Neurobiological Perspective and Personalized Treatment in Schizophrenia

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

A Hybrid PSO-PID Approach For Trajectory Tracking Application of A Liquid Level Control Process

Water level control is a crucial step for steam generators (SG) which are widely used to control the temperature of nuclear power plants. The control process is therefore a challenging task to improve the performance of water level control system. The performance assessment is another consideration to underline. In this paper, in order to get better control of water level, the nonlinear process was first expressed in terms of a transfer function (TF), a proportional-integral-derivative (PID) controller was then attached to the model. The parameters of the PID controller was finally optimized using particle swarm optmization (PSO). Simulation results indicate that the proposed approach can make an effective tracking of a given level set or reference trajectory

A Hybrid artificial intelligence method to classify trichotillomania and obsessive compulsive disorder

Classification of psychiatric disorders is becoming one of major focus of research using artificial intelligence approach. The combination of feature selection and classification methods generates satisfactory outcomes using biological biomarkers. The use of quantitative electroencephalography (EEG) cordance has enhanced the clinical utility of the EEG in psychiatric and neurological subjects. Trichotillomania (TTM), a kind of body focused repetitive behavior, is defined as a disorder characterized by repetitive hair pulling that results in noticeable hair loss. Phenomenological observations underline similarities between hair-pulling behaviors and compulsions seen in obsessive–compulsive disorder (OCD). Despite the recognized similarities between OCD and TTM, there is evidence of important differences between these two disorders. In order to dichotomize the subjects of each disorder, artificial intelligence approach was employed using quantitative EEG (QEEG) cordance values with 19 electrodes from 10 brain regions (prefrontal, frontocentral, central, left temporal, right temporal, left parietal, occipital, midline, left frontal and right frontal) in 4 frequency bands (delta, theta, alpha and beta). Machine learning methods, artificial neural networks (ANN), support vector machine (SVM), k-nearest neighbor (k-NN) and Naïve Bayes (NB), were used in order to classify 39 TTM and 40 OCD patients. SVM, with its relatively better performance, was then combined with an improved ant colony optimization (IACO) approach in order to select more informative features with less iterations. The noteworthy performance of the hybrid approach underline that it is possible to discriminate OCD and TTM subjects with 81,04% overall accuracy

PSİKİYATRİK BOZUKLUKLARDA YAPAY ZEKA YAKLAŞIMLARI

Potential utility of machine learning (ML) methods can be used as a clinical tool in administering diagnosis and therapy to a targeted group of subjects suffering from psychiatric diseae. The use of ML methodology is more potentially useful to the clinician as a classification or treatment response prediction tool . It is worth using feature selection algorithms to raise the sensitivity and accuracy of the models to contribute to the hybrid approach of artificial intelligence methodologies

Process Control Using Genetic Algorithm And Ant Colony Optimization Algorithm

Artificial life uses biological knowledge and techniques to solve different engineering, management, control and computational problems. Natural systems teach us that very simple individual organisms can form systems capable of performing highly complex tasks by dynamically interacting with each other. In this study, artificial life based approaches are handled and incorporated to enable a real-time water level control. The process was first modelled using NARX type Artificial Neural Network. A fuzzy controller was then attached to the model. For a better performance, fuzzy controller membership function boundary values and action values were optimized simultaneously. The optimization process was performed using genetic algorithm and ant colony optimization algorithm, respectively. Finally, the performance of the controllers was discussed further by considering the system outputs. The developed structure replaces the tedious process of trial-and-error for better combination of fuzzy parameters and can settle the problem of designing fuzzy controller without an expert’s experience

A Wrapper-Based Approach For Feature Selection And Disorder Classification

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

A Hybrid artificial intelligence method to classify trichotillomania and obsessive compulsive disorder

Classification of psychiatric disorders is becoming one of the major focuses of research using artificial intelligence approach. The combination of feature selection and classification methods generates satisfactory outcomes using biological biomarkers. The use of quantitative electroencephalography (EEG) cordance has enhanced the clinical utility of the EEG in psychiatric and neurological subjects. Trichotillomania (TTM), a kind of body focused repetitive behavior, is defined as a disorder characterized by repetitive hair pulling that results in noticeable hair loss. Phenomenological observations underline similarities between hair-pulling behaviors and compulsions seen in obsessive-compulsive disorder (OCD). Despite the recognized similarities between OCD and TTM, there is evidence of important differences between these two disorders. In order to dichotomize the subjects of each disorder, artificial intelligence approach was employed using quantitative EEG (QEEG) cordance values with 19 electrodes from 10 brain regions (prefrontal, frontocentral, central, left temporal, right temporal, left parietal, occipital, midline, left frontal and right frontal) in 4 frequency bands (delta, theta, alpha and beta). Machine learning methods, artificial neural networks (ANN), support vector machine (SVM), k-nearest neighbor (k-NN) and Naïve Bayes (NB), were used in order to classify 39 TTM and 40 OCD patients. SVM, with its relatively better performance, was then combined with an improved ant colony optimization (IACO) approach in order to select more informative features with less iterations. The noteworthy performance of the hybrid approach underline that it is possible to discriminate OCD and TTM subjects with 81.04% overall accuracy

A Class Activation Map-Based Interpretable Transfer Learning Model for Automated Detection of ADHD from fMRI Data

Automatic detection of Attention Deficit Hyperactivity Disorder (ADHD) based on the functional Magnetic Resonance Imaging (fMRI) through Deep Learning (DL) is becoming a quite useful methodology due to the curse of-dimensionality problem of the data is solved. Also, this method proposes an invasive and robust solution to the variances in data acquisition and class distribution imbalances. In this paper, a transfer learning approach, specifically ResNet-50 type pre-trained 2D-Convolutional Neural Network (CNN) was used to automatically classify ADHD and healthy children. The results demonstrated that ResNet-50 architecture with 10-k cross-validation (CV) achieves an overall classification accuracy of 93.45%. The interpretation of the results was done via the Class Activation Map (CAM) analysis which showed that children with ADHD differed from controls in a wide range of brain areas including frontal, parietal and temporal lobes