multiple tracts both in the vicinity and at distance from the epileptogenic focus. These techniques have also shown promising ability to predict cognitive status and response to pharmacological or surgical treatments. More recently, the hypothesis that focal epilepsy may be more adequately described as a system-level disorder has motivated a shift towards the study of macroscale brain connectivity. This review will cover emerging findings contributing to our understanding of white matter alterations in focal epilepsy, studied by means of histological and ultrastructural analyses, diffusion MRI, and large-scale network analysis. Focus is put on temporal lobe epilepsy and focal cortical dysplasia. This topic was addressed in a special interest group on neuroimaging at the 70th annual meeting of the American Epilepsy Society, held in Houston December 2-6, 2016.

Using machine learning to classify temporal lobe epilepsy based on diffusion MRI

BACKGROUND

It is common for patients diagnosed with medial temporal lobe epilepsy (TLE) to have extrahippocampal damage. However, it is unclear whether microstructural extrahippocampal abnormalities are consistent enough to enable classification using diffusion MRI imaging. Therefore, we implemented a support vector machine (SVM)-based method to predict TLE from three different imaging modalities: mean kurtosis (MK), mean diffusivity (MD), and fractional anisotropy (FA). While MD and FA can be calculated from traditional diffusion tensor imaging (DTI), MK requires diffusion kurtosis imaging (DKI).

METHODS

Thirty-two TLE patients and 36 healthy controls underwent DKI imaging. To measure predictive capability, a fivefold cross-validation (CV) was repeated for 1000 iterations. An ensemble of SVM models, each with a different regularization value, was trained with the subject images in the training set, and had performance assessed on the test set. The different regularization values were determined using a Bayesian-based method.

RESULTS

Mean kurtosis achieved higher accuracy than both FA and MD on every iteration, and had far superior average accuracy: 0.82 (MK), 0.68 (FA), and 0.51 (MD). Finally, the MK voxels with the highest coefficients in the predictive models were distributed within the inferior medial aspect of the temporal lobes.

CONCLUSION

These results corroborate our earlier publications which indicated that DKI shows more promise in identifying TLE-associated pathological features than DTI. Also, the locations of the contributory MK voxels were in areas with high fiber crossing and complex fiber anatomy. These traits result in non-Gaussian water diffusion, and hence render DTI less likely to detect abnormalities. If the location of consistent microstructural abnormalities can be better understood, then it may be possible in the future to identify the various phenotypes of TLE. This is important since treatment outcome varies dependent on type of TLE.

Diffusion-kurtosis imaging predicts early radiotherapy response in nasopharyngeal carcinoma patients

In this prospective study, we analyzed diffusion kurtosis imaging (DKI) parameters to predict the early response to radiotherapy in 23 nasopharyngeal carcinoma (NPC) patients. All patients underwent conventional magnetic resonance imaging (MRI) and DKI before and after radiotherapy. The patients were divided into response (RG; no residual tumors; 16/23 patients) and no-response (NRG; residual tumors; 7/23 patients) groups, based on MRI and biopsy results 3 months after radiotherapy. The maximum diameter of tumors in RG and NRG patients were similar prior to radiotherapy (p=0.103). The pretreatment diffusion coefficient (D) parameters (D_axis, D_mean and D_rad) were higher in RG than NRG patients (p=0.022, p=0.027 and p=0.027). Conversely, the pre-treatment fractional anisotropy (FA) and kurtosis coefficient (K) parameters (K_axis, K_fa, K_mean, K_rad and Mkt) were lower in RG than NRG patients (p=0.015, p=0.022, p=0.008, p=0.004, p=0.001, p=0.002). The K_rad coefficient (0.76) was the best parameter to predict the radiotherapy response. Based on receiver operating characteristic curve analysis K_rad showed 71.4% sensitivity and 93.7% specificity (AUC: 0.897, 95% CI, 0.756-1). Multivariate analysis indicated DKI parameters were independent prognostic factors for the short-term effect in NPC. Thus, DKI predicts the early response to radiotherapy in NPC patients.

Microstructural Abnormalities of Basal Ganglia and Thalamus in Bipolar and Unipolar Disorders: A Diffusion Kurtosis and Perfusion Imaging Study

OBJECTIVE

Bipolar disorder (BD) is often misdiagnosed as unipolar depression (UD), leading to mistreatment and poor clinical outcomes. However, little is known about the similarities and differences in subcorticalgray matter regions between BD and UD.

METHODS

Thirty-five BD patients, 30 UD patients and 40 healthy controls underwent diffusional kurtosis imaging (DKI) and three dimensional arterial spin labeling (3D ASL). The parameters including mean kurtosis (MK), axial kurtosis (Ka), radial kurtosis (Kr), fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (Da), radial diffusivity (Dr) and cerebral blood flow (CBF) were measured by using regions-of-interest analysis in the caudate, putamen and thalamus of the subcortical gray matter regions.

RESULTS

UD exhibited differences from controls for DKI measures and CBF in the left putamen and caudate. BD showed differences from controls for DKI measures in the left caudate. Additionally, BD showed lower Ka in right putamen, higher MD in right caudate compared with UD. Receiver operating characteristic analysis revealed the Kr of left caudate had the highest predictive power for distinguishing UD from controls.

CONCLUSION

The two disorders may have overlaps in microstructural abnormality in basal ganglia. The change of caudate may serve as a potential biomarker for UD.

Diffusion Kurtosis as an in vivo Imaging Marker of Early Radiation-Induced Changes in Radiation-Induced Temporal Lobe Necrosis in Nasopharyngeal Carcinoma Patients

PURPOSE

Diffusion kurtosis imaging (DKI), an extension of the popular diffusion tensor imaging (DTI) model, has been applied in clinical studies of brain tissue changes. We explored the value of DKI for the early detection of radiation-induced changes in temporal lobe necrosis (TLN) after radiotherapy (RT) for nasopharyngeal carcinoma (NPC).

METHODS

A total of 400 patients with NPC were retrospectively enrolled; all participants underwent MRI scans 0-7 days before RT, at 4 weeks during RT, and 1 month after completing RT. DKI-derived kurtosis parameters (mean kurtosis [MK], axial kurtosis [K_a], radial kurtosis [K_r]), and DKI-derived diffusion parameters (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity [λ_a], radial diffusivity [λ_r]) were assessed in temporal lobe white matter.

RESULTS

Analysis was performed for 20 patients with temporal lobe necrosis following long-term follow-up. No brain abnormalities were visible on conventional MRI in any patient at 4 weeks during RT and 1 month after RT. Of all DKI-derived parameters, MK was significantly lower at 1 month after RT than before RT (P < 0.05).

CONCLUSION

This study indicates DKI can detect the early presence of relatively subtle RT-induced brain abnormalities before TLN in patients with NPC and may provide a sensitive imaging technique for temporal white matter microstructural abnormalities that are silent on conventional modalities but precede TLN after RT.

White matter abnormalities at a regional and voxel level in focal and generalized epilepsy: A systematic review and meta-analysis

Objective

Since the introduction of diffusion tensor imaging, white matter abnormalities in epilepsy have been studied extensively. However, the affected areas reported, the extent of abnormalities and the association with relevant clinical parameters are highly variable. We aimed to obtain a more consistent estimate of white matter abnormalities and their association with clinical parameters in different epilepsy types.

Methods

We systematically searched for differences in white matter fractional anisotropy and mean diffusivity, at regional and voxel level, between people with epilepsy and healthy controls. Meta-analyses were used to quantify the directionality and extent of these differences. Correlations between white matter differences and age of epilepsy onset, duration of epilepsy and sex were assessed with meta-regressions.

Results

Forty-two studies, with 1027 people with epilepsy and 1122 controls, were included with regional data. Sixteen voxel-based studies were also included. People with temporal or frontal lobe epilepsy had significantly decreased fractional anisotropy (Δ –0.021, 95% confidence interval –0.026 to –0.016) and increased mean diffusivity (Δ0.026 × 10^–3 mm²/s, 0.012 to 0.039) in the commissural, association and projection white matter fibers. White matter was much less affected in generalized epilepsy. White matter changes in people with focal epilepsy correlated with age at onset, epilepsy duration and sex.

Significance

This study provides a better estimation of white matter changes in different epilepsies. Effects are particularly found in people with focal epilepsy. Correlations with the duration of focal epilepsy support the hypothesis that these changes are, at least partly, a consequence of seizures and may warrant early surgery. Future studies need to guarantee adequate group sizes, as white matter differences in epilepsy are small.

•

White matter FA and MD are more affected in focal than in generalized epilepsy.

•

Epilepsy subtypes show distinct patterns of affected white matter regions.

•

White matter integrity is altered both ipsi- and contralaterally in focal epilepsy.

•

White matter changes in focal epilepsy seem to be a consequence of seizures.

Rethinking cognition and behavior in the new classification for childhood epilepsy: Examples from frontal lobe and temporal lobe epilepsies

The new approach to classification of the epilepsies emphasizes the role of dysfunction in networks in defining types of epilepsies. This paper reviews the structural and neuropsychological deficits in two types of childhood epilepsy: frontal lobe and temporal lobe epilepsy. The evidence for and against a pattern of specificity of deficits in executive function and memory associated with these two types of epilepsies is presented. The evidence varies with the methodologies used in the studies, but direct comparison of the two types of epilepsies does not suggest a clear-cut mapping of function onto structure. These findings are discussed in light of the concept of network dysfunction. The evidence supports the conceptualization of epilepsy as a network disease. Implications for future work in the neuropsychology of pediatric epilepsy are suggested. This article is part of a Special Issue entitled "The new approach to classification: Rethinking cognition and behavior in epilepsy".

Brain white matter plasticity and functional reorganization underlying the central pathogenesis of trigeminal neuralgia

Peripheral nerve damage does not fully explain the pathogenesis of trigeminal neuralgia (TN). Central nervous system changes can follow trigeminal nerve dysfunction. We hypothesized that brain white matter and functional connectivity changes in TN patients were involved in pain perception, modulation, the cognitive-affective system, and motor function; moreover, changes in functional reorganization were correlated with white matter alterations. Twenty left TN patients and twenty-two healthy controls were studied. Diffusion kurtosis imaging was analyzed to extract diffusion and kurtosis parameters, and functional connectivity density (FCD) mapping was used to explore the functional reorganization in the brain. In the patient group, we found lower axial kurtosis and higher axial diffusivity in tracts participated in sensory, cognitive-affective, and modulatory aspects of pain, such as the corticospinal tract, superior longitudinal fasciculus, anterior thalamic radiation, inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, cingulated gyrus, forceps major and uncinate fasciculus. Patients exhibited complex FCD reorganization of hippocampus, striatum, thalamus, precentral gyrus, precuneus, prefrontal cortex and inferior parietal lobule in multiple modulatory networks that played crucial roles in pain perception, modulation, cognitive-affective system, and motor function. Further, the correlated structural-functional changes may be responsible for the persistence of long-term recurrent pain and sensory-related dysfunction in TN.

Restriction spectrum imaging reveals decreased neurite density in patients with temporal lobe epilepsy

OBJECTIVE

Diffusion tensor imaging (DTI) has become a popular tool for delineating the location and extent of white matter injury in temporal lobe epilepsy (TLE). However, DTI yields nonspecific measures that are confounded by changes occurring within both the intracellular and extracellular environments. This study investigated whether an advanced diffusion method, restriction spectrum imaging (RSI) could provide a more robust measure of white matter injury in TLE relative to DTI due to RSI's ability to separate intraaxonal diffusion (i.e., neurite density; ND) from diffusion associated with extraaxonal factors (e.g., inflammation; crossing fibers).

METHODS

RSI and DTI scans were obtained on 21 patients with TLE and 11 age-matched controls. RSI-derived maps of ND, isotropic-hindered (IH) and isotropic-free (IF) water, and crossing fibers (CFs) were compared to DTI-derived fractional anisotropy (FA) maps. Voxelwise and tract-based analyses were performed comparing patients with TLE to controls on each diffusion metric.

RESULTS

Reductions in FA were seen primarily in frontotemporal white matter in TLE, and they were most pronounced proximal to the seizure focus. Reductions in ND corresponded to those seen in the FA maps; however, ND reductions were greater in magnitude, more lateralized to the epileptogenic hemisphere, and showed a broader pattern. Increases in IF/IH and effects from CFs also contributed to reduced FA in the ipsilateral parahippocampal cingulum and fornix, with decreases in IH extending into extratemporal regions. Reduced ND of the uncinate fasciculus was associated with longer disease duration, whereas FA was not associated with any clinical variables.

SIGNIFICANCE

RSI may provide a more specific measure of white matter pathology in TLE, distinguishing regions primarily affected by axonal/myelin loss from those where CFs and increases in extracellular water also play a role. By providing a more specific measure of axonal/myelin loss, RSI-derived ND may better reflect overall white matter burden in epilepsy.

Epilepsy-related cytoarchitectonic abnormalities along white matter pathways

OBJECTIVE

Temporal lobe epilepsy (TLE) is one of the most common forms of epilepsy. Unfortunately, the clinical outcomes of TLE cannot be determined based only on current diagnostic modalities. A better understanding of white matter (WM) connectivity changes in TLE may aid the identification of network abnormalities associated with TLE and the phenotypic characterisation of the disease.

METHODS

We implemented a novel approach for characterising microstructural changes along WM pathways using diffusional kurtosis imaging (DKI). Along-the-tract measures were compared for 32 subjects with left TLE and 36 age-matched and gender-matched controls along the left and right fimbria-fornix (FF), parahippocampal WM bundle (PWMB), arcuate fasciculus (AF), inferior longitudinal fasciculus (ILF), uncinate fasciculus (UF) and cingulum bundle (CB). Limbic pathways were investigated in relation to seizure burden and control with antiepileptic drugs.

RESULTS

By evaluating measures along each tract, it was possible to identify abnormalities localised to specific tract subregions. Compared with healthy controls, subjects with TLE demonstrated pathological changes in circumscribed regions of the FF, PWMB, UF, AF and ILF. Several of these abnormalities were detected only by kurtosis-based and not by diffusivity-based measures. Structural WM changes correlated with seizure burden in the bilateral PWMB and cingulum.

CONCLUSIONS

DKI improves the characterisation of network abnormalities associated with TLE by revealing connectivity abnormalities that are not disclosed by other modalities. Since TLE is a neuronal network disorder, DKI may be well suited to fully assess structural network abnormalities related to epilepsy and thus serve as a tool for phenotypic characterisation of epilepsy.

Data for evaluation of fast kurtosis strategies, b-value optimization and exploration of diffusion MRI contrast

Here we describe and provide diffusion magnetic resonance imaging (dMRI) data that was acquired in neural tissue and a physical phantom. Data acquired in biological tissue includes: fixed rat brain (acquired at 9.4 T) and spinal cord (acquired at 16.4 T) and in normal human brain (acquired at 3 T). This data was recently used for evaluation of diffusion kurtosis imaging (DKI) contrasts and for comparison to diffusion tensor imaging (DTI) parameter contrast. The data has also been used to optimize b-values for ex vivo and in vivo fast kurtosis imaging. The remaining data was obtained in a physical phantom with three orthogonal fiber orientations (fresh asparagus stems) for exploration of the kurtosis fractional anisotropy. However, the data may have broader interest and, collectively, may form the basis for image contrast exploration and simulations based on a wide range of dMRI analysis strategies.

Parameters of diffusional kurtosis imaging for the diagnosis of acute cerebral infarction in different brain regions

Diffusional kurtosis imaging (DKI) is a new type diffusion-weighted sequence which measures the non-Gaussianity of water diffusion. The present study aimed to investigate whether the parameters of DKI could distinguish between differences in water molecule diffusion in various brain regions under the conditions of acute infarction and to identify the optimal DKI parameter for locating ischemic lesions in each brain region. A total of 28 patients with acute ischemic stroke in different brain regions were recruited for the present study. The relative values of DKI parameters were selected as major assessment indices, and the homogeneity of background image and contrast of adjacent structures were used as minor assessment indices. According to the brain region involved in three DKI parametric maps, including mean kurtosis (MK), axial kurtosis (Ka) and radial kurtosis (Kr), 112 groups of regions of interest were outlined in the following regions: Corpus callosum (n=17); corona radiata (n=26); thalamus (n=21); subcortical white matter (n=24); and cerebral cortex (n=24). For ischemic lesions in the corpus callosum and corona radiata, significant increases in relative Ka were detected, as compared with the other parameters (P<0.05). For ischemic lesions in the thalamus, subcortical white matter and cerebral cortices, an increase in the three parameters was detected, however this difference was not significant. Minor assessment indices demonstrated that Ka lacked tissue contrast and the background of Kr was heterogeneous; thus, MK was the superior assessment parameter for ischemic lesions in these regions. In conclusion, Ka is better suited for the diagnosis of acute ischemic lesions in highly anisotropic brain regions, such as the corpus callosum and corona radiate. MK may be appropriate for the lesions in low anisotropic or isotropic brain regions, such as the thalamus, subcortical white matter and cerebral cortices.

Comparison of image sensitivity between conventional tensor-based and fast diffusion kurtosis imaging protocols in a rodent model of acute ischemic stroke

Diffusion kurtosis imaging (DKI) can offer a useful complementary tool to routine diffusion MRI for improved stratification of heterogeneous tissue damage in acute ischemic stroke. However, its relatively long imaging time has hampered its clinical application in the emergency setting. A recently proposed fast DKI approach substantially shortens the imaging time, which may help to overcome the scan time limitation. However, to date, the sensitivity of the fast DKI protocol for the imaging of acute stroke has not been fully described. In this study, we performed routine and fast DKI scans in a rodent model of acute stroke, and compared the sensitivity of diffusivity and kurtosis indices (i.e. axial, radial and mean) in depicting acute ischemic lesions. In addition, we analyzed the contrast-to-noise ratio (CNR) between the ipsilateral ischemic and contralateral normal regions using both conventional and fast DKI methods. We found that the mean kurtosis shows a relative change of 47.1 ± 7.3% between the ischemic and contralateral normal regions, being the most sensitive parameter in revealing acute ischemic injury. The two DKI methods yielded highly correlated diffusivity and kurtosis measures and lesion volumes (R(2)  ⩾ 0.90, p < 0.01). Importantly, the fast DKI method exhibited significantly higher CNR of mean kurtosis (1.6 ± 0.2) compared with the routine tensor protocol (1.3 ± 0.2, p < 0.05), with its CNR per unit time (CNR efficiency) approximately doubled when the scan time was taken into account. In conclusion, the fast DKI method provides excellent sensitivity and efficiency to image acute ischemic tissue damage, which is essential for image-guided and individualized stroke treatment.

Initial Application of Diffusional Kurtosis Imaging in Evaluating Brain Development of Healthy Preterm Infants

Objective

To explore the parametric characteristics of diffusional kurtosis imaging (DKI) in the brain development of healthy preterm infants.

Materials and Methods

Conventional magnetic resonance imaging (MRI) and DKI were performed in 35 preterm (29 to 36 weeks gestational age [GA]; scanned at 33 to 44 weeks postmenstrual age [PMA]) and 10 term infants (37.4 to 40.7 weeks GA; scanned at 38.3 to 42.9 weeks PMA). Fractional anisotropy (FA), mean diffusivity (MD) and mean kurtosis (MK) values from 8 regions of interest, including both white matter (WM) and gray matter (GM), were obtained.

Results

MK and FA values were positively correlated with PMA in most selected WM regions, such as the posterior limbs of the internal capsule (PLIC) and the splenium of the corpus callosum (SCC). The positive correlation between MK value and PMA in the deep GM region was higher than that between FA and PMA. The MK value gradually decreased from the PLIC to the cerebral lobe. In addition, DKI parameters exhibited subtle differences in the parietal WM between the preterm and term control groups.

Conclusions

MK may serve as a more reliable imaging marker of the normal myelination process and provide a more robust characterization of deep GM maturation.

Experimental considerations for fast kurtosis imaging

PURPOSE

The clinical use of kurtosis imaging is impeded by long acquisitions and postprocessing. Recently, estimation of mean kurtosis tensor W¯ and mean diffusivity ( D¯) was made possible from 13 distinct diffusion weighted MRI acquisitions (the 1-3-9 protocol) with simple postprocessing. Here, we analyze the effects of noise and nonideal diffusion encoding, and propose a new correction strategy. We also present a 1-9-9 protocol with increased robustness to experimental imperfections and minimal additional scan time. This refinement does not affect computation time and also provides a fast estimate of fractional anisotropy (FA).

THEORY AND METHODS

1-3-9/1-9-9 data are acquired in rat and human brains, and estimates of D¯, FA, W¯ from human brains are compared with traditional estimates from an extensive diffusion kurtosis imaging data set. Simulations are used to evaluate the influence of noise and diffusion encodings deviating from the scheme, and the performance of the correction strategy. Optimal b-values are determined from simulations and data.

RESULTS

Accuracy and precision in D¯ and W¯ are comparable to nonlinear least squares estimation, and is improved with the 1-9-9 protocol. The compensation strategy vastly improves parameter estimation in nonideal data.

CONCLUSION

The framework offers a robust and compact method for estimating several diffusion metrics. The protocol is easily implemented. Magn Reson Med 76:1455-1468, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Grading of Gliomas by Using Monoexponential, Biexponential, and Stretched Exponential Diffusion-weighted MR Imaging and Diffusion Kurtosis MR Imaging

PURPOSE

To quantitatively compare the potential of various diffusion parameters obtained from monoexponential, biexponential, and stretched exponential diffusion-weighted imaging models and diffusion kurtosis imaging in the grading of gliomas.

MATERIALS AND METHODS

This study was approved by the local ethics committee, and written informed consent was obtained from all subjects. Both diffusion-weighted imaging and diffusion kurtosis imaging were performed in 69 patients with pathologically proven gliomas by using a 3-T magnetic resonance (MR) imaging unit. An isotropic apparent diffusion coefficient (ADC), true ADC, pseudo-ADC, and perfusion fraction were calculated from diffusion-weighted images by using a biexponential model. A water molecular diffusion heterogeneity index and distributed diffusion coefficient were calculated from diffusion-weighted images by using a stretched exponential model. Mean diffusivity, fractional anisotropy, and mean kurtosis were calculated from diffusion kurtosis images. All values were compared between high-grade and low-grade gliomas by using a Mann-Whitney U test. Receiver operating characteristic and Spearman rank correlation analysis were used for statistical evaluations.

RESULTS

ADC, true ADC, perfusion fraction, water molecular diffusion heterogeneity index, distributed diffusion coefficient, and mean diffusivity values were significantly lower in high-grade gliomas than in low-grade gliomas (U = 109, 56, 129, 6, 206, and 229, respectively; P < .05). Pseudo-ADC and mean kurtosis values were significantly higher in high-grade gliomas than in low-grade gliomas (U = 98 and 8, respectively; P < .05). Both water molecular diffusion heterogeneity index (area under the receiver operating characteristic curve [AUC] = 0.993) and mean kurtosis (AUC = 0.991) had significantly greater AUC values than ADC (AUC = 0.866), mean diffusivity (AUC = 0.722), and fractional anisotropy (AUC = 0.500) in the differentiation of low-grade and high-grade gliomas (P < .05).

CONCLUSION

Water molecular diffusion heterogeneity index and mean kurtosis values may provide additional information and improve the grading of gliomas compared with conventional diffusion parameters.

Diffusion-MRI in neurodegenerative disorders

The ability to image the whole brain through ever more subtle and specific methods/contrasts has come to play a key role in understanding the basis of brain abnormalities in several diseases. In magnetic resonance imaging (MRI), "diffusion" (i.e. the random, thermally-induced displacements of water molecules over time) represents an extraordinarily sensitive contrast mechanism, and the exquisite structural detail it affords has proven useful in a vast number of clinical as well as research applications. Since diffusion-MRI is a truly quantitative imaging technique, the indices it provides can serve as potential imaging biomarkers which could allow early detection of pathological alterations as well as tracking and possibly predicting subtle changes in follow-up examinations and clinical trials. Accordingly, diffusion-MRI has proven useful in obtaining information to better understand the microstructural changes and neurophysiological mechanisms underlying various neurodegenerative disorders. In this review article, we summarize and explore the main applications, findings, perspectives as well as challenges and future research of diffusion-MRI in various neurodegenerative disorders including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, Huntington's disease and degenerative ataxias.

White matter in temporal lobe epilepsy: clinico-pathological correlates of water diffusion abnormalities

Using magnetic resonance imaging, it is possible to measure the behavior of diffusing water molecules, and the metrics derived can be used as indirect markers of tissue micro-architectural properties. Numerous reports have demonstrated that patients with temporal lobe epilepsy (TLE) have water diffusion abnormalities in several white matter structures located within and beyond the epileptogenic temporal lobe, showing that TLE is not a focal disorder, but rather a brain network disease. Differences in severity and spatial extent between patients with or without mesial temporal sclerosis (MTS), as well as differences related to hemispheric seizure onset, are suggestive of different pathophysiological mechanisms behind different forms of TLE, which in turn result in specific cognitive disabilities. The biological interpretation of diffusion abnormalities is based on a wealth of information from animal models of white matter damage, and is supported by recent reports that directly correlate diffusion metrics with histological characteristics of surgical specimens of TLE patients. Thus, there is now more evidence showing that the increased mean diffusivity (MD) and concomitant reductions of diffusion anisotropy that are frequently observed in several white matter bundles in TLE patients reflect reduced axonal density (increased extra-axonal space) due to smaller-caliber axons, and abnormalities in the myelin sheaths of the remaining axons. Whether these histological and diffusion features are a predisposing factor for epilepsy or secondary to seizures is still uncertain; some reports suggest the latter. This article summarizes recent findings in this field and provides a synopsis of the histological features seen most frequently in post-surgical specimens of TLE patients in an effort to aid the interpretation of white matter diffusion abnormalities.

The potential role of novel diffusion imaging techniques in the understanding and treatment of epilepsy

Epilepsy is a common neurological disorder in which magnetic resonance imaging plays a key role. Diffusion imaging based on the molecular diffusion of water has been widely used clinically and in research for patients with epilepsy. Diffusion tensor imaging (DTI), the most common model, has been used for around two decades. Several parameters can be derived from DTI that are sensitive, but non-specific, to underlying structural changes. DTI assumes a single diffusion process following a Gaussian distribution within each voxel and is thus an overly simplistic representation of tissue microstructure. Several more advanced models of diffusion are now available that may have greater utility in the understanding of the effects of epilepsy on tissue microstructure. In this review, I summarise the principles, applications in epilepsy and future potential of three such techniques. Diffusion kurtosis imaging (DKI) characterises the degree to which diffusion deviates from Gaussian behaviour and gives an idea of the underlying tissue complexity. It has been used in both focal and generalised epilepsy and seems more sensitive than DTI. Multi-compartment models separate the signal from extra- and intra-axonal compartments in each voxel. The Composite Hindered and Restricted Model of Diffusion (CHARMED) can characterise axonal density but has not yet been applied in patients with epilepsy. The Neurite Orientation Dispersion and Density Imaging (NODDI) model can determine the intracellular volume fraction (ICVF) and degree of dispersion of neurite orientation. Preliminary data suggest it may more sensitive than conventional and diffusion imaging in localising focal epilepsy.

Microstructural brain abnormalities of children of idiopathic generalized epilepsy with generalized tonic-clonic seizure: a voxel-based diffusional kurtosis imaging study

PURPOSE

To investigate the diffusion abnormalities in the brain of children with idiopathic generalized epilepsy (IGE) with generalized tonic-clonic seizure (GTCS) by using diffusion kurtosis imaging (DKI).

MATERIALS AND METHODS

Twenty-one IGE children with GTCS and 16 controls were recruited. DKI was performed and maps of radial diffusivity (λ⊥ ), axial diffusivity (λ// ), mean diffusivity (MD), fractional anisotropy (FA), radial kurtosis (K⊥ ), axial kurtosis (K// ) and mean kurtosis (MK) were calculated. Voxel-based analyses were employed to compare diffusion metrics in epilepsy versus the controls.

RESULTS

In the case group, MD was found significantly higher in the right temporal lobe, the right occipital lobe, hippocampus, and some subcortical regions, while FA increased in bilateral supplementary motor area and the left superior frontal lobe (false discovery rate corrected P < 0.05). Analysis of λ⊥ and λ// showed that the increased MD was mainly due to the elevated λ// . Significantly decreased MK was also detected in bilateral temporo-occipital regions, the right hippocampus, the left insula, the left post-central area, and some subcortical regions (false discovery rate corrected P < 0.05). In most regions the changed MK were due to the decreased K// .

CONCLUSION

The kurtosis parameters (K⊥ , K// , and MK) reflect different microstructural information in the IGE children with GTCS, and this support the value of DKI in studying children GTCS.

Altered microstructure in temporal lobe epilepsy: a diffusional kurtosis imaging study

BACKGROUND AND PURPOSE

Temporal lobe epilepsy is associated with regional abnormalities in tissue microstructure, as demonstrated by DTI. However, the full extent of these abnormalities has not yet been defined because DTI conveys only a fraction of the information potentially accessible with diffusion MR imaging. In this study, we assessed the added value of diffusional kurtosis imaging, an extension of DTI, to evaluate microstructural abnormalities in patients with temporal lobe epilepsy.

MATERIALS AND METHODS

Thirty-two patients with left temporal lobe epilepsy and 36 matched healthy subjects underwent diffusion MR imaging. To evaluate abnormalities in patients, we performed voxelwise analyses, assessing DTI-derived mean diffusivity, fractional anisotropy, and diffusional kurtosis imaging-derived mean diffusional kurtosis, as well as diffusional kurtosis imaging and DTI-derived axial and radial components, comparing patients with controls.

RESULTS

We replicated findings from previous studies demonstrating a reduction in fractional anisotropy and an increase in mean diffusivity preferentially affecting, but not restricted to, the temporal lobe ipsilateral to seizure onset. We also noted a pronounced pattern of diffusional kurtosis imaging abnormalities in gray and white matter tissues, often extending into regions that were not detected as abnormal by DTI measures.

CONCLUSIONS

Diffusional kurtosis is a sensitive and complementary measure of microstructural compromise in patients with temporal lobe epilepsy. It provides additional information regarding the anatomic distribution and degree of damage in this condition. Diffusional kurtosis imaging may be used as a biomarker for disease severity, clinical phenotypes, and treatment monitoring in epilepsy.

Performances of diffusion kurtosis imaging and diffusion tensor imaging in detecting white matter abnormality in schizophrenia

Diffusion kurtosis imaging (DKI) is an extension of diffusion tensor imaging (DTI), exhibiting improved sensitivity and specificity in detecting developmental and pathological changes in neural tissues. However, little attention was paid to the performances of DKI and DTI in detecting white matter abnormality in schizophrenia. In this study, DKI and DTI were performed in 94 schizophrenia patients and 91 sex- and age-matched healthy controls. White matter integrity was assessed by fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), mean kurtosis (MK), axial kurtosis (AK) and radial kurtosis (RK) of DKI and FA, MD, AD and RD of DTI. Group differences in these parameters were compared using tract-based spatial statistics (TBSS) (P < 0.01, corrected). The sensitivities in detecting white matter abnormality in schizophrenia were MK (34%) > AK (20%) > RK (3%) and RD (37%) > FA (24%) > MD (21%) for DKI, and RD (43%) > FA (30%) > MD (21%) for DTI. DKI-derived diffusion parameters (RD, FA and MD) were sensitive to detect abnormality in white matter regions (the corpus callosum and anterior limb of internal capsule) with coherent fiber arrangement; however, the kurtosis parameters (MK and AK) were sensitive to reveal abnormality in white matter regions (the juxtacortical white matter and corona radiata) with complex fiber arrangement. In schizophrenia, the decreased AK suggests axonal damage; however, the increased RD indicates myelin impairment. These findings suggest that diffusion and kurtosis parameters could provide complementary information and they should be jointly used to reveal pathological changes in schizophrenia.

•

Kurtosis parameters are suitable to assess WM regions with complex fiber arrangement.

•

Diffusion parameters are suitable to assess WM regions with coherent fiber arrangement.

•

Increased RD suggests myelin abnormalities in schizophrenia.

•

Decreased AK indicates axonal abnormalities in schizophrenia.

Kurtosis analysis of neural diffusion organization

A computational framework is presented for relating the kurtosis tensor for water diffusion in brain to tissue models of brain microstructure. The tissue models are assumed to be comprised of non-exchanging compartments that may be associated with various microstructural spaces separated by cell membranes. Within each compartment the water diffusion is regarded as Gaussian, although the diffusion for the full system would typically be non-Gaussian. The model parameters are determined so as to minimize the Frobenius norm of the difference between the measured kurtosis tensor and the model kurtosis tensor. This framework, referred to as kurtosis analysis of neural diffusion organization (KANDO), may be used to help provide a biophysical interpretation to the information provided by the kurtosis tensor. In addition, KANDO combined with diffusional kurtosis imaging can furnish a practical approach for developing candidate biomarkers for neuropathologies that involve alterations in tissue microstructure. KANDO is illustrated for simple tissue models of white and gray matter using data obtained from healthy human subjects.

Diffusional kurtosis imaging reveals a distinctive pattern of microstructural alternations in idiopathic generalized epilepsy

OBJECTIVES

Idiopathic generalized epilepsy (IGE) arises from paroxysmal dysfunctions of the thalamo-cortical network. One of the hallmarks of IGE is the absence of visible abnormalities on routine magnetic resonance imaging (MRI). However, recent quantitative MRI studies showed cortical-subcortical structural abnormalities in IGE, but the extent of abnormalities has been inconsistent in the literature. The inconsistencies may be associated with complex microstructural abnormalities in IGE that are not completely detectable using conventional diffusion tensor imaging methods. The goal of this study was to investigate white-matter (WM) microstructural abnormalities in patients with IGE using diffusional kurtosis imaging (DKI).

MATERIALS AND METHODS

We obtained DKI and volumetric T1-weighted images from 14 patients with IGE and 25 matched healthy controls. Using tract-based spatial statistics, we performed voxel-wise group comparisons in the parametric maps generated from DKI: mean diffusivity (MD), fractional anisotropy (FA), and mean kurtosis (MK), and in probabilistic maps of WM volume generated by voxel-based morphometry.

RESULTS

We observed that conventional microstructural measures (MD and FA) revealed WM abnormalities in thalamo-cortical projections, whereas MK disclosed a broader pattern of WM abnormalities involving thalamo-cortical and cortical-cortical projections.

CONCLUSIONS

Even though IGE is traditionally considered a 'non-lesional' form of epilepsy, our results demonstrated pervasive thalamo-cortical WM microstructural abnormalities. Particularly, WM abnormalities shown by MK further extended into cortical-cortical projections. This suggests that the extent of microstructural abnormalities in thalamo-cortical projections in IGE may be better assessed through the diffusion metrics provided by DKI.