Multi-class classification model for psychiatric disorder discrimination.

BACKGROUND: Physicians follow-up a symptom-based approach in the diagnosis of psychiatric diseases. According to this approach, a process based on internationally valid diagnostic tools such as The Diagnostic and Statistical Manual of Mental Disorders (DSM) or International Classification of Diseases (ICD), patient reports and the observation and experience of the physician is monitored. As in other fields of medicine, the search for biomarkers that can be used in processes related to diseases continues in psychiatry and various researches are carried out in this field. OBJECTIVES: Within the scope of this study, a dataset containing electroencephalogram (EEG) measurements of individuals diagnosed with different psychiatric diseases were analyzed by machine learning methods and the diseases were differentiated/classified with the models obtained. Thus, it was investigated whether EEG data could be a biomarker for psychiatric diseases. MATERIALS AND METHODS: In the dataset analyzed within the scope of the study, for 550 patients (81 bipolar disorder, 95 attention deficit and hyperactivity disorder - ADHD, 67 depression, 34 obsessive compulsive disorder - OCD, 75 opioid, 146 posttraumatic stress disorder - PTSD, 52 schizophrenia) and 84 healthy individuals, there are 634 samples (rows), 77 variables (columns) in total. 76 of the variables consist of absolute power values belonging to 4 frequency bands (alpha, beta, delta, theta) collected from 19 different electrodes. 80 % of the dataset was used for training the models and 20 % of the data was used for testing the performance of the models. The 5-fold cross validation (CV) method, which repeats 3 times in the training dataset, was used and with this method, the hyperparameters used in the models were also optimized. Different models have been established with the selected hyperparameters and the performance of these models has been tested with the test dataset. C5.0, random forest (RF), support vector machine (SVM) and artificial neural networks (ANN) were used to build the models. RESULTS: Within the scope of the study, the absolute power values obtained from EEG measurements performed using 19 electrodes were analyzed by machine learning methods. It was concluded that classification between disease groups was feasible with a high accuracy (C5.0-0.841, SVM_radial - 0.841, RF - 0.762). It was observed that ADHD, depression and schizophrenia diseases can be differentiated better (F-score = 1, balanced accuracy = 1) once the results were evaluated on a class category basis according to the F- measure and balanced accuracy values. DISCUSSION AND CONCLUSION: Through the medium of the analyzes made within the scope of this study, it was investigated whether EEG data could be used as a biomarker for the detection and diagnosis of psychiatric diseases. The findings obtained from this study revealed that by using EEG data as a biomarker, it can be highly predicted whether a person has a psychiatric disease or not. Once evaluated with broad strokes, it is feasible to assert that it is possible to analyze whether the person who consults a physician with a complaint is ranked among the psychiatric disease class with EEG measurement. When trying to differentiate between numerous and diverse disease categories, it may be claimed that some diseases (ADHD, depression, schizophrenia) can be distinguished better by coming to the fore on a model basis. Considering the findings, it is anticipated that the analyzes obtained as a result of this study will contribute to the studies to be conducted using machine learning in the field of psychiatry.

Prediction of treatment resistance in obsessive compulsive disorder patients based on EEG complexity as a biomarker.

OBJECTIVE: This study aimed to identify an Electroencephalography (EEG) complexity biomarker that could predict treatment resistance in Obsessive compulsive disorder (OCD) patients. Additionally, the statistical differences between EEG complexity values in treatment-resistant and treatment-responsive patients were determined. Moreover, the existence of correlations between EEG complexity and Yale-Brown Obsessive Compulsive Scale (YBOCS) score were evaluated. METHODS: EEG data for 29 treatment-resistant and 28 treatment-responsive OCD patients were retrospectively evaluated. Approximate entropy (ApEn) method was used to extract the EEG complexity from both whole EEG data and filtered EEG data, according to 4 common frequency bands, namely delta, theta, alpha, and beta. The random forests method was used to classify ApEn complexity. RESULTS: ApEn complexity extracted from beta band EEG segments discriminated treatment-responsive and treatment-resistant OCD patients with an accuracy of 89.66% (sensitivity: 89.44%; specificity: 90.64%). Beta band EEG complexity was lower in the treatment-resistant patients and the severity of OCD, as measured by YBOCS score, was inversely correlated with complexity values. CONCLUSIONS: The results indicate that, EEG complexity could be considered a biomarker for predicting treatment response in OCD patients. SIGNIFICANCE: The prediction of treatment response in OCD patients might help clinicians devise and administer individualized treatment plans.

Mapping adaptation, deviance detection, and prediction error in auditory processing.

Various studies have suggested that auditory deviance detection is organized in a hierarchical manner with ascending levels of complexity. Event-related potentials (ERP) are considered to reflect different cortical processing stages. In the current electroencephalographic study, we employed an auditory sequence oddball paradigm to investigate different levels of cortical auditory processing and the contribution of neuronal habituation and prediction error mechanism to N1 and Mismatch Negativity (MMN). Our findings suggest that N1 reflects a lower cortical process primarily involved in the encoding of simple physical features and is thus mainly modulated by neuronal attenuation and not complex top-down mechanisms. By analyzing within-sequence signal differences, we divided the MMN into distinct subcomponents reflecting different hierachical levels of auditory processing. We determined a "first-order" MMN that reflects the processing of simple deviant features (such as frequency) and "higher-order" MMNs that occur at regularity violation of complex patterns or unexpected inputs that do not allow further predictions. In our source localization analysis, both the primary auditory cortex and left IFG were primarily involved in the detection of simple, physically deviant features, while the right IFG was associated with the processing of novel, unexpected auditory inputs and the ACC with regularity violation of known patterns. Summarizing, our results might contribute to a better understanding of the different complexities of neuronal habituation and prediction error mechanisms at different levels of cortical auditory processing.

Assessing the relationship between attachment, parental attitude and plasma oxytocin in schizophrenia patients and their unaffected siblings.

BACKGROUND: It is widely acknowledged that schizophrenia patients tend to have insecure attachment styles and improper parenting. However, the biological processes related to these adversities remain unclear and that the disturbance in oxytocin system is considered as one of the strongest predictors of such adversities. METHODS: Thirty-four patients with schizophrenia and their unaffected thirty-four healthy siblings were recruited for the study and they were compared with thirty-one healthy controls. We examined attachment styles via Experience in Close Relationship-Revised Test and perceived parental attitudes with the My Memories of Upbringing-Short Version Test. In addition, we evaluated plasma oxytocin levels across groups. RESULTS: The patients with schizophrenia had lower plasma oxytocin levels and obtained higher levels for attachment anxiety and avoidance with more parental rejection and over protection. There was a significantly negative relationship between the levels of plasma oxytocin in blood and parental over protection in the healthy sibling and healthy control groups. In contrast, there was a significantly positive relationship between the levels of plasma oxytocin in blood and parental over protection in the schizophrenia group. Results of the regression analyses revealed that the plasma oxytocin levels and over protection were notable factors in discriminating the groups from each other. CONCLUSION: Our findings suggested that disturbance in oxytocin is associated with susceptibility to schizophrenia predisposition. In sum, therapeutic interventions that address oxytocin and over protection may influence the outcomes in this severe mental disorder.

Binomial Logistic Regression and Artificial Neural Network Methods to Classify Opioid-Dependent Subjects and Control Group Using Quantitative EEG Power Measures.

Logistic regression (LR) and artificial neural networks (ANNs) are widely referred approaches in medical data classification studies. LR, a statistical fitting model, is suggested in medical problems because of its well-established methodology and coefficients contributing to the evaluation of clinical interpretations. ANNs are graphical models structured with node networks interconnected with arcs each of which is expressed in terms of weights discovered throughout the modeling process. Since ANNs have a complex structure with its layers and nodes in the layers, which provides ANNs the ability to model any data with complex relationships. Among the various models having origins in statistics and computer science, LR and ANNs have prevailed in the area of mass medical data classification. In this study, we introduce the 2 aforementioned approaches in order to generate a model dichotomizing 75 opioid-dependent patients and 59 control subjects from each other. Quantitative electroencephalography (QEEG) absolute power value of each electrode were calculated for 4 consecutive frequency bands namely delta, theta, alpha, and beta with the frequencies, 0.5 to 4, 4 to 8, 8 to 12, and 12 to 20 Hz, respectively. Significant independent variables contributing to the classification were underlined in LR while a feature selection (FS) method, genetic algorithm, is being applied to the ANN model to reveal more informative features. The performances of the classifiers were finally compared considering overall classification accuracies, area under receiver operating characteristic curve scores, and Gini coefficient. Although ANN-based classifier outperformed compared with LR, both models performed satisfactorily for absolute power measure in beta frequency band. Our results underline the potential benefit of the introduced methodology is promising and is to be treated as a clinical interface in dichotomizing substance use disorders subjects and for other medical data analysis studies.

[The Effect of Theory of Mind Capacities of Mothers of Patients with Schizophrenia on the Severity of the Diseases]. / Sizofreni Hastalarinin Annelerinin Zihin Kurami Kapasitelerinin Hastalik Siddeti Üzerine Etkisinin Incelenmesi.

OBJECTIVE: Studies conducted with patients with schizophrenia and first-degree relatives show that, the pathology in theory of mind may be related to the risk of psychosis. The theory of mind capacities of the mothers may be effective in the prognosis of schizophrenia. The purpose of this study is to examine the effects of theory of mind capacities of patient mothers on the severity of the disease. METHOD: The study was conducted with 34 schizophrenic patients and their mothers, and 31 healthy mothers. Sociodemographic data was captures for all groups. In addition, PANSS was collected from patients and Reading the Mind in the Eyes Test was collected from the each patient mother. RESULTS: There was no statistically significant difference between the age and education levels of the groups. The mothers of patients with schizophrenia performed significantly worse in the Reading the Mind in the Eyes Test than the control group. There was an inverse correlation between theory of mind capacity and the medication dose, PANSS general condition and PANSS total score. DISCUSSION: The significant difference between the groups suggests that the impairment in the function of the theory of mind can be regarded as an endo-phenotype for schizophrenia. The low theory of mind capacities of the mothers of schizophrenia patients can cause the mothers to misunderstand the feelings of their patient which could lead to increase in disease severity and drugs use.

Cortisol response to stress in schizophrenia: Associations with oxytocin, social support and social functioning.

Previous studies reported attenuated cortisol reactivity as one explanation for poor social functioning in schizophrenia. Recent research has demonstrated that both glucocorticoid and oxytocin systems are central to stress regulation. Here, we studied the associations between basal oxytocin, stress-induced cortisol levels, and social functioning and social support in schizophrenia. A mock job interview was used as an ecologically-valid social stressor in 32 schizophrenia patients. Blood samples were taken before and after stress induction to assess basal oxytocin and cortisol levels. In addition social functioning and social support scales were collected. Patients were divided into cortisol responders and non-responders according to percentage change following stress induction. Our findings revealed a possible subgroup of patients who did not exhibit attenuated cortisol responses. Importantly, cortisol responders had generally better social functioning, but perceived social support was not different between groups. There was also no evidence of a relationship between cortisol and oxytocin. This study highlights the heterogeneity of cortisol responses to stress in a schizophrenia population, and the importance of the relationship between social functioning and cortisol reactivity. These findings could be relevant when considering therapeutic interventions that manipulate endocrinology in order to improve real-world functioning.

The relationship between responsiveness to social and monetary rewards and ADHD symptoms.

Alterations in reward processing are frequently reported in attention deficit hyperactivity disorder (ADHD). One important factor affecting reward processing is the quality of reward as social and monetary rewards are processed by different neural networks. However, the effect of reward type on reward processing in ADHD has not been extensively studied. Hence, in the current study, an exploratory research was conducted to investigate the effect of reward type (i.e., social or monetary) on different phases of reward processing. We recorded event-related potentials (ERPs) during a spatial attention paradigm in which cues heralded availability and type of the upcoming reward and feedbacks informed about the reward earned. Thirty-nine (19 males) healthy individuals (age range: 19-27 years) participated in the study. ADHD symptoms were assessed by using ADHD self-report scale (ASRS). Our results revealed a consistent negative correlation between the hyperactivity subscale of ASRS and almost all social-feedback related ERPs (P2, P3, and FRN). ERP amplitudes after social feedbacks were less positive for P2 and P3 and more negative for FRN for individuals with greater hyperactivity levels. Our findings suggest that hyporesponsiveness to social feedbacks may be associated with hyperactivity. However, the results have to be confirmed with clinical populations.

Decreased functional connectivity in schizophrenia: The relationship between social functioning, social cognition and graph theoretical network measures.

Schizophrenia is a complex disorder in which abnormalities in brain connectivity and social functioning play a central role. The aim of this study is to explore small-world network properties, and understand their relationship with social functioning and social cognition in the context of schizophrenia, by testing functional connectivity differences in network properties and its relation to clinical behavioral measures. Resting-state fMRI time series data were acquired from 23 patients diagnosed with schizophrenia and 23 healthy volunteers. The results revealed that patients with schizophrenia show significantly decreased connectivity between a range of brain regions, particularly involving connections among the right orbitofrontal cortex, bilateral putamen and left amygdala. Furthermore, topological properties of functional brain networks in patients with schizophrenia were characterized by reduced path length compared to healthy controls; however, no significant difference was found for clustering coefficient, local efficiency or global efficiency. Additionally, we found that nodal efficiency of the amygdala and the putamen were significantly correlated with the independence-performance subscale of social functioning scale (SFC), and Reading the Mind in the Eyes test; however, the correlations do not survive correction for multiple comparison. The current results help to clarify the relationship between social functioning deficits and topological brain measures in schizophrenia.

The developmental origins of metacognitive deficits in schizophrenia.

The deficits in metacognition have been observed in schizophrenia but developmental roots of impaired metacognition are not well understood. Accordingly, this study compared metacognitive abilities of patients with schizophrenia and healthy group and examined the relationship between childhood trauma, attachment style and caregiver attitudes with metacognitive capacity which might contribute to metacognitive deficits in patient group. 35 patients with schizophrenia and 35 healthy people were included in the study. Metacognitive capacity was measured using the Metacognition Assessment Scale Abbreviated (MAS-A). This scale comprises four domains: self-reflectivity, understanding other's mind, decentration and mastery. Group comparisons revealed that schizophrenia patients had greater deficits in metacognitive ability. We found that the report of childhood emotional abuse, a pattern of anxious attachment and over protection by caregivers were uniquely related to metacognitive capacity.

Alterations in event-related potential responses to empathy for pain in schizophrenia.

Lack of empathy is a critical factor impacting on social functioning and quality of life in schizophrenia. There is, however, a paucity of research into the underlying neurophysiological correlates of empathy deficits in this disorder. Accordingly, we sought: (1) to identify whether dysfunctional empathic abilities in schizophrenia are related to alterations in early or late brain processes, and (2) to explore the potential relationship between brain activity and mood, self-reported empathy and symptom severity. Eighteen patients with schizophrenia and 21 healthy matched controls performed an empathy-for-pain paradigm where photographs of hands in neutral or painful situations were shown while we recorded their electroencephalography (EEG), and we examined mood, empathic concern for others and symptom severity. Significant group differences between patients and controls emerged in early (50-150ms after stimulus onset) and late (after 300ms) timeframes. Moreover, brain activity was related with unpleasantness ratings in all participants, with self-reported empathic concern only in controls and with negative mood and personal distress only in patients. Differences in social behavior in schizophrenia may be explained by early as well as late differences, affecting mostly the early frontocentral ERPs, i.e. those suggested to correspond to the emotional sharing component of empathy.

Reward modulates the mirror neuron system in schizophrenia: A study into the mu rhythm suppression, empathy, and mental state attribution.

Impairments in the mirror neuron system (MNS) have been implicated as a possible underlying neurological basis for deficits in higher-level social cognition in schizophrenia. Previous work testing this hypothesis has used the electroencephalographic mu rhythm as an index of MNS activity, with studies showing mixed results. Here we investigated the role that reward plays in modulating the mu rhythm, and its association with empathy and emotional mental state reasoning. A group of schizophrenia patients and a healthy control group completed an action observation paradigm in which they watched actions that were financially rewarding, punishing, or neutral. Patients showed intact reward-related modulation of the mu rhythm, and greater mu suppression was associated with greater negative symptoms. There was also a trend for reduced mu suppression in patients. Furthermore, both empathy and emotional mental state reasoning were associated with the degree of mu suppression, but only in healthy controls. These findings confirm the association between the mu suppression and high-level social cognition. It is possible that schizophrenia patients utilize different cognitive routes to infer mental states. The demonstration that reward influences the degree of mu suppression in schizophrenia patients may help to account for previous conflicting findings in the literature.

Symptom Dimensions, Smoking and Impulsiveness in Obsessive-Compulsive Disorder.

BACKGROUND: Obsessive-compulsive disorder (OCD) has distinct symptom dimensions with possibly subtle differences in the underlying neurobiology. One behavioral habit, smoking, has been widely investigated in psychiatric disorders, though received less attention in OCD. Here, we aimed to investigate the relationship between symptom dimensions and smoking behavior in OCD. SUBJECTS AND METHODS: OCD patients (n=167) with the symptom dimensions of washing, taboo thoughts and symmetry-counting-repeating-ordering (S+C+R+O) were questioned in terms of smoking status and assessed with the Yale-Brown Obsessive Compulsive Scale (Y-BOCS), Fagerström Test for Nicotine Dependence (FTND), Hamilton Depression Rating Scale-17 Items (HDRS-17), Beck Anxiety Inventory (BAI) and Barratt Impulsiveness Scale-11 (BIS 11). RESULTS: Smoking status differed significantly among patients with distinct symptom dimensions (p=0.009).The ratio of smokers was the lowest in those with the washing (30%, N=12) and the highest in the S+C+R+O (68.2%, N=15) group. Those with taboo thoughts had a smoking ratio of 37.14% (N=39). In post hoc analysis, smoking ratio was significantly higher in the S+C+R+O group than in those with washing symptoms (p=0.004) and taboo thoughts (p=0.007) though it did not differ significantly between washers and taboo thought groups. The BIS-11 did not differ across symptom dimensions. CONCLUSIONS: OCD is a heterogeneous disorder in terms of smoking. Impulsiveness, which does not significantly vary across distinct symptom dimensions, cannot explain this heterogeneity. The severity of addiction does not differ in smokers with OCD across symptom dimensions.

Gray matter abnormalities in patients with social anxiety disorder: A voxel-based morphometry study.

The main objective of this study was to investigate the gray matter volume (GMV) differences between the patients with social anxiety disorder (SAD) and healthy controls, using VBM analysis. A total of 27 consecutive patients (15 women and 12 men) with SAD and 27 age and sex-matched healthy control subjects were included in this study. With magnetic resonance imaging, we examined GMV differences between SAD and healthy control groups. We found that GMV in the right middle and inferior temporal, left superior parietal, left precuneus and right fusiform areas were significantly greater in patients with SAD than in healthy controls. In addition, GMV in the right inferior and middle temporal regions were positively correlated with the social avoidance and total social anxiety scores of the participants in the SAD group. Lastly, greater GMV in the left superior parietal and precuneal regions were correlated with the higher disability in the social life of the patients with SAD. Our results suggest that the regions that showed significant GMV differences between the two groups play an important role in the pathophysiology of SAD and increased GMV in these regions might reflect a pathological process of neural abnormalities in this disorder.

Neuroanatomical correlates of genetic risk for bipolar disorder: A voxel-based morphometry study in bipolar type I patients and healthy first degree relatives.

BACKGROUND: Bipolar disorder (BD) is a highly heritable mental illness which is associated with neuroanatomical abnormalities. Investigating healthy individuals at high genetic risk for bipolar disorder may help to identify neuroanatomical markers of risk and resilience without the confounding effects of burden of illness or medication. METHODS: Structural magnetic resonance imaging scans were acquired from 30 euthymic patients with BD-I (BP), 28 healthy first degree relatives of BD-I patients (HR), and 30 healthy controls (HC). Data was analyzed using DARTEL for voxel based morphometry in SPM8. RESULTS: Whole-brain analysis revealed a significant main effect of group in the gray matter volume in bilateral inferior frontal gyrus, left parahippocampal gyrus, left lingual gyrus and cerebellum, posterior cingulate gyrus, and supramarginal gyrus (alphasim corrected (≤0.05 FWE)). Post-hoc t-tests showed that inferior frontal gyrus volumes were bilaterally larger both in BP and HR than in HC. BP and HR also had smaller cerebellar volume compared with HC. In addition, BP had smaller left lingual gyrus volume, whereas HR had larger left parahippocampal and supramarginal gyrus volume compared with HC. LIMITATIONS: This study was cross-sectional and the sample size was not large. All bipolar patients were on medication, therefore we were not able to exclude medication effects in bipolar group in this study. CONCLUSIONS: Our findings suggest that increased inferior frontal gyrus and decreased cerebellar volumes might be associated with genetic predisposition for bipolar disorder. Longitudinal studies are needed to better understand the predictive and prognostic value of structural changes in these regions.

A wrapper-based approach for feature selection and classification of major depressive disorder-bipolar disorders.

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 nested cross-validation (CV) procedure.

EEG power, cordance and coherence differences between unipolar and bipolar depression.

INTRODUCTION: Understanding the biological underpinnings of unipolar (UD) and bipolar depression (BD) is vital for avoiding inappropriate treatment through the misdiagnosis of bipolar patients in their first depressive episode. One plausible way to distinguish between UD and BD is to compare EEG brain dynamics to identify potential neurophysiological biomarkers. Here we aimed to test group differences in EEG power, cordance and coherence values between UD and BD. METHODS: Twenty-five bipolar and 56 unipolar depression patients were recruited. Sociodemographic and clinical variables were collected in addition to resting state EEG. Data was analyzed with multivariate and repeated analyses of variance where parametric assumptions were met. RESULTS: Accordingly, we did not find any differences in the EEG absolute power and frontal asymmetry indexes between UD and BD. Regarding cordance, significant group differences were observed in the right theta cordance values (p=0.031). Regarding coherence, BD patients (as compared to UD) exhibited greater central-temporal theta (p=0.003), and parietal-temporal alpha (p=0.007) and theta (p=0.001) coherence. Lastly, less alpha coherence in BD was present at right frontal-central (p=0.007) and central inter-hemispheric (p=0.019) regions. CONCLUSIONS: Our results demonstrate that EEG cordance and coherence values have potential to discriminate between UD and BD. The loss of temporal synchronization in the frontal interhemispheric and right sided frontolimbic neuronal networks may be a unique feature that distinguishes between BD and UD.

Oxidative stress in schizophrenia: a case-control study on the effects on social cognition and neurocognition.

BACKGROUND: Schizophrenia is a debilitating mental disorder that presents impairments in neurocognition and social cognition. Several studies have suggested that the etiology of schizophrenia can be partly explained by oxidative stress. However, our knowledge about the implications of oxidative stress on illness-related cognitive deficits is still far from being clear. The aim of this work was to study the role of oxidative stress molecules on social cognition and neurocognition in patients with schizophrenia. METHODS: We assessed the peripheral levels of several molecules associated with oxidative stress, namely nitric oxide (NO), malondialdehyde (MDA), glutathione (GSH), homocysteine, superoxide dismutase (SOD) and neurotrophin 4/5 (NT4/5), in forty-one patients with schizophrenia and forty-three healthy participants. A battery of tests to measure neurocognition and social cognition was also administered to the schizophrenia group. RESULTS: We found that the schizophrenia group presented substantially higher levels of oxidative stress than the control group, as revealed by elevated quantities of the pro-oxidants NO and MDA, and decreased levels of the antioxidants GSH, SOD and NT4/5. Interestingly, the levels of NT4/5, which have been shown to have antioxidant effects, correlated with executive functioning, as measured by two distinct tests (WCST and TMT). However, social cognition and symptom severity were not found to be associated with oxidative stress. CONCLUSIONS: We propose a protective role of NT4/5 against oxidative stress, which appears to have a potentially beneficial impact on neurocognition in schizophrenia.

Social approach and avoidance behaviour for negative emotions is modulated by endogenous oxytocin and paranoia in schizophrenia.

Patients with schizophrenia suffer from dysfunctional social behaviour. Social approach and avoidance (AA) has been associated with motor responses, as the affective valence and gaze direction of facial stimuli can bias push and pull motor tendencies. The aim of this study was to investigate the role of endogenous oxytocin in social AA behaviour in schizophrenia. Basal plasma oxytocin levels were collected from 28 patients who were then given a joystick-based Approach-Avoidance Task (AAT). Reaction times were recorded and AAT effect scores calculated for responses to happy and angry faces, which either had direct or averted gaze. Individual differences in basal oxytocin had a significant relationship with AAT responses, and patients with higher levels of oxytocin tended to avoid angry faces more. Furthermore, greater avoidance of angry faces was correlated with more severe psychotic (positive and general) symptoms and greater paranoia. This suggests that the endogenous effects of oxytocin may be specific to the interpretation of negative threatening emotions in schizophrenia patients, and also provides evidence that psychotic symptoms and paranoia can impact on social AA behaviour by heightening threat avoidance.

Metacognition in psychosis: comparison of schizophrenia with bipolar disorder.

While deficits in metacognition have been observed in schizophrenia (SZ), it is less clear whether these are specific to the disorder. Accordingly, this study compared metacognitive abilities of patients with schizophrenia and bipolar disorder (BD) and examined the degree to which neurocognition contributed to metacognitive deficits in both groups. Participants were 30 patients with SZ and 30 with BD. Metacognitive capacity was measured using the Metacognition Assessment Scale Abbreviated (MAS-A). This scale comprises four domains: self-reflectivity, understanding others' minds, decentration and mastery. Verbal memory, executive functioning and symptoms were concurrently assessed. Group comparisons revealed that SZ patients had greater deficits in metacognitive self-reflectivity, which correctly classified 85.2% of patients with SZ in a logistic regression. Self-reflectivity and understanding others'minds were related to verbal memory and executive functioning in the SZ group, but not in the BD group. Furthermore, greater positive and general psychotic symptoms were associated with poorer metacognition in SZ. Results suggest SZ involves unique deficits in the ability to self-reflect and that these deficits may be uniquely linked with neurocognition.