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Lecture Notes of Methods and Techniques in Social Neuroscience () - EXAM 15th op APRIL

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In this document you can find all my lecture notes of the past 7 lectures form the course Methods and Techniques in Social Neuroscience in the year . The document is in English and each lecture has its supporting book chapters attached. I have also uploaded a summary of the book 'Methods in Social Neuroscience' which we had to read for this course. All important concepts are in bold.

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Documentinformatie

Geüpload op
22 maart 2021
Aantal pagina's
19
Geschreven in
2020/2021
Type
College aantekeningen
Docent(en)
Dennis schutter
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Voorbeeld van de inhoud

Methods and Techniques in Social
Neuroscience (200900352)
Lectures notes by Macy Bouwhuizen



Lecture 1: Electroencephalography by Dennis Schutter – Chapter 1,9,10
Electroencephalography (EEG) is very old technique, its easy and inexpensive to use and therefore still is very
popular. In this lecture we will cover the strengths and limitations.

A little history of EEG

Berger H. (1929) was a Deutsch scientist who worked in his free time on EEG. He tried to capture brain
potentials from the scalp. He worked on his project a long time, but it took a long time before he started
publishing, he had problems with extracting a signal from the noise. In 1929 he published his first results –
alpha waves from the brain of his son. Every human has alpha waves, and they are really good to record.
However, he was not the only one working on EEG. Pradvdich Neminski was a Russian scientist who probably
was the first-person recording EEG in 1912. This was probably earlier than Berger, but because of the distance
between Europe and Russia, the results didn’t make it here at that time.

What about EEG signals?

The brain consists of white and grey matter. The cortex consists of 6 layers  layer 1 and layer 5 are important
for EEG. In layer 5 there are pyramidalis interna neurons with dendrites pointing upwards until layer 1. These
cells are organized in an orderly vertical fashion. Only these layers can be picked up from the scalp. Input that
we measure with EEG is originated from layer 5.




EEG is the sum up of the inhibitory and excitatory signals. EPSP: depolarization of pyramidal cell caused by flow
of positively charged ions (Na+) into the cell and negatively charged ions (Cl-) out of the cell. IPSP:
hyperpolarization of pyramidal cell caused by the flow of negatively charged ions (Cl-) into the cell and
positively charged ions out (K+) out of the cell. This is a continuous effort, from 1000s of inputs at the same

,time. Therefore, it becomes really difficult to find out the input of just one neuron. However, because EEG is
very precise in time (milliseconds), the rapid changes can be measured by changes in microvolts. It is important
to remember that EEG does not measure action potentials. EEG cannot measure field potentials of single
neurons: it measures the summation of 1000s of neuron who all fire in the same direction: so, all excitatory or
all inhibitory. EEG signal is again a differential recording: you need a reference signal for a ‘ground’ signal.
Therewith you can compare both signals. For example, measured on the ear.

Brain activity is very complicated to measure. There are different waves which are very known:




The spontaneous EEG is a mix of all frequency’s bands. Fourier transformation frequency components can be
used to extract from the EEG and visualized in a spectrum as sinusoids have their own frequency, amplitude
and phase. EEG is very susceptible to artefacts and for example when measuring gamma waves, it gets really
difficult to extract the real signals from artefacts. Sources of artefacts can be: muscle contractions, movements
or lights.

Interesting studies as an example of using EEG in social experiments

A study about resting state (default mode nowadays) activity and gambling behavior. They used the Iowa
Gambling task in which low risk behavior will eventually make people earn more. Depending on the asymmetry
of the prefrontal cortex in the left side of the brain, they would be more reward driven and take more risks.
This is a simple example of how you can use resting state activity and alpha oscillations to predict people’s
behavior in different types of tasks.

Another study had a kind of similar design: they used symmetrical frontal cortical activity to measure
aggression. The participants (Texans) had to write essays about different political topic such as abortion. After
this they all got an EEG and an envelope. Some people were instructed to do nothing, others to contract their
right hand and the last group to contract their left hand. And after this EEG manipulation and the contracting:
the participants that got the essay started to get insulted by another – to provoke anger. And after this social
setup, the participant and the insulter played in a punishing task. They found that people who were in the

, assaulted condition and who had squeezed their right hand, punished the insulter the most. Squeezing the
right hand  increased activity in the left side of the brain in the frontal area  most pronounced anger.

There is some evidence with brain oscillations and their associations. Fast brain oscillations (alpha and beta) are
related to sensory, motor and cognitive processing. And slow brain oscillations (delta and theta) are associated
with reward, punishment and motivations. This is a framework based on a certain brain organization in which
brain oscillations get assigned to different parts of the brain, so this part of the brain is responsible for creating
a certain brain oscillation. This studied in the reward-punishment contingency experiment: in which taking risks
will first help you and then not and then again will help. People have to learn from their mistakes. Theta and
beta waves are used to show how people will act and people with high motivation (more beta activity) have
difficulties in learning in a changing environment.



Event-related potentials (ERP)




In this study a tone is presented many times to a participant. The participant only has to sit and listen. After
many trials (100-1000) there will be a very clear signal of the change that the stimulus makes in the signal –
noise will cancel out. You cannot only see the amplitude of the signal but also the latency of the signal and this
says something about the processing speed. This is very unique to EEG and cannot be measured by for example
fMRI.

A study on psychopaths was about monitoring your own actions and monitory actions of others. They used the
error related negativity (ERN) wave which is activated when you make an error. It is the signal that detects the
mismatch between what you should do and what you did. In the experiment the participants (psychopaths and
controls) had to do a simple task and the psychopaths had a higher ERN when they did the task themselves.
However, when psychopaths are watching someone else make a mistake then they did not generate an ERN. In
controls, this ERN signal was generated. This means that the ERN system is working for psychopaths, but they
are just really self-centered.

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