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Beschreibung: Since the first studies on electroencephalography (EEG) of Hans Berger, roughly one century ago, the main focus of human neurophysiological research was based around neural oscillations. However, recent studies pointed out the presence of the largely overlooked non-oscillatory - or aperiodic - signal component in human neurophysiology. Initially considered mere background noise, increasing evidence now suggests that the aperiodic signal contains meaningful physiological information and exhibits dynamic variability. However, the precise physiology and function of the aperiodic signal remains an open research question. The aim of this Bachelor thesis will be to explore the physiological and functional significance of the aperiodic signal in human neurophysiology, by investigating its systematic variations in response to specific task conditions and pharmacological manipulations.
Literature:
- Donoghue, T., Haller, M., Peterson, E. J., Varma, P., Sebastian, P., Gao, R., ... & Voytek, B. (2020). Parameterizing neural power spectra into periodic and aperiodic components. Nature neuroscience, 23(12), 1655-1665.
- Gyurkovics, M., Clements, G. M., Low, K. A., Fabiani, M., & Gratton, G. (2022). Stimulus-induced changes in 1/f-like background activity in EEG. Journal of Neuroscience, 42(37), 7144-7151.
- Lendner, J. D., Helfrich, R. F., Mander, B. A., Romundstad, L., Lin, J. J., Walker, M. P., ... & Knight, R. T. (2020). An electrophysiological marker of arousal level in humans. Elife, 9, e55092.
- Waschke, L., Donoghue, T., Fiedler, L., Smith, S., Garrett, D. D., Voytek, B., & Obleser, J. (2021). Modality-specific tracking of attention and sensory statistics in the human electrophysiological spectral exponent. Elife, 10, e70068.
Kontakt: Dr. Marius Tröndle, E-Mail
Beschreibung: Alzheimer's disease (AD) is the most common form of dementia nowadays and poses a number of challenges due to its complex and heterogeneous nature. While advances in machine learning offer promising prospects for understanding the disease complexities and its evolution, conventional methods often overlook neuroanatomical heterogeneity between individuals, relying on case-control analyses. This thesis explores the promise of normative modeling in comparison with conventional methods for understanding disease and capturing the individual variability inherent in neurodegenerative diseases, with the aim of improving the accuracy of diagnosis and enhancing our understanding of the disease process.
Literature:
- Marquand AF, Rezek I, Buitelaar J, Beckmann CF. Understanding Heterogeneity in Clinical Cohorts Using Normative Models: Beyond Case-Control Studies. Biol Psychiatry. 2016 Oct 1;80(7):552-61.
- Verdi S, Marquand AF, Schott JM, Cole JH. Beyond the average patient: how neuroimaging models can address heterogeneity in dementia. Brain. 2021 Nov 29;144(10):2946-53.
- Pinaya WHL, Scarpazza C, Garcia-Dias R, Vieira S, Baecker L, F da Costa P, et al. Using normative modelling to detect disease progression in mild cognitive impairment and Alzheimer?s disease in a cross-sectional multi-cohort study. Sci Rep. 2021 Aug 3;11(1):15746.
Kontakt: MSc Camille Elleaume, E-Mail
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