Neurite orientation dispersion and density imaging of the healthy cervical spinal cord in vivo

Here we present the application of neurite orientation dispersion and density imaging (NODDI) to the healthy spinal cord in vivo. NODDI provides maps such as the intra-neurite tissue volume fraction (vin), the orientation dispersion index (ODI) and the isotropic volume fraction (viso), and here we investigate their potential for spinal cord imaging. We scanned five healthy volunteers, four of whom twice, on a 3 T MRI system with a ZOOM-EPI sequence. In accordance to the published NODDI protocol, multiple b-shells were acquired at cervical level and both NODDI and diffusion tensor imaging (DTI) metrics were obtained and analysed to: i) characterise differences in grey and white matter (GM/WM); ii) assess the scan–rescan reproducibility of NODDI; iii) investigate the relationship between NODDI and DTI; and iv) compare the quality of fit of NODDI and DTI. Our results demonstrated that: i) anatomical features can be identified in NODDI maps, such as clear contrast between GM and WM in ODI; ii) the variabilities of vin and ODI are comparable to that of DTI and are driven by biological differences between subjects for ODI, have similar contribution from measurement errors and biological variation for vin, whereas viso shows higher variability, driven by measurement errors; iii) NODDI identifies potential sources contributing to DTI indices, as in the brain; and iv) NODDI outperforms DTI in terms of quality of fit. In conclusion, this work shows that NODDI is a useful model for in vivo diffusion MRI of the spinal cord, providing metrics closely related to tissue microstructure, in line with findings in the brain

Sodium ((23)Na) ultra-short echo time imaging in the human brain using a 3D-Cones trajectory

Object: Sodium magnetic resonance imaging ((23)Na-MRI) of the brain has shown changes in (23)Na signal as a hallmark of various neurological diseases such as stroke, Alzheimer's disease, Multiple Sclerosis and Huntington's disease. To improve scan times and image quality, we have implemented the 3D-Cones (CN) sequence for in vivo (23)Na brain MRI. Materials and Methods: Using signal-to-noise (SNR) as a measurement of sequence performance, CN is compared against more established 3D-radial k-space sampling schemes featuring cylindrical stack-of-stars (SOS) and 3D-spokes kooshball (KB) trajectories, on five healthy volunteers in a clinical setting. Resolution was evaluated by simulating the point-spread-functions (PSFs) and experimental measures on a phantom. Results: All sequences were shown to have a similar SNR arbitrary units (AU) of 6–6.5 in brain white matter, 7–9 in gray matter and 17–18 AU in cerebrospinal fluid. SNR between white and gray matter were significantly different for KB and CN (p = 0.046 and\0.001 respectively), but not for SOS (p = 0.1). Group mean standard deviations were significantly smaller for CN (p = 0.016). Theoretical full-width at half-maximum linewidth of the PSF for CN is broadened by only 0.1, compared to 0.3 and 0.8 pixels for SOS and KB respectively. Actual image resolution is estimated as 8, 9 and 6.3 mm for SOS, KB and CN respectively. Conclusion: The CN sequence provides stronger tissue contrast than both SOS and KB, with more reproducible SNR measurements compared to KB. For CN, a higher true resolution in the same amount of time with no significant trade-off in SNR is achieved. CN is therefore more suitable for 23Na-MRI in the brain

Sodium (Na) ultra-short echo time imaging in the human brain using a 3D-Cones trajectory

Object: Sodium magnetic resonance imaging (Na-MRI) of the brain has shown changes in Na signal as a hallmark of various neurological diseases such as stroke, Alzheimer's disease, Multiple Sclerosis and Huntington's disease. To improve scan times and image quality, we have implemented the 3D-Cones (CN) sequence for in vivo Na brain MRI. Materials and methods: Using signal-to-noise (SNR) as a measurement of sequence performance, CN is compared against more established 3D-radial k-space sampling schemes featuring cylindrical stack-of-stars (SOS) and 3D-spokes kooshball (KB) trajectories, on five healthy volunteers in a clinical setting. Resolution was evaluated by simulating the point-spread-functions (PSFs) and experimental measures on a phantom. Results: All sequences were shown to have a similar SNR arbitrary units (AU) of 6-6.5 in brain white matter, 7-9 in gray matter and 17-18 AU in cerebrospinal fluid. SNR between white and gray matter were significantly different for KB and CN (p = 0.046 and <0.001 respectively), but not for SOS (p = 0.1). Group mean standard deviations were significantly smaller for CN (p = 0.016). Theoretical full-width at half-maximum linewidth of the PSF for CN is broadened by only 0.1, compared to 0.3 and 0.8 pixels for SOS and KB respectively. Actual image resolution is estimated as 8, 9 and 6.3 mm for SOS, KB and CN respectively. Conclusion: The CN sequence provides stronger tissue contrast than both SOS and KB, with more reproducible SNR measurements compared to KB. For CN, a higher true resolution in the same amount of time with no significant trade-off in SNR is achieved. CN is therefore more suitable for Na-MRI in the brain. © 2013 The Author(s)

Spatial variability and changes of metabolite concentrations in the cortico-spinal tract in multiple sclerosis using coronal CSI

We characterized metabolic changes along the cortico-spinal tract (CST) in multiple sclerosis (MS) patients using a novel application of chemical shift imaging (CSI) and considering the spatial variation of metabolite levels. Thirteen relapsing-remitting (RR) and 13 primary-progressive (PP) MS patients and 16 controls underwent (1)H-MR CSI, which was applied to coronal-oblique scans to sample the entire CST. The concentrations of the main metabolites, i.e., N-acetyl-aspartate, myo-Inositol (Ins), choline containing compounds (Cho) and creatine and phosphocreatine (Cr), were calculated within voxels placed in regions where the CST is located, from cerebral peduncle to corona radiata. Differences in metabolite concentrations between groups and associations between metabolite concentrations and disability were investigated, allowing for the spatial variability of metabolite concentrations in the statistical model. RRMS patients showed higher CST Cho concentration than controls, and higher CST Ins concentration than PPMS, suggesting greater inflammation and glial proliferation in the RR than in the PP course. In RRMS, a significant, albeit modest, association between greater Ins concentration and greater disability suggested that gliosis may be relevant to disability. In PPMS, lower CST Cho and Cr concentrations correlated with greater disability, suggesting that in the progressive stage of the disease, inflammation declines and energy metabolism reduces. Attention to the spatial variation of metabolite concentrations made it possible to detect in patients a greater increase in Cr concentration towards the superior voxels as compared to controls and a stronger association between Cho and disability, suggesting that this step improves our ability to identify clinically relevant metabolic changes

The Importance of Cerebellar Connectivity on Simulated Brain Dynamics

The brain shows a complex multiscale organization that prevents a direct understanding of how structure, function and dynamics are correlated. To date, advances in neural modeling offer a unique opportunity for simulating global brain dynamics by embedding empirical data on different scales in a mathematical framework. The Virtual Brain (TVB) is an advanced data-driven model allowing to simulate brain dynamics starting from individual subjects’ structural and functional connectivity obtained, for example, from magnetic resonance imaging (MRI). The use of TVB has been limited so far to cerebral connectivity but here, for the first time, we have introduced cerebellar nodes and interconnecting tracts to demonstrate the impact of cerebro-cerebellar loops on brain dynamics. Indeed, the matching between the empirical and simulated functional connectome was significantly improved when including the cerebro-cerebellar loops. This positive result should be considered as a first step, since issues remain open about the best strategy to reconstruct effective structural connectivity and the nature of the neural mass or mean-field models generating local activity in the nodes. For example, signal processing is known to differ remarkably between cortical and cerebellar microcircuits. Tackling these challenges is expected to further improve the predictive power of functional brain activity simulations, using TVB or other similar tools, in explaining not just global brain dynamics but also the role of cerebellum in determining brain states in physiological conditions and in the numerous pathologies affecting the cerebro-cerebellar loop