This thesis adopts a data-driven, multimodal approach to explore the complexity of psychiatric disorders, emphasizing the integration of diverse datasets to achieve a deeper understanding of these... Show moreThis thesis adopts a data-driven, multimodal approach to explore the complexity of psychiatric disorders, emphasizing the integration of diverse datasets to achieve a deeper understanding of these conditions. It illustrates this methodology across several chapters, each focusing on different aspects and biomarkers relevant to stress-related psychiatric disorders. Chapter 2 reevaluates the effects of intranasal oxytocin through a multimodal lens. Chapter 3 connects cortisol's in-vivo effects with brain responses and molecular architectures. Chapter 4 introduces a multimodal machine learning model to predict remission in depressive disorder, aiming for both accuracy and explanatory power. Chapter 5 examines the concept of resilience as a discrepancy between expected and observed mental health post-stress, assessing its predictive value for mental health outcomes. Finally, Chapter 6 explores a novel genetic risk score for stress-related disorders, testing its utility and applicability in various contexts. Through a comprehensive and data-centric approach, the thesis aims to uncover new insights into the etiology and treatment of psychiatric disorders, highlighting the importance of considering their multimodal nature. Show less
BackgroundThe ability to predict the disease course of individuals with major depressive disorder (MDD) is essential for optimal treatment planning. Here, we used a data-driven machine learning... Show moreBackgroundThe ability to predict the disease course of individuals with major depressive disorder (MDD) is essential for optimal treatment planning. Here, we used a data-driven machine learning approach to assess the predictive value of different sets of biological data (whole-blood proteomics, lipid metabolomics, transcriptomics, genetics), both separately and added to clinical baseline variables, for the longitudinal prediction of 2-year remission status in MDD at the individual-subject level.MethodsPrediction models were trained and cross-validated in a sample of 643 patients with current MDD (2-year remission n = 325) and subsequently tested for performance in 161 individuals with MDD (2-year remission n = 82).ResultsProteomics data showed the best unimodal data predictions (area under the receiver operating characteristic curve = 0.68). Adding proteomic to clinical data at baseline significantly improved 2-year MDD remission predictions (area under the receiver operating characteristic curve = 0.63 vs. 0.78, p = .013), while the addition of other omics data to clinical data did not yield significantly improved model performance. Feature importance and enrichment analysis revealed that proteomic analytes were involved in inflammatory response and lipid metabolism, with fibrinogen levels showing the highest variable importance, followed by symptom severity. Machine learning models outperformed psychiatrists’ ability to predict 2-year remission status (balanced accuracy = 71% vs. 55%).ConclusionsThis study showed the added predictive value of combining proteomic data, but not other omics data, with clinical data for the prediction of 2-year remission status in MDD. Our results reveal a novel multimodal signature of 2-year MDD remission status that shows clinical potential for individual MDD disease course predictions from baseline measurements. Show less
The characteristic endogenous circadian rhythm of plasma glucocorticoid concentrations is made up from an underlying ultradian pulsatile secretory pattern. Recent evidence has indicated that this... Show moreThe characteristic endogenous circadian rhythm of plasma glucocorticoid concentrations is made up from an underlying ultradian pulsatile secretory pattern. Recent evidence has indicated that this ultradian cortisol pul-satility is crucial for normal emotional response in man. In this study, we investigate the anatomical tran-scriptional and cell type signature of brain regions sensitive to a loss of ultradian rhythmicity in the context of emotional processing. We combine human cell type and transcriptomic atlas data of high spatial resolution with functional magnetic resonance imaging (fMRI) data. We show that the loss of cortisol ultradian rhythm alters emotional processing response in cortical brain areas that are characterized by transcriptional and cellular profiles of GABAergic function. We find that two previously identified key components of rapid non-genomic GC signaling - the ANXA1 gene and retrograde endocannabinoid signaling - show most significant differential expression (q = 3.99e- 10) and enrichment (fold enrichment = 5.56, q = 9.09e-4). Our results further indicate that specific cell types, including a specific NPY-expressing GABAergic neuronal cell type, and specific G protein signaling cascades underly the cerebral effects of a loss of ultradian cortisol rhythm. Our results provide a biological mechanistic underpinning of our fMRI findings, indicating specific cell types and cascades as a target for manipulation in future experimental studies. Show less
Stress initiates a cascade of (neuro)biological, physiological, and behavioral changes, allowing us to respond to a challenging environment. The human response to acute stress can be studied in... Show moreStress initiates a cascade of (neuro)biological, physiological, and behavioral changes, allowing us to respond to a challenging environment. The human response to acute stress can be studied in detail in controlled settings, usually in a laboratory environment. To this end, many studies employ acute stress paradigms to probe stress related outcomes in healthy and patient populations. Though valuable, these studies in themselves often have relatively limited sample sizes. We established a data-sharing and collaborative interdisciplinary initiative, the STRESS-NL database, which combines (neuro)biological, physiological, and behavioral data across many acute stress studies in order to accelerate our understanding of the human acute stress response in health and disease (www.stressdatabase.eu). Researchers in the stress field from 12 Dutch research groups of 6 Dutch universities created a database to achieve an accurate inventory of (neuro)biological, physiological, and behavioral data from laboratory-based human studies that used acute stress tests. Currently, the STRESS-NL database consists of information on 5529 individual participants (2281 females and 3348 males, age range 6-99 years, mean age 27.7 +/- 16 years) stemming from 57 experiments described in 42 independent studies. Studies often did not use the same stress paradigm; outcomes were different and measured at different time points. All studies currently included in the database assessed cortisol levels before, during and after experimental stress, but cortisol measurement will not be a strict requirement for future study inclusion. Here, we report on the creation of the STRESS-NL database and infrastructure to illustrate the potential of accumulating and combining existing data to allow meta-analytical, proof-of-principle analyses. The STRESS-NL database creates a framework that enables human stress research to take new avenues in explorative and hypothesis-driven data analyses with high statistical power. Future steps could be to incorporate new studies beyond the borders of the Netherlands; or build similar databases for experimental stress studies in rodents. In our view, there are major scientific benefits in initiating and maintaining such international efforts. Show less
Stress initiates a cascade of (neuro)biological, physiological, and behavioral changes, allowing us to respond to a challenging environment. The human response to acute stress can be studied in... Show moreStress initiates a cascade of (neuro)biological, physiological, and behavioral changes, allowing us to respond to a challenging environment. The human response to acute stress can be studied in detail in controlled settings, usually in a laboratory environment. To this end, many studies employ acute stress paradigms to probe stress related outcomes in healthy and patient populations. Though valuable, these studies in themselves often have relatively limited sample sizes. We established a data-sharing and collaborative interdisciplinary initiative, the STRESS-NL database, which combines (neuro)biological, physiological, and behavioral data across many acute stress studies in order to accelerate our understanding of the human acute stress response in health and disease (www.stressdatabase.eu). Researchers in the stress field from 12 Dutch research groups of 6 Dutch universities created a database to achieve an accurate inventory of (neuro)biological, physiological, and behavioral data from laboratory-based human studies that used acute stress tests. Currently, the STRESS-NL database consists of information on 5529 individual participants (2281 females and 3348 males, age range 6-99 years, mean age 27.7 +/- 16 years) stemming from 57 experiments described in 42 independent studies. Studies often did not use the same stress paradigm; outcomes were different and measured at different time points. All studies currently included in the database assessed cortisol levels before, during and after experimental stress, but cortisol measurement will not be a strict requirement for future study inclusion. Here, we report on the creation of the STRESS-NL database and infrastructure to illustrate the potential of accumulating and combining existing data to allow meta-analytical, proof-of-principle analyses. The STRESS-NL database creates a framework that enables human stress research to take new avenues in explorative and hypothesis-driven data analyses with high statistical power. Future steps could be to incorporate new studies beyond the borders of the Netherlands; or build similar databases for experimental stress studies in rodents. In our view, there are major scientific benefits in initiating and maintaining such international efforts. Show less
Background: Synthetic glucocorticoids like dexamethasone can cause severe neuropsychiatric effects. They preferentially bind to the glucocorticoid receptor (GR) over the mineralocorticoid receptor ... Show moreBackground: Synthetic glucocorticoids like dexamethasone can cause severe neuropsychiatric effects. They preferentially bind to the glucocorticoid receptor (GR) over the mineralocorticoid receptor (MR). High dosages result in strong GR activation but likely also result in lower MR activation based on GR-mediated negative feedback on cortisol levels. Therefore, reduced MR activity may contribute to dexamethasone-induced neuropsychiatric symptoms. Objective: In this single case study, we evaluate whether dexamethasone leads to reduced MR activation in the human brain. Brain tissue of an 8-year-old brain tumor patient was used, who suffered chronically from dexamethasone-induced neuropsychiatric symptoms and deceased only hours after a high dose of dexamethasone. Main outcome measures: The efficacy of dexamethasone to induce MR activity was determined in HEK293T cells using a reporter construct. Subcellular localization of GR and MR was assessed in paraffin-embedded hippocampal tissue from the patient and two controls. In hippocampal tissue from the patient and eight controls, mRNA of MR/GR target genes was measured. Results: In vitro, dexamethasone stimulated MR with low efficacy and low potency. Immunofluorescence showed the presence of both GR and MR in the hippocampal cell nuclei after dexamethasone exposure. The putative MR target gene JDP2 was consistently expressed at relatively low levels in the dexamethasone-treated brain samples. Gene expression showed substantial variation in MR/GR target gene expression in two different hippocampus tissue blocks from the same patient. Conclusions: Dexamethasone may induce MR nuclear translocation in the human brain. Conclusions on in vivo effects on gene expression in the brain await the availability of more tissue of dexamethasone-treated patients. Show less
Neuroimaging studies suggest that intranasal oxytocin (IN-OXT) may modulate emotional and social processes by altering neural activity patterns. The extent of brain penetration after IN-OXT is... Show moreNeuroimaging studies suggest that intranasal oxytocin (IN-OXT) may modulate emotional and social processes by altering neural activity patterns. The extent of brain penetration after IN-OXT is unclear, and it is currently unknown whether IN-OXT can directly bind central oxytocin receptors (OXTRs). We investigated oxytocin pathway gene expression in regions affected by IN-OXT on task-based fMRI. We found that OXTR is more highly expressed in affected than unaffected subcortical regions; this effect did not vary by task type or sex. Cortical results revealed higher OXTR expression in regions affected by IN-OXT in emotional processing tasks and in male-only data. No significant differences were found in expression of the closely related vasopressin receptors. Our findings suggest that the mechanism by which IN-OXT may alter brain functionality involves direct activation of central OXTRs. Show less
