| INTRODUCTION
Even with dramatic technological advances, measuring the
"internal behavior" of the brain is a far more complex task than measuring the
body systems that it controls. At the present time the EEG signal recorded from the scalp
is the only reliable, real-time, and totally risk-free method for estimating this internal
behavior. Because the EEG reflects neuronal behavior, the method which uses this signal as
a source for physiological feedback has been given the separate label that we use here,
namely neurofeedback.
Studies in animals have taught us a great deal about the EEG signal and its origins
within the brain. Since we cannot conduct similar studies in humans we must depend upon
the insights provided by this animal research to guide our understanding of the EEG, and
to provide the strategies to shape some aspects of the internal behavior of the brain.
Fortunately, there are strong parallels between the EEG patterns seen in higher mammals
and in humans. Moreover, evidence provided by disorders of the human nervous system and by
accidental injuries suggests that the mechanisms underlying these patterns are also
similar. Therefore, much of the picture that we present here relating to the human EEG
was, of necessity, gained through careful and painstaking research conducted in higher
animals. In my experience, the models which have emerged apply effectively to the
interpretation of the human EEG.
Mapping the brain
The EEG measures small electrical potentials that can be recorded at the greatly
expanded surface of the brain, which is called the cerebral cortex. The cerebral cortex is
the highest level of nervous system organization. Cells here store our memories, generate
our thoughts, express our feelings and intentions, and determine our conscious awareness
of ourselves and of the outside world. They are organized into separate systems, some of
which are concerned with the regulation of specific functions, such as sensations and
movements, and others with more integrative processes, such as behavior sequencing and
memory. This organization allows us to position EEG electrodes in a pattern that maps the
surface of the brain and examines the status of these different functions. The method
used is called quantitative EEG topographic mapping.
The small electrical potentials that make up the EEG are generated by summated
extracellular currents in pools of cortical cells that are modulated by signals arriving
from the outside world and from other parts of the nervous system. These changes are
recorded by referencing a cortical electrode against a presumed "silent" site
such as the earlobe (referential recording), or against a different pool of neurons in a
different brain area (bipolar recording). For example, let’s say that we are sitting
at a table reading a boring book. If nothing of interest is found in this material, we
will become progressively inattentive and eventually be unable to "see" what we
are reading. We will read the same sentence over and over and still not be certain about
what it says. This behavior occurs because the sensory pathway and cortical cells that
convey this information to consciousness have begun to assume a "resting mode".
This change will be especially reflected in the EEG recorded from electrodes placed over
the visual part of our cortex. However, if a spider unexpectedly crawls onto the left side
of the page, the pathway that conveys this information from the eyes to the brain will
suddenly excite specific cells in the area of our cortex that records and interprets that
portion of our visual world. The EEG will show this change. Depending on our comfort with
spiders this event may also elicit automatic responses in other pathways which may cause
changes in our internal state, focus of attention, and motor behavior as we orient
behaviorally to the spider and take some action. These responses will be reflected by
other characteristic changes in the EEG.
By placing sensors or electrodes over appropriate areas of the cortex we can measure
the pattern of such changes in ongoing EEG signals. These changes are expressed as shifts
in the frequency and amplitude of the EEG signals. The term frequency refers to the timing
of the average interval between successive oscillations in the EEG, as we shall see below.
The term amplitude refers to the average height or "voltage" of the oscillations
in a given frequency range.
Traditionally, people in the EEG field have given Greek letter labels to various
frequency ranges seen in the EEG signal. This practice, which was useful at first, has
come under some question due to the fact that the same frequency patterns can be seen
under very different functional circumstances. For example, hypoxia, drowsiness, epilepsy,
ADD and head injury can all produce 4-8 Hz patterns which are not functionally
distinguished by the label theta. Similarly, brain excitation is associated with an EEG
pattern referred to as beta activity. In my opinion, this label is also unfortunate, since
it oversimplifies actual events.
Exciting the Pathways of the Brain
What causes the EEG to show such changes? To understand the answer to this question, we
must understand the different ways in which cortical cells (or neurons) are
influenced by incoming signals. Neurons are like little batteries, in that there is a
separation between negative ions, which are kept in abundance inside the cell, and
positive ions which are kept outside of the cell. This separation is produced by the
electrochemical properties of the cell’s outer layer, or membrane. Resting neurons
are thus said to be charged, or polarized, and this charge can be measured with
sensitive instruments.
Receptive sites can be excited by incoming signals that release chemicals called
excitatory neurotransmitters on the neuronal membrane. These chemicals act like keys that
then open doors, or channels, in the cell membrane and allow positive ions into the cell
and let negative ions out. This produces a small depolarizing current flow that moves
across the membrane and sets up ionic perturbations throughout the cell and its
surrounding extracellular space. It also reduces the charge across the membrane. This
current flow is called an excitatory pre-synaptic potential, or EPSP. If
many such pre-potentials accumulate they can depolarize, or discharge the cell, an event
referred to as an action potential. Action potentials flow to the cells’ axon
and are thus carried to other neurons with which the cell is connected, causing local
release of neurotransmitters on their membrane surfaces. The cell will rapidly charge up
again to be ready for the next bit of information.
Neurons can also show an increase in polarization, a phenomenon called hyperpolarization.
This response is caused by the action of a different class of pre-synaptically
released chemicals, called inhibitory neurotransmitters. These transmitters carry negative
chloride ions into the cell and/or keep another positive ion, potassium, out of the cell.
The opposite current flow produced makes the membrane more polarized and, therefore, the
cell more difficult to excite. The combination of currents produced by depolarizing and
hyperpolarizing dynamics and their interaction between deep and superficial layers of the
cortex are responsible for generating the field potentials that give rise to the EEG.
During the processing of relevant sensory information, such as the spider mentioned
above, excitation reaches the cell from many sources, including the incoming sensory
information, its interpretation and integration by other cortical systems, and its
affective associations. In the sensory pathway itself excitation may be selective, with
only certain cells discharging. This selectivity conveys the distinguishing features of
the stimulus, such as its location, shape, movements, etc. However, other nearby cells
that are not directly excited may actually be hyperpolarized. This process can produce an
area of cellular inhibition around the excited cells called "surround
inhibition", which is believed to help focus and resolve incoming signals. Because
many sources of excitation stimulate relevant cortical cells at many sites, while nearby
cells are unaffected or inhibited, the complex field potentials produced tend to cancel
each other when recorded from our distant surface electrode. This will produce a recorded
EEG trace that shows little frequency modulation or amplitude, which we call an activated
or desynchronized EEG pattern, and others call Beta. When this kind of EEG trace
appears suddenly in the visual cortex we know that the individual has seen the spider. If
it spreads rapidly to other cortical areas we can assume that they were startled and/or
not necessarily delighted by this perception.
Reduced excitation: Making Brain Waves
When they are not processing information, cortical cells can behave quite differently.
They are under a different kind of control. The pathways between external sensory and
internal regulatory systems and the cerebral cortex pass through a structure deep in the
brain called the thalamus. The thalamus, which in Greek means "inner
room" and in Egyptian "antechamber", is made up of clusters of functionally
related cell groups called nuclei. The Egyptian antechamber designation is very
appropriate, since these nuclei serve to regulate the access of information to the brain
from either sensory receptor systems concerned with the external world, such as the eyes
and ears, or from other structures in the brain concerned with internal regulation, such
as motor coordination, emotions, and other higher functions.
Thus, when the spider entered the picture, cells in the thalamic nucleus that relay
left visual field information from the retina of the eyes to the visual nucleus of the
thalamus were stimulated. The cells in this nucleus were also excited by internal neuromodulatory
responses to this unexpected perception that arise from such factors as arousal,
startle or fear. As such, they readily conveyed this information to the appropriate part
of the visual cortex and initiated an alerting sequence of events. However, in the absence
of the spider, the cells in both the thalamic and cortical elements of the visual pathway
would tend to go off-line if the individual was becoming uninterested.
In this case, a different EEG pattern would appear in the visual cortex and eventually
in many other cortical areas as well. This different pattern is caused by changes in both
the thalamus and cortex when we become inattentive. During attention, stimulation from the
world around us and our interest in certain things in that world combine with our general
state of alertness to excite the thalamus and cortex. This makes information conduction
efficient. However, during inattention these sources of excitation are all reduced. The
thalamic relay cells and their cortical targets are less excited and may even be inhibited
by competing brain processes. This causes these cells to become relatively hyperpolarized,
and to enter a "resting" or "stand-by" state. As mentioned above,
hyperpolarization makes these cells harder to excite.
Unlike many other brain cells, thalamic relay cells don’t just remain silent when
they are hyperpolarized. This is because the currents which develop within these cells are
changed by hyperpolarization, a property which makes their behavior "voltage
dependent". In thalamic relay cells this property causes the hyperpolarized membrane
to let certain positive ions creep in. As a result, instead of not firing when
hyperpolarized, these cells display a "built-in" slow depolarizing current which
gradually brings them to their firing threshold, even when they are at rest. When they
fire in this state they show unique bursts instead of single action potentials. Further,
these bursts are repetitive.
The repetitive burst discharge results from another unique characteristic of thalamic
organization. The axons of relay neurons simultaneously convey these bursts of excitation
to the cortex and to another nearby thalamic nucleus. The exclusive role of this nucleus
is to regulate the relay cells, and each relay cluster has its own special relationship
with adjacent segments of this nucleus. Bursts in relay cells cause sister cells in this
nucleus to burst also. When they burst, these cells feed back to the thalamic relay cells
that activated them, but instead of exciting these cells they release an inhibitory
neurotransmitter that reinstates the initial hyperpolarization. The re-hyperpolarized
relay cells begin to slowly depolarize again and the whole process is repeated. This
circuit organization results in an oscillation between the sister cells in these two
thalamic nuclei that produces the repetitive thalamic relay cell bursts. Such bursting
behavior can last for only a fraction of a second or for several seconds. Details about
this organization and its functional consequences for the EEG can be found in an abundant
neurophysiological literature 8-12.
The trains of thalamic relay cell bursts are directly relayed to associated cortical
cells and, if they are also relatively unexcited, can cause them to burst in a similar
pattern. As inattention is prolonged this oscillation encompasses more and more relay
cells and triggers the networks between cortical neurons to draw more cells into the
oscillation. The growing pool of oscillating cortical cells becomes synchronized over a
widespread area. Our surface EEG electrode will now record synchronized alternating
currents set up by this oscillatory pattern of discharge as repeated wave-like potential
changes, thus giving rise to the term "brain waves" .
Since these waves are repeated, the interval between them, or their frequency, can be
measured over time. In most adults the frequency of these waves in the inattentive state
is between 8 and 12 cycles per second. When sustained over the visual cortex this pattern
tells us that visual attention has decreased. We know that the individual is not
processing the words on the page of their book. They are not necessarily sleepy, they are
just inattentive. These oscillations in the EEG during visual inattention were first
described by the pioneering German scientist Hans Berger 13, who considered
them to be "first-order brain waves", and thus called them the "alpha
rhythm". The kind of activated EEG pattern seen with attention (as when the spider
was detected) was considered by Berger to represent "second-order brain waves",
which he called the "beta rhythm". In the alert and visually attentive
individual these "beta" waves briefly dominate the EEG from the posterior
cortical surface. However, when we are visually inattentive "alpha" waves will
be dominant. If we become generally unaroused and inattentive this activity will spread
more generally over the entire cortex, as other systems go "off-line".
Neuro-feedback training for the enhancement of this rhythm will increase visual
inattention and can lead, eventually, to general relaxation.
In addition to the alpha and beta rhythms
there is another important EEG rhythm that we need to discuss. This is a distinctive,
faster rhythm that is recorded over a specific brain area. The rhythm is in the 12-19
cycle/second range, and the brain area where it is dominant is concerned with our
conscious awareness of our body and the condition of our muscles and joints. It is thus
called the sensorimotor cortex. Accordingly, the rhythm recorded here has been labeled as
the "sensorimotor rhythm", or SMR. My laboratory was the first to study this
rhythm in animals and later in humans 14,15. The SMR is seen clearly in higher
mammals who are alert but in a state of sustained motionlessness, a condition some have
described as "expectant". It is suppressed by movement and only occurs when the
animal begins to shut down its voluntary motor pathway. That is, when the animal resists
any impulse to move. When this happens the thalamic relay cells in the pathway that convey
sensations from the muscles and joints to the sensorimotor cortex show a similar
characteristic oscillatory bursting as was described earlier. However, because this
condition represents motor inattention only, in an otherwise alert state, the interval
between bursts is shorter, giving rise to the faster SMR. The reason for this faster
frequency will be discussed later. The SMR behaves very much like the alpha rhythm but is
related to a specific suppression of movement rather than to a reduction of visual
attention 15, 16. Neurofeedback enhancement of the SMR thus produces a quiet
body in the context of an alert mind.
It should be pointed out that the SMR is more difficult to see in the surface EEG of
humans than are slower rhythms. There are several reasons for this discrepancy. First, in
animal studies EEG electrodes are placed directly onto the brain. Interference in the
conduction of brain wave signals through connective tissues, bone, and skin has been found
to affect faster frequencies more than slower ones. Secondly, as we have said, the faster
SMR is associated with relative alertness. In this state fewer thalamic and cortical cells
are drawn into synchronized oscillation, resulting in a lower voltage and a more limited
spread within the cortex. However, when electrodes are placed directly on the brain in
humans, for example after the surgical removal of the bone over the sensorimotor cortex),
this higher frequency activity is clearly apparent and resembles that seen in cats 17.
Thus, under normal circumstances we must rely on sensitive frequency analysis equipment to
aid us in the detection of the SMR.
Modulating the Frequency of Brain Waves
By now some of the reader’s own visual relay cells are beginning to show bursting
behavior. If we don’t counter this effect this process will not only expand to
include more and more cells but may lead, ultimately, to your falling asleep. If we were
recording from you, we would know this because the characteristics of your brain waves
would begin to change. Before this happens you might want to know how it happens.
The complex regulation of brain waves is directed by many different functional
influences, including 1) the information from our senses and other important pathways that
coordinate motor and internal responses, 2) influences from the various areas of the brain
which express our learned biases and direct our particular interests at the moment, and 3)
influences from other areas in the brain which set our general level of alertness. Thus,
when we are attentive, are emotionally motivated, or plan a particular action, excitatory
influences act on appropriate thalamic relay and cortical cells and promote selective
depolarization in the service of our needs. Such events are signaled in the EEG by an
activated pattern which can be either localized to a particular system or generalized to
many areas of the cortex during complex cognitive activities.
However, if we are unmotivated, confused, or exhausted, which could certainly be the
case here, a growing number of relay and cortical cells begin bursting. This will lead at
first to localized alpha rhythm activity, usually in the visual cortex. As inattention is
sustained, this rhythmic pattern increases in amplitude, becomes more widespread, and
includes a slower component of alpha rhythm activity. Our model attributes these changes
to the fact that the relay cells have become more hyperpolarized, more of them have joined
the oscillation and, because of an associated and generalized reduction in neuromodulatory
excitation, rhythmic activity has emerged in other relay systems as well. Eventually, the
frequency of these rhythms begins to slow and rhythmic trains in the range of 4-7
cycle/sec. are seen. This is one example of slower activity commonly referred to as the
"theta rhythm". It tells us that the individual has become drowsy and is falling
asleep. You will know that this has happened when your face hits your desk!
We suggest that the passive reduction of excitatory influences from all sources in this
state, together with conditioned inhibitory responses, results in a progressively greater
hyperpolarization of thalamic relay cells. Such an increasing hyperpolarization has been
shown to change cell membrane characteristics and to slow the built-in depolarizing
current. This effect lengthens the interval between relay cell bursts 18. This
happens because of the previously mentioned voltage dependent changes in membrane ionic
currents.
Thus, in the normal brain, very fast low voltage EEG activity is associated with active
excitation, the SMR is associated with reduced motor excitability and the absence of
movement, intermediate alpha rhythm frequencies are associated with inattentiveness, and
slower theta rhythm frequencies with drowsiness. Very slow delta frequencies are normally
generated in infancy and early childhood 19, 20, and during stage 3-4 sleep in
adults. While the SMR is localized, faster and slower frequencies can be either localized
or generalized, depending on the pathways that are activated, placed in stand-by mode,
shut-down, or disrupted.
Despite some contradictions in the EEG literature it is abundantly clear that all of
these rhythms depend primarily on the integrating and oscillatory functions of the
thalamus. It has long been known from clinical findings that structural injuries or
degenerating processes that separate the thalamus from the cortex produce a featureless
EEG, with only unstable slow activity 22. If the pathology is severe the
pattern seen is persistent "polymorphic delta activity" or PDA. In less severe
or more localized pathology the slow activity seen may be episodic and suppressed by
arousal or cognitive engegement. This is called "intermittent rhythmic delta
activity", or IRDA. These pathological rhythmic patterns occuring in relation to
lesion-induced isolation of cortical tissues appear to be generated by intrinsic currents
in these isolated cell populations.
The closest thing one sees to this activity in the normal brain occurs during stage 3-4
sleep, when thalamocortical interactions are functionally attenuated due to a deep
hyperpolarization of thalamic relay neurons during this phase of sleep. Steriade et al. 21,23
postulate " a progressive hyperpolarization of thalamocortical neurons with the
deepening of the behavioral state of EEG-synchronized sleep". One can presume that
this state is different from pathological isolation, since findings from these studies
indicate that cortical cells contribute to thalamic pacemaker functions, setting up a
thalamocortical-corticothalamic oscillation in the case of sleep-related delta activity.
Perhaps the most convincing evidence for the critical role of the thalamus in EEG
rhythm generation comes from a series of studies by Villablanca and associates in the
1970’s 24-26. With great skill and precision these investigators
surgically removed the entire thalamus in a series of cats. They called this preparation
the "athalamic cat". In other studies they simply separated the thalamus from
its cortical projections in one hemisphere. In the latter preparations normal EEG rhythmic
activity could be recorded from the intact hemisphere but the isolated cortex of the
operated side showed only slow rhythmic activity resembling clinical PDA. However, EEG
recordings from the separated thalamic relay nuclei showed normal rhythmic activity.
But the coup de gras was the athalamic cat. Initially, in these animals,
regardless of the behavioral state determined by other physiological markers, the only
cortical EEG pattern seen was the same slow PDA. This was true even during REM sleep
(normally associated with an activated EEG), and was completely unaltered by the
administration of barbiturate drugs. Only after a prolonged recovery period was active
arousal accompanied by attenuation of these slow waves, likely due to the functional
restoration of extra-thalamic excitatory neuromodulatory pathways.
I think that these considerations are very important in that they focus our thinking
concerning the primary mechanisms underlying relevant EEG rhythms to the thalamus. For
example, they provide a compelling argument for the fact that delta activity in the EEG
can only occur under rather special circumstances involving deep hyperpolarization of
thalamic and/or cortical neurons. Such hyperpolarization requires a significant reduction
in excitatory input or a profound source of functional inhibition. What then are people
talking about when they report quantitative increases in delta activity in normal awake
and responsive subjects? Do they provide actual EEG tracings showing this delta activity?
Can they show that this activity has the required amplitude and rhythmicity of true delta
activity? Such "delta activity" is often indicated as occuring simultaneously
with normal alpha activity. Can the mammalian nervous system do that? These reports come
only from extracted numerical data derived by mindless computer-based frequency analysis.
Such analysis is fraught with potential artifact and mathematical distortions in the low
frequency domain. There will never be a zero output in any defined frequency range with
current analysis methods. To those who think that these changes are relevant I say
"show me the delta, baby".
As we learn more and more about the principles of thalamocortical and corticothalamic
interactions, and how they relate to normal and abnormal circumstances, we acquire the
functional tools to understand what we are seeing in the EEG. In fact, in order to assist
the reader in remembering the position expressed here I have composed the following poems.
The Athalamic Cat
by M. Barry Sterman
"This is not the p*ssy in the hat, this is the
athalamic cat!
He can’t make alpha all the day, he can’t make theta any way!
Its delta, delta on and on, because, you see, his thalamus is gone!"
The Happy Cat
by M. Barry Sterman
"This is indeed the cat in the hat,
He is healthy, happy and even a bit fat,
He can make alpha when he is out of touch,
He can make theta when he is sleepy or such,
But only makes delta when he is in bed,
or if he suffers a knock on his head."
What can this physiological model tell us about our patients?
From this perspective we can presume that the presence or absence of sustained EEG
rhythms, as well as their location and their frequency, can tell us a great deal about how
the brain is directing attention and planning behavior. We can also interpret deviations
from a more informed perspective. For example, when adults close their eyes or become
visually inattentive the dominant 8-12 cycle/second alpha rhythm emerges over the back of
the head. This dominant activity is usually greatest and rather symmetrical over this
region where conscious visual perception is localized. This response is normally well
developed by the second decade of life. Prior to this age, however, the dominant frequency
is slower 19, 20. Thus, at age 5 it is in the range of 4-7 cycle/second. Why is
this dominant frequency slower in these younger children? As we saw above, the frequency
of EEG rhythms is believed to reflect the combined influence of many functional systems
acting on the thalamic and cortical generators of EEG rhythms. The more these generators
are excited the faster the rhythmic oscillation produced by the thalamic machinery
described above. It is reasonable to assume, therefore, that the immature status of the
pathways which mediate general alertness and excite thalamic relay cells in early
childhood results in less excitation and, accordingly, slower EEG rhythms. This condition
is not abnormal but instead reflects the normal course of brain maturation.
Using modern EEG brain mapping it is possible today to identify both topographic and
frequency deviations from this normal course of maturation. Thus, if a 15 year old child
has a localized or pervasive dominant frequency in the 5-7 cycle/second range, we can
presume that something is wrong. Brain mapping of the EEG has in fact demonstrated just
such abnormalities in many children with ADD, and this information provides the primary
rationale for neurofeedback therapy. In our experience EEG brain mapping is disclosing a
number of different patterns of abnormality in these children, a finding which may aid in
providing a more precise and reliable physiological differentiation of the various
patterns of this disorder.
Remembering that neurofeedback can be viewed as a focused exercise program for selected
pathways within the central nervous system and that its potential effects are driven by
the Law of Effect, we can speculate as to how this treatment might facilitate therapeutic
adjustments within thalamocortical networks. EEG electrodes placed over areas of EEG
abnormality can be used to guide functional reorganization within these loops. Once the
primary cortical area(s) showing this disturbance are identified (frequency and
localization may vary with age, severity, or idiosyncrasies of the disorder), a
neurofeedback strategy can be developed to promote the progressive normalization or
optimization of EEG patterns throughout the area. Neurofeedback alone cannot accomplish
this objective. As with any other behavioral treatment, it is essential to establish a
close working relationship with the client, and to function as a personal trainer and
source of motivation and educational feedback. With many disorders this will involve a
program which seeks theoretically to reduce cellular hyperpolarization in thalamocortical
circuits, and to raise the frequency of EEG rhythmic activity. Thus, the SMR protocol
requires the patient to gradually adjust thalamocortical excitation through increased
attention (suppression of slow activity) in the context of a quiet body (enhancement of
the SMR.) The desire for rewards will initially promote transient changes in functional
state. However, with repeated facilitation of normal interactions within relevant
circuits, this exercise may also elicit progressive and more permanent changes in both
functional and structural characteristics. We know that the neural plasticity required to
achieve such changes exists. We have only to harness it effectively with solid guiding
principles, methodology, and clinical expertise.
A focused and directed functional exercise effect, which we know can alter underlying
systems if applied correctly, seems to be the most unique contribution of neurofeedback.
It is hoped that these basic considerations will provide the reader with some framework
for interpretation of the interesting findings to be encountered in the many and diverse
papers which follow.
REFERENCES
1. Sterman, M.B., Lo Presti, R.W. and Fairchild, M.D. Electroencephalographic and
behavioral studies of monomethyl hydrazine toxicity in the cat. 1969, Technical Report,
AMRL-TR-69-3, Air Systems Command, Wright-Patterson Air Force Base, Ohio.
2. Sterman ,M.B., Howe, R.D. and Macdonald, L.R. Facilitation of spindleburst sleep by
conditioning of electroencephalographic activity while awake. 1970, Science, 167:
11461148.
3. Sterman, M.B. Neurophysiologic and clinical studies of sensorimotor EEG biofeedback
training: Some effects on epilepsy. (1) In: L.Birk (Ed.), Biofeedback: Behavioral
Medicine, 1973, Grune and Stratton, New York (Chapter 9), 1973: (2) Seminars in
Psychiatry, 5(4):507525.
4. Sterman, M.B. and Friar, L. Suppression of seizures in epileptic following
sensorimotor EEG feedback training. 1972, Electroenceph. Clin. Neurophysiol., 33:8995.
5. Lubar, J.F. and Shouse, M.N. EEG and Behavioral changes in a hyperkinetic child
concurrent with training of the sensorimotor rhythm (SMR): A preliminary report. 1976, Biofeedback
and Self-Regulation, 3 :293-306.
6. Hull, C.L. Principles of Behavior. Appleton-Century-Crofts, New York, 1943.
7. Thorndike, E.L. Educational Psychology: Volume II. The psychology of
learning. Teachers College, Columbia University, New York, 1913.
8. Bal, T. And McCormick, D.A. Ionic mechanisms of rhythmic burst firing and tonic
activity in the nucleus reticularis thalami: a mammalian pacemaker. 1993, J. Physiol.
(Lond), 468: 669-691.
9. Sherman, M.S. and Guillery, R.W. Functional organization of thalamocortical relays.
1996, J. Neurophysiol., 76(3): 1367-1395.
10. Steriade, M. and Llinas, R. The functional states of the thalamus and the
associated neuronal interplay. 1988, Physiol. Rev., 68: 649-742.
11. Steriade, M., McCormick, D.A. and Seinowski, T.J. Thalamocortical oscillations in
the sleeping and aroused brain. 1993, Science, 262: 679-685.
12. Von Krosig, M., Bal, T. and McCormick, D.A. Cellular mechanisms of a synchronized
oscillation in the thalamus. 1993, Science, 261: 361-364.
13. Berger, H. On the electroencephalogram of man, II. 1930, J. Psychol. and Neurol.,
40: 160-179.
14. Sterman, M.B. and Wyrwicka, W. EEG correlates of sleep: Evidence for separate
forebrain substrates. Brain Res., 6:319329, 1967.
15. Sterman, M.B. Physiological origins and functional correlates of EEG rhythmic
activities: implications for self-regulation. 1996, Biofeedback and Self-reg.,
3-33.
16. Kuhlman, W.N. Functional topography of the human mu rhythm. Electroenceph. clin.
Neurophysiol., 1978, 44: 83-93.
17. Sterman, M.B. Studies of EEG biofeedback training in man and cats. 1972, Highlights
of 17th Annual Conference: VA Cooperative Studies in Mental Health and Behavioral
Sciences. VA Dept. of Medicine and Surgery.
18. McCormick, D.A. & Huguenard, J.R. A model of the electrophysiological
properties of thalamocortical relay neurons. 1992, J. Neurophysiol., 68: 1384-1400.
19. Matousek, M. And Pertersen, I. Frequency analysis of the EEG in normal children and
adolescents. 1973, In: P. Kellaway & I. Pertersen (Eds.) Automation of Clinical
Electroencephalography. Raven Press, New York, pp. 75-102.
20. Matsuura, M., Yamamoto, K., Fukuzawa, H. et al. Age development and sex differences
of various EEG elements in healthy children and adults - Quantification by a computerized
waveform recognition method. 1985, Electroenceph. clin. Neurophysiol., 60: 394-406.
21. Steriade, M. Cellular substrates of brain rhythms. In: Niedermeyer, E. and Lopes da
Silva, F. (eds.), Electroencephalography: Basic Principles, Clinical Applications, and
Related Fields. 3rd Edition, Williams & Wilkins, Baltimore, 1993, pp. 1164.
22. Schaul, N. Green, L., Peyster, R. And Gotman, J. Structural determinants of
electroencephalographic findings in acute hemispheric lesions. 1986, Ann. Neurol.,
20: 703-711.
23. Steriade, M., Curro Dossi, R. and Nunez, A. Network modulation of a slow intrinsic
oscillation of cat thalamocortical neurons implicated in sleep delta waves: Cortically
induced synchronization and brainstem cholinergic suppression. 1991, J. Neurosci.,
11: 3200-3217.
24. Villablanca, J. and Ruiz de Pereda, G. The athalamic cat, Sleep-wakefulness
patterns. Arch. Biol. Med. Exp., 7: 45-46, 1971.
25. Villablanca, J. and Marcus, R. Sleep-wakefulness, EEG and behavioral studies of
chronic cats without neocortex and striatum: The diencephalic cat. Arch. Ital. Biol.,
110: 348-382, 1972. |
