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Biomarkers sensitive to prodromal or early pathophysiological changes in Alzheimer’s disease (AD) symptoms could improve disease detection and enable timely interventions. Changes in brain hemodynamics may be associated with the main clinical AD symptoms. To test this possibility, we measured the variability of blood oxygen level-dependent (BOLD) signal in individuals from three independent datasets (totaling 80 AD patients and 90 controls). We detected a replicable increase in brain BOLD signal variability in the AD populations, which constituted a robust biomarker for clearly differentiating AD cases from controls. Fast BOLD scans showed that the elevated BOLD signal variability in AD arises mainly from cardiovascular brain pulsations. Manifesting in abnormal cerebral perfusion and cerebrospinal fluid convection, present observation presents a mechanism explaining earlier observations of impaired glymphatic clearance associated with AD in humans.

During the COVID-19 pandemic, healthcare workers (HCWs) have faced unprecedented workloads and personal health risks leading to mental disorders and surges in sickness absence. Previous work has shown that interindividual differences in psychological resilience might explain why only some individuals are vulnerable to these consequences. However, no prognostic tools to predict individual HCW resilience during the pandemic have been developed. We deployed machine learning (ML) to predict psychological resilience during the pandemic. The models were trained in HCWs of the largest Finnish hospital, Helsinki University Hospital (HUS, N = 487), with a six-month follow-up, and prognostic generalizability was evaluated in two independent HCW validation samples (Social and Health Services in Kymenlaakso: Kymsote, N = 77 and the City of Helsinki, N = 322) with similar follow-ups never used for training the models. Using the most predictive items to predict future psychological resilience resulted in a balanced accuracy (BAC) of 72.7–74.3% in the HUS sample. Similar performances (BAC = 67–77%) were observed in the two independent validation samples. The models' predictions translated to a high probability of sickness absence during the pandemic. Our results provide the first evidence that ML techniques could be harnessed for the early detection of COVID-19-related distress among HCWs, thereby providing an avenue for potential targeted interventions.

Guideline-conform treatment of mental disorders is compromised in immigrant populations, but longitudinal pharmacoepidemiologic patterns in bipolar disorder (BD) remain unknown. We aimed to close this knowledge gap by applying state sequence analysis (SSA) to comprehensively assess individual-level medication use. Psychopharmacological medication use was assessed among Swedish-born, second-generation, non-refugee and refugee first-generation immigrants with incident BD diagnosed in Sweden 2006–2015 ( n  = 24,578, 16–65 years). Three years of medication-use were conceptualized with SSA as consecutive sequences of three-month periods. Anticonvulsant mood-stabilizer, lithium and antipsychotic use was considered adequate treatment. Typologies were identified by clustering and associated with population groups and covariates applying multinomial logistic regression, yielding odds ratios (OR) for comparison to the majority typology as well as estimated probabilities for each typology. Immigrant populations discontinued medication within 6 months more frequently than Swedish-born (42.1–45.7% vs 36.8%). Transitions from periods lacking medication to adequate treatment showed low likelihood across population groups (8.9–10.1%). T reatment failure (48.3% of refugees, 32.3% of Swedish-born), representing lack of adequate and antidepressant medication, predominated among seven identified typologies. Compared to Swedish-born and treatment failure , adjusted OR for other typologies were lower for refugees (0.3–0.5) and other immigrant groups (0.5–0.8). Adjusting for covariates, highest probabilities for treatment failure were computed for non-refugee (44%) and refugee first-generation immigrants (51%), followed by individuals with low education level (42%) and psychiatric comorbidities (attention-deficit/hyperactivity disorder 38%, substance-use disorder 37%). In conclusion, immigrant groups, particularly refugees, with incident BD are less likely to receive adequate treatment, requiring special emphasis on guideline-conformance.

Background and aimsPrepregnancy maternal obesity is a global health problem and has been associated with offspring metabolic and mental ill-health. However, there is a knowledge gap in understanding potential neurobiological factors related to these associations. This study explored the relation between maternal prepregnancy body mass index (BMI) and offspring brain white matter microstructure at the age of 6, 10, and 26 years in three independent cohorts.Subjects and methodsThe study used data from three European birth cohorts (n = 116 children aged 6 years, n = 2466 children aged 10 years, and n = 437 young adults aged 26 years). Information on maternal prepregnancy BMI was obtained before or during pregnancy and offspring brain white matter microstructure was measured at age 6, 10, or 26 years. We used magnetic resonance imaging-derived fractional anisotropy (FA) and mean diffusivity (MD) as measures of white matter microstructure in the brainstem, callosal, limbic, association, and projection tracts. Linear regressions were fitted to examine the association of maternal BMI and offspring white matter microstructure, adjusting for several socioeconomic and lifestyle-related confounders, including education, smoking, and alcohol use.ResultsMaternal BMI was associated with higher FA and lower MD in multiple brain tracts, for example, association and projection fibers, in offspring aged 10 and 26 years, but not at 6 years. In each cohort maternal BMI was related to different white matter tract and thus no common associations across the cohorts were found.ConclusionsMaternal BMI was associated with higher FA and lower MD in multiple brain tracts in offspring aged 10 and 26 years, but not at 6 years of age. Future studies should examine whether our observations can be replicated and explore the potential causal nature of the findings.

Introduction Benzodiazepines and related drugs (BZDRs) may be prescribed to treat anxiety and insomnia in persons with depression. Although BZDRs are recommended for short‐term use only, their use may be prolonged. Despite research on the use of BZDRs in depression, to our knowledge, long‐term use and its predictors have not been previously studied. The aim of this study was to estimate the incidence of long‐term BZDR use in persons with depression and to identify sociodemographic and clinical factors associated with prolonged use. Methods Data were extracted from Finnish nationwide registers. The study focused on persons with depression aged 16–65 years who initiated BZDR use between July 1, 2015 and June 30, 2018. Persons with a previous diagnosis of bipolar disorder, schizophrenia‐spectrum disorder, or dementia, or those who did not have at least 180 days of follow‐up, were excluded from the study. Sociodemographic and clinical factors associated with long‐term (≥180 days) versus shorter use were compared with logistic regression. The final study sample included 11,303 BZDR initiators. Results A total of 849 (7.5%, CI 95% 7.0–8.0) BZDR initiators became long‐term users. The mean age of long‐term users was 39.8 years (SD 13.3), and more than half of them were females (58.9%). Factors associated with long‐term BZDR use included age over 45 years (45–54: aOR 1.56, 95% CI 1.22–1.99, ≥55: 1.72, 1.32–2.25 compared to ≤24 years), male gender (1.49, 1.28–1.73), substance use disorder (excl. alcohol) (1.79, 1.27–2.29), use of opioid (1.75, 1.38–2.21) or quetiapine (1.65, 1.18–2.27), ≥3 antidepressants used in the previous year (1.63, 1.22–2.19), socioeconomic status other than employed, the use of antidepressants with hypnotic effects (1.25, 1.06–1.47) and anxiety disorder (1.16, 1.00–1.35). Conclusions This Finnish population‐based cohort study identified sociodemographic and clinical factors that should be considered in the initiation and monitoring of BZDR treatment among persons with depression.

The COVID-19 pandemic has caused unseen pressure on healthcare systems in many countries, jeopardizing the mental well-being of healthcare workers. The authors aimed to assess the mental well-being of Finnish healthcare workers from 2 hospital districts (Helsinki University Hospital [HUS] and Social and Health Services in Kymenlaakso [Kymsote]) with differing COVID-19 incidence rates during the first wave of the COVID-19 pandemic in spring 2020. A total number of 996 healthcare workers (HUS N = 862, Kymsote N = 134) participated in this prospectively conducted survey study during summer 2020. Symptom criteria of self-reported mental health symptoms followed ICD-10 classification, excluding duration criteria. Participants were divided into symptom categories \"often/sometimes\", and \"rarely/never\". These groups were compared to sociodemographic factors and factors related to work, workload, and well-being. The degree of mental health symptoms did not differ between the 2 healthcare districts despite differing COVID-19 incidences (p = 1). The authors observed a significant relationship between self-reported diagnostic mental health symptoms and experiences of insufficient instructions for protection against COVID-19 (in HUS cohort p < 0.001), insufficient recovery from work (p < 0.001), and subjective increased workload (p < 0.001). The authors' results show the importance of well-planned and sufficient instructions for protection from SARS-CoV-2 for healthcare workers, indicating their need to feel safe and protected at work. The workload of healthcare workers should be carefully monitored to keep it moderate and ensure sufficient recovery. Sufficient control of the epidemic to keep the burden of the healthcare system low is vital for healthcare workers' well-being. Int J Occup Med Environ Health. 2022;35(6):708-18.

The Northern Finland Birth Cohort 1986 is a large population-based birth cohort, which aims to promote health and wellbeing of the population. In this paper, we systematically review the psychiatric research performed in the cohort until today, i.e. at the age of 32 years of the cohort (2018). We conducted a systematic literature search using the databases of PubMed and Scopus and complemented it with a manual search. We found a total of 94 articles, which were classified as examining ADHD, emotional and behavioural problems, psychosis risk or other studies relating to psychiatric subjects. The articles are mainly based on two large comprehensive follow-up studies of the cohort and several substudies. The studies have often used also nationwide register data. The studies have found several early predictors for the aforementioned psychiatric outcomes, such as problems at pregnancy and birth, family factors in childhood, physical inactivity and substance use in adolescence. There are also novel findings relating to brain imaging and cognition, for instance regarding familial risk of psychosis in relation to resting state functional MRI. The Northern Finland Birth Cohort 1986 has been utilised frequently in psychiatric research and future data collections are likely to lead to new scientifically important findings. Abbreviations: attention deficit hyperactivity disorder (ADHD); magnetic resonance imaging (MRI)

Abstract It has remained unclear what factors relate to primary nonadherence to antipsychotic treatment and whether specific agents and routes of administration differ in how patients adhere to them. We collected electronic prescriptions and their dispensings from the Finnish electronic prescription database for 29 956 patients with schizophrenia prescribed antipsychotics via electronic prescription during 2015–2016. We defined primary nonadherence as being prescribed an antipsychotic, which was not dispensed from the pharmacy within one year from prescription. Using logistic regression, we analyzed whether several sociodemographic and clinical factors related to nonadherence. We found that 31.7% (N = 9506) of the patients demonstrated primary nonadherence to any of their prescribed antipsychotics. We found that young age (OR = 1.77, 95%CI = 1.59–1.96), concomitant benzodiazepines (OR = 1.47, 95%CI = 1.40–1.55) and mood stabilizers (OR = 1.29, 95%CI = 1.21–1.36), substance abuse (OR = 1.26 95%CI = 1.19–1.35), previous suicide attempt (OR = 1.21, 95%CI = 1.11–1.31), diabetes (OR = 1.15, 95%CI = 1.06–1.25), asthma/COPD (OR = 1.14, 95%CI = 1.04–1.25), and cardiovascular disease (OR = 1.12, 95%CI = 1.05–1.19), were related to primary nonadherence to antipsychotic treatment. Patients using clozapine showed the lowest nonadherence (4.77%, 95%CI = 4.66–4.89), and patients using long-acting injectables were more adherent to treatment (7.27%, 95%CI = 6.85–7.71) when compared to respective oral agents (10.26%, 95%CI = 10.02–10.49). These results suggest that selection between different pharmacological agents and routes of administration while taking into account patients’ concomitant medications (benzodiazepines in particular) and comorbidities play a key role in primary nonadherence to antipsychotic treatment.

Abstract Background and Hypothesis Individuals with psychosis have an increased risk of committing and being victims of violence. There are frequent media reports of psychotic individuals assaulting strangers, which may cause fear and stigmatization among the general public. We hypothesize that homicides targeting strangers by psychotic individuals are rare. Study Design Systematic review and meta-analysis to assess what percentage of homicide offenders suffering from psychosis target strangers. Medline database was searched with search term ‘psychosis OR schizophrenia AND homicide’ from inception to 10/2024. Articles published in peer-reviewed journals, written in English, and reporting the total number of homicide offenders with psychosis (ICD-10: F20, F22, F25, F30-F31, F32.3, F29) and stranger victims were included. PRISMA guidelines were followed. Studies with inadequate data were excluded. A random-effects meta-analysis using meta and metafor packages in R version 4.4.2 was conducted using the restricted maximum-likelihood (REML) method to account for variability across studies. The primary outcome was the pooled rate of stranger homicides among individuals with psychosis who committed a homicide, expressed as a proportion with 95% confidence intervals (CIs). Study Results Thirteen studies were included, comprising a total of 1,438 perpetrators who had killed 177 strangers. Meta-analysis of these studies indicates that 12.7% (95% CI: 7.85–17.56 and heterogeneity I2 89.49%) of the homicides by psychotic individuals are targeted at strangers. Male gender explained 26.9% of between-study variance (P-value<0.05). Conclusions Although still rare, the percentage of homicides committed by psychotic individuals and targeted at strangers is higher than previously reported.

Background and hypothesis Neuroimaging-based machine learning (ML) algorithms have the potential to aid the clinical diagnosis of schizophrenia. However, literature on the effect of prevalent comorbidities such as substance use disorder (SUD) and antisocial personality (ASPD) on these models’ performance has remained unexplored. We investigated whether the presence of SUD or ASPD affects the performance of neuroimaging-based ML models trained to discern patients with schizophrenia (SCH) from controls. Study design We trained an ML model on structural MRI data from public datasets to distinguish between SCH and controls (SCH = 347, controls = 341). We then investigated the model’s performance in two independent samples of individuals undergoing forensic psychiatric examination: sample 1 was used for sensitivity analysis to discern ASPD (N = 52) from SCH (N = 66), and sample 2 was used for specificity analysis to discern ASPD (N = 26) from controls (N = 25). Both samples included individuals with SUD. Study results In sample 1, 94.4% of SCH with comorbid ASPD and SUD were classified as SCH, followed by patients with SCH + SUD (78.8% classified as SCH) and patients with SCH (60.0% classified as SCH). The model failed to discern SCH without comorbidities from ASPD + SUD (AUC = 0.562, 95%CI = 0.400–0.723). In sample 2, the model’s specificity to predict controls was 84.0%. In both samples, about half of the ASPD + SUD were misclassified as SCH. Data-driven functional characterization revealed associations between the classification as SCH and cognition-related brain regions. Conclusion Altogether, ASPD and SUD appear to have effects on ML prediction performance, which potentially results from converging cognition-related brain abnormalities between SCH, ASPD, and SUD.