Sleep disturbances are prominent correlates of acute mood episodes and inadequate recovery in bipolar disorder (BD), yet the mechanistic relationship between sleep physiology and mood remains poorly understood. Using a series of pre-sleep mood inductions and overnight sleep recording, this study examined the relationship between overnight mood regulation and a marker of sleep intensity (non-rapid eye movement sleep slow wave activity; NREM SWA) during the interepisode phase of BD.

Adults with interepisode BD type 1 (BD; n = 20) and healthy adult controls (CTL; n = 23) slept in the laboratory for a screening night, a neutral mood induction night (baseline), a happy mood induction night, and a sad mood induction night. NREM SWA (0.75–4.75 Hz) was derived from overnight sleep EEG recordings. Overnight mood regulation was evaluated using an affect grid pleasantness rating post-mood induction (pre-sleep) and the next morning.

Overnight mood regulation did not differ between groups following the sad or happy inductions. SWA did not significantly change for either group on the sad induction night compared with baseline. In BD only, SWA on the sad night was related to impaired overnight negative mood regulation. On the happy induction night, SWA increased relative to baseline in both groups, though SWA was not related to overnight mood regulation for either group.

These findings indicate that SWA disruption may play a role in sustaining negative mood state from the previous night in interepisode BD. However, positive mood state could enhance SWA in bipolar patients and healthy adults.

Adults with major depressive disorder (MDD) demonstrate increased susceptibility to interfering effects of anxiety on cognitive control; although under certain conditions adults with MDD are able to compensate for these effects. The brain mechanisms that may facilitate the ability to compensate for anxiety either via the recruitment of additional cognitive resources or via the regulation of interference from anxiety remain largely unknown. To clarify these mechanisms, we examined the effects of anxiety on brain activity and amygdala–prefrontal functional connectivity in adults diagnosed with MDD.

A total of 22 unmedicated adults with MDD and 18 healthy controls (HCs) performed the Tower of London task under conditions designed to induce anxiety, while undergoing a functional magnetic resonance imaging assessment.

During the easy condition, the MDD group demonstrated equivalent planning accuracy, longer planning times, elevated amygdala activity and left rostrolateral prefrontal cortex (RLPFC) hyperactivity relative to HCs. Anxiety mediated observed group differences in planning times, as well as differences in amygdala activation, which subsequently mediated observed differences in RLPFC activation. During the easy condition, the MDD group also demonstrated increased negative amygdala–dorsolateral prefrontal cortex (DLPFC) connectivity which correlated with improved planning accuracy. During the hard condition, HCs demonstrated greater DLPFC activation and stronger negative amygdala–DLPFC connectivity, which was unrelated to planning accuracy.

Our results suggest that persons with MDD compensate for anxiety-related limbic activation during low-load cognitive tasks by recruiting additional RLPFC activation and through increased inhibitory amygdala–DLPFC communication. Targeting these neural mechanisms directly may improve cognitive functioning in MDD.

Alcohol use disorder (AUD) and schizophrenia (SCZ) frequently co-occur, and large-scale genome-wide association studies (GWAS) have identified significant genetic correlations between these disorders.

We used the largest published GWAS for AUD (total cases = 77 822) and SCZ (total cases = 46 827) to identify genetic variants that influence both disorders (with either the same or opposite direction of effect) and those that are disorder specific.

We identified 55 independent genome-wide significant single nucleotide polymorphisms with the same direction of effect on AUD and SCZ, 8 with robust effects in opposite directions, and 98 with disorder-specific effects. We also found evidence for 12 genes whose pleiotropic associations with AUD and SCZ are consistent with mediation via gene expression in the prefrontal cortex. The genetic covariance between AUD and SCZ was concentrated in genomic regions functional in brain tissues (p = 0.001).

Our findings provide further evidence that SCZ shares meaningful genetic overlap with AUD.

Technology-based interventions (TBIs) are a useful approach when attempting to provide therapy to more patients with psychosis.

Randomized controlled trials of outcomes of TBIs v. face-to-face interventions in psychosis were identified in a systematic search conducted in PubMed/Ovid MEDLINE. Data were extracted independently by two researchers, and standardized mean changes were pooled using a three-level model and network meta-analysis.

Fifty-eight studies were included. TBIs complementing treatment as usual (TAU) were generally superior to face-to-face interventions (g = 0.16, p ≤ 0.0001) and to specific outcomes, namely, neurocognition (g = 0.13, p ≤ 0.0001), functioning (g = 0.25, p = 0.006), and social cognition (g = 0.32, p ≤ 0.05). Based on the network meta-analysis, the effect of two TBIs differed significantly from zero; these were the TBIs cognitive training for the neurocognitive outcome [g = 0.16; 95% confidence interval (CI) 0.09–0.23] and cognitive behavioral therapy for quality of life (g = 1.27; 95% CI 0.46–2.08). The variables educational level, type of medication, frequency of the intervention, and contact during the intervention moderated the effectiveness of TBIs over face-to-face interventions in neurocognition and symptomatology.

TBIs are effective for the management of neurocognition, symptomatology, functioning, social cognition, and quality of life outcomes in patients with psychosis. The results of the network meta-analysis showed the efficacy of some TBIs for neurocognition, symptomatology, and quality of life. Therefore, TBIs should be considered a complement to TAU in patients with psychosis.

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.

Patients with bipolar disorder (BD) show reduced fractional anisotropy (FA) compared to patients with major depressive disorder (MDD). Little is known about whether these differences are mood state-independent or influenced by acute symptom severity. Therefore, the aim of this study was (1) to replicate abnormalities in white matter microstructure in BD v. MDD and (2) to investigate whether these vary across depressed, euthymic, and manic mood.

In this cross-sectional diffusion tensor imaging study, n = 136 patients with BD were compared to age- and sex-matched MDD patients and healthy controls (HC) (n = 136 each). Differences in FA were investigated using tract-based spatial statistics. Using interaction models, the influence of acute symptom severity and mood state on the differences between patient groups were tested.

Analyses revealed a main effect of diagnosis on FA across all three groups (ptfce-FWE = 0.003). BD patients showed reduced FA compared to both MDD (ptfce-FWE = 0.005) and HC (ptfce-FWE < 0.001) in large bilateral clusters. These consisted of several white matter tracts previously described in the literature, including commissural, association, and projection tracts. There were no significant interaction effects between diagnosis and symptom severity or mood state (all ptfce-FWE > 0.704).

Results indicated that the difference between BD and MDD was independent of depressive and manic symptom severity and mood state. Disruptions in white matter microstructure in BD might be a trait effect of the disorder. The potential of FA values to be used as a biomarker to differentiate BD from MDD should be further addressed in future studies using longitudinal designs.

Mismatch negativity (MMN) amplitude is reduced in psychotic disorders and associated with symptoms and functioning. Due to these robust associations, it is often considered a biomarker for psychotic illness. The relationship between MMN and clinical outcomes has been examined well in early onset psychotic illness; however, its stability and predictive utility in chronic samples are not clear.

We examined the five-year stability of MMN amplitude over two timepoints in individuals with established psychotic disorders (cases; N = 132) and never-psychotic participants (NP; N = 170), as well as longitudinal associations with clinical symptoms and functioning.

MMN amplitude exhibited good temporal stability (cases, r = 0.53; never-psychotic, r = 0.52). In cases, structural equation models revealed MMN amplitude to be a significant predictor of worsening auditory hallucinations (β = 0.19), everyday functioning (β = −0.13), and illness severity (β = −0.12) at follow-up. Meanwhile, initial IQ (β = −0.24), negative symptoms (β = 0.23), and illness severity (β = −0.16) were significant predictors of worsening MMN amplitude five years later.

These results imply that MMN measures a neural deficit that is reasonably stable up to five years. Results support disordered cognition and negative symptoms as preceding reduced MMN, which then may operate as a mechanism driving reductions in everyday functioning and the worsening of auditory hallucinations in chronic psychotic disorders. This pattern may inform models of illness course, clarifying the relationships amongst biological mechanisms of predictive processing and clinical deficits in chronic psychosis and allowing us to better understand the mechanisms driving such impairments over time.

There is evidence for a polygenic contribution to psychosis. One targetable mechanism through which polygenic variation may impact on individuals and interact with the social environment is stress sensitization, characterized by elevated reactivity to minor stressors in daily life. The current study aimed to investigate whether stress reactivity is modified by polygenic risk score for schizophrenia (PRS) in cases with enduring non-affective psychotic disorder, first-degree relatives of cases, and controls.

We used the experience sampling method to assess minor stressors, negative affect, positive affect and psychotic experiences in 96 cases, 79 first-degree relatives, i.e. siblings, and 73 controls at wave 3 of the Dutch Genetic Risk and Outcome of Psychosis (GROUP) study. Genome-wide data were collected at baseline to calculate PRS.

We found that associations of momentary stress with psychotic experiences, but not with negative and positive affect, were modified by PRS and group (all pFWE<0.001). In contrast to our hypotheses, siblings with high PRS reported less intense psychotic experiences in response to momentary stress compared to siblings with low PRS. No differences in magnitude of these associations were observed in cases with high v. low level of PRS. By contrast, controls with high PRS showed more intense psychotic experiences in response to stress compared to those with low PRS.

This tentatively suggests that polygenic risk may operate in different ways than previously assumed and amplify reactivity to stress in unaffected individuals but operate as a resilience factor in relatives by attenuating their stress reactivity.

Psychiatric disorders and homelessness are related, but temporal associations are unclear. We aimed to explore the overlap between hospital-based psychiatric disorders and sheltered homelessness.

This population-based cohort study was conducted using the Danish registers e.g., the Danish Homeless Register and the Danish National Patient Register. The study cohort included all individuals aged 15 years or older, living in Denmark at least one day during 2002–2021 (born 1984–2006). First psychiatric diagnosis was used to define psychiatric disorder and first homeless shelter contact to define homelessness. Adjusted incidence rate ratios (IRRs) and cumulative incidences were estimated.

Among 1 530 325 individuals accounting for 16 787 562 person-years at risk aged 15–38 years, 11 433 (0.8%) had at least one homeless shelter contact. Among 1 406 410 individuals accounting for 14 131 060 person-years at risk, 210 730 had at least one psychiatric disorder. People with any psychiatric disorder had increased risk of sheltered homelessness relative to individuals with no psychiatric disorder [IRR 9.2, 95% confidence interval (CI) 8.8–9.6]. Ten years after first psychiatric disorder, 3.0% (95% CI 2.9–3.1) had at least one homeless shelter contact. Individuals experiencing homelessness had increased risk of any psychiatric disorder compared to individuals with no homeless shelter contact (IRR 7.0, 95% CI 6.7–7.4). Ten years after first homeless shelter contact, 47.1% (45.3–48.0) had received a hospital-based psychiatric diagnosis.

Strong bidirectional associations between psychiatric disorders and homelessness were identified. Health and social care professionals should be aware of and address these high risks of accumulated psychiatric and social problems.

It is unclear whether the enhancing contact model (ECM) intervention is effective in reducing family caregiving burden and improving hope and quality of life (QOL) among family caregivers of persons with schizophrenia (FCPWS).

We conducted a cluster randomized controlled trial in FCPWS in eight rural townships in Xinjin, Chengdu, China. In total, 253 FCPWS were randomly allocated to the ECM, psychoeducational family intervention (PFI), or treatment as usual (TAU) group. FCPWS in three groups were assessed caregiving burden, QOL and state of hope at baseline (T0), post-intervention (T1), 3-month (T2), and 9-month (T3) follow-up, respectively.

Compared with participants in the TAU group, participants in the ECM group had statistically significantly lower caregiving burden scores both at T1 and T2 (p = 0.0059 and 0.0257, respectively). Compared with participants in the TAU group, participants in the PFI group had statistically significantly higher QOL scores in T1 (p = 0.0406), while participants in the ECM group had statistically significantly higher QOL scores in T3 (p = 0.0240). Participants in both ECM and PFI groups had statistically significantly higher hope scores than those in the TAU group at T1 (p = 0.0160 and 0.0486, respectively).

This is the first study to explore the effectiveness of ECM on reducing family caregiving burden and improving hope and QOL in rural China. The results indicate the ECM intervention, a comprehensive and multifaceted intervention, is more effective than the PFI in various aspects of mental wellbeing among FCPWS. Future research needs to confirm ECM's effectiveness in various population.

Major depressive disorder (MDD) is one of the growing human mental health challenges facing the global health care system. In this study, the structural connectivity between symptoms of MDD is explored using two different network modeling approaches.

Data are from ‘the Virginia Adult Twin Study of Psychiatric and Substance Use Disorders (VATSPSUD)’. A cohort of N = 2163 American Caucasian female-female twins was assessed as part of the VATSPSUD study. MDD symptoms were assessed using personal structured clinical interviews. Two network analyses were conducted. First, an undirected network model was estimated to explore the connectivity between the MDD symptoms. Then, using a Bayesian network, we computed a directed acyclic graph (DAG) to investigate possible directional relationships between symptoms.

Based on the results of the undirected network, the depressed mood symptom had the highest centrality value, indicating its importance in the overall network of MDD symptoms. Bayesian network analysis indicated that depressed mood emerged as a plausible driving symptom for activating other symptoms. These results are consistent with DSM-5 guidelines for MDD. Also, somatic weight and appetite symptoms appeared as the strongest connections in both networks.

We discuss how the findings of our study might help future research to detect clinically relevant symptoms and possible directional relationships between MDD symptoms defining major depression episodes, which would help identify potential tailored interventions. This is the first study to investigate the network structure of VATSPSUD data using both undirected and directed network models.