Introduction to analysis of neural data and brain connectivity (July 2 to 6)

Prof. A. Tauste, M. Gilson, A. Insabato, G. Zamora-López, Center for Brain and Cognition (UPF), Language: English, AFTERNOON: 3.00 to 6.00 pm.


Introduction to analysis of neural data and brain connectivity



Prof. A. Tauste, M. Gilson, A. Insabato, G. Zamora-López,  Center for Brain and Cognition (UPF)

A.Tauste,  double degree in Mathematics and Telecommunications Engineering  from UPC (2006),  Master in Innovation Technologies from  Univ. Pompeu Fabra, MS in Electrical Engineering from Stanford University  and PhD  in Engineering from the University of Cambridge. Researcher in the BarcelonaBeta Brain Research Center (BBRC) and  in the Centre for Brain and Cognition de la Univ. Pompeu Fabra.

M. Gilson, B.S. a l’ École Polytecnique (2001), M.S Electrical and electronic Engineering a l’École Polytechnique  of Montréal (Canada),  PhD in Electrical and Electronic Engineering  from the  Melbourne Univ.  (Australia) (2013). He is a   post-doc  in the  Computational Neuroscience Group at the  Univ. Pompeu Fabra.

A. Insabato,  Bachelor Degree in Philosophy, Università l’Orientale, Napoli (2007), MA in Philosophy of Science, Universitá la Sapienza, Roma (2009),  PhD in Computational Neuroscience from the  Univ. Pompeu Fabra (2014).   Post-doc  in the  Computational Neuroscience Group at the  Univ. Pompeu Fabra.

G. Zamora-López, PhD in Physics  from the  Potsdam University ( Germany). Post-doc  in the    Information and Communication Technologies Department in the  Univ. Pompeu Fabra,  he works in the    Human Brain Project.

Course language




July 2nd to 6th, from 15:00 to 18:00h.


Course goals

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.

Specific objectives:

  • 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.

Transversal objectives:

  • 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.


Course contents

  1. Introduction to neuroscience
    1. From brain structure to function
    2. Questions in analysis of brain connectivity
    3. Data in neuroscience (neuronal architecture and brain activity measurements)
  2. Methods to detect interactions from time series
    1. Graphical models and Partial Correlation
    2. Autoregressive processes and Granger Causality
    3. Non-linear model-free methods
  3. Graph Theory for brain connectivity
    1. Network analysis of brain's structural connections
    2. (Weighted) network analysis of brain's functional relations
  4. Machine Learning
    1. Supervised learning: classification and regression
    2. Classification algorithms
    3. Cross-validation
    4. Regularization 



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.


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. Students should bring their laptop to each session



  • 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.