Master 2020/2021

Research Seminar "Cognitive Sciences"

Type: Compulsory course (Cognitive Sciences and Technologies: From Neuron to Cognition)

Area of studies: Psychology

Delivered by: School of Psychology

Where: Institute for Cognitive Neuroscience

When: 2 year, 1-3 module

Mode of studies: offline

Instructors: Tommaso Fedele, Iiro Jaaskelainen

Master’s programme: Cognitive Sciences and Technologies: From Neuron to Cognition

Language: English

ECTS credits: 8

Contact hours: 70

Course Syllabus

Abstract

Research Seminar "Cognitive Sciences" (2nd year) is dedicated to advanced data analysis of neurophysiological data and provides the understanding of algorithmic pipelines routinely used in the analysis of EEG and MEG data. Given the quick development of analysis tools, it is always challenging to fully comprehend the machinery hidden behind the typical button-press toolbox packages. Instead of approaching data analysis packages as a “black box”, at the end of the course the students will be able to fully comprehend the meaning of their choices while setting options in their data analysis workflow. During this course, we will go through the details of data acquisition, data processing and step by step implementation of most advanced data analysis pipeline and the understanding of the main parameters involved. After quickly reviewing the physical principles of signal acquisition and introducing some mathematical tools, the course dives into the main topics of time-frequency analysis, source reconstruction, functional connectivity and statistical analysis. The course provides the students with the basic theory of neurophysiological data analysis which is useful not only in neuroscience and cognitive sciences but also in other scientific areas using similar mathematical framework.

Learning Objectives

• Analysis in the time-frequency domain
• Reconstruction of neurophysiological sources
• Use of naturalistic stimuli in cognitive stidues
• Connectivity analysis methods
• Statistical analysis of EEG/MEG data

Expected Learning Outcomes

• Know basic physical principle of signal acquisition
• Know how to build a time-frequency projection of EEG/MEG data
• Know how to track neural sources recorded by EEG/MEG
• Know how to compute and interpret connectivity analysis
• Know how to approach the statistical evaluation of analysis outcome

Course Contents

• Elements of Fourier Transform and its implementation
• Time-frequency analysis: from Fourier to wavelet and multitapers
• Source reconstruction: forward model, inverse model
• Source reconstruction: equivalent current dipole and distributed approaches
• Connectivity analysis: coherence, Granger causality and their interpretability
• Intersubject correlation method in fMRI/EEG/MEG data
• Application of naturalistic stimuli in cognitive studies
• Cross-frequency coupling
• Statistical analysis of multidimensional data: non parametric testing

Assessment Elements

• Homeworks
  The students who have the average test score (О test ) to be equal 7 or higher, may have it as the final grade (skipping the final exam option).
• Exam
  The students who have the average test score (О test ) to be equal 7 or higher, may have it as the final grade (skipping the final exam option).
• Participation in discussions

Interim Assessment

• Interim assessment (3 module)
  0.7 * Exam + 0.15 * Homeworks + 0.15 * Participation in discussions

Bibliography

Recommended Core Bibliography

• Cohen, M. X. (2014). Analyzing Neural Time Series Data : Theory and Practice. Cambridge, Massachusetts: The MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=689432

Recommended Additional Bibliography

• Anders M. Dale, K Arthur K. Liu, Bruce R. Fischl, Y L. Buckner, John W. Belliveau, & Jeffrey D. Lewine. (2000). R.: Dynamic statistical parametric neurotechnique mapping: combining fMRI and MEG for high-resolution imaging of cortical activity.
• Andre M Bastos, & Jan-Mathijs eSchoffelen. (2016). A tutorial review of functional connectivity analysis methods and their interpretational pitfalls. https://doi.org/10.3389/fnsys.2015.00175
• Baillet, S., Friston, K., & Oostenveld, R. (2011). Academic Software Applications for Electromagnetic Brain Mapping Using MEG and EEG. https://doi.org/10.1155/2011/972050
• Eugenio Rodriguez, Jacques Martinerie, & Francisco J. Varela. (1999). Measuring phase-synchrony in brain signals.
• Tort, A. B. L., Komorowski, R., Eichenbaum, H., & Kopell, N. (2010). Measuring Phase-Amplitude Coupling Between Neuronal Oscillations of Different Frequencies. https://doi.org/10.1152/jn.00106.2010