Developing a cognitive dysfunction risk score for use with opioid-dependent persons in drug treatment

Identification of Specific Abnormal Brain Functional Activity and Connectivity in Cancer Pain Patients: A Preliminary Resting-State fMRI Study

Objective

This study investigates the differences in brain functional activity and connectivity patterns between Cancer Pain (CP) patients and Healthy Controls (HCs) using resting-state functional magnetic resonance imaging (rs-fMRI) to identify potential neuroimaging biomarkers.

Methods

This study collected rs-fMRI data from 25 CP patients and 25 hCs, processed the functional MRI images, and calculated metrics such as amplitude of low-frequency fluctuation (ALFF), Regional Homogeneity (ReHo), and FC. Through statistical analysis, differences in brain functional activity and connectivity between the cancer pain group and the healthy control group were investigated, followed by machine learning classification.

Results

The results showed that compared to the normal group, reductions in the ALFF were primarily observed in the bilateral inferior temporal gyrus; ReHo increased in the right middle temporal gyrus and decreased in the left cerebellum Crus2. Using the statistically different brain areas as seed points to construct FC networks and performing statistical analysis, it was found that the regions with decreased FC connection strength between the cancer pain group and the normal group were mainly in the prefrontal cortex (PFC), the postcentral gyrus of the parietal lobe, and the cerebellum. Statistical results indicated that there was no significant correlation between pain scores (Numeric Rating Scale, NRS) and neuroimaging metrics. According to the machine learning classification, the FC features of the right precentral gyrus achieved higher diagnostic efficacy (AUC = 0.804) compared to ALFF and ReHo in distinguishing between CP patients and HCs.

Conclusion

Brain activity and FC in CP patients show abnormalities in regions such as the inferior temporal gyrus, middle temporal gyrus, prefrontal cortex, parietal lobe, and cerebellum. These areas may be interconnected through neural networks and jointly participate in functions related to pain perception, emotion regulation, cognitive processing, and motor control. However, the precise connections and mechanisms of action require further research.

Social and cognitive determinants of medications for opioid use disorder outcomes: A systematic review using a social determinants of health framework

Disparities exist in the engagement and success of individuals seeking medications for opioid use disorder (MOUD) treatment. Existing work suggests that individual-level factors such as cognitive functioning influence MOUD treatment, less is known about the role of environmental factors beyond the individual such as social determinants of health (SDOH). The aim of this systematic review was to summarize the literature of neuropsychological assessment in the context of MOUD treatment using an SDOH framework. We included peer-reviewed articles based in the United States and published in the English language that evaluated neuropsychological assessment on MOUD treatment outcomes. Three electronic databases were searched from January 2022 to September 2023 without restricting the date of publication for article inclusion. We identified 34 empirical articles that met inclusion criteria, the majority being nonrandomized clinical trials. Few studies examined differences in neuropsychological performance over time or in response to an adjunct intervention. Findings comparing cognitive functioning across MOUD and comparisons groups were mixed, as were findings from the studies that examined changes in cognitive functioning over time. Factors represented from the SDOH framework included educational attainment, premorbid intellectual functioning, and employment status. Neuropsychological domains and type of assessments varied, as did inclusion/exclusion and demographic characteristics. Existing literature is mixed on whether neuropsychological deficits in individuals with OUD are amenable to treatment, particularly among populations disproportionally disadvantaged by SDOH. More research is needed on the SDOH and other contextual factors that influence cognitive factors and MOUD treatment engagement and success.

Cognitive Alterations in Addictive Disorders: A Translational Approach

The cognitive decline in people with substance use disorders is well known and can be found during both the dependence and drug abstinence phases. At the clinical level, cognitive decline impairs the response to addiction treatment and increases dropout rates. It can be irreversible, even after the end of drug abuse consumption. Improving our understanding of the molecular and cellular alterations associated with cognitive decline could be essential to developing specific therapeutic strategies for its treatment. Developing animal models to simulate drug abuse-induced learning and memory alterations is critical to continue exploring this clinical situation. The main aim of this review is to summarize the most recent evidence on cognitive impairment and the associated biological markers in patients addicted to some of the most consumed drugs of abuse and in animal models simulating this clinical situation. The available information suggests the need to develop more studies to further explore the molecular alterations associated with cognitive impairment, with the ultimate goal of developing new potential therapeutic strategies.

Multi-domain prognostic models used in middle-aged adults without known cognitive impairment for predicting subsequent dementia

BACKGROUND

Dementia, a global health priority, has no current cure. Around 50 million people worldwide currently live with dementia, and this number is expected to treble by 2050. Some health conditions and lifestyle behaviours can increase or decrease the risk of dementia and are known as 'predictors'. Prognostic models combine such predictors to measure the risk of future dementia. Models that can accurately predict future dementia would help clinicians select high-risk adults in middle age and implement targeted risk reduction.

OBJECTIVES

Our primary objective was to identify multi-domain prognostic models used in middle-aged adults (aged 45 to 65 years) for predicting dementia or cognitive impairment. Eligible multi-domain prognostic models involved two or more of the modifiable dementia predictors identified in a 2020 Lancet Commission report and a 2019 World Health Organization (WHO) report (less education, hearing loss, traumatic brain injury, hypertension, excessive alcohol intake, obesity, smoking, depression, social isolation, physical inactivity, diabetes mellitus, air pollution, poor diet, and cognitive inactivity). Our secondary objectives were to summarise the prognostic models, to appraise their predictive accuracy (discrimination and calibration) as reported in the development and validation studies, and to identify the implications of using dementia prognostic models for the management of people at a higher risk for future dementia.

SEARCH METHODS

We searched MEDLINE, Embase, PsycINFO, CINAHL, and ISI Web of Science Core Collection from inception until 6 June 2022. We performed forwards and backwards citation tracking of included studies using the Web of Science platform.  SELECTION CRITERIA: We included development and validation studies of multi-domain prognostic models. The minimum eligible follow-up was five years. Our primary outcome was an incident clinical diagnosis of dementia based on validated diagnostic criteria, and our secondary outcome was dementia or cognitive impairment determined by any other method.

DATA COLLECTION AND ANALYSIS

Two review authors independently screened the references, extracted data using a template based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS), and assessed risk of bias and applicability of included studies using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). We synthesised the C-statistics of models that had been externally validated in at least three comparable studies.  MAIN RESULTS: We identified 20 eligible studies; eight were development studies and 12 were validation studies. There were 14 unique prognostic models: seven models with validation studies and seven models with development-only studies. The models included a median of nine predictors (range 6 to 34); the median number of modifiable predictors was five (range 2 to 11). The most common modifiable predictors in externally validated models were diabetes, hypertension, smoking, physical activity, and obesity. In development-only models, the most common modifiable predictors were obesity, diabetes, hypertension, and smoking. No models included hearing loss or air pollution as predictors. Nineteen studies had a high risk of bias according to the PROBAST assessment, mainly because of inappropriate analysis methods, particularly lack of reported calibration measures. Applicability concerns were low for 12 studies, as their population, predictors, and outcomes were consistent with those of interest for this review. Applicability concerns were high for nine studies, as they lacked baseline cognitive screening or excluded an age group within the range of 45 to 65 years. Only one model, Cardiovascular Risk Factors, Ageing, and Dementia (CAIDE), had been externally validated in multiple studies, allowing for meta-analysis. The CAIDE model included eight predictors (four modifiable predictors): age, education, sex, systolic blood pressure, body mass index (BMI), total cholesterol, physical activity and APOEƐ4 status. Overall, our confidence in the prediction accuracy of CAIDE was very low; our main reasons for downgrading the certainty of the evidence were high risk of bias across all the studies, high concern of applicability, non-overlapping confidence intervals (CIs), and a high degree of heterogeneity. The summary C-statistic was 0.71 (95% CI 0.66 to 0.76; 3 studies; very low-certainty evidence) for the incident clinical diagnosis of dementia, and 0.67 (95% CI 0.61 to 0.73; 3 studies; very low-certainty evidence) for dementia or cognitive impairment based on cognitive scores. Meta-analysis of calibration measures was not possible, as few studies provided these data.

AUTHORS' CONCLUSIONS

We identified 14 unique multi-domain prognostic models used in middle-aged adults for predicting subsequent dementia. Diabetes, hypertension, obesity, and smoking were the most common modifiable risk factors used as predictors in the models. We performed meta-analyses of C-statistics for one model (CAIDE), but the summary values were unreliable. Owing to lack of data, we were unable to meta-analyse the calibration measures of CAIDE. This review highlights the need for further robust external validations of multi-domain prognostic models for predicting future risk of dementia in middle-aged adults.

Assessing Clinically Significant Cognitive Impairment Using the NIH Toolbox in Individuals with Co-occurring Serious Mental Illness and Alcohol Use Disorder

OBJECTIVE

Serious mental illnesses (SMI) and alcohol use disorder (AUD) co-occurrence (SMI-AUD) is common, yet little is known about the prevalence and risk factors of cognitive impairment for this population. We used the National Institutes of Health (NIH) Toolbox to identify clinically significant cognitive impairment (CSCI), describe the cognitive profile, and investigate whether psychiatric and AUD severity measures are associated with CSCI in individuals with SMI-AUD.

METHODS

CSCI was defined as 2 or more fully corrected fluid subtest T scores below a set threshold based on an individual's crystalized composite score. Psychiatric severity measures included the Structured Clinical Interview for DSM-V (SCID-5) for SMI diagnosis and the Positive and Negative Syndrome Scale. AUD severity measures included the SCID-5 for AUD symptom severity score, years of alcohol use, and urine ethyl glucuronide levels. A multivariable logistic regression was used to investigate the adjusted effects of each variable on the probability of CSCI.

RESULTS

Forty-one percent (N = 55/135) of our sample had CSCI compared with the base rate of 15% from the NIH Toolbox normative sample. Subtests measuring executive function most frequently contributed to meeting criteria for CSCI (Flanker and Dimensional Change Card Sort). A history of head injury ( P = 0.033), increased AUD symptom severity score ( P = 0.007) and increased negative symptom severity score ( P = 0.027) were associated with CSCI.

CONCLUSIONS

Cognition should be considered in the treatment of people with SMI-AUD, particularly in those with history of brain injury, higher AUD symptom severity, and/or negative symptom severity.

Adapting behavioural interventions to compensate for cognitive dysfunction in persons with opioid use disorder

Treatment for opioid use disorder (OUD) is often in the context of biobehavioural interventions, consisting of medication for OUD (for example, methadone and buprenorphine), which is accompanied by psychoeducation and/or behavioural therapies. Patients with OUD often display weaknesses in cognitive function that may impact the efficacy of such behavioural interventions. A review of the literature was conducted to: (1) describe common cognitive dysfunction profiles among patients with OUD, (2) outline intervention approaches for patients with OUD, (3) consider the cognitive demands that interventions place on patients with OUD and (4) identify potential accommodation strategies that may be used to optimise treatment outcomes. Cognitive profiles of patients with OUD often include weaknesses in executive function, attention, memory and information processing. Behavioural interventions require the patients' ability to learn, understand and remember information (placing specific cognitive demands on patients). Accommodation strategies are, therefore, needed for patients with challenges in one or more of these areas. Research on accommodation strategies for patients with OUD is very limited. We applied research from populations with similar cognitive profiles to form a comprehensive collection of potential strategies to compensate for cognitive dysfunction among patients with OUD. The cognitive profiles and accommodation strategies included in this review are intended to inform future intervention research aimed at improving outcomes among patients with OUD.