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Advanced neuroimaging methods

General details of the subject

Face-to-face degree course

Description and contextualization of the subject

Basis of electrophysiological signal and synchrony; Sensor-level analysis, Component analysis; Source reconstruction: Dipole modeling, Distributed approaches, Frequency-domain methods, functional and effective connectivity

Teaching staff

NameInstitutionCategoryDoctorTeaching profileAreaE-mail
RICHTER , CRAIGBCBL- Basque Center on Cognition, Brain and
SOTO BLANCO, DAVIDBCBL- Basque Center on Cognition, Brain and


CE1. Advanced knowledge of research methods in electrophysiology.50.0 %
CE2. Applying knowledge creatively to identify research questions and plan data analysis in studies using electroencephalography and megnetoencephalography.50.0 %

Study types

TypeFace-to-face hoursNon face-to-face hoursTotal hours
Applied laboratory-based groups101020
Applied computer-based groups102535

Assessment systems

NameMinimum weightingMaximum weighting
Practical tasks50.0 % 50.0 %
Presentations50.0 % 50.0 %


This course will introduce the fundamental methods for the analysis of functional MRI, and electrophysiological (i.e. EEG and MEG) data.

The goal is to provide the students with the core ideas necessary to understand current data analysis methods used in the literature from a critical point of view, and provide sufficient hands-on experience on relevant software packages that implement them. The course is divided into three sections. The first part will consist of hands-on sessions covering the essentials of MRI data processing, using a standard neuroimaging software packages. There will be practical sessions on the analysis of electrophysiological data, mainly using BrainVision Analyzer and EEGlab.

In addition, the course will describe the basic theoretical background of the most common methods for analysis of functional MRI data, such as the general linear model and independent component analysis, the main software packages (AFNI, FSL), also with an introduction to multivariate pattern analyses (MVPA).


Compulsory materials

- FSL (see, AFNI, Brain Vision Analyzer 2. User Manual - User Manual of EEGLab ( - Statistical Parametric Mapping: The analysis of functional brain images. Edited by K. J. Friston, J.T. Ashburner, S. J. Kiebel, T. E. Nichols and W.E. Penny. Elsevier 2008. - Statistical analysis of fMRI data. F. G. Ashby. MIT Press 2011. - Handbook of functional MRI data analysis. R.A. Poldrack, J. A. Mumford and T.E. Nichols. Cambridge University Press 2011.

Basic bibliography

Hansen, P.C., et al. MEG: An Introduction to Methods (selected chapters)

Friston, K. J. et al. Statistical Parametric Mapping, The Analysis of Functional Brain Images (selected chapters)

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