M.Sc. Social, Cognitive and Affective Neuroscience

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Statistical Methods

The module comprises fives sections.

  1. A pre-course which serves to refresh and deepen basic mathematical knowledge.
  2. An introduction to Fourier analysis which introduces the mathematical foundations of frequency- domain analyses.
  3. An introduction to the theory of the General Linear Model (GLM). The GLM is a framework that unifies a number of methods such as simple and multiple linear egression, T-Tests, ANOVA, ANCOVA and provides a useful reference for introducing fundamental frequentist and Bayesian statistical techniques.
  4. An introduction to the mass-univariate analysis of FMRI data using the GLM.
  5. An introduction to Bayesian differential equation models of EEG and FMRI data, commonly referred to as “Dynamic Causal Models”.

The aim of the module is to familiarize students with, and enable students to, critically evaluate formal data-analytical methods.  Students will obtain an intuitive and mathematical understanding of standard statistical as well as model-based paradigms used in the analysis of neuroimaging data. Furthermore, they will be able to judge the relevance, quality, and limitations of empirical neuroscientific studies. 

The module is a lecture series with a 90 min written exam which is completed at the end of the first year.