Methods and Techniques in Social Neuroscience (200900352)
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Methods and Techniques in Social Neuroscience (200900352)
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Universiteit Utrecht (UU)
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Methods in Social Neuroscience
Samenvatting van het boek 'Methods in Social Neuroscience' voor het vak Methods and Techniques in Social Neuroscience aan Universiteit Utrecht.
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SUMMARY of Methods in Social Neuroscience by Eddie Harmon-Jones & Jennifer S. Beer for Methods and Techniques in Social Neuroscience (200900352)
Methods and Instruments in Cognitive and Affective Neuroscience Book Summary
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Methods and Techniques in Social Neuroscience – Book
CH1: Introduction to Social and Personality Neuroscience Methods
What is Social and Personality Neuroscience?
Social psychology: the scientific study of how the thoughts, feelings, and behaviors of an individual
are influenced by the actual, imagined, or implied presence of others.
Personality psychology: the scientific study of how dispositional aspects of the individual influence
his or her thoughts, feelings, and behavior.
Advantage of Social and Personality Neuroscience Methods
Humans can be noninvasively monitored in various situations.
Considerations for Using Neuroscience Methods
No single technique can measure all biological activities with excellent spatial and temporal
resolution. That is, no one measure of brain function captures neuronal activity as it unfolds on the
order of milliseconds or nanoseconds (temporal resolution), or can specify exactly which neurons are
activated on the order of millimeters or nanometers (spatial resolution).
Concerns with the drawing of causal inferences
Measures of brain activations obtained from ERPs or fMRI are essentially correlational, in that a
psychological state is manipulated and the brain activation is measured. From this type of
experimental design, it is impossible to determine whether the brain activation was necessarily or
sufficiently responsible for the psychological or behavioral effect.
Repetitive transcranial magnetic stimulation (rTMS) can be used to increase or decrease neuronal
activity temporarily and noninvasively over particular cortical areas. This approach permits causal
statements about the role of particular cortical regions in particular psychological or behavioral
outcomes.
However, both patient and rTMS methods are limited. With patients, the lesions often involve a
number of brain regions. With rTMS, the ‘virtual lesions’ cannot penetrate too deeply into the brain,
and the spatial resolution of the method is not very precise.
CH9: Electroencephalographic Methods in Social and Personality Psychology
Physiology Underlying Electroencephalography
,Electroencephalography (EEG): the recording of electrical brain activity from the human scalp.
- discovered by Hans Berger in the late 1920s.
Electrical activity associated with neurons comes from action potentials and postsynaptic potentials.
- Action potential: a discrete voltage spike that runs from the beginning of the axon at the cell body
to the axon terminals where neurotransmitters are released.
- Postsynaptic potential: a voltage that occurs when the neurotransmitters bind to receptors on the
membrane of the postsynaptic cell.
Recording of individual neurons or single-unit recordings measure action potentials and not
postsynaptic potentials.
Action potentials in different axons will typically cancel each other out. If one neuron fires shortly
after another one, then current at a given location will be flowing into one axon at the same time
that it is flowing out of another one, and thus they cancel each other out and produce a much
smaller signal at the electrode. Whereas the duration of an action potential is approximately 1 msec,
the duration of postsynaptic potentials is much longer, often tens or hundreds of milliseconds.
Postsynaptic potentials are also mostly confined to dendrites and cell bodies, and occur
instantaneously rather than traveling down axons at a fixed rate. These factors allows postsynaptic
potentials to summate rather than cancel each other out, and thus make it possible to record them
at the scalp. Because of the need for summation of electrical potentials, EEG activity is most likely the
result of postsynaptic potentials, which have a slower time course and are more likely to be
synchronous and summate than presynaptic potentials.
Thus scalp-recorded electrical activity is the result of activity of populations of neurons. Because
the activity generated by one neuron is small, it is thought that the activity recorded at the scalp is
the integrated, synchronous activity of numerous neurons. Moreover, for activity to be recorded at
the scalp, the electric fields generated by each neuron must be oriented in such a way that their
effects accumulate. That is, the neurons must be arranged in an open as opposed to a closed field. In
an open field, the neurons’ dendrites are all oriented on one side of the structure, while their axons
all depart from the other side. Open fields are present where neurons are organized in layers, as in
most of the cortex, parts of the thalamus, the cerebellum, and other structures.
The raw EEG signal is a complex waveform that can be analyzed in the following domains:
- Temporal domain (event-related potential)
- Frequency domain (hertz, cycles per second)
Recording
In contemporary social and personality research, EEG is recorded from 32, 64, 128, or more
electrodes. (in the literature, 32 or 64 electrodes are the most common).
Naming convention for electrode positions:
The first letter of the name of the electrode refers to the brain region over which the electrode sits:
Fp – frontal pole
F – frontal region
C – central region
P – parietal region
T – temporal region
O – occipital region
,Electrodes in between these regions are often designated by using two letters, such as FC for frontal
– central. After the letter is a number, as in F3, or another letter, as in Cz. Odd numbers are used to
designate sites on the left side of the head, and even numbers are used to designate sites on the
right side of the head;
Odd numbers – left side of the head
Even numbers – right side of the head
Numbers increase as distance from the middle of the head increases, so F7 is farther from the
midline than F3. The letter z is used to designate the midline, which runs from the front to the back
of the head.
Recording of eye movements (electro-oculography, or EOG) is also carried out, to facilitate artifact
scoring of the EEG.
Conductive gel is used as a medium between the scalp and electrodes. EEG, EOG, and other signals
are then amplified with bioamplifiers. For EEG frequency analyses, the raw signals are often
bandpass-filtered online (e.g., from 0.1 to 100Hz), because the frequencies of interest fall between 1
and 40 Hz. From the amplifiers, the raw signals are then digitized onto a computer at a sampling rate
greater than twice the highest frequency of interest. For example, if one is only interested in
frequencies below 40 Hz, then 80 samples per second would be collected. This sampling rate is
necessary because of the Nyquist theorem, which states that exact reconstruction of a continuous
signal from its samples is possible if the signal is of limited bands and is sampled at least twice as
great as the actual signal bandwidth. If this sampling condition is not met, then frequencies will
overlap; that is, frequencies above half the sampling rate will be reconstructed as, and appear as,
frequencies below half the sampling rate. This distortion is called ‘aliasing’, because the
reconstructed signal is said to be an alias of the original signal.
Artifacts
If artifacts are recorded in the EEG, procedures exist to handle them:
Muscle Artifact
Muscle artifacts (electromyography, or EMG) is typically of higher frequencies than EEG. Most EEG
signals of interest are less than 40 Hz, whereas EMG is typically greater than 40 Hz. However, some
EMG blends in with the EEG frequencies, so it is advisable to limit muscle artifact by training the
participants to limit muscle movements. If muscle artifacts do appear in studies in which muscle
movements should not occur, the artifacts can be removed during the data-processing stage.
One way to handle the EMG that may contaminate the EEG is to measure facial EMG and then use
the facial EMG responses (in EMG frequency ranges, such as 50 – 250 Hz) in covariance analyses, to
assess whether statistically adjusting the EEG data for several possible covariates has eliminated the
effects on EEG of interest.
Eye Movement Artifacts
Eye movement artifacts are also often best dealt with in advance of EEG recording. That is, training
participants to limit eye movements during EEG recording is recommended. However, researchers
must not encourage participants to control their blinking, because blinks and spontaneous eye
movements are controlled by several brain systems in a highly automatic fashion, and the instruction
to suppress these systems may act as a secondary task. Participants do blink, and these blinks will
influence the EEG data (particularly data obtained from frontal electrodes), so epochs containing
, blinks should be removed from the EEG or corrected via a computer algorithm. In general, the actual
EEG time series is regressed on the EOG time series, and the resulting residual time series represents
a new EEG from which the influence of the ocular activity is statistically removed.
Nonbiological Artifacts
Nonbiological artifacts are those that typically involve external electrical noise (coming from elevator
motors, electric lights, computers, etc.). Most of these problems can be dealt with through filtering
of the signal.
Offline Data Processing
Referencing
After the data are scored for artifacts, EEG signals are often re-referenced. All bioelectrical
measurements must reflect the difference in activity between at least two sites. In EEG research,
one site is typically placed on the scalp, whereas the other side could also be on the scalp or on a
nonscalp area, such as an earlobe or nose tip. Because researchers strive for obtaining measures that
reflect activity in particular brain regions, they often search for a relatively inactive reference, such as
the earlobe. However, there are no ‘inactive sites,’ and all sites near the scalp reflect some EEG
because of volume conduction. To address this issue, some researchers suggest using an average
reference consisting of the average of activity at all recorded EEG sites.
Other researchers have recommended the use of linked ears as a reference, because of the ears’
relatively low activity and because the linking of the ears should theoretically center the reference on
the head, making the determination of lateralized activity more accurate. However, the physical
linking of the ears into one reference electrode has been questioned. But, issues do not happen
when a linked-ears reference is created offline, after the data have been collected. To create an
offline linked- or averaged-ears reference, the collected EEG data need to be references online to
one of the ears or some other location (e.g., Cz). Then the other ear (if the reference is one ear) or
both ears (if the reference is Cz) will need to be collected with the EEG. Offline, the data will be re-
referenced to the average of the two ears.
Obtaining the Frequencies of the EEG Signal
Several steps are involved in transforming EEG signals into indices used in data analyses. First, a
signal is collected in the time domain; it is then converted to a frequency domain representation,
usually in the form of a power spectrum. The spectrum, which collapses data across time,
summarizes which frequencies are present. Spectral analysis involves examining the frequency
composition of short windows of time (epochs), often 1 or 2 sec each. The spectra are averaged
across many epochs. Epochs of 1 or 2 sec in length are used to meet an assumption underlying the
Fourier transform, which is the method used to derive power spectra. The Fourier transform
assumes a periodic signal, or one that is repeated at uniformly spaced intervals. Any periodic signal
can be decomposed into a series of sine and cosine functions of various frequencies, with the
function for each frequency beginning at its own particular phase. EEG signals are not exactly
periodic, because the repetition of features is not precisely spaced at uniform intervals. However, the
use of short epochs allows one to analyze small segments of data with features that are repeated in a
highly similar fashion at other points in the waveform.
The fast Fourier transform (FFT) of each epoch produces many power spectra results, and the
average of these power spectra is used in analyses. Further reduction is accomplished by
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