Model-based analysis of brain connectivity from neuroimaging data: estimation, analysis and classification.

Organizers: Date and place:

13th July 2019 (whole day)
CNS 2019 in Barcelona (Spain)


Description:

Brain connectivity analysis has become central in nowadays neuroscience. We propose a systematic overview of the abundance of methods in this ever-growing field. This is necessary to answer questions like "how should I pick an appropriate connectivity measure for this type of experimental data?" or "how should I interpret the outcomes of my connectivity analysis?", which are not usually addressed by textbooks or papers.
In this one-day tutorial we will offer a guide to navigate through the main concepts and methods of this field, including hands-on coding exercises. The morning session will be devoted to theory and concepts. We will focus on (i) time series analysis methods to estimate connectivity from BOLD fMRI data (extension to other types of data is possible), (ii) network theory to describe and analyze estimated networks and (iii) machine learning techniques to relate connectivity to cognitive states (e.g. tasks performed by subjects) or to pathological states (e.g. Alzheimer's disease or MCI). Theory and concepts will be presented along with simple code examples. The afternoon session will comprise of a hands-on session, focusing on the applications of the reviewed connectivity methods to fMRI data. All code examples and exercises will be in Python using Jupyter notebooks, extending the existing framework http://github.com/MatthieuGilson/WBLEC_toolbox to incorporate recent developments [1].

References:
  1. Gilson M, Zamora-López G, Pallarés V, Adhikari MH, Senden M, Tauste Campo A, Mantini D, Corbetta M, Deco G, Insabato A (bioRxiv) "MOU-EC: model-based whole-brain effective connectivity to extract biomarkers for brain dynamics from fMRI data and study distributed cognition"; doi: http:// doi.org/10.1101/531830
  2. further:

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