Advanced neuroimaging methods
General details of the subject
- Face-to-face degree course
Description and contextualization of the subjectBasis of electrophysiological signal and synchrony; Sensor-level analysis, Component analysis; Source reconstruction: Dipole modeling, Distributed approaches, Frequency-domain methods, functional and effective connectivity
|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 %|
|Type||Face-to-face hours||Non face-to-face hours||Total hours|
|Applied laboratory-based groups||10||10||20|
|Applied computer-based groups||10||25||35|
|Name||Minimum weighting||Maximum weighting|
|Attendance and participation||20.0 %||20.0 %|
|Works and projects||80.0 %||80.0 %|
TemaryThis 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 http://fsl.fmrib.ox.ac.uk/fslcourse), AFNI, Brain Vision Analyzer 2. User Manual - User Manual of EEGLab (http://sccn.ucsd.edu/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 bibliographyHansen, 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)