Diffusion Kurtosis Imaging of neonatal Spinal Cord in clinical routine

Diffusion kurtosis imaging (DKI) has undisputed advantages over the more classical diffusion magnetic resonance imaging (dMRI) as witnessed by the fast-increasing number of clinical applications and software packages widely adopted in brain imaging. However, in the neonatal setting, DKI is still largely underutilized, in particular in spinal cord (SC) imaging, because of its inherently demanding technological requirements. Due to its extreme sensitivity to non-Gaussian diffusion, DKI proves particularly suitable for detecting complex, subtle, fast microstructural changes occurring in this area at this early and critical stage of development, which are not identifiable with only DTI. Given the multiplicity of congenital anomalies of the spinal canal, their crucial effect on later developmental outcome, and the close interconnection between the SC region and the brain above, managing to apply such a method to the neonatal cohort becomes of utmost importance. This study will (i) mention current methodological challenges associated with the application of advanced dMRI methods, like DKI, in early infancy, (ii) illustrate the first semi-automated pipeline built on Spinal Cord Toolbox for handling the DKI data of neonatal SC, from acquisition setting to estimation of diffusion measures, through accurate adjustment of processing algorithms customized for adult SC, and (iii) present results of its application in a pilot clinical case study. With the proposed pipeline, we preliminarily show that DKI is more sensitive than DTI-related measures to alterations caused by brain white matter injuries in the underlying cervical SC

Phase and amplitude electroencephalography correlations change with disease progression in people with idiopathic rapid eye-movement sleep behavior disorder

Study Objectives Increased phase synchronization in electroencephalography (EEG) bands might reflect the activation of compensatory mechanisms of cognitive decline in people with neurodegenerative diseases. Here, we investigated whether altered large-scale couplings of brain oscillations could be linked to the balancing of cognitive decline in a longitudinal cohort of people with idiopathic rapid eye-movement sleep behavior disorder (iRBD). Methods We analyzed 18 patients (17 males, 69.7 +/- 7.5 years) with iRBD undergoing high-density EEG (HD-EEG), presynaptic dopaminergic imaging, and clinical and neuropsychological (NPS) assessments at two time points (time interval 24.2 +/- 5.9 months). We thus quantified the HD-EEG power distribution, orthogonalized amplitude correlation, and weighted phase-lag index at both time points and correlated them with clinical, NPS, and imaging data. Results Four patients phenoconverted at follow-up (three cases of parkinsonism and one of dementia). At the group level, NPS scores decreased over time, without reaching statistical significance. However, alpha phase synchronization increased and delta amplitude correlations decreased significantly at follow-up compared to baseline. Both large-scale network connectivity metrics were significantly correlated with NPS scores but not with sleep quality indices or presynaptic dopaminergic imaging data. Conclusions These results suggest that increased alpha phase synchronization and reduced delta amplitude correlation may be considered electrophysiological signs of an active compensatory mechanism of cognitive impairment in people with iRBD. Large-scale functional modifications may be helpful biomarkers in the characterization of prodromal stages of alpha-synucleinopathies.Peer reviewe

Applicability of Advanced Diffusion Magnetic Resonance Imaging in clinical routine of Neonatal Data

Diffusion Magnetic Resonance Imaging (dMRI), a variant of conventional MRI based on the tissue water diffusion rate, has recently gathered an extraordinary interest among the scientific community due to the relationships found between several neurological and neurosurgical pathologies and alterations in diffusivity of both white and gray matter. It can thus be considered the imaging method of choice to study the brain, and several steps forward have been made from Diffusion Tensor Imaging (DTI) - the method which first showed the capabilities of dMRI - to advanced diffusion analysis methods. Applying these cutting-edge imaging techniques to investigate pediatric subjects is gaining increasing popularity precisely for the unparalleled sensitivity to tissue microstructure compared to conventional MRI. Indeed, advanced dMRI models turn out to be ideal for investigating fast tissue growth and differentiation characterizing early infancy and not detectable with the same degree of sensitivity with structural MRI. If, with regard to infant brain, most recent dMRI techniques have already been successfully applied in research studies and are entering clinical routine, their use in imaging of neonatal spinal cord is still unexplored. Nonetheless, we are dealing with an innovative, up-to-date domain which holds great promise for diagnosis and understanding of pathological conditions due to injury of both grey and white matter tracts. However, there are considerable challenges to this kind of imaging and research at present is focusing its effort on sorting them out. Further issues concern the application of this imaging in a pediatric clinical setting, which presents specific requirements in terms of acquisition sequences in contrastto current advanced diffusion methods. The main goal of my PhD project, in collaboration with the LIFT (Laboratorio di Imaging Funzionale a 3Tesla) of Gaslini Children’s Hospital in Genoa, has been to allow translation of advanced dMRI methods into clinical routine for the analysis of neonatal data, both in brain and spinal cord, considering the close interconnection between these two districts. Specifically, during my PhD work, I have paid particular attention to: (i) the design of ad-hoc acquisition sequences and preprocessing pipelines tailored for neonates - a crucial step at this delicate age-range; (ii) the application of Diffusion Kurtosis Imaging (DKI) model, a promising extension of DTI quantifying non-gaussian diffusion in biological tissues; and (iii) the investigation of preterm birth in order to find new potential biomarkers, given its still high incidence and adverse impact worldwide

Influence of adaptive denoising on Diffusion Kurtosis Imaging at 3T and 7T

Background and objective: Choosing the most appropriate denoising method to improve the quality of diagnostic images maximally is key in pre-processing of diffusion MRI images. Recent advancements in acquisition and reconstruction techniques have questioned traditional noise estimation methods favoring adaptive denoising frameworks, circumventing the need to know a priori information that is hardly avail-able in a clinical setting. In this observational study, we compared two innovative adaptive techniques sharing some features, Patch2Self and Nlsam, through application on reference adult data at 3T and 7T. The primary aim was identifying the most effective method in case of Diffusion Kurtosis Imaging (DKI) data -particularly susceptible to noise and signal fluctuations -at 3T and 7T fields. A side goal consisted of investigating the dependence of kurtosis metrics' variability with respect to the magnetic field on the adopted denoising methodology.Methods: For comparison purposes, we focused on qualitative and quantitative analysis of DKI data and related microstructural maps before and after applying the two denoising approaches. Specifically, we assessed computational efficiency, preservation of anatomical details via perceptual metrics, consistency of microstructure model fitting, alleviation of degeneracies in model estimation, and joint variability with varying field strength and denoising method.Results: Accounting for all these factors, Patch2Self framework has turned out to be specifically suitable for DKI data, with improving performance at 7T. Nlsam method is more robust in alleviating degenerate black voxels while introducing some blurring, which in turn is reflected in an overall loss of image sharp-ness. Regarding the impact of denoising on field-dependent variability, both methods have been shown to make variations from standard to Ultra-High Field more concordant with theoretical evidence, claiming that kurtosis metrics are sensitive to susceptibility-induced background gradients, directly proportional to the magnetic field strength and sensitive to the microscopic distribution of iron and myelin.Conclusions: This study serves as a proof-of-concept stressing the need for an accurate choice of a denois-ing methodology, specifically tailored for the data under analysis and allowing higher spatial resolution acquisition within clinically compatible timings, with all the potential benefits that improving suboptimal quality of diagnostic images entails.(c) 2023 Published by Elsevier B.V.Peer reviewe