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Accelerated intermittent Theta Burst Stimulation (aiTBS) has been shown to be an effective antidepressant treatment. Although neurobiological changes shortly after this intervention have been reported, whether aiTBS results in structural brain changes must still be determined. Furthermore, it possible that rapid volumetric changes are driven by factors other than neurotrophic processes. We examined whether possible grey matter volumetric (GMV) increases after aiTBS treatment could be driven by increased brain perfusion, measured by Arterial Spin Labeling (ASL). 46 treatment-resistant depressed patients were randomized to receive 20 sessions of active or sham iTBS applied to the left dorsolateral prefrontal cortex. All sessions were delivered over 4 days at 5 sessions per day (trial registration: http://clinicaltrials.gov/show/NCT01832805). Patients were scanned the day before starting stimulation and three days after aiTBS. There was a significant cluster of increased left hippocampal GMV in the dentate gyrus related to HRSD changes after active aiTBS, but not after sham stimulation. These GMV increases became more pronounced when accounting for changes in cerebral perfusion. Active, but not sham, aiTBS, resulted in acute volumetric changes in parts of the left dentate gyrus, suggesting a connection with adult neurogenesis. Furthermore, taking cerebral perfusion measurements into account impacts on detection of the GMV changes. Whether these hippocampal volumetric changes produced by active aiTBS are necessary for long-term clinical improvement remains to be determined. • Active aiTBS results in increased grey matter volume (GMV) in parts of the left hippocampus. • Left dentate gyrus volumetric change may relate to adult neurogenesis, not to perfusion increments. • Cerebral perfusion measurements impact on detection of the GMV changes. [ABSTRACT FROM AUTHOR]

Background: Postoperative delirium (POD), as an acute brain failure, is widely reported as a very common postoperative complication, and it is closely associated with increased morbidity and mortality. This study aimed to investigate potential risk factors including C-reactive protein/albumin ratio (CAR) for POD in elderly subjects after total joint arthroplasty (TJA).Methods: A total of 272 elderly patients (aged 65∼85 years) who were scheduled to undergo elective TJA with epidural anesthesia were consecutively recruited. The data of baseline characteristics, operation-associated indexes, and preoperative laboratory tests were collected. POD assessment was performed daily within postoperative 7 days. Receiver operating characteristic curve analysis was utilized for evaluating the predictive and cut-off value of CAR for POD. Risk factors for POD were evaluated by the binary univariate and multivariate logistic regression analyses.Results: Within postoperative 7 days, there were 55 patients who had suffered POD with an incidence of 20.2% (55/272). The area under the curve of CAR for POD was 0.804, with the cut-off value of 2.35, a sensitivity of 66.82%, and a specificity of 80.00%, respectively (95% confidence interval [CI]: 0.737-0.872, P < .001). Age (odds ratio: 2.02, 95% CI: 1.03-3.96, P = .038) and preoperative CAR level (odds ratio: 3.04, 95% CI: 1.23-7.23, P = .016) were 2 independent risk factors for POD in elderly subjects undergoing TJA.Conclusions: Preoperative CAR level may be a promising predictor for POD in elderly subjects following TJA. [ABSTRACT FROM AUTHOR]

Background: Postoperative cognitive dysfunction (POCD), which refers to a cognitive impairment subsequent to surgical procedures, is a common complication in the elderly subjects. This study aimed to investigate potential risk factors for POCD in elderly subjects undergoing hip joint arthroplasty.Methods: Consecutive elderly osteoarthritis patients who were scheduled to undergo hip arthroplasty under epidural anesthesia were enrolled into this single-center, prospective observational study. Serum phosphorylated neurofilament heavy subunit-H (pNF-H) was measured by the enzyme-linked immunosorbent assay method. A level of >70.5 pg/mL was accepted as pNF-H positivity. Neuropsychological assessment at baseline (1 day before the surgery) and postoperative day 7 was conducted. POCD was defined according to the calculated Z scores. Risk factors for POCD were evaluated by univariate and multivariate logistic regression analyses.Results: In final, 287 patients were enrolled, and 55 had suffered POCD within postoperative 7 days with an incidence of 19.2%. The final multiple logistic regression analysis revealed a higher pNF-H positivity was the only independent risk factor for POCD (odds ratio: 2.03, 95% confidence interval: 1.21-3.29, P = .012).Conclusions: Our results revealed an increased preoperative serum pNF-H expression was an independent risk factor for POCD development in elderly subjects undergoing hip joint arthroplasty, suggesting the close association between anatomical damage in central nervous system and POCD. [ABSTRACT FROM AUTHOR]

Well-dispersion of fillers in polymers is crucial for the preparation of mixed matrix membranes(MMMs). In this paper, COF-LZU1 particles were in-situ prepared in poly(ether- block -amide) membranes for the first time, and the as-obtained MMMs exhibited higher n-butanol/water sorption selectivity and pervaporation selectivity in comparison with that of MMMs prepared by conventional blending methods. The effects of reactants concentrations, growth temperature and solvents on property and pervaporation performance of MMMs were investigated. For the benzene-1,3,5-tricarbaldehyde (TFB) concentration of 2.0 mmol L−1, growth temperature of 70 °C, TFB to p-phenylenediamine molar ratio of 1:1.5, and solvent of n-butanol, the MMMs showed maximum separation factor (22.2) (increased by 139% in comparison with that of pristine membranes) and peameation flux of 611 g m−2 h−1, demonstrating great prospect for the recovery of n-butanol. • COF-LZU1 particles were in-situ prepared in poly(ether- block -amide) membranes. • The membranes exhibited stronger hydrophobicity, higher n-butanol/water sorption and pervaporation selectivity. • The separation factor increased by 139% in comparison with that of pristine membranes. [ABSTRACT FROM AUTHOR]

Abstract Submicron PAF-56 particles with narrow size-distribution were prepared by bottom-up method for the first time, and incorporated in polydimethylsiloxane (PDMS) membranes for pervaporation of 5% n-butanol solution. The effects of PAF-56 loading on the hydrophilicity, swelling degree, partition coefficient and pervaporation performance of the mixed matrix membranes (MMMs) were studied. It was found that, the PAF-56/PDMS MMMs displayed n-butanol sorption-selectivity, and the permeation flux and separation factor increased with the decreased PAF-56 particles size at the same loading, demonstrating size-effects of PAF-56 particles. With the increased PAF-56 loading, the permeation flux and separation factor exhibited maximum values at 2–2.5% loading. For MMMs with PAF-56 size of 0.83 μm and 2.5 wt% loading, the separation factor and permeation fluxes were 48.4 and 1906 g m−2 h−1 (increased by 183% and 66% in comparison with that of pristine PDMS membrane), indicating great potential in recovery of n-butanol. Graphical abstract Image 1 Highlights • Sub-micron PAF-56 particles were prepared by bottom-up method for the first time. • Size-effects of PAF-56 particles were found in pervaporation. • Apparent anti-trade-off effects between permeation flux and separation factor were demonstrated. [ABSTRACT FROM AUTHOR]

Metal-organic frameworks (MOFs)/polymer mixed matrix membranes (MMMs) have great potential in pervaporation separation due to the ease of design and modification of MOFs, along with the compatibility between MOFs and polymer matrix. This article reviews the current status of MOFs MMMs for pervaporation, including polymer (hydrophobicity/hydrophilicity, structure stability), MOFs (stability, hydrophobicity/hydrophilicity, surface functional structure, particles morphology and pores size), mass transfer, and applications (dehydration of organic solvents, removal of dilute organic compounds from aqueous streams, separation of organic-organic mixtures, and membrane reactor). The perspectives and suggestions of MOFs MMMs are given. [ABSTRACT FROM AUTHOR]

Multi-atlas segmentation is a powerful approach to automated anatomy delineation via fusing label information from a set of spatially normalized atlases. For simplicity, many existing methods perform pairwise image registration, leading to inaccurate segmentation especially when shape variation is large. In this paper, we propose a dynamic tree-based strategy for effective large-deformation registration and multi-atlas segmentation. To deal with local minima caused by large shape variation, coarse estimates of deformations are first obtained via alignment of automatically localized landmark points. The dynamic tree capturing the structural relationships between images is then employed to further reduce misalignment errors. Evaluation based on two real human brain datasets, ADNI and LPBA40, shows that our method significantly improves registration and segmentation accuracy. [ABSTRACT FROM AUTHOR]

The mean porosity within a micro-unit should be properly calculated in discrete element simulation of gas-solid fluidization systems. This article presents a three-dimensional porosity model for fine grid calculation. All nine cases of particle-grid overlap are considered in the model. The three-dimensional porosity is analytically expressed, employing a series of volume formulas for pertinent curved top cylinders. The double integrals involved in the volume expressions are numerically calculated according to compound trapezoid formula. The adopted integral scheme and numerical method can be easily realized, which are suitable for complicated three-dimensional fine grid calculation. [ABSTRACT FROM AUTHOR]

Abstract: In discrete element model (DEM) for simulation of gas–solid fluidized bed, drag force is correlated by use of local parameters which would be excessively smoothed within coarse grid. This work studies the method of calculating drag force for coarse grid DEM simulation of bubbling fluidized bed. The so-called complete circumstance-dependent local porosity (CCLP) model is proposed to correlate drag force taking the local circumstance of particle into account. The resulting circumstance-dependent drag force is employed in two-dimensional DEM simulations of a real small-scale bubbling fluidized system, using coarse grid. The local porosity is shown to be revised to a greater extent at the junctions of the gas-rich and particle-rich regions by the proposed CCLP model, which indicates that the circumstance influences on drag force are highlighted in the dense–dilute transitional regions. The parameter, i.e. the amplification factor, involved in the CCLP model is rationalized by investigating the comprehensive capability of predicting the fluidization features. The bubble size and shape simulated at the optimal parameter are generally consistent with the experimental observations. The simulated fluctuation time scales and amplitudes of solid volume fraction, relative pressure and bed layer height are close to the experimental results. Simulations show that DEM performs well in modeling the time-varying waveforms for the physical quantities in bubbling fluidized bed by use of the revised drag calculation method, which proves the unfamiliar merits of coarse grid simulation in some ways. [Copyright &y& Elsevier]

Abstract: In this paper, a novel image registration method is proposed to achieve accurate registration between images having large shape differences with the help of a set of appropriate intermediate templates. We first demonstrate that directionality is a key factor in both pairwise image registration and groupwise registration, which is defined in this paper to describe the influence of the registration direction and paths on the registration performance. In our solution, the intermediate template selection and intermediate template guided registration are two coherent steps with directionality being considered. To take advantage of the directionality, a directed graph is built based on the asymmetric distance defined on all ordered image pairs in the image population, which is fundamentally different from the undirected graph with symmetric distance metrics in all previous methods, and the shortest distance between template and subject on the directed graph is calculated. The allocated directed path can be thus utilized to better guide the registration by successively registering the subject through the intermediate templates one by one on the path towards the template. The proposed directed graph based solution can also be used in groupwise registration. Specifically, by building a minimum spanning arborescence (MSA) on the directed graph, the population center, i.e., a selected template, as well as the directed registration paths from all the rest of images to the population center, is determined simultaneously. The performance of directed graph based registration algorithm is demonstrated by the spatial normalization on both synthetic dataset and real brain MR images. It is shown that our method can achieve more accurate registration results than both the undirected graph based solution and the direct pairwise registration. [Copyright &y& Elsevier]

Abstract: In this paper, by solving the relaxed quasiconcave programming problem in outcome space, a new global optimization algorithm for convex multiplicative programming is presented. Two kinds of techniques are employed to establish the algorithm. The first one is outer approximation technique which is applied to shrink relaxation area of quasiconcave programming problem and to compute appropriate feasible points and to raise the capacity of bounding. And the other one is branch and bound technique which is used to guarantee global optimization. Some numerical results are presented to demonstrate the effectiveness and feasibility of the proposed algorithm. [Copyright &y& Elsevier]

Tissue-clearing methods allow every cell in the mouse brain to be imaged without physical sectioning. However, the computational tools currently available for cell quantification in cleared tissue images have been limited to counting sparse cell populations in stereotypical mice. Here, we introduce NuMorph, a group of analysis tools to quantify all nuclei and nuclear markers within the mouse cortex after clearing and imaging by light-sheet microscopy. We apply NuMorph to investigate two distinct mouse models: a Topoisomerase 1 (Top1) model with severe neurodegenerative deficits and a Neurofibromin 1 (Nf1) model with a more subtle brain overgrowth phenotype. In each case, we identify differential effects of gene deletion on individual cell-type counts and distribution across cortical regions that manifest as alterations of gross brain morphology. These results underline the value of whole-brain imaging approaches, and the tools are widely applicable for studying brain structure phenotypes at cellular resolution. [Display omitted] • NuMorph accurately quantifies all cortical nuclei and cell-type-specific markers • Top1 deletion results in increased neuronal degeneration in frontal areas • Nf1 deletion leads to structural changes that recapitulate human MRI findings • Structural changes are driven by increased gliogenesis in the Nf1 knockout model Krupa et al. develop an image analysis toolbox called NuMorph that accurately quantifies all nuclei within the mouse cortex after tissue clearing and light-sheet imaging. They implement NuMorph to characterize region- and cell-type-specific deficits present in two distinct mouse models, demonstrating the advantages of a whole-brain imaging approach. [ABSTRACT FROM AUTHOR]

Novel sandwich PES-SPES/amino-MIL-53(Al)/PTFE adsorptive membranes were prepared by a filtration/immersion precipitation method and employed for the removal of p-nitrophenol (PNP) from water. The static adsorption thermodynamics, kinetics, dynamic adsorption/desorption and membrane reusability were investigated. The results showed that, amino-MIL-53(Al) displays very high saturation adsorption capacity towards PNP (297.85 mg g −1 ). The sandwich membranes showed much higher adsorption performance (static adsorption capacity, breakthrough volume, saturation volume, dynamic adsorption capacity) and lower pressure drop than those of blank and mixed matrix membranes. With increased amino-MIL-53(Al) content, the maximum static adsorption capacity, saturation volume and dynamic adsorption capacity significantly increased. The sandwich membranes exhibited excellent reusability even after four recycles. [ABSTRACT FROM AUTHOR]

Comparison of human brain MR images is often challenged by large inter-subject structural variability. To determine correspondences between MR brain images, most existing methods typically perform a local neighborhood search, based on certain morphological features. They are limited in two aspects: (1) pre-defined morphological features often have limited power in characterizing brain structures, thus leading to inaccurate correspondence detection, and (2) correspondence matching is often restricted within local small neighborhoods and fails to cater to images with large anatomical difference. To address these limitations, we propose a novel method to detect distinctive landmarks for effective correspondence matching. Specifically, we first annotate a group of landmarks in a large set of training MR brain images. Then, we use regression forest to simultaneously learn (1) the optimal sets of features to best characterize each landmark and (2) the non-linear mappings from the local patch appearances of image points to their 3D displacements towards each landmark. The learned regression forests are used as landmark detectors to predict the locations of these landmarks in new images. Because each detector is learned based on features that best distinguish the landmark from other points and also landmark detection is performed in the entire image domain, our method can address the limitations in conventional methods. The deformation field estimated based on the alignment of these detected landmarks can then be used as initialization for image registration. Experimental results show that our method is capable of providing good initialization even for the images with large deformation difference, thus improving registration accuracy. [ABSTRACT FROM AUTHOR]

Investigation of the effects of anthropogenic pollutants on the mass concentrations, particle number concentrations, and chemical compositions of secondary organic aerosol (SOA) formation is essential in understanding the photooxidation mechanism of volatile organic compounds but has been proven to be a very challenging experimental target because of their complex processes. Here, the effects of NO and SO 2 on SOA formation from isoprene photooxidation were studied by a number of laboratory studies. The results indicate the accumulated O 3 upon the NO conversion triggers the main oxidation reaction, by which the oxidants (i.e., OH and SO 4 −) are derived. The SOA mass concentrations and particle number concentrations are enhanced by NO under low NO concentrations but suppressed by NO under high NO concentrations, which are consistent with previous studies (Kroll et al., 2005 , 2006). This could be rationalized that the large amount of accumulated O 3 inhibit the multi-step isoprene oxidation and a large number of less oxidized products (m/z < 130) absorb or condense on the surface of existing particles, which decelerate the SOA nucleation and growth. The presence of SO 2 accelerates the conversion of NO and the accumulation of O 3 , which promotes the main oxidation reaction of isoprene. New compounds are observed at m/z = 134, 137, 150, 152, and 179 measured by threshold photoionization (positive ion mode) with a tunable vacuum ultraviolet free electron laser and are found to be mainly formed from the isoprene oxidization by O 3 and OH. The present findings provide both macroscopic and microscopic information to advance understanding of atmospheric components affected by the anthropogenic-biogenic interactions in the neighborhood of emission origins. [Display omitted] • The study investigates the atmospheric components affected by the anthropogenic-biogenic interactions. • The accumulated O 3 upon the NO conversion triggers the isoprene oxidation. • The presence of SO 2 accelerates the conversion of NO and the accumulation of O 3 , which promotes the main oxidation reaction of isoprene. • Newly-observed compounds measured by threshold photoionization advance understanding of atmospheric components. [ABSTRACT FROM AUTHOR]

Abstract The (1 ＋1)-dimensional mathematical model had been extensively derived to describe Rossby solitary waves in a line in the past few decades. But as is well known, the (1 ＋1)-dimensional model cannot reflect the generation and evolution of Rossby solitary waves in a plane. In this paper, a (2 ＋1)-dimensional nonlinear Zakharov–Kuznetsov–Burgers equation is derived to describe the evolution of Rossby wave amplitude by using methods of multiple scales and perturbation expansions from the quasi-geostrophic potential vorticity equations with the generalized beta effect. The effects of the generalized beta and dissipation are presented by the Zakharov–Kuznetsov–Burgers equation. We also obtain the new solitary solution of the Zakharov–Kuznetsov equation when the dissipation is absent with the help of the Bernoulli equation, which is different from the common classical solitary solution. Based on the solution, the features of the variable coefficient are discussed by geometric figures Meanwhile, the approximate solitary solution of Zakharov–Kuznetsov–Burgers equation is given by using the homotopy perturbation method. And the amplitude of solitary waves changing with time is depicted by figures. Undoubtedly, these solitary solutions will extend previous results and better help to explain the feature of Rossby solitary waves. [ABSTRACT FROM AUTHOR]

Objective The central mechanisms underlying postherpetic neuralgia (PHN) pain remains unknown. The primary purpose of this study was to identify microstructural white matter changes closely related to the PHN pain by means of diffusion tensor imaging (DTI) and tract-based spatial statistics (TBSS) analysis. Methods DTI data of the brains were obtained from 8 PHN patients and 8 healthy controls (HC) that were matched in age, gender, and educational level. DTI metrics, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD), were separately compared between the two groups using TBSS analysis to detect subtle microstructural changes. Partial correlation analyses were also conducted to evaluate the association between the altered DTI measures and clinical features. Results Average diffusion indices of white matter skeletons in the whole-brain showed no significant difference between the two groups. However, compared to the HC group, patients with PHN pain revealed reductions in localized FA and AD values in white matter underlying insula, occipital lobe, cerebellum, precentral gyrus, and many other regions, but without distinct change in regional MD and RD levels. In addition, decline of FA and AD values in patients represented significant negative correlations with PHN pain duration when the effect of VAS scores were excluded. Conclusion The current study suggest that there exists altered microstructure integrity of white matter in multiple brain regions in patients with PHN, and these changes increase in size as the duration of the pain increases. These findings might provide a new insight into the mechanism of PHN pain in brain. [ABSTRACT FROM AUTHOR]

The first year of life is the most dynamic and perhaps the most critical phase of postnatal brain development. The ability to accurately measure structure changes is critical in early brain development study, which highly relies on the performances of image segmentation and registration techniques. However, either infant image segmentation or registration, if deployed independently, encounters much more challenges than segmentation/registration of adult brains due to dynamic appearance change with rapid brain development. In fact, image segmentation and registration of infant images can assists each other to overcome the above challenges by using the growth trajectories (i.e., temporal correspondences) learned from a large set of training subjects with complete longitudinal data. Specifically, a one-year-old image with ground-truth tissue segmentation can be first set as the reference domain. Then, to register the infant image of a new subject at earlier age, we can estimate its tissue probability maps, i.e., with sparse patch-based multi-atlas label fusion technique, where only the training images at the respective age are considered as atlases since they have similar image appearance. Next, these probability maps can be fused as a good initialization to guide the level set segmentation. Thus, image registration between the new infant image and the reference image is free of difficulty of appearance changes, by establishing correspondences upon the reasonably segmented images. Importantly, the segmentation of new infant image can be further enhanced by propagating the much more reliable label fusion heuristics at the reference domain to the corresponding location of the new infant image via the learned growth trajectories, which brings image segmentation and registration to assist each other. It is worth noting that our joint segmentation and registration framework is also flexible to handle the registration of any two infant images even with significant age gap in the first year of life, by linking their joint segmentation and registration through the reference domain. Thus, our proposed joint segmentation and registration method is scalable to various registration tasks in early brain development studies. Promising segmentation and registration results have been achieved for infant brain MR images aged from 2-week-old to 1-year-old, indicating the applicability of our method in early brain development study. [ABSTRACT FROM AUTHOR]

Automatic labeling of the hippocampus in brain MR images is highly demanded, as it has played an important role in imaging-based brain studies. However, accurate labeling of the hippocampus is still challenging, partially due to the ambiguous intensity boundary between the hippocampus and surrounding anatomies. In this paper, we propose a concatenated set of spatially-localized random forests for multi-atlas-based hippocampus labeling of adult/infant brain MR images. The contribution in our work is two-fold. First , each forest classifier is trained to label just a specific sub-region of the hippocampus, thus enhancing the labeling accuracy. Second , a novel forest selection strategy is proposed, such that each voxel in the test image can automatically select a set of optimal forests, and then dynamically fuses their respective outputs for determining the final label. Furthermore , we enhance the spatially-localized random forests with the aid of the auto-context strategy. In this way, our proposed learning framework can gradually refine the tentative labeling result for better performance. Experiments show that, regarding the large datasets of both adult and infant brain MR images, our method owns satisfactory scalability by segmenting the hippocampus accurately and efficiently. [ABSTRACT FROM AUTHOR]

Recently, multi-atlas patch-based label fusion has achieved many successes in medical imaging area. The basic assumption in the current state-of-the-art approaches is that the image patch at the target image point can be represented by a patch dictionary consisting of atlas patches from registered atlas images. Therefore, the label at the target image point can be determined by fusing labels of atlas image patches with similar anatomical structures. However, such assumption on image patch representation does not always hold in label fusion since (1) the image content within the patch may be corrupted due to noise and artifact; and (2) the distribution of morphometric patterns among atlas patches might be unbalanced such that the majority patterns can dominate label fusion result over other minority patterns. The violation of the above basic assumptions could significantly undermine the label fusion accuracy. To overcome these issues, we first consider forming label-specific group for the atlas patches with the same label. Then, we alter the conventional flat and shallow dictionary to a deep multi-layer structure, where the top layer ( label-specific dictionaries ) consists of groups of representative atlas patches and the subsequent layers ( residual dictionaries ) hierarchically encode the patchwise residual information in different scales. Thus, the label fusion follows the representation consensus across representative dictionaries. However, the representation of target patch in each group is iteratively optimized by using the representative atlas patches in each label-specific dictionary exclusively to match the principal patterns and also using all residual patterns across groups collaboratively to overcome the issue that some groups might be absent of certain variation patterns presented in the target image patch. Promising segmentation results have been achieved in labeling hippocampus on ADNI dataset, as well as basal ganglia and brainstem structures, compared to other counterpart label fusion methods. [ABSTRACT FROM AUTHOR]

It is fundamentally important to fuse the brain atlas from magnetic resonance (MR) images for many imaging-based studies. Most existing works focus on fusing the atlases from high-quality MR images. However, for low-quality diagnostic images (i.e., with high inter-slice thickness), the problem of atlas fusion has not been addressed yet. In this paper, we intend to fuse the brain atlas from the high-thickness diagnostic MR images that are prevalent for clinical routines. The main idea of our works is to extend the conventional groupwise registration by incorporating a novel super-resolution strategy. The contribution of the proposed super-resolution framework is two-fold. First, each high-thickness subject image is reconstructed to be isotropic by the patch-based sparsity learning. Then, the reconstructed isotropic image is enhanced for better quality through the random-forest-based regression model. In this way, the images obtained by the super-resolution strategy can be fused together by applying the groupwise registration method to construct the required atlas. Our experiments have shown that the proposed framework can effectively solve the problem of atlas fusion from the low-quality brain MR images. [ABSTRACT FROM AUTHOR]

Pain is a complex experience that comprises both sensory and affective dimensions. Mammalian target of rapamycin (mTOR) plays an important role in the modulation of neuronal plasticity associated with the pathogenesis of pain sensation. However, the role of mTOR in pain affect is unclear. Using a formalin-induced conditioned place avoidance (F-CPA) test, the current study investigated the effects of the mTOR specific inhibitor rapamycin on noxious stimulation induced aversion in the rostral anterior cingulate cortex (rACC). Intraplantar injection of 5% formalin was associated with significant activation of mTOR, as well as p70 ribosomal S6 protein (p70S6K), its downstream effector, in the rACC. The inhibition of mTOR activation with rapamycin disrupted pain-related aversion; however, this inhibition did not affect formalin-induced spontaneous nociceptive behaviors in rats. These findings demonstrated for the first time that mTOR and its downstream pathway in the rACC contribute to the induction of pain-related negative emotion. [ABSTRACT FROM AUTHOR]