Reward processing has been proposed to underpin the atypical social feature of autism spectrum disorder (ASD). However, previous neuroimaging studies have yielded inconsistent results regarding the specificity of atypicalities for social reward processing in ASD.

Utilising a large sample, we aimed to assess reward processing in response to reward type (social, monetary) and reward phase (anticipation, delivery) in ASD.

Functional magnetic resonance imaging during social and monetary reward anticipation and delivery was performed in 212 individuals with ASD (7.6–30.6 years of age) and 181 typically developing participants (7.6–30.8 years of age).

Across social and monetary reward anticipation, whole-brain analyses showed hypoactivation of the right ventral striatum in participants with ASD compared with typically developing participants. Further, region of interest analysis across both reward types yielded ASD-related hypoactivation in both the left and right ventral striatum. Across delivery of social and monetary reward, hyperactivation of the ventral striatum in individuals with ASD did not survive correction for multiple comparisons. Dimensional analyses of autism and attention-deficit hyperactivity disorder (ADHD) scores were not significant. In categorical analyses, post hoc comparisons showed that ASD effects were most pronounced in participants with ASD without co-occurring ADHD.

Our results do not support current theories linking atypical social interaction in ASD to specific alterations in social reward processing. Instead, they point towards a generalised hypoactivity of ventral striatum in ASD during anticipation of both social and monetary rewards. We suggest this indicates attenuated reward seeking in ASD independent of social content and that elevated ADHD symptoms may attenuate altered reward seeking in ASD.

To evaluate the construct validity of the NIH Toolbox Cognitive Battery (NIH TB-CB) in the healthy oldest-old (85+ years old).

Our sample from the McKnight Brain Aging Registry consists of 179 individuals, 85 to 99 years of age, screened for memory, neurological, and psychiatric disorders. Using previous research methods on a sample of 85 + y/o adults, we conducted confirmatory factor analyses on models of NIH TB-CB and same domain standard neuropsychological measures. We hypothesized the five-factor model (Reading, Vocabulary, Memory, Working Memory, and Executive/Speed) would have the best fit, consistent with younger populations. We assessed confirmatory and discriminant validity. We also evaluated demographic and computer use predictors of NIH TB-CB composite scores.

Findings suggest the six-factor model (Vocabulary, Reading, Memory, Working Memory, Executive, and Speed) had a better fit than alternative models. NIH TB-CB tests had good convergent and discriminant validity, though tests in the executive functioning domain had high inter-correlations with other cognitive domains. Computer use was strongly associated with higher NIH TB-CB overall and fluid cognition composite scores.

The NIH TB-CB is a valid assessment for the oldest-old samples, with relatively weak validity in the domain of executive functioning. Computer use’s impact on composite scores could be due to the executive demands of learning to use a tablet. Strong relationships of executive function with other cognitive domains could be due to cognitive dedifferentiation. Overall, the NIH TB-CB could be useful for testing cognition in the oldest-old and the impact of aging on cognition in older populations.

After reading this article, it is hoped that readers will be able to:

1. (1) Conceptualise why surveys can be useful in cognitive behavioural therapy.

2. (2) Implement collaborative and individualised survey design, delivery and feedback as part of a CBT intervention.

Smartphones have the potential for capturing subtle changes in cognition that characterize preclinical Alzheimer’s disease (AD) in older adults. The Ambulatory Research in Cognition (ARC) smartphone application is based on principles from ecological momentary assessment (EMA) and administers brief tests of associative memory, processing speed, and working memory up to 4 times per day over 7 consecutive days. ARC was designed to be administered unsupervised using participants’ personal devices in their everyday environments.

We evaluated the reliability and validity of ARC in a sample of 268 cognitively normal older adults (ages 65–97 years) and 22 individuals with very mild dementia (ages 61–88 years). Participants completed at least one 7-day cycle of ARC testing and conventional cognitive assessments; most also completed cerebrospinal fluid, amyloid and tau positron emission tomography, and structural magnetic resonance imaging studies.

First, ARC tasks were reliable as between-person reliability across the 7-day cycle and test-retest reliabilities at 6-month and 1-year follow-ups all exceeded 0.85. Second, ARC demonstrated construct validity as evidenced by correlations with conventional cognitive measures (r = 0.53 between composite scores). Third, ARC measures correlated with AD biomarker burden at baseline to a similar degree as conventional cognitive measures. Finally, the intensive 7-day cycle indicated that ARC was feasible (86.50% approached chose to enroll), well tolerated (80.42% adherence, 4.83% dropout), and was rated favorably by older adult participants.

Overall, the results suggest that ARC is reliable and valid and represents a feasible tool for assessing cognitive changes associated with the earliest stages of AD.

1. (1) To recognise common misconceptions about trauma-focused CBT for PTSD and the evidence against them.

2. (2) To widen understanding of the application of cognitive therapy for PTSD (CT-PTSD) to a broad range of presentations.

3. (3) To increase confidence in the formulation-driven, flexible, active and creative delivery of CT-PTSD.

Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA).

A 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample.

In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors.

Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

Clinically significant weight gain (CSWG) is associated with increased morbidity and mortality. This study describes CSWG and comorbidities observed in patients with bipolar I disorder (BD-I) and schizophrenia (SZ) after initiating select second-generation antipsychotics (SGAs).

Percent change in weight, CSWG (=7% weight increase), and incident comorbidities within 12 months of treatment were assessed among patients initiating oral SGAs of moderate-to-high weight gain risk using medical records/claims (OM1 Real-World Data Cloud; January 2013-February 2020). Oral SGAs included clozapine (SZ), iloperidone (SZ), paliperidone (SZ), olanzapine, olanzapine/fluoxetine (BD-I), quetiapine, and risperidone. Outcomes were stratified by baseline body mass index and reported descriptively.

Among patients with BD-I (N = 9142) and SZ (N = 8174), approximately three-quarters were overweight/obese at baseline. During treatment (mean duration = 30 weeks), average percent weight increase was 3.7% (BD-I) and 3.3% (SZ). Average percent weight increase was highest for underweight/normal weight patients (BD-I = 5.5%; SZ = 4.8%), followed by overweight (BD-I = 3.8%; SZ = 3.4%) and obese patients (BD-I = 2.7%; SZ = 2.3%). Within 3 months of treatment, 12% of all patients experienced CSWG. A total of 11.3% (BD-I) and 14.7% (SZ) of patients developed coronary artery disease, hypertension, dyslipidemia, or type 2 diabetes within 12 months of treatment; development of comorbidities was highest among overweight/obese patients and those with CSWG.

Patients who were underweight/normal weight at baseline had the greatest percent change in weight during treatment. Increased comorbidities were observed within 12 months of treatment, specifically among overweight/obese patients and those with CSWG. The magnitude of weight gain and development of comorbidities were similar for patients with BD-I and SZ.

Alkermes, Inc.

Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan.

This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018–2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample.

32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables.

Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.

Perceived discrimination is associated with worse mental health. Few studies have assessed whether perceived discrimination (i) is associated with the risk of psychotic disorders and (ii) contributes to an increased risk among minority ethnic groups relative to the ethnic majority.

We used data from the European Network of National Schizophrenia Networks Studying Gene-Environment Interactions Work Package 2, a population-based case−control study of incident psychotic disorders in 17 catchment sites across six countries. We calculated odds ratios (OR) and 95% confidence intervals (95% CI) for the associations between perceived discrimination and psychosis using mixed-effects logistic regression models. We used stratified and mediation analyses to explore differences for minority ethnic groups.

Reporting any perceived experience of major discrimination (e.g. unfair treatment by police, not getting hired) was higher in cases than controls (41.8% v. 34.2%). Pervasive experiences of discrimination (≥3 types) were also higher in cases than controls (11.3% v. 5.5%). In fully adjusted models, the odds of psychosis were 1.20 (95% CI 0.91–1.59) for any discrimination and 1.79 (95% CI 1.19–1.59) for pervasive discrimination compared with no discrimination. In stratified analyses, the magnitude of association for pervasive experiences of discrimination appeared stronger for minority ethnic groups (OR = 1.73, 95% CI 1.12–2.68) than the ethnic majority (OR = 1.42, 95% CI 0.65–3.10). In exploratory mediation analysis, pervasive discrimination minimally explained excess risk among minority ethnic groups (5.1%).

Pervasive experiences of discrimination are associated with slightly increased odds of psychotic disorders and may minimally help explain excess risk for minority ethnic groups.

1. (1) To recognise PTSD following admissions to intensive care units (ICUs).

2. (2) To understand how the ICU experience can lead to PTSD development.

3. (3) To understand how Ehlers and Clark’s (2000) cognitive model of PTSD can be applied to post-ICU PTSD.

4. (4) To be able to apply cognitive therapy for PTSD to patients with post-ICU PTSD.

Environmental risk factors for dementia are poorly understood. Aluminium and fluorine in drinking water have been linked with dementia but uncertainties remain about this relationship.

In the largest longitudinal study in this context, we set out to explore the individual effect of aluminium and fluoride in drinking water on dementia risk and, as fluorine can increase absorption of aluminium, we also examine any synergistic influence on dementia.

We used Cox models to investigate the association between mean aluminium and fluoride levels in drinking water at their residential location (collected 2005–2012 by the Drinking Water Quality Regulator for Scotland) with dementia in members of the Scottish Mental Survey 1932 cohort who were alive in 2005.

A total of 1972 out of 6990 individuals developed dementia by the linkage date in 2012. Dementia risk was raised with increasing mean aluminium levels in women (hazard ratio per s.d. increase 1.09, 95% CI 1.03–1.15, P < 0.001) and men (1.12, 95% CI 1.03–1.21, P = 0.004). A dose-response pattern of association was observed between mean fluoride levels and dementia in women (1.34, 95% CI 1.28–1.41, P < 0.001) and men (1.30, 95% CI 1.22–1.39, P < 0.001), with dementia risk more than doubled in the highest quartile compared with the lowest. There was no statistical interaction between aluminium and fluoride levels in relation with dementia.

Higher levels of aluminium and fluoride were related to dementia risk in a population of men and women who consumed relatively low drinking-water levels of both.