Association of pharmacological treatments and real-world outcomes in borderline personality disorder.

OBJECTIVE: Most patients with borderline personality disorder (BPD) receive psychopharmacological treatment, but clinical guidelines on BPD lack consensus on the role of pharmacotherapy. We investigated the comparative effectiveness of pharmacological treatments for BPD. METHODS: We identified patients with BPD with treatment contact during 2006-2018 using Swedish nationwide register databases. By leveraging within-individual design, in which each individual was used as their own control to eliminate selection bias, we assessed the comparative effectiveness of pharmacotherapies. For each medication, we calculated the hazard ratios (HRs) for the following outcomes: (1) psychiatric hospitalization and (2) hospitalization owing to any cause or death. RESULTS: We identified 17,532 patients with BPD (2649 men; mean [SD] age = 29.8 [9.9]). Treatment with benzodiazepines (HR = 1.38, 95% CI = 1.32-1.43), antipsychotics (HR = 1.19, 95% CI = 1.14-124), and antidepressants (HR = 1.18, 95% CI = 1.13-1.23) associated with increased risk of psychiatric rehospitalization. Similarly, treatment with benzodiazepines (HR = 1.37, 95% CI = 1.33-1.42), antipsychotics (HR = 1.21, 95% CI = 1.17-1.26), and antidepressants (HR = 1.17, 95% CI = 1.14-1.21) was associated with a higher risk of all-cause hospitalization or death. Treatment with mood stabilizers did not have statistically significant associations with the outcomes. Treatment with ADHD medication was associated with decreased risk of psychiatric hospitalization (HR = 0.88, 95% CI = 0.83-0.94) and decreased risk of all-cause hospitalization or death (HR = 0.86, 95% CI = 0.82-0.91). Of the specific pharmacotherapies, clozapine (HR = 0.54, 95% CI = 0.32-0.91), lisdexamphetamine (HR = 0.79, 95% CI = 0.69-0.91), bupropion (HR = 0.84, 95% CI = 0.74-0.96), and methylphenidate (HR = 0.90, 95% CI = 0.84-0.96) associated with decreased risk of psychiatric rehospitalization. CONCLUSIONS: ADHD medications were associated with a reduced risk of psychiatric rehospitalization or hospitalization owing to any cause or death among individuals with BPD. No such associations were found for benzodiazepines, antidepressants, antipsychotics, or mood stabilizers.

Maternal prepregnancy body mass index and offspring white matter microstructure: results from three birth cohorts.

BACKGROUND AND AIMS: Prepregnancy 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 METHODS: The 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. RESULTS: Maternal 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. CONCLUSIONS: Maternal 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.

Early adversity and brain response to faces in young adulthood.

Early stressors play a key role in shaping interindividual differences in vulnerability to various psychopathologies, which according to the diathesis-stress model might relate to the elevated glucocorticoid secretion and impaired responsiveness to stress. Furthermore, previous studies have shown that individuals exposed to early adversity have deficits in emotion processing from faces. This study aims to explore whether early adversities associate with brain response to faces and whether this association might associate with the regional variations in mRNA expression of the glucocorticoid receptor gene (NR3C1). A total of 104 individuals drawn from the Northern Finland Brith Cohort 1986 participated in a face-task functional magnetic resonance imaging (fMRI) study. A large independent dataset (IMAGEN, N = 1739) was utilized for reducing fMRI data-analytical space in the NFBC 1986 dataset. Early adversities were associated with deviant brain response to fearful faces (MANCOVA, P = 0.006) and with weaker performance in fearful facial expression recognition (P = 0.01). Glucocorticoid receptor gene expression (data from the Allen Human Brain Atlas) correlated with the degree of associations between early adversities and brain response to fearful faces (R2 = 0.25, P = 0.01) across different brain regions. Our results suggest that early adversities contribute to brain response to faces and that this association is mediated in part by the glucocorticoid system. Hum Brain Mapp 38:4470-4478, 2017. © 2017 Wiley Periodicals, Inc.

Dopamine D2 receptor antagonism of antipsychotics and the risk of death due to choking.

The risk of fatal choking for people with schizophrenia and associations with antipsychotic medication are largely unknown. Therefore, we calculated the choking-related standardized mortality ratio for schizophrenia relative to the general population (SMRchoking). We also computed adjusted hazard ratios (aHR) of choking-related mortality for antipsychotics in a nationwide cohort of patients with schizophrenia (N = 59,916). SMRchoking was 20.5 (95 % confidence interval (CI)=17.1-23.9). The aHR was 1.74 (95 %CI=1.19-2.55) for strong dopamine 2-antagonists. For other antipsychotics, CIs included 1. Importantly, aHRs were particularly high for high dose categories of strong dopamine D2 receptor (D2R) antagonists. In conclusion, a schizophrenia diagnosis is associated with a 20-fold risk of death due to choking. This risk is elevated during use of strong D2R antagonist antipsychotics, particularly when prescribed in high dosages.

Development and Validation of a Machine Learning-Based Model of Mortality Risk in First-Episode Psychosis.

Importance: There is an absence of mortality risk assessment tools in first-episode psychosis (FEP) that could enable personalized interventions. Objective: To examine the feasibility of machine learning (ML) in discerning mortality risk in FEP and to assess whether such risk predictions can inform pharmacotherapy choices. Design, Setting, and Participants: In this prognostic study, Swedish nationwide cohort data (from July 1, 2006, to December 31, 2021) were harnessed for model development and validation. Finnish cohort data (from January 1, 1998, to December 31, 2017) were used for external validation. Data analyses were completed between December 2022 and December 2023. Main Outcomes and Measures: Fifty-one nationwide register variables, encompassing demographics and clinical and work-related histories, were subjected to ML to predict future mortality risk. The ML model's performance was evaluated by calculating the area under the receiver operating characteristic curve (AUROC). The comparative effectiveness of pharmacotherapies in patients was assessed and was stratified by the ML model to those with predicted high mortality risk (vs low risk), using the between-individual hazard ratio (HR). The 5 most important variables were then identified and a model was retrained using these variables in the discovery sample. Results: This study included 24 052 Swedish participants (20 000 in the discovery sample and 4052 in the validation sample) and 1490 Finnish participants (in the validation sample). Swedish participants had a mean (SD) age of 29.1 (8.1) years, 62.1% were men, and 418 died with 2 years. Finnish participants had a mean (SD) age of 29.7 (8.0) years, 61.7% were men, and 31 died within 2 years. The discovery sample achieved an AUROC of 0.71 (95% CI, 0.68-0.74) for 2-year mortality prediction. Using the 5 most important variables (ie, the top 10% [substance use comorbidities, first hospitalization duration due to FEP, male sex, prior somatic hospitalizations, and age]), the final model resulted in an AUROC of 0.70 (95% CI, 0.63-0.76) in the Swedish sample and 0.67 (95% CI, 0.56-0.78) in the Finnish sample. Individuals with predicted high mortality risk had an elevated 15-year risk in the Swedish sample (HR, 3.77 [95% CI, 2.92-4.88]) and an elevated 20-year risk in the Finnish sample (HR, 3.72 [95% CI, 2.67-5.18]). For those with predicted high mortality risk, long-acting injectable antipsychotics (HR, 0.45 [95% CI, 0.23-0.88]) and mood stabilizers (HR, 0.64 [95% CI, 0.46-0.90]) were associated with decreased mortality risk. Conversely, for those predicted to survive, only oral aripiprazole (HR, 0.38 [95% CI, 0.20-0.69]) and risperidone (HR, 0.38 [95% CI, 0.18-0.82]) were associated with decreased mortality risk. Conclusions and Relevance: In this prognostic study, an ML-based model was developed and validated to predict mortality risk in FEP. These findings may help to develop personalized interventions to mitigate mortality risk in FEP.

Real-world effectiveness of antidepressants, antipsychotics and their combinations in the maintenance treatment of psychotic depression. Evidence from within-subject analyses of two nationwide cohorts.

Psychotic depression (PD) is a severe mental disorder leading to functional disability and high risk of suicide, but very little is known about the comparative effectiveness of medications used in its maintenance treatment. The objective of this study was to investigate the comparative effectiveness of specific antipsychotics and antidepressants, and their combinations, on the risk of psychiatric hospitalization among persons with PD in routine care. Persons aged 16-65 years with a first-time diagnosis of PD were identified from Finnish (years 2000-2018) and Swedish (years 2006-2021) nationwide registers of inpatient care, specialized outpatient care, sickness absence, and disability pension. The main exposures were specific antipsychotics and antidepressants, and the main outcome measure was psychiatric hospitalization as a marker of severe relapse. The risk of hospitalization associated with periods of use vs. non-use of medications (expressed as adjusted hazard ratio, aHR) was assessed by a within-individual design, using each individual as his/her own control, and analyzed with stratified Cox models. The two national cohorts were first analyzed separately, and then combined using a fixed-effect meta-analysis. The Finnish cohort included 19,330 persons (mean age: 39.8±14.7 years; 57.9% women) and the Swedish cohort 13,684 persons (mean age: 41.3±14.0 years; 53.5% women). Individual antidepressants associated with a decreased risk of relapse vs. non-use of antidepressants were bupropion (aHR=0.73, 95% CI: 0.63-0.85), vortioxetine (aHR=0.78, 95% CI: 0.63-0.96) and venlafaxine (aHR=0.92, 95% CI: 0.86-0.98). Any long-acting injectable antipsychotic (LAI) (aHR=0.60, 95% CI: 0.45-0.80) and clozapine (aHR=0.72, 95% CI: 0.57-0.91) were associated with a decreased risk of relapse vs. non-use of antipsychotics. Among monotherapies, only vortioxetine (aHR=0.67, 95% CI: 0.47-0.95) and bupropion (aHR=0.71, 95% CI: 0.56-0.89) were associated with a significantly decreased risk of relapse vs. non-use of both antidepressants and antipsychotics. In an exploratory analysis of antidepressant-antipsychotic combinations, a decreased relapse risk was found for amitriptyline-olanzapine (aHR=0.45, 95% CI: 0.28-0.71), sertraline-quetiapine (aHR=0.79, 95% CI: 0.67-0.93) and venlafaxine-quetiapine (aHR=0.82, 95% CI: 0.73-0.91) vs. non-use of antidepressants and antipsychotics. Benzodiazepines and related drugs (aHR=1.29, 95% CI: 1.24-1.34) and mirtazapine (aHR=1.17, 95% CI: 1.07-1.29) were associated with an increased risk of relapse. These data indicate that, in the maintenance treatment of PD, bupropion, vortioxetine, venlafaxine, any LAI, clozapine, and only few specific antidepressant-antipsychotic combinations are associated with a decreased risk of relapse. These findings challenge the current recommendation by treatment guidelines to combine an antipsychotic with an antidepressant (without further specification) as standard treatment in PD.

The contribution of first-episode illness characteristics and cumulative antipsychotic usage to progressive structural brain changes over a long-term follow-up in schizophrenia.

Exposure to antipsychotics as well as certain first-episode illness characteristics have been associated with greater gray matter (GM) deficits in the early phase of schizophrenia. Whether the first-episode illness characteristics affect the long-term progression of the structural brain changes remain unexplored. We therefore assessed the role of first-episode illness characteristics and life-time antipsychotic use in relation to long-term structural brain GM changes in schizophrenia. Individuals with schizophrenia (SZ, n = 29) and non-psychotic controls (n = 61) from the Northern Finland Birth Cohort 1966 underwent structural MRI at the ages of 34 (baseline) and 43 (follow-up) years. At follow-up, the average duration of illness was 19.8 years. Voxel-based morphometry was used to assess the effects of predictors on longitudinal GM changes in schizophrenia-relevant brain areas. Younger age of onset (AoO), higher cumulative antipsychotic dose and severity of symptoms were associated with greater GM deficits in the SZ group at follow-up. None of the first-episode illness characteristics were associated with longitudinal GM changes during 9-year follow-up period. We conclude that a younger AoO and high life-time antipsychotic use may contribute to progression of structural brain changes in schizophrenia. Apart from AoO, other first-episode illness characteristics may not contribute to longitudinal GM changes in midlife.

Effects of Substance Use and Antisocial Personality on Neuroimaging-Based Machine Learning Prediction of Schizophrenia.

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.

Comparative Effectiveness of Pharmacotherapies for the Risk of Attempted or Completed Suicide Among Persons With Borderline Personality Disorder.

Importance: Suicidal behavior is a significant clinical concern in individuals with borderline personality disorder (BPD), but the effectiveness of pharmacotherapy on reducing suicide risk has remained unknown. Objective: To study the comparative effectiveness of different pharmacotherapies in preventing attempted or completed suicides in patients with BPD in Sweden. Design, Setting and Participants: In this comparative effectiveness research study, nationwide Swedish register databases of inpatient care, specialized outpatient care, sickness absences, and disability pensions were used to identify patients aged 16 to 65 years with registered treatment contact due to BPD during 2006 to 2021. Data were analyzed from September to December 2022. A within-individual design was used, in which each patient was used as their own control to eliminate selection bias. To control protopathic bias, sensitivity analyses were conducted, in which the first 1 or 2 months of medication exposure were omitted from the analyses. Main outcomes and Measures: Hazard ratio (HR) for attempted or completed suicide. Results: A total of 22 601 patients with BPD (3540 [15.7%] men; mean [SD] age, 29.2 [9.9] years) were included. During the 16-year follow-up (mean [SD] follow-up, 6.9 [5.1] years), 8513 hospitalizations due to attempted suicide and 316 completed suicides were observed. Attention-deficit/hyperactive disorder (ADHD) medication treatment, compared with its nonuse, was associated with a decrease in the risk of attempted or completed suicide (HR, 0.83; 95% CI, 0.73-0.95; false discovery rate [FDR]-corrected P = .001). Treatment with mood stabilizers did not have a statistically significant association with the main outcome (HR, 0.97; 95% CI, 0.87-1.08; FDR-corrected P = .99). Antidepressant (HR, 1.38; 95% CI, 1.25-1.53; FDR-corrected P < .001) and antipsychotic (HR, 1.18; 95% CI, 1.07-1.30; FDR-corrected P < .001) treatments were associated with an elevated risk of attempted or completed suicide. Of the investigated pharmacotherapies, treatment with benzodiazepines was associated with the highest risk of attempted or completed suicide (HR, 1.61; 95% CI, 1.45-1.78; FDR-corrected P < .001). These results remained similar when controlling for potential protopathic bias. Conclusions and Relevance: In this comparative effectiveness research study of a Swedish nationwide cohort, ADHD medication was the only pharmacological treatment associated with reduced risk of suicidal behavior among patients with BPD. Conversely, the findings suggest that benzodiazepines should be used with care among patients with BPD due to their association with increased risk of suicide.

Patterns of risk-Using machine learning and structural neuroimaging to identify pedophilic offenders.

Background: Child sexual abuse (CSA) has become a focal point for lawmakers, law enforcement, and mental health professionals. With high prevalence rates around the world and far-reaching, often chronic, individual, and societal implications, CSA and its leading risk factor, pedophilia, have been well investigated. This has led to a wide range of clinical tools and actuarial instruments for diagnosis and risk assessment regarding CSA. However, the neurobiological underpinnings of pedosexual behavior, specifically regarding hands-on pedophilic offenders (PO), remain elusive. Such biomarkers for PO individuals could potentially improve the early detection of high-risk PO individuals and enhance efforts to prevent future CSA. Aim: To use machine learning and MRI data to identify PO individuals. Methods: From a single-center male cohort of 14 PO individuals and 15 matched healthy control (HC) individuals, we acquired diffusion tensor imaging data (anisotropy, diffusivity, and fiber tracking) in literature-based regions of interest (prefrontal cortex, anterior cingulate cortex, amygdala, and corpus callosum). We trained a linear support vector machine to discriminate between PO and HC individuals using these WM microstructure data. Post hoc, we investigated the PO model decision scores with respect to sociodemographic (age, education, and IQ) and forensic characteristics (psychopathy, sexual deviance, and future risk of sexual violence) in the PO subpopulation. We assessed model specificity in an external cohort of 53 HC individuals. Results: The classifier discriminated PO from HC individuals with a balanced accuracy of 75.5% (sensitivity = 64.3%, specificity = 86.7%, P 5000 = 0.018) and an out-of-sample specificity to correctly identify HC individuals of 94.3%. The predictive brain pattern contained bilateral fractional anisotropy in the anterior cingulate cortex, diffusivity in the left amygdala, and structural prefrontal cortex-amygdala connectivity in both hemispheres. This brain pattern was associated with the number of previous child victims, the current stance on sexuality, and the professionally assessed risk of future sexual violent reoffending. Conclusion: Aberrant white matter microstructure in the prefronto-temporo-limbic circuit could be a potential neurobiological correlate for PO individuals at high-risk of reoffending with CSA. Although preliminary and exploratory at this point, our findings highlight the general potential of MRI-based biomarkers and particularly WM microstructure patterns for future CSA risk assessment and preventive efforts.

Are Brain Responses to Emotion a Reliable Endophenotype of Schizophrenia? An Image-Based Functional Magnetic Resonance Imaging Meta-analysis.

BACKGROUND: Impaired emotion processing constitutes a key dimension of schizophrenia and a possible endophenotype of this illness. Empirical studies consistently report poorer emotion recognition performance in patients with schizophrenia as well as in individuals at enhanced risk of schizophrenia. Functional magnetic resonance imaging studies also report consistent patterns of abnormal brain activation in response to emotional stimuli in patients, in particular, decreased amygdala activation. In contrast, brain-level abnormalities in at-risk individuals are more elusive. We address this gap using an image-based meta-analysis of the functional magnetic resonance imaging literature. METHODS: Functional magnetic resonance imaging studies investigating brain responses to negative emotional stimuli and reporting a comparison between at-risk individuals and healthy control subjects were identified. Frequentist and Bayesian voxelwise meta-analyses were performed separately, by implementing a random-effect model with unthresholded group-level T-maps from individual studies as input. RESULTS: In total, 17 studies with a cumulative total of 677 at-risk individuals and 805 healthy control subjects were included. Frequentist analyses did not reveal significant differences between at-risk individuals and healthy control subjects. Similar results were observed with Bayesian analyses, which provided strong evidence for the absence of meaningful brain activation differences across the entire brain. Region of interest analyses specifically focusing on the amygdala confirmed the lack of group differences in this region. CONCLUSIONS: These results suggest that brain activation patterns in response to emotional stimuli are unlikely to constitute a reliable endophenotype of schizophrenia. We suggest that future studies instead focus on impaired functional connectivity as an alternative and promising endophenotype.

Primary Nonadherence to Antipsychotic Treatment Among Persons with Schizophrenia.

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.

Mental well-being of healthcare workers in 2 hospital districts during the first wave of the COVID-19 pandemic in Finland: a cross-sectional study.

OBJECTIVES: 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. MATERIAL AND METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.

A machine learning approach to predict resilience and sickness absence in the healthcare workforce during the COVID-19 pandemic.

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.

Early Adversity and Emotion Processing From Faces: A Meta-analysis on Behavioral and Neurophysiological Responses.

BACKGROUND: Although the link between early adversity (EA) and later-life psychiatric disorders is well established, it has yet to be elucidated whether EA is related to distortions in the processing of different facial expressions. We conducted a meta-analysis to investigate whether exposure to EA relates to distortions in responses to different facial emotions at three levels: 1) event-related potentials of the P100 and N170, 2) amygdala functional magnetic resonance imaging responses, and 3) accuracy rate or reaction time in behavioral data. METHODS: The systematic literature search (PubMed and Web of Science) up to April 2020 resulted in 29 behavioral studies (n = 8555), 32 functional magnetic resonance imaging studies (n = 2771), and 3 electroencephalography studies (n = 197) for random-effect meta-analyses. RESULTS: EA was related to heightened bilateral amygdala reactivity to sad faces (but not other facial emotions). Exposure to EA was related to faster reaction time but a normal accuracy rate in response to angry and sad faces. In response to fearful and happy faces, EA was related to a lower accuracy rate only in individuals with recent EA exposure. This effect was more pronounced in individuals with exposure to EA before (vs. after) the age of 3 years. These findings were independent of psychiatric diagnoses. Because of the low number of eligible electroencephalography studies, no conclusions could be reached regarding the effect of EA on the event-related potentials. CONCLUSIONS: EA relates to alterations in behavioral and neurophysiological processing of facial emotions. Our study stresses the importance of assessing age at exposure and time since EA because these factors mediate some EA-related perturbations.

The progression of disorder-specific brain pattern expression in schizophrenia over 9 years.

Age plays a crucial role in the performance of schizophrenia vs. controls (SZ-HC) neuroimaging-based machine learning (ML) models as the accuracy of identifying first-episode psychosis from controls is poor compared to chronic patients. Resolving whether this finding reflects longitudinal progression in a disorder-specific brain pattern or a systematic but non-disorder-specific deviation from a normal brain aging (BA) trajectory in schizophrenia would help the clinical translation of diagnostic ML models. We trained two ML models on structural MRI data: an SZ-HC model based on 70 schizophrenia patients and 74 controls and a BA model (based on 561 healthy individuals, age range = 66 years). We then investigated the two models' predictions in the naturalistic longitudinal Northern Finland Birth Cohort 1966 (NFBC1966) following 29 schizophrenia and 61 controls for nine years. The SZ-HC model's schizophrenia-specificity was further assessed by utilizing independent validation (62 schizophrenia, 95 controls) and depression samples (203 depression, 203 controls). We found better performance at the NFBC1966 follow-up (sensitivity = 75.9%, specificity = 83.6%) compared to the baseline (sensitivity = 58.6%, specificity = 86.9%). This finding resulted from progression in disorder-specific pattern expression in schizophrenia and was not explained by concomitant acceleration of brain aging. The disorder-specific pattern's progression reflected longitudinal changes in cognition, outcomes, and local brain changes, while BA captured treatment-related and global brain alterations. The SZ-HC model was also generalizable to independent schizophrenia validation samples but classified depression as control subjects. Our research underlines the importance of taking account of longitudinal progression in a disorder-specific pattern in schizophrenia when developing ML classifiers for different age groups.

The relationship of genetic susceptibilities for psychosis with physiological fluctuation in functional MRI data.

Previously, schizophrenia is found to be related to the variability of the functional magnetic resonance imaging (fMRI) signal in the white matter. However, evidence about the relationship between genetic vulnerabilities and physiological fluctuation in the brain is lacking. We investigated whether familial risk for psychosis (FR) and polygenic risk score for schizophrenia (PRS) are linked with physiological fluctuation in fMRI data. We used data from the Oulu Brain and Mind study (n = 140-149, aged 20-24 years) that is a substudy of the Northern Finland Birth Cohort 1986. The participants underwent a resting-state fMRI scan. Coefficient of variation (CV) of blood oxygen level dependent (BOLD) signal (CVBOLD) was used as a proxy of physiological fluctuation in the brain. Familial risk was defined to be present if at least one parent had been diagnosed with psychosis previously. PRS was computed based on the results of the prior GWAS by the Schizophrenia Working Group. FR or PRS were not associated with CVBOLD in cerebrospinal fluid, white matter, or grey matter. The findings did not provide evidence for the previous suggestions that genetic vulnerabilities for schizophrenia become apparent in alterations of the variation of the BOLD signal in the brain.

Symptomatic psychosis risk and physiological fluctuation in functional MRI data.

BACKGROUND: Physiological brain pulsations have been shown to play a critical role in maintaining interstitial homeostasis in the glymphatic brain clearance mechanism. We investigated whether psychotic symptomatology is related to the physiological variation of the human brain using fMRI. METHODS: The participants (N = 277) were from the Northern Finland Birth Cohort 1986. Psychotic symptoms were evaluated with the Positive Symptoms Scale of the Structured Interview for Prodromal Syndromes (SIPS). We used the coefficient of variation of BOLD signal (CVBOLD) as a proxy for physiological brain pulsatility. The CVBOLD-analyses were controlled for motion, age, sex, and educational level. The results were also compared with fMRI and voxel-based morphometry (VBM) meta-analyses of schizophrenia patients (data from the Brainmap database). RESULTS: At the global level, participants with psychotic-like symptoms had higher CVBOLD in cerebrospinal fluid (CSF) and white matter (WM), when compared to participants with no psychotic symptoms. Voxel-wise analyses revealed that CVBOLD was increased, especially in periventricular white matter, basal ganglia, cerebellum and parts of the cortical structures. Those brain regions, which included alterations of physiological fluctuation in symptomatic psychosis risk, overlapped <6% with the regions that were found to be affected in the meta-analyses of previous fMRI and VBM studies in schizophrenia patients. Motion did not vary as a function of SIPS. CONCLUSIONS: Psychotic-like symptoms were associated with elevated CVBOLD in a variety of brain regions. The CVBOLD findings may produce new information about cerebral physiological fluctuations that have been out of reach in previous fMRI and VBM studies.

Structural and functional alterations in the brain gray matter among first-degree relatives of schizophrenia patients: A multimodal meta-analysis of fMRI and VBM studies.

OBJECTIVE: We conducted a multimodal coordinate-based meta-analysis (CBMA) to investigate structural and functional brain alterations in first-degree relatives of schizophrenia patients (FRs). METHODS: We conducted a systematic literature search from electronic databases to find studies that examined differences between FRs and healthy controls using whole-brain functional magnetic resonance imaging (fMRI) or voxel-based morphometry (VBM). A CBMA of 30 fMRI (754 FRs; 959 controls) and 11 VBM (885 FRs; 775 controls) datasets were conducted using the anisotropic effect-size version of signed differential mapping. Further, we conducted separate meta-analyses about functional alterations in different cognitive tasks: social cognition, executive functioning, working memory, and inhibitory control. RESULTS: FRs showed higher fMRI activation in the right frontal gyrus during cognitive tasks than healthy controls. In VBM studies, there were no differences in gray matter density between FRs and healthy controls. Furthermore, multi-modal meta-analysis obtained no differences between FRs and healthy controls. By utilizing the BrainMap database, we showed that the brain region which showed functional alterations in FRs (i) overlapped only slightly with the brain regions that were affected in the meta-analysis of schizophrenia patients and (ii) correlated positively with the brain regions that exhibited increased activity during cognitive tasks in healthy individuals. CONCLUSIONS: Based on this meta-analysis, FRs may exhibit only minor functional alterations in the brain during cognitive tasks, and the alterations are much more restricted and only slightly overlapping with the regions that are affected in schizophrenia patients. The familial risk did not relate to structural alterations in the gray matter.

The variability of functional MRI brain signal increases in Alzheimer's disease at cardiorespiratory frequencies.

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.