IntroductionDysfunction of the cerebral vasculature is considered one of the key components of Alzheimer’s disease (AD), but the mechanisms affecting individual brain vessels are poorly understood.MethodsHere, using in vivo two-photon microscopy in superficial cortical layers and ex vivo imaging across brain regions, we characterized blood–brain barrier (BBB) function and neurovascular coupling (NVC) at the level of individual brain vessels in adult female 5xFAD mice, an aggressive amyloid-β (Aβ) model of AD.ResultsWe report a lack of abnormal increase in adsorptive-mediated transcytosis of albumin and preserved paracellular barrier for fibrinogen and small molecules despite an extensive load of Aβ. Likewise, the NVC responses to somatosensory stimulation were preserved at all regulatory segments of the microvasculature: penetrating arterioles, precapillary sphincters, and capillaries. Lastly, the Aβ plaques did not affect the density of capillary pericytes.ConclusionOur findings provide direct evidence of preserved microvascular function in the 5xFAD mice and highlight the critical dependence of the experimental outcomes on the choice of preclinical models of AD. We propose that the presence of parenchymal Aβ does not warrant BBB and NVC dysfunction and that the generalized view that microvascular impairment is inherent to Aβ aggregation may need to be revised.
Computed tomography (CT) is a widely used imaging modality for medical diagnosis and treatment. In electroencephalography (EEG), CT imaging is necessary for co-registering with magnetic resonance imaging (MRI) and for creating more accurate head models for the brain electrical activity due to better representation of bone anatomy. Unfortunately, CT imaging exposes patients to potentially harmful sources of ionizing radiation. Image synthesis methods present a solution for avoiding extra radiation exposure. In this paper, we perform image synthesis to create a realistic, synthetic CT image from MRI of the same subject, and we present a comparison of different image synthesis techniques. Using a dataset of 30 paired MRI and CT image volumes, our results compare image synthesis using deep neural network regression, state-ofthe-art adversarial deep learning, as well as atlas-based synthesis utilizing image registration. We also present a novel synthesis method that combines multi-atlas registration as a prior to deep learning algorithms, in which we perform a weighted addition of synthetic CT images, derived from atlases, to the output of a deep neural network to obtain a residual type of learning. In addition to evaluating the quality of the synthetic CT images, we also demonstrate that image synthesis methods allow for more accurate bone segmentation using the synthetic CT imaging than would otherwise be possible by segmenting the bone in the MRI directly.
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