Multi-phase state estimation featuring industrial-grade distribution network models

This paper proposes a novel implementation of a multi-phase distribution network state estimator which employs industrial-grade modeling of power components and measurements. Unlike the classical voltage-based and current-based state estimators, this paper presents the implementation details of a constrained weighted least squares state calculation method that includes standard three-phase state estimation capabilities in addition to practical modeling requirements from the industry; these requirements comprise multi-phase line configurations, unsymmetrical and incomplete transformer connections, power measurements on 4-connected loads, cumulative-type power measurements, line-to-line voltage magnitude measurements, and reversible line drop compensators. The enhanced modeling equips the estimator with capabilities that make it superior to a recently presented state-of-the-art distribution network load estimator that is currently used in real-life distribution management systems; comparative performance results demonstrate the advantage of the proposed estimator under practical measurement schemes

Neural correlates of dynamic emotion perception in schizophrenia and the influence of prior expectations.

Impaired emotion perception is a well-established and stable deficit in schizophrenia; however, there is limited knowledge about the underlying aberrant cognitive and brain processes that result in emotion perception deficits. Recent influential work has shown that perceptual deficits in schizophrenia may result from aberrant precision in prior expectations, associated with disrupted activity in frontal regions. In the present study, we investigated the perception of dynamic, multisensory emotion, the influence of prior expectations and the underlying aberrant brain processes in schizophrenia. During a functional Magnetic Resonance Imaging scan, participants completed the Dynamic Emotion Perception task, which induces prior expectations with emotion instruction cues. We delineated neural responses and functional connectivity in whole-brain large-scale networks underlying emotion perception. Compared to healthy individuals, schizophrenia patients had lower accuracy specifically for emotions that were congruent with prior expectations. At the neural level, schizophrenia patients had less engagement of right inferior frontal and parietal regions, as well as right amygdala dysconnectivity during discrimination of emotions congruent with prior expectations. The results indicate that individuals with schizophrenia may have aberrant prior expectations about emotional expressions, associated with under-activity in inferior frontoparietal regions and right amygdala dysconnectivity, which results in impaired perception of emotion

Association between schizophrenia polygenic risk and neural correlates of emotion perception.

The neural correlates of emotion perception have been shown to be significantly altered in schizophrenia (SCZ) patients as well as their healthy relatives, possibly reflecting genetic susceptibility to the disease. The aim of the study was to investigate the association between SCZ polygenic risk and brain activity whilst testing perception of multisensory, dynamic emotional stimuli. We created SCZ polygenic risk scores (PRS) for a sample of twenty-eight healthy individuals. The PRS was based on data from the Psychiatric Genomics Consortium and was used as a regressor score in the neuroimaging analysis. The results of a multivariate brain-behaviour analysis show that higher SCZ PRS are related to increased activity in brain regions critical for emotion during the perception of threatening (angry) emotions. These results suggest that individuals with higher SCZ PRS over-activate the neural correlates underlying emotion during perception of threat, perhaps due to an increased experience of fear or neural inefficiency in emotion-regulation areas. Moreover, over-recruitment of emotion regulation regions might function as a compensation to maintain normal emotion regulation during threat perception. If replicated in larger studies, these findings may have important implications for understanding the neurophysiological biomarkers relevant in SCZ

Statistical learning and inference is impaired in the non-clinical continuum of psychosis

Our perceptions result from the brain’s ability to make inferences, or predictive models, of sensory information. Recently, it has been proposed that psychotic traits may be linked to impaired predictive processes. Here, we examine the brain dynamics underlying statistical learning and inference in stable and volatile environments, in a population of healthy human individuals (N=75; Male/Female=36/39) with a range of psychotic-like experiences. We measured prediction error responses to sound sequences with electroencephalography, gauged sensory inference explicitly by behaviourally recording sensory statistical learning errors, and used dynamic causal modelling to tap into the underlying neural circuitry. We discuss the findings that were robust to replication across the two experiments (N=31 and N=44 for the Discovery and the Validation datasets, respectively). First, we found that during stable conditions, participants demonstrated greater precision in their predictive model, reflected in a larger prediction error response to unexpected sounds, and decreased statistical learning errors. Moreover, individuals with attenuated prediction errors in stable conditions were found to make greater incorrect predictions about sensory information. Critically, we show that greater errors in statistical learning and inference are related to increased psychotic-like experiences. These findings link neurophysiology to behaviour during statistical learning and prediction formation, as well as providing further evidence for the idea of a continuum of psychosis in the healthy, non-clinical population

Randomised controlled trial of Compensatory Cognitive Training and a Computerised Cognitive Remediation programme

Background: Compensation and adaptation therapies have been developed to improve community functioning via improving neurocognitive abilities in people with schizophrenia. Various modes of delivering compensation and adaptation therapies have been found to be effective. The aim of this trial is to compare two different cognitive interventions, Compensatory Cognitive Training (CCT) and Computerised Interactive Remediation of Cognition-Training for Schizophrenia (CIRCuiTS). The trial also aims to identify if mismatch negativity (MMN) can predict an individual's response to the compensation and adaptation programmes. Methods: This study will use a randomised, controlled trial of two cognitive interventions to compare the impact of these programmes on measures of neurocognition and function. One hundred clinically stable patients aged between 18 and 65 years with a diagnosis of a schizophrenia spectrum disorder will be recruited. Participants will be randomised to either the CCT or the CIRCuiTS therapy groups. The outcome measures are neurocognition (BACS), subjective sense of cognitive impairment (SSTICS), social functioning (SFS), and MMN (measured by EEG) in people with schizophrenia spectrum disorders. Discussion: This trial will determine whether different approaches to addressing the cognitive deficits found in schizophrenia spectrum disorders are of comparable benefit using the outcome measures chosen. This has implications for services where cost and lack of computer technology limit the implementation and dissemination of interventions to address cognitive impairment in routine practice. The trial will contribute to the emerging evidence of MMN as a predictor of response to cognitive interventions. Trial registration: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12618000161224. Registered on 2 February 2018. Protocol version: 4.0, 18 June 2018