Image analysis and superimposition of 3-dimensional cone-beam computed tomography models

Three-dimensional (3D) imaging techniques can provide valuable information to clinicians and researchers. But as we move from traditional 2-dimensional (2D) cephalometric analysis to new 3D techniques, it is often necessary to compare 2D with 3D data. Cone-beam computed tomography (CBCT) provides simulation tools that can help bridge the gap between image types. CBCT acquisitions can be made to simulate panoramic, lateral, and posteroanterior cephalometric radioagraphs so that they can be compared with preexisting cephalometric databases. Applications of 3D imaging in orthodontics include initial diagnosis and superimpositions for assessing growth, treatment changes, and stability. Three-dimensional CBCT images show dental root inclination and torque, impacted and supernumerary tooth positions, thickness and morphology of bone at sites of mini-implants for anchorage, and osteotomy sites in surgical planning. Findings such as resorption, hyperplasic growth, displacement, shape anomalies of mandibular condyles, and morphological differences between the right and left sides emphasize the diagnostic value of computed tomography acquisitions. Furthermore, relationships of soft tissues and the airway can be assessed in 3 dimensions

Exercise Degrades Bone in Caloric Restriction, Despite Suppression of Marrow Adipose Tissue (MAT)

Marrow adipose tissue (MAT) and its relevance to skeletal health during caloric restriction (CR) is unknown: It remains unclear whether exercise, which is anabolic to bone in a calorie-replete state, alters bone or MAT in CR. We hypothesized that response of bone and MAT to exercise in CR differs from the calorie-replete state. Ten-week-old female B6 mice fed a regular diet (RD) or 30% CR diet were allocated to sedentary (RD, CR, n = 10/group) or running exercise (RD-E, CR-E, n = 7/group). After 6 weeks, CR mice weighed 20% less than RD, p < 0.001; exercise did not affect weight. Femoral bone volume (BV) via 3D MRI was 20% lower in CR versus RD (p < 0.0001). CR was associated with decreased bone by μCT: Tb.Th was 16% less in CR versus RD, p < 0.003, Ct.Th was 5% less, p < 0.07. In CR-E, Tb.Th was 40% less than RD-E, p < 0.0001. Exercise increased Tb.Th in RD (+23% RD-E versus RD, p <; 0.003) but failed to do so in CR. Cortical porosity increased after exercise in CR (+28%, p = 0.04), suggesting exercise during CR is deleterious to bone. In terms of bone fat, metaphyseal MAT/ BV rose 159% in CR versus RD, p = 0.003 via 3D MRI. Exercise decreased MAT/BV by 52% in RD, p < 0.05, and also suppressed MAT in CR (−121%, p = 0.047). Histomorphometric analysis of adipocyte area correlated with MAT by MRI (R2 = 0.6233, p < 0.0001). With respect to bone, TRAP and Sost mRNA were reduced in CR. Intriguingly, the repressed Sost in CR rose with exercise and may underlie the failure of CR-bone quantity to increase in response to exercise. Notably, CD36, a marker of fatty acid uptake, rose 4088% in CR (p < 0.01 versus RD), suggesting that basal increases in MAT during calorie restriction serve to supply local energy needs and are depleted during exercise with a negative impact on bone

White matter development from birth to 6 years of age: A longitudinal study

Human white matter development in the first years of life is rapid, setting the foundation for later development. Microstructural properties of white matter are linked to many behavioral and psychiatric outcomes; however, little is known about when in development individual differences in white matter microstructure are established. The aim of the current study is to characterize longitudinal development of white matter microstructure from birth through 6 years to determine when in development individual differences are established. Two hundred and twenty-four children underwent diffusion-weighted imaging after birth and at 1, 2, 4, and 6 years. Diffusion tensor imaging data were computed for 20 white matter tracts (9 left-right corresponding tracts and 2 commissural tracts), with tract-based measures of fractional anisotropy and axial and radial diffusivity. Microstructural maturation between birth and 1 year are much greater than subsequent changes. Further, by 1 year, individual differences in tract average values are consistently predictive of the respective 6-year values, explaining, on average, 40% of the variance in 6-year microstructure. Results provide further evidence of the importance of the first year of life with regard to white matter development, with potential implications for informing early intervention efforts that target specific sensitive periods

Neonatal hippocampal volume moderates the effects of early postnatal enrichment on cognitive development

Environmental enrichment, particularly during the early life phases of enhanced neuroplasticity, can stimulate cognitive development. However, individuals exhibit considerable variation in their response to environmental enrichment. Recent evidence suggests that certain neurophenotypes such as hippocampal size may index inter-individual differences in sensitivity to environmental conditions. We conducted a prospective, longitudinal investigation in a cohort of 75 mother-child dyads to investigate whether neonatal hippocampal volume moderates the effects of the postnatal environment on cognitive development. Newborn hippocampal volume was quantified shortly after birth (26.2 ± 12.5 days) by structural MRI. Measures of infant environmental enrichment (assessed by the IT-HOME) and cognitive state (assessed by the Bayley-III) were obtained at 6 months of age (6.09 ± 1.43 months). The interaction between neonatal hippocampal volume and enrichment predicted infant cognitive development (b = 0.01, 95 % CI [0.00, 0.02], t = 2.08, p =.04), suggesting that exposure to a stimulating environment had a larger beneficial effect on cognitive outcomes among infants with a larger hippocampus as neonates. Our findings suggest that the effects of the postnatal environment on infant cognitive development are conditioned, in part, upon characteristics of the newborn brain, and that newborn hippocampal volume is a candidate neurophenotype in this context

A deep neural network to assess spontaneous pain from mouse facial expressions

Grimace scales quantify characteristic facial expressions associated with spontaneous pain in rodents and other mammals. However, these scales have not been widely adopted largely because of the time and effort required for highly trained humans to manually score the images. Convoluted neural networks were recently developed that distinguish individual humans and objects in images. Here, we trained one of these networks, the InceptionV3 convolutional neural net, with a large set of human-scored mouse images. Output consists of a binary pain/no-pain assessment and a confidence score. Our automated Mouse Grimace Scale integrates these two outputs and is highly accurate (94%) at assessing the presence of pain in mice across different experimental assays. In addition, we used a novel set of “pain” and “no pain” images to show that automated Mouse Grimace Scale scores are highly correlated with human scores (Pearson’s r = 0.75). Moreover, the automated Mouse Grimace Scale classified a greater proportion of images as “pain” following laparotomy surgery when compared to animals receiving a sham surgery or a post-surgical analgesic. Together, these findings suggest that the automated Mouse Grimace Scale can eliminate the need for tedious human scoring of images and provide an objective and rapid way to quantify spontaneous pain and pain relief in mice

Use of high resolution 3D diffusion tensor imaging to study brain white matter development in live neonatal rats

High resolution diffusion tensor imaging (DTI) can provide important information on brain development, yet it is challenging in live neonatal rats due to the small size of neonatal brain and motion-sensitive nature of DTI. Imaging in live neonatal rats has clear advantages over fixed brain scans, as longitudinal and functional studies would be feasible to understand neuro-developmental abnormalities. In this study, we developed imaging strategies that can be used to obtain high resolution 3D DTI images in live neonatal rats at postnatal day 5 (PND5) and PND14, using only 3 h of imaging acquisition time. An optimized 3D DTI pulse sequence and appropriate animal setup to minimize physiological motion artifacts are the keys to successful high resolution 3D DTI imaging. Thus, a 3D rapid acquisition relaxation enhancement DTI sequence with twin navigator echoes was implemented to accelerate imaging acquisition time and minimize motion artifacts. It has been suggested that neonatal mammals possess a unique ability to tolerate mild-to-moderate hypothermia and hypoxia without long term impact. Thus, we additionally utilized this ability to minimize motion artifacts in magnetic resonance images by carefully suppressing the respiratory rate to around 15/min for PND5 and 30/min for PND14 using mild-to-moderate hypothermia. These imaging strategies have been successfully implemented to study how the effect of cocaine exposure in dams might affect brain development in their rat pups. Image quality resulting from this in vivo DTI study was comparable to ex vivo scans. fractional anisotropy values were also similar between the live and fixed brain scans. The capability of acquiring high quality in vivo DTI imaging offers a valuable opportunity to study many neurological disorders in brain development in an authentic living environment

A 3D Fully Convolutional Neural Network With Top-Down Attention-Guided Refinement for Accurate and Robust Automatic Segmentation of Amygdala and Its Subnuclei

Recent advances in deep learning have improved the segmentation accuracy of subcortical brain structures, which would be useful in neuroimaging studies of many neurological disorders. However, most existing deep learning based approaches in neuroimaging do not investigate the specific difficulties that exist in segmenting extremely small but important brain regions such as the subnuclei of the amygdala. To tackle this challenging task, we developed a dual-branch dilated residual 3D fully convolutional network with parallel convolutions to extract more global context and alleviate the class imbalance issue by maintaining a small receptive field that is just the size of the regions of interest (ROIs). We also conduct multi-scale feature fusion in both parallel and series to compensate the potential information loss during convolutions, which has been shown to be important for small objects. The serial feature fusion enabled by residual connections is further enhanced by a proposed top-down attention-guided refinement unit, where the high-resolution low-level spatial details are selectively integrated to complement the high-level but coarse semantic information, enriching the final feature representations. As a result, the segmentations resulting from our method are more accurate both volumetrically and morphologically, compared with other deep learning based approaches. To the best of our knowledge, this work is the first deep learning-based approach that targets the subregions of the amygdala. We also demonstrated the feasibility of using a cycle-consistent generative adversarial network (CycleGAN) to harmonize multi-site MRI data, and show that our method generalizes well to challenging traumatic brain injury (TBI) datasets collected from multiple centers. This appears to be a promising strategy for image segmentation for multiple site studies and increased morphological variability from significant brain pathology

Gut microbiome and brain functional connectivity in infants-a preliminary study focusing on the amygdala

Recently, there has been a surge of interest in the possibility that microbial communities inhabiting the human gut could affect cognitive development and increase risk for mental illness via the “microbiome-gut-brain axis.” Infancy likely represents a critical period for the establishment of these relationships, as it is the most dynamic stage of postnatal brain development and a key period in the maturation of the microbiome. Indeed, recent reports indicate that characteristics of the infant gut microbiome are associated with both temperament and cognitive performance. The neural circuits underlying these relationships have not yet been delineated. To address this gap, resting-state fMRI scans were acquired from 39 1-year-old human infants who had provided fecal samples for identification and relative quantification of bacterial taxa. Measures of alpha diversity were generated and tested for associations with measures of functional connectivity. Primary analyses focused on the amygdala as manipulation of the gut microbiota in animal models alters the structure and neurochemistry of this brain region. Secondary analyses explored functional connectivity of nine canonical resting-state functional networks. Alpha diversity was significantly associated with functional connectivity between the amygdala and thalamus and between the anterior cingulate cortex and anterior insula. These regions play an important role in processing/responding to threat. Alpha diversity was also associated with functional connectivity between the supplementary motor area (SMA, representing the sensorimotor network) and the inferior parietal lobule (IPL). Importantly, SMA-IPL connectivity also related to cognitive outcomes at 2 years of age, suggesting a potential pathway linking gut microbiome diversity and cognitive outcomes during infancy. These results provide exciting new insights into the gut-brain axis during early human development and should stimulate further studies into whether microbiome-associated changes in brain circuitry influence later risk for psychopathology

A novel framework for the local extraction of extra-axial cerebrospinal fluid from MR brain images

The quantification of cerebrospinal fluid (CSF) in the human brain has shown to play an important role in early postnatal brain developmental. Extr a-axial fluid (EA-CSF), which is characterized by the CSF in the subarachnoid space, is promising in the early detection of children at risk for neurodevelopmental disorders. Currently, though, there is no tool to extract local EA-CSF measurements in a way that is suitable for localized analysis. In this paper, we propose a novel framework for the localized, cortical surface based analysis of EA-CSF. In our proposed processing, we combine probabilistic brain tissue segmentation, cortical surface reconstruction as well as streamline based local EA-CSF quantification. For streamline computation, we employ the vector field generated by solving a Laplacian partial differential equation (PDE) between the cortical surface and the outer CSF hull. To achieve sub-voxel accuracy while minimizing numerical errors, fourth-order Runge-Kutta (RK4) integration was used to generate the streamlines. Finally, the local EA-CSF is computed by integrating the CSF probability along the generated streamlines. The proposed local EA-CSF extraction tool was used to study the early postnatal brain development in typically developing infants. The results show that the proposed localized EA-CSF extraction pipeline can produce statistically significant regions that are not observed in previous global approach

The UNC-Wisconsin rhesus macaque neurodevelopment database: A structural MRI and DTI database of early postnatal development

Rhesus macaques are commonly used as a translational animal model in neuroimaging and neurodevelopmental research. In this report, we present longitudinal data from both structural and diffusion MRI images generated on a cohort of 34 typically developing monkeys from 2 weeks to 36 months of age. All images have been manually skull stripped and are being made freely available via an online repository for use by the research community