Enduring ethnic inequalities exist in mental healthcare. The COVID-19 pandemic has widened these.

To explore stakeholder perspectives on how the COVID-19 pandemic has increased ethnic inequalities in mental healthcare.

A qualitative interview study of four areas in England with 34 patients, 15 carers and 39 mental health professionals from National Health Service (NHS) and community organisations (July 2021 to July 2022). Framework analysis was used to develop a logic model of inter-relationships between pre-pandemic barriers and COVID-19 impacts.

Impacts were largely similar across sites, with some small variations (e.g. positive service impacts of higher ethnic diversity in area 2). Pre-pandemic barriers at individual level included mistrust and thus avoidance of services and at a service level included the dominance of a monocultural model, leading to poor communication, disengagement and alienation. During the pandemic remote service delivery, closure of community organisations and media scapegoating exacerbated existing barriers by worsening alienation and communication barriers, fuelling prejudice and division, and increasing mistrust in services. Some minority ethnic patients reported positive developments, experiencing empowerment through self-determination and creative activities.

During the COVID-19 pandemic some patients showed resilience and developed adaptations that could be nurtured by services. However, there has been a reduction in the availability of group-specific NHS and third-sector services in the community, exacerbating pre-existing barriers. As these developments are likely to have long-term consequences for minority ethnic groups’ engagement with mental healthcare, they need to be addressed as a priority by the NHS and its partners.

International guidelines present overall symptom severity as the key dimension for clinical characterisation of major depressive disorder (MDD). However, differences may reside within severity levels related to how symptoms interact in an individual patient, called symptom dynamics.

To investigate these individual differences by estimating the proportion of patients that display differences in their symptom dynamics while sharing the same overall symptom severity.

Participants with MDD (n = 73; mean age 34.6 years, s.d. = 13.1; 56.2% female) rated their baseline symptom severity using the Inventory for Depressive Symptomatology Self-Report (IDS-SR). Momentary indicators for depressive symptoms were then collected through ecological momentary assessments five times per day for 28 days; 8395 observations were conducted (average per person: 115; s.d. = 16.8). Each participant's symptom dynamics were estimated using person-specific dynamic network models. Individual differences in these symptom relationship patterns in groups of participants sharing the same symptom severity levels were estimated using individual network invariance tests. Subsequently, the overall proportion of participants that displayed differential symptom dynamics while sharing the same symptom severity was calculated. A supplementary simulation study was conducted to investigate the accuracy of our methodology against false-positive results.

Differential symptom dynamics were identified across 63.0% (95% bootstrapped CI 41.0–82.1) of participants within the same severity group. The average false detection of individual differences was 2.2%.

The majority of participants within the same depressive symptom severity group displayed differential symptom dynamics. Examining symptom dynamics provides information about person-specific psychopathological expression beyond severity levels by revealing how symptoms aggravate each other over time. These results suggest that symptom dynamics may be a promising new dimension for clinical characterisation, warranting replication in independent samples. To inform personalised treatment planning, a next step concerns linking different symptom relationship patterns to treatment response and clinical course, including patterns related to spontaneous recovery and forms of disorder progression.

A significant proportion of people with clozapine-treated schizophrenia develop ‘checking’ compulsions, a phenomenon yet to be understood.

To use habit formation models developed in cognitive neuroscience to investigate the dynamic interplay between psychosis, clozapine dose and obsessive–compulsive symptoms (OCS).

Using the anonymised electronic records of a cohort of clozapine-treated patients, including longitudinal assessments of OCS and psychosis, we performed longitudinal multi-level mediation and multi-level moderation analyses to explore associations of psychosis with obsessiveness and excessive checking. Classic bivariate correlation tests were used to assess clozapine load and checking compulsions. The influence of specific genetic variants was tested in a subsample.

A total of 196 clozapine-treated individuals and 459 face-to-face assessments were included. We found significant OCS to be common (37.9%), with checking being the most prevalent symptom. In mediation models, psychosis severity mediated checking behaviour indirectly by inducing obsessions (r = 0.07, 95% CI 0.04–0.09; P < 0.001). No direct effect of psychosis on checking was identified (r = −0.28, 95% CI −0.09 to 0.03; P = 0.340). After psychosis remission (n = 65), checking compulsions correlated with both clozapine plasma levels (r = 0.35; P = 0.004) and dose (r = 0.38; P = 0.002). None of the glutamatergic and serotonergic genetic variants were found to moderate the effect of psychosis on obsession and compulsion (SLC6A4, SLC1A1 and HTR2C) survived the multiple comparisons correction.

We elucidated different phases of the complex interplay of psychosis and compulsions, which may inform clinicians’ therapeutic decisions.

Major depressive disorder (MDD) has been increasingly understood as a disruption of brain connectome. Investigating grey matter structural networks with a large sample size can provide valuable insights into the structural basis of network-level neuropathological underpinnings of MDD.

Using a multisite MRI data-set including nearly 2000 individuals, this study aimed to identify robust topology and connectivity abnormalities of grey matter structural network linked to MDD and relevant clinical phenotypes.

A total of 955 MDD patients and 1009 healthy controls were included from 23 sites. Individualised structural covariance networks (SCN) were established based on grey matter volume maps. Following data harmonisation, network topological metrics and focal connectivity were examined for group-level comparisons, individual-level classification performance and association with clinical ratings. Various validation strategies were applied to confirm the reliability of findings.

Compared with healthy controls, MDD individuals exhibited increased global efficiency, abnormal regional centralities (i.e. thalamus, precentral gyrus, middle cingulate cortex and default mode network) and altered circuit connectivity (i.e. ventral attention network and frontoparietal network). First-episode drug-naive and recurrent patients exhibited different patterns of deficits in network topology and connectivity. In addition, the individual-level classification of topological metrics outperforms that of structural connectivity. The thalamus-insula connectivity was positively associated with the severity of depressive symptoms.

Based on this high-powered data-set, we identified reliable patterns of impaired topology and connectivity of individualised SCN in MDD and relevant subtypes, which adds to the current understanding of neuropathology of MDD and might guide future development of diagnostic and therapeutic markers.