Purpose: Introduce and validate a novel, fast, and fully automated deep learning pipeline (FatSegNet) to accurately identify, segment, and quantify visceral and subcutaneous adipose tissue (VAT and SAT) within a consistent, anatomically defined abdominal region on Dixon MRI scans. Method: FatSegNet is composed of three stages: (i) Consistent localization of the abdominal region using two 2D-Competitive Dense Fully Convolutional Networks (CDFNet), (ii) Segmentation of adipose tissue on three views by independent CDFNets, and (iii) View-aggregation. FatSegNet is validated by: 1) comparison of segmentation accuracy (sixfold cross-validation), 2) test-retest reliability, 3) generalizability to randomly selected manually re-edited cases, and 4) replication of age and sex effects in the Rhineland Studya large prospective population cohort. Results: The CDFNet demonstrates increased accuracy and robustness compared to traditional deep learning networks. FatSegNet Dice score outperforms manual raters on VAT (0.850 vs. 0.788),and produces comparable results on SAT (0.975 vs. 0.982). The pipeline has excellent agreement for both test-retest (ICC VAT 0.998 and SAT 0.996) and manual re-editing (ICC VAT 0.999 and SAT 0.999). 2 SUBMITTED TO MAGNETIC RESONANCE IN MEDICINE Conclusion: FatSegNet generalizes well to different body shapes, sensitively replicates known VAT and SAT volume effects in a large cohort study, and permits localized analysis of fat compartments. Furthermore, it can reliably analyze a 3D Dixon MRI in ∼ 1 min, providing an efficient and validated pipeline for abdominal adipose tissue analysis in the Rhineland Study. K E Y W O R D S Subcutaneous adipose tissue, Visceral adipose tissue, Dixon MRI, Neural networks, Deep learning, Semantic segmentation 1 | INTRODUCTION The excess of body fat depots is an increasing major public health issue worldwide and an important risk factor for the development of metabolic disorders and reduced quality of life [1, 2]. While the body mass index (BMI) is a widely used indicator of adipose tissue accumulation in the body, it does not provide information on fat distribution [3] -neither with respect to different fat tissue types nor with respect to deposit location. Different compartments of adipose tissue are associated with different physiopathological effects [4, 5]. Abdominal adipose tissue (AAT), composed of subcutaneous and visceral adipose tissue (SAT and VAT), has long been associated with an increased risk of chronic cardiovascular diseases, glucose impairment, and dyslipidemia [6, 7]. Recently, several studies have indicated a stronger relation between the accumulation of VAT with an adverse metabolic and inflammatory profile compared to SAT [8, 9]. Therefore, an accurate and independent measurement of VAT and SAT volumes (VAT-V and SAT-V) is of significant clinical and research interest. Currently, the gold standard for measuring VAT-V and SAT-V is the manual segmentation of abdominal fat images from Dixon magnetic resonance (MR) scans -a very expensive and time-consu...

AIM:To study the modulation of glutamate on post-ischemic intestinal and cerebral inflammatory responses in a ischemic and excitotoxic rat model. METHODS:Adult male rats were subjected to bilateral carotid artery occlusion for 15 min and injection of monosodium glutamate intraperitoneally, to decapitate them at selected time points. Tumor necrosis factor alpha (TNF-) level and nuclear factor kappa B (NF-B) activity were determined by enzyme-linked immunosorbant assay (ELISA) and electrophoretic mobility shift assay (EMSA), respectively. Hemodynamic parameters were monitored continuously during the whole process of cerebral ischemia and reperfusion. RESULTS:Monosodium glutamate (MSG) treated rats displayed statistically significant high levels of TNF- in cerebral and intestinal tissues within the first 6 h of ischemia. The rats with cerebral ischemia showed a minor decrease of TNF- production in cerebral and intestinal tissues. The rats with cerebral ischemia and treated with MSG displayed statistically significant low levels of TNF- in cerebral and intestinal tissues. These results correlated significantly with NF-B production calculated at the same intervals. During experiment, the mean blood pressure and heart rates in all groups were stable. CONCLUSION:Glutamate is involved in the mechanism of intestinal and cerebral inflammation responses. The effects of glutamate on cerebral and intestinal inflammatory responses after ischemia are up-regulated at the transcriptional level, through the NF-B signal transduction pathway.

Metabolic syndrome (MetS) is a major public health burden worldwide and associated with brain abnormalities. Although insulin resistance is considered a pivotal feature of MetS, its role in the pathogenesis of MetS-related brain alterations in the general population is unclear. Therefore, in 973 participants (mean age 52.5 years) of the population-based Rhineland Study, we assessed brain morphology in relation to MetS and insulin resistance, and evaluated to what extent the pattern of structural brain changes seen in MetS overlap with those associated with insulin resistance. Cortical reconstruction and volumetric segmentation were obtained from high-resolution brain images at 3 Tesla using FreeSurfer. The relations between metabolic measures and brain structure were assessed through (generalized) linear models. Both MetS and insulin resistance were associated with smaller cortical gray matter volume and thickness, but not with white matter or subcortical gray matter volume. Age-and sex-adjusted vertex-based brain morphometry demonstrated that MetS and insulin resistance were related to cortical thinning in a similar spatial pattern. Importantly, no independent effect of MetS on cortical gray matter was observed beyond the effect of insulin resistance. Our findings suggest that addressing insulin resistance is critical in the prevention of MetS-related brain changes in later life.

IMPORTANCEOlfactory dysfunction is a prodromal manifestation of many neurodegenerative disorders, including Alzheimer and Parkinson disease. However, its neuroanatomical basis is largely unknown.OBJECTIVE To assess the association between olfactory brain structures and olfactory function in adults 30 years or older and to examine the extent to which olfactory bulb volume (OBV) mediates the association between central olfactory structures and olfactory function. DESIGN, SETTING, AND PARTICIPANTSThis cross-sectional study analyzed baseline data from the first 639 participants with brain magnetic resonance imaging (MRI) in the Rhineland Study, an ongoing population-based cohort study in Bonn, Germany. Participants were enrolled between

Highlights First publicly available deep learning pipeline to segment the olfactory bulbs (OBs) in sub-millimeter T2-weighted whole-brain MRI. Rigorous validation in the Rhineland Study - an ongoing large population-based cohort study - in terms of segmentation accuracy, stability and reliability of volume estimates, as well as sensitivity to replicate known OB volume associations (e.g. age effects). Good generalizability to an unseen heterogeneous independent dataset (the Human Connectome Project). Robustness even for individuals without apparent OBs, as can be encountered in large cohort studies.