Task 4 ERPs Flashcards
What is MEG?
- dipoles produce small magnetic fields perpendicular to current flow (can be traced/ averaged to produce event-related fields ERF´s)
- same temporal resolution as EEG, but superior spatial res. !
→ magnetic fields are not distorted when passing through tissue & skull, thus modelling techniques are more accurate in localizing the signals - Useful for neurosurgery: locate center of seizures (epilepsy) or tumourous areas
SQUIDS
“superconducting quantum interference devices”
- highly sensitive to magnetic fields, used as detectors in the MEG device
- around 306 SQUIDs take measurements of the whole scalp
- have to be immersed in liquid helium at -269 C so that magnetic interference is limited
(femto-)Tesla
- unit of magnetic flux density (essentially magnetic field strength
- MEG instruments measure fields in the femtotesla range (10e-15T) –> 1:Ten-billionth of the earth´s magnetic field
Limitations of MEG
- only able to detect tangential current flow (mainly found in apical dendrites of neurons in sulci)
- magnetic fields are extremely weak, thus rooms have to be shielded from any external electrical/magnetic distortions
- SQUID sensors encased in liquid helium can´t be placed closer to the scalp than 3cm
Neural currents underlying MEG signals
- neurons generate APs which travel along axons
- when APs reaches synapses, NT gets released –> triggers PSP in next neuron
- allows transmission of electrical signal to continue through brain
–> if thousands of neurons fire at the same time in the same region: parallel currents in neighbouring neurons produce dipolar net current
Direction of magnetic flux
- direction of magnetic flux outside the head is determined by direction of current within group of neurons: right hand rule of electromagnetism
Inverse problem (MEG)
- difficult to identify which brain areas are responsible for particular MEG signal
- Assumptions:
1. brain is approx. spherical
2. active areas can be adequately represented by one/multiple current dipoles
ERFs
MEG traces can be recorded and averaged over a series of trials to obtain event-related fields
What are ERPs?
- event-related potentials
- represent summation of post-synaptic potentials from populations of synchronously active neurons located primarily in the cortex
- columnar structure aligns electrical field –> summated signal is strong enough to be detected at scalp
How is ERP extracted?
ERP must be extracted from noise in EEG
1. filtering raw EEG signal: application of algorithms to attenuate frequencies that are not of interest
2. aligning signals to events of interest
3. averaging signals
Nyquist theorem: sampling rate should be at least 2x the highest frequency present in the signal under investigation to prevent introduction of spurious low-frequency –> can make irreparable distortion to digital waveform
Artefacts
- biological and non-biological sources of noise: subject’s movements, muscle activities, eye movements, heartbeat, sweating, powerlines, electrical noise
–> can be detected and removed
Classes of ERP waveforms
- Stimulus locked waveform: aligning EEG epochs to onset of stimulus: ERP arises in response to specific stimulus –> reflects some aspect of perceptual/attentional processing with earlier deflections suggesting automatic processes
- Response-locked waveform: aligning EEG epochs to moment of behavioural response
Quantifying ERPs
- determination of time window in which component of interest emerges
- measuring average voltage within that window for each subject
Alternative: peak-picking algorithm to determine maximal voltage of component of interest within specified time range
Spectral analysis (basis and aim)
- basis: any oscillatory activity can be characterised by sum of different sinusoidal waves with distinct frequencies and amplitude
aim: estimate contribution of various frequencies to measured EEG signal
Fast Fourier Transform (FFT)
- analyses power (square of amplitude) used to quantify the contribution to measured signal
=> Fourier coefficient: strength of signal at given frequency - absolute power: amount of given frequency within EEG
- relative power: amount of given frequency divided by total power
Assumptions for FFT
- EEG is stationary signal (=linear analysis)
–> segments of approx. 60 sec and artifact-free
Time-frequency analysis (basis and examples)
- basis: time-frequency analysis gives insight into when in time the frequency shift occurs
- Short-time FFT: compute FFT-based time-dependent spectrum (spectogram) –> EEG signals viewed as more composite sine waves with varying frequencies
- Wavelet analysis: resolves EEG waveforms into specific time and frequency components –> signals are viewed as shifted and scaled versions of a wavelet (mathematical function)
EEG oscillations: local-scale vs. large-scale synchronisation
local scale synchronisation: occurs among neighbouring neurons
large scale synchronisation: occurs between neuronal assemblies of distant brain regions
Frequency bands: Delta bands
- 1- 4 Hz
- low frequency, high amplitude
- inhibitory rhythm
- predominant in infants (1-2 yo)
- inverse relationship with glucose metabolism
- increase in proximity of brain lesions and tumours, during anaesthesia and during sleep
- diminishes with age
Frequency bands: Theta bands
- 4-8 Hz
- low frequency, high amplitude
- prominently seen during sleep
- Implicated brain areas: septo-hippocampal, ACC, entorhinal cortex,
- diminishes with age
- Two types of wakefulness:
1. Drowsiness: widespread scalp theta wave distribution
2. Focused attention: frontal midline theta activity - diminishes with age
Frequency bands: Alpha bands
- 8-13 Hz
- amplitude between 1- and 45 uV for health adults
- greatest amplitudes over posterior regions (occipito-temporal and parietal) when eyes are closed
–> associated with visual system functions in absence of visual input - increases with age
Subbands:
- 8-10 Hz: lower alpha desynchronisation: stimulus and task-nonspecific increases in attentional demands
- 10-12 Hz: upper alpha desynchronisation: processing of sensory-semantic information –> better semantic memory performance
Frequency bands: Beta bands
- 13-30 Hz
- high frequency, low amplitude: 10-20 uV
- symmetric fronto-central distribution
- replaces alpha rhythm during cognitive activity
- increased excitatory activity with focused attention, diffuse arousal and vigilance
- increases with age
Frequency bands: Gamma bands
- 36-42 Hz
- high frequency, low amplitude
- attention, arousal, object-recognition and top-down modulation of sensory processes and perceptual binding
- directly associated with brain activation
- reflects large-scale integration and synchrony amongst widely distributed neurons
