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Background: Electroencephalography (EEG) signal-based machine learning models are among the most cost-effective methods for information retrieval. In this context, we aimed to investigate the cortical activities of psychotic criminal subjects by deploying an explainable feature engineering (XFE) model using an EEG psychotic criminal dataset. Methods: In this study, a new EEG psychotic criminal dataset was curated, containing EEG signals from psychotic criminal and control groups. To extract meaningful findings from this dataset, we presented a new channel-based feature extraction function named Zipper Pattern (ZPat). The proposed ZPat extracts features by analyzing the relationships between channels. In the feature selection phase of the proposed XFE model, an iterative neighborhood component analysis (INCA) feature selector was used to choose the most distinctive features. In the classification phase, we employed an ensemble and iterative distance-based classifier to achieve high classification performance. Therefore, a t-algorithm-based k-nearest neighbors (tkNN) classifier was used to obtain classification results. The Directed Lobish (DLob) symbolic language was used to derive interpretable results from the identities of the selected feature vectors in the final phase of the proposed ZPat-based XFE model. Results: To obtain the classification results from the ZPat-based XFE model, leave-one-record-out (LORO) and 10-fold cross-validation (CV) methods were used. The proposed ZPat-based model achieved over 95% classification accuracy on the curated EEG psychotic criminal dataset. Moreover, a cortical connectome diagram related to psychotic criminal detection was created using a DLob-based explainable artificial intelligence (XAI) method. Conclusions: In this regard, the proposed ZPat-based XFE model achieved both high classification performance and interpretability. Thus, the model contributes to feature engineering, psychiatry, neuroscience, and forensic sciences. Moreover, the presented ZPat-based XFE model is one of the pioneering XAI models for investigating psychotic criminal/criminal individuals. [ABSTRACT FROM AUTHOR]

Background and Objectives: Electroencephalography (EEG) signals, often termed the letters of the brain, are one of the most cost-effective methods for gathering valuable information about brain activity. This study presents a new explainable feature engineering (XFE) model designed to classify EEG data for violence detection. The primary objective is to assess the classification capability of the proposed XFE model, which uses a next-generation feature extractor, and to obtain interpretable findings for EEG-based violence and stress detection. Materials and Methods: In this research, two distinct EEG signal datasets were used to obtain classification and explainable results. The recommended XFE model utilizes a channel-based minimum and maximum pattern (ChMinMaxPat) feature extraction function, which generates 15 distinct feature vectors from EEG data. Cumulative weight-based neighborhood component analysis (CWNCA) is employed to select the most informative features from these vectors. Classification is performed by applying an iterative and ensemble t-algorithm-based k-nearest neighbors (tkNN) classifier to each feature vector. Information fusion is achieved through iterative majority voting (IMV), which consolidates the 15 tkNN classification results. Finally, the Directed Lobish (DLob) symbolic language generates interpretable outputs by leveraging the identities of the selected features. Together, the tkNN classifier, IMV-based information fusion, and DLob-based explainable feature extraction transform the model into a self-organizing explainable feature engineering (SOXFE) framework. Results: The ChMinMaxPat-based model achieved over 70% accuracy on both datasets with leave-one-record-out (LORO) cross-validation (CV) and over 90% accuracy with 10-fold CV. For each dataset, 15 DLob strings were generated, providing explainable outputs based on these symbolic representations. Conclusions: The ChMinMaxPat-based SOXFE model demonstrates high classification accuracy and interpretability in detecting violence and stress from EEG signals. This model contributes to both feature engineering and neuroscience by enabling explainable EEG classification, underscoring the potential importance of EEG analysis in clinical and forensic applications. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) signals contain information about the brain's state as they reflect the brain's functioning. However, the manual interpretation of EEG signals is tedious and time-consuming. Therefore, automatic EEG translation models need to be proposed using machine learning methods. In this study, we proposed an innovative method to achieve high classification performance with explainable results. We introduce channel-based transformation, a channel pattern (ChannelPat), the t algorithm, and Lobish (a symbolic language). By using channel-based transformation, EEG signals were encoded using the index of the channels. The proposed ChannelPat feature extractor encoded the transition between two channels and served as a histogram-based feature extractor. An iterative neighborhood component analysis (INCA) feature selector was employed to select the most informative features, and the selected features were fed into a new ensemble k-nearest neighbor (tkNN) classifier. To evaluate the classification capability of the proposed channel-based EEG language detection model, a new EEG language dataset comprising Arabic and Turkish was collected. Additionally, Lobish was introduced to obtain explainable outcomes from the proposed EEG language detection model. The proposed channel-based feature engineering model was applied to the collected EEG language dataset, achieving a classification accuracy of 98.59%. Lobish extracted meaningful information from the cortex of the brain for language detection. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to investigate the methylation changes of related genes in the peripheral blood and their clinical significance in Parkinson's disease (PD) and whether the methylation change of the gene encoding long noncoding RNA was different in the blood of patients and controls. Patients and methods: This prospective cross-sectional, controlled study was conducted with 45 participants (22 males, 23 females; mean age: 60.7±5.9 years; range, 53 to 75 years) between June 2020 and June 2021. Drug-naive patients diagnosed with PD were included in this study. Those with PD and a Mini-Mental State Examination (MMSE) score >23 were defined as Group 1 (n=15), and those with PD and an MMSE score ≤23 were defined as Group 2 (n=15). Controls were included in Group 3 (n=15). The methylation changes of genes HSP70, HSP90, heat shock factor 1 (HSF1), heat shock RNA 1 (HSR1), and eukaryotic translation elongation factor 1 alpha (eEF1a) were investigated with methylation-specific real-time quantitative polymerase chain reaction analysis. Results: The eEF1a was significantly more hypermethylated in Group 1. In Group 2, HSP70, HSP90 HSF1, HSR1, and eEF1a were significantly hypomethylated compared to Group 1 and Group 3 (for all genes p<0.001). The HSF1 hypomethylation was negatively correlated with MMSE and positively correlated with depression scores (p=0.03 and p=0.013, respectively). The correlation of eEF1a with MMSE and depression was the opposite of HSF1 (p<0.001 and p=0.013, respectively). Conclusion: Cell line and autopsy studies indicate that eEF1a hypermethylation might be one of the main molecules triggering alpha-synuclein aggregation in the pathogenesis of PD. Therefore, eEF1a may be a molecule that can be used as a peripheral biomarker. The findings supported this idea as it was more hypermethylated in PD patients than in controls, whereas its negative regulator HSF1 was hypomethylated and correlated with the clinic. Furthermore, the worsening of cognitive functions and depression in PD patients may affect methylation levels of chaperone genes in the peripheral blood. [ABSTRACT FROM AUTHOR]

Lymphocytes have dopamine receptors, and low dopamine levels increase receptor synthesis. Lymphocytes may move slower in Parkinson's, which is characterized by low dopamine levels. We hypothesized that longer telomeres would indicate less lymphocyte division. We investigated whether leukocyte telomere length (LTL) is different in naïve Parkinson's disease (PD) patients and whether telomere length has clinical significance in determining telomere length in naïve Parkinson's patients. Naïve patients diagnosed with PD were included in this study. 29 naïve PD patients and 15 controls were included in the study. Subgroup analyses were performed according to MMSE and depression scores of PD patients. LTL was measured by RT-qPCR. Differences in LTL between the groups were examined. Clinical and demographic findings and LTL were examined and correlated using appropriate statistical methods. Forty-four participants meeting the inclusion criteria were included in the study. LTL was significantly longer in PD patients than in the control group (p = 0.043) and was positively correlated with clinical worsening of the disease. According to the Analysis of Moment Structures, evaluation total MMSE was 1.82, UPDRS was -1.53, and depression score was -.31 negatively correlated with telomere. LTL was found to be longer in naïve PD patients than in controls. Without other factors that could affect telomere; these findings support the hypothesis that leukocyte division could be slower in PD than the control group at the same age. Additional studies are needed on this subject. Additionally, a longer TL could be a marker for a better clinical course in PD. [ABSTRACT FROM AUTHOR]

Objective: The association of psychiatric disorders with epilepsy was shown in many publications, but there have been very few studies dealing with cognitive distortions in epilepsy patients. In our study, we aimed to investigate the metacognitive systems, dysfunctional attitudes, and automatic thoughts of epilepsy patients. Method: Sixty patients diagnosed with epilepsy based on anamnesis and electroencephalographic examination and 50 healthy controls were included in the study. The sociodemographic data form, Beck Anxiety Inventory (BAI), Beck Depression Inventory (BDI), Automatic Thoughts Questionnaire (ATQ), Dysfunctional Attitudes Scale (DAS), and Metacognition Questionnaire-30 (MCQ) were administered to all participants. Results: BAI and BDI scores were significantly higher in the epilepsy group compared to the control group (p=0.001, p<0.05, respectively). The "uncontrollability-danger" and "cognitive awareness" subscale scores and the total score of MCQ in the epilepsy group were also higher than in the control group (p=0.001, p<0.05, p=0.001, respectively). The ATQ total score was found to be significantly higher in the epilepsy group compared to the control group (p=0.001). Conclusion: Based on the findings from the present study, in order to understand and support epilepsy patients better and provide diagnosis/treatment for psychiatric diseases that may be overlooked, it is recommended that clinical neurologists and psychiatrists evaluate patients in cooperation. [ABSTRACT FROM AUTHOR]