The expansion and severity of chronic MS lesions follows a periventricular gradient

Background and Objectives: Expansion of chronic lesions in MS patients and recently described CSF-related gradient of tissue damage are linked to microglial activation. The aim of the current study was to investigate whether lesion expansion is associated with proximity to ventricular CSF spaces. Methods: Pre- and post-gadolinium 3D-T1, 3D FLAIR and diffusion tensor images were acquired from 36 RRMS patients. Lesional activity was analysed between baseline and 48 months at different distances from the CSF using successive 1-mm thick concentric rings radiating from the ventricles. Results: Voxel-based analysis of the rate of lesion expansion demonstrated a clear periventricular gradient decreasing away from the ventricles. This was particularly apparent when lesions of equal diameter were analysed. Periventricular lesional tissue showed higher degree of tissue distraction at baseline that significantly increased during follow-up in rings close to CSF. This longitudinal change was proportional to degree of lesion expansion. Lesion-wise analysis revealed a gradual, centrifugal decrease in the proportion of expanding lesions from the immediate periventricular zone. Discussion: Our data suggest that chronic white matter lesions in close proximity to the ventricles are more destructive, show a higher degree of expansion at the lesion border and accelerated tissue loss in the lesion core.

Tuberculoma – a great mimicker: can diffusion tensor imaging and tractography help?

Background Central nervous system (CNS) tuberculomas often mimic tumors on conventional imaging, differentiation of which may not be possible without invasive tissue sampling. Diffusion tensor imaging (DTI), owing to its unrivalled property of characterizing molecular diffusion, may help in better lesion characterization and tractography may help understand the pattern of white matter involvement by tuberculomas. Purpose To estimate qualitative and quantitative diffusion tensor changes in brain tuberculomas and to evaluate patterns of white matter involvement using 3D tractography. Material and Methods Thirty patients with brain tuberculomas were evaluated on a 3-T magnetic resonance scanner. Diffusion tensor images were acquired along 20 non-colinear encoding directions with two b-values (b = 0, b = 1000). Regions of interest (ROIs) were drawn on quantitative fractional anisotropy (FA) and apparent diffusion coefficient (ADC) maps in the center of the tuberculoma and perilesional area. Similar ROIs were placed in contralateral hemispheres for comparison. Tractography maps were also generated. Results Mean FA in the center and perilesional area of tuberculomas were 0.098 ± 0.041 and 0.311 ± 0.135, respectively. ADC values in corresponding regions were 0.920 ± 0.272 ×10−3 mm2/s and 1.157 ± 0.277 ×10−3 mm2/s. These values were significantly different compared to contralateral similar brain parenchyma. Tractography revealed interruption of white fibers in the center with deviation of fibers at the periphery in the majority of tuberculomas with none showing infiltration of white matter described in tumors. Conclusion Significant qualitative as well as quantitative DTI changes were seen in tuberculoma and perilesional areas compared to contralateral hemisphere with tractography showing a pattern different from that described in tumors. These findings may help to differentiate tuberculomas from infiltrating tumors.

Comparison of Five Conductivity Tensor Models and Image Reconstruction Methods Using MRI

Imaging of the electrical conductivity distribution inside the human body has been investigated for numerous clinical applications. The conductivity tensors of biological tissue have been obtained from water diffusion tensors by applying several models, which may not cover the entire phenomenon. Recently, a new conductivity tensor imaging (CTI) method was developed through a combination of B1 mapping, and multi-b diffusion weighted imaging. In this study, we compared the most recent CTI method with the four existing models of conductivity tensors reconstruction. Two conductivity phantoms were designed to evaluate the accuracy of the models. Applied to five human brains, the conductivity tensors using the four existing models and CTI were imaged and compared with the values from the literature. The conductivity image of the phantoms by the CTI method showed relative errors between 1.10% and 5.26%. The images by the four models using DTI could not measure the effects of different ion concentrations subsequently due to prior information of the mean conductivity values. The conductivity tensor images obtained from five human brains through the CTI method were comparable to previously reported literature values. The images by the four methods using DTI were highly correlated with the diffusion tensor images, showing a coefficient of determination (R2) value of 0.65 to 1.00. However, the images by the CTI method were less correlated with the diffusion tensor images and exhibited an averaged R2 value of 0.51. The CTI method could handle the effects of different ion concentrations as well as mobilities and extracellular volume fractions by collecting and processing additional B1 map data. It is necessary to select an application-specific model taking into account the pros and cons of each model. Future studies are essential to confirm the usefulness of these conductivity tensor imaging methods in clinical applications, such as tumor characterization, EEG source imaging, and treatment planning for electrical stimulation.

An interpretable multiparametric radiomics model for the diagnosis of schizophrenia using magnetic resonance imaging of the corpus callosum

There is a growing need to develop novel strategies for the diagnosis of schizophrenia using neuroimaging biomarkers. We investigated the robustness of the diagnostic model for schizophrenia using radiomic features from T1-weighted and diffusion tensor images of the corpus callosum (CC). A total of 165 participants [86 schizophrenia and 79 healthy controls (HCs)] were allocated to training (N = 115) and test (N = 50) sets. Radiomic features of the CC subregions were extracted from T1-weighted, apparent diffusion coefficient (ADC), and fractional anisotropy (FA) images (N = 1605). Following feature selection, various combinations of classifiers were trained, and Bayesian optimization was adopted in the best performing classifier. Discrimination, calibration, and clinical utility of the model were assessed. An online calculator was constructed to offer the probability of having schizophrenia. SHapley Additive exPlanations (SHAP) was applied to explore the interpretability of the model. We identified 30 radiomic features to differentiate participants with schizophrenia from HCs. The Bayesian optimized model achieved the highest performance, with an area under the curve (AUC), accuracy, sensitivity, and specificity of 0.89 (95% confidence interval: 0.81–0.98), 80.0, 83.3, and 76.9%, respectively, in the test set. The final model offers clinical probability in an online calculator. The model explanation by SHAP suggested that second-order features from the posterior CC were highly associated with the risk of schizophrenia. The multiparametric radiomics model focusing on the CC shows its robustness for the diagnosis of schizophrenia. Radiomic features could be a potential source of biomarkers that support the biomarker-based diagnosis of schizophrenia and improve the understanding of its neurobiology.

Expansion of chronic MS lesions is associated with an increase of radial diffusivity in periplaque white matter

Background: Expansion of chronic multiple sclerosis (MS) lesion is associated with slow-burning inflammation at lesion rim. However, the underlying mechanisms leading to expansion are not fully understood. Objective: To investigate the relationship between diffusivity markers of demyelination and axonal loss in perilesional white matter and lesion expansion in relapsing-remitting MS (RRMS). Methods: T1, FLAIR and diffusion tensor images were acquired from 30 patients. Novel single-streamline technique was used to estimate diffusivity in lesions, perilesional white matter and normal-appearing white matter (NAWM). Results: Significant association was found between baseline periplaque radial diffusivity (RD) and subsequent lesion expansion. Conversely, periplaque axial diffusivity (AD) did not correlate with lesion growth. Baseline RD (but not AD) in periplaque white matter of expanding lesions was significantly higher compared with non-expanding lesions. Correlation between increase of both RD and AD in the periplaque area during follow-up period and lesion expansion was noticeably stronger for RD. Increase of RD in periplaque area was also much higher compared to AD. There was significant increase of AD and RD in the periplaque area of expanding, but not in non-expanding, lesions. Conclusion: Periplaque demyelination is likely to be an initial step in a process of lesion expansion and, as such, potentially represents a suitable target for remyelinating therapies.

Exploiting Spatial Information to Enhance DTI Segmentations via Spatial Fuzzy c-Means with Covariance Matrix Data and Non-Euclidean Metrics

A diffusion tensor models the covariance of the Brownian motion of water at a voxel and is required to be symmetric and positive semi-definite. Therefore, image processing approaches, designed for linear entities, are not effective for diffusion tensor data manipulation, and the existence of artefacts in diffusion tensor imaging acquisition makes diffusion tensor data segmentation even more challenging. In this study, we develop a spatial fuzzy c-means clustering method for diffusion tensor data that effectively segments diffusion tensor images by accounting for the noise, partial voluming, magnetic field inhomogeneity, and other imaging artefacts. To retain the symmetry and positive semi-definiteness of diffusion tensors, the log and root Euclidean metrics are used to estimate the mean diffusion tensor for each cluster. The method exploits spatial contextual information and provides uncertainty information in segmentation decisions by calculating the membership values for assigning a diffusion tensor at one voxel to different clusters. A regularisation model that allows the user to integrate their prior knowledge into the segmentation scheme or to highlight and segment local structures is also proposed. Experiments on simulated images and real brain datasets from healthy and Spinocerebellar ataxia 2 subjects showed that the new method was more effective than conventional segmentation methods.

Evaluation of White Matter Tracts in Autistic Individuals: A Review of Diffusion Tensor Imaging Studies

Introduction: Many cognitive and social deficits in autism are caused by abnormal functional connections between brain networks, which are manifested by impaired integrity of white matter tracts. White matter tracts are like the "highways" of the brain, which allow fast and efficient communication in different areas of the brain. The purpose of this article is to review the results of autism studies that have used diffusion tensor images (DTI). Diffusion tensor images is a neuroimaging technique to examine the integrity of tracts. Conclusion: The results of these studies suggest that neural tracts can be abnormal in people with Autism spectrum disorder (ASD) due to impaired white matter integrity. Thus, changes in these tracts in the brains of people with ASD are helpful in identifying individual differences. Although most studies have reported decreased FA and increased MD, RD, and AD in white matter tracts, some studies have reported increased FA or no significant difference between the control and autistic groups.

Assessment of fractional anisotropy outcomes in combat sport athletes with mild traumatic brain injury

The practice of combat sports increases the risk of suffering white matter injuries. That is why, it is required the early damage detection to determine to what extent the athlete may be active preserving their performance and health status. The integrity of the white matter can be quantitatively characterized in diffusion tensor images, using fractional anisotropy. This study aims at characterizing the fractional anisotropy of white matter injuries in combat athletes that are exposed to repetitive trauma and also, to detect changes in fractional anisotropy between cerebral hemispheres with and without lesions. It is proposed a global and structural analysis of the hemispheres, as well as the selection of ROI in the lesions. 14 athletes, from Boxing, Karate and Taekwondo sports, participated. The sample was divided into two groups of seven subjects each: Injured (23.428${\pm}$4.157 years old) and Healthy (24.285${\pm}$5.023 years old) paired by sport denomination. Diffusion tensor images were used to obtain FA values in the analysis of the hemispheres and lesions. Global and structural analysis of the hemispheres did not detect the presence of white matter lesions; however, the use of ROI selection permitted maximum approximation of the injuries location. It also improved the breakdown of FA values as it allows a local analysis of the lesion. As an additional result, there were found ROIs values, FA$_{med}=0.454{\pm}0.062$, which exceed the average fractional anisotropy of the white matter. The cohesion of acute and chronic phase lesions were found in the same subject. The apparently contradictory results in FA values are related to the stage of the lesions.

Hybrid diffusion tensor imaging feature-based AD classification

BACKGROUND: Effective detection of Alzheimer’s disease (AD) is still difficult in clinical practice. Therefore, establishment of AD detection model by means of machine learning is of great significance to assist AD diagnosis. OBJECTIVE: To investigate and test a new detection model aiming to help doctors diagnose AD more accurately. METHODS: Diffusion tensor images and the corresponding T1w images acquired from subjects (AD = 98, normal control (NC) = 100) are used to construct brain networks. Then, 9 types features (198×90×9 in total) are extracted from the 3D brain networks by a graph theory method. Features with low correction in both groups are selected through the Pearson correlation analysis. Finally, the selected features (198×33, 198×26, 198×30, 198×42, 198×36, 198×23, 198×29, 198×14, 198×25) are separately used into train 3 machine learning classifier based detection models in which 60% of study subjects are used for training, 20% for validation and 20% for testing. RESULTS: The best detection accuracy levels of 3 models are 90%, 98% and 90% with the corresponding sensitivity of 92%, 96%, and 72% and specificity of 88%, 100% and 94% when using a random forest classifier trained with the Shortest Path Length (SPL) features (198×14), a support vector machine trained with the Degree Centrality features (198×33), and a convolution neural network trained with SPL features, respectively. CONCLUSIONS: This study demonstrates that the new method and models not only improve the accuracy of detecting AD, but also avoid bias caused by the method of direct dimensionality reduction from high dimensional data.