Event archive

Host: Department of Neurology

Dr. Joachim Lange | The rhythms of temporal perception

MindBrainBody Lecture

Thomas J. Baumgarten, Ph.D | Effects of intrinsic and extrinsic neural activity changes on stimulus processing and perception

MindBrainBody Lecture

Professor Svenja Caspers | Interindividual variability of brain phenotypes – towards population neuroimaging

Guest Lecture

PhD Louise P. Kirsch | What’s so special about touch? A multidimensional approach to study social touch

Guest Lecture

Dr Marlene Bönstrup | Low-frequency brain oscillations as a target for an on-demand brain stimulation in human motor rehabilitation

Cognitive Neurology Lecture

PhD Katherine Storrs | Learning About the World By Learning About Images

Guest Lecture
Computational visual neuroscience has come a long way in the past 10 years. For the first time, we have fully explicit, image-computable models that can recognise objects with near-human accuracy, and predict brain activity in high-level visual regions. I will present evidence that diverse deep neural network architectures all predict brain representations well, and that task-training and subsequent reweighting of model features is critical to this high performance. However, vision is not yet explained. The most successful models are deep neural networks that have been supervised using ground-truth labels for millions of images. Brains have no such access to the ground truth, and must instead learn directly from sensory data. Unsupervised deep learning, in which networks learn statistical regularities in their data by compressing, extrapolating or predicting images and videos, is an ecologically feasible alternative. I will show that an unsupervised deep network trained on an environment of 3D rendered surfaces with varying shape, material and illumination, spontaneously comes to encode those factors in its internal representations. Most strikingly, the network makes patterns of errors in its perception of material which follow, on an image-by-image basis, the patterns of errors made by human observers. Unsupervised deep learning may provide a coherent framework for how our perceptual dimensions arise. [more]

Dr Andreas Horn | Connectomic Brain Stimulation

Guest Lecture

Prof. Margaret A. Sheridan | Deprivation and threat, testing conceptual model of adversity exposure and developmental outcomes

Guest Lecture
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]

PhD Peter Johannes Uhlhaas | Using Magnetoencephalography to Identify Circuit Dysfunctions and Biomarkers in Schizophrenia

Guest Lecture
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]

PhD Hadas Okon-Singer | Cognitive-Emotional Biases in Psychopathology: Searching for New Treatment Strategies

Guest Lecture
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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