Harmonizing Norwegian registries onto OMOP common data model: Mapping challenges and opportunities for pregnancy and COVID-19 research.

OBJECTIVE: Norwegian health registries covering entire population are used for administration, research, and emergency preparedness. We harmonized these data onto the Observational Medical Outcomes Partnership common data model (OMOP CDM) and enrich real-world data in OMOP format with COVID-19 related data. METHODS: Data from six registries (2018-2021) covering birth registrations, selected primary and secondary care events, vaccinations, and communicable disease notifications were mapped onto the OMOP CDM v5.3. An Extract-Transform-Load (ETL) pipeline was developed on simulated data using data characterization documents and scanning tools. We ran dashboard quality checks, cohort generations, investigated differences between source and mapped data, and refined the ETL accordingly. RESULTS: We mapped 1.5 billion rows of data of 5,673,845 individuals. Among these, there were 804,277 pregnancies, 483,585 mothers together with 792,477 children, and 472,948 fathers. We identified 382,516 positive tests for COVID-19 in 380,794 patients. These figures are consistent with results from source data. In addition to 11 million source codes mapped automatically, we mapped 237 non-standard codes to standard concepts and introduced 38 custom concepts to accommodate pregnancy-related terminologies that were not supported by OMOP CDM vocabularies. A total of 3,700/3,705 (99.8%) checks passed. The 5 failed checks could be explained by the nature of the data and only represent a small number of records. DISCUSSION AND CONCLUSION: Norwegian registry data were successfully harmonized onto OMOP CDM with high level of concordance and provides valuable source for federated COVID-19 related research. Our mapping experience is highly valuable for data partners with Nordic health registries.

COVID-19 and pregnancy: A European study on pre- and post-infection medication use.

PURPOSE: The COVID-19 pandemic has impacted medication needs and prescribing practices, including those affecting pregnant women. Our goal was to investigate patterns of medication use among pregnant women with COVID-19, focusing on variations by trimester of infection and location. METHODS: We conducted an observational study using six electronic healthcare databases from six European regions (Aragon/Spain; France; Norway; Tuscany, Italy; Valencia/Spain; and Wales/UK). The prevalence of primary care prescribing or dispensing was compared in the 30-day periods before and after a positive COVID-19 test or diagnosis. RESULTS: The study included 294,126 pregnant women, of whom 8943 (3.0%) tested positive for, or were diagnosed with, COVID-19 during their pregnancy. A significantly higher use of antithrombotic medications was observed particularly after COVID-19 infection in the second and third trimesters. The highest increase was observed in the Valencia region where use of antithrombotic medications in the third trimester increased from 3.8% before COVID-19 to 61.9% after the infection. Increases in other countries were lower; for example, in Norway, the prevalence of antithrombotic medication use changed from around 1-2% before to around 6% after COVID-19 in the third trimester. Smaller and less consistent increases were observed in the use of other drug classes, such as antimicrobials and systemic corticosteroids. CONCLUSION: Our findings highlight the substantial impact of COVID-19 on primary care medication use among pregnant women, with a marked increase in the use of antithrombotic medications post-COVID-19. These results underscore the need for further research to understand the broader implications of these patterns on maternal and neonatal/fetal health outcomes.

Data-driven health deficit assessment improves a frailty index's prediction of current cognitive status and future conversion to dementia: results from ADNI.

Frailty is a dementia risk factor commonly measured by a frailty index (FI). The standard procedure for creating an FI requires manually selecting health deficit items and lacks criteria for selection optimization. We hypothesized that refining the item selection using data-driven assessment improves sensitivity to cognitive status and future dementia conversion, and compared the predictive value of three FIs: a standard 93-item FI was created after selecting health deficit items according to standard criteria (FIs) from the ADNI database. A refined FI (FIr) was calculated by using a subset of items, identified using factor analysis of mixed data (FAMD)-based cluster analysis. We developed both FIs for the ADNI1 cohort (n = 819). We also calculated another standard FI (FIc) developed by Canevelli and coworkers. Results were validated in an external sample by pooling ADNI2 and ADNI-GO cohorts (n = 815). Cluster analysis yielded two clusters of subjects, which significantly (pFDR < .05) differed on 26 health items, which were used to compute FIr. The data-driven subset of items included in FIr covered a range of systems and included well-known frailty components, e.g., gait alterations and low energy. In prediction analyses, FIr outperformed FIs and FIc in terms of baseline cognition and future dementia conversion in the training and validation cohorts. In conclusion, the data show that data-driven health deficit assessment improves an FI's prediction of current cognitive status and future dementia, and suggest that the standard FI procedure needs to be refined when used for dementia risk assessment purposes.

No add-on effect of tDCS on fatigue and depression in chronic stroke patients: A randomized sham-controlled trial combining tDCS with computerized cognitive training.

BACKGROUND: Fatigue and emotional distress rank high among self-reported unmet needs in life after stroke. Transcranial direct current stimulation (tDCS) may have the potential to alleviate these symptoms for some patients, but the acceptability and effects for chronic stroke survivors need to be explored in randomized controlled trials. METHODS: Using a randomized sham-controlled parallel design, we evaluated whether six sessions of 1 mA tDCS (anodal over F3, cathodal over O2) combined with computerized cognitive training reduced self-reported symptoms of fatigue and depression. Among the 74 chronic stroke patients enrolled at baseline, 54 patients completed the intervention. Measures of fatigue and depression were collected at five time points spanning a 2 months period. RESULTS: While symptoms of fatigue and depression were reduced during the course of the intervention, Bayesian analyses provided evidence for no added beneficial effect of tDCS. Less severe baseline symptoms were associated with higher performance improvement in select cognitive tasks, and study withdrawal was higher in patients with more fatigue and younger age. Time-resolved symptom analyses by a network approach suggested higher centrality of fatigue items (except item 1 and 2) than depression items. CONCLUSION: The results reveal no add-on effect of tDCS on fatigue or depression but support the notion of fatigue as a relevant clinical symptom with possible implications for treatment adherence and response.

The genetic architecture of human brainstem structures and their involvement in common brain disorders.

Brainstem regions support vital bodily functions, yet their genetic architectures and involvement in common brain disorders remain understudied. Here, using imaging-genetics data from a discovery sample of 27,034 individuals, we identify 45 brainstem-associated genetic loci, including the first linked to midbrain, pons, and medulla oblongata volumes, and map them to 305 genes. In a replication sample of 7432 participants most of the loci show the same effect direction and are significant at a nominal threshold. We detect genetic overlap between brainstem volumes and eight psychiatric and neurological disorders. In additional clinical data from 5062 individuals with common brain disorders and 11,257 healthy controls, we observe differential volume alterations in schizophrenia, bipolar disorder, multiple sclerosis, mild cognitive impairment, dementia, and Parkinson's disease, supporting the relevance of brainstem regions and their genetic architectures in common brain disorders.

Brain Connectome Mapping of Complex Human Traits and Their Polygenic Architecture Using Machine Learning.

BACKGROUND: Mental disorders and individual characteristics such as intelligence and personality are complex traits sharing a largely unknown neuronal basis. Their genetic architectures are highly polygenic and overlapping, which is supported by heterogeneous phenotypic expression and substantial clinical overlap. Brain network analysis provides a noninvasive means of dissecting biological heterogeneity, yet its sensitivity, specificity, and validity in assessing individual characteristics relevant for brain function and mental health and their genetic underpinnings in clinical applications remain a challenge. METHODS: In a machine learning approach, we predicted individual scores for educational attainment, fluid intelligence and dimensional measures of depression, anxiety, and neuroticism using functional magnetic resonance imaging-based static and dynamic temporal synchronization between large-scale brain network nodes in 10,343 healthy individuals from the UK Biobank. In addition to using age and sex to serve as our reference point, we also predicted individual polygenic scores for related phenotypes and 13 different neuroticism traits and schizophrenia. RESULTS: Beyond high accuracy for age and sex, supporting the biological sensitivity of the connectome-based features, permutation tests revealed above chance-level prediction accuracy for trait-level educational attainment and fluid intelligence. Educational attainment and fluid intelligence were mainly negatively associated with static brain connectivity in frontal and default mode networks, whereas age showed positive correlations with a more widespread pattern. In contrast, prediction accuracy was at chance level for depression, anxiety, neuroticism, and polygenic scores across traits. CONCLUSIONS: These novel findings provide a benchmark for future studies linking the genetic architecture of individual and mental health traits with functional magnetic resonance imaging-based brain connectomics.

Attentional bias modification is associated with fMRI response toward negative stimuli in individuals with residual depression: a randomized controlled trial

Background: Attentional bias modification (ABM) may lead to more adaptive emotion perception and emotion regulation. Understanding the neural basis of these effects may lead to greater precision for the development of future treatments. Task-related functional MRI (fMRI) after ABM training has not been investigated in depression so far. The main aim of this randomized controlled trial was to explore differences in brain activity after ABM training, in response to emotional stimuli. Methods: A total of 134 people with previous depression, who had been treated for depression and had various degrees of residual symptoms, were randomized to 14 days of active ABM or a closely matched placebo training, followed by an fMRI emotion regulation task. The training procedure was a classical dot­probe task with emotional face stimuli. In the active ABM condition, the probes replaced the more positively valenced face of a given pair. As participants implicitly learned to predict the probe location, this would be likely to induce a more positive attentional bias. The placebo condition was identical, except for the contingency of the probe, which appeared equally behind positive and negative stimuli. We compared depression symptoms and subjective ratings of perceived negativity during fMRI between the training groups. We explored brain activation in predefined regions of interest and across the whole brain. We explored activation in areas associated with changes in attentional bias and degree of depression. Results: Compared with the placebo group, the ABM group showed reduced activation in the amygdala and the anterior cingulate cortex when passively viewing negative images. We found no group differences in predefined regions of interest associated with emotion regulation strategies. Response in the temporal cortices was associated with the degree of change in attentional bias and the degree of depressive symptoms in ABM versus placebo. Limitations: These findings should be replicated in other samples of patients with depression, and in studies using fMRI designs that allow analyses of within-group variability from baseline to follow-up. Conclusion: Attentional bias modification training has an effect on brain function in the circuitry associated with emotional appraisal and the generation of affective states. Clinicaltrials.gov identifier: NCT02931487

Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis.

Previous structural and functional neuroimaging studies have implicated distributed brain regions and networks in depression. However, there are no robust imaging biomarkers that are specific to depression, which may be due to clinical heterogeneity and neurobiological complexity. A dimensional approach and fusion of imaging modalities may yield a more coherent view of the neuronal correlates of depression. We used linked independent component analysis to fuse cortical macrostructure (thickness, area, gray matter density), white matter diffusion properties and resting-state functional magnetic resonance imaging default mode network amplitude in patients with a history of depression (n = 170) and controls (n = 71). We used univariate and machine learning approaches to assess the relationship between age, sex, case-control status, and symptom loads for depression and anxiety with the resulting brain components. Univariate analyses revealed strong associations between age and sex with mainly global but also regional specific brain components, with varying degrees of multimodal involvement. In contrast, there were no significant associations with case-control status, nor symptom loads for depression and anxiety with the brain components, nor any interaction effects with age and sex. Machine learning revealed low model performance for classifying patients from controls and predicting symptom loads for depression and anxiety, but high age prediction accuracy. Multimodal fusion of brain imaging data alone may not be sufficient for dissecting the clinical and neurobiological heterogeneity of depression. Precise clinical stratification and methods for brain phenotyping at the individual level based on large training samples may be needed to parse the neuroanatomy of depression.

Population-based neuroimaging reveals traces of childbirth in the maternal brain.

Maternal brain adaptations have been found across pregnancy and postpartum, but little is known about the long-term effects of parity on the maternal brain. Using neuroimaging and machine learning, we investigated structural brain characteristics in 12,021 middle-aged women from the UK Biobank, demonstrating that parous women showed less evidence of brain aging compared to their nulliparous peers. The relationship between childbirths and a "younger-looking" brain could not be explained by common genetic variation or relevant confounders. Although prospective longitudinal studies are needed, the results suggest that parity may involve neural changes that could influence women's brain aging later in life.

Effects of Attentional Bias Modification on residual symptoms in depression: a randomized controlled trial.

BACKGROUND: Following treatment, many depressed patients have significant residual symptoms. However, large randomised controlled trials (RCT) in this population are lacking. When Attention bias modification training (ABM) leads to more positive emotional biases, associated changes in clinical symptoms have been reported. A broader and more transparent picture of the true advantage of ABM based on larger and more stringent clinical trials have been requested. The current study evaluates the early effect of two weeks ABM training on blinded clinician-rated and self-reported residual symptoms, and whether changes towards more positive attentional biases (AB) would be associated with symptom reduction. METHOD: A total of 321 patients with a history of depression were included in a preregistered randomized controlled double-blinded trial. Patients were randomised to an emotional ABM paradigm over fourteen days or a closely matched control condition. Symptoms based on the Hamilton Rating Scale for Depression (HRSD) and Beck Depression Inventory II (BDI-II) were obtained at baseline and after ABM training. RESULTS: ABM training led to significantly greater decrease in clinician-rated symptoms of depression as compared to the control condition. No differences between ABM and placebo were found for self-reported symptoms. ABM induced a change of AB towards relatively more positive stimuli for participants that also showed greater symptom reduction. CONCLUSION: The current study demonstrates that ABM produces early changes in blinded clinician-rated depressive symptoms and that changes in AB is linked to changes in symptoms. ABM may have practical potential in the treatment of residual depression. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT02658682 (retrospectively registered in January 2016).

Data-Driven Clustering Reveals a Link Between Symptoms and Functional Brain Connectivity in Depression.

BACKGROUND: Depression is a complex disorder with large interindividual variability in symptom profiles that often occur alongside symptoms of other psychiatric domains, such as anxiety. A dimensional and symptom-based approach may help refine the characterization of depressive and anxiety disorders and thus aid in establishing robust biomarkers. We use resting-state functional magnetic resonance imaging to assess the brain functional connectivity correlates of a symptom-based clustering of individuals. METHODS: We assessed symptoms using the Beck Depression and Beck Anxiety Inventories in individuals with or without a history of depression (N = 1084) and high-dimensional data clustering to form subgroups based on symptom profiles. We compared dynamic and static functional connectivity between subgroups in a subset of the total sample (n = 252). RESULTS: We identified five subgroups with distinct symptom profiles, which cut across diagnostic boundaries with different total severity, symptom patterns, and centrality. For instance, inability to relax, fear of the worst, and feelings of guilt were among the most severe symptoms in subgroups 1, 2, and 3, respectively. The distribution of individuals was 32%, 25%, 22%, 10%, and 11% in subgroups 1 to 5, respectively. These subgroups showed evidence of differential static brain-connectivity patterns, in particular comprising a frontotemporal network. In contrast, we found no significant associations with clinical sum scores, dynamic functional connectivity, or global connectivity. CONCLUSIONS: Adding to the pursuit of individual-based treatment, subtyping based on a dimensional conceptualization and unique constellations of anxiety and depression symptoms is supported by distinct patterns of static functional connectivity in the brain.

Within-Network Connectivity in the Salience Network After Attention Bias Modification Training in Residual Depression: Report From a Preregistered Clinical Trial.

Alterations in resting state networks (RSNs) are associated with emotional- and attentional control difficulties in depressed individuals. Attentional bias modification (ABM) training may lead to more adaptive emotional processing in depression, but little is known about the neural underpinnings associated with ABM. In the current study a sample of 134 previously depressed individuals were randomized into 14 days of computerized ABM- or a closely matched placebo training regime followed by a resting state magnetic resonance imaging (MRI) scan. Using independent component analysis (ICA) we examined within-network connectivity in three major RSN's, the default mode network (DMN), the salience network (SN) and the central executive network (CEN) after 2 weeks of ABM training. We found a significant difference between the training groups within the SN, but no difference within the DMN or CEN. Moreover, a significant symptom improvement was observed in the ABM group after training. Clinical Trial Registration: www.ClinicalTrials.gov, identifier NCT02931487.

Multimodal functional and structural neuroimaging investigation of major depressive disorder following treatment with duloxetine.

BACKGROUND: Longitudinal neuroimaging studies of major depressive disorder (MDD) have most commonly assessed the effects of antidepressants from the serotonin reuptake inhibitor class and usually reporting a single measure. Multimodal neuroimaging assessments were acquired from MDD patients during an acute depressive episode with serial measures during a 12-week treatment with the serotonin-norepinephrine reuptake inhibitor (SNRI) duloxetine. METHODS: Participants were medication-free MDD patients (n = 32; mean age 40.2 years) in an acute depressive episode and healthy controls matched for age, gender, and IQ (n = 25; mean age 38.8 years). MDD patients received treatment with duloxetine 60 mg daily for 12 weeks with an optional dose increase to 120 mg daily after 8 weeks. All participants had serial imaging at weeks 0, 1, 8, and 12 on a 3 Tesla magnetic resonance imaging (MRI) scanner. Neuroimaging tasks included emotional facial processing, negative attentional bias (emotional Stroop), resting state functional MRI and structural MRI. RESULTS: A significant group by time interaction was identified in the anterior default mode network in which MDD patients showed increased connectivity with treatment, while there were no significant changes in healthy participants. In the emotional Stroop task, increased posterior cingulate activation in MDD patients normalized following treatment. No significant group by time effects were observed for happy or sad facial processing, including in amygdala responsiveness, or in regional cerebral volumes. Reduced baseline resting state connectivity within the orbitofrontal component of the default mode network was predictive of clinical response. An early increase in hippocampal volume was predictive of clinical response. CONCLUSIONS: Baseline resting state functional connectivity was predictive of subsequent clinical response. Complementary effects of treatment were observed from the functional neuroimaging correlates of affective facial expressions, negative attentional bias, and resting state. No significant effects were observed in affective facial processing, while the interaction effect in negative attentional bias and individual group effects in resting state connectivity could be related to the SNRI class of antidepressant medication. The specificity of the observed effects to SNRI pharmacological treatments requires further investigation. TRIAL REGISTRATION: Registered at clinicaltrials.gov ( NCT01051466 ).