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Signal processing for functional brain imaging

MICRO-513

Enseignant(s) :

Van De Ville Dimitri Nestor Alice

Langue:

English

Summary

Computational methods for the analysis of human brain imaging data

Content

Human brain imaging such as magnetic resonance imaging (MRI) and electroencephalography (EEG) allows non-invasive investigation of the human brain in health and disease.  Datasets are large, noisy, and richely structured, thus their analysis needs to rely on a broad range of mathematical and signal processing tools.  Students will learn to understand, implement, and tailor general tools including linear regression (mass univariate models), multivariate models (principal components analysis, partial least squares, independent component analysis), pattern recognition (machine learning), and graphical models. Exercises and lab exercises (in Python) provide insights into the analysis of brain imaging data. A journal club emphasizes application of brain imaging tools in fundamental and clinical neuroscience. Students will read, present and critique original research papers.

Keywords

neuroimaging, functional MRI, EEG, brain mapping, systems-level neuroscience

Teaching methods

Weekly lectures (2h) following by an exercise session (1h)

Three lab exercises during the semester

Journal club at the end of the semester

Assessment methods

Attendance and completion of three lab exercises during the semester

Written exam

Dans les plans d'études

Semaine de référence

 LuMaMeJeVe
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15   AAC137
ELD020		 
15-16    
16-17   CM1103
ELD020		 
17-18     
18-19     
19-20     
20-21     
21-22     
 
      Cours
      Exercice, TP
      Projet, autre

légende

  • Semestre d'automne
  • Session d'hiver
  • Semestre de printemps
  • Session d'été
  • Cours en français
  • Cours en anglais
  • Cours en allemand