SUPERVISORS: PROF. DR. WILLEM VERWEY (UT), DR. FATMEH YAVARI (IFADO), PROF. DR. MICHAEL NITSCHE (IFADO)
Fear extinction is relevant for adaptive behaviour. Deficits are associated with psychiatric diseases such as phobia and depression. For its physiological foundation, an anterior network which incorporates the amygdala, the hippocampus, and the prefrontal cortex, has been described (Baldi & Bucherelli, 2015). A potential cerebellar contribution, including interactions with the anterior network, has been also discussed (Thürling et al., 2015). Here, we explore cerebellar-anterior network functional connectivity via multi-electrode tACS approaches. A well-established two-day aversive fear-conditioning paradigm will be used. Skin Conduction Responses (SCR), breathing and pulse frequency will be measured to provide an index of conditioned fear. We will also obtain spontaneous EEG and stimulus-locked event-related potentials (ERP). We expect that enhancing oscillatory functional connectivity of cerebellar-anterior areas -ventromedial prefrontal cortex and cerebellar hemispheres- in the theta frequency band improve extinction learning. The experiment will be performed at the Ifado in Dortmund. Students are welcome to contact the following email for more details: Yavari@ifado.de
Baldi, E., & Bucherelli, C. (2015). Brain sites involved in fear memory reconsolidation and extinction of rodents. Neurosci Biobehav Rev, 53, 160-190. doi:10.1016/j.neubiorev.2015.04.003
Thurling, M., Kahl, F., Maderwald, S., Stefanescu, R. M., Schlamann, M., Boele, H. J., . . . Timmann, D. (2015). Cerebellar cortex and cerebellar nuclei are concomitantly activated during eyeblink conditioning: a 7T fMRI study in humans. J Neurosci, 35(3), 1228-1239. doi:10.1523/JNEUROSCI.2492-14.2015
Blatt, G. J., Oblak, A. L., & Schmahman, J. D. (2013). Cerebellar connections with limbic circuits: anatomy and functional implications. Handbook of the cerebellum and cerebellar disorders(Springer Science and Business Media Dordrecht), 479-496.
Hoffmann, L. C., & Berry, S. D. (2009). Cerebellar theta oscillations are synchronized during hippocampal theta contingent trace conditioning. Proc Natl Acad Sci U S A, 106(50), 21371-21376. doi:10.1073/pnas.0908403106
Mauk, M. D., & Ohyama, T. (2004). Extinction as new learning versus unlearning: considerations from a computer simulation of the cerebellum. Learn Mem, 11(5), 566-571. doi:10.1101/lm.83504
Milad, M. R., & Quirk, G. J. (2012). Fear extinction as a model for translational neuroscience: ten years of progress. Annu Rev Psychol, 63, 129-151. doi:10.1146/annurev.psych.121208.131631
Nitsche, M. A., Cohen, L. G., Wassermann, E. M., Priori, A., Lang, N., Antal, A., . . . Pascual-Leone, A. (2008). Transcranial direct current stimulation: State of the art 2008. Brain Stimul, 1(3), 206-223. doi:10.1016/j.brs.2008.06.004
SUPERVISOR: PROF.DR. WILLEM VERWEY
An important issue is how people develop motor automaticity. This is the capacity to execute a series of successive movements while little attention is needed for executing them. It is as if the limbs know what to do.
Such motor skills can be investigated in the laboratory with a sequential key pressing task called the Discrete Sequence Production (DSP) task (Abrahamse et al., 2013). Current theorizing indicates that participants initially develop a central-symbolic representation for each keying sequence consisting of spatial and verbal codes (Verwey et al., 2016). With extensive practice another type of representation would develop, a motor chunk that chains representations at the motor level (Verwey et al., 2015).
The aim of this theory-driven project is to show that central-symbolic representations develop due to repeated preparation in short-term memory of the responses before they are executed, whereas motor chunks result from repeatedly executing the sequence, irrespective of whether they are prepared. This will be investigated in two relatively simple keying experiments in BMS lab in which participants have to learn various discrete keying sequences.
Abrahamse, E. L., Ruitenberg, M. F. L., De Kleine, E., & Verwey, W. B. (2013). Control of automated behaviour: Insights from the Discrete Sequence Production task. Frontiers in Human Neuroscience, 7(82), 1-16.
Verwey, W. B., Groen, E. C., & Wright, D. L. (2016). The stuff that motor chunks are made of: Spatial instead of motor representations? Experimental Brain Research, 234(2), 353-366.
Verwey, W. B., Shea, C. H., & Wright, D. L. (2015). A cognitive framework for explaining serial processing and sequence execution strategies. Psychonomic Bulletin & Review, 22(1), 54-77.
SUPERVISORS: PROF. DR. WILLEM VERWEY (UT), MAURO LARRA (IFADO)
The stress response is characterized by an activation of peripheral bodily response systems culminating in the release of stress hormones, most notably adrenaline and cortisol. These messengers act in concert to alter brain function leading to changes in perception, cognition and behavior under stress. While it is generally assumed that stress inhibits top-down control, recent evidence suggests rather process specific effects enabling a fine-tuned adaptation of psychological functions. The master project will focus on how stress influences the top-down control of spatial attention. An EEG experiment will be conducted in which spatial attention is manipulated by endogenous and exogenous cues after participants have been exposed to a laboratory stressor (Cold Pressor Test) or a control procedure. Stress responses are quantified via analysis of salivary stress hormone concentrations along with cardiovascular and psychological parameters. Attentional effects are assessed in lateralized power spectra of the EEG as well as behavioral data (manual reactions). This project offers the opportunity to gather insights into psychobiological stress research and acquire experience in the acquisition and analysis of electrophysiological data. The experiment will be carried out at the IfADo in Dortmund, Germany, and will be supervised by Dr. Mauro Larra.
Larra, M.F., Pramme, L., Schachinger, H., Frings, C., 2016. Stress and selective attention: Immediate and delayed stress effects on inhibition of return. Brain Cogn 108, 66-72.
Joels, M., Baram, T.Z., 2009. The neuro-symphony of stress. Nat Rev Neurosci 10, 459-466.
Fries, P., Reynolds, J.H., Rorie, A.E., Desimone, R., 2001. Modulation of oscillatory neuronal synchronization by selective visual attention. Science 291, 1560-1563.
Supervisors: dr. Rob Van der Lubbe, dr. Simone Borsci
Several authors argued that retrieval of an item from visual short term memory (internal spatial attention) and focusing attention on an externally presented item (external spatial attention) are similar. In a recent EEG study (Van der Lubbe et al., 2014) we presented four-stimulus arrays and observed increased power in the alpha and theta bands at ipsilateral sites above occipital cortex with precues and with postcues appearing 3,000 ms after array offset. These findings indeed support the idea of a common underlying mechanism. Nevertheless, this support may crucially depend on the time interval between the stimulus array and the postcue, and also on the specific strategy employed. In the planned research project we want to examine whether participants shift to a more abstract non-spatial type of representation in the case of longer time intervals. Thus, goal of the project is to determine the boundary conditions for overlapping mechanisms by systematically varying the array-postcue time interval.
SUPERVISORS: DR. RUSSEL CHAN, DR. ROB VAN DER LUBBE
The study of active and healthy aging is a primary focus for social and neuroscientific communities. Motor learning is an important aspect of maintaining functional capacity and autonomy in the society. This project aims to assess electrophysiological neuronal activity (via EEG) differences in the brain between elder and younger adults. You will have the opportunity to learn EEG techniques and apply them to participants who are practicing two fixed 6-element motor learning sequences till automaticity. As a Masters student you will have an opportunity to use R as a statistical programming language to perform mixed-effects models analysis in both EEG and behavioural data in the first instance. Further and working closely with the supervisor, you may learn to apply a multimodal computational methods like neural network models to combine both EEG and behavioural data, aimed to further differentiate the cortical representation between experts and poorer motor sequence learners between younger and elder adults.
Requirement: 1 Masters student with some understanding of R and/or Python for analysis.
Grady, C. (2012). The cognitive neuroscience of ageing. Nat Rev Neurosci, 13(7), 491-505. doi:10.1038/nrn3256
Popal, H., Wang, Y., & Olson, I. R. (2019). A Guide to Representational Similarity Analysis for Social Neuroscience. Soc Cogn Affect Neurosci, 14(11), 1243-1253. doi:10.1093/scan/nsz099
Verwey, W. B., Shea, C. H., & Wright, D. L. (2015). A cognitive framework for explaining serial processing and sequence execution strategies. Psychon Bull Rev, 22(1), 54-77. doi:10.3758/s13423-014-0773-4
SUPERVISORS: DR. RUSSEL CHAN, PROF. DR. ING. WILLEM VERWEY
The aging process slows down movement speed, impairs coordination and reduces postural stability leading to falls. Generic motor training programs often result in less effective outcomes. The goal of the project is to improve motor learning expertise and use the Discrete Sequence Production task in a “dancelike-step task” created by Prof. Verwey to improve motor sequence learning. Meditation training will also be provided as a form of cognitive training for elder adults. We will measure motor function performance such as reaction time, balance changes (Center of Mass) using 3D XSens technology. You will have the opportunity to learn how to implement and work with 3D technology and also about meditation training. Analysis will be performed using mixed-effects models in R to understand changes pre-post program changes and compared to a wait-listed group of elder adults.
Requirement: 1 Masters student with some understanding of R and/or Python for analysis.
Predevelopment and pilot from October 2020. Aim for data collection start in Jan 2021 onwards.
SUPERVISOR: PROF. DR. FRANK VAN DER VELDE
Concepts and their relations play a crucial role in human cognition. In particular, they are the building blocks of our semantic cognition, with which we understanding our environment. Concepts can vary from concrete, as given by the concept "dog", to abstract, such as the concept "honesty". Learning concepts can be based on learning perceptual classifications, such as learning the concept "dog" from classifying individual dogs, or by classifying or recognizing actions as performed by certain agents. But concepts can also be learned by combining other concepts and their relations. So, the concept "animal" could be learned from understanding the similarities between concepts such as "dogs" and "cats" and their differences with other concepts like "chair" or "house". In this way, we also learn relations between concepts, for example that a dog is an animal, but not every animal is a dog. Because concepts (such as actions) are typically learned in (certain) relations to each other, a 'conceptual space' (or knowledge base) can arise, which forms the basis for our semantic cognition.
How we learn concepts and conceptual spaces, and how they are represented in the brain, is a topic of very active research. Learning of concepts and relations is also an important theme in machine learning. The key issue in this project concerns the way in which concepts and their relations in a given domain are learned and how they are combined to form a conceptual space. The domain can be chosen one, such as the "sport domain" (with concepts like "player" or "game") or the "health domain" (with concepts like "virus" or "medicine"). Or it could be designed for the project to study how humans learn such a new domain. The chosen topic can be studied with experimental techniques such as card sorting or priming studies. Or the conceptual space in a chosen domain could be designed (e.g., for use in machines) and evaluated by humans, for example by using questionnaires. Aspects of concept learning and conceptual spaces can also be modeled with computer modeling, such as Deep Learning or other techniques.
This is a general topic that can be specified into a 25EC or a 35EC thesis project.
Rouder, Jeffrey; Ratcliff, Roger (2006). "Comparing Exemplar and Rule-Based Theories of Categorization". Current Directions in Psychological Science. 15: 9–13. doi:10.1111/j.0963-7214.2006.00397.x.
Lambon-Ralph, M. A., Jefferies, E., Patterson, K. and Timothy T. Rogers, T. T. (2017). The neural and computational bases of semantic cognition. Nature Reviews Neuroscience 18, 42–55. doi:10.1038/nrn.2016.150
Huth, A. G., de Heer, W. A., Griffiths, T. L., Theunissen, F. E., & Gallant, J. L. (2016). Natural speech reveals the semantic maps that tile human cerebral cortex. Nature, 532(7600), 453-458. doi:10.1038/nature17637