The neurobiological underpinnings of negative symptoms in schizophrenia remain unclear. Previous studies have revealed that in schizophrenia, the anticipatory component of the hedonic experience (anticipatory anhedonia, failure to anticipate reward or pleasurable experiences) is more markedly impaired than the consummatory aspect of pleasure (consummatory anhedonia, in the moment experience of pleasure during pleasurable situations). Several neuroimaging focused on reward prediction deficit have shown dysfunctions in the neuronal circuits that sustain these processes in patients, but findings have not been consistent.

The current study aimed at investigating the impairment of reward anticipation in subjects with schizophrenia (SCZ) during the “Monetary Incentive Delay task” (MID task), employing the topographic analysis of event-related potentials (ERPs) with EEG recordings. Furthermore, the associations with negative symptoms and anticipatory and consummatory hedonic experience were investigated.

EEG data were recorded in thirty SCZ and twenty-three matched HC, during the MID task in which reward and loss cues (incentive cues of positive and negative value) of different magnitude, as well as neutral cues were presented. Anticipation and experience of pleasure were measured by the Temporal Experience of Pleasure Scale (TEPS), while negative symptom dimensions by the Schedule for the Deficit Syndrome (SDS). For the EEG data analysis, the topographic analysis of variance (TANOVA) that uses the global field power of difference maps was used to evaluate between-group differences in scalp topography. Correlation analyses between hedonic experience, negative symptoms and ERPs were performed.

The TANOVA interaction effect (group x cue) was significant in the time window between 140.6 and 195.3 msec after cue presentation (p<.05). Post-hoc analysis showed that significant differences in topography were observed for the reward condition (p=.0006) but not for the loss one (p=.6732) between SCZ and HC. Finally, a significant correlation (p<.01) between t-maps values obtained in the same time-frame and the anticipation of pleasure scores was detected, while no significant correlations were found with the experience of pleasure scores or the severity negative symptom.

SCZ are unable to integrate the incentive magnitude and reward value of future events in the context of their ongoing task. Topographic abnormalities in ERP could be traced already during early stages of reward processing and were associated with anticipation of pleasure, but not with the experience of pleasure or the avolition, suggesting that these constructs might be partially separate.

None Declared

Ensuring equitable access to health care is a widely agreed-upon goal in medicine, yet access to care is a multidimensional concept that is difficult to measure. Although frameworks exist to evaluate access to care generally, the concept of “access to genomic medicine” is largely unexplored and a clear framework for studying and addressing major dimensions is lacking.

Comprised of seven clinical genomic research projects, the Clinical Sequencing Evidence-Generating Research consortium (CSER) presented opportunities to examine access to genomic medicine across diverse contexts. CSER emphasized engaging historically underrepresented and/or underserved populations. We used descriptive analysis of CSER participant survey data and qualitative case studies to explore anticipated and encountered access barriers and interventions to address them.

CSER’s enrolled population was largely lower income and racially and ethnically diverse, with many Spanish-preferring individuals. In surveys, less than a fifth (18.7%) of participants reported experiencing barriers to care. However, CSER project case studies revealed a more nuanced picture that highlighted the blurred boundary between access to genomic research and clinical care. Drawing on insights from CSER, we build on an existing framework to characterize the concept and dimensions of access to genomic medicine along with associated measures and improvement strategies.

Our findings support adopting a broad conceptualization of access to care encompassing multiple dimensions, using mixed methods to study access issues, and investing in innovative improvement strategies. This conceptualization may inform clinical translation of other cutting-edge technologies and contribute to the promotion of equitable, effective, and efficient access to genomic medicine.

Despite innovative treatments, the impairment in real-life functioning in subjects with schizophrenia (SCZ) remains an unmet need in the care of these patients. Recently, real-life functioning in SCZ was associated with abnormalities in different electrophysiological indices. It is still not clear whether this relationship is mediated by other variables, and how the combination of different EEG abnormalities influences the complex outcome of schizophrenia.

The purpose of the study was to find EEG patterns which can predict the outcome of schizophrenia and identify recovered patients.

Illness-related and functioning-related variables were measured in 61 SCZ at baseline and after four-years follow-up. EEGs were recorded at the baseline in resting-state condition and during two auditory tasks. We performed Sparse Partial Least Square (SPLS) Regression, using EEG features, age and illness duration to predict clinical and functional features at baseline and follow up. Through a Linear Support Vector Machine (Linear SVM) we used electrophysiological and clinical scores derived from SPLS regression, in order to classify recovered patients at follow-up.

We found one significant latent variable (p<0.01) capturing correlations between independent and dependent variables at follow-up (RHO=0.56). Among individual predictors, age and illness-duration showed the highest scores; however, the score for the combination of the EEG features was higher than all other predictors. Within dependent variables, negative symptoms showed the strongest correlation with predictors. Scores resulting from SPLS Regression classified recovered patients with 90.1% of accuracy.

A combination of electrophysiological markers, age and illness-duration might predict clinical and functional outcome of schizophrenia after 4 years of follow-up.

Different electrophysiological indices have been investigated to identify diagnostic and prognostic markers of schizophrenia (SCZ). However, these indices have limited use in clinical practice, since both specificity and association with illness outcome remain unclear. In recent years, machine learning techniques, through the combination of multidimensional data, have been used to better characterize SCZ and to predict illness course.

The aim of the present study is to identify multimodal electrophysiological biomarkers that could be used in clinical practice in order to improve precision in diagnosis and prognosis of SCZ.

Illness-related and functioning-related variables were measured at baseline in 113 subjects with SCZ and 57 healthy controls (HC), and after four-year follow-up in 61 SCZ. EEGs were recorded at baseline in resting-state condition and during two auditory tasks (MMN-P3a and N100-P3b). Through a Linear Support Vector Machine, using EEG data as predictors, four models were generated in order to classify SCZ and HC. Then, we combined unimodal classifiers’ scores through a stacking procedure. Pearson’s correlations between classifiers score with illness-related and functioning-related variables, at baseline and follow-up, were performed.

Each EEG model produced significant classification (p < 0.05). Global classifier discriminated SCZ from HC with accuracy of 75.4% (p < 0.01). A significant correlation (r=0.40, p=0.002) between the global classifier scores with negative symptoms at follow-up was found. Within negative symptoms, blunted affect showed the strongest correlation.

Abnormalities in electrophysiological indices might be considered trait markers of schizophrenia. Our results suggest that multimodal electrophysiological markers might have prognostic value for negative symptoms.

Substance use disorders are highly prevalent among people with schizophrenia. Dually diagnosed patients present with unfavorable course and poor long-term outcomes. Integrated, motivation-based treatment for both disorders in the same setting is considered the treatment of choice. However, integrated treatment programs are not readily available and effect sizes of the programs are modest.

To evaluate an integrated psychosocial treatment program for people with schizophrenia and substance use disorders in the setting of a community psychiatric hospital.

100 in-patients with schizophrenia and substance use disorders were randomized to Integrated Treatment (IT) or Treatment as usual (TAU). The IT group was initially treated in a specialized open ward; upon discharge they were offered treatment in a specialized out-patient program of the hospital. The TAU group was initially treated in another non-specialized open ward; upon discharge they were offered treatment in the non-specialized out-patient unit of the hospital. TAU included pharmacotherapy, medical treatment, supportive psychotherapy and further aids by nursing staff and social workers. IT included all elements of TAU plus manualized group therapy with motivational interviewing, psychoeducation and cognitive-behavioral approaches. Assessments were performed at baseline and after 3, 6 and 12 months.

The IT group had slightly less drop-outs in the follow-up period (non-significant). The IT group was more satisfied with treatment and they developed a higher motivation to reduce substance use. Both groups succeeded in reducing substance use during follow-up, whereas the IT group did slightly better (non-significant).

Negative symptoms are a core feature of schizophrenia but their pathophysiology remains elusive. They cluster in a motivation-related domain, including apathy, anhedonia, asociality and in an expression-related domain, including alogia and blunted affect.

Our aim was to investigate the different neurobiological underpinnings of the two domains using the brain electrical microstates (MS), which reflect global patterns of functional connectivity with high temporal resolution.

We recorded multichannel resting EEGs in 142 schizophrenia patients (SCZ) and in 64 healthy controls (HC), recruited to the Italian network for research on psychoses study. Four microstates (MS) classes were computed from resting EEG data using the K-Mean clustering algorithm. Pearson's coefficient was used to investigate correlations of microstates measures with negative symptom domains, assessed by the Brief Negative Symptoms Scale (BNSS).

SCZ, in comparison to HC, showed increased contribution and duration of MS-C. Only the avolition domain of BNSS correlated with the contribution and occurrence of MS-A. Within the same domain, anticipatory anhedonia, apathy and asociality, but not consummatory anhedonia, were positively correlated with contribution and occurrence of microstate A. Asociality was also negatively correlated with contribution and occurrence of MS-D.

Our findings support different neurobiological underpinnings of the negative symptom domains, avolition and expressive deficit. Furthermore, our results lend support to the hypothesis that only anticipatory anhedonia is linked to the avolition domain of the negative symptoms. Mixed results in the literature concerning the presence of MS-A and D abnormalities in schizophrenia might be related to the syndrome heterogeneity.

The authors have not supplied their declaration of competing interest.

In subjects with schizophrenia (SCZ), the disorganization factor was found to be a strong predictor of real-life functioning. “Conceptual disorganization” (P2), “difficulties in abstract thinking” (N5) and “poor attention” (G11) are considered core aspects of the disorganization factor, as assessed by PANSS. The overlap of these items with neurocognitive functions is debated and should be further investigated.

Within the Italian network for research on psychoses study, electrophysiological and neurocognitive correlates of the disorganization factor and its component items were investigated.

Resting state EEGs were recorded in 145 stabilized SCZ and 69 matched healthy controls (HC). Spectral amplitude (SAmp) was averaged in nine frequency bands. MATRICS consensus cognitive battery (MCCB) was used for neurocognitive assessment. Band SAmp differences and correlations with psychopathology and MCCB scores were explored by global randomization statistics.

SCZ showed increased delta, theta, and beta1 and decreased alpha2 SAmp. A negative correlation between alpha1 and disorganization was observed in SCZ. At the item level, only N5 showed this correlation. MCCB neurocognitive composite was associated with P2 and N5 but not with alpha1 SAmp.

Our findings suggest an heterogeneity of the disorganization dimension and a partial overlap with neurocognitive domains. The N5, “difficulties in abstract thinking”, had a unique association with alpha1 SAmp, which is thought to be involved in the formation of conceptual maps.

The authors have not supplied their declaration of competing interest.

Disaster Medicine (DM) education for Emergency Medicine (EM) residents is highly variable due to time constraints, competing priorities, and program expertise. The investigators’ aim was to define and prioritize DM core competencies for EM residency programs through consensus opinion of experts and EM professional organization representatives.

Investigators utilized a modified Delphi methodology to generate a recommended, prioritized core curriculum of 40 DM educational topics for EM residencies.

The DM topics recommended and outlined for inclusion in EM residency training included: patient triage in disasters, surge capacity, introduction to disaster nomenclature, blast injuries, hospital disaster mitigation, preparedness, planning and response, hospital response to chemical mass-casualty incident (MCI), decontamination indications and issues, trauma MCI, disaster exercises and training, biological agents, personal protective equipment, and hospital response to radiation MCI.

This expert-consensus-driven, prioritized ranking of DM topics may serve as the core curriculum for US EM residency programs.