Functional connectivity changes and their relationship with clinical disability and white matter integrity in patients with relapsing-remitting multiple sclerosis

Background and objective: To define the pathological substrate underlying disability in multiple sclerosis by evaluating the relationship of resting-state functional connectivity with microstructural brain damage, as assessed by diffusion tensor maging, and clinical impairments. Methods: Thirty relapsing–remitting patients and 24 controls underwent 3T-MRI; motor abilities were evaluated by using measures of walking speed, hand dexterity and balance capability, while information processing speed was evaluated by a paced auditory serial addiction task. Independent component analysis and tract-based spatial statistics were applied to RS-fMRI and diffusion tensor imaging data using FSL software. Group differences, after dual regression, and clinical correlations were modelled with GeneralLinear-Model and corrected for multiple comparisons. Results: Patients showed decreased functional connectivity in 5 of 11 resting-state-networks (cerebellar, executive-control, medial-visual, basal ganglia and sensorimotor), changes in inter-network correlations and widespread white matter microstructural damage. In multiple sclerosis, corpus callosum microstructural damage positively correlated with functional connectivity in cerebellar and auditory networks. Moreover, functional connectivity within the medial-visual network inversely correlated with information processing speed. White matter widespread microstructural damage inversely correlated with both the paced auditory serial addiction task and hand dexterity. Conclusions: Despite the within-network functional connectivity decrease and the widespread microstructural damage, the inter-network functional connectivity changes suggest a global brain functional rearrangement in multiple sclerosis. The correlation between functional connectivity alterations and callosal damage uncovers a link between functional and structural connectivity. Finally, functional connectivity abnormalities affect information processing speed rather than motor abilities

Towards a comprehensive framework for movement and distortion correction of diffusion MR images: Within volume movement

Most motion correction methods work by aligning a set of volumes together, or to a volume that represents a reference location. These are based on an implicit assumption that the subject remains motionless during the several seconds it takes to acquire all slices in a volume, and that any movement occurs in the brief moment between acquiring the last slice of one volume and the first slice of the next. This is clearly an approximation that can be more or less good depending on how long it takes to acquire one volume and in how rapidly the subject moves. In this paper we present a method that increases the temporal resolution of the motion correction by modelling movement as a piecewise continous function over time. This intra-volume movement correction is implemented within a previously presented framework that simultaneously estimates distortions, movement and movement-induced signal dropout. We validate the method on highly realistic simulated data containing all of these effects. It is demonstrated that we can estimate the true movement with high accuracy, and that scalar parameters derived from the data, such as fractional anisotropy, are estimated with greater fidelity when data has been corrected for intra-volume movement. Importantly, we also show that the difference in fidelity between data affected by different amounts of movement is much reduced when taking intra-volume movement into account. Additional validation was performed on data from a healthy volunteer scanned when lying still and when performing deliberate movements. We show an increased correspondence between the “still” and the “movement” data when the latter is corrected for intra-volume movement. Finally we demonstrate a big reduction in the telltale signs of intra-volume movement in data acquired on elderly subjects

The association between first and second wave COVID-19 mortality in Italy

Background: The relation between the magnitude of successive waves of the COVID-19 outbreak within the same communities could be useful in predicting the scope of new outbreaks. Methods: We investigated the extent to which COVID-19 mortality in Italy during the second wave was related to first wave mortality within the same provinces. We compared data on province-specific COVID-19 2020 mortality in two time periods, corresponding to the first wave (February 24\u2013June 30, 2020) and to the second wave (September 1\u2013December 31, 2020), using cubic spline regression. Results: For provinces with the lowest crude mortality rate in the first wave (February\u2013June), i.e. < 22 cases/100,000/month, mortality in the second wave (September\u2013December) was positively associated with mortality during the first wave. In provinces with mortality greater than 22/100,000/month during the first wave, higher mortality in the first wave was associated with a lower second wave mortality. Results were similar when the analysis was censored at October 2020, before the implementation of region-specific measures against the outbreak. Neither vaccination nor variant spread had any role during the study period. Conclusions: These findings indicate that provinces with the most severe initial COVID-19 outbreaks, as assessed through mortality data, faced milder second waves

Covalent functionalization enables good dispersion and anisotropic orientation of multi-walled carbon nanotubes in a poly(l-lactic acid) electrospun nanofibrous matrix boosting neuronal differentiation

A biocompatible porous scaffold obtained via electrospinning a nanocomposite solution of poly(l-lactic acid) and 4-methoxyphenyl functionalized multi-walled carbon nanotubes is presented here for the first time for the enhancement of neurite outgrowth. Optimization of blend preparation and deposition parameters paves the way to the obtainment of defect-free random networks of nanofibers with homogeneous diameters in the hundreds of nanometers length scale. The tailored covalent functionalization of nanotube surfaces allows a homogeneous dispersion of the nanofillers within the polymer matrix, diminishing their natural tendency to aggregate and form bundles. This results in a remarkable effect on the crystallization temperature, as evidenced through differential scanning calorimetry. Furthermore, transmission electron microscopy shows carbon nanotubes anisotropically aligned along the fiber axes, a feature believed to enhance neurite adhesion and growth. Indeed, microscopy images show neurites extension along the direction of nanofibers, highlighting the extreme relevance of scaffold morphology in engineering complex tissue environments. Furthermore, a remarkable effect on increasing the neurite outgrowth results when using the fibrous scaffold containing dispersed carbon nanotubes in comparison with an analogous one made of only polymer, providing further evidence of the key role played by carbon nanostructures in inducing neuronal differentiation