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Deep Learning Algorithms for Brain Image Analysis
Published in Mridu Sahu, G. R. Sinha, Brain and Behavior Computing, 2021
Sai Darahas Akkineni, S. P. K. Karri
Schizophrenia is a mental disorder of the brain that results in hallucinations, delusions, or some similar abnormal behavior in individuals. This is still an area of active research, and many diagnostic tools were being developed to accurately find reliable discriminating features of schizophrenia in brain imaging studies. The challenge has been the heterogeneous nature of schizophrenic patients due to various other mental disorders. Some popular databases for schizophrenia diagnosis are Northwestern University Schizophrenia Data and Software Tool (NUSDAST), BrainGluSchi, and the Center of Biomedical Research Excellence (COBRE).10 Multiple deep learning algorithms were developed alongside having high specificity and sensitivity to automate the schizophrenia clinical diagnosis. A discriminator auto-encoder neural network was proposed with a sparsity constraint [44]. Their work obtained an accuracy of approximately 85% on classifying schizophrenic patients from healthy ones. The discriminator autoencoder neural network was trained on multi-atlas fcMRI data with 474 schizophrenic patients.
Multivariate Statistics Neural Network Models
Published in Basilio de Bragança Pereira, Calyampudi Radhakrishna Rao, Fábio Borges de Oliveira, Statistical Learning Using Neural Networks, 2020
Basilio de Bragança Pereira, Calyampudi Radhakrishna Rao, Fábio Borges de Oliveira
Rozenthal [182] and Rozenthal et al. [183] applied an ART neural network to analyze data from 53 schizophrenic (not addicted, physically capable, below age 50) patients who met the Diagnostic and Statistical Manual for Mental Disorders (DSMIV) criteria and submitted to neuropsychological tests. Schizophrenia patients exhibit at least two functional symptom patterns: those who have hallucinations, disorderly thoughts and low self-esteem (negative dimension); those who have poor speech and disorderly thoughts (disorganized dimension). This application of the neural network (along with classical statistical clustering for comparison) indicated two important clusters. The low IQ and negative dimension cluster remained stable when the number of clusters was increased. This cluster seemed to act as an attractor that impacted the more severe cases and did not respond readily to treatment. Tables 4.1 though 4.4 present the results of the study.
Applications of imaging genomics beyond oncology
Published in Ruijiang Li, Lei Xing, Sandy Napel, Daniel L. Rubin, Radiomics and Radiogenomics, 2019
Xiaohui Yao, Jingwen Yan, Li Shen
Genetic factors were found to play a major role in the etiology of schizophrenia. A meta-analysis using pooled data from 12 twin studies estimated the heritability of schizophrenia to be approximately 80% [107]. To date, around 30 schizophrenia-associated loci have been identified through GWAS to play a role in conferring the risk of schizophrenia, such as catechol-O-methyltransferase (COMT), Disrupted In Schizophrenia 1 (DISC1), regulator of G protein signaling 4 (RGS4), neuregulin 1 (NRG1), dystrobrevin binding protein 1 (DTNBP1), D-amino acid oxidase activator (DAOA), phosphodiesterase 4B (PDE4B), Dopamine- and cAMP-regulated phosphoprotein, Mr 32 kDa (DARPP-32) protein phosphatase 1 regulatory subunit 3B and glutamate metabotropic receptor 3 (GRM3) [108]. There are also growing evidences from exome sequencing studies indicating that some risk genes and pathways are affected by both common and rare variants [109], which implies large effects of rare variants on individual risk. This can be best exemplified by 11 large, rare recurrent CNVs and loss-of-function variants in set domain containing 1A, histone lysine methyltransferase (SETD1A) [109,110]. Evidences from other exome sequencing studies imply more other rare variants conferring substantial individual risk [111,112]. Despite the remarkable progress in the search for risk genes associated with schizophrenia, translation of genetic associations into targetable mechanisms related to disease pathogenesis remains poorly understood.
Louvain clustering integration within density-based graph classification (Louvain dbGC) in Schizophrenia
Published in IISE Transactions on Healthcare Systems Engineering, 2022
Mai Abdulla, Mohammad T. Khasawneh
Schizophrenia (SZ) is a functional mental disorder caused by genetic factors and environmental effects. Patients with SZ share silent symptoms that do not produce any clinically obvious signs and are usually diagnosed at advanced stages. Those signs include depression, hallucinations, cognitive impairment and disorganized thinking (Marín, 2012). The current way of diagnosing SZ depends on self-reported symptoms and observed behavior through an extended period of time, and until now there are no quantitative tests or biomarkers to diagnose mental disorders (Rice et al., 1992). However, this diagnosis method is subjective and not effective for early diagnosis. Scientific research shows the importance of early diagnosis in minimizing disability and restoring the functionality of the brain (National Institute of Mental Health, 2019). Therefore, it is urgent to find an objective method to automatically identify SZ and improve the accuracy of the recognition.
Effects of several atypical antipsychotics closapine, sertindole or ziprasidone on hepatic antioxidant enzymes: Possible role in drug-induced liver dysfunction
Published in Journal of Toxicology and Environmental Health, Part A, 2021
Lena Platanić Arizanović, Aleksandra Nikolić-Kokić, Jelena Brkljačić, Nikola Tatalović, Marko Miler, Zorana Oreščanin-Dušić, Teodora Vidonja Uzelac, Milan Nikolić, Verica Milošević, Duško Blagojević, Snežana Spasić, Čedo Miljević
Antipsychotic drugs are primarily used for the treatment of schizophrenia, a disorder which affects approximately 1% of the world population. Antipsychotic drugs may be divided into first-generation such as haloperidol, and second-generation, the latter referred to atypical antipsychotic drugs (APD) including clozapine, sertindole, and ziprasidone. Despite their exceptional clinical efficacy, antipsychotic drugs are frequently associated with numerous side effects, including extrapyramidal symptoms which are most prevalent in patients treated with first-generation compounds, while metabolic side effects appear to be more frequent in patients using APDs (Stroup and Gray 2018). Risk of development of adverse metabolic effects such as weight gain, obesity, and nonalcoholic fatty liver disease, dyslipidemia, and disturbed glucose metabolism appears to be different among consequences attributed to antipsychotic drug actions, as APDs represent a heterogeneous group of compounds (Newcomer 2005). The highest rates of weight gain, obesity, hyperlipidemia, disturbed glucose metabolism, and diabetes are associated with clozapine and olanzapine (Newcomer 2005). Risperidone and quetiapine pose a moderate risk, while ziprasidone appears to exhibit relatively benign metabolic side effects (Newcomer 2005).
Clozapine, ziprasidone, and sertindole-induced morphological changes in the rat heart and their relationship to antioxidant enzymes function
Published in Journal of Toxicology and Environmental Health, Part A, 2018
Aleksandra Nikolić-Kokić, Nikola Tatalović, Jelena Nestorov, Milica Mijović, Ana Mijusković, Marko Miler, Zorana Oreščanin-Dušić, Milan Nikolić, Verica Milošević, Duško Blagojević, Mihajlo Spasić, Čedo Miljević
Atypical antipsychotics AAP, also referred to as second-generation antipsychotics constitute, a group of pharmaceutical agents used to treat psychiatric conditions, primarily schizophrenia and schizophrenia-related disorders. However, in recent years these potent medications were also utilized for treatment of a broad range of symptoms and disorders, including bipolar disorder and depression as well as personality disorders and obsessive-compulsive disorder. Clozapine was the first AAP developed in 1958 and after 30 years of investigations and clinical trials, it was approved by the FDA (Crilly 2007). Currently, clozapine is considered to be one of the most effective antipsychotic drug for treatment-resistant schizophrenia (Leucht et al. 2009). Despite its effectiveness, clozapine usage is limited due to the risk of several potentially fatal adverse reactions including myocarditis and sudden death (Haas et al. 2007). Myocarditis was reported as clinically important complication observed in patients on clozapine therapy without preexisting cardiovascular diseases (Haas et al. 2007). In contrast to clozapine, ziprasidone, and sertindole are AAP that generally are considered as a low risk for development of myocarditis and heart failure (Ward et al. 2013). However, ziprasidone and sertindole were found to induce prolongation of QT interval and TdP in some patients (Naguy 2016; Nielsen et al. 2015a, 2015b)