BACKGROUND AND PURPOSE:Quantitative MR imaging techniques are gaining interest as methods of reducing acquisition times while additionally providing robust measurements. This study aimed to implement a synthetic MR imaging method on a new scanner type and to compare its diagnostic accuracy and volumetry with conventional MR imaging in patients with MS and controls.

Rodent models are developed to enhance understanding of the underlying biology of different brain disorders. However, before interpreting findings from animal models in a translational aspect to understand human disease, a fundamental step is to first have knowledge of similarities and differences of the biological systems studied. In this study, we analyzed and verified four known networks termed: default mode network, motor network, dorsal basal ganglia network, and ventral basal ganglia network using resting state functional MRI (rsfMRI) in humans and rats. Our work supports the notion that humans and rats have common robust resting state brain networks and that rsfMRI can be used as a translational tool when validating animal models of brain disorders. In the future, rsfMRI may be used, in addition to short-term interventions, to characterize longitudinal effects on functional brain networks after long-term intervention in humans and rats.

Objective Magnetic resonance imaging (MRI) is essential for multiple sclerosis diagnostics but is conventionally not specific to demyelination. Myelin imaging is often hampered by long scanning times, complex postprocessing, or lack of clinical approval. This study aimed to assess the specificity, robustness, and clinical value of Rapid Estimation of Myelin for Diagnostic Imaging, a new myelin imaging technique based on time‐efficient simultaneous T1/T2 relaxometry and proton density mapping in multiple sclerosis. Methods Rapid myelin imaging was applied using 3T MRI ex vivo in 3 multiple sclerosis brain samples and in vivo in a prospective cohort of 71 multiple sclerosis patients and 21 age/sex‐matched healthy controls, with scan–rescan repeatability in a subcohort. Disability in patients was assessed by the Expanded Disability Status Scale and the Symbol Digit Modalities Test at baseline and 2‐year follow‐up. Results Rapid myelin imaging correlated with myelin‐related stains (proteolipid protein immunostaining and Luxol fast blue) and demonstrated good precision. Multiple sclerosis patients had, relative to controls, lower normalized whole‐brain and normal‐appearing white matter myelin fractions, which correlated with baseline cognitive and physical disability. Longitudinally, these myelin fractions correlated with follow‐up physical disability, even with correction for baseline disability. Interpretation Rapid Estimation of Myelin for Diagnostic Imaging provides robust myelin quantification that detects diffuse demyelination in normal‐appearing tissue in multiple sclerosis, which is associated with both cognitive and clinical disability. Because the technique is fast, with automatic postprocessing and US Food and Drug Administration/CE clinical approval, it can be a clinically feasible biomarker that may be suitable to monitor myelin dynamics and evaluate treatments aiming at remyelination. ANN NEUROL 2020;87:710–724

PurposeCharacterize the static and dynamic functional connectivity for subjects with juvenile myoclonic epilepsy (JME) using a quantitative data-driven analysis approach.MethodsWhole-brain resting-state functional MRI data were acquired on a 3 T whole-body clinical MRI scanner from 18 subjects clinically diagnosed with JME and 25 healthy control subjects. 2-min sliding-window approach was incorporated in the quantitative data-driven data analysis framework to assess both the dynamic and static functional connectivity in the resting brains. Two-sample t-tests were performed voxel-wise to detect the differences in functional connectivity metrics based on connectivity strength and density.ResultsThe static functional connectivity metrics based on quantitative data-driven analysis of the entire 10-min acquisition window of resting-state functional MRI data revealed significantly enhanced functional connectivity in JME patients in bilateral dorsolateral prefrontal cortex, dorsal striatum, precentral and middle temporal gyri. The dynamic functional connectivity metrics derived by incorporating a 2-min sliding window into quantitative data-driven analysis demonstrated significant hyper dynamic functional connectivity in the dorsolateral prefrontal cortex, middle temporal gyrus and dorsal striatum. Connectivity strength metrics (both static and dynamic) can detect more extensive functional connectivity abnormalities in the resting-state functional networks (RFNs) and depict also larger overlap between static and dynamic functional connectivity results.ConclusionIncorporating a 2-min sliding window into quantitative data-driven analysis of resting-state functional MRI data can reveal additional information on the temporally fluctuating RFNs of the human brain, which indicate that RFNs involving dorsolateral prefrontal cortex have temporal varying hyper dynamic characteristics in JME patients. Assessing dynamic along with static functional connectivity may provide further insights into the abnormal function connectivity underlying the pathological brain functioning in JME.