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ATRX is a chromatin remodelling ATPase that is involved in transcriptional regulation, DNA damage repair and heterochromatin maintenance. It has been widely studied for its role in ALT-positive cancers, but its role in neurological function remains elusive. Hypomorphic mutations in the X-linked ATRX gene cause a rare form of intellectual disability combined with alpha-thalassemia called ATR-X syndrome in hemizygous males. Clinical features also include facial dysmorphism, microcephaly, short stature, musculoskeletal defects and genital abnormalities. Since complete deletion of ATRX in mice results in early embryonic lethality, the field has largely relied on conditional knockout models to assess the role of ATRX in multiple tissues. Given that null alleles are not found in patients, a more patient-relevant model was needed. Here, we have produced and characterised the first patient mutation knock-in model of ATR-X syndrome, carrying the most common causative mutation, R246C. This is one of a cluster of missense mutations located in the chromatin binding domain and disrupts its function. The knock-in mice recapitulate several aspects of the patient disorder, including craniofacial defects, microcephaly, reduced body size and impaired neurological function. They provide a powerful model for understanding the molecular mechanisms underlying ATR-X syndrome and for testing potential therapeutic strategies.

For information from sensory organs to be processed by the brain, it is usually passed to appropriate areas of the cerebral cortex. Almost all of this information passes through the thalamus, a relay structure that reciprocally connects to the vast majority of the cortex. The thalamus facilitates this information transfer through a set of thalamocortical connections that vary in cellular structure, molecular profiles, innervation patterns, and firing rates. Additionally, corticothalamic connections allow for intracortical information transfer through the thalamus. These efferent and afferent connections between the thalamus and cortex have been the focus of many studies, and the importance of cortical connectivity in defining thalamus anatomy is demonstrated by multiple studies that parcellate the thalamus based on cortical connectivity profiles.Here, we examine correlated morphological variation between the thalamus and cortex, or thalamocortical structural covariance. For each voxel in the thalamus as a seed, we construct a cortical structural covariance map that represents correlated cortical volume variation, and examine whether high structural covariance is observed in cortical areas that are functionally relevant to the seed. Then, using these cortical structural covariance maps as features, we subdivide the thalamus into six non-overlapping regions (clusters of voxels), and assess whether cortical structural covariance is associated with cortical connectivity that specifically originates from these regions.We show that cortical structural covariance is high in areas of the cortex that are functionally related to the seed voxel, cortical structural covariance varies along cortical depth, and sharp transitions in cortical structural covariance profiles are observed when varying seed locations in the thalamus. Subdividing the thalamus based on structural covariance, we additionally demonstrate that the six thalamic clusters of voxels stratify cortical structural covariance along the dorsal-ventral, medial-lateral, and anterior-posterior axes. These cluster-associated structural covariance patterns are prominently detected in cortical regions innervated by fibers projecting out of their related thalamic subdivisions.Together, these results advance our understanding of how the thalamus and the cortex couple in their volumes. Our results indicate that these volume correlations reflect functional organization and structural connectivity, and further provides a novel segmentation of the mouse thalamus that can be used to examine thalamic structural variation and thalamocortical structural covariation in disease models.

1AbstractAlthough initially showing great potential, oxytocin treatment has encountered a translational hurdle in its promise of treating the social deficits of autism. Some debate surrounds the ability of oxytocin to successfully enter the brain, and therefore modify neuroanatomy. Moreover, given the heterogeneous nature of autism, treatment will only amerliorate symptoms in a subset of patients. Therefore, to determine whether oxytocin changes brain circuitry, and whether it does so variably, depending on genotype, we implemented a large randomized, blinded, placebo-controlled, preclinical study on chronic intranasal oxytocin treatment in three different mouse models related to autism with a focus on using neuroanatomical phenotypes to assess and subset treatment response. Intranasal oxytocin (0.6IU) was administered daily, for 28 days, starting at 5 weeks of age to the 16p11.2 deletion, Shank3 (exon 4-9) knockout, and Fmr1 knockout mouse models. Given the sensitivity of structural magnetic resonance imaging (MRI) to the neurological effects of interventions like drugs, along with many other advantages, the mice underwent in vivo longitudinal and high-resolution ex vivo imaging with MRI. The scans included three in vivo T1-weighted, 90um isotropic resolution scans and a T2-weighted, 3D fast spin echo with 40um isotropic resolution ex vivo scan to assess the changes in neuroanatomy using established automated image registration and deformation based morphometry approaches in response to oxytocin treatment. The behavior of the mice was assessed in multiple domains, including social behaviours and repetitive behaviours, among others. Treatment effect on the neuroanatomy did not reach significance, although the pattern of trending effects was promising. No significant effect of treatment was found on social behavior in any of the strains, although a significant effect of treatment was found in the Fmr1 mouse, with treatment normalizing a grooming deficit. No other treatment effect on behavior was observed that survived multiple comparisons correction. Overall, chronic treatment with oxytocin had limited effects on the three mouse models related to autism, and no promising pattern of response susceptibility emerged.

Contains fulltext : 280328.pdf (Publisher’s version ) (Open Access) The ever-increasing use of mouse models in preclinical neuroscience research calls for an improvement in the methods used to translate findings between mouse and human brains. Previously we showed that the brains of primates can be compared in a direct quantitative manner using a common reference space built from white matter tractography data (Rogier B. Mars et al., 2018b). Here we extend the common space approach to evaluate the similarity of mouse and human brain regions using openly accessible brain-wide transcriptomic data sets. We show that mouse-human homologous genes capture broad patterns of neuroanatomical organization, but that the resolution of cross-species correspondences can be improved using a novel supervised machine learning approach. Using this method, we demonstrate that sensorimotor subdivisions of the neocortex exhibit greater similarity between species, compared with supramodal subdivisions, and that mouse isocortical regions separate into sensorimotor and supramodal clusters based on their similarity to human cortical regions. We also find that mouse and human striatal regions are strongly conserved, with the mouse caudoputamen exhibiting an equal degree of similarity to both the human caudate and putamen. 24 p.

Abnormally pulsatile umbilical artery (UA) Doppler ultrasound velocity waveforms are a hallmark of severe or early onset placental-mediated intrauterine growth restriction (IUGR), whereas milder late onset IUGR pregnancies typically have normal UA pulsatility. The diagnostic utility of these waveforms to detect placental pathology is thus limited and hampered by factors outside of the placental circulation, including fetal cardiac output. In view of these limitations, we hypothesized that these Doppler waveforms could be more clearly understood as a reflection phenomenon and that a reflected pulse pressure wave is present in the UA that originates from the placenta and propagates backward along the UA. To investigate this, we developed a new ultrasound approach to isolate that portion of the UA Doppler waveform that arises from a pulse pressure wave propagating backward along the UA. Ultrasound measurements of UA lumen diameter and flow waveforms were used to decompose the observed flow waveform into its forward and reflected components. Evaluation of CD1 and C57BL/6 mice at embryonic day (E)15.5 and E17.5 demonstrated that the reflected waveforms diverged between the strains at E17.5, mirroring known changes in the fractal geometry of fetoplacental arteries at these ages. These experiments demonstrate the feasibility of noninvasively measuring wave reflections that originate from the fetoplacental circulation. The observed reflections were consistent with theoretical predictions based on the area ratio of parent to daughters at bifurcations in fetoplacental arteries suggesting that this approach could be used in the diagnosis of fetoplacental vascular pathology that is prevalent in human IUGR. Given that the proposed measurements represent a subset of those currently used in human fetal surveillance, the adaptation of this technology could extend the diagnostic utility of Doppler ultrasound in the detection of placental vascular pathologies that cause IUGR.NEW & NOTEWORTHY Here, we describe a novel approach to noninvasively detect microvascular changes in the fetoplacental circulation using ultrasound. The technique is based on detecting reflection pulse pressure waves that travel along the umbilical artery. Using a proof-of-principle study, we demonstrate the feasibility of the technique in two strains of experimental mice.

Contains fulltext : 219632.pdf (Publisher’s version ) (Open Access) Preclinical applications of resting-state functional magnetic resonance imaging (rsfMRI) offer the possibility to non-invasively probe whole-brain network dynamics and to investigate the determinants of altered network signatures observed in human studies. Mouse rsfMRI has been increasingly adopted by numerous laboratories worldwide. Here we describe a multi-centre comparison of 17 mouse rsfMRI datasets via a common image processing and analysis pipeline. Despite prominent cross-laboratory differences in equipment and imaging procedures, we report the reproducible identification of several large-scale resting-state networks (RSN), including a mouse default-mode network, in the majority of datasets. A combination of factors was associated with enhanced reproducibility in functional connectivity parameter estimation, including animal handling procedures and equipment performance. RSN spatial specificity was enhanced in datasets acquired at higher field strength, with cryoprobes, in ventilated animals, and under medetomidine-isoflurane combination sedation. Our work describes a set of representative RSNs in the mouse brain and highlights key experimental parameters that can critically guide the design and analysis of future rodent rsfMRI investigations.

Fragile X syndrome (FXS) is a neurodevelopmental disorder that is caused by mutations in the FMR1 gene that are known to cause neuroanatomical alterations. The morphological underpinnings of these alterations have not been elucidated. Furthermore, while alterations have been identified in both male and female individuals, neuroanatomy in female rodent models has not been assessed. We identified structural differences in regions that are also altered in FXS in male and female rat models, including the splenium of the corpus callosum. Interestingly, different sets of regions were disrupted in male and female rat models and, remarkably, male rats had higher brain-wide diffusion than female rats overall. We found reduced axonal caliber in the splenium, offering a mechanism for its structural changes. Our results provide insight into which brain regions are vulnerable to a loss of Fmr1 expression and suggest a potential mechanism for how its loss causes white matter dysfunction in FXS.

Background The serotonin (5-HT) system has long been implicated in autism spectrum disorder (ASD) as indicated by elevated whole blood and platelet 5-HT, altered platelet and brain receptor and transporter binding, and genetic linkage and association findings. Based upon work in genetically modified mice, 5-HT is known to influence several aspects of brain development, but systematic neuroimaging studies have not previously been reported. In particular, the 5-HT transporter (serotonin transporter, SERT; 5-HTT) gene, Slc6a4, has been extensively studied. Methods Using a 7-T MRI and deformation-based morphometry, we assessed neuroanatomical differences in an Slc6a4 knockout mouse on a C57BL/6 genetic background, along with an Slc6a4 Ala56 knockin mouse on two different genetic backgrounds (129S and C57BL/6). Results Individually (same sex, same background, same genotype), the only differences found were in the female Slc6a4 knockout mouse; all the others had no significant differences. However, an analysis of variance across the whole study sample revealed a significant effect of Slc6a4 on the amygdala, thalamus, dorsal raphe nucleus, and lateral and frontal cortices. Conclusions This work shows that an increase or decrease in SERT function has a significant effect on the neuroanatomy in 5-HT relevant regions, particularly the raphe nuclei. Notably, the Slc6a4 Ala56 knockin alone appears to have an insignificant, but suggestive, effect compared to the KO, which is consistent with Slc6a4 function. Despite the small number of 5-HT neurons and their localization to the brainstem, it is clear that 5-HT plays an important role in neuroanatomical organization. Electronic supplementary material The online version of this article (10.1186/s13229-018-0210-z) contains supplementary material, which is available to authorized users.

An organizational pattern seen in the brain, termed structural covariance, is the statistical association of pairs of brain regions in their anatomical properties. These associations, measured across a population as covariances or correlations usually in cortical thickness or volume, are thought to reflect genetic and environmental underpinnings.Here, we examine the biological basis of structural volume covariance in the mouse brain. We first examined large scale associations between brain region volumes using an atlas-based approach that parcellated the entire mouse brain into 318 regions over which correlations in volume were assessed, for volumes obtained from 153 mouse brain images via high-resolution MRI. We then used a seed-based approach and determined, for 108 different seed regions across the brain and using mouse gene expression and connectivity data from the Allen Institute for Brain Science, the variation in structural covariance data that could be explained by distance to seed, transcriptomic similarity to seed, and connectivity to seed.We found that overall, correlations in structure volumes hierarchically clustered into distinct anatomical systems, similar to findings from other studies and similar to other types of networks in the brain, including structural connectivity and transcriptomic similarity networks. Across seeds, this structural covariance was significantly explained by distance (17% of the variation, up to a maximum of 49% for structural covariance to the visceral area of the cortex), transcriptomic similarity (13% of the variation, up to maximum of 28% for structural covariance to the primary visual area) and connectivity (15% of the variation, up to a maximum of 36% for structural covariance to the intermediate reticular nucleus in the medulla) of covarying structures. Together, distance, connectivity, and transcriptomic similarity explained 37% of structural covariance, up to a maximum of 63% for structural covariance to the visceral area. Additionally, this pattern of explained variation differed spatially across the brain, with transcriptomic similarity playing a larger role in the cortex than subcortex, while connectivity explains structural covariance best in parts of the cortex, midbrain, and hindbrain. These results suggest that both gene expression and connectivity underlie structural volume covariance, albeit to different extents depending on brain region, and this relationship is modulated by distance.