Venue: Universitat Politecnica de Barcelona (XII Summer School UPC-UB edition)

Date: from 2nd to 6th of July 2018 (afternoons), 15 hours in total

Registration: http://mesioupcub.masters.upc.edu/en/xii-summer-school-2018

Organizers:

- Adrià Tauste Campo (BarcelonaBeta Brain Research Center, Barcelona)
- Andrea Insabato (Universitat Pompeu Fabra, Barcelona)
- Gorka Zamora-López (Universitat Pompeu Fabra, Barcelona)
- Matthieu Gilson (Universitat Pompeu Fabra, Barcelona)

The course is an introduction to data analysis in neuroscience. The aim is to understand standard concepts and methods used to study and interpret brain connectivity (e.g., Granger causality, graph theory, machine learning) with a focus on their statistical aspects. During the course, students will use real data (whole-brain functional and structural MRI) and improve their programming skills (Python with numpy, scipy and scikit-learn) to implement such methods and discuss their findings.

- Familiarize with the use of generative models for time series, with graph theory and supervised learning algorithms.
- Understand neuroscience data.
- Use tools for brain connectivity analysis.

- Improve Python programming skills.
- Learn to compare results from different types of analyses about the same question.
- Present the results of statistical analysis in a clear and appealing format.

The students will be evaluated via a hands-on project developed during the course in small groups (up to three participants). They will be asked to summarize their results and present them in front of the whole class.

For those students willing to have a certificate, an evaluation will be given by the UPC.

Students are expected to have basic knowledge in applied maths (linear algebra and calculus), as well as some programming skills. Knowledge of Python is recommended, but we will provide a small set of exercises to level up before the course for those that are familiar with other programming languages.

- Lütkepohl, H. (2005). New introduction to multiple time series analysis. Springer Science & Business Media.
- Murphy, K. P. (2012). Machine Learning: a Probabilistic Perspective. MIT Press.
- Wasseerman, S. & Faust, K. (1999). Social network analysis: Methods and Applications. Cambridge University Press.