The blood-brain barrier (BBB) consists of specialized cells that tightly regulate the in- and outflow of molecules from the blood to brain parenchyma, protecting the brain's microenvironment. If one of the BBB components starts to fail, its dysfunction can lead to a cascade of neuroinflammatory events leading to neuronal dysfunction and degeneration. Preliminary imaging findings suggest that BBB dysfunction could serve as an early diagnostic and prognostic biomarker for a number of neurological diseases. This review aims to provide clinicians with an overview of the emerging field of BBB imaging in humans by answering three key questions: (1. Disease) In which diseases could BBB imaging be useful? (2. Device) What are currently available imaging methods for evaluating BBB integrity? And (3. Distribution) what is the potential of BBB imaging in different environments, particularly in resource limited settings? We conclude that further advances are needed, such as the validation, standardization and implementation of readily available, low-cost and non-contrast BBB imaging techniques, for BBB imaging to be a useful clinical biomarker in both resource-limited and well-resourced settings.

White matter hyperintensities (WMHs) are common in normally functioning older adults. While greater WMH volume may be associated with an increased risk of cognitive dysfunction and dementia, the impact of the location of WMHs is less well understood. We investigated whether WMH burden in specific white matter (WM) tracts is associated with clinical classification and global cognitive scores in at-risk groups for Alzheimer’s disease (AD). Participants from Dementia Prevention Research Clinics, New Zealand, were classified as control, subjective cognitive decline, single-domain amnestic mild cognitive impairment (MCI), multiple-domain amnestic MCI, or early AD by a multidisciplinary team (Table 1). Several tract-WMH “overlap scores” (the percentage of total tract voxels containing WMHs) were calculated for each participant’s binarised WMH map (Lesion Segmentation Toolbox, SPM) using seven tract probability templates from a 140-subject tract atlas (www.megatrackatlas.org). Tract templates were of the superior longitudinal fasciculus (SLF), inferior longitudinal fasciculus (ILF), inferior fronto-occipital fasciculus (IFOF), uncinate fasciculus (UF), cingulum (CB), fornix, and corpus callosum (CC). Between-group differences in overlap scores were tested with ANOVAs. Pearson’s correlations examined associations between tract-WMH overlap and global cognitive scores. Figure 1 shows WMH overlap with one tract, the IFOF, in a representative participant. Considering the whole sample, the highest tract-WMH overlap scores can be seen in the IFOF and CC. Significant between-group differences were found for the SLF, ILF, IFOF, UF, and CC (p < .05), but not the CB or fornix (Fig. 2), with significant linear trends describing the relationship between overlap scores and clinical classification. Post-hoc tests confirmed significant pairwise comparisons for overlap scores in all aforementioned tracts except the SLF (Fig. 2). We found Addenbrooke’s Cognitive Examination-III scores negatively correlated with overlap scores in the ILF (r = -0.19), IFOF(r = -0.22), UF(r = -0.22), and CC(r = -0.19) (all p < .01), but not the SLF, CB, or fornix (p > .05). Our findings highlight that WMH burden within specific WM tracts is associated with degree of clinical impairment in groups at-risk of AD. Future research will investigate relationships between overlap scores in specific WM tracts and executive functioning, episodic memory, and processing speed performance.

OBJECTIVE:Bioelectrial signals known as slow waves play a key role in coordinating gastric motility. Slow wave dysrhythmias have been associated with a number of functional motility disorders. However, there have been limited human recordings obtained in the consious state or over an extended period of time. This study aimed to evaluate a robust ambulatory recording platform. APPROACH:A commercially available multi-sensor recording system (Shimmer3, ShimmerSensing) was applied to acquire slow wave information from the stomach of six humans and four pigs. First, acute experiments were conducted in pigs to verify the accuracy of the recording module by comparing to a standard widely employed electrophysiological mapping system (ActiveTwo, BioSemi). Then, patients with medically refractory gastroparesis undergoing temporary gastric stimulator implantation were enrolled and gastric slow waves were recorded from mucosally-implanted electrodes for 5 d continuously. Accelerometer data was also collected to exclude data segments containing excessive patient motion artefact. MAIN RESULTS:Slow wave signals and activation times from the Shimmer3 module were closely comparable to a standard electrophysiological mapping system. Slow waves were able to be recorded continuously for 5 d in human subjects. Over the 5 d, slow wave frequency was 2.8  ±  0.6 cpm and amplitude was 0.2  ±  0.3 mV. SIGNIFICANCE:A commercial multi-sensor recording module was validated for recording electrophysiological slow waves for 5 d, including in ambulatory patients. Multiple modules could be used simultaneously in the future to track the spatio-temporal propagation of slow waves. This framework can now allow for patho-electrophysiological studies to be undertaken to allow symptom correlation with dysrhythmic slow wave events.

Often times, the development of physical models of materials behavior is hindered not only by the incompleteness of the theoretical approach, but also by uncertainties that arise from limitations in the experimental observations used to validate and calibrate these models. In this work, we present a Bayesian framework for both the calibration of physical models as well as the quantification of likely uncertainty in experimental observations and apply it to a model for the plastic response of multi-phase Transformation Induced Plasticity (TRIP) steels. The model is based on a formulation of irreversible thermodynamics of plastic deformation and accounts for the presence of multiple phases through homogenization theories based on the iso-work approximation. Bayesian calibration through Metropolis-Hastings Markov Chain Monte Carlo has been used to calibrate a subset of the model parameters against experimental data sequentially and simultaneously. The calibrated parameters obtained from sequential training were in turn used to assess the uncertainty in a subset of experimental data—i.e. phase volume fractions—used as input to the models themselves. The viability of the calibration approach has also been examined using synthetic data generated from simultaneous calibrated model.

Blood-brain barrier (BBB) dysfunction has been observed in multiple neurodegenerative conditions, including mild cognitive impairment (MCI) and Alzheimer’s disease (AD). However recent concerns on the repeated use of Gadolinium based contrast agents (GBCAs), prompted us to investigate alternative, non-invasive methods for measuring BBB health. Diffusion-prepared arterial spin labelling (DP-ASL) imaging was implemented at 3T to determine water exchange rates (Kw) in 55 participants, comprising MCI, early AD and control participants. We found Kw to be associated with cognitive performance, suggesting it may be a useful imaging biomarker of early AD pathology.