Anorexia nervosa (AN) is characterized by sizable, widespread gray matter (GM) reductions in the acutely underweight state. However, evidence for persistent alterations after weight-restoration has been surprisingly scarce despite high relapse rates, frequent transitions to other psychiatric disorders, and generally unfavorable outcome. While most studies investigated brain regions separately (univariate analysis), psychiatric disorders can be conceptualized as brain network disorders characterized by multivariate alterations with only subtle local effects. We tested for persistent multivariate structural brain alterations in weight-restored individuals with a history of AN, investigated their putative biological substrate and relation with 1-year treatment outcome.

We trained machine learning models on regional GM measures to classify healthy controls (HC) (N = 289) from individuals at three stages of AN: underweight patients starting intensive treatment (N = 165, used as baseline), patients after partial weight-restoration (N = 115), and former patients after stable and full weight-restoration (N = 89). Alterations after weight-restoration were related to treatment outcome and characterized both anatomically and functionally.

Patients could be classified from HC when underweight (ROC-AUC = 0.90) but also after partial weight-restoration (ROC-AUC = 0.64). Alterations after partial weight-restoration were more pronounced in patients with worse outcome and were not detected in long-term weight-recovered individuals, i.e. those with favorable outcome. These alterations were more pronounced in regions with greater functional connectivity, not merely explained by body mass index, and even increases in cortical thickness were observed (insula, lateral orbitofrontal, temporal pole).

Analyzing persistent multivariate brain structural alterations after weight-restoration might help to develop personalized interventions after discharge from inpatient treatment.

People with severe mental illness (SMI) have more physical health conditions than the general population, resulting in higher rates of hospitalisations and mortality. In this study, we aimed to determine the rate of emergency and planned physical health hospitalisations in those with SMI, compared to matched comparators, and to investigate how these rates differ by SMI diagnosis.

We used Clinical Practice Research DataLink Gold and Aurum databases to identify 20,668 patients in England diagnosed with SMI between January 2000 and March 2016, with linked hospital records in Hospital Episode Statistics. Patients were matched with up to four patients without SMI. Primary outcomes were emergency and planned physical health admissions. Avoidable (ambulatory care sensitive) admissions and emergency admissions for accidents, injuries and substance misuse were secondary outcomes. We performed negative binomial regression, adjusted for clinical and demographic variables, stratified by SMI diagnosis.

Emergency physical health (aIRR:2.33; 95% CI 2.22–2.46) and avoidable (aIRR:2.88; 95% CI 2.60–3.19) admissions were higher in patients with SMI than comparators. Emergency admission rates did not differ by SMI diagnosis. Planned physical health admissions were lower in schizophrenia (aIRR:0.80; 95% CI 0.72–0.90) and higher in bipolar disorder (aIRR:1.33; 95% CI 1.24–1.43). Accident, injury and substance misuse emergency admissions were particularly high in the year after SMI diagnosis (aIRR: 6.18; 95% CI 5.46–6.98).

We found twice the incidence of emergency physical health admissions in patients with SMI compared to those without SMI. Avoidable admissions were particularly elevated, suggesting interventions in community settings could reduce hospitalisations. Importantly, we found underutilisation of planned inpatient care in patients with schizophrenia. Interventions are required to ensure appropriate healthcare use, and optimal diagnosis and treatment of physical health conditions in people with SMI, to reduce the mortality gap due to physical illness.

Although poverty associated with severe mental illness (SMI) has been documented in many studies, little long-term evidence of social drift exists. This study aimed to unravel the poverty transitions among persons with SMI in a fast change community in China.

Two mental health surveys, using the International Classification of Disease (ICD-10), were conducted in the same six townships of Xinjin county, Chengdu, China in 1994 and 2015. A total of 308 persons with SMI identified in 1994 were followed up in 2015. The profiles of poverty transitions were identified and regression modelling methods were applied to determine the predictive factors of poverty transitions.

The poverty rate of persons with SMI increased from 39.9% to 49.4% in 1994 and 2015. A larger proportion of them had fallen into poverty (27.3%) rather than moved out of it (17.8%). Those persons with SMI who had lost work ability, had physical illness and more severe mental disabilities in 1994, as well as those who had experienced negative changes on these factors were more likely to live in persistent poverty or fall into poverty. Higher education level and medical treatment were major protective factors of falling into poverty.

This study shows long-term evidence on the social drift of persons with SMI during the period of rapid social development in China. Further targeted poverty alleviation interventions should be crucial for improving treatment and mental recovery and alleviating poverty related to SMI.

Reappraisal, an adaptive emotion regulation strategy, is associated with frontal engagement. In internalizing psychopathologies (IPs) such as anxiety and depression frontal activity is atypically reduced suggesting impaired regulation capacity. Yet, successful reappraisal is often demonstrated at the behavioral level. A data-driven approach was used to clarify brain and behavioral relationships in IPs.

During functional magnetic resonance imaging, anxious [general anxiety disorder (n = 43), social anxiety disorder (n = 72)] and depressed (n = 47) patients reappraised negative images to reduce negative affect (‘ReappNeg’) and viewed negative images (‘LookNeg’). After each trial, the affective state was reported. A cut-point (i.e. values <0 based on ΔReappNeg-LookNeg) demarcated successful reappraisers. Neural activity for ReappNeg-LookNeg, derived from 37 regions of interest, was submitted to Principal Component Analysis (PCA) to identify unique components of reappraisal-related brain response. PCA factors, symptom severity, and self-reported habitual reappraisal were submitted to discriminant function analysis and linear regression to examine whether these data predicted successful reappraisal (yes/no) and variance in reappraisal ability.

Most patients (63%) were successful reappraisers according to the behavioral criterion (values<0; ΔReappNeg-LookNeg). Discriminant function analysis was not significant for PCA factors, symptoms, or habitual reappraisal. For regression, more activation in a factor with high loadings for frontal regions predicted better reappraisal facility. Results were not significant for other variables.

At the individual level, more activation in a ‘frontal’ factor corresponded with better reappraisal facility. However, neither brain nor behavioral variables classified successful reappraisal (yes/no). Findings suggest individual differences in regions strongly implicated in reappraisal play a role in on-line reappraisal capability.

Considerable heterogeneity exists in treatment response to first-line posttraumatic stress disorder (PTSD) treatments, such as Cognitive Processing Therapy (CPT). Relatively little is known about the timing of when during a course of care the treatment response becomes apparent. Novel machine learning methods, especially continuously updating prediction models, have the potential to address these gaps in our understanding of response and optimize PTSD treatment.

Using data from a 3-week (n = 362) CPT-based intensive PTSD treatment program (ITP), we explored three methods for generating continuously updating prediction models to predict endpoint PTSD severity. These included Mixed Effects Bayesian Additive Regression Trees (MixedBART), Mixed Effects Random Forest (MERF) machine learning models, and Linear Mixed Effects models (LMM). Models used baseline and self-reported PTSD symptom severity data collected every other day during treatment. We then validated our findings by examining model performances in a separate, equally established, 2-week CPT-based ITP (n = 108).

Results across approaches were very similar and indicated modest prediction accuracy at baseline (R2 ~ 0.18), with increasing accuracy of predictions of final PTSD severity across program timepoints (e.g. mid-program R2 ~ 0.62). Similar findings were obtained when the models were applied to the 2-week ITP. Neither the MERF nor the MixedBART machine learning approach outperformed LMM prediction, though benefits of each may differ based on the application.

Utilizing continuously updating models in PTSD treatments may be beneficial for clinicians in determining whether an individual is responding, and when this determination can be made.

Treatment-resistant schizophrenia (TRS) and non-TRS may be associated with different dopaminergic and glutamatergic regulations. The concept of dysregulated glutamatergic concentrations in specific brain regions remains controversial. Herein, we aimed to assess (i) the distribution of the glutamatergic concentration in the brain, (ii) the association between working memory (WM) differences in TRS and non-TRS patients, and (iii) whether an alteration in the glutamate (Glu) level is associated with WM.

The participants included 38 TRS patients, 35 non-TRS patients, and 19 healthy controls (HCs), all of whom underwent 1.5-Tesla proton magnetic resonance spectroscopy of anterior cingulate cortex (ACC) and medial prefrontal cortex (MPFC). The ratios of glutamatergic neurometabolites to N-acetylaspartate + N-acetyl aspartylglutamate (NAAx) were calculated. Cognitive function was assessed using the Wechsler Adult Intelligence Scales, 4th Edition, which included the working memory index (WMI).

The TRS patients had a higher glutamate + glutamine (Glx)/NAAx ratio compared to the non-TRS patients and HCs in the ACC, but this was not significantly different in the MPFC. WM was negatively correlated with Glx/NAAx in the ACC among the non-TRS patients, but not in the TRS patients or HCs.

Our findings were consistent with most studies indicating that the glutamatergic concentration in the ACC plays important roles in the classification of TRS and cognition. Our results may provide potential evidence for predictors and treatment response biomarkers in TRS patients. Further research is needed to probe the value using the relationship between Glu and WM as a potential prognostic predictor of schizophrenia.

The COVID-19 pandemic highlighted the need for mental health interventions that can be easily disseminated during a crisis. Behavioural activation (BA) is a cost-effective treatment that can be administered by non-specialists; however, it is unclear whether it is still effective during a time of lockdown and social distancing, when opportunities for positive activity are significantly constrained.

Between May and October 2020, we randomised 68 UK participants with mild to moderate low mood to either a 4-week online programme of non-specialist administered BA or to a passive control group. Before and after the intervention, we collected self-report data on mood and COVID-related disruption, as well as measuring emotional cognition as an objective marker of risk for depression.

In comparison to the control group, the BA group showed a significant decrease in depression, anxiety and anhedonia after the intervention, as well as an increase in self-reported activation and social support. Benefits persisted at 1-month follow-up. BA also decreased negative affective bias on several measures of the Facial Emotion Recognition Task and early change in bias was associated with later therapeutic gain. Participants rated the intervention as highly acceptable.

This study highlights the benefits of online BA that can be administered by non-specialists after brief training. These findings can help inform the policy response towards the rising incidence of mental health problems during a crisis situation such as a pandemic. They also highlight the use of objective cognitive markers of risk across different treatment modalities.

The aims were to investigate patterns of labour market integration following an adult diagnosis of attention-deficit/hyperactivity disorder (ADHD) and its relation to sociodemographic factors and comorbid disorders.

Multiple Swedish nationwide registers were used to identify 8045 individuals, aged 20–29, with an incident diagnosis of ADHD 2006–2011. Labour market integration was conceptualized according to the core-peripheral model as a continuum from a strong (core) to a weak (peripheral) connection to the labour market. Sequence analyses categorized clusters of labour market integration, from 1 year before to 5 years after their ADHD diagnosis for individuals diagnosed with ADHD and a matched control group without ADHD. Multinomial logistic regression computed odds ratios (ORs) with 95% confidence intervals (CIs) between sociodemographic factors and comorbid disorders and the identified clusters.

About one-fourth of the young adults diagnosed with ADHD belonged to clusters characterized by a transition to a mainly peripheral labour market position, which was approximately four-times higher compared to controls without ADHD. Foremost, those living in small cities/villages (OR 1.9; CI 1.5–2.2), those having comorbid autism-spectrum disorder (OR 13.7; CI 6.8–27.5) or schizophrenia/psychoses (OR 7.8; CI 3.8–15.9) were associated with a transition towards a peripheral labour market position throughout the study period. Those with a high educational level (OR 0.1; CI 0.1–0.1), and men (OR 0.7; CI 0.6–0.8) were less likely to have a peripheral labour market position.

Young adults diagnosed with ADHD are four-times more likely to be in the peripheral labour market position compared to those without ADHD. To increase labour market participation, special attention is warranted to those with low educational level, those living outside big cities and those with comorbid mental disorders.

To determine, in a general population, how much rates of stress reactions (SR), major depression (MD), alcohol-use disorder (AUD) and drug-use disorder (DUD) increase after the death of close relatives.

SR, MD, AUD, and DUD registrations were assessed from national Swedish registries. From the population followed from 2000 to 2018, those exposed to death of a close relative in 2002–2016 were matched to unexposed controls and analyzed in males and females by a controlled pre-post design using a difference-in-difference method.

Substantial, brief increases in risk for SR and more modest prolonged increases in MD were observed after death of relatives in both men and women greatest with children, followed by spouses, parents, and siblings. Relatively long-lasting modest increases in AUD but not DUD were also observed following death of relatives. The absolute increases for SR and MD were greater in females than males and for AUD greater in males than females. However, logistic regression analyses showed most effects did not differ significantly by sex. Consistently larger increases in disorder risk were seen with the death of younger v. older parents, siblings, and spouses and with accidental v. non-accidental death in children.

Applying a matched cohort design to Swedish population registries, death of close relatives was associated with, and likely caused, substantial increases in rates of SR, MD, and AUD, consistent with smaller prior clinical investigations. Through such registries, we can, in large representative samples, integrate the impact of exposures to selected environmental adversities into disorder risk pathways.

Accumulating evidence suggests that alterations in inflammatory biomarkers are important in depression. However, previous meta-analyses disagree on these associations, and errors in data extraction may account for these discrepancies.

PubMed/MEDLINE, Embase, PsycINFO, and the Cochrane Library were searched from database inception to 14 January 2020. Meta-analyses of observational studies examining the association between depression and levels of tumor necrosis factor-α (TNF-α), interleukin 1-β (IL-1β), interleukin-6 (IL-6), and C-reactive protein (CRP) were eligible. Errors were classified as follows: incorrect sample sizes, incorrectly used standard deviation, incorrect participant inclusion, calculation error, or analysis with insufficient data. We determined their impact on the results after correction thereof.

Errors were noted in 14 of the 15 meta-analyses included. Across 521 primary studies, 118 (22.6%) showed the following errors: incorrect sample sizes (20 studies, 16.9%), incorrect use of standard deviation (35 studies, 29.7%), incorrect participant inclusion (7 studies, 5.9%), calculation errors (33 studies, 28.0%), and analysis with insufficient data (23 studies, 19.5%). After correcting these errors, 11 (29.7%) out of 37 pooled effect sizes changed by a magnitude of more than 0.1, ranging from 0.11 to 1.15. The updated meta-analyses showed that elevated levels of TNF- α, IL-6, CRP, but not IL-1β, are associated with depression.

These findings show that data extraction errors in meta-analyses can impact findings. Efforts to reduce such errors are important in studies of the association between depression and peripheral inflammatory biomarkers, for which high heterogeneity and conflicting results have been continuously reported.

Vitamin D is engaged in various neural processes, with low vitamin D linked to depression and cognitive dysfunction. There are gender differences in depression and vitamin D level. However, the relationship between depression, gender, vitamin D, cognition, and brain function has yet to be determined.

One hundred and twenty-two patients with major depressive disorder (MDD) and 119 healthy controls underwent resting-state functional MRI and fractional amplitude of low-frequency fluctuations (fALFF) was calculated to assess brain function. Serum concentration of vitamin D (SCVD) and cognition (i.e. prospective memory and sustained attention) were also measured.

We found a significant group-by-gender interaction effect on SCVD whereby MDD patients showed a reduction in SCVD relative to controls in females but not males. Concurrently, there was a female-specific association of SCVD with cognition and MDD-related fALFF alterations in widespread brain regions. Remarkably, MDD- and SCVD-related fALFF changes mediated the relation between SCVD and cognition in females.

Apart from providing insights into the neural mechanisms by which low vitamin D contributes to cognitive impairment in MDD in a gender-dependent manner, these findings might have clinical implications for assignment of female patients with MDD and cognitive dysfunction to adjuvant vitamin D supplementation therapy, which may ultimately advance a precision approach to personalized antidepressant choice.