What we know is shaped by our perceptual experiences. But how does our knowledge shape perception? To resolve this outstanding question, our research sheds light on how visual attention and visual expectations affect:

1) the efficiency with which our brain encodes natural image information

2) the speed with which our brain translates image information into meaningful concepts

3) the flow of visual information across the human cerebral cortex

To address these open questions, we deploy a variety of multivariate analysis techniques to fMRI and EEG data, including a multivariate measure of brain connectivity. Furthermore, we use advanced psychophysical methods, facilitated by trained deep convolutional neural networks, to create natural images for which we can quantify how informative each image detail is for image recognition.

Our ability to successfully communicate with other people is an essential skill in everyday life. Therefore, unravelling how the human brain is able to derive meaning from acoustic speech signals and to recognize our communication partner based on seeing a face represents an important scientific endeavour. Speech recognition depends on both the clarity of the acoustic input and on what we expect to hear. For example, in noisy listening conditions, listeners of the identical speech input can differ in their perception of what was said. Similarly for face recognition, brain responses to faces depend on expectations and do not simply reflect the presented facial features. These findings are compatible with the more general view that perception is an active process in which incoming sensory information is interpreted with respect to expectations. The neural mechanisms supporting such integration of sensory signals and expectations, however, remain to be identified. Read more...

Our group studies how aging influences cognition and emotion using behavioral tools, peripheral physiology as well as structural, functional and pharmacological neuroimaging methodologies. We have a major current focus being the differentiation between successful and non-successful aging with respect to emotional health. The latter seems to be maintained by compensatory and adaptive mechanisms in response to age-related life and brain changes. We want to know how the brain contributes to this adaptation and whether/why this adaptation is lacking in non-successful aging like late-life depression. In our current studies, we investigate how emotional resilience in aging but also in younger age is modulated by cognitive and emotional regulation strategies, lifestyle behavior (i.e. weight management) and – as part of the newly established CRC 289 - treatment expectations. Read more...

Using neuroscientific methods such as functional magnetic resonance imaging (fMRI) we study the neural mechanisms of emotional processing and how emotions affect other cognitive functions such as learning or decision making. In part, we employ very simple paradigms such as classical conditioning, because this enables a link of our research to existing animal work. Methodologically, we are also bridging the gap between human neuroscientific research and animal neurophysiology by using high resolution fMRI for instance to investigate emotional processing in different subnuclei of the amygdala and the striatum. The interaction between emotion and cognition is also exemplified by the modulation of pain processing by various cognitive factors. A prime example of this effect is placebo analgesia in which expectation and experience shape pain perception. Using fMRI (including novel MR protocols to investigate spinal cord responses), pharmacological interventions and computational models, we investigate the neuronal mechanisms underlying this effect. Read more...

The MR Physics group is working on the development of new and improved methods for magnetic resonance imaging (MRI) and spectroscopy (MRS) that aim to provide a non-invasive access to the structure, organization, function, and metabolism of the human central nervous system in vivo. Read more...

Every day in our lives is plastered with decisions, from the minute (What shall I have for
breakfast?) to the grand (Shall I pursue a career in decision neuroscience?). Common theoretical
accounts posit that the human brain accomplishes decision making through a series of neural
computations, in which the expected future reward of different decision options are compared with one another and then the option with the highest expected value is usually selected.
Thus, valuation lies at the heart of the puzzle of human decision making.
Our group is interested in tracking down these values in the brain and the representations of
choice options using computational modeling of choice behavior in combination with fMRI and
more recently with EEG. This approach allows us to identify neural structures that participate in the computation of expected values and their updating through learning.
In general, we try to understand valuation by perturbing this process through experimental manipulation, record the change in valuation and therefore learn how the brain organizes value computations. More recently, we have been focussing decision making in real-time social interactions and seek to understand how social reasoning about others affects our own choices. Moreover, we also investigate how stimulus properties, pharmacological manipulations, behavioral genetics are biasing human valuation.. Read more...

Our research focuses on neurotransmitter systems involved in aversive learning. In particular, we investigate how neurotransmitters such as Dopamine, Opioids and Cannabinoids contribute to emotional states of fear and anxiety. Read more...

Social cooperation underlies most – if not ultimately all – human achievements. When joining forces, individuals can achieve more than in isolation. However, cooperation risks exploitation. Each individual faces the temptation to defect and reap the benefits of cooperation without paying its costs. If too many individuals defect, cooperation breaks down and everybody ends up worse. Cooperation thus exposes a dilemma between collective and individual rationality. Decisions to cooperate – or not – thus necessarily involve thinking about decisions of others and about possible payoffs.
The group’s goal is to address the following broad questions: How do humans make decisions to cooperate with each other? What are the computational and neural processes of such decisions? The planned projects combine computational modeling of behavioral data, evolutionary game theory, model-based and multivariate analyses of fMRI data, pupillometry, virtual reality, and testing of psychiatric patients. Read more...

Our research focuses on the neurobiological underpinnings of fear and anxiety related processes. Thereby we use fear conditioning, extinction as well as return of fear manipulations as laboratory models for the acquisition and (behavioural) treatment of anxiety disorders as well as relapse respectively. In addition, we are interested in emotional attention processes as well as the molecular genetic associations with fear and anxiety related processes. Specifically, we are interested in how acute and chronic stress experiences over the lifetime affects behavioural and neural correlates of fear conditioning, extinction as well as attention processes. We use a multidimensional approach including peripheral psychophysiological (skin conductance responses, fear potentiated startle), hormone measurements, as well as functional neuroimaging (fMRI) methods. Read more...

Our work group continuously contributes to national and international research evaluating new therapy approaches to headaches and facial pain. Projects focus on the effectiveness of new pharmaceutical approaches but also on invasive and non-invasive neuromodulation and/or neurostimulation. Patients who are interested in participating in research might be given the opportunity to contribute to clinical trials subject to eligibility.
We are looking forward to receive your email: [email protected] . Read more...

Understanding basic mechanisms underlying normal and diseased human brain developments crucially depends on reliable knowledge of its anatomical microstructure and connectivity. Our long-term goal is to estimate microstructural properties non-invasively in living subjects using biophysical models of the MRI signal.We are developing novel methods for fusion of high-resolution quantitative MRI (qMRI) to enable MRI-based in vivo histology (hMRI) in the brain and spinal cord. The main focus is to develop valid MRI metrics that characterize key white matter microstructure properties as known from ex vivo histology (e.g. myelin density, fiber density, and the g-ratio ? i.e. the ratio between inner and outer fiber diameter). Read more

We are developing a classification system of learning and memory systems based on the information processing properties of the relevant brain areas. Read more