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Neuroinflammation plays a central role in the neuropathogenesis of a wide-spectrum of neurologic and psychiatric disease, but current neuroimaging methods to detect and characterize neuroinflammation are limited. We explored the sensitivity of quantitative multi-compartment diffusion MRI, and specifically neurite orientation dispersion and density imaging (NODDI), to detect changes in microglial density in the brain. Monte Carlo simulations of water diffusion using a NODDI acquisition scheme were performed to measure changes in a virtual MRI signal following modeled cellular changes within the extra-neurite space. 12-week-old C57BL/6J male mice ( = 48; 24 control, 24 treated with colony stimulating factor 1 receptor (CSF1R) inhibitor, PLX5622) were sacrificed at 0, 1, 3, and 7 days following withdrawal of CSF1R inhibition and were imaged to obtain measures of the orientation dispersion index (ODI). Following imaging, all brains were immunostained with Iba-1, NeuN, and GFAP for quantitative fluorescence microscopy. Cell populations were calculated with the ImageJ particle analyzer tool; correlation between microglial density and mean ODI values were calculated with Kendall's tau. Monte Carlo simulations demonstrate the sensitivity and positive correlation of ODI to increased occupancy in the extra-neurite space. Commensurate with our simulation data, NODDI imaging demonstrates an increase in ODI as microglia repopulate the brain following the withdrawal of CSF1R inhibition. Quantitative immunofluorescence of microglial density reveals that microglial density is positively correlated with ODI and greater hindered diffusion in the extra-neurite space (τ = 0.386, < 0.05). Our results demonstrate that clinically feasible multi-compartment diffusion weighted imaging techniques such as NODDI are sensitive to microglial density and the cellular changes associated with microglial activation and highlights its potential to improve clinical diagnostic accuracy, patient risk stratification, and therapeutic monitoring of neuroinflammation in neurologic and psychiatric disease.

Background Dual degree program MD/PhD candidates typically train extensively in basic science research and in clinical medicine, but often receive little formal experience or mentorship in clinical and translational research. Methods To address this educational and curricular gap, the University of Wisconsin Medical Scientist Training Program partnered with the University of Wisconsin Institute for Clinical and Translational Research to create a new physician-scientist preceptorship in clinical and translational research. This six-week apprentice-style learning experience—guided by a physician-scientist faculty mentor—integrates both clinical work and a translational research project, providing early exposure and hands-on experience with clinically oriented research and the integrated career of a physician-scientist. Five years following implementation, we retrospectively surveyed students and faculty members to determine the outcomes of this preceptorship. Results Over five years, 38 students and 36 faculty members participated in the physician-scientist preceptorship. Based on student self-assessments ( n  = 29, response rate 76%), the course enhanced competency in conducting translational research and understanding regulation of clinical research among other skills. Mentor assessments ( n  = 17, response rate 47%) supported the value of the preceptorship in these same areas. Based on work during the preceptorship, half of the students produced a peer-reviewed publication or a meeting abstract. At least eleven peer-reviewed manuscripts were generated. The preceptorship also provided a structure for physician-scientist mentorship in the students’ clinical specialty of choice. Conclusion The physician-scientist preceptorship provides a new curricular model to address the gap of clinical research training and provides for mentorship of physician-scientists during medical school. Future work will assess the long-term impact of this course on physician-scientist career trajectories.

Interactions between intestinal microbiota and the central nervous system profoundly influence brain structure and function. Over the past 15 years, intense research efforts have uncovered the significant association between gut microbial dysbiosis and neurologic, neurodegenerative, and psychiatric disorders; however, our understanding of the effect of gut microbiota on quantitative neuroimaging measures of brain microstructure and function remains limited. Many current gut microbiome studies specifically focus on discovering correlations between specific microbes and neurologic disease states that, while important, leave critical mechanistic questions unanswered. To address this significant gap in knowledge, quantitative structural and functional brain imaging has emerged as a vital bridge and as the next step in understanding how the gut microbiome influences the brain. In this review, we examine the current state-of-the-art, raise awareness of this important topic, and aim to highlight immense new opportunities—in both research and clinical imaging—for the imaging community in this emerging field of study. Our review also highlights the potential for preclinical imaging of germ-free and gnotobiotic models to significantly advance our understanding of the causal mechanisms by which the gut microbiome alters neural microstructure and function. Key Points • Alterations to the gut microbiome can significantly influence brain structure and function in health and disease. • Quantitative neuroimaging can help elucidate the effect of gut microbiota on the brain and with future translational advances, neuroimaging will be critical for both diagnostic assessment and therapeutic monitoring.

Altered gut microbiome populations are associated with a broad range of neurodevelopmental disorders including autism spectrum disorder and mood disorders. In animal models, modulation of gut microbiome populations via dietary manipulation influences brain function and behavior and has been shown to ameliorate behavioral symptoms. With striking differences in microbiome-driven behavior, we explored whether these behavioral changes are also accompanied by corresponding changes in neural tissue microstructure. Utilizing diffusion tensor imaging, we identified global changes in white matter structural integrity occurring in a diet-dependent manner. Analysis of 16S ribosomal RNA sequencing of gut bacteria also showed changes in bacterial populations as a function of diet. Changes in brain structure were found to be associated with diet-dependent changes in gut microbiome populations using a machine learning classifier for quantitative assessment of the strength of microbiome-brain region associations. These associations allow us to further test our understanding of the gut-brain-microbiota axis by revealing possible links between altered and dysbiotic gut microbiome populations and changes in brain structure, highlighting the potential impact of diet and metagenomic effects in neuroimaging.

Nisin is a posttranslationally modified antimicrobial peptide that is widely used as a food preservative. It contains five cyclic thioethers of varying sizes that are installed by a single enzyme, NisC. Reported here are the in vitro reconstitution of the cyclization process and the x-ray crystal structure of the NisC enzyme. The structure reveals similarities in fold and substrate activation with mammalian farnesyl transferases, suggesting that human homologs of NisC posttranslationally modify a cysteine of a protein substrate.

Diffusion tensor imaging (DTI) has provided remarkable insight into our understanding of white matter microstructure and brain connectivity across a broad spectrum of psychiatric disease. While DTI and other diffusion weighted magnetic resonance imaging (MRI) methods have clarified the axonal contribution to the disconnectivity seen in numerous psychiatric diseases, absent from these studies are quantitative indices of neurite density and orientation that are especially important features in regions of high synaptic density that would capture the synaptic contribution to the psychiatric disease state. Here we report the application of neurite orientation dispersion and density imaging (NODDI), an emerging microstructure imaging technique, to a novel Disc1 svΔ2 rat model of psychiatric illness and demonstrate the complementary and more specific indices of tissue microstructure found in NODDI than those reported by DTI. Our results demonstrate global and sex-specific changes in white matter microstructural integrity and deficits in neurite density as a consequence of the Disc1 svΔ2 genetic variation and highlight the application of NODDI and quantitative measures of neurite density and neurite dispersion in psychiatric disease.

Abstract Background Appropriate indications for radiologic evaluation of central venous ports are not fully understood. We aimed to quantitatively assess the utility of clinical history and imaging in the evaluation of malfunctioning central venous ports. Methods Clinical history, plain radiographs, and line injections intended to evaluate central venous port malfunction in 153 consecutive cases over a nearly 4-year period were retrospectively reviewed by 2 radiologists. Radiographs and line injections were separately categorized as normal or abnormal, and a consensus was reached on the final imaging diagnosis. The likelihood of a port-related abnormality necessitating immediate intervention was determined for all represented combinations of clinical history, radiographic findings, and line injection results. Results A radiologic diagnosis was made in 96.1% of cases; 19.7% of these diagnoses were classified as critical, requiring prompt intervention. Very low risk histories had a 0.0% incidence of critical port abnormalities in our cohort, regardless of imaging findings. Low risk histories had a 10.5% incidence of a critical abnormality and were best evaluated either by line injection, either directly or following an abnormal chest radiograph. Intermediate and high risk histories were associated with a 30.5% and 61.1% incidence of critical port abnormalities, respectively, and were best evaluated by line injection without preceding chest radiograph. Conclusions There are several scenarios in which imaging does not meaningfully affect management of malfunctioning central venous ports. Recognizing these inefficiencies may allow for more appropriate and cost-effective use of radiographs and line injections to evaluate the cause of port malfunction.

After successful cardiopulmonary resuscitation, the patient underwent CT scan which revealed dependent layering of contrast and severe venous reflux ( figure 1 ) as well as lack of forward flow into the left heart ( figure 2 ), indicating impending circulatory collapse.

Synaptosomes are isolated nerve terminals that contain synaptic components, including neurotransmitters, metabolites, adhesion/fusion proteins, and nerve terminal receptors. The essential role of synaptosomes in neurotransmission has stimulated keen interest in understanding both their proteomic and metabolic composition. Mass spectrometric (MS) quantification of synaptosomes has illuminated their proteomic composition, but the determination of the metabolic composition by MS has been met with limited success. In this study, we report a proof-of-concept application of one- and two-dimensional nuclear magnetic resonance (NMR) spectroscopy for analyzing the metabolic composition of synaptosomes. We utilize this approach to compare the metabolic composition synaptosomes from a wild-type rat with that from a newly generated genetic rat model (Disc1 svΔ2), which qualitatively recapitulates clinically observed early DISC1 truncations associated with schizophrenia. This study demonstrates the feasibility of using NMR spectroscopy to identify and quantify metabolites within synaptosomal fractions.

Magnetic resonance imaging (MRI) is foundational tool in neuroscience, enabling characterization of neuroanatomical markers of disease, behavior, and cognition. However, the precise cellular processes driving the structural and functional readouts provided by MRI remain opaque. Non-invasively assessing cell type, abundance, and location using MRI has the potential to revolutionize both basic science and clinical practice. To this end, we developed SpaTial Representation and Analysis using Topological Architecture (STRATA), an image-based gradient-boosted machine learning framework, which quantifies cell type proportions of neurons, astrocytes, oligodendrocytes, and microglia from MR images. Here we demonstrate and validate STRATA on diverse disease models, species, and regions of interest that together highlight the generalizability of the STRATA framework.