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There is evidence that subtle breakdown of the blood–brain barrier (BBB) is a pathophysiological component of several diseases, including cerebral small vessel disease and some dementias. Dynamic contrast-enhanced MRI (DCE-MRI) combined with tracer kinetic modelling is widely used for assessing permeability and perfusion in brain tumours and body tissues where contrast agents readily accumulate in the extracellular space. However, in diseases where leakage is subtle, the optimal approach for measuring BBB integrity is likely to differ since the magnitude and rate of enhancement caused by leakage are extremely low; several methods have been reported in the literature, yielding a wide range of parameters even in healthy subjects. We hypothesised that the Patlak model is a suitable approach for measuring low-level BBB permeability with low temporal resolution and high spatial resolution and brain coverage, and that normal levels of scanner instability would influence permeability measurements. DCE-MRI was performed in a cohort of mild stroke patients (n=201) with a range of cerebral small vessel disease severity. We fitted these data to a set of nested tracer kinetic models, ranking their performance according to the Akaike information criterion. To assess the influence of scanner drift, we scanned 15 healthy volunteers that underwent a “sham” DCE-MRI procedure without administration of contrast agent. Numerical simulations were performed to investigate model validity and the effect of scanner drift. The Patlak model was found to be most appropriate for fitting low-permeability data, and the simulations showed vp and KTrans estimates to be reasonably robust to the model assumptions. However, signal drift (measured at approximately 0.1% per minute and comparable to literature reports in other settings) led to systematic errors in calculated tracer kinetic parameters, particularly at low permeabilities. Our findings justify the growing use of the Patlak model in low-permeability states, which has the potential to provide valuable information regarding BBB integrity in a range of diseases. However, absolute values of the resulting tracer kinetic parameters should be interpreted with extreme caution, and the size and influence of signal drift should be measured where possible. •We performed DCE-MRI in 201 patients with a range of small vessel disease severity.•We tested tracer kinetic model performance via simulations and statistical analysis.•The Patlak model was optimal for assessing leakage in normal tissues and lesions.•Scanner drift leads to substantial errors in measured tracer kinetic parameters.•DCE-MRI measurements of subtle leakage should be interpreted with caution.

Whole-rock major- and trace-element data are presented on a sample collection from the >3 Ga Amikoq Layered Complex (ALC), and hosting amphibolites within the Mesoarchean Akia terrane, SW Greenland. The lithologies range from leuconorite to melanorite/feldspathic orthopyroxenite, orthopyroxenite to harzburgite through to dunite, and tholeiitic basaltic–picritic mafic host rocks. The Amikoq Layered Complex samples are primitive (Mg#: 65–89) with elevated Ni and Cr contents. However, the absence of troctolitic lithologies and the presence of two orthopyroxene compositional trends, suggests that the successions might not be comagmatic. On the basis of trace-element cumulate models, relatively low Ni contents and minor negative Sr-Eu anomalies in some high-Ti ultramafic rocks, it is not possible to exclude a petrogenesis related to a melt similar to that of the mafic host-rocks. Ultramafic samples with U-shaped trace-element distribution patterns are petrogenetically related to the noritic sequences, either through cumulus mineral accumulation or melt-rock reactions. Assimilation-fractional-crystallisation modelling of melanorites nevertheless require the parental melt to have been contaminated/mixed with a component of island-arc-like tholeiite affinity. A boninite-like parental melt might have been derived from the subcontinental lithospheric mantle of the Akia terrane, or alternatively via assimilation of an ultramafic parental melt with island-arc-like tholeiite. Given the complex geological evolution and high-grade metamorphic overprint of the Amikoq Layered Complex, we are unable to differentiate between the two models.

Anti-gliadin antibodies (AGA) occur in approximately 10% of the general population, produced as a response to gluten. Autoimmune gluten-related disorders can have detrimental neurological effects if not properly controlled but the relevance of such \"incidental\" AGA is not properly established; any harm caused would indicate the gluten-free diet as a means for affected people to protect their brain health. We explored this question by comparing brain MRI scanning, cognitive testing and other measures between healthy volunteers with and without AGA. Healthy volunteers aged 50-70 (without celiac disease, on a gluten-containing diet) underwent blood testing to confirm AGA status. Any AGA+ subjects were matched to AGA- controls on age, sex, BMI, level of education, hypertension diagnosis and smoking history. These subgroups underwent a cognitive test battery, quality-of-life (QoL) surveys and brain MRI scanning. Groups were compared between all outcome measures. Secondary analyses correlated AGA titre with outcomes across the whole cohort. Groupwise comparisons of cognitive, QoL and MRI studies were all negative. Repeating these analyses as correlations with AGA titre across the cohort, a single significant result was found concerning the error rate on the subtle cognitive impairment test, in a direction indicating increased IgG AGA to predict worse performance. This did not survive multiple comparisons correction. Our analysis is the most comprehensive to date and utilises a number of outcome measures known to be sensitive to subtle shifts in neurophysiology and cognition. Incidental AGA does not appear to be associated with any indications of neuropsychological deficit.

Enrichment of the heavy rare earth elements (HREE) in carbonatites is rare as carbonatite petrogenesis favours the light (L)REE. We describe HREE enrichment in fenitized phonolite breccia, focusing on small satellite occurrences 1–2 km from the Songwe Hill carbonatite, Malawi. Within the breccia groundmass, a HREE-bearing mineral assemblage comprises xenotime, zircon, anatase/rutile and minor huttonite/thorite, as well as fluorite and apatite. A genetic link between HREE mineralization and carbonatite emplacement is indicated by the presence of Sr-bearing carbonate veins, carbonatite xenoliths and extensive fenitization. We propose that the HREE are retained in hydrothermal fluids which are residually derived from a carbonatite after precipitation of LREE minerals. Brecciation provides a focusing conduit for such fluids, enabling HREE transport and xenotime precipitation in the fenite. Continued fluid–rock interaction leads to dissolution of HREE-bearing minerals and further precipitation of xenotime and huttonite/thorite. At a maximum Y content of 3100 µg g−1, HREE concentrations in the presented example are not sufficient to constitute ore, but the similar composition and texture of these rocks to other cases of carbonatite-related HREE enrichment suggests that all form via a common mechanism linked to fenitization. Precipitation of HREE minerals only occurs where a pre-existing structure provides a focusing conduit for fenitizing fluids, reducing fluid – country-rock interaction. Enrichment of HREE and Th in fenite breccia serves as an indicator of fluid expulsion from a carbonatite, and may indicate the presence of LREE mineralization within the source carbonatite body at depth.

Dynamic contrast-enhanced MRI (DCE-MRI) is increasingly used to quantify and map the spatial distribution of blood-brain barrier (BBB) leakage in neurodegenerative disease, including cerebral small vessel disease and dementia. However, the subtle nature of leakage and resulting small signal changes make quantification challenging. While simplified one-dimensional simulations have probed the impact of noise, scanner drift, and model assumptions, the impact of spatio-temporal effects such as gross motion, k-space sampling and motion artefacts on parametric leakage maps has been overlooked. Moreover, evidence on which to base the design of imaging protocols is lacking due to practical difficulties and the lack of a reference method. To address these problems, we present an open-source computational model of the DCE-MRI acquisition process for generating four dimensional Digital Reference Objects (DROs), using a high-resolution brain atlas and incorporating realistic patient motion, extra-cerebral signals, noise and k-space sampling. Simulations using the DROs demonstrated a dominant influence of spatio-temporal effects on both the visual appearance of parameter maps and on measured tissue leakage rates. The computational model permits greater understanding of the sensitivity and limitations of subtle BBB leakage measurement and provides a non-invasive means of testing and optimising imaging protocols for future studies. [Display omitted]

One of the fundamental challenges when dealing with medical imaging datasets is class imbalance. Class imbalance happens where an instance in the class of interest is relatively low, when compared to the rest of the data. This study aims to apply oversampling strategies in an attempt to balance the classes and improve classification performance. We evaluated four different classifiers from k-nearest neighbors (k-NN), support vector machine (SVM), multilayer perceptron (MLP) and decision trees (DT) with 73 oversampling strategies. In this work, we used imbalanced learning oversampling techniques to improve classification in datasets that are distinctively sparser and clustered. This work reports the best oversampling and classifier combinations and concludes that the usage of oversampling methods always outperforms no oversampling strategies hence improving the classification results.

The metamorphic history of the Mesoarchean Amikoq Layered Complex within the Akia terrane of SW Greenland was characterised by electron microprobe mineral data and detailed petrography on 12 representative samples, integrated with zircon U–Pb geochronology and petrology. The complex intruded into a >3004 Ma supracrustal association now consisting of granoblastic metabasites with subordinate quartz-rich gneiss. Supracrustal host rocks contain a relict high-temperature assemblage of orthopyroxene–clinopyroxene (± pigeonite exsolution lamellae, exsolved at ~975–1010°C), which is interpreted to pre-date the Amikoq intrusion. Cumulate to granoblastic-textured rocks of the main Amikoq Layered Complex range modally from leuconorite to melanorite, orthopyroxenite to harzburgite/dunite and rare hornblende melagabbro. Observed mineralogy of main complex noritic lithologies is essentially relict igneous with orthopyroxene–biotite and hornblende–plagioclase thermometers yielding temperatures of ~800–1070°C. An anatectic zircon megacryst from a patchy quartzo–feldspathic leucosome hosted in an orthopyroxene-dominated Amikoq rock reflects local anatexis at peak metamorphic P–T conditions and yields an intrusion minimum age of 3004 ± 9 Ma. Field observations indicate local anatexis of orthopyroxene-dominated lithologies, possibly indicating a post-intrusion peak temperature of >900°C. The last preserved stages of retrogression are recorded in paragneiss plagioclase–garnet, biotite–garnet and host rock ilmenite–magnetite pairs (≤3 kbar and ~380–560°C). The Amikoq Complex intruded a MORB-like crustal section and the former remained relatively undisturbed in terms of modal mineralogy. Preservation of igneous textures and mineralogy are related to an anhydrous, high-grade metamorphic history that essentially mimics igneous crystallisation conditions, whereas local high-strain zones acted as fluid pathways resulting in hydrous breakdown of igneous minerals. There is no evidence of equilibration of the intrusion at sub-amphibolite-facies conditions.

Pyrexia soon after stroke is associated with severe stroke and poor functional outcome. Few studies have assessed brain temperature after stroke in patients, so little is known of its associations with body temperature, stroke severity, or outcome. We measured temperatures in ischemic and normal-appearing brain using 1H-magnetic resonance spectroscopy and its correlations with body (tympanic) temperature measured four-hourly, infarct growth by 5 days, early neurologic (National Institute of Health Stroke Scale, NIHSS) and late functional outcome (death or dependency). Among 40 patients (mean age 73 years, median NIHSS 7, imaged at median 17 hours), temperature in ischemic brain was higher than in normal-appearing brain on admission (38.6°C-core, 37.9°C-contralateral hemisphere, P = 0.03) but both were equally elevated by 5 days;both were higher than tympanic temperature. Ischemic lesion temperature was not associated with NIHSS or 3-month functional outcome;in contrast, higher contralateral normal-appearing brain temperature was associated with worse NIHSS, infarct expansion and poor functional outcome, similar to associations for tympanic temperature. We conclude that brain temperature is higher than body temperature;that elevated temperature in ischemic brain reflects a local tissue response to ischemia, whereas pyrexia reflects the systemic response to stroke, occurs later, and is associated with adverse outcomes.

In this work, we develop the Single-Input Multi-Output U-Net (SIMOU-Net), a hybrid network for foetal brain segmentation inspired by the original U-Net fused with the holistically nested edge detection (HED) network. The SIMOU-Net is similar to the original U-Net but it has a deeper architecture and takes account of the features extracted from each side output. It acts similar to an ensemble neural network, however, instead of averaging the outputs from several independently trained models, which is computationally expensive, our approach combines outputs from a single network to reduce the variance of predications and generalization errors. Experimental results using 200 normal foetal brains consisting of over 11,500 2D images produced Dice and Jaccard coefficients of 94.2 ± 5.9% and 88.7 ± 6.9%, respectively. We further tested the proposed network on 54 abnormal cases (over 3500 images) and achieved Dice and Jaccard coefficients of 91.2 ± 6.8% and 85.7 ± 6.6%, respectively.

BackgroundCT is commonly used to image patients with ischaemic stroke but radiologist interpretation may be delayed. Machine learning techniques can provide rapid automated CT assessment but are usually developed from annotated images which necessarily limits the size and representation of development data sets. We aimed to develop a deep learning (DL) method using CT brain scans that were labelled but not annotated for the presence of ischaemic lesions.MethodsWe designed a convolutional neural network-based DL algorithm to detect ischaemic lesions on CT. Our algorithm was trained using routinely acquired CT brain scans collected for a large multicentre international trial. These scans had previously been labelled by experts for acute and chronic appearances. We explored the impact of ischaemic lesion features, background brain appearances and timing of CT (baseline or 24–48 hour follow-up) on DL performance.ResultsFrom 5772 CT scans of 2347 patients (median age 82), 54% had visible ischaemic lesions according to experts. Our DL method achieved 72% accuracy in detecting ischaemic lesions. Detection was better for larger (80% accuracy) or multiple (87% accuracy for two, 100% for three or more) lesions and with follow-up scans (76% accuracy vs 67% at baseline). Chronic brain conditions reduced accuracy, particularly non-stroke lesions and old stroke lesions (32% and 31% error rates, respectively).ConclusionDL methods can be designed for ischaemic lesion detection on CT using the vast quantities of routinely collected brain scans without the need for lesion annotation. Ultimately, this should lead to more robust and widely applicable methods.