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Personalized treatment is the focus of researchers and comes into prominence for both genetic sciences and neurotechnology. Recently, clinical practice tries to follow the idea and principles of personalized medicine. Besides predicting an individual’s sensibility or predisposition for developing schizophrenia, pharmacogenetic and pharmacogenomic approaches attempt to define and acknowledge important indicators of clinical response to antipsychotics namely their efficacy and adverse effects. Particularly in the treatment of schizophrenia, clinicians are very helpless in resistant cases, and clinical pharmacogenomics contributes in a revolutionary way. With both phenotyping, namely Therapeutic Drug Monitoring (TDM) and genotyping, “big expectations” emerged both with the right drug, the right dose, and the right time. Both pharmacokinetic genotyping, CYP400 enzyme activity, and pharmacodynamic genotyping could be measured. The chapter handles schizophrenia with neurobiological views and covers personalized treatment approaches from various perspectives. Personalized treatment in the diagnosis and treatment of schizophrenia is presented first. Following comorbid schizophrenia in addition to the use of various substances, psychopharmacology of schizophrenia and the mechanism of action of antipsychotic drugs are presented. Genetics and epigenetics in schizophrenia are studied in detail and in silico application and computational approaches covering the feature extraction process and destructive impact of the metaverse are shared lastly.

Feature selection (FS) and classification are consecutive artificial intelligence (AI) methods used in data analysis, pattern classification, data mining and medical informatics. Beside promising studies in the application of AI methods to health informatics, working with more informative features is crucial in order to contribute to early diagnosis. Being one of the prevalent psychiatric disorders, depressive episodes of bipolar disorder (BD) is often misdiagnosed as major depressive disorder (MDD), leading to suboptimal therapy and poor outcomes. Therefore discriminating MDD and BD at earlier stages of illness could help to facilitate efficient and specific treatment. In this study, a nature inspired and novel FS algorithm based on standard Ant Colony Optimization (ACO), called Improved ACO (IACO), was used to reduce the number of features by removing irrelevant and redundant data. The selected features were then fed into support vector machine (SVM), a powerful mathematical tool for data classification, regression, function estimation and modeling processes, in order to classify MDD and BD subjects. Proposed method used coherence, a promising quantitative electroencephalography (EEG) biomarker, values calculated from alpha, theta and delta frequency bands. The noteworthy performance of novel IACO-SVM approach stated that it is possible to discriminate 46 BD and 55 MDD subjects using 22 of 48 features with 80.19% overall classification accuracy. The performance of IACO algorithm was also compared to the performance of standard ACO, genetic algorithm (GA) and particle swarm optimization (PSO) algorithms in terms of their classification accuracy and number of selected features. In order to provide an almost unbiased estimate of classification error, the validation process was performed using the nested cross-validation (CV) procedure.

WOS: 000430198600004 PubMed ID: 29284291 The behavioral variant frontotemporal dementia (bvFTD) usually emerges with behavioral changes similar to changes in late-life bipolar disorder (BD) especially in the early stages. According to the literature, a substantial number of bvFTD cases have been misdiagnosed as BD. Since the literature lacks studies comparing differential diagnosis ability of electrophysiological and neuroimaging findings in BD and bvFTD, we aimed to show their classification power using an artificial neural network and genetic algorithm based approach. Eighteen patients with the diagnosis of bvFTD and 20 patients with the diagnosis of late-life BD are included in the study. All patients' clinical magnetic resonance imaging (MRI) scan and electroencephalography recordings were assessed by a double-blind method to make diagnosis from MRI data. Classification of bvFTD and BD from total 38 participants was performed using feature selection and a neural network based on general algorithm. The artificial neural network method classified BD from bvFTD with 76% overall accuracy only by using on EEG power values. The radiological diagnosis classified BD from bvFTD with 79% overall accuracy. When the radiological diagnosis was added to the EEG analysis, the total classification performance raised to 87% overall accuracy. These results suggest that EEG and MRI combination has more powerful classification ability as compared with EEG and MRI alone. The findings may support the utility of neurophysiological and structural neuroimaging assessments for discriminating the 2 pathologies.