Background-Heart failure is associated with neurological deficits, including cognitive dysfunction. However, the molecular mechanisms underlying reduced cerebral blood flow in the early stages of heart failure, particularly when blood pressure is minimally affected, are not known. Methods and Results-Using a myocardial infarction model in mice, we demonstrate a tumor necrosis factor-␣ (TNF␣)-dependent enhancement of posterior cerebral artery tone that reduces cerebral blood flow before any overt changes in brain structure and function. TNF␣ expression is increased in mouse posterior cerebral artery smooth muscle cells at 6 weeks after myocardial infarction. Coordinately, isolated posterior cerebral arteries display augmented myogenic tone, which can be fully reversed in vitro by the competitive TNF␣ antagonist etanercept. TNF␣ mediates its effect via a sphingosine-1-phosphate (S1P)-dependent mechanism, requiring sphingosine kinase 1 and the S1P 2 receptor. In vivo, sphingosine kinase 1 deletion prevents and etanercept (2-week treatment initiated 6 weeks after myocardial infarction) reverses the reduction of cerebral blood flow, without improving cardiac function. Conclusions-Cerebral artery vasoconstriction and decreased cerebral blood flow occur early in an animal model of heart failure; these perturbations are reversed by interrupting TNF␣/S1P signaling. This signaling pathway may represent a potential therapeutic target to improve cognitive function in heart failure.

Morphometric analysis of anatomical landmarks allows researchers to identify specific morphological differences between natural populations or experimental groups, but manually identifying landmarks is time‐consuming. We compare manually and automatically generated adult mouse skull landmarks and subsequent morphometric analyses to elucidate how switching from manual to automated landmarking will impact morphometric analysis results for large mouse (Mus musculus) samples (n = 1205) that represent a wide range of ‘normal’ phenotypic variation (62 genotypes). Other studies have suggested that the use of automated landmarking methods is feasible, but this study is the first to compare the utility of current automated approaches to manual landmarking for a large dataset that allows the quantification of intra‐ and inter‐strain variation. With this unique sample, we investigated how switching to a non‐linear image registration‐based automated landmarking method impacts estimated differences in genotype mean shape and shape variance‐covariance structure. In addition, we tested whether an initial registration of specimen images to genotype‐specific averages improves automatic landmark identification accuracy. Our results indicated that automated landmark placement was significantly different than manual landmark placement but that estimated skull shape covariation was correlated across methods. The addition of a preliminary genotype‐specific registration step as part of a two‐level procedure did not substantially improve on the accuracy of one‐level automatic landmark placement. The landmarks with the lowest automatic landmark accuracy are found in locations with poor image registration alignment. The most serious outliers within morphometric analysis of automated landmarks displayed instances of stochastic image registration error that are likely representative of errors common when applying image registration methods to micro‐computed tomography datasets that were initially collected with manual landmarking in mind. Additional efforts during specimen preparation and image acquisition can help reduce the number of registration errors and improve registration results. A reduction in skull shape variance estimates were noted for automated landmarking methods compared with manual landmarking. This partially reflects an underestimation of more extreme genotype shapes and loss of biological signal, but largely represents the fact that automated methods do not suffer from intra‐observer landmarking error. For appropriate samples and research questions, our image registration‐based automated landmarking method can eliminate the time required for manual landmarking and have a similar power to identify shape differences between inbred mouse genotypes.

Magel2 belongs to the MAGE/necdin family of proteins, which have roles in cell cycle, differentiation, and apoptosis. The Magel2 gene is expressed in various brain regions, most notably the hypothalamus. Mice with a targeted deletion of Magel2 display hypoactivity, blunted circadian rhythm, decreased fertility, and increased adiposity. The human ortholog, MAGEL2, is one of a set of paternally expressed, imprinted genes inactivated in most cases of Prader-Willi syndrome, a complex neurodevelopmental disorder. To explore the role of Magel2, brain morphology, brain neurochemistry, and behavior were measured in Magel2-null mice. Brain volume was reduced in specific regions, particularly in the parieto-temporal lobe of the cerebral cortex, the amygdala, the hippocampus, and the nucleus accumbens, as measured by quantitative magnetic resonance imaging. Abnormal neurochemistry was detected in brain samples from adult mice, consisting of decreased serotonin and 5-hydroxyindoleacetic acid in the cortex and the hypothalamus, and decreased dopamine in the hypothalamus. Magel2-null mice displayed relatively normal motor and learning abilities, but exhibited abnormal behavior in novel environments. This study lends support to the important role of the circadian rhythm output gene Magel2 in brain structure and behavior.

BackgroundThe increasing use of the ketogenic diet (KD), particularly by women of child-bearing age, raises a question about its suitability during gestation. To date, no studies have thoroughly investigated the direct implications of a gestational ketogenic diet on embryonic development.MethodsTo fill this knowledge gap we imaged CD-1 mouse embryos whose mothers were fed either a Standard Diet (SD) or a KD 30 days prior to, as well as during gestation. Images were collected at embryonic days (E) 13.5 using Optical Projection Tomography (OPT) and at E17.5 using Magnetic Resonance Imaging (MRI).ResultsAn anatomical comparison of the SD and KD embryos revealed that at E13.5 the average KD embryo was volumetrically larger, possessed a relatively larger heart but smaller brain, and had a smaller pharynx, cervical spinal cord, hypothalamus, midbrain, and pons, compared with the average SD embryo. At E17.5 the KD embryo was found to be volumetrically smaller with a relatively smaller heart and thymus, but with enlarged cervical spine, thalamus, midbrain and pons.ConclusionA ketogenic diet during gestation results in alterations in embryonic organ growth. Such alterations may be associated with organ dysfunction and potentially behavioral changes in postnatal life.

The ability to visualize behaviourally evoked neural activity patterns across the rodent brain is essential for understanding the distributed brain networks mediating particular behaviours. However, current imaging methods are limited in their spatial resolution and/or ability to obtain brain-wide coverage of functional activity. Here, we describe a new automated method for obtaining cellular-level, whole-brain maps of behaviourally induced neural activity in the mouse. This method combines the use of transgenic immediate-early gene reporter mice to visualize neural activity; serial two-photon tomography to image the entire brain at cellular resolution; advanced image processing algorithms to count the activated neurons and align the datasets to the Allen Mouse Brain Atlas; and statistical analysis to identify the network of activated brain regions evoked by behaviour. We demonstrate the use of this approach to determine the whole-brain networks activated during the retrieval of fear memories. Consistent with previous studies, we identified a large network of amygdalar, hippocampal, and neocortical brain regions implicated in fear memory retrieval. Our proposed methods can thus be used to map cellular networks involved in the expression of normal behaviours as well as to investigate in depth circuit dysfunction in mouse models of neurobiological disease.