Magnetic Resonance Elastography-derived Stiffness Predicts Renal Function Loss and Is Associated With Microvascular Inflammation in Kidney Transplant Recipients.

Background: Organ stiffening can be caused by inflammation and fibrosis, processes that are common causes of transplant kidney dysfunction. Magnetic resonance elastography (MRE) is a contrast-free, noninvasive imaging modality that measures kidney stiffness. The objective of this study was to assess the ability of MRE to serve as a prognostic factor for renal outcomes. Methods: Patients were recruited from the St Michael's Hospital Kidney Transplant Clinic. Relevant baseline demographic, clinical, and Banff histologic information, along with follow-up estimated glomerular filtration rate (eGFR) data, were recorded. Two-dimensional gradient-echo MRE imaging was performed to obtain kidney "stiffness" maps. Binary logistic regression analyses were performed to examine for relationships between stiffness and microvascular inflammation score. Linear mixed-effects modeling was used to assess the relationship between stiffness and eGFR change over time controlling for other baseline variables. A G2-likelihood ratio Chi-squared test was performed to compare between the baseline models with and without "stiffness." Results: Sixty-eight transplant kidneys were scanned in 66 patients (mean age 56 ± 12 y, 24 females), with 38 allografts undergoing a contemporaneous biopsy. Mean transplant vintage was 7.0 ± 6.8 y. In biopsied allografts, MRE-derived allograft stiffness was associated only with microvascular inflammation (Banff g + ptc score, Spearman ρ = 0.43, P = 0.01), but no other histologic parameters. Stiffness was negatively associated with eGFR change over time (Stiffness × Time interaction ß = -0.80, P < 0.0001), a finding that remained significant even when adjusted for biopsy status and baseline variables (Stiffness × Time interaction ß = -0.46, P = 0.04). Conversely, the clinical models including "stiffness" showed significantly better fit (P = 0.04) compared with the baseline clinical models without "stiffness." Conclusions: MRE-derived renal stiffness provides important prognostic information regarding renal function loss for patients with allograft dysfunction, over and above what is provided by current clinical variables.

Hyperglycemia associated with acute brain injury in neonatal encephalopathy.

OBJECTIVE: To identify how alterations in glucose levels are associated with regional brain injury in neonatal encephalopathy. METHODS: This was a prospective cohort study of 102 newborns with neonatal encephalopathy, with continuous glucose monitoring for 72 h. 97 (95%) completed 72 h of therapeutic hypothermia. Brain imaging around day 5 of life included diffusion tensor imaging and MR spectroscopy. Regions of interest were placed for both DTI and MR spectroscopy, and tractography of the optic radiation and corticospinal tract were evaluated. Linear regression models related each MR metric with minimum and maximum glucose values during each day of life, adjusting for 5-minute Apgar scores and umbilical artery pH. RESULTS: Higher maximum glucose levels on the first day of life were associated with widespread changes in mean diffusivity in the anterior and posterior white matter, splenium of the corpus callosum, lentiform nucleus, pulvinar nucleus of the thalamus, posterior limb of the internal capsule, and optic radiations, thus including regions traditionally associated with hypoxia-ischemia or hypoglycemia. No associations were found between lower minimum glucose levels and DTI changes in any regions tested, or between glucose levels and MR spectroscopy. CONCLUSIONS: In this cohort of neonatal encephalopathy with therapeutic hypothermia, higher maximal glucose on the first day of life was associated with widespread microstructural changes, but lower minimum glucose levels were not associated with changes in any of the regions tested. Long-term follow-up will determine if imaging findings translate to long-term outcomes.

Original and Modified Graeb Score Correlation With Intraventricular Hemorrhage and Clinical Outcome Prediction in Hyperacute Intracranial Hemorrhage.

Background and Purpose- The Graeb score is a visual rating scale of intraventricular hemorrhage (IVH) on noncontrast head CT. Little data exist in the hyperacute (<6 hour) period for reliability and predictive value of the modified Graeb Score (mGS) or the original Graeb Score (oGS) for clinical outcomes or their correlation with quantitative IVH volumes. Methods- A retrospective analysis of multicenter prospective intracranial hemorrhage study was performed. oGS and mGS inter-observer agreement and IVH volume correlation on the baseline noncontrast head CT were calculated by intraclass correlation coefficient and Pearson coefficient respectively. Predictors of poor outcome (modified Rankin Scale scores ≥4) at 3 months were identified using a backward stepwise selection multivariable analysis. oGS and mGS performance for modified Rankin Scale scores ≥4 was determined by receiver operating characteristic analysis. Results- One hundred forty-one patients (65±12 years) with median (interquartile range) time to CT of 82.5 (70.3-157.5) minutes were included. IVH was observed in 43 (30%) patients. Inter-observer agreement was excellent for both oGS (intraclass correlation coefficient, 0.90 [95% CI, 0.80-0.95]) and mGS (intraclass correlation coefficient, 0.97 [95% CI, 0.84-0.99]). mGS (R=0.79; P<0.01) correlated better than oGS (R=0.71; P<0.01) with IVH volumes (P=0.02). Models of thresholded oGS and mGS were not different from a model of planimetric baseline intracranial hemorrhage and IVH volume for poor outcome prediction. Area under the curves were 0.70, 0.73, and 0.72, respectively. Conclusions- Excellent correlation for oGS and mGS with IVH volume was seen. Thresholded oGS and mGS are reasonable surrogates for planimetric IVH volume for hyperacute intracranial hemorrhage studies.

Comparisons between multi-component myelin water fraction, T1w/T2w ratio, and diffusion tensor imaging measures in healthy human brain structures.

Various MRI techniques, including myelin water imaging, T1w/T2w ratio mapping and diffusion-based imaging can be used to characterize tissue microstructure. However, surprisingly few studies have examined the degree to which these MRI measures are related within and between various brain regions. Therefore, whole-brain MRI scans were acquired from 31 neurologically-healthy participants to empirically measure and compare myelin water fraction (MWF), T1w/T2w ratio, fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) in 25 bilateral (10 grey matter; 15 white matter) regions-of-interest (ROIs). Except for RD vs. T1w/T2w, MD vs. T1w/T2w, moderately significant to highly significant correlations (p < 0.001) were found between each of the other measures across all 25 brain structures [T1w/T2w vs. MWF (Pearson r = 0.33, Spearman ρ = 0.31), FA vs. MWF (r = 0.73, ρ = 0.75), FA vs. T1w/T2w (r = 0.25, ρ = 0.22), MD vs. AD (r = 0.57, ρ = 0.58), MD vs. RD (r = 0.64, ρ = 0.61), AD vs. MWF (r = 0.43, ρ = 0.36), RD vs. MWF (r = -0.49, ρ = -0.62), MD vs. MWF (r = -0.22, ρ = -0.29), RD vs. FA (r = -0.62, ρ = -0.75) and MD vs. FA (r = -0.22, ρ = -0.18)]. However, while all six MRI measures were correlated with each other across all structures, there were large intra-ROI and inter-ROI differences (i.e., with no one measure consistently producing the highest or lowest values). This suggests that each quantitative MRI measure provides unique, and potentially complimentary, information about underlying brain tissues - with each metric offering unique sensitivity/specificity tradeoffs to different microstructural properties (e.g., myelin content, tissue density, etc.).

Single-phase CT angiography: collateral grade is independent of scan weighting.

PURPOSE: Collateral grading may vary on single-phase CTA (sCTA) depending on whether the CTA is arterial (A), arteriovenous (AV), or venous (V) weighted. We studied the impact of sCTA weighting on collateral grading using the Tan, MAAS, and Menon methods, and their ability to predict infarct and clinical outcome hypothesizing that AV-weighted sCTA should better predict these outcomes. METHODS: Multicenter retrospective analysis of 212 patients undergoing baseline CTP/sCTA. sCTA weighting was determined by comparing ICA to torcula AV ratios with those from concomitant CTP time-density curves at peak arterial or venous contrast attenuation. A generalized linear mixed model investigated the predictive value for infarct volume or 90-day mRS of the three collateral scores stratified by sCTA weighting and adjusting for age, sex, clot burden score (CBS), and NIHSS. Bayesian information criterion (BIC) differences were calculated between the null and fitted models. RESULTS: Mean age, baseline median NIHSS, ASPECTS, and onset to treatment time were 69.89 ± 14.45, 13 (6-18), 10 (8-10), and 128 (66-181) minutes. sCTA scans were AV-weighted in 137/212 (65%) and A-weighted in 73 (34%). No association was demonstrated between sCTA weighting, hospital site, and sCTA technique. All collateral scores were related to infarct volume irrespective of sCTA weighting, with greatest fit with the regional leptomeningeal score (BIC 18.29, p = 0.0001). No association was shown between sCTA weighting, collateral grade, and clinical outcome. CONCLUSION: sCTA weighting did not significantly impact collateral grade using three common collateral scores or their ability to predict final infarct.

Quantitative Ex Vivo MRI Changes due to Progressive Formalin Fixation in Whole Human Brain Specimens: Longitudinal Characterization of Diffusion, Relaxometry, and Myelin Water Fraction Measurements at 3T.

PURPOSE: Postmortem MRI can be used to reveal important pathologies and establish radiology-pathology correlations. However, quantitative MRI values are altered by tissue fixation. Therefore, the purpose of this study was to investigate time-dependent effects of formalin fixation on MRI relaxometry (T1 and T2), diffusion tensor imaging (fractional anisotropy, FA; and mean diffusivity, MD), and myelin water fraction (MWF) measurements throughout intact human brain specimens. METHODS: Two whole, neurologically-healthy human brains were immersed in 10% formalin solution and scanned at 13 time points between 0 and 1,032 h. Whole-brain maps of longitudinal (T1) and transverse (T2) relaxation times, FA, MD, and MWF were generated at each time point to illustrate spatiotemporal changes, and region-of-interest analyses were then performed in eight brain structures to quantify temporal changes with progressive fixation. RESULTS: Although neither of the diffusion measures (FA nor MD) showed significant changes as a function of formalin fixation time, both T1 and T2-relaxation times significantly decreased, and MWF estimates significantly increased with progressive fixation until (and likely beyond) our final measurements were taken at 1,032 h. CONCLUSION: These results suggest that T1-relaxation, T2-relaxation and MWF estimates must be performed quite early in the fixation process to avoid formalin-induced changes compared to in vivo values; and furthermore, that different ex vivo scans within an experiment must be acquired at consistent (albeit still early) fixation intervals to avoid fixative-related differences between samples. Conversely, ex vivo diffusion measures (FA and MD) appear to depend more on other factors (e.g., pulse sequence optimization, sample temperature, etc.).

A Method for Whole Brain Ex Vivo Magnetic Resonance Imaging with Minimal Susceptibility Artifacts.

Magnetic resonance imaging (MRI) is a non-destructive technique that is capable of localizing pathologies and assessing other anatomical features (e.g., tissue volume, microstructure, and white matter connectivity) in postmortem, ex vivo human brains. However, when brains are removed from the skull and cerebrospinal fluid (i.e., their normal in vivo magnetic environment), air bubbles and air-tissue interfaces typically cause magnetic susceptibility artifacts that severely degrade the quality of ex vivo MRI data. In this report, we describe a relatively simple and cost-effective experimental setup for acquiring artifact-free ex vivo brain images using a clinical MRI system with standard hardware. In particular, we outline the necessary steps, from collecting an ex vivo human brain to the MRI scanner setup, and have also described changing the formalin (as might be necessary in longitudinal postmortem studies). Finally, we share some representative ex vivo MRI images that have been acquired using the proposed setup in order to demonstrate the efficacy of this approach. We hope that this protocol will provide both clinicians and researchers with a straight-forward and cost-effective solution for acquiring ex vivo MRI data from whole postmortem human brains.

Heads in the Cloud: A Primer on Neuroimaging Applications of High Performance Computing.

With larger data sets and more sophisticated analyses, it is becoming increasingly common for neuroimaging researchers to push (or exceed) the limitations of standalone computer workstations. Nonetheless, although high-performance computing platforms such as clusters, grids and clouds are already in routine use by a small handful of neuroimaging researchers to increase their storage and/or computational power, the adoption of such resources by the broader neuroimaging community remains relatively uncommon. Therefore, the goal of the current manuscript is to: 1) inform prospective users about the similarities and differences between computing clusters, grids and clouds; 2) highlight their main advantages; 3) discuss when it may (and may not) be advisable to use them; 4) review some of their potential problems and barriers to access; and finally 5) give a few practical suggestions for how interested new users can start analyzing their neuroimaging data using cloud resources. Although the aim of cloud computing is to hide most of the complexity of the infrastructure management from end-users, we recognize that this can still be an intimidating area for cognitive neuroscientists, psychologists, neurologists, radiologists, and other neuroimaging researchers lacking a strong computational background. Therefore, with this in mind, we have aimed to provide a basic introduction to cloud computing in general (including some of the basic terminology, computer architectures, infrastructure and service models, etc.), a practical overview of the benefits and drawbacks, and a specific focus on how cloud resources can be used for various neuroimaging applications.