Quantification of normal-appearing white matter damage in early relapse-onset multiple sclerosis through neurite orientation dispersion and density imaging.

Background Neurodegeneration is a major contributor of neurological disability in multiple sclerosis (MS). The possibility to fully characterize normal appearing white matter (NAWM) damage could provide the missing information needed to clarify the mechanisms beyond disability accumulation. Objective In the present study we aimed to characterize the presence and extent of NAWM damage and its correlation with clinical disability. Methods We applied Diffusion Tensor Imaging (DTI) and Neurite Orientation Dispersion and Density Imaging (NODDI) in a cohort of 27 early relapse-onset MS patients (disease duration < 5 years) compared to a population of 26 age- and sex-matched healthy controls (HCs). All patients underwent a neurological examination, including the Expanded Disability Status Scale (EDSS). Results MS patients showed lower fractional anisotropy (FA) and higher mean diffusivity (MD) values in the main WM bundles, such as the corticospinal tract, corpus callosum, superior and middle cerebellar peduncles, posterior thalamic radiation (which includes optic radiation), cingulum and external capsule. All brain areas with reduced FA/increased MD also displayed a reduction in neurite density index (NDI). However, comparing individual voxels of the WM skeleton between MS and HCs, a higher number of NDI significant voxels was disclosed compared to FA/MD (56.4% vs 11.2%/41.2%). No significant correlations were observed between DTI/NODDI metrics and EDSS. Conclusions Our findings suggest that NDI may allow for a better characterization and understanding of the microstructural changes in the NAWM since the early relapsing-remitting MS phases. Future longitudinal studies including a larger cohort of patients with different clinical phenotypes may clarify the association between NODDI metrics and disability progression.

Assessment of structural disconnections in gliomas: comparison of indirect and direct approaches.

Gliomas are amongst the most common primary brain tumours in adults and are often associated with poor prognosis. Understanding the extent of white matter (WM) which is affected outside the tumoral lesion may be of paramount importance to explain cognitive deficits and the clinical progression of the disease. To this end, we explored both direct (i.e., tractography based) and indirect (i.e., atlas-based) approaches to quantifying WM structural disconnections in a cohort of 44 high- and low-grade glioma patients. While these methodologies have recently gained popularity in the context of stroke and other pathologies, to our knowledge, this is the first time they are applied in patients with brain tumours. More specifically, in this work, we present a quantitative comparison of the disconnection maps provided by the two methodologies by applying well-known metrics of spatial similarity, extension, and correlation. Given the important role the oedematous tissue plays in the physiopathology of tumours, we performed these analyses both by including and excluding it in the definition of the tumoral lesion. This was done to investigate possible differences determined by this choice. We found that direct and indirect approaches offer two distinct pictures of structural disconnections in patients affected by brain gliomas, presenting key differences in several regions of the brain. Following the outcomes of our analysis, we eventually discuss the strengths and pitfalls of these two approaches when applied in this critical field.

Diffusion-based microstructure models in brain tumours: Fitting in presence of a model-microstructure mismatch.

Diffusion-based biophysical models have been used in several recent works to study the microenvironment of brain tumours. While the pathophysiological interpretation of the parameters of these models remains unclear, their use as signal representations may yield useful biomarkers for monitoring the treatment and the progression of this complex and heterogeneous disease. Up to now, however, no study was devoted to assessing the mathematical stability of these approaches in cancerous brain regions. To this end, we analyzed in 11 brain tumour patients the fitting results of two microstructure models (Neurite Orientation Dispersion and Density Imaging and the Spherical Mean Technique) and of a signal representation (Diffusion Kurtosis Imaging) to compare the reliability of their parameter estimates in the healthy brain and in the tumoral lesion. The framework of our between-tissue analysis included the computation of 1) the residual sum of squares as a goodness-of-fit measure 2) the standard deviation of the models' derived metrics and 3) models' sensitivity functions to analyze the suitability of the employed protocol for parameter estimation in the different microenvironments. Our results revealed no issues concerning the fitting of the models in the tumoral lesion, with similar goodness of fit and parameter precisions occurring in normal appearing and pathological tissues. Lastly, with the aim of highlight possible biomarkers, in our analysis we briefly discuss the correlation between the metrics of the three techniques, identifying groups of indices which are significantly collinear in all tissues and thus provide no additional information when jointly used in data-driven analyses.