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Modern topics in cognitive neuroscience

Informacje ogólne

Kod przedmiotu: 2500-EN-COG-OB1L-5 Kod Erasmus / ISCED: 14.4 / (0313) Psychologia
Nazwa przedmiotu: Modern topics in cognitive neuroscience
Jednostka: Wydział Psychologii
Grupy: Cognitive Science
Punkty ECTS i inne: 5.00
Język prowadzenia: angielski
Skrócony opis: (tylko po angielsku)

The aim of the course is to introduce participants to a range of techniques and methodological advancements which can be used to create complex models human behavior and its neural underpinnings than standard mass univariate approaches, which have been traditionally applied throughout cognitive neuroscience research.

Zajęcia w cyklu "Semestr letni 2019/20" (w trakcie)

Okres: 2020-02-17 - 2020-08-02
Wybrany podział planu:


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Typ zajęć: Ćwiczenia, 30 godzin więcej informacji
Wykład, 30 godzin więcej informacji
Koordynatorzy: Łukasz Okruszek
Prowadzący grup: (brak danych)
Lista studentów: (nie masz dostępu)
Zaliczenie: Przedmiot - Egzamin
Ćwiczenia - Zaliczenie na ocenę
Wykład - Egzamin
Pełny opis: (tylko po angielsku)

Cognitive neuroscience is a multidisciplinary field which main focuses on exploring neurobiological underpinnings of behavior by the means of neuroimaging methods. Recently, it has been emphasized that complex models of the human behavior cannot be created without developing methods which integrate data from various neuroimaging methods and synthesizing large scale data which are already publicly available. The course will cover a range of methodological advancements which are believed to be necessary for further progressing the cognitive neuroscience field. The list of topics will include among others: advanced EEG data analysis, multimodal neuroimaging, open (neuro)science and online repositories, functional connectivity analysis. Furthermore, methods which extend the ecological validity of cognitive neuroscience research (e.g. via virtual reality techniques) will be introduced.

Literatura: (tylko po angielsku)

Main blocks:

14 h – Virtual reality applications (Grzegorz Pochwatko, PhD)

14 h – EEG time-frequecy analysiss (Anna Anzulewicz, PhD)

10 h – EEG source localization (Mateusz Rusiniak, PhD)

14 h – open neuroscience and fMRI functional connectivity

8 h – multimodal fusion of EEG and fMRI data

14 h Virtual reality applications

1. Introduction and historical perspective (Idea;

a. Technological and biological foundations

b. Theoretical models and review of research methods

Virtual environments - digital representation of reality

a. Research and training environments

b. Serious games

c. Spatial presence, copresence

Automata, virtual humans and artificial intelligence

a. Robots, bots, agents, dolls - More about the social

presence

b. Man-machine relationship

c. Meeting with the virtual Another

Avatars, human interactions in virtual worlds

a. Digital representation of self and others

b. Avatars and digital doppelgangers

c. Proteus effect

Identification with avatar and digital body

a. Research review

b. Consequences of identification.

c. Integration of the body and its digital representation.

Applications of virtual reality in research practice.

Creating virtual environments in Vizard and ModBox(Unity)

1. Gathering materials

a. Planning the study

b. First sketch

c. selection of materials, models, etc.

d. Introduction to Vizard and Modbox

2. Creating environment

a. Tools and functionality Vizard

b. Creating a research environment

c. Building and testing elements of research

3. Testing the environment

a. data collection and analysis

Bibliography:

Blascovich, J., Loomis, J., Beall, A. C., Swinth, K. R., Hoyt, C. L., & Bailenson,

J. N. (2002). Immersive virtual environment technology as a

methodological tool for social psychology. Psychological Inquiry, 13(2),

103-124.

Osimo, S. A., Pizarro, R., Spanlang, B., & Slater, M. (2015). Conversations

between self and self as Sigmund Freud—A virtual body ownership

paradigm for self counselling. Scientific reports, 5, 13899.

Banakou, D., Kishore, S., & Slater, M. (2018). Virtually being einstein

results in an improvement in cognitive task performance and a decrease

in age bias. Frontiers in Psychology, 9, 917.

Slater, M. M., Navarro, X., Valenzuela, J., Oliva, R., Beacco, A., Thorn, J., &

Watson, Z. (2018). Virtually Being Lenin Enhances Presence and

Engagement in a Scene From the Russian Revolution. Frontiers in Robotics

and AI, 5.

14 h - EEG time-frequency analysis:

1. Advantages and limitations of time-frequency domain analyses

2. Time-frequency decomposition methods

3. Time-frequency power and baseline normalisations

4. The basic connectivity analyses

5. Interpretation of time-frequency results

6. Reporting results of time-frequency analyses

Bibliography:

Mike X Cohen (2014). Analyzing Neural Time Series Data: Theory and

Practice. The MIT Press.

Mike X Cohen (2014). Fundamentals of Time-Frequency Analyses in

Matlab/Octave. sinc(x) Press.

10 h - EEG source localization:

1. Introduction and initial setup

2. Discrete Source Analysis (single dipoles vs. regional sources)

3. Co-registration of EEG and MRI data

4. Distributed Source Analysis I (Comparison of different volume

techniques, e.g. LAURA, sLORETA), Brain Atlases

Bibliography https://www.besa.de/science/publications/besa-methods/

14 h – fMRI functional connectivity

1. Open (neuro)science and online repositories (2 x 45)

2. rfMRI Preprocessing and Denoising (2 x 45)

3. rfMRI Network Analysis Strategies (3 x 45)

4. Using Human Connectome Project data for your own research

(workshop – 7 x 45)

Bibliography:

Bijsterbosch J., Smith, S., Beckmann, C. (2017). Oxford Neuroimaging

Primers – Introduction to Resting State fMRI Functional Connectivity,

Oxford University Press

Fornito, A., Zalesky, A., Bullmore, E. (2016). Fundamentals of Brain

Network Analysis, Academic Press

WU-Minn Human Connectome Project 1200 Subjects Data Release

Reference Manual

8 h – Multimodal neuroimaging:

1. Pros and cons of multimodal neuroimaging (2 x 45)

2. How to integrate EEG and fMRI data? (2 x 45)

3. EEG-informed fMRI Analysis (2 x 45)

4. Fusion of EEG and fMRI data by ICA (2 x 45)

Bibliography:

Ullsperger M., Debener, S. (2010) Simultaneous EEG and fMRI. Recording,

Analysis and Application:

- Snyder, A.Z., Raichle, M.E. (2010). Studies of the Human Brain Combining

Functional Neuroimaging and Electrophysiological Methods (ch. 1.3)

- Ullsperger M. (2010) EEG-informed fMRI Analysis (ch. 3.3.)

- Caloun, V.D., Eichele, T. (2010) Fusion of EEG and fMRI by Parallel Group

ICA (ch. 3.4)

Mijovic B. et al. (2012). The “why” and “how” of JointICA: Results from a

visual detection task. Neuroimage, 60, 1171-1185.

Wynn J. et al. (2016). Impaired target detection in schizophrenia and the

ventral attentional network: Findings from a joint event-related

potential–functional MRI analysis. Neuroimage: Clinical, 9, 95-102.

Opisy przedmiotów w USOS i USOSweb są chronione prawem autorskim.
Właścicielem praw autorskich jest Uniwersytet Warszawski.