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  • Open Access


    Signal Conducting System with Effective Optimization Using Deep Learning for Schizophrenia Classification

    V. Divya1,*, S. Sendil Kumar2, V. Gokula Krishnan3, Manoj Kumar4

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1869-1886, 2023, DOI:10.32604/csse.2023.029762

    Abstract Signal processing based research was adopted with Electroencephalogram (EEG) for predicting the abnormality and cerebral activities. The proposed research work is intended to provide an automatic diagnostic system to determine the EEG signal in order to classify the brain function which shows whether a person is affected with schizophrenia or not. Early detection and intervention are vital for better prognosis. However, the diagnosis of schizophrenia still depends on clinical observation to date. Without reliable biomarkers, schizophrenia is difficult to detect in its early phase and hence we have proposed this idea. In this work, the… More >

  • Open Access


    Machine Learning in Detecting Schizophrenia: An Overview

    Gurparsad Singh Suri1, Gurleen Kaur1, Sara Moein2,*

    Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 723-735, 2021, DOI:10.32604/iasc.2021.015049

    Abstract Schizophrenia (SZ) is a mental heterogeneous psychiatric disorder with unknown cause. Neuroscientists postulate that it is related to brain networks. Recently, scientists applied machine learning (ML) and artificial intelligence for the detection, monitoring, and prognosis of a range of diseases, including SZ, because these techniques show a high performance in discovering an association between disease symptoms and disease. Regions of the brain have significant connections to the symptoms of SZ. ML has the power to detect these associations. ML interests researchers because of its ability to reduce the number of input features when the data More >

  • Open Access


    Down-regulation of N-methyl-D-aspartate receptor subunits 1 affects neurogenesis of hippocampal neural stem cells


    BIOCELL, Vol.45, No.2, pp. 417-426, 2021, DOI:10.32604/biocell.2021.013842

    Abstract Schizophrenia is a common and serious mental illness characterized by severe impairments in thinking, emotions, and behaviors. Usually, the cognitive deficits of schizophrenia are closely associated with abnormal neurogenesis due to the hypofunction of certain neural receptors such as N-methyl-D-aspartate receptors (NMDARs), which mediates neurotransmission. However, little is known about the involvement of NMDAR1 in regulating hippocampal neurogenesis in schizophrenia. In the current study, we present evidence suggesting that NMDAR1 regulates hippocampal neurogenesis as lentivirus-mediated shRNA silencing NMDAR1 gene or blocking with MK-801 results in abnormal neurogenesis consistently found in schizophrenia. The important finding was More >

  • Open Access


    Exploring the Abnormal Brain Regions and Abnormal Functional Connections in SZ by Multiple Hypothesis Testing Techniques

    Lan Yang1, Shun Qi2,3,#, Chen Qiao1,*, Yanmei Kang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 215-237, 2020, DOI:10.32604/cmes.2020.010796

    Abstract Schizophrenia (SZ) is one of the most common mental diseases. Its main characteristics are abnormal social behavior and inability to correctly understand real things. In recent years, the magnetic resonance imaging (MRI) technique has been popularly utilized to study SZ. However, it is still a great challenge to reveal the essential information contained in the MRI data. In this paper, we proposed a biomarker selection approach based on the multiple hypothesis testing techniques to explore the difference between SZ and healthy controls by using both functional and structural MRI data, in which biomarkers represent both… More >

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