Domain specificity of error monitoring: An ERP study in young and older adults.

Metacognition refers to the ability to monitor and control one's cognitive processes, which plays an important role in decision-making throughout the lifespan. It is still debated whether metacognitive abilities decline with age. Neuroimaging evidence suggests that metacognition is served by domain-specific mechanisms. These domains may differentially decline with increasing age. The current investigates whether the error-related negativity (ERN) and the error positivity (Pe) which reflect error detection and error awareness, respectively, differ across perceptual and memory domains in young and older adults. In total, 38 young adults and 37 older adults completed a classic Flanker Task (perceptual) and an adapted memory-based version. No difference in ERN amplitude was found between young and older adults and across domains. Perceptual ERN peaked earlier than Memory ERN. Memory ΔERN was larger than Perceptual ΔERN. Pe was smaller in older adults and ΔPe was larger for perceptual than memory flanker. Memory Pe peaked earlier in young as compared to older adults. Multivariate analyses of whole scalp data supported cross-domain differences. During the task, ERN decreased in young but not in older adults. Memory Pe decreased in young adults but increased in older adults while no significant change in perceptual Pe was found. The study's findings suggest that neural correlates of error monitoring differ across cognitive domains. Moreover, it was shown that error awareness declines in old age but its within-task dynamics vary across cognitive domains. Possible mechanisms underlying metacognition impairments in aging are discussed.

The neural correlates of reinforcement sensitivity theory: A systematic review of the frontal asymmetry and spectral power literature.

The original Reinforcement Sensitivity Theory (oRST) proposes two systems of approach (BAS) and avoidance (BIS) motivation to underpin personality and behavior. The revised-RST (rRST) model separates avoidance motivation into passive (BIS; anxiety) and active (FFFS; fear) systems. Prior research has attempted to map RST onto lateralized frontal asymmetry to provide a neurophysiological marker of RST. The main aim is to examine the relationships of the o/rRST scales with trait (baseline) and state (manipulated through experimental paradigms) frontal asymmetry. A systematic review was conducted, resulting in 158 studies designated to neuroimaging research. In total, 54 studies were included in this review using either frontal asymmetry or spectral power. The results were split into three main categories: resting frontal alpha asymmetry (N = 23), emotional induction and state-related frontal alpha asymmetry (N = 20), and spectral analysis (N = 16). Findings indicated that BAS was associated with enhanced left frontal asymmetry at baseline and during state-related paradigms. Findings for BIS were more inconsistent, especially at rest, suggesting that BIS, in particular, may require active engagement with the environment. Only 9 of the 54 papers included used the revised RST model, highlighting the need for more rRST research.

Classification of Sleep Quality and Aging as a Function of Brain Complexity: A Multiband Non-Linear EEG Analysis.

Understanding and classifying brain states as a function of sleep quality and age has important implications for developing lifestyle-based interventions involving sleep hygiene. Current studies use an algorithm that captures non-linear features of brain complexity to differentiate awake electroencephalography (EEG) states, as a function of age and sleep quality. Fifty-eight participants were assessed using the Pittsburgh Sleep Quality Inventory (PSQI) and awake resting state EEG. Groups were formed based on age and sleep quality (younger adults n = 24, mean age = 24.7 years, SD = 3.43, good sleepers n = 11; older adults n = 34, mean age = 72.87; SD = 4.18, good sleepers n = 9). Ten non-linear features were extracted from multiband EEG analysis to feed several classifiers followed by a leave-one-out cross-validation. Brain state complexity accurately predicted (i) age in good sleepers, with 75% mean accuracy (across all channels) for lower frequencies (alpha, theta, and delta) and 95% accuracy at specific channels (temporal, parietal); and (ii) sleep quality in older groups with moderate accuracy (70 and 72%) across sub-bands with some regions showing greater differences. It also differentiated younger good sleepers from older poor sleepers with 85% mean accuracy across all sub-bands, and 92% at specific channels. Lower accuracy levels (<50%) were achieved in predicting sleep quality in younger adults. The algorithm discriminated older vs. younger groups excellently and could be used to explore intragroup differences in older adults to predict sleep intervention efficiency depending on their brain complexity.

Depression, anxiety and worry in young Pacific adults in New Zealand during the COVID-19 pandemic.

OBJECTIVE: To measure symptoms of anxiety, depression and hopelessness in a sample of young Pacific adults living in Auckland, New Zealand during the 2020/2021 COVID-19 pandemic and identify protective factors. METHODS: Participants were 267 Pacific adults (58% female) who completed a survey online. Analyses included descriptive statistics, correlations, linear regression and symptom network analysis. RESULTS: Around 25% of the sample scored in the range for moderate to severe anxiety and 10% for moderate to severe depression on standard measures. Almost 40% indicated that they found the first lockdown very stressful and 55% noted that some members of their family found it stressful. Only 16% worried about COVID-19 and their future quite a bit or constantly, while another 25% worried sometimes. Self-compassion and Pacific Identity had moderate, negative correlations, and Worry about COVID-19 had weak positive correlations, with anxiety, depression, hopelessness and perceived stress. CONCLUSION: These results suggest that, while the prevalence of depression and anxiety are quite high among this population, fostering ethnic identity and self-compassion in Pacific children and adolescents might protect against developing depression and anxiety.

Prediction of Tinnitus Treatment Outcomes Based on EEG Sensors and TFI Score Using Deep Learning.

Tinnitus is a hearing disorder that is characterized by the perception of sounds in the absence of an external source. Currently, there is no pharmaceutical cure for tinnitus, however, multiple therapies and interventions have been developed that improve or control associated distress and anxiety. We propose a new Artificial Intelligence (AI) algorithm as a digital prognostic health system that models electroencephalographic (EEG) data in order to predict patients' responses to tinnitus therapies. The EEG data was collected from patients prior to treatment and 3-months following a sound-based therapy. Feature selection techniques were utilised to identify predictive EEG variables with the best accuracy. The patients' EEG features from both the frequency and functional connectivity domains were entered as inputs that carry knowledge extracted from EEG into AI algorithms for training and predicting therapy outcomes. The AI models differentiated the patients' outcomes into either therapy responder or non-responder, as defined by their Tinnitus Functional Index (TFI) scores, with accuracies ranging from 98%-100%. Our findings demonstrate the potential use of AI, including deep learning, for predicting therapy outcomes in tinnitus. The research suggests an optimal configuration of the EEG sensors that are involved in measuring brain functional changes in response to tinnitus treatments. It identified which EEG electrodes are the most informative sensors and how the EEG frequency and functional connectivity can better classify patients into the responder and non-responder groups. This has potential for real-time monitoring of patient therapy outcomes at home.

A novel way to quantify schizophrenia symptoms in clinical trials.

BACKGROUND: A major problem in quantifying symptoms of schizophrenia is establishing a reliable distinction between enduring and dynamic aspects of psychopathology. This is critical for accurate diagnosis, monitoring and evaluating treatment effects in both clinical practice and trials. MATERIALS AND METHODS: We applied Generalizability Theory, a robust novel method to distinguish between dynamic and stable aspects of schizophrenia symptoms in the widely used Positive and Negative Symptom Scale (PANSS) using a longitudinal measurement design. The sample included 107 patients with chronic schizophrenia assessed using the PANSS at five time points over a 24-week period during a multi-site clinical trial of N-Acetylcysteine as an add-on to maintenance medication for the treatment of chronic schizophrenia. RESULTS: The original PANSS and its three subscales demonstrated good reliability and generalizability of scores (G = 0.77-0.93) across sample population and occasions making them suitable for assessment of psychosis risks and long-lasting change following a treatment, while subscales of the five-factor models appeared less reliable. The most enduring symptoms represented by the PANSS were poor attention, delusions, blunted affect and poor rapport. More dynamic symptoms with 40%-50% of variance explained by patient transient state including grandiosity, preoccupation, somatic concerns, guilt feeling and hallucinatory behaviour. CONCLUSIONS: Identified dynamic symptoms are more amendable to change and should be the primary target of interventions aiming at effectively treating schizophrenia. Separating out the dynamic symptoms would increase assay sensitivity in trials, reduce the signal to noise ratio and increase the potential to detect the effects of novel therapies in clinical trials.

Interpretability of Spatiotemporal Dynamics of the Brain Processes Followed by Mindfulness Intervention in a Brain-Inspired Spiking Neural Network Architecture.

Mindfulness training is associated with improvements in psychological wellbeing and cognition, yet the specific underlying neurophysiological mechanisms underpinning these changes are uncertain. This study uses a novel brain-inspired artificial neural network to investigate the effect of mindfulness training on electroencephalographic function. Participants completed a 4-tone auditory oddball task (that included targets and physically similar distractors) at three assessment time points. In Group A (n = 10), these tasks were given immediately prior to 6-week mindfulness training, immediately after training and at a 3-week follow-up; in Group B (n = 10), these were during an intervention waitlist period (3 weeks prior to training), pre-mindfulness training and post-mindfulness training. Using a spiking neural network (SNN) model, we evaluated concurrent neural patterns generated across space and time from features of electroencephalographic data capturing the neural dynamics associated with the event-related potential (ERP). This technique capitalises on the temporal dynamics of the shifts in polarity throughout the ERP and spatially across electrodes. Findings support anteriorisation of connection weights in response to distractors relative to target stimuli. Right frontal connection weights to distractors were associated with trait mindfulness (positively) and depression (inversely). Moreover, mindfulness training was associated with an increase in connection weights to targets (bilateral frontal, left frontocentral, and temporal regions only) and distractors. SNN models were superior to other machine learning methods in the classification of brain states as a function of mindfulness training. Findings suggest SNN models can provide useful information that differentiates brain states based on distinct task demands and stimuli, as well as changes in brain states as a function of psychological intervention.

Neurophysiological correlates of excitement in men with recent-onset psychosis.

OBJECTIVE: Right frontal function, as indicated by the N200 component of the event-related potential during target detection, has previously been associated with excitement (excitement, impulsivity, hostility, uncooperativeness) in men with a long-term diagnosis of schizophrenia. The current study investigated excitement in relation to N200 in men who had recently experienced their first episode of psychosis. SUBJECTS AND METHODS: Twenty men who had recently suffered their first psychotic episode underwent a clinical interview and auditory oddball task. RESULTS: Multiple linear regression analysis showed that 58% of the variance in the excitement symptom cluster was explained by a positive association with frontal midline N200 amplitude and an inverse association with right frontal N200 amplitude. The latter was not apparent in the initial correlation, suggesting suppression by the midline activity. These associations were not explained by drug use, medication or negative symptoms. However, the correlation between excitement and midline N200 was stronger in drug users, and that between right frontal N200 and excitement was stronger in nonusers. CONCLUSION: Findings support the independent contributions to excitement of mechanisms reflected in midline and right frontal N200 amplitude respectively during the early stages of psychosis.

Associations between immunological function and memory recall in healthy adults.

Studies in clinical and aging populations support associations between immunological function, cognition and mood, although these are not always in line with animal models. Moreover, very little is known about the relationship between immunological measures and cognition in healthy young adults. The present study tested associations between the state of immune system and memory recall in a group of relatively healthy adults. Immediate and delayed memory recall was assessed in 30 participants using the computerised cognitive battery. CD4, CD8 and CD69 subpopulations of lymphocytes, Interleukin-6 (IL-6) and cortisol were assessed with blood assays. Correlation analysis showed significant negative relationships between CD4 and the short and long delay memory measures. IL-6 showed a significant positive correlation with long-delay recall. Generalized linear models found associations between differences in all recall challenges and CD4. A multivariate generalized linear model including CD4 and IL-6 exhibited a stronger association. Results highlight the interactions between CD4 and IL-6 in relation to memory function. Further study is necessary to determine the underlying mechanisms of the associations between the state of immune system and cognitive performance.

Acute effects of radiofrequency electromagnetic field emitted by mobile phone on brain function.

Due to its attributes, characteristics, and technological resources, the mobile phone (MP) has become one of the most commonly used communication devices. Historically, ample evidence has ruled out the substantial short-term impact of radiofrequency electromagnetic field (RF-EMF) emitted by MP on human cognitive performance. However, more recent evidence suggests potential harmful effects associated with MP EMF exposure. The aim of this review is to readdress the question of whether the effect of MP EMF exposure on brain function should be reopened. We strengthen our argument focusing on recent neuroimaging and electroencephalography studies, in order to present a more specific analysis of effects of MP EMF exposure on neurocognitive function. Several studies indicate an increase in cortical excitability and/or efficiency with EMF exposure, which appears to be more prominent in fronto-temporal regions and has been associated with faster reaction time. Cortical excitability might also underpin disruption to sleep. However, several inconsistent findings exist, and conclusions regarding adverse effects of EMF exposure are currently limited. It also should be noted that the crucial scientific question of the effect of longer-term MP EMF exposure on brain function remains unanswered and essentially unaddressed. Bioelectromagnetics. 38:329-338, 2017. © 2017 Wiley Periodicals, Inc.

Lower anterior cingulate volume in seriously violent men with antisocial personality disorder or schizophrenia and a history of childhood abuse.

OBJECTIVE: Antisocial personality disorder (ASPD) and schizophrenia, as well as childhood abuse, are associated with violent behaviour and show marked volumetric reduction in the anterior cingulate (AC), a brain region implicated in regulation of violence through its involvement in decision making, empathy, impulse control, and emotion regulation. The present study examined, for the first time to the authors' knowledge, the grey matter volume of the AC in relation to seriously violent behaviour and childhood psychosocial deprivation (including physical and sexual abuse) in the context of a mental disorder (schizophrenia or ASPD). METHODS: Fifty-seven men [14 with ASPD and a history of serious violence; 13 with schizophrenia and a history of serious violence (VSZ); 15 with schizophrenia without a violence history (SZ); 15 nonviolent healthy participants] underwent whole-brain magnetic resonance imaging and were rated on the presence of physical abuse, sexual abuse, neglect, extreme poverty, foster home placement, criminal parent, severe family conflict, and broken home (collectively 'psychosocial deprivation'). Stereological volumetric ratings of the AC were examined for group differences and their association with childhood psychosocial deprivation. RESULTS: A higher proportion of ASPD and VSZ patients had suffered psychosocial deprivation as children, in particular severe physical abuse, relative to SZ patients and healthy participants. ASPD and VSZ, but not SZ, patients had significantly lower AC volume relative to healthy participants. AC volumes correlated negatively with (total) psychosocial deprivation as well as physical and sexual abuse ratings. Group differences in AC volume became nonsignificant when psychosocial deprivation ratings were covaried for. CONCLUSIONS: Violent mentally disordered individuals with ASPD or schizophrenia suffer from a significant AC volume loss and this deficit, at least in part, is explained by their histories of stressful childhood experiences. Current and future therapies aiming to reduce violence in such populations would benefit by attending to biological (and other) correlates of childhood abuse.

Graph-based analysis of EEG for schizotypy classification applying flicker Ganzfeld stimulation.

Ganzfeld conditions induce alterations in brain function and pseudo-hallucinatory experiences, particularly in people with high positive schizotypy. The current study uses graph-based parameters to investigate and classify brain networks under Ganzfeld conditions as a function of positive schizotypy. Participants from the general population (14 high schizotypy (HS), 29 low schizotypy (LS)) had an electroencephalography assessment during Ganzfeld conditions, with varying visual activation (8 frequencies of random light flicker) and soundscape-induced mood (neutral, serenity, and anxiety). Weighted functional networks were computed in six frequency sub-bands (delta, theta, alpha-low, alpha-high, beta, and gamma) as a function of light-flicker frequency and mood. The brain network was analyzed using graph theory parameters, including clustering coefficient (CC), strength, and global efficiency (GE). It was found that the LS groups had higher CC and strength than the HS groups, especially in bilateral temporal and frontotemporal brain regions. Moreover, some decreases in CC and strength measures were found in LS groups among occipital and parieto-occipital brain regions. LS groups also had significantly higher GE in all Ganzfeld conditions compared to the HS groups. The random under-sampling boosting (RUSBoost) algorithm achieved the best classification performance with an accuracy of 95.34%, specificity of 96.55%, and sensitivity of 92.85% during an anxiety-induction Ganzfeld condition. This is the first exploration of the relationship between brain functional state changes under Ganzfeld conditions in individuals who vary in positive schizotypy. The accuracy of graph-based parameters in classifying brain states as a function of schizotypy is shown, particularly for brain activity during anxiety induction, and should be investigated in psychosis.

The relationships between expressed emotion, cortisol, and EEG alpha asymmetry.

Families can express high criticism, hostility and emotional over-involvement towards a person with or at risk of mental health problems. Perceiving such high expressed emotion (EE) can be a major psychological stressor for individuals, especially those at risk of mental health problems. To reveal the biological mechanisms underlying the effect of EE on health, this study investigated physiological response (salivary cortisol, frontal alpha asymmetry (FAA)) to verbal criticism and their relationship to anxiety and perceived EE. Using a repeated-measures design, healthy participants attended three testing sessions on non-consecutive days. On each day, participants listened to one of three types of auditory stimuli, namely criticism, neutral or praise, and Electroencephalography (EEG) and salivary cortisol were measured. Results showed a reduction in cortisol following criticism but there was no significant change in FAA. Post-criticism cortisol concentration negatively correlated with perceived EE after controlling for baseline mood. Our findings suggest that salivary cortisol change responds to criticism in non-clinical populations and this response might be largely driven by individual differences in the perception of criticism (e.g., arousal and relevance). Criticisms expressed by audio comments may not be explicitly perceived as an acute emotional stressor, and thus, physiological response to criticisms could be minimum.

Constrained neuro fuzzy inference methodology for explainable personalised modelling with applications on gene expression data.

Interpretable machine learning models for gene expression datasets are important for understanding the decision-making process of a classifier and gaining insights on the underlying molecular processes of genetic conditions. Interpretable models can potentially support early diagnosis before full disease manifestation. This is particularly important yet, challenging for mental health. We hypothesise this is due to extreme heterogeneity issues which may be overcome and explained by personalised modelling techniques. Thus far, most machine learning methods applied to gene expression datasets, including deep neural networks, lack personalised interpretability. This paper proposes a new methodology named personalised constrained neuro fuzzy inference (PCNFI) for learning personalised rules from high dimensional datasets which are structurally and semantically interpretable. Case studies on two mental health related datasets (schizophrenia and bipolar disorders) have shown that the relatively short and simple personalised fuzzy rules provided enhanced interpretability as well as better classification performance compared to other commonly used machine learning methods. Performance test on a cancer dataset also showed that PCNFI matches previous benchmarks. Insights from our approach also indicated the importance of two genes (ATRX and TSPAN2) as possible biomarkers for early differentiation of ultra-high risk, bipolar and healthy individuals. These genes are linked to cognitive ability and impulsive behaviour. Our findings suggest a significant starting point for further research into the biological role of cognitive and impulsivity-related differences. With potential applications across bio-medical research, the proposed PCNFI method is promising for diagnosis, prognosis, and the design of personalised treatment plans for better outcomes in the future.

Understanding suicidality in Pacific adolescents in New Zealand using network analysis.

INTRODUCTION: Pacific adolescents in New Zealand (NZ) are three to four times more likely than NZ European adolescents to report suicide attempts and have higher rates of suicidal plans. Suicidal thoughts, plans, and attempts, termed suicidality in this study, result from a complex dynamic interplay of factors, which emerging methodologies like network analysis aim to capture. METHODS: This study used cross-sectional network analysis to model the relationships between suicidality, self-harm, and individual depression symptoms, whilst conditioning on a multi-dimensional set of variables relevant to suicidality. A series of network models were fitted to data from a community sample of New Zealand-born Pacific adolescents (n = 550; 51% male; Mean age (SD) = 17 (0.35)). RESULTS: Self-harm and the depression symptom measuring pessimism had the strongest associations with suicidality, followed by symptoms related to having a negative self-image about looks and sadness. Nonsymptom risk factors for self-harm and suicidality differed markedly. CONCLUSIONS: Depression symptoms varied widely in terms of their contribution to suicidality, highlighting the valuable information gained from analysing depression at the symptom-item level. Reducing the sources of pessimism and building self-esteem presented as potential targets for alleviating suicidality amongst Pacific adolescents in New Zealand. Suicide prevention strategies need to include risk factors for self-harm.

RNA-sequencing of peripheral whole blood of individuals at ultra-high-risk for psychosis - A longitudinal perspective.

BACKGROUND: The peripheral blood is an attractive source of prognostic biomarkers for psychosis conversion. There is limited research on the transcriptomic changes associated with psychosis conversion in the peripheral whole blood. STUDY DESIGN: We performed RNA-sequencing of peripheral whole blood from 65 ultra-high-risk (UHR) participants and 70 healthy control participants recruited in the Longitudinal Youth-at-Risk Study (LYRIKS) cohort. 13 UHR participants converted in the study duration. Samples were collected at 3 timepoints, at 12-months interval across a 2-year period. We examined whether the genes differential with psychosis conversion contain schizophrenia risk loci. We then examined the functional ontologies and GWAS associations of the differential genes. We also identified the overlap between differentially expressed genes across different comparisons. STUDY RESULTS: Genes containing schizophrenia risk loci were not differentially expressed in the peripheral whole blood in psychosis conversion. The differentially expressed genes in psychosis conversion are enriched for ontologies associated with cellular replication. The differentially expressed genes in psychosis conversion are associated with non-neurological GWAS phenotypes reported to be perturbed in schizophrenia and psychosis but not schizophrenia and psychosis phenotypes themselves. We found minimal overlap between the genes differential with psychosis conversion and the genes that are differential between pre-conversion and non-conversion samples. CONCLUSION: The associations between psychosis conversion and peripheral blood-based biomarkers are likely to be indirect. Further studies to elucidate the mechanism behind potential indirect associations are needed.

Investigation of social and cognitive predictors in non-transition ultra-high-risk' individuals for psychosis using spiking neural networks.

Finding predictors of social and cognitive impairment in non-transition Ultra-High-Risk individuals (UHR) is critical in prognosis and planning of potential personalised intervention strategies. Social and cognitive functioning observed in youth at UHR for psychosis may be protective against transition to clinically relevant illness. The current study used a computational method known as Spiking Neural Network (SNN) to identify the cognitive and social predictors of transitioning outcome. Participants (90 UHR, 81 Healthy Control (HC)) completed batteries of neuropsychological tests in the domains of verbal memory, working memory, processing speed, attention, executive function along with social skills-based performance at baseline and 4 × 6-month follow-up intervals. The UHR status was recorded as Remitters, Converters or Maintained. SNN were used to model interactions between variables across groups over time and classify UHR status. The performance of SNN was examined relative to other machine learning methods. Higher interaction between social and cognitive variables was seen for the Maintained, than Remitter subgroup. Findings identified the most important cognitive and social variables (particularly verbal memory, processing speed, attention, affect and interpersonal social functioning) that showed discriminative patterns in the SNN models of HC vs UHR subgroups, with accuracies up to 80%; outperforming other machine learning models (56-64% based on 18 months data). This finding is indicative of a promising direction for early detection of social and cognitive impairment in UHR individuals that may not anticipate transition to psychosis and implicate early initiated interventions to stem the impact of clinical symptoms of psychosis.

Enhancing biofeedback-driven self-guided virtual reality exposure therapy through arousal detection from multimodal data using machine learning.

Virtual reality exposure therapy (VRET) is a novel intervention technique that allows individuals to experience anxiety-evoking stimuli in a safe environment, recognise specific triggers and gradually increase their exposure to perceived threats. Public-speaking anxiety (PSA) is a prevalent form of social anxiety, characterised by stressful arousal and anxiety generated when presenting to an audience. In self-guided VRET, participants can gradually increase their tolerance to exposure and reduce anxiety-induced arousal and PSA over time. However, creating such a VR environment and determining physiological indices of anxiety-induced arousal or distress is an open challenge. Environment modelling, character creation and animation, psychological state determination and the use of machine learning (ML) models for anxiety or stress detection are equally important, and multi-disciplinary expertise is required. In this work, we have explored a series of ML models with publicly available data sets (using electroencephalogram and heart rate variability) to predict arousal states. If we can detect anxiety-induced arousal, we can trigger calming activities to allow individuals to cope with and overcome distress. Here, we discuss the means of effective selection of ML models and parameters in arousal detection. We propose a pipeline to overcome the model selection problem with different parameter settings in the context of virtual reality exposure therapy. This pipeline can be extended to other domains of interest where arousal detection is crucial. Finally, we have implemented a biofeedback framework for VRET where we successfully provided feedback as a form of heart rate and brain laterality index from our acquired multimodal data for psychological intervention to overcome anxiety.

Neural processing of social rejection: the role of schizotypal personality traits.

A fear of being rejected can cause perceptions of more insecurity and stress in close relationships. Healthy individuals activate the dorsal anterior cingulate cortex (dACC) when experiencing social rejection, while those who are vulnerable to depression deactivate the dACC presumably to downregulate salience of rejection cues and minimize distress. Schizotypal individuals, characterized by unusual perceptual experiences and/or odd beliefs, are more rejection sensitive than normal. We tested the hypothesis, for the first time, that individuals with high schizotypy also have an altered dACC response to rejection stimuli. Twenty-six healthy individuals, 14 with low schizotypy (LS) and 12 with high schizotypy (HS), viewed depictions of rejection and acceptance and neutral scenes while undergoing functional MRI. Activation maps in LS and HS groups during each image type were compared using SPM5, and their relation to participant mood and subjective ratings of the images was examined. During rejection relative to neutral scenes, LS activated and HS deactivated the bilateral dACC, right superior frontal gyrus, and left ventral prefrontal cortex. Across both groups, a temporo-occipito-parieto-cerebellar network was active during rejection, and a left fronto-parietal network during acceptance, relative to neutral scenes, and the bilateral lingual gyrus during rejection relative to acceptance scenes. Our finding of dACC-dorso-ventral PFC activation in LS, but deactivation in HS individuals when perceiving social rejection scenes suggests that HS individuals attach less salience to and distance themselves from such stimuli. This may enable them to cope with their higher-than-normal sensitivity to rejection.

Neural changes following cognitive behaviour therapy for psychosis: a longitudinal study.

A growing body of evidence demonstrates that persistent positive symptoms, particularly delusions, can be improved by cognitive behaviour therapy for psychosis. Heightened perception and processing of threat are believed to constitute the genesis of delusions. The present study aimed to examine functional brain changes following cognitive behaviour therapy for psychosis. The study involved 56 outpatients with one or more persistent positive distressing symptoms of schizophrenia. Twenty-eight patients receiving cognitive behaviour therapy for psychosis for 6-8 months in addition to their usual treatment were matched with 28 patients receiving treatment as usual. Patients' symptoms were assessed by a rater blind to treatment group, and they underwent functional magnetic resonance imaging during an affect processing task at baseline and end of treatment follow-up. The two groups were comparable at baseline in terms of clinical and demographic parameters and neural and behavioural responses to facial and control stimuli. The cognitive behaviour therapy for psychosis with treatment-as-usual group (22 subjects) showed significant clinical improvement compared with the treatment-as-usual group (16 subjects), which showed no change at follow-up. The cognitive behaviour therapy for psychosis with treatment-as-usual group, but not the treatment-as-usual group, showed decreased activation of the inferior frontal, insula, thalamus, putamen and occipital areas to fearful and angry expressions at treatment follow-up compared with baseline. Reduction of functional magnetic resonance imaging response during angry expressions correlated directly with symptom improvement. This study provides the first evidence that cognitive behaviour therapy for psychosis attenuates brain responses to threatening stimuli and suggests that cognitive behaviour therapy for psychosis may mediate symptom reduction by promoting processing of threats in a less distressing way.