Themen für Bachelorarbeiten

Beschreibung: Neuropsychological studies indicate that healthy ageing is associated with a decline of inhibitory control of attentional and behavioural systems, to inhibit prepotent responses is critical for successful goal-directed behaviours.
A widely accepted measure of inhibitory control is the antisaccade task that requires both the inhibition of a reflexive saccadic response toward a visual target and the initiation of a voluntary eye movement in the opposite direction.
This thesis aims to bring together and discuss evidence of decreasing inhibitory control in older adults using electroencephalography and eye-tracking recordings from the antisaccade task.

Literature:
[1] Hwang, Kai, et al. "Cortical neurodynamics of inhibitory control." Journal of Neuroscience 34.29 (2014): 9551-9561.
[2] Plomecka, Martyna Beata, et al. "Aging effects and test/retest reliability of inhibitory control for saccadic eye movements." Eneuro 7.5 (2020).
[3] Constantinidis, Christos, and Beatriz Luna. "Neural substrates of inhibitory control maturation in adolescence." Trends in neurosciences 42.9 (2019): 604-616.
Kontakt: Martyna Plomecka, E-Mail

Beschreibung: 3 Papers:

Martinez-Conde, S., Macknik, S. & Hubel, D. The role of fixational eye movements in visual perception. Nat Rev Neurosci 5, 229?240 (2004). https://doi.org/10.1038/nrn1348

Rucci, M., & Poletti, M. (2015). Control and Functions of Fixational Eye Movements. Annual review of vision science, 1, 499?518. https://doi.org/10.1146/annurev-vision-082114-035742

Krauzlis Richard J. , Goffart Laurent and Hafed Ziad M. 2017 Neuronal control of fixation and fixational eye movementsPhil. Trans. R. Soc. B3722016020520160205

Kontakt: Dr. Tzvetan Popov, E-Mail

Beschreibung: 3 Papers:

Kingstone A, Klein RM. What are human express saccades? Percept Psychophys. 1993 Aug;54(2):260-73. doi: 10.3758/bf03211762. PMID: 8361841.

Fischer B, Boch R (1983) Saccadic eye movements after extremely short reaction times in the monkey. Brain Res 260: 21?26

Fischer, B., & Weber, H. (1993). Express saccades and visual attention. Behavioral and Brain Sciences, 16(3), 553-567. doi:10.1017/S0140525X00031575

Kontakt: Dr. Tzvetan Popov, E-Mail

Beschreibung: The learning process is one of the main topics of research in multiple disciplines such as psychology, neuroscience, behavioral ecology, evolutionary theory and computer science. The neural mechanism of memory formation and how it changes with age remains unclear. The use of neurophysiological measures can offer valuable insights into the learning process due to the ability of linking neural signals to complex behaviors. Electroencephalographic studies show that some components of event-related potentials (ERP) may provide information about the learning process itself and objectively measure learning success. These components include P300, a positive deflection with a latency of around 300 after stimulus onset.

Polich, J. (2007). Updating P300: An integrative Theory of P3a and P3b. Clin. Neurophysiol., 118(10): 2128-2148.
Tinga, A. M., de Back, T. T., Louwerse, M. M. (2019). Non-invasive neurophysiological measures of learning: A meta-analysis. Neuroscience and Biobehavioral Reviews, 99: 59-99.
Kontakt: Dawid Strzelczyk, E-Mail

Beschreibung: Literatur:

Woo CW, Chang LJ, Lindquist MA, Wager TD. (2017). Building better biomarkers: brain models in translational neuroimaging. Nature Neuroscience

Zarley, D. (2019, January 28). Meet the scientists who are training AI to diagnose mental illness [Web log post]. Retrieved from https://www.theverge.com/2019/1/28/18197253/ai-mental-illness-artificial-intelligence-science-neuroimaging-mri

Wardenaar, K. J., & De Jonge, P. (2013). Diagnostic heterogeneity in psychiatry: towards an empirical solution. BMC Medicine, 11(1). doi:10.1186/1741-7015-11-201

Walsh, C. G., Ribeiro, J. D., & Franklin, J. C. (2017). Predicting Risk of Suicide Attempts Over Time Through Machine Learning. Clinical Psychological Science, 5(3), 457-469. doi:10.1177/2167702617691560

Vieira, S., Pinaya, W. H., & Mechelli, A. (2017). Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications. Neuroscience & Biobehavioral Reviews, 74, 58-75. doi:10.1016/j.neubiorev.2017.01.002

Torous, J., Onnela, J., & Keshavan, M. (2017). New dimensions and new tools to realize the potential of RDoC: digital phenotyping via smartphones and connected devices. Translational Psychiatry, 7(3), e1053-e1053. doi:10.1038/tp.2017.25

Stark, H. (2017, September/October 30). Artificial intelligence is here and it wants to revolutionize psychiatry. Forbes

Torous, J. (2014). Mobile technology and global mental health. Asian Journal of Psychiatry, 10, 69-70. doi:10.1016/j.ajp.2013.07.004

Rutledge, R. B., Chekroud, A. M., & Huys, Q. J. (2019). Machine learning and big data in psychiatry: toward clinical applications. Current Opinion in Neurobiology, 55, 152-159. doi:10.1016/j.conb.2019.02.006

Reece, A. G., & Danforth, C. M. (2017). Erratum to: Instagram photos reveal predictive markers of depression. EPJ Data Science, 6(1). doi:10.1140/epjds/s13688-017-0118-4

Place, S., Blanch-Hartigan, D., Rubin, C., Gorrostieta, C., Mead, C., Kane, J., ? Azarbayejani, A. (2017). Behavioral Indicators on a Mobile Sensing Platform Predict Clinically Validated Psychiatric Symptoms of Mood and Anxiety Disorders. Journal of Medical Internet Research, 19(3), e75. doi:10.2196/jmir.6678

Neighborhood Psychiatry. (2018, February 13). Can artificial intelligence improve psychiatric diagnosis? Retrieved from https://www.psychologytoday.com/intl/blog/psychiatry-the-people/201802/can-artificial-intelligence-improve-psychiatric-diagnosis

Meyer-Lindenberg, A. (2018). Künstliche Intelligenz in der Psychiatrie ? ein Überblick. Der Nervenarzt, 89(8), 861-868. doi:10.1007/s00115-018-0557-6

Just, M. A., Pan, L., Cherkassky, V. L., McMakin, D. L., Cha, C., Nock, M. K., & Brent, D. (2017). Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth. Nature Human Behaviour, 1(12), 911-919. doi:10.1038/s41562-017-0234-y

Corcoran, C. M., Carrillo, F., Fernández-Slezak, D., Bedi, G., Klim, C., Javitt, D. C., ? Cecchi, G. A. (2018). Prediction of psychosis across protocols and risk cohorts using automated language analysis. World Psychiatry, 17(1), 67-75. doi:10.1002/wps.20491

Deshpande, G., Wang, P., Rangaprakash, D., & Wilamowski, B. (2015). Fully Connected Cascade Artificial Neural Network Architecture for Attention Deficit Hyperactivity Disorder Classification From Functional Magnetic Resonance Imaging Data. IEEE Transactions on Cybernetics, 45(12), 2668-2679. doi:10.1109/tcyb.2014.2379621

Bedi, G., Carrillo, F., Cecchi, G. A., Slezak, D. F., Sigman, M., Mota, N. B., Ribeiro, S., Javitt, D. C., Copelli,
M., & Corcoran, C. M. (2015). Automated analysis of free speech predicts psychosis onset in high-risk
youths. Npj Schizophrenia, 1(1), 15030. https://doi.org/10.1038/npjschz.2015.30

Bedi, G., Cecchi, G. A., Slezak, D. F., Carrillo, F., Sigman, M., & de Wit, H. (2014). A Window into the Intoxicated
Mind? Speech as an Index of Psychoactive Drug Effects. Neuropsychopharmacology, 39(10),
2340?2348. https://doi.org/10.1038/npp.2014.80
Kontakt: Prof. Dr. Nicolas Langer, E-Mail

Beschreibung: Der Rey–Osterrieth Complex Figure Test (ROCF) ist ein neuropsychologischer Test zur Erfassung der Fähigkeit der räumlich visuellen Konstruktion und der visuellen Gedächtnisleistung. Auch wird der Test zur Erfassung exekutiver Funktionen eingesetzt. Der Test wurde ursprünglich 1941 von André Rey entwickelt und 1944 von Paul Alexandre Osterrieth standardisiert. Das Ziel des Tests war es, zwischen Wahrnehmungs- und Gedächtnisstörungen unterscheiden zu können, und ob die Störungen auf Lernschwierigkeiten oder hirnorganische Ursachen zurückzuführen sind.
Kontakt: Prof. Dr. Nicolas Langer, E-Mail

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Kontakt: Prof. Dr. Nicolas Langer, E-Mail