Thought this paper was interesting in its implications for smart vagal tone in increasing heart rate variability, that is, the positive effects of doing EE in general and cognitive health. It was published in Applied Psychophysiology and Biofeedback, June 2013, Volume 38, Issue 2, pp 143-155. It touches the subject of systems theory and mathematical models which I don't know much about. So for those of you who have a clue, I'll quote the article extensively in case it might be of interest.
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Dynamic Processes in Regulation and Some Implications for Biofeedback and Biobehavioral Interventions
Paul Lehrer • David Eddie
Appl Psychophysiol Biofeedback (2013) 38:143–155
DOI 10.1007/s10484-013-9217-6
Abstract
Systems theory has long been used in psychology, biology, and sociology. This paper applies newer methods of control systems modeling for assessing system stability in health and disease. Control systems can be characterized as open or closed systems with feedback loops. Feedback produces oscillatory activity, and the complexity of naturally occurring oscillatory patterns reflects the multiplicity of feedback mechanisms, such that many mechanisms operate simultaneously to control the system. Unstable systems, often associated with poor health, are characterized by absence of oscillation, random noise, or a very simple pattern of oscillation. This modeling approach can be applied to a diverse range of phenomena, including cardiovascular and brain activity, mood and thermal regulation, and social system stability. External system stressors such as disease, psychological stress, injury, or interpersonal conflict may perturb a system, yet simultaneously stimulate oscillatory processes and exercise control mechanisms. Resonance can occur in systems with negative feedback loops, causing high-amplitude oscillations at a single frequency. Resonance effects can be used to strengthen modulatory oscillations, but may obscure other information and control mechanisms, and weaken system stability. Positive as well as negative feedback loops are important for system function and stability. Examples are presented of oscillatory processes in heart rate variability, and regulation of autonomic, thermal, pancreatic and central nervous system processes, as well as in social/organizational systems such as marriages and business organizations. Resonance in negative feedback loops can help stimulate oscillations and exercise control reflexes, but also can deprive the system of important information. Empirical hypotheses derived from this approach are presented, including that moderate stress may enhance health and functioning.
Introduction
Concepts of ‘cybernetics’, ‘systems’ (dynamic and otherwise),
‘complexity’, ‘chaos’, ‘catastrophe’, and ‘oscillation’
have long been part of discourse in the behavioral,
social, and biological sciences. Without the ready availability
of mathematical models for the social and biological
sciences, a nonmathematical approach to systems theory
was prominent in early biofeedback work, and was well
articulated by Schwartz (1981 and Schwartz et al. 1979).
However, a mathematical approach for aircraft communication
and control systems had been articulated by Wiener
as early as the 1940s (Wiener 1948, 1961). This model
served as a heuristic for describing behavioral, economic,
biological, and astronomical systems, among others (Mindell
et al. 2003; Wiener 1948). In recent years, mathematical
versions of systems theory have been applied in
biology and behavioral science, as described below.
Because Wiener’s theory had limited predictive power, it
was soon combined and supplanted by theoretical systems
with greater complexity, such as Shannon’s information
theory (Shannon and Weaver 1949), which incorporates
concepts of channel capacity, noise, and entropy (here
reflecting the uncertainty in prediction rather than the conventional
meaning implying dissolution of order), as well as
chaos theory (Gleik 1987; Kellert 1993), which emphasizes
deterministic nonrandom but complex nonlinear relationships
among a large number of co-occurring processes, for
which statistical prediction is possible. Applications of both
information theory (Attneave 1959; Yockey 2005) and chaos
theory (Elbert et al. 1994; Guastello et al. 2009; Rossler and
Rossler 1994; Weiss et al. 1994) have been made to the
biological and behavioral sciences. The present article draws
upon insights from this work.
For the purposes of this article, we define a system as a
variety of elements that interact with one another to form a
whole entity. A system’s distinct parts are not isolated from
each other, thus the characteristics of the whole entity
cannot generally be deduced from characteristics of its
components. A stable system retains important characteristics
even in the face of perturbations that disturb the
behavior of specific elements. Dynamical systems show
changing patterns of action over time, but retain characteristics
of an integrated whole. Cybernetics is the study of
control and information processes, including system characteristics
like stability, feedback, adaptation, regulation,
and the relationships among them. Dynamical systems
have been described as ‘‘organized’’ or ‘‘disorganized’’ and
of greater or lesser complexity. Simpler organized systems
can often be described and modeled in linear terms,
although nonlinear approaches usually are more appropriate
for more complex systems.
A control system is defined as a stable system, in which
system elements interact to preserve system stability, both
for internal control and in response to perturbations caused
by external influences. Systems can be modeled as closedloop
or open-loop. An open-loop system involves only a
system response to an outside event. It does not provide an
internal mechanism for monitoring system performance or
preserving system stability. For example, open-loop models
have been used to biomimetically describe the flying
patterns of drosophila melanogaster after stimulation (Fry
et al. 2008). Behavioral and chemical interventions
designed to change system behavior are examples of open
loop control processes, such as when we teach a person to
lower muscle tension or blood pressure directly, rather than
by using natural feedback mechanisms.
A closed-loop model includes internal regulation,
whereby system responses to outside stimulation are processed
through internal feedback loops that monitor and
adjust the response of the system itself. As a discipline,
biofeedback is particularly concerned with closed-loop,
internal regulatory systems, and tries to maximize their
effectiveness. A good example of a closed loop system is
the baroreflex (Vaschillo et al. 2002), as will be described
in more detail later in this article. However, when
biofeedback or relaxation is used as a strategic intervention
to control acute symptomatology, the resultant process can
be described as an open-loop system.
A common control mechanism leading to system stability
in closed loop systems is the negative feedback loop. Negative
feedback loops preserve stability by activating opposing
processes that act to modulate change, though
importantly, stable systems tend to contain both positive and
negative feedback loops (Cinquin and Demongeot 2002;
Pigolott et al. 2007). Negative feedback systems usually
have inherent delays caused by the time needed for opposing
system elements to effect change in each other. Negative
feedback loops with delays cause oscillations at particular
frequencies Cinquin and Demongeot 2002). A very simple
negative feedback system is the thermostat, which responds
to changes in ambient temperature by switching a heating or
air conditioning system on and off. Here the delay is
dependent on such factors as outside air temperature, insulation,
and volume of inside air. Thus, room temperature
oscillates around the thermostatic setting, but is rarely constant.
The negative feedback loop in the baroreflex system
also contains delays, as will be described below.
Oscillatory properties have been observed in systems as
small as the cell, and as large as societies. For example, at a
cellular level, oscillations have been used to map various
negative feedback loops in protein concentrations, and
circadian gene expressions in bacteria (Pigolott et al.
2007). In psychological and social systems oscillations
describe patterns of voting, marital relationships, and
mood, as described below.
The baroreflex system is a good example of a stabilizing
oscillatory process. It produces periodic fluctuations in
blood pressure, heart rate, and vascular tone that reflect
modulatory control. External influences on blood pressure
(e.g., aperiodic stress or exercise) induce blood pressure
changes that, without a control mechanism, could burst
blood vessels or cause circulatory insufficiency, depending
on the direction of blood pressure change. The baroreflex
responds to blood pressure increases by slowing heart rate
and causing vasodilatation, which, in turn, produces a
decrease in blood pressure. Each time blood pressure
decreases, the same mechanism produces vasoconstriction
and heart rate acceleration, which causes blood pressure to
rise again. Changes in blood pressure are delayed by a
number of factors, including inertia in blood flow and
blood vessel plasticity (Vaschillo et al. 2002, 2006). The
delay in blood pressure changes following heart rate
change averages about 5 s, thus yielding the ubiquitous
heart rate oscillation of about 10 s (Vaschillo et al. 2002),
i.e., 5 s for the baroreflex to cause an increase in blood
pressure when the baroreflex responds to a decrease in
blood pressure, and 5 s to cause a decrease in blood pressure
following a baroreflex response to a blood pressure
increase. In actuality, however, the baroreflex system is
composed of two closed linear systems with negative
feedback loops—it also contains a slower vascular tone
loop with a rhythm of approximately 0.02–0.03 Hz, which
has a slower response time than the heart rate loop (Rahman
et al. 2011; Vaschillo et al. 2012).
It should be noted that a full description of baroreflex
control would not be restricted to purely mechanical properties
of the reflex. The baroreflex system shows characteristics
of neuroplasticity. Dworkin (1993) demonstrated that
the baroreflex can adapt to changing environments through
classical conditioning. Consistent with Dworkin’s findings,
Lehrer and colleagues demonstrated that regular stimulation
of the baroreflex through biofeedback can increase baseline
baroreflex gain, suggesting an improvement in regulatory
capacity over blood pressure changes (Lehrer et al. 2003).
Complex Control of Systems
Most psychological and biological systems are much more
complex than thermostats, with multiple control mechanisms,
and multiple overlapping oscillations, the quantitative
characteristics of which depend on the frequency and
phase of each component oscillator, as well as the interactions
among them (Hyndman 1974). Changes to one
system component (control mechanism) affect others, so
the individual control mechanisms are interdependent, thus
promoting biological vitality and complexity. Additionally,
it should be noted that closed loop systems with negative
feedback loops often require simultaneous open loop processes
in order to function properly, to provide sufficient
stimulation to stimulate negative feedback loop activity
(Feigl 1998). This adds to the complexity.
Simple control, such as control of room temperature, can
typically be described with linear mathematical models,
but when multiple controllers operate, each with a characteristic
frequency, attendant mathematical computations
become more complex. Existence of multiple overlapping
control systems assures system stability in the face of
various perturbations by providing multiple ‘‘backups’’. A
high degree of entropy, reflecting a high degree of nonlinearity
and/or a need for multiple linear formulas to
describe the system, is often related to health and biological
system stability. Conversely, simple periodicity or
random variability (‘‘white noise’’) often underlies
pathology in disordered systems (Goldberger et al. 2002),
reflecting, respectively, either the presence of only a single
control oscillator, or absence of any regulatory activity.
Oscillatory patterns with greater complexity, such as
those that occur when a number of oscillatory patterns
overlap, are described as ‘‘chaotic’’. Chaos reflects the
simultaneous operation of numerous control processes.
Although the healthy cardiovascular system can be effectively
modeled using linear statistics (Berntson et al. 1997),
it also has chaotic properties, which diminish when cardiac
function is impaired or an individual is in danger of dying
from physiological decompensation (Arzeno et al. 2007).
Similarly, healthy adjustment is characterized by chaotic
rhythms in appetite (Berthoud 2006) and mood (Haffen and
Sechter 2006), among other biobehavioral dimensions.
Also, negative feedback loops often work in concert with
open loops as described above, and with positive feedback
loops and resonance characteristics, as described below, to
maintain optimal functionality.
Oscillation, Control, and Health
Many oscillatory systems in the body, such as neural
(Berthouze et al. 2010), cardiovascular (Berntson et al.
1997), and circadian (Garau et al. 2006) tend to degrade
with age, reflecting a decrease in adaptive capacity. In
addition, there are also pronounced age-related decreases in
baroreflex gain (Brown et al. 2003), while blood pressure
oscillations tend to increase with age (Cugini et al. 2003),
perhaps reflecting degrading of control over this otherwise
tightly regulated function. Generally speaking, oscillatory
patterns in a variety of biological control systems tend to be
a characteristic of youth, fitness, and health.
Oscillatory properties do not only represent activation of
specific control reflexes (e.g., the baroreflex). They provide
information as well. Periodic stimulation is required for
system control in feedback networks. This principle has
been proposed for cellular behavior (Klevecz et al. 2008)
as well as macro biological and behavioral systems, as in
discussions of open loop processes described above, and
both resonance and stochastic processes, described below.
External stimulation ‘‘perturbs’’ a control system causing it
to oscillate. Without such oscillations, and hence, without
noise (which can function as the perturbation that pushes
the system to oscillate), systems may fail to get the information
they need to function properly. The tendency for
noise to stimulate system oscillation is called stochastic
resonance. There is evidence that interoceptive and
exteroceptive noise enhances the sensitivity of a variety of
control processes, from micro to macro levels (Wiesenfeld
and Moss 1995). Studies of stochastic resonance show that
noise can facilitate control functions, as in optimization of
monosynaptic afferent reflexes in the motor system of the
cat by causing stretches in a synergistic muscle (Martinez
et al. 2007). In humans, extreme absence of system perturbation,
as in sensory deprivation, can cause regulation
go awry, resulting in psychotic-like sensory and emotional
symptoms (Mason and Brady 2009).
One could, therefore, speculate that psychological stress
may have some beneficial effects, by introducing noise into
neural regulatory processes, just as exercise helps maintain
many physiological processes. Since, in biological systems,
oscillations tend to reflect the operation of reflex systems
involved in modulation and self-control, presumably a
complete lack of stimulation would deny exercise to these
reflexes, causing them to atrophy. Under normal circumstances,
however, the environment provides sufficient
perturbations in biological and behavioral systems to preserve
their normal function. In psychophysiological theories
of stress effects, this formulation overlaps with the
more recent concept of ‘‘allostasis’’ and ‘‘allostatic overload’’
(Juster et al. 2010). Allostasis refers to stability
achieved through variability, and is analogous to the
activity of oscillating control processes stimulated by stochastic
noise, or general unorganized environmental stimulation.
A well functioning personality or biological system
responds well to moderate environmental stressors, and, in
fact, may be energized by them, as in Yerkes and Dodson’s
model of stress and performance, wherein moderate stress
enhances performance in moderately difficult tasks, while
very low or very high levels of stress can be detrimental
(Yerkes and Dodson 1908). Allostatic overload (McEwen
2004) occurs when environmental stresses are either too
great or too prolonged, or when various control reflexes
become exhausted.
Resonance: A Source of Stimulation and Simplicity
in System Oscillation
In addition to oscillation, another characteristic of negative
feedback systems with a delay, is resonance (Grodins
1963). When outside stimulation causes a system oscillation
at a particular frequency, and no other forces are at
work to dampen the oscillations, negative feedback loops
can themselves destabilize the system by causing increasingly
large resonance frequency oscillations, to the point
where information from other frequencies no longer gets
processed. A common example of a ‘run-away’ negative
feedback loop, resonance effect is the ‘‘Larsen effect’’ that
occurs when a microphone is placed near a speaker,
causing a high-pitched squeal at a single frequency
(Weaver and Lobkis 2006). In such a case, a single high amplitude
oscillation, triggered either by noise (stochastic
resonance) or by pulsatile stimulation close to the resonance
frequency, can obscure the effect of otherwise
meaningful perturbations. In the human body, resonance
effects in the postural control loop have been implicated in
postural impairment in Parkinson’s disease (Maurer et al.
2004). Resonance has been similarly implicated in age related
impairment in gait control (Thurner et al. 2002).
In terms of psychophysiology, it is conceivable that
resonance effects might inhibit the fight-or-flight reflex
because a single homeostatic process may overwhelm
reflexes needed to confront an environmental stressor. One
could speculate that resonance effects, stochastic and
otherwise, might deprive the system of information from
various internal control mechanisms, as when resonance frequency
oscillations are at such a high amplitude that
they obliterate information from reflexes operating at different
frequencies. Thus, it is possible that resonant oscillations
in the heart rate baroreflex system at 6/min could
weaken the effects of other control reflexes, by greatly
diminishing their relative size and depriving the system of
information. This is a hypothesis for future research. To
prevent a ‘run-away’ resonance process, systems must be
dampened (Siebert 1986). Dampening effects on heart rate
amplitude during heart rate variability biofeedback probably
stem from inherent limitations in the ability of the heart
to respond at extreme levels.
The literature on heart rate variability biofeedback,
however, shows that not all resonance effects are detrimental.
Resonance in the cardiovascular system produced
by a rhythm in the baroreflex is the basis of the beneficial
effects of heart rate variability biofeedback. The baroreflex
provides a negative feedback loop for controlling blood
pressure, such that heart rate falls when blood pressure
rises, and vice versa when blood pressure falls, thus
modulating blood pressure fluctuations. The resonance
frequency of this system appears to be related to the individual’s
blood volume (Vaschillo et al. 2006), with a lower
resonance frequency among people with a larger blood
supply (men and taller people, vs. women and shorter
people), and to changes in hemodynamic inertia resulting
from fluctuating heart rate. When this resonance system is
activated by a respiration rate close to the resonance frequency,
oscillations in heart rate become very large. This
high-amplitude stimulation of the baroreflexes appears to
strengthen them (Lehrer et al. 2003), and is perhaps the
cause of the various salutary effects of heart rate variability
biofeedback.
Heart rate variability biofeedback has been shown to
restore autonomic control that has been acutely repressed
by experimental exposure to inflammatory cytokines
(Lehrer et al. 2010), and appears to ameliorate a number of
disorders characterized by autonomic and/or emotional
dysregulation (Lehrer 2007), including hypertension (Lin
et al. 2012; Nolan et al. 2010; Reineke 2008), asthma
(Lehrer et al. 2004), anxiety/stress (Hallman et al. 2011;
Henriques et al. 2011; Shenefelt 2010), depression (Beckham
et al. 2013; Karavidas et al. 2007; Patron et al. 2013;
Siepmann et al. 2008), and chronic pain (Hallman et al.
2011; Sowder et al. 2010; Strine 2004; Yetwin 2012),
while improving athletic performance (Paul and Garg
2012). We might, however, theorize that the effects of
constant breathing at resonance frequency would not be
advantageous. Chronic resonance frequency breathing
could theoretically weaken or obstruct reflexes dependent
on oscillations at other frequencies. Thus, individuals
practicing heart rate variability biofeedback are instructed
to practice for a relatively brief period of time daily
(usually about 20 min) and to use the technique strategically
when symptomatic.
Positive Feedback Loops
The body also contains multiple positive feedback loops,
whereby change in a particular direction facilitates greater
change in that direction. Although positive feedback can
provide a stimulating effect in an open loop system, it also
plays a role in closed loop systems. In psychophysiology,
positive feedback loops often are involved in maladaptive
processes (Thayer and Lane 2000). A prime example is
anxiety and depression’s propensity to amplify their inherent
symptomology by triggering greater sensitivity to anxious or
depressive thoughts, thus creating a vicious cycle.
However, positive feedback loops can also play an
important role in enhancing emotional control. The psychological
augmenting effects of positive feedback may
add to oscillation amplitude during HRV biofeedback, or
the sympatholytic effect of muscle relaxation. This may
result from a spiraling relaxation effect. Just as anxiety
begets more anxiety by sensitizing anxiogenic brain circuits
(a positive feedback loop) (Chemtob et al. 1988;
Krantz et al. 1987; Thayer and Lane 2000), heart rate
variability biofeedback or muscle relaxation therapy may
compound relaxation effects. On the cellular level, positive
feedback loops may be necessary to stimulate modulatory
negative feedback loops (Kurbel 2012; Nishi et al. 2000;
Pomerening et al. 2005; Prochazka et al. 1997; Tsai et al.
2008).
Positive feedback loops can also play a role in creating
system complexity, which may increase stability by creating
a multiplicity of stable states (Plahte et al. 1995), as
well as by maintaining oscillation amplitude during frequency
adjustment (Tsai et al. 2008). Positive feedback
loops have been shown to promote load compensation in
control of movement (Prochazka et al. 1997), and to be
necessary to prevent dampening of oscillations in cellular
function (Pomerening et al. 2005). Positive feedback loops
are important for the maintenance of the ovulatory cycle
(Kurbel 2012), and for propagating dopaminergic signaling
(Nishi et al. 2000). Without some form of regular perturbations,
oscillations will gradually decline in amplitude
and disappear. Positive feedback loops can provide these
perturbations.
Simplicity, Complexity, and Randomness
The dimensions of simplicity and complexity are important
for understanding psychobiological control, and can be
usefully incorporated into theories of applied psychophysiology.
Simplicity in a biological or behavioral control
system can have multiple sources. In addition to reflecting
the effects of resonance, simplicity may result from system
fatigue due to allostatic overload (Juster et al. 2010), or
from biological damage to system components, such as
when heart failure leads to diminished entropy in heart rate
(Ho et al. 2011; Isler and Kuntalp 2007; Liu et al. 2011).
Simplicity in heart rhythms as a sign of cardiac pathology
was discussed earlier. Emotional rigidity, or simplicity, is
well described in DSM-IV (American Psychiatric Association
1994) as a sign of psychopathology, which is characterized
by a tendency to respond to a wide variety of
situations with a stereotyped response: sadness, anxiety,
suspiciousness, anger, etc. However, just as simplicity is a
sign of poor adaptation, so is random variation, suggesting
lack of modulatory control. Pathological examples of random
fluctuations include manic or depressive episodes in
bipolar disorder, as well as certain cardiac arrhythmias
such as preventricular or preatrial contractions, asthma
exacerbations, and so on.
Work in the tradition of chaos and information theories
has provided mathematical tools for describing organized
complexity of temporal patterns, as described in information
theory, including Shannon’s ‘‘spectral entropy’’ calculations
(Shannon 1948), Pincus’ ‘‘approximate entropy’’
(Pincus 1991, 1998), and Lempel and Ziv’s method for
evaluating ‘‘randomness’’ (Lempel and Ziv 1976). These
measures represent various approaches to calculating relative
unpredictability of fluctuation patterns in a time series,
or the number of dimensions, or fractals (Mandelbrot
1983) necessary to describe a data set mathematically.
Autonomic Regulation
Healthy regulation is often characterized as sympathetic
and parasympathetic activity converging in a limit cycle
around a critical value, such as is the case of cortisolvasopressin
or acetylcholine-epinephrine reactions, where
each process induces a compensatory reaction in the other
(Bernard-Weil 1986). Thus, sympathetic activity may
simultaneously suppress parasympathetic activity, but
increase parasympathetic reactivity, thereby producing an
oscillation. During extremely stressful stimulation, an
individual may show wild oscillations in various autonomic
functions, both sympathetic and parasympathetic
(e.g., bronchodilation and constriction, increased and
decreased blood pressure, energy and fatigue, high versus
low rates of peristalsis as in constipation and diarrhea,
etc.), although these oscillations are sometimes paradoxically
suppressed in severe emotional reactions (Gellhorn
1969, 1970).
Heart rate changes in response to stimulation appear to
be important modulators of systemic response. Reduced
cardiac variability in response to stimulation is related to a
variety of pathophysiological states (Montano et al. 2009),
while increased heart rate variability and vagal influence on
the heart are positively correlated with recovery after
physical exercise stress (Chen et al. 2011). Thus flexibility
in cardiac response to stimulation, and higher vagal influence
on the heart (measured as high-frequency heart rate
variability, reflecting both baroreflex control and modulation
of respiratory function) are related to better adaptation
to interoceptive and exteroceptive demands (as evinced by
rapid recovery). This is another example of ways in which
complexity and oscillatory function are related to health
and adaptability.
Thermoregulation
[...]
Oscillation, Complexity, and Disease
Heart rate variability complexity is indicative of cardiac
and neurocardiac flexibility and adaptability, and may be
diminished significantly by pathology. A healthy heart can
respond to various moment-to-moment physical and emotional
demands, while the diseased heart does not exhibit
such flexibility. Although oscillations may persist in the
diseased heart, aging and disease cause increasingly more
regular oscillatory patterns reflecting a decrease in mechanisms
of cardiac control (Goldberger et al. 2002). Such
noncomplex patterns of variability ultimately predict death
in the critically ill (Arzeno et al. 2007; Norris et al.
2008a, b).
[...]
Mental Illness
Mood and behavior appear to be governed by many of the
same concepts used to describe oscillatory behavior in
physiological processes. As with physiological systems,
organized complexity is the hallmark of healthy psychological
behavior. Both hyper and hypo-stabilities may
characterize a breakdown in normal mental functioning,
and leave a person psychologically vulnerable to the deleterious
effects of stress. Gottschalk et al. (1995) found that
while participants with bipolar disorder showed some brief
periods of fairly well defined cycling in mood, their overall
pattern of mood changes were less complex than controls.
Paulus et al. compared patients with schizophrenia to
healthy individuals in a binary choice study, and found that
participants with schizophrenia showed a simpler and more
predictable pattern of response, apparently reflecting
decreased adaptability in cognitive processing (Paulus and
Braff 2003; Paulus et al. 1996). A study of laterality in
electrodermal activity found decreased activity and complexity
in the left hand, compared with the right, among
depressed patients, and decreased activity and complexity
in the right hand, compared with the left, among patients
with schizophrenia, while there were no laterality differences
among healthy individuals (Bob 2007). These results
are consistent with theories of right hemisphere dysfunction
in depression (represented by decreased adaptiveness
in this instance), and left hemisphere dysfunction in
schizophrenia. However, even some psychopathological
states appear to possess chaotic organization. Tschacher
et al. (1997) recorded occurrence of psychotic symptoms
among 14 patients with schizophrenia over time, and found
a complex, nonlinear time course in eight of them. Others
showed more random psychotic behavior, suggesting that
the patients were responding to environmental cues, not
modulated by control processes.
EEG Oscillations Reflect System Functions
Systems theory also applies to neurons’ activity in the
brain, whose functioning has been shown to be interdependent
with other nearby neurons and even neurons in
different brain regions. The complexity and chaotic characteristics
of brain function, particularly as studied by
electroencephalogram (EEG), has been thoroughly
reviewed elsewhere (Buzsaki 2006). Briefly, EEG reflects
summations of excitatory postsynaptic potentials that
neurons emit as discrete events. When neurons fire synchronously,
relatively high-amplitude slow waves occur, in
a relatively simple pattern; when they fire desynchronously,
the numerous potentials can both augment and
cancel each other out, generally resulting in lower-amplitude,
more complex signals at higher frequencies (Onton
et al. 2006). Fast-firing cells in the cortex may contribute to
these effects, as does the interaction between excitatory
and inhibitory processes. Faster, low-amplitude oscillations
tend to predominate during states of arousal, attention, and
cognitive processing (Timofeev and Chauvette 2011), and
in general, during periods of greater cortical activity
(Klimesch 1996).
Oscillations in EEG signal reflect the interplay of various
brain processes involved in cortical integration, such
that specific kinds of mental activity may be reflected in
definable patterns of activity at specific frequencies, and at
specific electrode locations (Gottesmann 1999; Jacobs
2001). Slower waves from the cortex reflect decreased
arousal at the measured EEG sites (i.e., fewer simultaneous
processes producing potentials at various frequencies).
Low levels of alpha and beta activity (the faster, higherfrequency
ranges) and/or increased waking slow-wave
activity are associated with a lack of inhibitory control over
behavior (Knyazev 2007). Similarly a greater frontal theta
beta ratio is related to decreased emotional response inhibition
(Putman et al. 2010). Greater delta and theta activity
and lower alpha activity have been found in the EEGs of
children with fetal alcohol syndrome (Kaneko et al. 1996).
This pattern is also found in attention deficit disorder,
lower cognitive abilities, impaired motor control, hypoglycemia,
and antisocial behaviors. It is even found during
deep relaxation and sleep, while exposed to hypoxia,
fasting, and in sexual arousal and orgasm (Knyazev 2007).
Conversely, delta, theta, and lower-frequency alpha
activity are inhibited when people are awake and cognitively
active. A recent study found that, during tasks with
increased cognitive demand, increases occurred in the EEG
frequency with the greatest activity across electrode sites,
but a decrease in EEG entropy (Zarjam et al. 2011).
Oscillations are not smoothly distributed across the
frequency spectrum. Resonance structures have been
identified at frequencies of 4, 10, 20, and 40 Hz (Erol Basar
1999) with the largest body of research on the 10 Hz
rhythm, the center of the alpha frequency band (8–12 Hz).
Resonance properties can be determined by a variety of
methods. For instance, one can stimulate the system at
specific frequencies with photic stimulation, and determine
the frequencies at which high-amplitude oscillations are
obtained (Fedotchev et al. 1990; Herrmann 2001; Spiegler
et al. 2011). One may also use computer simulations based
on known frequency characteristics of particular nerve
cells (Kasevich and LaBerge 2011) or from evoked
potentials (Basar et al. 1976; Bayram et al. 2011; Winterer
et al. 1999). Alternatively, one can stimulate an individual
with audible noise and measure the obtained frequency
peaks (stochastic resonance) (Ward et al. 2010). Resonances
at these frequencies suggest the existence of specific
positive feedback reflex arcs at each resonance
frequency, related to particular neural processes contributing
to each resonance frequency.
Thalamocortical processes have been studied and modeled,
and rhythm bands containing each of these frequencies
have been described in a large literature (theta or delta
for 4 Hz, alpha for 10 Hz, beta for 20 Hz, and gamma for
40 Hz) (Knyazev 2007). It has been theorized that delta
oscillations reflect activity of motivational systems, while
theta oscillations reflect emotional regulation, and alpha
oscillations reflect inhibitory processes (Knyazev 2007).
Some theorists have related specific resonance frequencies
to the period length of experimentally evoked action
potentials, event-related potentials, synchronies, or asynchronies
(Pfurtscheller and Lopes da Silva 1999). Patterns
of theta and alpha-frequency resonances triggered by
memory consolidation processes have been identified
(Klimesch et al. 2005). It is also known that memory
consolidation occurs during sleep (Fogel and Smith 2011),
which is characterized by high-amplitude slow EEG waves
primarily stemming from the brain stem, with cortical
modulation of lower centers, perhaps from a single oscillator
(Crunelli and Hughes 2010).
Phase relationships in EEG activity among various brain
areas have also been studied, appearing to vary systematically.
For example, occipital phase synchrony in the alpha
rhythm tends to be greatest during relaxation with eyes
closed, and decreases with greater arousal (Gengerelli
1978). Experience of ‘‘pure consciousness’’ in transcendental
meditation appears to be characterized by alpha
phase synchrony from the frontal and central areas (Orme-
Johnson and Haynes 1981). Although phase synchrony
may reflect tight coordination among brain sites, more
complex forms of coordination have also been modeled,
often involving varying frequencies, varying orderly phase
relationships, and nonlinear relationships, where frequent
changes in phase relationships among brain areas reflect
complexity of cognitive processing (Basar 2006; Panzeri
et al. 2010; Tognoli and Kelso 2009).
Measures of chaos and complexity applied to EEG data
suggest a simpler pattern of brain organization, with, fewer
control processes, among patients with schizophrenia
(Roschke et al. 1995) and depression (Nandrino et al. 1994;
Pezard et al. 1996). Pezard et al. found that EEG patterns
became more complex among depressed patients, whose
depression improved, becoming indistinguishable from
those among healthy individuals, while the pattern of
decreased complexity persisted among individuals whose
depression did not improve (Pezard et al. 1996). Other
studies have found risk for autistic spectrum disorder (Bosl
et al. 2011) and the presence of attention deficit hyperactivity
disorder (Sohn et al. 2010) to be related to decreased
average EEG complexity.
Social Systems
Humans live as couples, in families and in neighborhoods
or larger social and economic networks. Systems theory
has been applied to social systems, and, indeed, oscillatory
patterns of social system behavior can reflect negative
feedback loops, which could contribute to stability. Stability
in a social system generally represents a stable pattern
of interaction, implying the action of mechanisms that
resist change. Oscillatory patterns of openness and closedness
to new ideas have been described in societies
(Klapp 1975), as have variations in societal norms about
expression of emotion (Cancian and Gordon 1988),
although presumably extremes in either of these characteristics
trigger a consequent tendency in the opposite
direction. Voter patterns appear to have oscillatory characteristics,
which, in the United States, have been quantified
as thirty-year oscillatory patterns in party alignment
(Mayhew 2002). The cause for this oscillation period may
reflect generational change. Perhaps people see the problems
caused by results of their parents’ voting patterns, and
react to this by changing course. Although delays, chaos,
and resonance characteristics in social feedback loops have
not been measured, the existence of specific frequency
patterns in some of them suggest that these characteristics
may be present.
Dyadic systems may also have oscillatory qualities.
Gottman et al. have taken a mathematical approach to
assessing marital systems (Gottman et al. 2002). They have
applied concepts of nonlinear dynamics and catastrophe
theory, which posit complete periodic restructuring of
systems when perturbed by certain forces. They propose
that marriages have one or more ‘‘set points’’ for nature and
style of interaction, some of which may be favorable to
marital satisfaction and stability, and others unfavorable.
From this perspective, they have developed formulas that
predict marital stability and divorce. They record verbatim
transcripts of marital interactions, where they note the
number of times that individuals respond to each other’s
content with specific affect, including such variables as
threshold for negativity, and frequency of positive and
negative interactions. In marriages characterized by reciprocal
negativity and paucity of positivity in interactions,
couples can be trained to modify their individual behavior.
Thus, based on positive feedback loops, marital systems,
wherein negativity begets negativity, may be altered so that
positivity begets positivity. The overarching goal is to
move the interaction system from a negative steady state to
one that is more positive. Repair and dampening functions
have been calculated, which can modify the positive or
negative influence of one partner on another in various
steady state conditions. The investigators report conflicting
data about whether rigidity in behavior predicts poor
marriage outcome, but show how intervention can affect
the system of interaction and, ultimately, marital satisfaction.
Their model includes the interaction of perception of
well-being in the relationship, the flux over of negative and
positive behaviors, and physiological responses.
Systems theory has also been applied in analysis of
corporate structures (Weick 2009), but concepts of oscillation,
delay, and influences of positive and negative
feedback loops have not been studied. Corporations are
systems, just as those defined above. They are constantly
bombarded by changes in market conditions, competition,
etc., and need to maximize the adaptiveness with which
they respond to changing conditions, while still maintaining
their structure. Information and communication, both
from outside of and within the organization, has been
proposed as a medium for achieving these ends. Utilization
of feedback from information allows an organization to
monitor both external events and internal processes. For
example, if an organization’s norms do not allow for
transmission of bad news to its CEO, then poor, uninformed
decisions are likely to be made. Various feedback
mechanisms can prevent change from happening (e.g., not
heeding information identified as coming from the ‘‘wrong
channel’’). Although we have found little research using
time series analysis to discover patterns of oscillations in
negative feedback loops comprising organizational structures,
examples of feedback loops in organizational
behavior have been described (Weick 2009). Presumably
oscillations occur in organizational communication as well
as in other control systems, and might be quantifiable on a
number of dimensions, such as commands to change
operational procedures to adapt to external pressures,
oscillating with instructions to follow internal protocols,
pressured work versus relaxation, and supportive versus
critical communications from superiors.
***
Dynamic Processes in Regulation and Some Implications for Biofeedback and Biobehavioral Interventions
Paul Lehrer • David Eddie
Appl Psychophysiol Biofeedback (2013) 38:143–155
DOI 10.1007/s10484-013-9217-6
Abstract
Systems theory has long been used in psychology, biology, and sociology. This paper applies newer methods of control systems modeling for assessing system stability in health and disease. Control systems can be characterized as open or closed systems with feedback loops. Feedback produces oscillatory activity, and the complexity of naturally occurring oscillatory patterns reflects the multiplicity of feedback mechanisms, such that many mechanisms operate simultaneously to control the system. Unstable systems, often associated with poor health, are characterized by absence of oscillation, random noise, or a very simple pattern of oscillation. This modeling approach can be applied to a diverse range of phenomena, including cardiovascular and brain activity, mood and thermal regulation, and social system stability. External system stressors such as disease, psychological stress, injury, or interpersonal conflict may perturb a system, yet simultaneously stimulate oscillatory processes and exercise control mechanisms. Resonance can occur in systems with negative feedback loops, causing high-amplitude oscillations at a single frequency. Resonance effects can be used to strengthen modulatory oscillations, but may obscure other information and control mechanisms, and weaken system stability. Positive as well as negative feedback loops are important for system function and stability. Examples are presented of oscillatory processes in heart rate variability, and regulation of autonomic, thermal, pancreatic and central nervous system processes, as well as in social/organizational systems such as marriages and business organizations. Resonance in negative feedback loops can help stimulate oscillations and exercise control reflexes, but also can deprive the system of important information. Empirical hypotheses derived from this approach are presented, including that moderate stress may enhance health and functioning.
Introduction
Concepts of ‘cybernetics’, ‘systems’ (dynamic and otherwise),
‘complexity’, ‘chaos’, ‘catastrophe’, and ‘oscillation’
have long been part of discourse in the behavioral,
social, and biological sciences. Without the ready availability
of mathematical models for the social and biological
sciences, a nonmathematical approach to systems theory
was prominent in early biofeedback work, and was well
articulated by Schwartz (1981 and Schwartz et al. 1979).
However, a mathematical approach for aircraft communication
and control systems had been articulated by Wiener
as early as the 1940s (Wiener 1948, 1961). This model
served as a heuristic for describing behavioral, economic,
biological, and astronomical systems, among others (Mindell
et al. 2003; Wiener 1948). In recent years, mathematical
versions of systems theory have been applied in
biology and behavioral science, as described below.
Because Wiener’s theory had limited predictive power, it
was soon combined and supplanted by theoretical systems
with greater complexity, such as Shannon’s information
theory (Shannon and Weaver 1949), which incorporates
concepts of channel capacity, noise, and entropy (here
reflecting the uncertainty in prediction rather than the conventional
meaning implying dissolution of order), as well as
chaos theory (Gleik 1987; Kellert 1993), which emphasizes
deterministic nonrandom but complex nonlinear relationships
among a large number of co-occurring processes, for
which statistical prediction is possible. Applications of both
information theory (Attneave 1959; Yockey 2005) and chaos
theory (Elbert et al. 1994; Guastello et al. 2009; Rossler and
Rossler 1994; Weiss et al. 1994) have been made to the
biological and behavioral sciences. The present article draws
upon insights from this work.
For the purposes of this article, we define a system as a
variety of elements that interact with one another to form a
whole entity. A system’s distinct parts are not isolated from
each other, thus the characteristics of the whole entity
cannot generally be deduced from characteristics of its
components. A stable system retains important characteristics
even in the face of perturbations that disturb the
behavior of specific elements. Dynamical systems show
changing patterns of action over time, but retain characteristics
of an integrated whole. Cybernetics is the study of
control and information processes, including system characteristics
like stability, feedback, adaptation, regulation,
and the relationships among them. Dynamical systems
have been described as ‘‘organized’’ or ‘‘disorganized’’ and
of greater or lesser complexity. Simpler organized systems
can often be described and modeled in linear terms,
although nonlinear approaches usually are more appropriate
for more complex systems.
A control system is defined as a stable system, in which
system elements interact to preserve system stability, both
for internal control and in response to perturbations caused
by external influences. Systems can be modeled as closedloop
or open-loop. An open-loop system involves only a
system response to an outside event. It does not provide an
internal mechanism for monitoring system performance or
preserving system stability. For example, open-loop models
have been used to biomimetically describe the flying
patterns of drosophila melanogaster after stimulation (Fry
et al. 2008). Behavioral and chemical interventions
designed to change system behavior are examples of open
loop control processes, such as when we teach a person to
lower muscle tension or blood pressure directly, rather than
by using natural feedback mechanisms.
A closed-loop model includes internal regulation,
whereby system responses to outside stimulation are processed
through internal feedback loops that monitor and
adjust the response of the system itself. As a discipline,
biofeedback is particularly concerned with closed-loop,
internal regulatory systems, and tries to maximize their
effectiveness. A good example of a closed loop system is
the baroreflex (Vaschillo et al. 2002), as will be described
in more detail later in this article. However, when
biofeedback or relaxation is used as a strategic intervention
to control acute symptomatology, the resultant process can
be described as an open-loop system.
A common control mechanism leading to system stability
in closed loop systems is the negative feedback loop. Negative
feedback loops preserve stability by activating opposing
processes that act to modulate change, though
importantly, stable systems tend to contain both positive and
negative feedback loops (Cinquin and Demongeot 2002;
Pigolott et al. 2007). Negative feedback systems usually
have inherent delays caused by the time needed for opposing
system elements to effect change in each other. Negative
feedback loops with delays cause oscillations at particular
frequencies Cinquin and Demongeot 2002). A very simple
negative feedback system is the thermostat, which responds
to changes in ambient temperature by switching a heating or
air conditioning system on and off. Here the delay is
dependent on such factors as outside air temperature, insulation,
and volume of inside air. Thus, room temperature
oscillates around the thermostatic setting, but is rarely constant.
The negative feedback loop in the baroreflex system
also contains delays, as will be described below.
Oscillatory properties have been observed in systems as
small as the cell, and as large as societies. For example, at a
cellular level, oscillations have been used to map various
negative feedback loops in protein concentrations, and
circadian gene expressions in bacteria (Pigolott et al.
2007). In psychological and social systems oscillations
describe patterns of voting, marital relationships, and
mood, as described below.
The baroreflex system is a good example of a stabilizing
oscillatory process. It produces periodic fluctuations in
blood pressure, heart rate, and vascular tone that reflect
modulatory control. External influences on blood pressure
(e.g., aperiodic stress or exercise) induce blood pressure
changes that, without a control mechanism, could burst
blood vessels or cause circulatory insufficiency, depending
on the direction of blood pressure change. The baroreflex
responds to blood pressure increases by slowing heart rate
and causing vasodilatation, which, in turn, produces a
decrease in blood pressure. Each time blood pressure
decreases, the same mechanism produces vasoconstriction
and heart rate acceleration, which causes blood pressure to
rise again. Changes in blood pressure are delayed by a
number of factors, including inertia in blood flow and
blood vessel plasticity (Vaschillo et al. 2002, 2006). The
delay in blood pressure changes following heart rate
change averages about 5 s, thus yielding the ubiquitous
heart rate oscillation of about 10 s (Vaschillo et al. 2002),
i.e., 5 s for the baroreflex to cause an increase in blood
pressure when the baroreflex responds to a decrease in
blood pressure, and 5 s to cause a decrease in blood pressure
following a baroreflex response to a blood pressure
increase. In actuality, however, the baroreflex system is
composed of two closed linear systems with negative
feedback loops—it also contains a slower vascular tone
loop with a rhythm of approximately 0.02–0.03 Hz, which
has a slower response time than the heart rate loop (Rahman
et al. 2011; Vaschillo et al. 2012).
It should be noted that a full description of baroreflex
control would not be restricted to purely mechanical properties
of the reflex. The baroreflex system shows characteristics
of neuroplasticity. Dworkin (1993) demonstrated that
the baroreflex can adapt to changing environments through
classical conditioning. Consistent with Dworkin’s findings,
Lehrer and colleagues demonstrated that regular stimulation
of the baroreflex through biofeedback can increase baseline
baroreflex gain, suggesting an improvement in regulatory
capacity over blood pressure changes (Lehrer et al. 2003).
Complex Control of Systems
Most psychological and biological systems are much more
complex than thermostats, with multiple control mechanisms,
and multiple overlapping oscillations, the quantitative
characteristics of which depend on the frequency and
phase of each component oscillator, as well as the interactions
among them (Hyndman 1974). Changes to one
system component (control mechanism) affect others, so
the individual control mechanisms are interdependent, thus
promoting biological vitality and complexity. Additionally,
it should be noted that closed loop systems with negative
feedback loops often require simultaneous open loop processes
in order to function properly, to provide sufficient
stimulation to stimulate negative feedback loop activity
(Feigl 1998). This adds to the complexity.
Simple control, such as control of room temperature, can
typically be described with linear mathematical models,
but when multiple controllers operate, each with a characteristic
frequency, attendant mathematical computations
become more complex. Existence of multiple overlapping
control systems assures system stability in the face of
various perturbations by providing multiple ‘‘backups’’. A
high degree of entropy, reflecting a high degree of nonlinearity
and/or a need for multiple linear formulas to
describe the system, is often related to health and biological
system stability. Conversely, simple periodicity or
random variability (‘‘white noise’’) often underlies
pathology in disordered systems (Goldberger et al. 2002),
reflecting, respectively, either the presence of only a single
control oscillator, or absence of any regulatory activity.
Oscillatory patterns with greater complexity, such as
those that occur when a number of oscillatory patterns
overlap, are described as ‘‘chaotic’’. Chaos reflects the
simultaneous operation of numerous control processes.
Although the healthy cardiovascular system can be effectively
modeled using linear statistics (Berntson et al. 1997),
it also has chaotic properties, which diminish when cardiac
function is impaired or an individual is in danger of dying
from physiological decompensation (Arzeno et al. 2007).
Similarly, healthy adjustment is characterized by chaotic
rhythms in appetite (Berthoud 2006) and mood (Haffen and
Sechter 2006), among other biobehavioral dimensions.
Also, negative feedback loops often work in concert with
open loops as described above, and with positive feedback
loops and resonance characteristics, as described below, to
maintain optimal functionality.
Oscillation, Control, and Health
Many oscillatory systems in the body, such as neural
(Berthouze et al. 2010), cardiovascular (Berntson et al.
1997), and circadian (Garau et al. 2006) tend to degrade
with age, reflecting a decrease in adaptive capacity. In
addition, there are also pronounced age-related decreases in
baroreflex gain (Brown et al. 2003), while blood pressure
oscillations tend to increase with age (Cugini et al. 2003),
perhaps reflecting degrading of control over this otherwise
tightly regulated function. Generally speaking, oscillatory
patterns in a variety of biological control systems tend to be
a characteristic of youth, fitness, and health.
Oscillatory properties do not only represent activation of
specific control reflexes (e.g., the baroreflex). They provide
information as well. Periodic stimulation is required for
system control in feedback networks. This principle has
been proposed for cellular behavior (Klevecz et al. 2008)
as well as macro biological and behavioral systems, as in
discussions of open loop processes described above, and
both resonance and stochastic processes, described below.
External stimulation ‘‘perturbs’’ a control system causing it
to oscillate. Without such oscillations, and hence, without
noise (which can function as the perturbation that pushes
the system to oscillate), systems may fail to get the information
they need to function properly. The tendency for
noise to stimulate system oscillation is called stochastic
resonance. There is evidence that interoceptive and
exteroceptive noise enhances the sensitivity of a variety of
control processes, from micro to macro levels (Wiesenfeld
and Moss 1995). Studies of stochastic resonance show that
noise can facilitate control functions, as in optimization of
monosynaptic afferent reflexes in the motor system of the
cat by causing stretches in a synergistic muscle (Martinez
et al. 2007). In humans, extreme absence of system perturbation,
as in sensory deprivation, can cause regulation
go awry, resulting in psychotic-like sensory and emotional
symptoms (Mason and Brady 2009).
One could, therefore, speculate that psychological stress
may have some beneficial effects, by introducing noise into
neural regulatory processes, just as exercise helps maintain
many physiological processes. Since, in biological systems,
oscillations tend to reflect the operation of reflex systems
involved in modulation and self-control, presumably a
complete lack of stimulation would deny exercise to these
reflexes, causing them to atrophy. Under normal circumstances,
however, the environment provides sufficient
perturbations in biological and behavioral systems to preserve
their normal function. In psychophysiological theories
of stress effects, this formulation overlaps with the
more recent concept of ‘‘allostasis’’ and ‘‘allostatic overload’’
(Juster et al. 2010). Allostasis refers to stability
achieved through variability, and is analogous to the
activity of oscillating control processes stimulated by stochastic
noise, or general unorganized environmental stimulation.
A well functioning personality or biological system
responds well to moderate environmental stressors, and, in
fact, may be energized by them, as in Yerkes and Dodson’s
model of stress and performance, wherein moderate stress
enhances performance in moderately difficult tasks, while
very low or very high levels of stress can be detrimental
(Yerkes and Dodson 1908). Allostatic overload (McEwen
2004) occurs when environmental stresses are either too
great or too prolonged, or when various control reflexes
become exhausted.
Resonance: A Source of Stimulation and Simplicity
in System Oscillation
In addition to oscillation, another characteristic of negative
feedback systems with a delay, is resonance (Grodins
1963). When outside stimulation causes a system oscillation
at a particular frequency, and no other forces are at
work to dampen the oscillations, negative feedback loops
can themselves destabilize the system by causing increasingly
large resonance frequency oscillations, to the point
where information from other frequencies no longer gets
processed. A common example of a ‘run-away’ negative
feedback loop, resonance effect is the ‘‘Larsen effect’’ that
occurs when a microphone is placed near a speaker,
causing a high-pitched squeal at a single frequency
(Weaver and Lobkis 2006). In such a case, a single high amplitude
oscillation, triggered either by noise (stochastic
resonance) or by pulsatile stimulation close to the resonance
frequency, can obscure the effect of otherwise
meaningful perturbations. In the human body, resonance
effects in the postural control loop have been implicated in
postural impairment in Parkinson’s disease (Maurer et al.
2004). Resonance has been similarly implicated in age related
impairment in gait control (Thurner et al. 2002).
In terms of psychophysiology, it is conceivable that
resonance effects might inhibit the fight-or-flight reflex
because a single homeostatic process may overwhelm
reflexes needed to confront an environmental stressor. One
could speculate that resonance effects, stochastic and
otherwise, might deprive the system of information from
various internal control mechanisms, as when resonance frequency
oscillations are at such a high amplitude that
they obliterate information from reflexes operating at different
frequencies. Thus, it is possible that resonant oscillations
in the heart rate baroreflex system at 6/min could
weaken the effects of other control reflexes, by greatly
diminishing their relative size and depriving the system of
information. This is a hypothesis for future research. To
prevent a ‘run-away’ resonance process, systems must be
dampened (Siebert 1986). Dampening effects on heart rate
amplitude during heart rate variability biofeedback probably
stem from inherent limitations in the ability of the heart
to respond at extreme levels.
The literature on heart rate variability biofeedback,
however, shows that not all resonance effects are detrimental.
Resonance in the cardiovascular system produced
by a rhythm in the baroreflex is the basis of the beneficial
effects of heart rate variability biofeedback. The baroreflex
provides a negative feedback loop for controlling blood
pressure, such that heart rate falls when blood pressure
rises, and vice versa when blood pressure falls, thus
modulating blood pressure fluctuations. The resonance
frequency of this system appears to be related to the individual’s
blood volume (Vaschillo et al. 2006), with a lower
resonance frequency among people with a larger blood
supply (men and taller people, vs. women and shorter
people), and to changes in hemodynamic inertia resulting
from fluctuating heart rate. When this resonance system is
activated by a respiration rate close to the resonance frequency,
oscillations in heart rate become very large. This
high-amplitude stimulation of the baroreflexes appears to
strengthen them (Lehrer et al. 2003), and is perhaps the
cause of the various salutary effects of heart rate variability
biofeedback.
Heart rate variability biofeedback has been shown to
restore autonomic control that has been acutely repressed
by experimental exposure to inflammatory cytokines
(Lehrer et al. 2010), and appears to ameliorate a number of
disorders characterized by autonomic and/or emotional
dysregulation (Lehrer 2007), including hypertension (Lin
et al. 2012; Nolan et al. 2010; Reineke 2008), asthma
(Lehrer et al. 2004), anxiety/stress (Hallman et al. 2011;
Henriques et al. 2011; Shenefelt 2010), depression (Beckham
et al. 2013; Karavidas et al. 2007; Patron et al. 2013;
Siepmann et al. 2008), and chronic pain (Hallman et al.
2011; Sowder et al. 2010; Strine 2004; Yetwin 2012),
while improving athletic performance (Paul and Garg
2012). We might, however, theorize that the effects of
constant breathing at resonance frequency would not be
advantageous. Chronic resonance frequency breathing
could theoretically weaken or obstruct reflexes dependent
on oscillations at other frequencies. Thus, individuals
practicing heart rate variability biofeedback are instructed
to practice for a relatively brief period of time daily
(usually about 20 min) and to use the technique strategically
when symptomatic.
Positive Feedback Loops
The body also contains multiple positive feedback loops,
whereby change in a particular direction facilitates greater
change in that direction. Although positive feedback can
provide a stimulating effect in an open loop system, it also
plays a role in closed loop systems. In psychophysiology,
positive feedback loops often are involved in maladaptive
processes (Thayer and Lane 2000). A prime example is
anxiety and depression’s propensity to amplify their inherent
symptomology by triggering greater sensitivity to anxious or
depressive thoughts, thus creating a vicious cycle.
However, positive feedback loops can also play an
important role in enhancing emotional control. The psychological
augmenting effects of positive feedback may
add to oscillation amplitude during HRV biofeedback, or
the sympatholytic effect of muscle relaxation. This may
result from a spiraling relaxation effect. Just as anxiety
begets more anxiety by sensitizing anxiogenic brain circuits
(a positive feedback loop) (Chemtob et al. 1988;
Krantz et al. 1987; Thayer and Lane 2000), heart rate
variability biofeedback or muscle relaxation therapy may
compound relaxation effects. On the cellular level, positive
feedback loops may be necessary to stimulate modulatory
negative feedback loops (Kurbel 2012; Nishi et al. 2000;
Pomerening et al. 2005; Prochazka et al. 1997; Tsai et al.
2008).
Positive feedback loops can also play a role in creating
system complexity, which may increase stability by creating
a multiplicity of stable states (Plahte et al. 1995), as
well as by maintaining oscillation amplitude during frequency
adjustment (Tsai et al. 2008). Positive feedback
loops have been shown to promote load compensation in
control of movement (Prochazka et al. 1997), and to be
necessary to prevent dampening of oscillations in cellular
function (Pomerening et al. 2005). Positive feedback loops
are important for the maintenance of the ovulatory cycle
(Kurbel 2012), and for propagating dopaminergic signaling
(Nishi et al. 2000). Without some form of regular perturbations,
oscillations will gradually decline in amplitude
and disappear. Positive feedback loops can provide these
perturbations.
Simplicity, Complexity, and Randomness
The dimensions of simplicity and complexity are important
for understanding psychobiological control, and can be
usefully incorporated into theories of applied psychophysiology.
Simplicity in a biological or behavioral control
system can have multiple sources. In addition to reflecting
the effects of resonance, simplicity may result from system
fatigue due to allostatic overload (Juster et al. 2010), or
from biological damage to system components, such as
when heart failure leads to diminished entropy in heart rate
(Ho et al. 2011; Isler and Kuntalp 2007; Liu et al. 2011).
Simplicity in heart rhythms as a sign of cardiac pathology
was discussed earlier. Emotional rigidity, or simplicity, is
well described in DSM-IV (American Psychiatric Association
1994) as a sign of psychopathology, which is characterized
by a tendency to respond to a wide variety of
situations with a stereotyped response: sadness, anxiety,
suspiciousness, anger, etc. However, just as simplicity is a
sign of poor adaptation, so is random variation, suggesting
lack of modulatory control. Pathological examples of random
fluctuations include manic or depressive episodes in
bipolar disorder, as well as certain cardiac arrhythmias
such as preventricular or preatrial contractions, asthma
exacerbations, and so on.
Work in the tradition of chaos and information theories
has provided mathematical tools for describing organized
complexity of temporal patterns, as described in information
theory, including Shannon’s ‘‘spectral entropy’’ calculations
(Shannon 1948), Pincus’ ‘‘approximate entropy’’
(Pincus 1991, 1998), and Lempel and Ziv’s method for
evaluating ‘‘randomness’’ (Lempel and Ziv 1976). These
measures represent various approaches to calculating relative
unpredictability of fluctuation patterns in a time series,
or the number of dimensions, or fractals (Mandelbrot
1983) necessary to describe a data set mathematically.
Autonomic Regulation
Healthy regulation is often characterized as sympathetic
and parasympathetic activity converging in a limit cycle
around a critical value, such as is the case of cortisolvasopressin
or acetylcholine-epinephrine reactions, where
each process induces a compensatory reaction in the other
(Bernard-Weil 1986). Thus, sympathetic activity may
simultaneously suppress parasympathetic activity, but
increase parasympathetic reactivity, thereby producing an
oscillation. During extremely stressful stimulation, an
individual may show wild oscillations in various autonomic
functions, both sympathetic and parasympathetic
(e.g., bronchodilation and constriction, increased and
decreased blood pressure, energy and fatigue, high versus
low rates of peristalsis as in constipation and diarrhea,
etc.), although these oscillations are sometimes paradoxically
suppressed in severe emotional reactions (Gellhorn
1969, 1970).
Heart rate changes in response to stimulation appear to
be important modulators of systemic response. Reduced
cardiac variability in response to stimulation is related to a
variety of pathophysiological states (Montano et al. 2009),
while increased heart rate variability and vagal influence on
the heart are positively correlated with recovery after
physical exercise stress (Chen et al. 2011). Thus flexibility
in cardiac response to stimulation, and higher vagal influence
on the heart (measured as high-frequency heart rate
variability, reflecting both baroreflex control and modulation
of respiratory function) are related to better adaptation
to interoceptive and exteroceptive demands (as evinced by
rapid recovery). This is another example of ways in which
complexity and oscillatory function are related to health
and adaptability.
Thermoregulation
[...]
Oscillation, Complexity, and Disease
Heart rate variability complexity is indicative of cardiac
and neurocardiac flexibility and adaptability, and may be
diminished significantly by pathology. A healthy heart can
respond to various moment-to-moment physical and emotional
demands, while the diseased heart does not exhibit
such flexibility. Although oscillations may persist in the
diseased heart, aging and disease cause increasingly more
regular oscillatory patterns reflecting a decrease in mechanisms
of cardiac control (Goldberger et al. 2002). Such
noncomplex patterns of variability ultimately predict death
in the critically ill (Arzeno et al. 2007; Norris et al.
2008a, b).
[...]
Mental Illness
Mood and behavior appear to be governed by many of the
same concepts used to describe oscillatory behavior in
physiological processes. As with physiological systems,
organized complexity is the hallmark of healthy psychological
behavior. Both hyper and hypo-stabilities may
characterize a breakdown in normal mental functioning,
and leave a person psychologically vulnerable to the deleterious
effects of stress. Gottschalk et al. (1995) found that
while participants with bipolar disorder showed some brief
periods of fairly well defined cycling in mood, their overall
pattern of mood changes were less complex than controls.
Paulus et al. compared patients with schizophrenia to
healthy individuals in a binary choice study, and found that
participants with schizophrenia showed a simpler and more
predictable pattern of response, apparently reflecting
decreased adaptability in cognitive processing (Paulus and
Braff 2003; Paulus et al. 1996). A study of laterality in
electrodermal activity found decreased activity and complexity
in the left hand, compared with the right, among
depressed patients, and decreased activity and complexity
in the right hand, compared with the left, among patients
with schizophrenia, while there were no laterality differences
among healthy individuals (Bob 2007). These results
are consistent with theories of right hemisphere dysfunction
in depression (represented by decreased adaptiveness
in this instance), and left hemisphere dysfunction in
schizophrenia. However, even some psychopathological
states appear to possess chaotic organization. Tschacher
et al. (1997) recorded occurrence of psychotic symptoms
among 14 patients with schizophrenia over time, and found
a complex, nonlinear time course in eight of them. Others
showed more random psychotic behavior, suggesting that
the patients were responding to environmental cues, not
modulated by control processes.
EEG Oscillations Reflect System Functions
Systems theory also applies to neurons’ activity in the
brain, whose functioning has been shown to be interdependent
with other nearby neurons and even neurons in
different brain regions. The complexity and chaotic characteristics
of brain function, particularly as studied by
electroencephalogram (EEG), has been thoroughly
reviewed elsewhere (Buzsaki 2006). Briefly, EEG reflects
summations of excitatory postsynaptic potentials that
neurons emit as discrete events. When neurons fire synchronously,
relatively high-amplitude slow waves occur, in
a relatively simple pattern; when they fire desynchronously,
the numerous potentials can both augment and
cancel each other out, generally resulting in lower-amplitude,
more complex signals at higher frequencies (Onton
et al. 2006). Fast-firing cells in the cortex may contribute to
these effects, as does the interaction between excitatory
and inhibitory processes. Faster, low-amplitude oscillations
tend to predominate during states of arousal, attention, and
cognitive processing (Timofeev and Chauvette 2011), and
in general, during periods of greater cortical activity
(Klimesch 1996).
Oscillations in EEG signal reflect the interplay of various
brain processes involved in cortical integration, such
that specific kinds of mental activity may be reflected in
definable patterns of activity at specific frequencies, and at
specific electrode locations (Gottesmann 1999; Jacobs
2001). Slower waves from the cortex reflect decreased
arousal at the measured EEG sites (i.e., fewer simultaneous
processes producing potentials at various frequencies).
Low levels of alpha and beta activity (the faster, higherfrequency
ranges) and/or increased waking slow-wave
activity are associated with a lack of inhibitory control over
behavior (Knyazev 2007). Similarly a greater frontal theta
beta ratio is related to decreased emotional response inhibition
(Putman et al. 2010). Greater delta and theta activity
and lower alpha activity have been found in the EEGs of
children with fetal alcohol syndrome (Kaneko et al. 1996).
This pattern is also found in attention deficit disorder,
lower cognitive abilities, impaired motor control, hypoglycemia,
and antisocial behaviors. It is even found during
deep relaxation and sleep, while exposed to hypoxia,
fasting, and in sexual arousal and orgasm (Knyazev 2007).
Conversely, delta, theta, and lower-frequency alpha
activity are inhibited when people are awake and cognitively
active. A recent study found that, during tasks with
increased cognitive demand, increases occurred in the EEG
frequency with the greatest activity across electrode sites,
but a decrease in EEG entropy (Zarjam et al. 2011).
Oscillations are not smoothly distributed across the
frequency spectrum. Resonance structures have been
identified at frequencies of 4, 10, 20, and 40 Hz (Erol Basar
1999) with the largest body of research on the 10 Hz
rhythm, the center of the alpha frequency band (8–12 Hz).
Resonance properties can be determined by a variety of
methods. For instance, one can stimulate the system at
specific frequencies with photic stimulation, and determine
the frequencies at which high-amplitude oscillations are
obtained (Fedotchev et al. 1990; Herrmann 2001; Spiegler
et al. 2011). One may also use computer simulations based
on known frequency characteristics of particular nerve
cells (Kasevich and LaBerge 2011) or from evoked
potentials (Basar et al. 1976; Bayram et al. 2011; Winterer
et al. 1999). Alternatively, one can stimulate an individual
with audible noise and measure the obtained frequency
peaks (stochastic resonance) (Ward et al. 2010). Resonances
at these frequencies suggest the existence of specific
positive feedback reflex arcs at each resonance
frequency, related to particular neural processes contributing
to each resonance frequency.
Thalamocortical processes have been studied and modeled,
and rhythm bands containing each of these frequencies
have been described in a large literature (theta or delta
for 4 Hz, alpha for 10 Hz, beta for 20 Hz, and gamma for
40 Hz) (Knyazev 2007). It has been theorized that delta
oscillations reflect activity of motivational systems, while
theta oscillations reflect emotional regulation, and alpha
oscillations reflect inhibitory processes (Knyazev 2007).
Some theorists have related specific resonance frequencies
to the period length of experimentally evoked action
potentials, event-related potentials, synchronies, or asynchronies
(Pfurtscheller and Lopes da Silva 1999). Patterns
of theta and alpha-frequency resonances triggered by
memory consolidation processes have been identified
(Klimesch et al. 2005). It is also known that memory
consolidation occurs during sleep (Fogel and Smith 2011),
which is characterized by high-amplitude slow EEG waves
primarily stemming from the brain stem, with cortical
modulation of lower centers, perhaps from a single oscillator
(Crunelli and Hughes 2010).
Phase relationships in EEG activity among various brain
areas have also been studied, appearing to vary systematically.
For example, occipital phase synchrony in the alpha
rhythm tends to be greatest during relaxation with eyes
closed, and decreases with greater arousal (Gengerelli
1978). Experience of ‘‘pure consciousness’’ in transcendental
meditation appears to be characterized by alpha
phase synchrony from the frontal and central areas (Orme-
Johnson and Haynes 1981). Although phase synchrony
may reflect tight coordination among brain sites, more
complex forms of coordination have also been modeled,
often involving varying frequencies, varying orderly phase
relationships, and nonlinear relationships, where frequent
changes in phase relationships among brain areas reflect
complexity of cognitive processing (Basar 2006; Panzeri
et al. 2010; Tognoli and Kelso 2009).
Measures of chaos and complexity applied to EEG data
suggest a simpler pattern of brain organization, with, fewer
control processes, among patients with schizophrenia
(Roschke et al. 1995) and depression (Nandrino et al. 1994;
Pezard et al. 1996). Pezard et al. found that EEG patterns
became more complex among depressed patients, whose
depression improved, becoming indistinguishable from
those among healthy individuals, while the pattern of
decreased complexity persisted among individuals whose
depression did not improve (Pezard et al. 1996). Other
studies have found risk for autistic spectrum disorder (Bosl
et al. 2011) and the presence of attention deficit hyperactivity
disorder (Sohn et al. 2010) to be related to decreased
average EEG complexity.
Social Systems
Humans live as couples, in families and in neighborhoods
or larger social and economic networks. Systems theory
has been applied to social systems, and, indeed, oscillatory
patterns of social system behavior can reflect negative
feedback loops, which could contribute to stability. Stability
in a social system generally represents a stable pattern
of interaction, implying the action of mechanisms that
resist change. Oscillatory patterns of openness and closedness
to new ideas have been described in societies
(Klapp 1975), as have variations in societal norms about
expression of emotion (Cancian and Gordon 1988),
although presumably extremes in either of these characteristics
trigger a consequent tendency in the opposite
direction. Voter patterns appear to have oscillatory characteristics,
which, in the United States, have been quantified
as thirty-year oscillatory patterns in party alignment
(Mayhew 2002). The cause for this oscillation period may
reflect generational change. Perhaps people see the problems
caused by results of their parents’ voting patterns, and
react to this by changing course. Although delays, chaos,
and resonance characteristics in social feedback loops have
not been measured, the existence of specific frequency
patterns in some of them suggest that these characteristics
may be present.
Dyadic systems may also have oscillatory qualities.
Gottman et al. have taken a mathematical approach to
assessing marital systems (Gottman et al. 2002). They have
applied concepts of nonlinear dynamics and catastrophe
theory, which posit complete periodic restructuring of
systems when perturbed by certain forces. They propose
that marriages have one or more ‘‘set points’’ for nature and
style of interaction, some of which may be favorable to
marital satisfaction and stability, and others unfavorable.
From this perspective, they have developed formulas that
predict marital stability and divorce. They record verbatim
transcripts of marital interactions, where they note the
number of times that individuals respond to each other’s
content with specific affect, including such variables as
threshold for negativity, and frequency of positive and
negative interactions. In marriages characterized by reciprocal
negativity and paucity of positivity in interactions,
couples can be trained to modify their individual behavior.
Thus, based on positive feedback loops, marital systems,
wherein negativity begets negativity, may be altered so that
positivity begets positivity. The overarching goal is to
move the interaction system from a negative steady state to
one that is more positive. Repair and dampening functions
have been calculated, which can modify the positive or
negative influence of one partner on another in various
steady state conditions. The investigators report conflicting
data about whether rigidity in behavior predicts poor
marriage outcome, but show how intervention can affect
the system of interaction and, ultimately, marital satisfaction.
Their model includes the interaction of perception of
well-being in the relationship, the flux over of negative and
positive behaviors, and physiological responses.
Systems theory has also been applied in analysis of
corporate structures (Weick 2009), but concepts of oscillation,
delay, and influences of positive and negative
feedback loops have not been studied. Corporations are
systems, just as those defined above. They are constantly
bombarded by changes in market conditions, competition,
etc., and need to maximize the adaptiveness with which
they respond to changing conditions, while still maintaining
their structure. Information and communication, both
from outside of and within the organization, has been
proposed as a medium for achieving these ends. Utilization
of feedback from information allows an organization to
monitor both external events and internal processes. For
example, if an organization’s norms do not allow for
transmission of bad news to its CEO, then poor, uninformed
decisions are likely to be made. Various feedback
mechanisms can prevent change from happening (e.g., not
heeding information identified as coming from the ‘‘wrong
channel’’). Although we have found little research using
time series analysis to discover patterns of oscillations in
negative feedback loops comprising organizational structures,
examples of feedback loops in organizational
behavior have been described (Weick 2009). Presumably
oscillations occur in organizational communication as well
as in other control systems, and might be quantifiable on a
number of dimensions, such as commands to change
operational procedures to adapt to external pressures,
oscillating with instructions to follow internal protocols,
pressured work versus relaxation, and supportive versus
critical communications from superiors.