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Exposure to childhood adversity is common and associated with a host of negative developmental outcomes as well as differences in neural structure and function. It is commonly posited that these social experiences “get under the skin” in early childhood, increasing long-term risk through disruptions to biology. In this talk I propose a novel approach to studying the link between adversity, brain development, and risk for psychopathology, the dimensional model of adversity and psychopathology (DMAP). In this model we propose that adversity exposure can be defined according to different dimensions which we expect to impact health and well-being through different neural substrates. Whereas we expect deprivation to primarily disrupt function and structure of lateral association cortex (e.g., dorsolateral prefrontal cortex and superior parietal cortex) and thus complex cognitive processing such as executive functioning. In contrast, we expect threat to alter structure and function of subcortical structures such as the hippocampus and amygdala and midline regions associated with emotion regulation such as the ventral medial prefrontal cortex and thus, associated emotion reactivity and automatic regulation processes. In a series of studies I test the basic tenants of the DMAP concluding that initial evidence, using both a priori hypothesis testing and data-driven approaches is consistent with the proposed model. I conclude by describing future work addressing multiple dimensions of adversity and potential adjustments to the model. [more]

A considerable body of work over the last 10 years combining non-invasive electrophysiology (electroencephalography/magnetoencephalography) in patient populations with preclinical research has contributed to the conceptualization of schizophrenia as a disorder associated with aberrant neural dynamics and disturbances in excitation/inhibition (E/I) balance parameters. Specifically, I will propose that recent technological and analytic advances in MEG provide novel opportunities to address these fundamental questions as well as establish important links with translational research. We have carried out several studies which have tested the importance of neural oscillations in the pathophysiology of schizophrenia through a combination of MEG-measurements in ScZ-patients and pharmacological manipulations in healthy volunteers which target the NMDA-receptor. These results highlight a pronounced impairment in high-frequency activity in both chronic and unmedicated patients which could provide novel insights into basic circuit mechanisms underlying cognitive and perceptual dysfunctions. However, acute Ketamine only partly recreates abnormalities observed in both resting-state and task-related neural oscillations in ScZ, suggesting potentially shortcoming of this pharmacological model for capturing large-scale network dysfunctions. Our recent work has employed MEG to develop a biomarker for early detection and diagnosis of ScZ. We have obtained MEG- and MRS-data from 125 participants meeting clinical high-risk criteria (CHR), 90 controls and 30 FEP-patients. We found marked changes in the synchrony of gamma-band oscillations in visual and auditory cortices during sensory processing which predicted clinical outcomes. In addition, CHR-participants were characterized by elevated broad-band gamma-band activity at rest which correlated with increased glutamate levels. Together, these findings highlight the potential of MEG-based biomarkers for the early diagnosis of ScZ in at-risk populations. [more]

Various psychological disorders are characterized by pronounced cognitive biases, including biased orienting of attention to certain stimuli, distorted expectation of the likelihood to encounter specific objects, biased interpretation of ambiguous information and biased perception. Although these biases are common in psychopathology, most of the studies so far focused on one bias by employing traditional analysis methods. Therefore, little is known about the correlational and causal relations between different biases and about combined patterns that may characterize certain disorders. In this talk, I will discuss recent behavioral, fMRI and autonomic data showing links between biases, as well as modulation of biased emotional processing in different populations. Moreover, by employing machine-learning based analysis, we managed to specify specific behavioral patterns that characterize anxiety vs. depression, two disorders that share many characteristics and show high comorbidity. Finally, I will discuss recent evidence for abnormalities in the blood pressure reaction to aversive pictures among individuals with pre- hypertension, a population that is usually not studied in the context of psychological reactions. Taken together, these findings suggest new strategies to explore and treat maladaptive behaviors that have fundamental implications on the patients’ life. [more]

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Recent studies have questioned the reliability of many functional neuroimaging findings reported in the literature over the past decades. In my talk I will illustrate how novel analytic workflows (Dukart et al., 2018, Scientific Reports; Holiga et al. 2019, Science Translational Medicine) may overcome some of the critical limitations of functional neuroimaging analyses improving the reliability of the methods as well as providing an improved interpretation of potential signals with respect to underlying biology and for identification of biomarkers for neurological and psychiatric diseases. [more]

What drives us to trust someone we just met? Did we eat spaghetti for lunch because we saw our colleague eat spaghetti? Can we become happier when we are nicer to our neighbors? How does the content of our breakfast have anything to do with our social interactions throughout the day? Research from different disciplines such as economics, psychology and neuroscience have attempted to investigate the motives and modulators of human decision making. Our decisions can be flexibly modulated by the different experiences we have in our daily lives. These modulations can occur through our social networks, through the impact of our own behavior on the social environment, but also simply by the food we have eaten. Here, I will present a series of recent studies from my lab in which we shed light on the psychological, neural and metabolic motives and modulators of human decision making. [more]

The dopaminergic circuits lie at the core of learning and motivational processes through which we are able to form predictions about the future and take action accordingly. Studies in animals have shown that midbrain dopaminergic neurons projecting to the striatum signal events in the environment that deviate from what we expect. A prevalent model of the dopaminergic function suggests that these so-called prediction errors –propagated by the D1 dopamine receptors to cortical areas – modulate synaptic plasticity and thereby facilitate learning and initiation of action. While D2 dopamine receptors in the striatum as well as prefrontal striatal projection regulate and modulate this signal propagation. How this neurobiological model of dopaminergic activity relates to behaviour has been difficult to address. In my talk I will present several pharmacogenetic studies that map manipulations of the dopaminergic system on to various computational phenotypes. First of all, we are interested in the role of dopamine in updating beliefs in a social as well as a non-social context. And second, we explored dopamine’s involvement in model-based decision making. Specifically, how does blocking D2 transmission affect our ability to keep the regularities and knowledge about the world online as we make decisions and learn about the states of the world that are not directly observed? [more]

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