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MAVS is an adapter protein involved in RIG-I-like receptor (RLR) signaling in mitochondria, peroxisomes, and mitochondria-associated ER membranes (MAMs). However, the role of MAVS in glucose metabolism and RLR signaling cross-regulation and how these signaling pathways are coordinated among these organelles have not been defined. This study reports that RLR action drives a switch from glycolysis to the pentose phosphate pathway (PPP) and the hexosamine biosynthesis pathway (HBP) through MAVS. We show that peroxisomal MAVS is responsible for glucose flux shift into PPP and type III interferon (IFN) expression, whereas MAMs-located MAVS is responsible for glucose flux shift into HBP and type I IFN expression. Mechanistically, peroxisomal MAVS interacts with G6PD and the MAVS signalosome forms at peroxisomes by recruiting TNF receptor-associated factor 6 (TRAF6) and interferon regulatory factor 1 (IRF1). By contrast, MAMs-located MAVS interact with glutamine-fructose-6-phosphate transaminase, and the MAVS signalosome forms at MAMs by recruiting TRAF6 and TRAF2. Our findings suggest that MAVS mediates the interaction of RLR signaling and glucose metabolism. MAVS is an adapter protein involved in RIG-I-like receptor (RLR) signaling. Here, the authors show how MAVS link RLR-mediated signaling and glucose metabolism, employing distinct mechanisms in different organelles. [ABSTRACT FROM AUTHOR]

The precuneus shows considerable heterogeneity in multiple dimensions including anatomy, function, and involvement in brain disorders. Leveraging the state-of-the-art functional gradient approach, we aimed to investigate the hierarchical organization of the precuneus, which may hold promise for a unified understanding of precuneus heterogeneity. Resting-state functional MRI data from 793 healthy individuals were used to discover and validate functional gradients of the precuneus, which were calculated based on the voxel-wise precuneus-to-cerebrum functional connectivity patterns. Then, we further explored the potential relationships of the precuneus functional gradients with cortical morphology, intrinsic geometry, canonical functional networks, and behavioral domains. We found that the precuneus principal and secondary gradients showed dorsoanterior-ventral and ventroposterior-dorsal organizations, respectively. Concurrently, the principal gradient was associated with cortical morphology, and both the principal and secondary gradients showed geometric distance dependence. Importantly, precuneus functional subdivisions corresponding to canonical functional networks (behavioral domains) were distributed along both gradients in a hierarchical manner, i.e., from the sensorimotor network (somatic movement and sensation) at one extreme to the default mode network (abstract cognitive functions) at the other extreme for the principal gradient and from the visual network (vision) at one end to the dorsal attention network (top-down control of attention) at the other end for the secondary gradient. These findings suggest that the precuneus functional gradients may provide mechanistic insights into the multifaceted nature of precuneus heterogeneity. [ABSTRACT FROM AUTHOR]

Heterogeneity of the cingulate cortex is evident in multiple dimensions including anatomy, function, connectivity, and involvement in networks and diseases. Using the recently developed functional connectivity gradient approach and resting-state functional MRI data, we found three functional connectivity gradients that captured distinct dimensions of cingulate hierarchical organization. The principal gradient exhibited a radiating organization with transitions from the middle toward both anterior and posterior parts of the cingulate cortex and was related to canonical functional networks and corresponding behavioral domains. The second gradient showed an anterior–posterior axis across the cingulate cortex and had prominent geometric distance dependence. The third gradient displayed a marked differentiation of subgenual and caudal middle with other parts of the cingulate cortex and was associated with cortical morphology. Aside from providing an updated framework for understanding the multifaceted nature of cingulate heterogeneity, the observed hierarchical organization of the cingulate cortex may constitute a novel research agenda with potential applications in basic and clinical neuroscience. Analysis of resting-state fMRI data reveals the presence of functional connectivity gradients of the human cingulate cortex, which updates our understanding of cingulate heterogeneity. [ABSTRACT FROM AUTHOR]

Type 2 diabetes mellitus (T2DM) is associated with brain damage and cognitive decline. Despite the fact that the thalamus involves aspects of cognition and is typically affected in T2DM, existing knowledge of subregion-level thalamic damage and its associations with cognitive performance in T2DM patients is limited. The thalamus was subdivided into 8 subregions in each hemisphere. Resting-state functional and structural MRI data were collected to calculate resting-state functional connectivity (rsFC) and gray matter volume (GMV) of each thalamic subregion in 62 T2DM patients and 50 healthy controls. Compared with controls, T2DM patients showed increased rsFC of the medial pre-frontal thalamus, posterior parietal thalamus, and occipital thalamus with multiple cortical regions. Moreover, these thalamic functional hyperconnectivity were associated with better cognitive performance and lower glucose variability in T2DM patients. However, there were no group differences in GMV for any thalamic subregions. These findings suggest a possible neural compensation mechanism whereby selective thalamocortical functional hyperconnectivity facilitated by better glycemic control help to preserve cognitive ability in T2DM patients, which may ultimately inform intervention and prevention of T2DM-related cognitive decline in real-world clinical settings. [ABSTRACT FROM AUTHOR]

Prior neuroimaging studies of the relationship between the kidney and the brain have been limited to clinical populations and have largely relied on a single modality. We sought to examine the kidney-brain associations in healthy subjects using a combined analysis of multi-modal imaging data. Structural, diffusion, and perfusion magnetic resonance imaging (MRI) scans were performed to measure cortical thickness, white matter integrity, and cerebral blood flow in 157 healthy young adults. Peripheral venous blood samples were collected to measure serum renal function indexes. Correlation analyses were performed to investigate the relations between brain MRI measures and renal function indexes. Results showed that higher serum uric acid level was associated with increased cortical thickness in the transverse temporal gyrus. We also found that decreased serum creatinine level was linked to lower white matter integrity in the sagittal stratum, anterior corona radiata, superior corona radiata, and external capsule. Furthermore, we observed that increased serum uric acid level was related to hyperperfusion in the opercular and triangular parts of inferior frontal gyrus and supramarginal gyrus, and hypoperfusion in the calcarine sulcus, cuneus and lingual gyrus. More importantly, mediation analysis revealed that the relationship between serum uric acid and working memory performance was mediated by perfusion in the supramarginal gyrus and lingual gyrus. These findings not only may extend current knowledge regarding the relationship between the kidney and the brain, but also may inform real-world clinical practice by identification of potential brain regions vulnerable to renal dysfunction. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the semi-supervised person re-identification (re-ID) task, where the training data includes some labeled data and most unlabeled data. Since the re-ID task is used for cross-camera scenes, learning camera invariant deep features become critical. We propose a novel end-to-end semi-supervised person re-ID method by introducing the context information, i.e., the camera information (camera ID) which could be easily collected without any manual annotation. Specifically, we design a camera-based hard triplet loss for (pseudo-) labeled data to learn the camera invariant features. The loss not only learns the similar features between the cross-camera anchor and the hard positive sample but also learns the distinguishing features between the within-camera anchor and the hard negative sample. For unlabeled data, we use both diversity loss and similarity loss to diversify unlabeled data features and mine similar samples. And we design an adaptive feature fusion module, which could adaptively combine the Global Average Pooling (GAP) and Global Max Pooling (GMP) features to learn person-specific discriminative information in a global-local manner. Furthermore, to validate the effectiveness of our approach, we conduct extensive experiments on two large-scale image re-ID datasets, including Market-1501 and DukeMTMC-reID. The experimental results demonstrate that our approach outperforms the state-of-the-art method by 4.8% on Market-1501, and 7.2% on DukeMTMC-reID. [ABSTRACT FROM AUTHOR]

We focus on the one-example person re-identification (Re-ID) task, where each identity has only one labeled example along with many unlabeled examples. Since each identity has only one labeled example, the number of initialized label examples is small, and the body parts of person are not aligned due to changes in person pose and camera angle under the camera. Therefore, the distinguishing information of learning labeled and unlabeled examples is challenging. To overcome these problems, we propose an end-to-end multi-task training network for semi-supervised Re-ID. First, we impose a part segmentation (PS) constraint on feature maps, forcing a module to predict part labels from the feature maps and enhance alignment. Second, we carefully design the network named Multiple Branch Network (MBN). MBN is a multi-branch deep network architecture, which consisting of one branch for global feature representation and two branches for local feature representation, local feature representation that including horizontal stripes representation and PS representation, respectively. Finally, loss function fusion is designed to learn discriminative features for semi-supervised Re-ID. Specifically, the MBN model is optimized by mining the object classification loss, exclusive loss and PS loss simultaneously. We validate the effectiveness of our approach by demonstrating its superiority over the state-of-the-art methods on the standard benchmark datasets, including Market-1501, DukeMTMC-reID. Notably, the rank-1 accuracy of our method outperforms the state-of-the-art method by 15.9 points (absolute, i.e., 71.7% vs. 55.8%) on Market-1501 and 8.9 points on DukeMTMC-reID. [ABSTRACT FROM AUTHOR]

Several behavioral and neuroimaging markers could be used to predict eventual antidepressant medication (ADM) outcomes in patients with major depressive disorder (MDD). However, these predictors are either subjective or complex, which has limited their clinical use. Thus, we aimed to identify an objective and easy-to-get marker to predict early therapeutic efficacy. Forty-seven drug-naïve patients with MDD and 47 age-, gender- and education-matched healthy controls underwent resting-state functional magnetic resonance imaging (fMRI) scans. We calculated the variable coefficient (VC) of the global signal for each subject. Baseline Hamilton Rating Scale for Depression (HRSD) score and that after 2 weeks of ADM were assessed for patients. Although there was no difference in VC between patients with MDD and healthy controls, we found a significant positive correlation between the VC and the decline rate of HRSD scores in the patients. Compared with the non-responding depression (NRD) group, the treatment-responsive depression (TRD) group had a higher VC. Receiver operator characteristic curve analysis revealed that the VC exhibited a good ability to differentiate TRD from NRD. In addition, the linear and logistic regression analyses showed that the VC was a significant predictor of the decline rate of HRSD scores and the antidepressant treatment response. These findings suggest that variance of the global signal may serve as a useful marker to help clinicians find an appropriate drug for individuals with MDD at the earliest opportunity and then further to facilitate personalized therapy. [ABSTRACT FROM AUTHOR]

Patients with neuromyelitis optica (NMO) have shown structural and functional impairments in the visual cortex. We aimed to characterize subregional grey matter volume (GMV) and resting-state functional connectivity (rsFC) changes in the visual cortex in NMO. Thirty-seven NMO patients and forty-two controls underwent structural and functional MRI scans. The GMV and rsFC of each visual subregion were compared between the groups. Compared with controls, NMO patients had GMV reductions in the bilateral V1, V2, V3d, VP, and LO and in the left V3A. In canonical visual pathways, the relatively low-level subregions showed more significant GMV reductions than did the high-level ones. Regardless of GMV correction, NMO patients showed reduced rsFC in the bilateral LO and V4v and in the left V2. The GMVs of the bilateral V1 and LO and of the left V2 and V3d were negatively correlated with clinical disability in NMO patients; these correlation coefficients were associated with hierarchical positions in the visual pathways. These findings suggest that in NMO, the low-level visual subregions have more severe structural damage; structural damage is not the only factor affecting rsFC alterations of visual subregions; GMV reduction in the low-level visual subregions has the highest predictive value for clinical disability. [ABSTRACT FROM AUTHOR]