This presentation discusses the basic principles governing EEG Rhythm Generation, and discusses the various circuits that generate and maintain cerebral oscillations.
3. Outline for the session
⢠History and Introduction
⢠Concept of field potentials and dipoles
⢠The net result !!
⢠Mechanisms of Synchronization
⢠Modulation of Synchronized rhythms â Why ?
⢠Summary
5. Discovery of Brain Electrical Activity
1875 - Richard Caton, a physician in England - Rabbit cortex electrical activity
1890 - Adolph Beck of Poland
1929 - Hans Berger (1873 - 1941), an Austrian psychiatrist
- The discoverer of human EEG (= ElectroEncephaloGram)
- Alpha and Beta waves, eye closed and open, mental task
- Sleep and Awake
- Illness and EEG
- Drug and EEG: Phenobarbital, morphine, cocaine
- Telepathic transmission
6. Outline for the session
⢠History and Introduction
⢠Concept of field potentials and dipoles
7. EEG
Many neurons need to sum their activity in order to be detected by EEG
electrodes. The timing of their activity is crucial. Synchronized neural activity
produces larger signals.
8. Layers of the cerebral cortex
The 2 mm thick
cortex can be
divided into six
distinct layers.
Each layer is
distinguished both
by the type of
neurons that it
contains and by the
connections that it
makes with other
areas of the brain.
It is believed that
the activation of the
large pyramidal
cells of layer V is
what is reflected in
most EEGs.
9. Cortical pyramidal neurons
Pyramidal cells (layer V) are the
primary output neurons of the
cerebral cortex.
Pyramidal neurons are
glutamatergic and compose
approximately 80% of the neurons of
the cortex.
They have a triangularly shaped
cell body, a single apical dendrite
extending towards the pial surface,
multiple basal dendrites, and a
single axon.
Pyramidal cells are grouped
closely together and organized in
the same orientation.
10. EEGs reflect synchronous firing of pyramidal cells
There are perhaps 105 neurons under a mm2 of cortical surface.
EEG electrodes measure the space-averaged activity of >107 neurons.
A Âľvolt signal will only be detected if pyramidal neurons are synchronously
activated and many small dipoles are combined.
15. Scalp EEG: positive polarity
EPSP
IPSP
+
EPSP deep layer
+
-
EPSP superficial layer
+
+
-
-
IPSP superficial layer
Scalp EEG: negative polarity
-
+
-
IPSP deep layer
-
+
Note:
- At a surface electrode, both positive and negative polarity may
indicate depolarization (EPSPs) depending on the orientation of
the dipole.
- EPSPs in superficial layers and IPSPs in deeper layers appear
(at a surface electrode) as a negative potential.
17. Possible dipole orientations
In a sphere (the head), dipoles can be radial (perpendicular to the
surface of the skull), tangential (parallel to the surface of the skull)
or partially radial and partially tangential.
18. detectable
electrical field
undetectable
electrical field
undetectable
magnetic field
detectable
magnetic field
The activation of pyramidal neurons in layer V of the cortex that are oriented
perpendicular to the surface of the skull will contribute to an EEG signal.
It takes approximately 100,000 adjacent neurons acting in temporal synchrony
to produce a measurable change in electric field outside the head.
19. Outline for the session
⢠History and Introduction
⢠Concept of field potentials and dipoles
⢠The net result !!
20. The Electroencephalogram
Two ways of generating synchronicity:
a) pacemaker; b) mutual coordination
1600 oscillators (excitatory cells)
un-coordinated
coordinated
22. Outline for the session
⢠History and Introduction
⢠Concept of field potentials and dipoles
⢠The net result !!
⢠Mechanisms of Synchronization
26. Thalamo-cortical reentrant loops.
Steriade, M. (1999). Coherent oscillations and
short-term plasticity in corticothalamic networks. TINS, Vol. 22 (8), 337-344.
Basic Circuitry:
Cortex
RE
Cortex
Dorsal Thal. = Relay Nuclei
RE
L-circ
Th-cx
Dendro-dendr.
Th-cx
RE
L-circ
Aff
âSecondary neuronsâ
27. 1,2
Afferent brainstem input to Th-cx (1),
Activation of RE and Cortex (2)
Cortex
L-circ
RE
Th-cx
Dendro-dendr.
Th-cx
RE
L-circ
Aff
28. 3 Excitatory processes in Cortex;
Inhibition of primary L-circ;
Inhibition of other RE cells
Cortex
L-circ
RE
Th-cx
Dendro-dendr.
Th-cx
RE
L-circ
Aff
29. 4
Excitatory feedback response from cortex.
Disinhibition of primary L-circ neurons.
Inhibition of secondary Th-cx neurons.
The resulting effect is that during time 4, Th-cx are again under inhibitory control from L-circ neurons and, at the same time are activated
from cortico-thalamic cells. Thus, only strong (converging and/or amplified) cortical feedback will trigger another excitatory activation
wave into the cortex in time 5.
Cortex
The strong
inhibition of the
secondary Th-cx
cell may lead to low
threshold spikes
(LTS) and, thus, to
a 10 Hz oscillation.
L-circ
RE
Th-cx
Th-cx
RE
L-circ
Aff
30. 5 The primary Th-cx cell may start a new excitatory burst into the cortex. At this stage
(because released from the L-circ inhibition), a new afferent input will have a strong effect.
The secondary Th-cx cells remain under inhibition
RESULT: Center-surround âon-offâ effect with a resulting strong focal activation of
cortical target neurons.
Cortex
RE
L-circ
Th-cx
Th-cx
RE
L-circ
Aff
31. Summary of findings:
Afferent brainstem activation is missing and cortical activation is strong:
- Th-cx cells are hyperpolarized and oscillate with spindle frequency Note that a depol.
current pulse during maximal hyperpol. leads to high frequency bursts. The result is
increased oscillatory cortical activation leading to Delta activity.
- The effect of increased cortical activation is even larger if stimulation patterns are
oscillatory
SLEEP: Spindles and Delta
RE
Cortex
Th-cx hyperpolarized,
Sleep spindles
L-circ
Th-cx
Missing brainstem afferents
32.
33. Izhikevich Spiking Neuron
Model (2003)
⢠Claimed to be as realistic as
Hodgkin-Huxley neurons.
⢠As computationally efficient as
simple integrate-and-fire.
Izhikevich, E. M. (2003). "Simple model of spiking
neurons." IEEE Transactions on Neural Networks
14(6): 1569-1572.
34.
35. Variables
v: Membrane potential.
u: Membrane Recovery variable, giving -ve feedback to v (higher value
means neuron less likely to fire), representing Na+ and K+ ionic
currents.
4 Parameters
c: Reset value for v; higher value means easier / more likely to fire
again.
a: Speed of recovery of u; higher value means faster recovery.
b: Sensitivity of u to v; higher values tightly couple the 2 variables
resulting in
possible sub-threshold oscillations and low threshold
spiking.
d: Additive reset value for u; higher value means harder / less likely for
neuron to
fire again.
36. Figure 3.15 Two Representations of Neural Circuitry (Part 2)
37. Outline for the session
⢠History and Introduction
⢠Concept of field potentials and dipoles
⢠The net result !!
⢠Mechanisms of Synchronization
⢠Modulation of Synchronized rhythms â Why ?
39. Fourier Transform: The inner product
Rodrigo Quian Quiroga
Sloan-Swartz Center for Theoretical Neurobiology
California Institute of Technology
http://www.vis.caltech.edu/~rodri
42. Roles of Neuronal Oscillation
⢠Memory processes are most closely related to theta and
gamma oscillations;
⢠Attention seems closely associated with alpha and gamma
oscillations;
⢠Conscious awareness may arise from synchronous neural
oscillations occurring globally throughout the brain;
⢠Gamma wave and epileptics seizure.
Neuronal Oscillations represent a dynamical interplay
between cellular and synaptic mechanisms.
- Lawrence M. Ward, TRENDS in Cognitive Sciences Vol.7 No.12 December 2003,
553-559
43. Rhythmic activity appears to play a major role in
information processing in the brain
Object recognition
Feature extraction/abstraction
Associative learning
Selective attention
Novelty detection
44. Outline for the session
⢠History and Introduction
⢠Concept of field potentials and dipoles
⢠The net result !!
⢠Mechanisms of Synchronization
⢠Modulation of Synchronized rhythms â Why ?
⢠Summary
Diagram reproduced from Izhikevich (2003) without permission
Shows close resemblance of model spike trains to real recorded neurons
Diagram reproduced from Izhikevich (2003) with permission available from his website
Shows what parameter values equate to which spiking behaviour. Input I(t) is shown below each spike train v(t). TC neurons have input above and below the baseline
RS, IB, CH are cortical excitatory neurons and have equal (a,b) values, ie they vary only in their reset values (c,d)
FS, LTS are cortical inhibitory with the same reset values (c,d), ie vary only in their scaling/recovery values (a,b)
RZ is bistable â the initiating pulse must be timed correctly with the subthreshold oscillations
Parameter c (reset value after a spike) is the only parameter that directly affects v
c and d are reset values
a and b are scaling values
We introduce the first method of analysis of EEGs, the Fourier transform. It can be seen as the inner product of the signal with sinusoids of different frequencies. This gives a representation of the signal in the frequency domain. The problem of Fourier is that it has no time resolution. Moreover it assumes stationarity.
The occurrence of wave sequences in the EEG time series is accompanied by peaks in the PSDt that provide the basis for labeling the waves in time series. The 1/fa form of the spectra is revealed in log log coordinates. The slopes of the spectra are highly variable between subjects and areas of scalp. These are spectra from the frontal area of nine subjects at rest with eyes closed. Intracranial spectra have the same form but steeper slopes.