Cognitive Enhancement Therapy Improves Frontolimbic Regulation of Emotion in Alcohol and/or Cannabis Misusing Schizophrenia: A Preliminary Study

Functional connectivity associated with improvement in emotion management after cognitive enhancement therapy in early-course schizophrenia

BACKGROUND

The ability to manage emotions is an important social-cognitive domain impaired in schizophrenia and linked to functional outcome. The goal of our study was to examine the impact of cognitive enhancement therapy (CET) on the ability to manage emotions and brain functional connectivity in early-course schizophrenia.

METHODS

Participants were randomly assigned to CET (n = 55) or an enriched supportive therapy (EST) control group (n = 45). The resting-state functional magnetic resonance imaging scans and measures of emotion management performances were collected at baseline, 9, and 18 months follow-up. The final sample consisted of 37 CET and 25 EST participants, including 19 CET and 12 EST participants with imaging data. Linear mixed-effects models investigated the impact of treatment on emotion management and functional connectivity from the amygdala to ventrolateral and dorsolateral prefrontal cortex (dlPFC).

RESULTS

The CET group showed significant improvement over time in emotion management compared to EST. Neither functional connectivity changes nor main group differences were observed following treatment. However, a significant between-group interaction showed that improved emotion management ability was associated with increased functional connectivity between the left amygdala and the left dlPFC in the CET group exclusively.

CONCLUSION

Our results replicate the previous work demonstrating that CET is effective at improving some aspects of social cognition in schizophrenia. We found evidence that improvement in emotion management may be associated with a change in amygdala-dlPFC connectivity. This fronto-limbic circuit may provide a mechanistic link between the biology of emotion management processes that can be enhanced in individuals with schizophrenia.

Detection of functional and structural brain alterations in female schizophrenia using elastic net logistic regression

Neuroimaging technique is a powerful tool to characterize the abnormality of brain networks in schizophrenia. However, the neurophysiological substrate of schizophrenia is still unclear. Here we investigated the patterns of brain functional and structural changes in female patients with schizophrenia using elastic net logistic regression analysis of resting-state functional magnetic resonance imaging data. Data from 52 participants (25 female schizophrenia patients and 27 healthy controls) were obtained. Using an elastic net penalty, the brain regions most relevant to schizophrenia pathology were defined in the models using the amplitude of low-frequency fluctuations (ALFF) and gray matter, respectively. The receiver operating characteristic analysis showed reliable classification accuracy with 85.7% in ALFF analysis, and 77.1% in gray matter analysis. Notably, our results showed eight common regions between the ALFF and gray matter analyses, including the Frontal-Inf-Orb-R, Rolandic-Oper-R, Olfactory-R, Angular-L, Precuneus-L, Precuenus-R, Heschl-L, and Temporal-Pole-Mid-R. In addition, the severity of symptoms was found positively associated with the ALFF within the Rolandic-Oper-R and Frontal-Inf-Orb-R. Our findings indicated that elastic net logistic regression could be a useful tool to identify the characteristics of schizophrenia -related brain deterioration, which provides novel insights into schizophrenia diagnosis and prediction.

Psychosocial interventions for people with both severe mental illness and substance misuse

BACKGROUND

Even low levels of substance misuse by people with a severe mental illness can have detrimental effects.

OBJECTIVES

To assess the effects of psychosocial interventions for reduction in substance use in people with a serious mental illness compared with standard care.

SEARCH METHODS

The Information Specialist of the Cochrane Schizophrenia Group (CSG) searched the CSG Trials Register (2 May 2018), which is based on regular searches of major medical and scientific databases.

SELECTION CRITERIA

We included all randomised controlled trials (RCTs) comparing psychosocial interventions for substance misuse with standard care in people with serious mental illness.

DATA COLLECTION AND ANALYSIS

Review authors independently selected studies, extracted data and appraised study quality. For binary outcomes, we calculated standard estimates of risk ratio (RR) and their 95% confidence intervals (CIs) on an intention-to-treat basis. For continuous outcomes, we calculated the mean difference (MD) between groups. Where meta-analyses were possible, we pooled data using a random-effects model. Using the GRADE approach, we identified seven patient-centred outcomes and assessed the quality of evidence for these within each comparison.

MAIN RESULTS

Our review now includes 41 trials with a total of 4024 participants. We have identified nine comparisons within the included trials and present a summary of our main findings for seven of these below. We were unable to summarise many findings due to skewed data or because trials did not measure the outcome of interest. In general, evidence was rated as low- or very-low quality due to high or unclear risks of bias because of poor trial methods, or inadequately reported methods, and imprecision due to small sample sizes, low event rates and wide confidence intervals. 1. Integrated models of care versus standard care (36 months) No clear differences were found between treatment groups for loss to treatment (RR 1.09, 95% CI 0.82 to 1.45; participants = 603; studies = 3; low-quality evidence), death (RR 1.18, 95% CI 0.39 to 3.57; participants = 421; studies = 2; low-quality evidence), alcohol use (RR 1.15, 95% CI 0.84 to 1.56; participants = 143; studies = 1; low-quality evidence), substance use (drug) (RR 0.89, 95% CI 0.63 to 1.25; participants = 85; studies = 1; low-quality evidence), global assessment of functioning (GAF) scores (MD 0.40, 95% CI -2.47 to 3.27; participants = 170; studies = 1; low-quality evidence), or general life satisfaction (QOLI) scores (MD 0.10, 95% CI -0.18 to 0.38; participants = 373; studies = 2; moderate-quality evidence). 2. Non-integrated models of care versus standard care There was no clear difference between treatment groups for numbers lost to treatment at 12 months (RR 1.21, 95% CI 0.73 to 1.99; participants = 134; studies = 3; very low-quality evidence). 3. Cognitive behavioural therapy (CBT) versus standard care There was no clear difference between treatment groups for numbers lost to treatment at three months (RR 1.12, 95% CI 0.44 to 2.86; participants = 152; studies = 2; low-quality evidence), cannabis use at six months (RR 1.30, 95% CI 0.79 to 2.15; participants = 47; studies = 1; very low-quality evidence) or mental state insight (IS) scores by three months (MD 0.52, 95% CI -0.78 to 1.82; participants = 105; studies = 1; low-quality evidence). 4. Contingency management versus standard care We found no clear differences between treatment groups for numbers lost to treatment at three months (RR 1.55, 95% CI 1.13 to 2.11; participants = 255; studies = 2; moderate-quality evidence), number of stimulant positive urine tests at six months (RR 0.83, 95% CI 0.65 to 1.06; participants = 176; studies = 1) or hospitalisations (RR 0.21, 95% CI 0.05 to 0.93; participants = 176; studies = 1); both low-quality evidence. 5. Motivational interviewing (MI) versus standard care We found no clear differences between treatment groups for numbers lost to treatment at six months (RR 1.71, 95% CI 0.63 to 4.64; participants = 62; studies = 1). A clear difference, favouring MI, was observed for abstaining from alcohol (RR 0.36, 95% CI 0.17 to 0.75; participants = 28; studies = 1) but not other substances (MD -0.07, 95% CI -0.56 to 0.42; participants = 89; studies = 1), and no differences were observed in mental state general severity (SCL-90-R) scores (MD -0.19, 95% CI -0.59 to 0.21; participants = 30; studies = 1). All very low-quality evidence. 6. Skills training versus standard care At 12 months, there were no clear differences between treatment groups for numbers lost to treatment (RR 1.42, 95% CI 0.20 to 10.10; participants = 122; studies = 3) or death (RR 0.15, 95% CI 0.02 to 1.42; participants = 121; studies = 1). Very low-quality, and low-quality evidence, respectively. 7. CBT + MI versus standard care At 12 months, there was no clear difference between treatment groups for numbers lost to treatment (RR 0.99, 95% CI 0.62 to 1.59; participants = 327; studies = 1; low-quality evidence), number of deaths (RR 0.60, 95% CI 0.20 to 1.76; participants = 603; studies = 4; low-quality evidence), relapse (RR 0.50, 95% CI 0.24 to 1.04; participants = 36; studies = 1; very low-quality evidence), or GAF scores (MD 1.24, 95% CI -1.86 to 4.34; participants = 445; studies = 4; very low-quality evidence). There was also no clear difference in reduction of drug use by six months (MD 0.19, 95% CI -0.22 to 0.60; participants = 119; studies = 1; low-quality evidence).

AUTHORS' CONCLUSIONS

We included 41 RCTs but were unable to use much data for analyses. There is currently no high-quality evidence to support any one psychosocial treatment over standard care for important outcomes such as remaining in treatment, reduction in substance use or improving mental or global state in people with serious mental illnesses and substance misuse. Furthermore, methodological difficulties exist which hinder pooling and interpreting results. Further high-quality trials are required which address these concerns and improve the evidence in this important area.

A Roadmap for Integrating Neuroscience Into Addiction Treatment: A Consensus of the Neuroscience Interest Group of the International Society of Addiction Medicine

Although there is general consensus that altered brain structure and function underpins addictive disorders, clinicians working in addiction treatment rarely incorporate neuroscience-informed approaches into their practice. We recently launched the Neuroscience Interest Group within the International Society of Addiction Medicine (ISAM-NIG) to promote initiatives to bridge this gap. This article summarizes the ISAM-NIG key priorities and strategies to achieve implementation of addiction neuroscience knowledge and tools for the assessment and treatment of substance use disorders. We cover two assessment areas: cognitive assessment and neuroimaging, and two interventional areas: cognitive training/remediation and neuromodulation, where we identify key challenges and proposed solutions. We reason that incorporating cognitive assessment into clinical settings requires the identification of constructs that predict meaningful clinical outcomes. Other requirements are the development of measures that are easily-administered, reliable, and ecologically-valid. Translation of neuroimaging techniques requires the development of diagnostic and prognostic biomarkers and testing the cost-effectiveness of these biomarkers in individualized prediction algorithms for relapse prevention and treatment selection. Integration of cognitive assessments with neuroimaging can provide multilevel targets including neural, cognitive, and behavioral outcomes for neuroscience-informed interventions. Application of neuroscience-informed interventions including cognitive training/remediation and neuromodulation requires clear pathways to design treatments based on multilevel targets, additional evidence from randomized trials and subsequent clinical implementation, including evaluation of cost-effectiveness. We propose to address these challenges by promoting international collaboration between researchers and clinicians, developing harmonized protocols and data management systems, and prioritizing multi-site research that focuses on improving clinical outcomes.

Integration of Nanobots Into Neural Circuits As a Future Therapy for Treating Neurodegenerative Disorders

Recent neuroscientific research demonstrates that the human brain is becoming altered by technological devices. Improvements in biotechnologies and computer based technologies are now increasing the likelihood for the development of brain augmentation devices in the next 20 years. We have developed the idea of an “Endomyccorhizae like interface” (ELI) nanocognitive device as a new kind of future neuroprosthetic which aims to facilitate neuronal network properties in individuals with neurodegenerative disorders. The design of our ELI may overcome the problems of invasive neuroprosthetics, post-operative inflammation, and infection and neuroprosthetic degradation. The method in which our ELI is connected and integrated to neuronal networks is based on a mechanism similar to endomyccorhizae which is the oldest and most widespread form of plant symbiosis. We propose that the principle of Endomyccorhizae could be relevant for developing a crossing point between the ELI and neuronal networks. Similar to endomyccorhizae the ELI will be designed to form webs, each of which connects multiple neurons together. The ELI will function to sense action potentials and deliver it to the neurons it connects to. This is expected to compensate for neuronal loss in some neurodegenerative disorders, such as Alzheimer's disease and Parkinson's disease.