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Schizophrenia is associated with an elevated risk for impulsive aggression for which there are few psychosocial treatment options. Neurocognitive and social cognitive deficits have been associated with aggression with social cognitive deficits seemingly a more proximal contributor. The current study examined the effects of combining cognitive and social cognition treatment on impulsive aggression among inpatients with chronic schizophrenia and schizoaffective disorder and a history of aggression compared to cognitive remediation treatment alone.The two-center study randomized 130 participants to receive 36 sessions of either a combination of cognitive remediation and social cognition treatment or cognitive remediation plus a computer-based control. Participants had at least one aggressive incident within the past year or a Life History of Aggression (LHA) score of 5 or more. Participants completed measures of neurocognition, social cognition, symptom severity, and aggression at baseline and endpoint.Study participants were mostly male (84.5 %), had a mean age 34.9 years, and 11.5 years of education. Both Cognitive Remediation Training (CRT) plus Social Cognition Training (SCT) and CRT plus control groups were associated with significant reductions in aggression measures with no group differences except on a block of the Taylor Aggression Paradigm (TAP), a behavioral task of aggression which favored the CRT plus SCT group. Both groups showed significant improvements in neurocognition and social cognition measures with CRT plus SCT being associated with greater improvements.CRT proved to be an effective non-pharmacological treatment in reducing impulsive aggression in schizophrenia inpatient participants with a history of aggressive episodes. The addition of social cognitive training did not enhance this anti-aggression treatment effect but did augment the CRT effect on cognitive functions, on emotion recognition and on mentalizing capacity of our participants.

Background Patient-centered outcomes research (PCOR) emphasizes the patient perspective and input to inform the research process with the aim to improve the quality of care. Given PCOR's emphasis on the patient perspective, methods to incorporate patient-reported outcomes (PROs) are important. Electronic PROs (ePROs) have been implemented successfully in many populations; however, many of these measurements do not incorporate patient perspective in the development of ePROs. For epilepsy and seizure disorders, user perspectives are key to developing measurements that capture real-time data, as seizures are not timed events; therefore, patients can wait days or even weeks and then try to recall their experience which can lead to variations in recall. ePRO can provide the necessary assurance that data were entered by the patient at the time the episode occurs. The aim of the present study was to assess patient perceptions of completing ePROs, expectations of ePRO devices for PCOR and on-site clinical visit in order to guide the development of successful ePRO deployment in seizure-related disorders. Methods This study used a naturalistic cohort design. A sample of 713 persons completed an online survey which consisted of 11 situational questions. Of the 713 individuals, results from 640 participants were included. Results were compared using a Wilcoxon signed-rank test. Results Most participants (71.9%) were able to accurately identify a seizure and 86.3% of participants felt it would be beneficial to have a short training on seizure symptoms prior to completing a daily seizure diary, and seizures should be reasonably reported within 10 mins (n = 426, 66.6%). Participants endorsed that repetitive movements and loss of consciousness as the most predominant symptoms they would look for in an ePRO. A majority of participants, 67.0% indicated that they regularly use accessibility features on using smartphones and tablets, and 38.6% indicated they would like to see more than one item per screen but only if they are related and to see all text in a larger size with scrolling features using fingers (n = 246; 38.4%). Conclusion This study has demonstrated the importance of developing ePROs that satisfy the needs of the participants and caregivers without compromising the scientific and clinical aspects of the disease construct. Developing tools using participant needs, observations, characteristics and input is essential to putting the participant perspective in patient-centered outcomes research.

OBJECTIVE: While clozapine is recognized as the most effective antipsychotic for individuals with treatment-resistant schizophrenia, its effects on neurocognition remain unclear. This study aimed to compare the neurocognitive effects of clozapine treatment to those of non-clozapine antipsychotics in patients with schizophrenia and to examine the role of anticholinergic burden on cognitive impairments. DESIGN: This was a naturalistic study. Cross-sectional data were drawn from participants with chronic schizophrenia in two clinical trials assessing cognition. Cognition was evaluated using the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery (MCCB). Anticholinergic burden was calculated for each medication using the Anticholinergic Cognitive Burden (ACB) scoring system. We stratified the participants treated with non-clozapine antipsychotics into high ACB score versus low ACB score groups. RESULTS: One hundred and seventy participants were enrolled and treated with clozapine (n=58) or non-clozapine antipsychotics (n=112). We observed no significant differences in the MCCB T-scores between the clozapine and the total non-clozapine groups for the cognitive composite score and the seven domain scores. However, the non-clozapine high ACB group showed significant impairments in processing speed and attention/vigilance, in contrast to the non-clozapine low ACB group (p

Background Studies have shown a convergence of neural systems implicated in cognitive control and social emotional functions with mechanisms involved in emotion regulation, awareness, and impulsive aggression. In a sample of patients with a history of aggression, we examined the association of cognitive and social cognitive functions with emotion regulation capacity, emotional awareness, impulse control, and aggression. Methods Participants were 78 individuals with schizophrenia or schizoaffective disorder that participated in a cognitive training study. Emotion regulation capacity was indexed by obtaining participants’ heart rate variability (HRV), respiration, and skin conductance while participants viewed pictures selected from the International Affective Picture System (IAPS). Three blocks of pictures were presented—a neutral block depicting pictures of everyday objects preceded by audio recordings of their description; an emotionally-evocative block preceded by their description, and a reappraisal block of emotionally-evocative pictures preceded by reappraisal statements. A subset of participants (N=37) completed the emotion regulation task. The MATRICS Consensus Cognitive Battery (MCCB), the Reading the Mind in the Eyes Task (Eyes Task), and the Emotion Recognition-40 (ER-40) were administered to assess cognitive functioning, mentalizing, and facial affect recognition respectively. Negative affectivity was captured using the Positive and Negative Affect Schedule (PANAS) whereas aggression was measured with the Taylor Aggression Paradigm (TAP) and the Overt Aggression Scale Modified (OAS-M). In a smaller subsample of 12 individuals, we performed analyses of activation patterns during the performance of the emotion regulation task. Regions of interest (ROI) included the dACC, DLPFC, VLPFC, vmPFC, left and right amygdala, and the subcallosal cingulate. We created a regional mask of the cognitive control and emotional appraisal regions for event-related fMRI. Results There were inverse associations between performance on the MCCB and the Eyes Task and indices of emotion regulation capacity—in particular, significant associations were obtained with Low Frequency/High Frequency HRV ratio (r=-0.324, p There were inverse associations between activation within the event-related regional mask and low frequency/high frequency heart rate variability ratio at rest (r=-0.504, p=0.095) and during the viewing of emotionally-evocative pictures (r = -0.264, p=0.36). Associations with HRV were especially remarkable for the dorsomedial, ventrolateral, and right amygdala regions. Remarkably, increased activation in the regional mask when reappraisal statements were provided with emotionally-evocative pictures was inversely correlated with the life history of aggression LHA (r=-0.32, p=0.28, n=14) and OAS (r=-0.284, p=0.34, n=14) aggression scores. This pattern was present when the dACC, right and left amygdala, DLPFC, VLPFC, and the subcallosal cingulate were set as the seed region. Discussion The results show an association between cognitive functions and emotion regulation capacity indexed by measuring physiological arousal during the performance of the picture viewing task. Emotion regulation capacity also showed significant association with aggression. Deficits in cognitive and social cognitive abilities may contribute to and be viable targets for improving emotion regulation capacity and decreasing aggression risk.

Background Many individuals with chronic or treatment resistant schizophrenia experience multiple relapses or treatment failures during the course of the illness. Some of these relapses are due to poor treatment compliance that is inherent to schizophrenia. Poor insight may be a leading cause for partial- or non-adherence to treatment as a high proportion of individuals with schizophrenia are partially or completely unaware of their mental disorder. The first two years after stabilization are thought to be key for long-term functional and clinical prognosis. In the present study, we first estimated the rate of treatment failure or relapse following clinical stability at one and two years, investigate the time to occurrence of relapse among individuals with chronic schizophrenia and the association between baseline clinical features recorded during the inpatient hospitalization and relapse at one- and two-year time points. We hypothesized that lack of insight, as measured by the PANSS item G12, would be a greater predictor of relapse than other characteristics previously suggested as possible predictors, including other symptoms from the PANSS, age, duration of illness, age at onset of illness, substance use, number of prior hospitalizations, and length of stay of the pre-discharge hospitalization. Methods A total of 138 participants diagnosed with schizophrenia or schizo-affective disorder were assessed with a comprehensive assessment at one year and two-year following discharge from a long-term psychiatric facility. Regression models were used to determine factors predicting time to relapse and other elements of functioning. Baseline factors examined included PANSS, MCCB, PSP, demographics and treatment variables. Results Relapse rates were 56.52% (n=78 of 138) by Year 1, and 69.56% (n=96 of 138) by end of Year 2. The estimated relapse-free period for all individuals at the end of the study was 8.78 months. The backward elimination (–2 log likelihood=189.59, χ2=9.01, df=2, p=.021) showed that the best predictive variables for relapse were lack of insight/judgment as assessed by PANSS item G12 at baseline (B=.20, SE=.09, df=1, p=.011, Exp[B]=1.36), lifetime years of substance use (B=.16, SE=.11, df=1, p=.029, Exp[B]=1.33), PANSS Factor baseline score on Disorganization (B=.15, SE=.12, df=1, p=.031, Exp[B]=1.56), and number of previous hospitalizations (B=.13, SE=.11, df=1, p=.048, Exp[B]=1.23). No other baseline variables were found to be significant. Discussion Poor insight is a fundamental symptom of schizophrenia that, while not entirely and uniformly expressed in all individuals, is among the most common symptoms across subjects. Our study shows that the numerous negative consequences of lack of insight should lead clinicians and researchers to make insight a high priority for allocation of clinical resources. Effective approaches to managing these predictive characteristics will allow affected individuals and their families and care providers to take part in collaborative treatment and relapse risk-management paradigms.

Background An individual’s mental health is best captured by considering the overall associations of biological, behavioral, and social functions that comprise the framework of individual experience. As such, accessing data on various health indicators concurrently can influence prediction of disease progression or change in response to treatment. Data generated passively by smartphones to measure human behavior has generated significant research interest and has increasingly been utilized in psychiatric disorders. In schizophrenia, passive and continuous assessment of how an individual uses their mobile device may give rise to clinically useful markers that can be used to improve treatment processes, adapt treatment choices, identify early risk for relapse to initiate clinical intervention, and develop new clinical models. A promising approach is to leverage current advances in mobile technology, data analytics and machine learning to enable automated and fast phenotyping of digital data. . In this context, the workflow for phenotyping (passive data collection → data storage and curation → trait extraction → machine learning/classification → models/apps for decision support) has to be carefully designed and efficiently executed to minimize resource usage and maximize utility. Digital phenotyping can be used in conjunction with standard care to reduce time to recognition and acknowledgement that worsening of a symptom needs to be addressed, to reduce time to receiving appropriate level of care, to increase ability to analyze and collect data from a variety of sources to improve mental health needs assessment and delivery of services, and to advance outcome measurement through comparison of passive and active data sets. Aim The aim of this pilot study is to test whether a smartphone digital phenotyping application can help detect early signs of treatment failure or response in individuals with chronic schizophrenia after discharge from hospital. Methods 17 individuals with DSM-5 schizophrenia were provided with a smartphone and digital phenotyping app, MindStrong Health app, following discharge from an inpatient psychiatric facility. The participants were followed for 6 months with monthly rater administered evaluations assessing neurocognition (Brief Assessment of Cognition in Schizophrenia (BACS)), symptomatology (PANSS; CGI-S/I), quality of life (SF-36), healthcare utilization, alcohol/drug use, level of clinical insight and depression (Calgary Depression Rating Scale, CDSS). Digital phenotyping data included gestures (swipes, taps, other), orientation (the way the phone is pointing), acceleration (sudden movements of the phone), keystroke patterns with characters encoded, number of calls made, number of emails sent, number of text messages sent, and location information from the GPS. Predictive models were built using multiple machine learning techniques - random forest plots, linear regression and gradient boosting, to predict the target scores based on phone usage patterns. Results Of the 17 enrolled participants, 10 provided analyzable data (i.e., had at least 22 target days with data). There was a gradual reduction of passive data generation due to either non-use of the smart phone or due to non-recharging of the device. The mean PANSS score was 80.12 (14.56). BACS scores corresponding to motor speed (token motor task), verbal fluency (category instances, letter fluency), and attention and processing speed (symbol coding) were found to be highly correlated with a composite digital phenotyping marker while scores on the PANSS or CDSS were not. Discussion The study provides a basis for using smartphone-base mobile apps to use as an augmentation within clinical practice to gather further information on patients outside the clinic setting, focusing on their behavior in the ‘real world.’ In particular, the cognitive data derived from the digital device correlated well with rater administered traditional cognitive ratings. Collecting digital data can provide a much needed window into the lives of patients in between normally scheduled visits, while minimizing costs and inconvenience to the patient. Studies with larger sample sizes are required to assess relationships with relapse, rehospitalizations and treatment failure.