MEG Ecog Flashcards
applications: attitudes, affective priming,
social categorization, stereotyping;
fMRI Study which did investigate inner conflicts. Such as in social categorization- the activity of the ACC did correspond with an inner conflict.
The ACC works as a monitor for inner conflicts.
SQUIDs - femto-)Tesla, mu-metal
- super conductive quantum inference device
- -> measures the magnetic field of the brain.
- femto tesla- pico tesla is the range of measurement that can be detected by the MEG
- need to be cooled at - 265 degree to work. ( That’s why they are kept in those huge tanks. With Helium to keep them cold. )
MEG vs. EEG sensitivity, measurement
EEG
- Measures synchronised synaptic activity.
- Electrodes placed on the scalp - detection of the sum of extracellular positive / negative charges. Many dipoles summed together.
- The voltage gets measured between a reference and an active electrode
frequency analysis, time frequency analysis
Shows when in time frequency shifts occur
● Short FFT: compute FFT-based time-dependent spectrum (spectrogram). EEG signals viewed
as more composite sine waves with varying frequencies
● Wavelet analysis: more adaptive approach, allows flexible resolution. Resolves EEG
waveforms into specific time and frequency components. The signals are viewed as shifted and scaled versions of a wavelet (mathematical function) rather than composition of sine waves of varying frequencies.
ECOG
- electrodes placed directly on the sculp
–> invasive
Event Related Potentials
- Extraction of response evoked by an external event from EEG data, in order to examine how tasks modulate brain activity.
Exogenous sensory Components:
Specific Event Related Potentials that are known (triggered by a stimulus)
C1: generated by primary visual cortex, negative for upper field stimuli and positive for lower field stimuli. Influenced by sensory factors, not task.
● P1: generated in extrastriate areas of visual cortex, influenced by sensory factors, attention and arousal.
● N1: consists of several distinct subcomponents (e.g., N170), which produce N-waves in the same approximate time frame
o N170: involved in face processing (increased stimulation when a face is recognised), can distinguish a face from not-a-face approximately 150ms after onset. Suggests that the N170 component lies in visual cortex and is modulated by attention under some conditions.
Endogenous sensory components
conditions.
Endogenous sensory components: reflect task-dependent neural processes
● P3/P300: larger for infrequent stimuli, correlated with RT. Composed of P3b and P3a
o P3b: sensitive to task-defined probability. Larger when task requires stimulus sorting in manner which makes given category more improbable
o P3a: sensitive to highly distinctive, improbably stimuli, even when the task doesn’t require stimuli discrimination.
Covert attention / overt attention
Covert attention: allocation of attention without eye movements
Overt attention: shift in attention accompanied by shift in gaze
Both are independent of exogenous and endogenous attention
Cocktail party effect: voluntary focus on what to perceive and process
Lunch-line effect: subconsciously tracking the environment for salient stimuli
Early / late selection
Early or late selection models
● Determines how much an input is processed before it is selected or rejected by attentional mechanisms: therefore, determines whether additional processing is necessary and how it will be represented in awareness.
o Early selection: stimulus is tossed out before perceptual analysis
o Late selection: all inputs are processed equally by perceptual system
● Feature attenuation rather than a hard selection filter (as indicated by the lunch-line effect)
MEG
Detection of tiny magnetic fields generated from the weak electrical impulses between brain cells.
–> Squids are used to detect the tiny magnetic fields that stem from the weak current running between brain cells.
- Helmet shaped magentometers cover the whole scalp and uses sensor units to measure the
magnetic field in x, y and z directions.
the inverse problem
Difficult to identify which brain currents are responsible for a particular MEG signal. Therefore, some assumptions must be made to obtain a unique solution:
o That the brain is approximately spherical
o That active areas can be adequately represented by one/ multiple current dipoles
● Computers make a guess as to where the dipoles are based on measured magnetic field
distribution. It then calculates the external magnetic field that that dipole would make, and compares it to the reality. This is repeated for the dipoles at different positions until the expected/ measured match as closely as possible.
parameters used to characterise the current dipole.
Five parameters are used to characterise the current dipole used to explain MEG data:
o 3 parameters: position in 3D space (x, y, z)
o 1 parameter: orientation (tangential currents produce magnetic fields externally) o 1 parameter: strength
● If 2+ regions are active, measured magnetic field depends on (1) position and strength of the dipoles and (2) extent to which the neurons are firing at the same time
Event related fields
The average of recorded MEG signals. The fields have the same resolution as ERPs, but allow for more accurate estimations of source localisations due to their minimal distortion.
Role of thalamo cortical activity in EEG activity
Interaction of thalamus and cortical networks assumed to play a key role in rhythmical EEG
activities
o Thalamic, thalamocortical and cortical neurons display oscillatory patterns which
produce rhythmic EEG oscillations
o Thalamus important for pacing of rhythmical activities
o Cortex provides the coherent output in response to thalamic input and generates
majority of oscillations that are recorded at the scalp
Delta Band ( 1-4Hz)
Low frequency, high amplitude inhibitory rhythm
● Sleep, proximity of brain lesions and tumours, during anaesthesia
● Diminishes with age
Theta Band ( 4-8 Hz)
Low frequency, high amplitude
● Mostly sleep
● Two types of wakefulness
o Decreased alertness/ impaired information processing: widespread theta wave distribution
o Focused attention/ mental effort: frontal midline theta activity, ACC as potential generator
● Implicated areas: septo-hippocampal, ACC, entorhinal cortex and medial septum areas.
Alpha Band ( 8-13Hz)
Amplitude between 10 and 45μV
● Relaxed wakefulness of resting periods with eyes closed
● Alpha blockage/ desynchronisation: can be diminished or abolished by eye
opening, sudden alertness or mental concentration
● Greatest amplitude over posterior occipito-temporal and parietal regions
● Associated with visual system functions in the absence of visual input
● Different sub-bands are functionally dissociated
o Lower alpha (8-10Hz) desynchronisation: stimulus and task-non-specific increases in attentional demands
o Upper alpha (10-12Hz) desynchronisation: processing of sensory-semantic information, better semantic memory performance and stimulus-specific expectancy
Beta band ( 13- 30Hz)
High frequency, low amplitude
● Symmetric fronto-central distribution
● Replaces alpha rhythm during cognitive activity
● Increased excitatory activity coming with focused attention, diffuse arousal
and vigilance
Gamma Band ( 36-42Hz)
High frequency, low amplitude
● Attention, arousal, object-recognition and top-down modulation of sensory
processes
● Directly associated with brain activation
● Reflect large-scale integration and synchrony amongst widely distributed
neurons
● Gamma bursts occur within periods of theta phases
Signal Analysis
Methods to investigate EEG signals: waveform frequencies, amplitudes, phase and coherence. Two forms of quantitative EEG analysis:
● Linear: assume EEG signal is a stationary process, include spectral/ coherence analysis
● Non-linear: most useful regarding transient and irregular EEG patterns, incorporate
higher-order statistics, information theory or chaos theory.
Spectral analysis
Based on notion that any oscillatory activity can be characterised by the sum of different sinusoidal waves with distinct frequencies and amplitude
Aim: estimate the contribution of various frequencies to measured EEG signal.
Assumes that EEG is a stationary signal.
● Fast Fourier Transform (FFT) to analyse: power (square of the amplitude) used to quantify the
contribution
o Absolute power: amount of a given frequency within the EEG o Relative power: same thing divided by total power
Certain conditions for use:
● Must enter artifact-free EEG segments
● Length of segment determines frequency resolution
● Segments cannot be too long, or the stationarity assumption would be violated
● At least 60seconds should be used to allow reliable estimation and eliminate second-to-second
variability
● Taper transformations should be used to avoid random changes at the beginning and end of the
segment
o Hanning cosine window
Time frequency Analysis
Shows when in time frequency shifts occur
● Short FFT: compute FFT-based time-dependent spectrum (spectrogram). EEG signals viewed
as more composite sine waves with varying frequencies
● Wavelet analysis: more adaptive approach, allows flexible resolution. Resolves EEG
waveforms into specific time and frequency components. The signals are viewed as shifted and scaled versions of a wavelet (mathematical function) rather than composition of sine waves of varying frequencies.
Jittering
- a method to overcome the poorer temporal resolution of fMRI . ( the long time it takes to make repeated fMRI pictures)
- if one wants to make fMRI the fMRI can not get a full cyclus (normally it takes 6-10 seconds in between )
the parts of the
Spectral Analysis
Based on notion that any oscillatory activity can be characterised by the sum of different sinusoidal waves with distinct frequencies and amplitude
Aim: estimate the contribution of various frequencies to measured EEG signal.
