Dopamine Release Dynamics in the Nucleus Accumbens Are Modulated by the Timing of Electrical Stimulation Pulses When Applied to the Medial Forebrain Bundle and Medial Prefrontal Cortex.

Electrical brain stimulation has been used in vivo and in vitro to investigate neural circuitry. Historically, stimulation parameters such as amplitude, frequency, and pulse width were varied to investigate their effects on neurotransmitter release and behavior. These experiments have traditionally employed fixed-frequency stimulation patterns, but it has previously been found that neurons are more precisely tuned to variable input. Introducing variability into the interpulse interval of stimulation pulses will inform on how dopaminergic release can be modulated by variability in pulse timing. Here, dopaminergic release in rats is monitored in the nucleus accumbens (NAc), a key dopaminergic center which plays a role in learning and motivation, by fast-scan cyclic voltammetry. Dopaminergic release in the NAc could also be modulated by stimulation region due to differences in connectivity. We targeted two regions for stimulation─the medial forebrain bundle (MFB) and the medial prefrontal cortex (mPFC)─due to their involvement in reward processing and projections to the NAc. Our goal is to investigate how variable interpulse interval stimulation patterns delivered to these regions affect the time course of dopamine release in the NAc. We found that stimulating the MFB with these variable stimulation patterns saw a highly responsive, frequency-driven dopaminergic response. In contrast, variable stimulation patterns applied to the mPFC were not as sensitive to the variable frequency changes. This work will help inform on how stimulation patterns can be tuned specifically to the stimulation region to improve the efficiency of electrical stimulation and control dopamine release.

DenseNet weed recognition model combining local variance preprocessing and attention mechanism.

Introduction: The purpose of this paper is to effectively and accurately identify weed species in crop fields in complex environments. There are many kinds of weeds in the detection area, which are densely distributed. Methods: The paper proposes the use of local variance pre-processing method for background segmentation and data enhancement, which effectively removes the complex background and redundant information from the data, and prevents the experiment from overfitting, which can improve the accuracy rate significantly. Then, based on the optimization improvement of DenseNet network, Efficient Channel Attention (ECA) mechanism is introduced after the convolutional layer to increase the weight of important features, strengthen the weed features and suppress the background features. Results: Using the processed images to train the model, the accuracy rate reaches 97.98%, which is a great improvement, and the comprehensive performance is higher than that of DenseNet, VGGNet-16, VGGNet-19, ResNet-50, DANet, DNANet, and U-Net models. Discussion: The experimental data show that the model and method we designed are well suited to solve the problem of accurate identification of crop and weed species in complex environments, laying a solid technical foundation for the development of intelligent weeding robots.

Quantitative evaluation of hepatic steatosis using novel ultrasound technology normalized local variance (NLV) and its standard deviation with different ROIs in patients with metabolic-associated fatty liver disease: a pilot study.

PURPOSE: The purpose of this study was to evaluate the diagnostic performance of novel ultrasound technology normalized local variance (NLV) and the standard deviation of NLV (NLV-SD) using different ROIs for hepatic steatosis in patients with metabolic-associated fatty liver disease (MAFLD) and to identify the factors that influence the NLV value and NLV-SD value, using pathology results as the gold standard. METHODS: We prospectively enrolled 34 consecutive patients with suspected MAFLD who underwent percutaneous liver biopsy for evaluation of hepatic steatosis from June 2020 to December 2020. All patients underwent ultrasound and NLV examinations. NLV values and NLV-SD values were measured using different ROIs just before the liver biopsy procedure. RESULTS: The distribution of hepatic steatosis grade on histopathology was 4/19/6/5 for none (< 5%)/ mild (5-33%)/ moderate (> 33-66%)/ and severe steatosis (> 66%), respectively. The NLV value with 50-mm-diameter ROI and NLV-SD value with 50-mm-diameter ROI showed a significant negative correlation with hepatic steatosis (spearman correlation coefficient: - 0.449, p = 0.008; - 0.471, p = 0.005). The AUROC of NLV (50 mm) for the detection of mild, moderate, and severe hepatic steatosis was 0.875, 0.735, and 0.583, respectively. The AUROC of NLV-SD (50 mm) for the detection of mild, moderate, and severe hepatic steatosis was 0.900, 0.745, and 0.603, respectively. NLV (50 mm) values and NLV-SD (50 mm) values between two readers showed excellent repeatability and the intraclass correlation coefficient (ICC) was 0.930 (p < 0.001) and 0.899 (p < 0.001). Hepatic steatosis was the only determinant factor for NLV value and NLV-SD value (p = 0.012, p = 0.038). CONCLUSION: The NLV (50 mm) and NLV-SD (50 mm) provided good diagnostic performance in detecting the varying degrees of hepatic steatosis with great reproducibility. This study showed that the degree of steatosis was the only significant factor affecting the NLV value and NLV-SD value.

Quantitative Evaluation of Hepatic Steatosis Using Normalized Local Variance in a Rat Model: Comparison with Histopathology as the Reference Standard.

OBJECTIVE: To evaluate the diagnostic performance of the normalized local variance (NLV) ultrasound technique in the assessment of hepatic steatosis, and to identify the factors that influence the NLV value using histopathological examination as the reference standard. MATERIALS AND METHODS: Forty male Sprague-Dawley rats were fed a methionine-choline-deficient diet for variable periods (0, 2, 4, 6, 8, 10, or 12 days or 2, 3, or 4 weeks; four rats per group). At the end of each diet duration, magnetic resonance spectroscopy (MRS) and NLV examination were performed. Thereafter, the rats were sacrificed and their livers were histopathologically evaluated. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic capability of the NLV value in the detection of varying degrees of hepatic steatosis. Univariate and multivariate linear regressions were used to determine the factors associated with the NLV value. RESULTS: The areas under the ROC curve for the detection of mild, moderate, and severe hepatic steatosis were 0.953, 0.896, and 0.735, respectively. The NLV value showed comparable diagnostic performance to that of MRS in the detection of ≥ mild or ≥ moderate hepatic steatosis. Multivariate linear regression analysis revealed that the degree of hepatic steatosis was the only significant factor affecting the NLV value (p < 0.001). CONCLUSION: The NLV value of ultrasound demonstrated satisfactory diagnostic performance in the assessment of varying degrees of hepatic steatosis. The degree of hepatic steatosis was the only significant factor that affected the NLV value.

Self-adaptive weighted level set evolution based on local intensity difference for parotid ducts segmentation.

BACKGROUND: Parotid ducts (PDs) play an important role in the diagnosis and treatment of parotid lesions. Segmentation of PDs from Cone beam computed tomography (CBCT) images has a significant impact to the pathological analysis of the parotid gland. Although level set methods (LSMs) have achieved considerable success in medical imaging segmentation, it is still a challenging task for existing LSMs to precisely and self-adaptively segment PDs from parotid duct (PD) images with both noise, intensity inhomogeneity, and vague boundary. In this paper, we propose a novel Self-adaptive Weighted level set method via Local intensity Difference (SWLD) to comprehensively solve the above issues. METHOD: Firstly, a new adaptive weighted operator based on local intensity variance difference has been proposed to overcome the limitations of previous LSMs that are sensitive to parameters, which achieves the aim of automatic segmentation. Secondly, we introduce local intensity mean difference into the energy function to improve the curve evolution efficiency. Thirdly, we eliminate the effects of intensity inhomogeneity, noise, and boundary blur in the parotid image through a local similarity factor with two different neighborhood sizes. RESULTS: Using the same dataset, segmentation of PDs is performed using the proposed SWLD algorithm and existing LSM algorithms. The mean Dice score for the proposed algorithm is 91.3%, and the corresponding mean Hausdorff distance (HD) is 1.746. CONCLUSION: Experimental results demonstrate that the proposed algorithm is superior to many existing level set segmentation algorithms, and it can accurately and automatically segment the PDs even in complex gradient boundaries.

Intensity based image registration by minimizing exponential function weighted residual complexity.

In this paper, we propose a novel intensity-based similarity measure for medical image registration. Traditional intensity-based methods are sensitive to intensity distortions, contrast agent and noise. Although residual complexity can solve this problem in certain situations, relative modification of the parameter can generate dramatically different results. By introducing a specifically designed exponential weighting function to the residual term in residual complexity, the proposed similarity measure performed well due to automatically weighting the residual image between the reference image and the warped floating image. We utilized local variance of the reference image to model the exponential weighting function. The proposed technique was applied to brain magnetic resonance images, dynamic contrast enhanced magnetic resonance images (DCE-MRI) of breasts and contrast enhanced 3D CT liver images. The experimental results clearly indicated that the proposed approach has achieved more accurate and robust performance than mutual information, residual complexity and Jensen-Tsallis.

Denoising Medical Images using Calculus of Variations.

We propose a method for medical image denoising using calculus of variations and local variance estimation by shaped windows. This method reduces any additive noise and preserves small patterns and edges of images. A pyramid structure-texture decomposition of images is used to separate noise and texture components based on local variance measures. The experimental results show that the proposed method has visual improvement as well as a better SNR, RMSE and PSNR than common medical image denoising methods. Experimental results in denoising a sample Magnetic Resonance image show that SNR, PSNR and RMSE have been improved by 19, 9 and 21 percents respectively.