Suicide is a major concern among active-duty military personnel. Aggression represents a salient risk factor for suicide among civilians, yet is relatively understudied among military populations. Although several theories posit a relation between aggression and suicide with putative underlying mechanisms of social isolation, access to firearms, and alcohol use, researchers have yet to test these potential mediators. This study uses rich, longitudinal data from the Army Study to Assess Risk and Resilience (STARRS) Pre/Post Deployment Study (PPDS) to examine whether aggression longitudinally predicts suicide attempts and to identify mediators of this association.

Army soldiers (N = 8483) completed assessments 1 month prior to deployment and 1, 2–3, and 9–12 months post-deployment. Participants reported on their physical and verbal aggression, suicide attempts, social network size, firearm ownership, and frequency of alcohol use.

As expected, pre-deployment aggression was significantly associated with suicide attempts at 12-months post-deployment even after controlling for lifetime suicide attempts. Social network size and alcohol use frequency mediated this association, but firearm ownership did not.

Findings further implicate aggression as an important suicide risk factor among military personnel and suggest that social isolation and alcohol use may partially account for this association.

ADHD symptoms are associated with emotional problems such as depressive and anxiety symptoms from early childhood to adulthood, with the association increasing with age. A shared aetiology and/or a causal relationship could explain their correlation. In the current study, we explore these explanations for the association between ADHD symptoms and emotional problems from childhood to adulthood.

Data were drawn from the Twins Early Development Study (TEDS), including 3675 identical and 7063 non-identical twin pairs. ADHD symptoms and emotional symptoms were reported by parents from childhood to adulthood. Self-report scales were included from early adolescence. Five direction of causation (DoC) twin models were fitted to distinguish whether associations were better explained by shared aetiology and/or causal relationships in early childhood, mid-childhood, early adolescence, late adolescence, and early adulthood. Follow-up analyses explored associations for the two subdomains of ADHD symptoms, hyperactivity-impulsivity and inattention, separately.

The association between ADHD symptoms and emotional problems increased in magnitude from early childhood to adulthood. In the best-fitting models, positive genetic overlap played an important role in this association at all stages. A negative causal effect running from ADHD symptoms to emotional problems was also detected in early childhood and mid-childhood. When distinguishing ADHD subdomains, the apparent protective effect of ADHD symptoms on emotional problems in childhood was mostly driven by hyperactivity-impulsivity.

Genetic overlap plays an important role in the association between ADHD symptoms and emotional problems. Hyperactivity-impulsivity may protect children from emotional problems in childhood, but this protective effect diminishes after adolescence.

Cognitive impairment constitutes a prevailing issue in the schizophrenia spectrum, severely impacting patients' functional outcomes. A global cognitive score, sensitive to the stages of the spectrum, would benefit the exploration of potential factors involved in the cognitive decline.

First, we performed principal component analysis on cognitive scores from 768 individuals across the schizophrenia spectrum, including first-degree relatives of patients, individuals at ultra-high risk, who had a first-episode psychosis, and chronic schizophrenia patients, alongside 124 healthy controls. The analysis provided 10 g-factors as global cognitive scores, validated through correlations with intelligence quotient and assessed for their sensitivity to the stages on the spectrum using analyses of variance. Second, using the g-factors, we explored potential mechanisms underlying cognitive impairment in the schizophrenia spectrum using correlations with sociodemographic, clinical, and developmental data, and linear regressions with genotypic data, pooled through meta-analyses.

The g-factors were highly correlated with intelligence quotient and with each other, confirming their validity. They presented significant differences between subgroups along the schizophrenia spectrum. They were positively correlated with educational attainment and the polygenic risk score (PRS) for cognitive performance, and negatively correlated with general psychopathology of schizophrenia, neurodevelopmental load, and the PRS for schizophrenia.

The g-factors appeared as valid estimators of global cognition, enabling discerning cognitive states within the schizophrenia spectrum. Educational attainment and genetics related to cognitive performance may have a positive influence on cognitive functioning, while general psychopathology of schizophrenia, neurodevelopmental load, and genetic liability to schizophrenia may have an adverse impact.

Transition to psychosis rates within ultra-high risk (UHR) services have been declining. It may be possible to ‘enrich’ UHR cohorts based on the environmental characteristics seen more commonly in first-episode psychosis cohorts. This study aimed to determine whether transition rates varied according to the accumulated exposure to environmental risk factors at the individual (migrant status, asylum seeker/refugee status, indigenous population, cannabis/methamphetamine use), family (family history or parental separation), and neighborhood (population density, social deprivation, and fragmentation) level.

The study included UHR people aged 15–24 who attended the PACE clinic from 2012 to 2016. Cox proportional hazards models (frequentist and Bayesian) were used to assess the association between individual and accumulated factors and transition to psychosis. UHR status and transition was determined using the CAARMS. Benjamini–Hochberg was used to correct for multiple comparisons in frequentist analyses.

Of the 461 young people included, 55.5% were female and median follow-up was 307 days (IQR: 188–557) and 17.6% (n = 81) transitioned to a psychotic disorder. The proportion who transitioned increased incrementally according to the number of individual-level risk factors present (HR = 1.51, 95% CIs 1.19–1.93, p < 0.001, pcorr = 0.01). The number of family- and neighborhood-level exposures did not increase transition risk (p > 0.05). Cannabis use was the only specific risk factor significantly associated with transition (HR = 1.89, 95% CIs 1.22–2.93, pcorr = 0.03, BF = 6.74).

There is a dose–response relationship between exposure to individual-level psychosis-related environmental risk factors and transition risk in UHR patients. If replicated, this could be incorporated into a novel approach to identifying the highest-risk individuals within clinical services.

Late-life depression (LLD) predisposes individuals to cognitive decline, often leading to misdiagnoses as mild cognitive impairment (MCI). Voxel-based morphometry (VBM) can distinguish the profiles of these disorders according to gray matter (GM) volumes. We integrated findings from previous VBM studies for comparative analysis and extended the research into molecular profiles to facilitate inspection and intervention.

We comprehensively searched PubMed and Web of Science for VBM studies that compared LLD and MCI cases with matched healthy controls (HCs) from inception to 31st December 2023. We included 13 studies on LLD (414 LLDs, 350 HCs) and 50 on MCI (1878 MCIs, 2046 HCs). Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) was used for voxel-based meta-analysis to assess GM atrophy, spatially correlated with neuropsychological profiles. We then used multimodal and linear-model analysis to assess the similarities and differences in GM volumetric changing patterns. Partial least squares (PLS) regression and gene enrichment were employed for transcription-neuroimaging associations.

GM volumes in the left hippocampus and right parahippocampal gyrus are more affected in MCI, along with memory impairment. MCI was spatially correlated with a more extensive reduction in the levels of neurotransmitters and a severe downregulation of genes related to cellular potassium ion transport and metal ion transmembrane transporter activity.

Compared to LLD, MCI exhibited more GM atrophy in the hippocampus and parahippocampal gyrus and lower gene expression of ion transmembrane transport. Our findings provided imaging-transcriptomic-genetic integrative profiles for differential diagnosis and precise intervention between LLD and MCI.

Mental disorders and physical-health conditions frequently co-occur, impacting treatment outcomes. While most prior research has focused on single pairs of mental disorders and physical-health conditions, this study explores broader associations between multiple mental disorders and physical-health conditions.

Using the Norwegian primary-care register, this population-based cohort study encompassed all 2 203 553 patients born in Norway from January 1945 through December 1984, who were full-time residents from January 2006 until December 2019 (14 years; 363 million person-months). Associations between seven mental disorders (sleep disturbance, anxiety, depression, acute stress reaction, substance-use disorders, phobia/compulsive disorder, psychosis) and 16 physical-health conditions were examined, diagnosed according to the International Classification of Primary Care.

Of 112 mental-disorder/physical-health condition pairs, 96% of associations yielded positive and significant ORs, averaging 1.41 and ranging from 1.05 (99.99% CI 1.00–1.09) to 2.38 (99.99% CI 2.30–2.46). Across 14 years, every mental disorder was associated with multiple different physical-health conditions. Across 363 million person-months, having any mental disorder was associated with increased subsequent risk of all physical-health conditions (HRs:1.40 [99.99% CI 1.35–1.45] to 2.85 [99.99% CI 2.81–2.89]) and vice versa (HRs:1.56 [99.99% CI 1.54–1.59] to 3.56 [99.99% CI 3.54–3.58]). Associations were observed in both sexes, across age groups, and among patients with and without university education.

The breadth of associations between virtually every mental disorder and physical-health condition among patients treated in primary care underscores a need for integrated mental and physical healthcare policy and practice. This remarkable breadth also calls for research into etiological factors and underlying mechanisms that can explain it.

Observational studies consistently report associations between tobacco use, cannabis use and mental illness. However, the extent to which this association reflects an increased risk of new-onset mental illness is unclear and may be biased by unmeasured confounding.

A systematic review and meta-analysis (CRD42021243903). Electronic databases were searched until November 2022. Longitudinal studies in general population samples assessing tobacco and/or cannabis use and reporting the association (e.g. risk ratio [RR]) with incident anxiety, mood, or psychotic disorders were included. Estimates were combined using random-effects meta-analyses. Bias was explored using a modified Newcastle–Ottawa Scale, confounder matrix, E-values, and Doi plots.

Seventy-five studies were included. Tobacco use was associated with mood disorders (K = 43; RR: 1.39, 95% confidence interval [CI] 1.30–1.47), but not anxiety disorders (K = 7; RR: 1.21, 95% CI 0.87–1.68) and evidence for psychotic disorders was influenced by treatment of outliers (K = 4, RR: 3.45, 95% CI 2.63–4.53; K = 5, RR: 2.06, 95% CI 0.98–4.29). Cannabis use was associated with psychotic disorders (K = 4; RR: 3.19, 95% CI 2.07–4.90), but not mood (K = 7; RR: 1.31, 95% CI 0.92–1.86) or anxiety disorders (K = 7; RR: 1.10, 95% CI 0.99–1.22). Confounder matrices and E-values suggested potential overestimation of effects. Only 27% of studies were rated as high quality.

Both substances were associated with psychotic disorders and tobacco use was associated with mood disorders. There was no clear evidence of an association between cannabis use and mood or anxiety disorders. Limited high-quality studies underscore the need for future research using robust causal inference approaches (e.g. evidence triangulation).

Associations between childhood trauma, neurodevelopment, alcohol use disorder (AUD), and posttraumatic stress disorder (PTSD) are understudied during adolescence.

Using 1652 participants (51.75% female, baseline Mage = 14.3) from the Collaborative Study of the Genetics of Alcoholism, we employed latent growth curve models to (1) examine associations of childhood physical, sexual, and non-assaultive trauma (CPAT, CSAT, and CNAT) with repeated measures of alpha band EEG coherence (EEGc), and (2) assess whether EEGc trajectories were associated with AUD and PTSD symptoms. Sex-specific models accommodated sex differences in trauma exposure, AUD prevalence, and neural development.

In females, CSAT was associated with higher mean levels of EEGc in left frontocentral (LFC, ß = 0.13, p = 0.01) and interhemispheric prefrontal (PFI, ß = 0.16, p < 0.01) regions, but diminished growth in LFC (ß = −0.07, p = 0.02) and PFI (ß = −0.07, p = 0.02). In males, CPAT was associated with lower mean levels (ß = −0.17, p = 0.01) and increased growth (ß = 0.11, p = 0.01) of LFC EEGc. Slope of LFC EEGc was inversely associated with AUD symptoms in females (ß = −1.81, p = 0.01). Intercept of right frontocentral and PFI EEGc were associated with AUD symptoms in males, but in opposite directions. Significant associations between EEGc and PTSD symptoms were also observed in trauma-exposed individuals.

Childhood assaultive trauma is associated with changes in frontal alpha EEGc and subsequent AUD and PTSD symptoms, though patterns differ by sex and trauma type. EEGc findings may inform emerging treatments for PTSD and AUD.

In contemporary neuroimaging studies, it has been observed that patients with major depressive disorder (MDD) exhibit aberrant spontaneous neural activity, commonly quantified through the amplitude of low-frequency fluctuations (ALFF). However, the substantial individual heterogeneity among patients poses a challenge to reaching a unified conclusion.

To address this variability, our study adopts a novel framework to parse individualized ALFF abnormalities. We hypothesize that individualized ALFF abnormalities can be portrayed as a unique linear combination of shared differential factors. Our study involved two large multi-center datasets, comprising 2424 patients with MDD and 2183 healthy controls. In patients, individualized ALFF abnormalities were derived through normative modeling and further deconstructed into differential factors using non-negative matrix factorization.

Two positive and two negative factors were identified. These factors were closely linked to clinical characteristics and explained group-level ALFF abnormalities in the two datasets. Moreover, these factors exhibited distinct associations with the distribution of neurotransmitter receptors/transporters, transcriptional profiles of inflammation-related genes, and connectome-informed epicenters, underscoring their neurobiological relevance. Additionally, factor compositions facilitated the identification of four distinct depressive subtypes, each characterized by unique abnormal ALFF patterns and clinical features. Importantly, these findings were successfully replicated in another dataset with different acquisition equipment, protocols, preprocessing strategies, and medication statuses, validating their robustness and generalizability.

This research identifies shared differential factors underlying individual spontaneous neural activity abnormalities in MDD and contributes novel insights into the heterogeneity of spontaneous neural activity abnormalities in MDD.

Robust clinical indices of etiologic heterogeneity for psychiatric disorders are rare. We investigate whether age at onset (AAO) reflects genetic heterogeneity, utilizing Genetic Risk Ratios (GRR) derived from Family Genetic Risk Scores (FGRS).

We examined, in individuals born in Sweden 1940–2003, whether AAO for five primary disorders -- drug use disorder (DUD), alcohol use disorder (AUD), major depression (MD), bipolar disorder (BD), and schizophrenia (SZ)-- was associated with varying levels of GRRs with a range of informative secondary disorders and traits.

Our disorders displayed a varying pattern of change of GRRs with increasing AAO. At one end was SZ, where all GRRs rose with increasing AAO meaning that SZ became increasing genetically heterogeneous with later AAO. The most balanced disorder was AUD where, with increasing AAO, GRRs rose for AD, BD, and MD and declined for DUD, CB, and ADHD. That is, at young AAO, AUD had high levels of genetic risk for other externalizing disorders while at older AAO, high genetic risk for internalizing disorders were more prominent. MD was at the continuum's other end where all GRRs, except for AD, decreased with higher AAO, meaning that MD became increasingly genetically homogeneous with later AAO.

Genetic heterogeneity was robustly associated with AAO across our five primary disorders with substantial inter-disorder differences in the observed patterns. In particular, young AAO was associated with maximal genetic homogeneity for SZ and DUD while older AAO had greater genetic homogeneity for MD with AUD falling in between.

Involuntary admissions to psychiatric hospitals are on the rise. If patients at elevated risk of involuntary admission could be identified, prevention may be possible. Our aim was to develop and validate a prediction model for involuntary admission of patients receiving care within a psychiatric service system using machine learning trained on routine clinical data from electronic health records (EHRs).

EHR data from all adult patients who had been in contact with the Psychiatric Services of the Central Denmark Region between 2013 and 2021 were retrieved. We derived 694 patient predictors (covering e.g. diagnoses, medication, and coercive measures) and 1134 predictors from free text using term frequency-inverse document frequency and sentence transformers. At every voluntary inpatient discharge (prediction time), without an involuntary admission in the 2 years prior, we predicted involuntary admission 180 days ahead. XGBoost and elastic net models were trained on 85% of the dataset. The models with the highest area under the receiver operating characteristic curve (AUROC) were tested on the remaining 15% of the data.

The model was trained on 50 634 voluntary inpatient discharges among 17 968 patients. The cohort comprised of 1672 voluntary inpatient discharges followed by an involuntary admission. The best XGBoost and elastic net model from the training phase obtained an AUROC of 0.84 and 0.83, respectively, in the test phase.

A machine learning model using routine clinical EHR data can accurately predict involuntary admission. If implemented as a clinical decision support tool, this model may guide interventions aimed at reducing the risk of involuntary admission.

Childhood bullying is a public health priority. We evaluated the effectiveness and costs of KiVa, a whole-school anti-bullying program that targets the peer context.

A two-arm pragmatic multicenter cluster randomized controlled trial with embedded economic evaluation. Schools were randomized to KiVa-intervention or usual practice (UP), stratified on school size and Free School Meals eligibility. KiVa was delivered by trained teachers across one school year. Follow-up was at 12 months post randomization. Primary outcome: student-reported bullying-victimization; secondary outcomes: self-reported bullying-perpetration, participant roles in bullying, empathy and teacher-reported Strengths and Difficulties Questionnaire. Outcomes were analyzed using multilevel linear and logistic regression models.

Between 8/11/2019–12/02/2021, 118 primary schools were recruited in four trial sites, 11 111 students in primary analysis (KiVa-intervention: n = 5944; 49.6% female; UP: n = 5167, 49.0% female). At baseline, 21.6% of students reported being bullied in the UP group and 20.3% in the KiVa-intervention group, reducing to 20.7% in the UP group and 17.7% in the KiVa-intervention group at follow-up (odds ratio 0.87; 95% confidence interval 0.78 to 0.97, p value = 0.009). Students in the KiVa group had significantly higher empathy and reduced peer problems. We found no differences in bullying perpetration, school wellbeing, emotional or behavioral problems. A priori subgroup analyses revealed no differences in effectiveness by socioeconomic gradient, or by gender. KiVa costs £20.78 more per pupil than usual practice in the first year, and £1.65 more per pupil in subsequent years.

The KiVa anti-bullying program is effective at reducing bullying victimization with small-moderate effects of public health importance.

The study was funded by the UK National Institute for Health and Care Research (NIHR) Public Health Research program (17-92-11). Intervention costs were funded by the Rayne Foundation, GwE North Wales Regional School Improvement Service, Children's Services, Devon County Council and HSBC Global Services (UK) Ltd.

Short-term exposure to antipsychotics has proven to be beneficial. However, naturalistic studies are lacking regarding the long-term use of antipsychotics. This study aimed to investigate changes in use of antipsychotics over 20 years after a first-episode schizophrenia.

This study is part of the Danish OPUS trial (1998–2000), including 496 participants with first-episode schizophrenia. Participants were reassessed four times over 20 years. The main outcomes were days on medication, redeemed prescriptions of clozapine, psychiatric hospitalizations, and employment.

At the 20-year follow-up, an attrition of 71% was detected. In total, 143 out of 496 participated, with 36% (n = 51) in remission-of-psychotic-symptoms-off-medication. The lowest number of days on medication (mean [s.d.], 339 [538] days) was observed in this group over 20 years. Register data on redeemed antipsychotics were available for all trial participants (n = 416). Individuals in treatment with antipsychotics (n = 120) at the 20-year follow-up had spent significantly more days in treatment (5405 [1857] v. 1434 [1819] mean days, p = 0.00) and more had ever redeemed a prescription of clozapine (25% v. 7.8%, p = 0.00) than individuals who had discontinued antipsychotics (n = 296). Further, discontinuers had significantly higher employment at the 20-year follow-up (28.4% v. 12.5%, p = 0.00).

In a cohort of individuals with first-episode schizophrenia, 36% were in remission-of-psychotic-symptoms-off-medication. However, high attrition was detected, potentially affecting study results by inflating results from individuals with favorable outcomes. From register data, free from attrition, approximately 30% were in treatment with antipsychotics, and 70% had discontinued antipsychotics. Individuals in treatment had the least favorable outcomes, implying greater illness severity.

Childhood sexual abuse (CSA) and emotional maltreatment are salient risk factors for the development of major depressive disorder (MDD) in women. However, the type- and timing-specific effects of emotional maltreatment experienced during adolescence on future depressive symptomatology in women with CSA have not been explored. The goal of this study was to fill this gap.

In total, 203 women (ages 20–32) with current depressive symptoms and CSA (MDD/CSA), remitted depressive symptoms and CSA (rMDD/CSA), and current depressive symptoms without CSA (MDD/no CSA) were recruited from the community and completed self-report measures. Depressive symptoms were assessed using the Beck Depression Inventory (BDI-II) and a detailed maltreatment history was collected using the Maltreatment and Abuse Chronology of Exposure (MACE). Differences in maltreatment exposure characteristics, including multiplicity and severity of maltreatment, as well as the chronologies of emotional maltreatment subtypes were compared among groups. A random forest machine-learning algorithm was utilized to assess the impact of exposure to emotional maltreatment subtypes at specific ages on current depressive symptoms.

MDD/CSA women reported greater prevalence and severity of emotional maltreatment relative to rMDD/CSA and MDD/no CSA women [F(2,196) = 9.33, p < 0.001], specifically from ages 12 to 18. The strongest predictor of current depressive symptoms was parental verbal abuse at age 18 for both MDD/CSA women (variable importance [VI] = 1.08, p = 0.006) and MDD/no CSA women (VI = 0.68, p = 0.004).

Targeting emotional maltreatment during late adolescence might prove beneficial for future intervention efforts for MDD following CSA.

Individuals with major depressive disorder (MDD) can experience reduced motivation and cognitive function, leading to challenges with goal-directed behavior. When selecting goals, people maximize ‘expected value’ by selecting actions that maximize potential reward while minimizing associated costs, including effort ‘costs’ and the opportunity cost of time. In MDD, differential weighing of costs and benefits are theorized mechanisms underlying changes in goal-directed cognition and may contribute to symptom heterogeneity.

We used the Effort Foraging Task to quantify cognitive and physical effort costs, and patch leaving thresholds in low effort conditions (reflecting perceived opportunity cost of time) and investigated their shared versus distinct relationships to clinical features in participants with MDD (N = 52, 43 in-episode) and comparisons (N = 27).

Contrary to our predictions, none of the decision-making measures differed with MDD diagnosis. However, each of the measures was related to symptom severity, over and above effects of ability (i.e. performance). Greater anxiety symptoms were selectively associated with lower cognitive effort cost (i.e. greater willingness to exert effort). Anhedonia and behavioral apathy were associated with increased physical effort costs. Finally, greater overall depression was related to decreased patch leaving thresholds.

Markers of effort-based decision-making may inform understanding of MDD heterogeneity. Increased willingness to exert cognitive effort may contribute to anxiety symptoms such as worry. Decreased leaving threshold associations with symptom severity are consistent with reward rate-based accounts of reduced vigor in MDD. Future research should address subtypes of depression with or without anxiety, which may relate differentially to cognitive effort decisions.

Severe fatigue and cognitive complaints are frequently reported after SARS-CoV-2 infection and may be accompanied by depressive symptoms and/or limitations in physical functioning. The long-term sequelae of COVID-19 may be influenced by biomedical, psychological, and social factors, the interplay of which is largely understudied over time. We aimed to investigate how the interplay of these factors contribute to the persistence of symptoms after COVID-19.

RECoVERED, a prospective cohort study in Amsterdam, the Netherlands, enrolled participants aged⩾16 years after SARS-CoV-2 diagnosis. We used a structural network analysis to assess relationships between biomedical (initial COVID-19 severity, inflammation markers), psychological (illness perceptions, coping, resilience), and social factors (loneliness, negative life events) and persistent symptoms 24 months after initial disease (severe fatigue, difficulty concentrating, depressive symptoms and limitations in physical functioning). Causal discovery, an explorative data-driven approach testing all possible associations and retaining the most likely model, was performed.

Data from 235/303 participants (77.6%) who completed the month 24 study visit were analysed. The structural model revealed associations between the putative factors and outcomes. The outcomes clustered together with severe fatigue as its central point. Loneliness, fear avoidance in response to symptoms, and illness perceptions were directly linked to the outcomes. Biological (inflammatory markers) and clinical (severity of initial illness) variables were connected to the outcomes only via psychological or social variables.

Our findings support a model where biomedical, psychological, and social factors contribute to the development of long-term sequelae of SARS-CoV-2 infection.

Drug use Disorder (DUD), the risk for which is substantially influenced by both genetic and social factors, is geographically concentrated in high-risk regions. An important step toward understanding this pattern is to examine geographical distributions of the genetic liability to DUD and a key demographic risk factor – social deprivation.

We calculated the mean family genetic risk score (FGRS) for DUD ((FGRSDUD) and social deprivation for each of the 5983 areas Demographic Statistical Areas (DeSO) for all of Sweden and used geospatial techniques to analyze and map these factors.

Using 2018 data, substantial spatial heterogeneity was seen in the distribution of the genetic risk for DUD in Sweden as a whole and in its three major urban centers which was confirmed by hot-spot analyses. Across DeSOs, FGRSDUD and s.d. levels were substantially but imperfectly correlated (r = + 0.63), with more scattering at higher FGRSDUD and s.d. scores. Joint mapping across DeSOs for FGRSDUD and s.d. revealed a diversity of patterns across Sweden. The stability of the distributions of FGRSDUD and s.d. in DeSOs within Sweden over the years 2012–2018 was quite high.

The geographical distribution of the genetic risk to DUD is quite variable in Sweden. DeSO levels of s.d. and FRGSDUD were substantially correlated but also disassociated in a number of regions. The observed patterns were largely consistent with known trends in the human geography of Sweden. This effort lays the groundwork for further studies of the sources of geographic variation in rates of DUD.

Motor activity fluctuations in healthy adults exhibit fractal patterns characterized by consistent temporal correlations across wide-ranging time scales. However, these patterns are disrupted by aging and psychiatric conditions. This study aims to investigate how fractal patterns vary across the sleep–wake cycle, differ based on individuals' recency of depression diagnosis, and change before and after a depressive episode.

Using actigraphy from two cohorts (n = 378), we examined fractal motor activity patterns both between individuals without depression and with varying recencies of depression and within individuals before and after depressive symptom recurrence. To evaluate fractal patterns, we quantified temporal correlations in motor activity fluctuations across different time scales using a scaling exponent, α. Linear mixed models were utilized to assess the influence of the sleep–wake cycle, (recency of) depression, and their interaction on α.

Fractal activity patterns in all individuals varied across the sleep–wake cycle, showing stronger temporal correlations during wakefulness (larger α = 1.035 ± 0.003) and more random activity fluctuations during sleep (smaller α = 0.784 ± 0.004, p < 0.001). This sleep–wake difference was reduced in recently depressed individuals (1–6 months), leading to larger α during sleep (0.836 ± 0.017), compared to currently depressed (0.781 ± 0.018, p = 0.006), remitted (0.776 ± 0.014, p < 0.001), and never-depressed individuals (0.773 ± 0.016, p < 0.001). Moreover, remitted individuals who experienced depressive symptom recurrence during antidepressant tapering exhibited a larger α during sleep after the symptom onset as compared to before (after: α = 0.703 ± 0.022; before: α = 0.680 ± 0.022; p < 0.001).

These findings suggest a link between fractal motor activity patterns during sleep and depressive symptom recurrence in remitted individuals and those with recent depression.

Past studies indicate daily increases in estrogen across the menstrual cycle protect against binge-eating (BE) phenotypes (e.g. emotional eating), whereas increases in progesterone enhance risk. Two previous studies from our laboratory suggest these associations could be due to differential genomic effects of estrogen and progesterone. However, these prior studies were unable to directly model effects of daily changes in hormones on etiologic risk, instead relying on menstrual cycle phase or mean hormone levels. The current study used newly modified twin models to examine, for the first time, the effects of daily changes in estradiol and progesterone on genetic/environmental influences on emotional eating in our archival twin sample assessed across 45 consecutive days.

Participants included 468 female twins from the Michigan State University Twin Registry. Daily emotional eating was assessed with the Dutch Eating Behavior Questionnaire, and daily saliva samples were assayed for ovarian hormone levels. Modified genotype × environment interaction models examined daily changes in genetic/environmental effects across hormone levels.

Findings revealed differential effects of daily changes in hormones on etiologic risk, with increasing genetic influences across progesterone levels, and increasing shared environmental influences at the highest estradiol levels. Results were consistent across primary analyses examining all study days and sensitivity analyses within menstrual cycle phases.

Findings are significant in being the first to identify changes in etiologic risk for BE symptoms across daily hormone levels and highlighting novel mechanisms (e.g. hormone threshold effects, regulation of conserved genes) that may contribute to the etiology of BE.

Executive control over low-level information processing is impaired proximal to psychosis onset with evidence of recovery over the first year of illness. However, previous studies demonstrating diminished perceptual modulation via attention are complicated by simultaneously impaired perceptual responses. The present study examined the early auditory gamma-band response (EAGBR), a marker of early cortical processing that appears preserved in first-episode psychosis (FEP), and its modulation by attention in a longitudinal FEP sample.

Magnetoencephalography was recorded from 25 FEP and 32 healthy controls (HC) during active and passive listening conditions in an auditory oddball task at baseline and follow-up (4–12 months) sessions. EAGBR inter-trial phase coherence (ITPC) and evoked power were measured from responses to standard tones. Symptoms were assessed using the Positive and Negative Syndrome Scale (PANSS).

There was no group difference in EAGBR power or ITPC. While EAGBR ITPC increased with attention in HC, this modulation was impaired among FEP. Diminished EAGBR modulation in FEP persisted at longitudinal follow-up. However, among FEP, recovery of EAGBR modulation was associated with reduced PANSS negative scores.

FEP exhibit impaired executive control over the flow of information at the earliest stages of sensory processing within auditory cortex. In contrast to previous work, this deficit was observed despite an intact measure of sensory processing, mitigating potential confounds. Recovery of sensory gain modulation over time was associated with reductions in negative symptoms, highlighting a source of potential resiliency against some of the most debilitating and treatment refractory symptoms in early psychosis.