Smoothing that does not blur: effects of the anisotropic approach for evaluating diffusion tensor imaging data in the clinic

White matter microstructural changes in short-term learning of a continuous visuomotor sequence

Efficient neural transmission is crucial for optimal brain function, yet the plastic potential of white matter (WM) has long been overlooked. Growing evidence now shows that modifications to axons and myelin occur not only as a result of long-term learning, but also after short training periods. Motor sequence learning (MSL), a common paradigm used to study neuroplasticity, occurs in overlapping learning stages and different neural circuits are involved in each stage. However, most studies investigating short-term WM plasticity have used a pre-post design, in which the temporal dynamics of changes across learning stages cannot be assessed. In this study, we used multiple magnetic resonance imaging (MRI) scans at 7 T to investigate changes in WM in a group learning a complex visuomotor sequence (LRN) and in a control group (SMP) performing a simple sequence, for five consecutive days. Consistent with behavioral results, where most improvements occurred between the two first days, structural changes in WM were observed only in the early phase of learning (d1-d2), and in overall learning (d1-d5). In LRNs, WM microstructure was altered in the tracts underlying the primary motor and sensorimotor cortices. Moreover, our structural findings in WM were related to changes in functional connectivity, assessed with resting-state functional MRI data in the same cohort, through analyses in regions of interest (ROIs). Significant changes in WM microstructure were found in a ROI underlying the right supplementary motor area. Together, our findings provide evidence for highly dynamic WM plasticity in the sensorimotor network during short-term MSL.

Validation of DWI pre-processing procedures for reliable differentiation between human brain gliomas

Diffusion magnetic resonance imaging (dMRI) is a powerful tool in clinical applications, in particular, in oncology screening. dMRI demonstrated its benefit and efficiency in the localisation and detection of different types of human brain tumours. Clinical dMRI data suffer from multiple artefacts such as motion and eddy-current distortions, contamination by noise, outliers etc. In order to increase the image quality of the derived diffusion scalar metrics and the accuracy of the subsequent data analysis, various pre-processing approaches are actively developed and used. In the present work we assess the effect of different pre-processing procedures such as a noise correction, different smoothing algorithms and spatial interpolation of raw diffusion data, with respect to the accuracy of brain glioma differentiation. As a set of sensitive biomarkers of the glioma malignancy grades we chose the derived scalar metrics from diffusion and kurtosis tensor imaging as well as the neurite orientation dispersion and density imaging (NODDI) biophysical model. Our results show that the application of noise correction, anisotropic diffusion filtering, and cubic-order spline interpolation resulted in the highest sensitivity and specificity for glioma malignancy grading. Thus, these pre-processing steps are recommended for the statistical analysis in brain tumour studies.

Anisotropic Smoothing Improves DT-MRI-Based Muscle Fiber Tractography

Purpose

To assess the effect of anisotropic smoothing on fiber tracking measures, including pennation angle, fiber tract length, and fiber tract number in the medial gastrocnemius (MG) muscle in healthy subjects using diffusion-weighted magnetic resonance imaging (DW-MRI).

Materials and Methods

3T DW-MRI data were used for muscle fiber tractography in the MG of healthy subjects. Anisotropic smoothing was applied at three levels (5%, 10%, 15%), and pennation angle, tract length, fiber tract number, fractional anisotropy, and principal eigenvector orientation were quantified for each smoothing level.

Results

Fiber tract length increased with pre-fiber tracking smoothing, and local heterogeneities in fiber direction were reduced. However, pennation angle was not affected by smoothing.

Conclusion

Modest anisotropic smoothing (10%) improved fiber-tracking results, while preserving structural features.

Anomalous diffusion process applied to magnetic resonance image enhancement

Diffusion process is widely applied to digital image enhancement both directly introducing diffusion equation as in anisotropic diffusion (AD) filter, and indirectly by convolution as in Gaussian filter. Anomalous diffusion process (ADP), given by a nonlinear relationship in diffusion equation and characterized by an anomalous parameters q, is supposed to be consistent with inhomogeneous media. Although classic diffusion process is widely studied and effective in various image settings, the effectiveness of ADP as an image enhancement is still unknown. In this paper we proposed the anomalous diffusion filters in both isotropic (IAD) and anisotropic (AAD) forms for magnetic resonance imaging (MRI) enhancement. Filters based on discrete implementation of anomalous diffusion were applied to noisy MRI T2w images (brain, chest and abdominal) in order to quantify SNR gains estimating the performance for the proposed anomalous filter when realistic noise is added to those images. Results show that for images containing complex structures, e.g. brain structures, anomalous diffusion presents the highest enhancements when compared to classical diffusion approach. Furthermore, ADP presented a more effective enhancement for images containing Rayleigh and Gaussian noise. Anomalous filters showed an ability to preserve anatomic edges and a SNR improvement of 26% for brain images, compared to classical filter. In addition, AAD and IAD filters showed optimum results for noise distributions that appear on extreme situations on MRI, i.e. in low SNR images with approximate Rayleigh noise distribution, and for high SNR images with Gaussian or non central χ noise distributions. AAD and IAD filter showed the best results for the parametric range 1.2 < q < 1.6, suggesting that the anomalous diffusion regime is more suitable for MRI. This study indicates the proposed anomalous filters as promising approaches in qualitative and quantitative MRI enhancement.

Statistical Instability of TBSS Analysis Based on DTI Fitting Algorithm

Voxel-based DTI analysis is an important approach in the comparison of subject groups by detecting and localizing gray and white matter changes in the brain. One of the principal problems for intersubject comparison is the absence of a "gold standard" processing pipeline. As a result, contradictory results may be obtained from identical data using different data processing pipelines, for example, in the data normalization or smoothing procedures. Tract-based spatial statistics (TBSS) shows potential to overcome this problem by automatic detection of white matter changes and decreasing variation in the performed analysis. However, skeleton projection approaches, such as TBSS, critically depend on the accuracy of the diffusion scalar metric estimations. In this work, we demonstrate that the agreement and reliability of TBSS results depend on the applied DTI data processing algorithm. Statistical tests have been performed using two in vivo measured datasets and compared with different implementations of the least squares algorithm. As a result, we recommend repeating TBSS analysis using different fitting algorithms, in particular, using on iteratively-assessed robust estimators, as accurate and more reliable approach in voxel-based analysis, particularly, for TBSS. Repeating TBSS analysis allows one to detect and localize suspicious regions in white matter which were estimated as the regions with significant difference. Finally, we did not find a favorite fitting algorithm (or class of them) which can be marked as more reliable for group comparison.

The structural neuroanatomy of metacognitive insight in schizophrenia and its psychopathological and neuropsychological correlates

Lack of insight into illness is a multidimensional phenomenon that has relevant implications on clinical course and therapy compliance. Here, we focused on metacognitive insight in schizophrenia, that is, the ability to monitor one's changes in state of mind and sensations, with the aim of investigating its neuroanatomical, psychopathological, and neuropsychological correlates. Fifty-seven consecutive patients with Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition, Text Revision) diagnosis of schizophrenia were administered the Insight Scale, and comprehensive psychopathological and neuropsychological batteries. They underwent a high-resolution T1-weighted magnetic resonance imaging investigation. Gray matter (GM) and white matter (WM) volumes were analyzed on a voxel-by-voxel basis using Statistical Parametric Mapping 8. Reduced metacognitive insight was related to reduced GM volumes in the left ventrolateral prefrontal cortex, right dorsolateral prefrontal cortex and insula, and bilateral premotor area and putamen. Further, it was related to reduced WM volumes of the right superior longitudinal fasciculum, left corona radiata, left forceps minor, and bilateral cingulum. Increased metacognitive insight was related to increased depression severity and attentional control impairment, while the latter was related to increased GM volumes in brain areas linked to metacognitive insight. Results of this study suggest that prefrontal GM and WM bundles, all implied in cognitive control and self-reflection, may be the neuroanatomical correlates of metacognitive insight in schizophrenia. Further, higher metacognitive insight is hypothesized to be a risk factor for depression which may subsequently impair attention. This line of research may provide the basis for the development of cognitive interventions aimed at improving self-monitoring and compliance to treatment.

Brain microstructural changes and cognitive correlates in patients with pure obsessive compulsive disorder

OBJECT

The aim of this study was to investigate macrostructural and microstructural brain changes in patients with pure obsessive compulsive disorder (OCD) and to examine the relationship between brain structure and neuropsychological deficits.

METHOD

20 patients with OCD underwent a comprehensive neuropsychological battery. A combined voxel-based morphometry (VBM) and diffusion tensor imaging (DTI) analysis was used to capture gray matter (GM) and white matter changes in OCD patients as compared to pair-matched healthy volunteers. Multiple regression designs explored the relationship between cognition and neuroimaging parameters.

RESULTS

OCD patients had increased mean diffusivity (MD) in GM nodes of the orbitofronto-striatal loop (left dorsal anterior cingulate [Z = 3.67, P < 0.001] left insula [Z = 3.35 P < 0.001] left thalamus [Z = 3.59, P < 0.001] left parahippocampal gyrus [Z = 3.77 P < 0.001]) and in lateral frontal and posterior associative cortices (right frontal operculum [Z = 3.42 P < 0.001], right temporal lobe [Z = 3.79 P < 0.001] left parietal lobe [Z = 3.91 P < 0.001]). Decreased fractional anisotropy (FA) was detected in intrahemispheric (left superior longitudinal fasciculus [Z = 4.07 P < 0.001]) and interhemispheric (body of corpus callosum [CC, Z = 4.42 P < 0.001]) bundles. Concurrently, the semantic fluency score, a measure of executive control processes, significantly predicted OCD diagnosis (Odds Ratio = 1.37; 95% Confidence Intervals = 1.09-1.73; P = 0.0058), while variation in performance was correlated with increased MD in left temporal (Z = 4.25 P < 0.001) and bilateral parietal regions (left Z = 3.94, right Z = 4.19 P < 0.001), and decreased FA in the right posterior corona radiata (Z = 4.07 P < 0.001) and the left corticospinal tract (Z = 3.95 P < 0.001).

CONCLUSIONS

The reported deficit in executive processes and the underlying microstructural alterations may qualify as behavioral and biological markers of OCD.

Feature-preserving smoothing of diffusion weighted images using nonstationarity adaptive filtering

Although promising for studying the microstructure of in vivo tissues, the performance and the potentiality of diffusion tensor magnetic resonance imaging are hampered by the presence of high-level noise in diffusion weighted (DW) images. This paper proposes a novel smoothing approach, called the nonstationarity adaptive filtering, which estimates the intensity of a pixel by averaging intensities in its adaptive homogeneous neighborhood. The latter is determined according to five constraints and spatiodirectional nonstationarity measure maps. The proposed approach is compared with an anisotropic diffusion method used in DW image smoothing. Experimental results on both synthetic and real human DW images show that the proposed method achieves a better compromise between the smoothness of homogeneous regions and the preservation of desirable features such as boundaries, even for highly noisy data, thus leading to homogeneously consistent tensor fields and consequently more coherent fibers.

Methodological improvements in voxel-based analysis of diffusion tensor images: applications to study the impact of apolipoprotein E on white matter integrity

PURPOSE

To identify regional differences in apparent diffusion coefficient (ADC) and fractional anisotropy (FA) using customized preprocessing before voxel-based analysis (VBA) in 14 normal subjects with the specific genes that decrease (apolipoprotein [APO] E ε2) and that increase (APOE ε4) the risk of Alzheimer's disease.

MATERIALS AND METHODS

Diffusion tensor images (DTI) acquired at 1.5 Tesla were denoised with a total variation tensor regularization algorithm before affine and nonlinear registration to generate a common reference frame for the image volumes of all subjects. Anisotropic and isotropic smoothing with varying kernel sizes was applied to the aligned data before VBA to determine regional differences between cohorts segregated by allele status.

RESULTS

VBA on the denoised tensor data identified regions of reduced FA in APOE ε4 compared with the APOE ε2 healthy older carriers. The most consistent results were obtained using the denoised tensor and anisotropic smoothing before statistical testing. In contrast, isotropic smoothing identified regional differences for small filter sizes alone, emphasizing that this method introduces bias in FA values for higher kernel sizes.

CONCLUSION

Voxel-based DTI analysis can be performed on low signal to noise ratio images to detect subtle regional differences in cohorts using the proposed preprocessing techniques.

Improved diffusion imaging through SNR-enhancing joint reconstruction

Quantitative diffusion imaging is a powerful technique for the characterization of complex tissue microarchitecture. However, long acquisition times and limited signal-to-noise ratio represent significant hurdles for many in vivo applications. This article presents a new approach to reduce noise while largely maintaining resolution in diffusion weighted images, using a statistical reconstruction method that takes advantage of the high level of structural correlation observed in typical datasets. Compared to existing denoising methods, the proposed method performs reconstruction directly from the measured complex k-space data, allowing for gaussian noise modeling and theoretical characterizations of the resolution and signal-to-noise ratio of the reconstructed images. In addition, the proposed method is compatible with many different models of the diffusion signal (e.g., diffusion tensor modeling and q-space modeling). The joint reconstruction method can provide significant improvements in signal-to-noise ratio relative to conventional reconstruction techniques, with a relatively minor corresponding loss in image resolution. Results are shown in the context of diffusion spectrum imaging tractography and diffusion tensor imaging, illustrating the potential of this signal-to-noise ratio-enhancing joint reconstruction approach for a range of different diffusion imaging experiments.

Biomarkers of increased diffusion anisotropy in semi-acute mild traumatic brain injury: a longitudinal perspective

Mild traumatic brain injury is the most prevalent neurological insult and frequently results in neurobehavioural sequelae. However, little is known about the pathophysiology underlying the injury and how these injuries change as a function of time. Although diffusion tensor imaging holds promise for in vivo characterization of white matter pathology, both the direction and magnitude of anisotropic water diffusion abnormalities in axonal tracts are actively debated. The current study therefore represents both an independent replication effort (n = 28) of our previous findings (n = 22) of increased fractional anisotropy during semi-acute injury, as well as a prospective study (n = 26) on the putative recovery of diffusion abnormalities. Moreover, new analytical strategies were applied to capture spatially heterogeneous white matter injuries, which minimize implicit assumptions of uniform injury across diverse clinical presentations. Results indicate that whereas a general pattern of high anisotropic diffusion/low radial diffusivity was present in various white matter tracts in both the replication and original cohorts, this pattern was only consistently observed in the genu of the corpus callosum across both samples. Evidence for a greater number of localized clusters with increased anisotropic diffusion was identified across both cohorts at trend levels, confirming heterogeneity in white matter injury. Pooled analyses (50 patients; 50 controls) suggested that measures of diffusion within the genu were predictive of patient classification, albeit at very modest levels (71% accuracy). Finally, we observed evidence of recovery in lesion load in returning patients across a 4-month interval, which was correlated with a reduction in self-reported post-concussive symptomatology. In summary, the corpus callosum may serve as a common point of injury in mild traumatic brain injury secondary to anatomical (high frequency of long unmyelinated fibres) and biomechanics factors. A spatially heterogeneous pattern of increased anisotropic diffusion exists in various other white matter tracts, and these white matter anomalies appear to diminish with recovery. This macroscopic pattern of diffusion abnormalities may be associated with cytotoxic oedema following mechanical forces, resulting in changes in ionic homeostasis, and alterations in the ratio of intracellular and extracellular water. Animal models more specific to the types of mild traumatic brain injury typically incurred by humans are needed to confirm the histological correlates of these macroscopic markers of white matter pathology.

Disrupted white matter connectivity is associated with reduced cortical thickness in the cingulate cortex in schizophrenia

INTRODUCTION

Both impaired white matter connectivity and alterations in gray matter morphometry have repeatedly been reported in schizophrenia. Neurodevelopmental models propose a close linkage between gray matter alterations and white matter deficits. However, there are no studies investigating alterations in cortical thickness in relation to white matter connectivity changes.

METHODS

This combined diffusion tensor imaging (DTI) - surface based morphometry study examined a potential linkage between disruption in white matter connectivity and alterations in cortical thickness. Cortical thickness was analyzed using the FreeSurfer software package (version 4.0.5, http://surfer.nmr.harvard.edu) in a sample of 19 patients with schizophrenia and 20 healthy controls.

RESULTS

Whole brain node-by-node correlational analysis revealed a highly significant association ( r= -.8, p < .0001) between disturbed white matter connectivity in the superior temporal cortex and diminished cortical thickness in the posterior part of the cingulate cortex (Brodmann area 23/31).

CONCLUSIONS

This result indicates a significant linkage between disturbed white matter connectivity and reduced cortical thickness in a relevant node of the default mode network that is held to be of high pathophysiological relevance in schizophrenia. The result moreover provides support for the assumption of a neurodevelopmental pathogenesis of the disorder.