White matter microstructural changes in tuberous sclerosis: Evaluation by neurite orientation dispersion and density imaging (NODDI) and diffusion tensor images

The role of unidentified bright objects in the neurocognitive profile of neurofibromatosis type 1 children: a volumetric MRI analysis

PURPOSE

Cognitive impairment is described in 80% of Neurofibromatosis type 1 (NF1) patients. Brain focal areas of T2w increased signal intensity on MRI, the so-called Unidentified Bright Objects (UBOs) have been hypothesized to be related to cognitive dysfunction, although conflicting results are available in literature. Here, we investigated the possible relation between UBOs' volume, cognitive impairment, and language disability in NF1 patients.

MATERIAL AND METHODS

In this retrospective study, clinical and MRI data of 21 NF1 patients (M/F = 12/9; mean age 10.1 ± 4.5) were evaluated. Brain intellectual functioning and language abilities were assessed with specific scales, while the analyzed MRI sequences included axial 2D-T2-weighted and FLAIR sequences. These images were used independently for UBOs segmentation with a semiautomatic approach and obtained volumes were normalized for biparietal diameters to take into account for brain volume. Possible differences in terms of normalized UBOs volumes were probed between cognitively affected and preserved patients, as well as between subjects with or without language impairment.

RESULTS

Patients cognitively affected were not different in terms of UBOs volume compared to those preserved (p = 0.35 and p = 0.30, for T2-weighted and FLAIR images, respectively). Similarly, no differences were found between patients with and without language impairment (p = 0.47 and p = 0.40, for the two sequences).

CONCLUSIONS

The relation between UBOs and cognition in children with NF1 has been already investigated in literature, although leading to conflicting results. Our study expands the current knowledge, showing a lack of correlation between UBOs volume and both cognitive impairment and language disability in NF1 patients.

Multi-organ imaging demonstrates the heart-brain-liver axis in UK Biobank participants

Medical imaging provides numerous insights into the subclinical changes that precede serious diseases such as heart disease and dementia. However, most imaging research either describes a single organ system or draws on clinical cohorts with small sample sizes. In this study, we use state-of-the-art multi-organ magnetic resonance imaging phenotypes to investigate cross-sectional relationships across the heart-brain-liver axis in 30,444 UK Biobank participants. Despite controlling for an extensive range of demographic and clinical covariates, we find significant associations between imaging-derived phenotypes of the heart (left ventricular structure, function and aortic distensibility), brain (brain volumes, white matter hyperintensities and white matter microstructure), and liver (liver fat, liver iron and fibroinflammation). Simultaneous three-organ modelling identifies differentially important pathways across the heart-brain-liver axis with evidence of both direct and indirect associations. This study describes a potentially cumulative burden of multiple-organ dysfunction and provides essential insight into multi-organ disease prevention.

Estimating axial diffusivity in the NODDI model

To estimate microstructure-related parameters from diffusion MRI data, biophysical models make strong, simplifying assumptions about the underlying tissue. The extent to which many of these assumptions are valid remains an open research question. This study was inspired by the disparity between the estimated intra-axonal axial diffusivity from literature and that typically assumed by the Neurite Orientation Dispersion and Density Imaging (NODDI) model (d_∥=1.7μm²/ms). We first demonstrate how changing the assumed axial diffusivity results in considerably different NODDI parameter estimates. Second, we illustrate the ability to estimate axial diffusivity as a free parameter of the model using high b-value data and an adapted NODDI framework. Using both simulated and in vivo data we investigate the impact of fitting to either real-valued or magnitude data, with Gaussian and Rician noise characteristics respectively, and what happens if we get the noise assumptions wrong in this high b-value and thus low SNR regime. Our results from real-valued human data estimate intra-axonal axial diffusivities of ∼2-2.5μm²/ms, in line with current literature. Crucially, our results demonstrate the importance of accounting for both a rectified noise floor and/or a signal offset to avoid biased parameter estimates when dealing with low SNR data.

White matter disruptions related to inattention and autism spectrum symptoms in tuberous sclerosis complex

Tuberous sclerosis complex is a rare genetic multisystem condition that is associated with a high prevalence of neurodevelopmental disorders such as autism and attention-deficit/hyperactivity disorder. The underlying neural mechanisms of the emergence of these symptom domains in tuberous sclerosis complex remain unclear. Here, we use fixel-based analysis of diffusion-weighted imaging, which allows for the differentiation between multiple fibre populations within a voxel, to compare white matter properties in 16 participants with tuberous sclerosis complex (aged 11-19) and 12 age and sex matched control participants. We further tested associations between white matter alterations and autism and inattention symptoms as well as cognitive ability in participants with tuberous sclerosis complex. Compared to controls, participants with tuberous sclerosis complex showed reduced fibre density cross-section (FDC) in the dorsal branch of right superior longitudinal fasciculus and bilateral inferior longitudinal fasciculus, reduced fibre density (FD) in bilateral tapetum, and reduced fibre cross-section (FC) in the ventral branch of right superior longitudinal fasciculus. In participants with tuberous sclerosis complex, the extent of FDC reductions in right superior longitudinal fasciculus was significantly associated with autism traits (social communication difficulties and restricted, repetitive behaviours), whereas FDC reductions in right inferior longitudinal fasciculus were associated with inattention. The observed white matter alterations were unrelated to cognitive ability. Our findings shed light on the fibre-specific biophysical properties of white matter alterations in tuberous sclerosis complex and suggest that these regional changes are selectively associated with the severity of neurodevelopmental symptoms.

MR Imaging Detection of CNS Lesions in Tuberous Sclerosis Complex: The Usefulness of T1WI with Chemical Shift Selective Images

BACKGROUND AND PURPOSE

CNS lesions of tuberous sclerosis complex are diagnosed mainly by T2WI, FLAIR, and sometimes T1WI with magnetization transfer contrast. The usefulness of T1WI with chemical shift selective images was recently reported in focal cortical dysplasia type IIb, which has histopathologic and imaging features similar to those of tuberous sclerosis complex. We investigated the usefulness of the T1WI with chemical shift selective images in detecting CNS lesions of tuberous sclerosis complex.

MATERIALS AND METHODS

We retrospectively reviewed 25 consecutive patients with tuberous sclerosis complex (mean age, 11.9 [SD, 8.9] years; 14 males) who underwent MR imaging including T1WI, T1WI with magnetization transfer contrast, T1WI with chemical shift selective, T2WI, and FLAIR images. Two neuroradiologists assessed the number of CNS lesions in each sequence and compared them in 2 steps: among T1WI, T1WI with magnetization transfer contrast and T1WI with chemical shift selective images, and among T2WI, FLAIR, and T1WI with chemical shift selective images. We calculated the contrast ratio of the cortical tubers and of adjacent normal-appearing gray matter and the contrast ratio of radial migration lines and adjacent normal-appearing white matter in each sequence and compared them.

RESULTS

T1WI with chemical shift selective images was significantly superior to T1WI with magnetization transfer contrast for the detection of radial migration lines and contrast ratio of radial migration lines. There was no significant difference between T1WI with chemical shift selective images and T1WI with magnetization transfer contrast for the detection of cortical tubers and the contrast ratio of the cortical tubers. Both T2WI and FLAIR were statistically superior to T1WI with chemical shift selective images for the detection of cortical tubers. T1WI with chemical shift selective images was significantly superior to T2WI and FLAIR for the detection of radial migration lines.

CONCLUSIONS

The usefulness of T1WI with chemical shift selective images in detecting radial migration lines was demonstrated. Our findings suggest that the combination of T1WI with chemical shift selective images, T2WI, and FLAIR would be useful to evaluate the CNS lesions of patients with tuberous sclerosis complex in daily clinical practice.

Convolutional Neural Network-aided Tuber Segmentation in Tuberous Sclerosis Complex Patients Correlates with EEG

OBJECTIVE

One of the clinical hallmarks of tuberous sclerosis complex is radiologically-identified cortical tubers present in most patients. Intractable epilepsy may require surgery, often involving invasive diagnostic procedures such as intracranial EEG. Identifying the location of the dominant tuber responsible for generating epileptic activities, is a critical issue. However, the link between cortical tubers and epileptogenesis is poorly understood. Given this, we hypothesized that tuber voxel intensity may be an indicator of the dominant epileptogenic tuber. Also, via tuber segmentation based on deep learning, we explore whether an automatic quantification of the tuber burden is feasible.

METHODS

We annotated tubers from structural MRIs across 29 TSC subjects, summarized tuber statistics in eight brain lobes, and determined suspected epileptogenic lobes from the same group using EEG monitoring data. Then logistic regression analyses are performed to demonstrate the linkage between the statistics of cortical tuber and the epileptogenic zones. Furthermore, we test the ability of a neural network to identify and quantify tuber burden.

RESULTS

Logistic regression analyses show that the volume and count of tubers per lobe, not the mean or variance of tuber voxel intensity, are positively correlated with electrophysiological data. In 47.6% of subjects, the lobe with the largest tuber volume concurred with the epileptic brain activity. A neural network model on the test dataset shows a sensitivity of 0.83 for localizing individual tubers. The predicted masks from the model highly correlated with the neurologist labels, thus may be a useful tool for determining tuber burden and searching for epileptogenic zone.

SIGNIFICANCE

we prove the feasibility of an automatic segmentation of tubers and a derivation of tuber burden across brain lobes. Our method may provide crucial insights in the treatment and outcome of TSC patients.

Neurite density of white matter significantly correlates with tuberous sclerosis complex disease severity

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Whole-brain white matter neurite density significantly reduces with TSC severity.

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A white matter quantification may be important for the evaluation of TSC patients.

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Low neurite density clusters are larger in severe TSC patients.

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Neurite density is an accurate MRI metric for the evaluation of TSC white-matter.

Objective

To assess whether white matter (WM) diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) derived measures correlate with tuberous sclerosis complex (TSC) disease severity.

Cohort and methods

A multi-shell diffusion protocol was added to the clinical MRI brain scans of thirteen patients including 6 males and 7 females with a mean ± std age of 17.2 ± 5.8 years. Fractional anisotropy (FA) and mean diffusivity (MD) were generated from DTI and neurite density index (NDI), orientation dispersion index (ODI) and free water index (fiso) were generated from NODDI. A clinical score was determined for each patient according to the existence of epilepsy, developmental delay, autism or psychiatric disorders. Whole-brain segmented WM was averaged for each parametric map and 3 group k-means clustering was performed on the NDI and FA maps. MRI quantitative parameters were correlated with the clinical scores.

Results

Segmented whole brain WM averages of MD and NDI values showed significant negative (p = 0.0058) and positive (p = 0.0092) correlations with the clinical scores, respectively. Additionally, the contribution of the low and high NDI-based clusters to the whole brain WM significantly correlated with the clinical scores (p = 0.03 and p = 0.00047, respectively). No correlation was found when the clusters were based on the FA maps.

Conclusion

Advanced diffusion MRI of TSC patients revealed widespread WM alterations. Neurite density showed significant correlations with disease severity and is therefore suggested to reflect an underlying biological process in TSC WM. The quantification of WM alterations by advanced diffusion MRI may be an additional biomarker for TSC and may be advantageous as a complementary MR protocol for the evaluation of TSC patients.

T2-Pseudonormalization and Microstructural Characterization in Advanced Stages of Late-infantile Metachromatic Leukodystrophy

PURPOSE

T2-weighted signal hyperintensities in white matter (WM) are a diagnostic finding in brain magnetic resonance imaging (MRI) of patients with metachromatic leukodystrophy (MLD). In our systematic investigation of the evolution of T2-hyperintensities in patients with the late-infantile form, we describe and characterize T2-pseudonormalization in the advanced stage of the natural disease course.

METHODS

The volume of T2-hyperintensities was quantified in 34 MRIs of 27 children with late-infantile MLD (median age 2.25 years, range 0.5-5.2 years). In three children with the most advanced clinical course (age >4 years) and for whom the T2-pseudonormalization was the most pronounced, WM microstructure was investigated using a multimodal MRI protocol, including diffusion-weighted imaging, MR spectroscopy (MRS), myelin water fraction (MWF), magnetization transfer ratio (MTR), T1-mapping and quantitative susceptibility mapping.

RESULTS

T2-hyperintensities in cerebral WM returned to normal in large areas of 3 patients in the advanced disease stage. Multimodal assessment of WM microstructure in areas with T2-pseudonormalization revealed highly decreased values for NAA, neurite density, isotropic water, mean and radial kurtosis, MWF and MTR, as well as increased radial diffusivity.

CONCLUSION

In late-infantile MLD patients, we found T2-pseudonormalization in WM tissue with highly abnormal microstructure characterizing the most advanced disease stage. Pathological hallmarks might be a loss of myelin, but also neuronal loss as well as increased tissue density due to gliosis and accumulated storage material. These results suggest that a multimodal MRI protocol using more specific microstructural parameters than T2-weighted sequences should be used when evaluating the effect of treatment trials in MLD.

In vivo restricted diffusion imaging (RDI) is sensitive to differences in axonal density in typical children and adults

The ability to dissociate axonal density in vivo from other microstructural properties is important for the diagnosis and treatment of neurologic disease, and new methods to do so are being developed. We investigated one such method-restricted diffusion imaging (RDI)-to see whether it can more accurately replicate histological axonal density patterns in the corpus callosum (CC) of adults and children compared to diffusion tensor imaging (DTI), neurite orientation dispersion and density imaging (NODDI), and generalized q-sampling imaging (GQI) methods. To do so, we compared known axonal density patterns defined by histology to diffusion-weighted imaging (DWI) scans of 840 healthy 20- to 40-year-old adults, and to DWI scans of 129 typically developing 7-month-old to 18-year-old children and adolescents. Contrast analyses were used to compare pattern similarities between the in vivo metric and previously published histological density models. We found that RDI was effective at mapping axonal density of small (Cohen's d = 2.60) and large fiber sizes (Cohen's d = 2.84) in adults. The same pattern was observed in the developing sample (Cohen's d = 3.09 and 3.78, respectively). Other metrics, notably NODDI's intracellular volume fraction in adults and GQI generalized fractional anisotropy in children, were also sensitive metrics. In conclusion, the study showed that the novel RDI metric is sensitive to density of small and large axons in adults and children, with both single- and multi-shell acquisition DWI data. Its effectiveness and availability to be used on standard as well as advanced DWI acquisitions makes it a promising method in clinical settings.

Diffusion Tensor Imaging Group Analysis Using Tract Profiling and Directional Statistics

Group analysis in diffusion tensor imaging is challenging. Comparisons of tensor morphology across groups have typically been performed on scalar measures of diffusivity, such as fractional anisotropy (FA), disregarding the complex three-dimensional morphologies of diffusion tensors. Scalar measures consider only the magnitude of the diffusion but not directions. In the present study, we have introduced a new approach based on directional statistics to use directional information of diffusion tensors in statistical group analysis based on Bingham distribution. We have investigated different directional statistical models to find the best fit. During the experiments, we confirmed that carrying out directional statistical analysis along the tract is much more effective than voxel- or skeleton-guided directional statistics. Hence, we propose a new method called tract profiling and directional statistics (TPDS) applicable to fiber bundles. As a case study, the method has been applied to identify connectivity differences of patients with major depressive disorder. The results obtained with the directional statistic-based analysis are consistent with those of NBS, but additionally, we found significant changes in the right hemisphere striatum, ACC, and prefrontal, parietal, temporal, and occipital connections as well as left hemispheric differences in the limbic areas such as the thalamus, amygdala, and hippocampus. The results are also evaluated with respect to fiber lengths. Comparison with the output of the network-based statistical toolbox indicated that the benefit of the proposed method becomes much more distinctive as the tract length increases. The likelihood of finding clusters of voxels that differ in long tracts is higher in TPDS, while that relationship is not clearly established in NBS.

NODDI in clinical research

Diffusion MRI (dMRI) has proven to be a useful imaging approach for both clinical diagnosis and research investigating the microstructures of nervous tissues, and it has helped us to better understand the neurophysiological mechanisms of many diseases. Though diffusion tensor imaging (DTI) has long been the default tool to analyze dMRI data in clinical research, acquisition with stronger diffusion weightings beyond the DTI regimen is now possible with modern clinical scanners, potentially enabling even more detailed characterization of tissue microstructures. To take advantage of such data, neurite orientation dispersion and density imaging (NODDI) has been proposed as a way to relate the dMRI signal to tissue features via biophysically inspired modeling. The number of reports demonstrating the potential clinical utility of NODDI is rapidly increasing. At the same time, the pitfalls and limitations of NODDI, and general challenges in microstructure modeling, are becoming increasingly recognized by clinicians. dMRI microstructure modeling is a rapidly evolving field with great promise, where people from different scientific backgrounds, such as physics, medicine, biology, neuroscience, and statistics, are collaborating to build novel tools that contribute to improving human healthcare. Here, we review the applications of NODDI in clinical research and discuss future perspectives for investigations toward the implementation of dMRI microstructure imaging in clinical practice.

Increased Dendritic Orientation Dispersion in the Left Occipital Gyrus is Associated with Atypical Visual Processing in Adults with Autism Spectrum Disorder

In autism spectrum disorder (ASD), the complexity-specific hypothesis explains that atypical visual processing is attributable to selective functional changes in visual pathways. We investigated dendritic microstructures and their associations with functional connectivity (FC). Participants included 28 individuals with ASD and 29 typically developed persons. We explored changes in neurite orientation dispersion and density imaging (NODDI) and brain areas whose FC was significantly correlated with NODDI parameters in the explored regions of interests. Individuals with ASD showed significantly higher orientation dispersion index (ODI) values in the left occipital gyrus (OG) corresponding to the secondary visual cortex (V2). FC values between the left OG and the left middle temporal gyrus (MTG) were significantly negatively correlated with mean ODI values. The mean ODI values in the left OG were significantly positively associated with low registration of the visual quadrants of the Adolescent/Adult Sensory Profile (AASP), resulting in a significant positive correlation with passive behavioral responses of the AASP visual quadrants; additionally, the FC values between the left OG and the left MTG were significantly negatively associated with reciprocal social interaction. Our results suggest that abnormal V2 dendritic arborization is associated with atypical visual processing by altered intermediation in the ventral visual pathway.