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Methods of Neuroscience > Task 4 ERPs > Flashcards

Task 4 ERPs Flashcards

1
Q

What is MEG?

A
  • 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
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2
Q

SQUIDS

A

“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

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3
Q

(femto-)Tesla

A
  • 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
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4
Q

Limitations of MEG

A
  • 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
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5
Q

Neural currents underlying MEG signals

A
  • 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
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6
Q

Direction of magnetic flux

A
  • direction of magnetic flux outside the head is determined by direction of current within group of neurons: right hand rule of electromagnetism
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7
Q

Inverse problem (MEG)

A
  • 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
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8
Q

ERFs

A

MEG traces can be recorded and averaged over a series of trials to obtain event-related fields

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9
Q

What are ERPs?

A
  • 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
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10
Q

How is ERP extracted?

A

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

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11
Q

Artefacts

A
  • biological and non-biological sources of noise: subject’s movements, muscle activities, eye movements, heartbeat, sweating, powerlines, electrical noise
    –> can be detected and removed
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12
Q

Classes of ERP waveforms

A
  1. 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
  2. Response-locked waveform: aligning EEG epochs to moment of behavioural response
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13
Q

Quantifying ERPs

A
  • 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

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14
Q

Spectral analysis (basis and aim)

A
  • 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

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15
Q

Fast Fourier Transform (FFT)

A
  • 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
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16
Q

Assumptions for FFT

A
  • EEG is stationary signal (=linear analysis)
    –> segments of approx. 60 sec and artifact-free
17
Q

Time-frequency analysis (basis and examples)

A
  • basis: time-frequency analysis gives insight into when in time the frequency shift occurs
  1. Short-time FFT: compute FFT-based time-dependent spectrum (spectogram) –> EEG signals viewed as more composite sine waves with varying frequencies
  2. Wavelet analysis: resolves EEG waveforms into specific time and frequency components –> signals are viewed as shifted and scaled versions of a wavelet (mathematical function)
18
Q

EEG oscillations: local-scale vs. large-scale synchronisation

A

local scale synchronisation: occurs among neighbouring neurons
large scale synchronisation: occurs between neuronal assemblies of distant brain regions

19
Q

Frequency bands: Delta bands

A
  • 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
20
Q

Frequency bands: Theta bands

A
  • 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
21
Q

Frequency bands: Alpha bands

A
  • 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

22
Q

Frequency bands: Beta bands

A
  • 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
23
Q

Frequency bands: Gamma bands

A
  • 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

Methods of Neuroscience (10 decks)

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