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Introduction: Sensory processing is profoundly regulated by brain neuromodulatory systems. One of the main neuromodulators is serotonin which influences higher cognitive functions, such as different aspects of perceptual processing. Accordingly, malfunction in the serotonergic system may lead to visual illusion in psychiatric disorders, such as autism and schizophrenia. This study aims to investigate the serotonergic modulation of visual responses of neurons to stimulus orientation in the primary visual cortex. Methods: Eight-week-old naive mice were anesthetized and a craniotomy was done on the region of interest in the primary visual cortex. Spontaneous and visual-evoked activities of neurons were recorded before and during the electrical stimulation of the dorsal raphe nucleus using in vivo whole-cell patch-clamp recording. The square-wave grating of 12 orientations was presented. The data were analyzed and the Wilcoxon signed-rank test was used to compare the data of two conditions that belong to the same neurons, with or without electrical stimulation. Results: The serotonergic system changed the orientation tuning of nearly 60% of recorded neurons by decreasing the mean firing rate in two independent visual response components, namely gain and baseline response. It also increased the mean firing rate in a small number of neurons (about 20%). Additionally, it left the preferred orientation and sensitivity of neurons unchanged. Conclusion: Serotonergic modulation showed a bidirectional effect. It causes predominately divisive and subtractive decreases in the visual responses of the neurons in the primary visual cortex that can modify the balance between internal and external sensory signals and result in disorders. Highlights: The serotonergic system predominantly decreased the mean firing rate of neurons in the primary visual cortex.The serotonergic system decreased responses of visual cortical neurons by subtractive and divisive changes of orientation tuning.The serotonergic system leaves the spontaneous activity of visual cortical neurons unchanged. Plain Language Summary: Serotonin is one of the well-known neuromodulators involved in many physiological functions of the brain, such as sensory processing. It can play an essential role in producing perceptual psychotic episodes following the use of psychedelic drugs. Neural mechanisms of changes in cortical processing by the serotonergic system are not elucidated enough. In this study, we showed the electrical stimulation of the dorsal raphe nucleus as the main resource for projecting serotonergic neurons to the visual cortex, causing to decrease in visual-evoked responses of neurons in the primary visual cortex without changing the spontaneous activity. This effect may lead to an imbalance between the brain's intrinsic and stimulus-evoked activity and result in various kinds of psychiatric disorders, such as visual hallucinogenic experiences in schizophrenia and autism. Accordingly, it is crucial to understand the mechanisms by which serotonin affects the rapid and long-term activity of neocortical circuits. Such studies can be helpful in the diagnosis and treatment of disorders related to the neuromodulatory roles of the serotonergic system by providing new methods for rebalancing these intricate components.
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BACKGROUND: Given the extensive use and preferred diagnostic method in common mammography tests for screening and diagnosis of breast cancer, there is concern about the increased dose absorbed by the patient due to the sensitivity of the breast tissue. OBJECTIVE: This study aims to evaluate the entrance surface air kerma (ESAK) before irradiation to the patient through its estimation. MATERIAL AND METHODS: In this descriptive paper, firstly, a phantom was used to measure some data, including ESAK, Kvp, mAs, HVL, and type of filter/target. Secondly, the MultiLayer Perceptron (MLP) neural network model was trained with Levenberg-Marquardt (LM) backpropagation training algorithm and finally, ESAK was estimated. RESULTS: Based on results obtained from the program in different neuron numbers, it was found that the number of 35 neurons is the most optimal value, offering a regression coefficient of 95.7%. The Mean Squared Error (MSE) for all data was 0.437 mGy and accounting for 4.8% of the output range changes, predicting 95.2% accuracy in the present research. CONCLUSION: Using neural networks in ESAK prediction, the method proposed in the present research leads to the possible ESAK estimation of patients before X-Ray. The results suggested that the regression coefficient represented 4.3% difference between the kerma measured by solid-state dosimeter in the radiation field and the value predicted in the research. In comparison with the Monte-Carlo simulation method, this method has better accuracy.
