The basic pattern of breathing generated in the medulla is extensively modified by several control mechanisms. Multiple controls provide a greater capability for regulating breathing under a larger number of conditions. Their interactions modify each other and provide for backup in case of failure. The set of strategies for controlling a given variable, such as minute ventilation, typically includes individual schemes that differ in several respects, including choices of sensors and effectors, magnitudes of effects, speeds of action, and optimum operating points.
The use of multiple control mechanisms in breathing can be illustrated by considering some of the ways breathing changes in response to exercise. Perhaps the simplest strategies are feedforward mechanisms, in which breathing responds to some component of exercise but without recognition of how well the response meets the demand. One such mechanism would be for the central nervous system (CNS) to vary the activity of the medullary pattern generator in parallel with, and in proportion to, the excitation of the muscles used during exercise. Another prospective feedforward scheme involves sensing the magnitude of the carbon dioxide load delivered to the lungs by systemic venous return and then driving ventilation in response to the magnitude of that load. Experimental evidence supports this mechanism, but the identity of the required intrapul-monary sensor remains uncertain. Still another recognized feedforward mechanism is the enhancement of breathing in response to increased receptor activity in skeletal joints as joint motion increases with exercise.
Although feedforward methods bring about changes in the appropriate direction, they do not provide control in response to the difference between desired and prevailing conditions, as can be done with feedback control. For example, if Paco2 deviates from a reference point, say 40 mm Hg, ventilation could be adjusted by feedback control to reduce the discrepancy. This well-known control system, diagrammed according to the principles given in Chapter 1, is shown in Figure 22.4. Unlike feedforward control, feedback control requires a sensor, a reference (set point), and a comparator that together generate an error signal, which drives the effector. Negative-feedback systems provide good control in the presence of considerable variations of other properties of the system, such as lung stiffness or respiratory muscle strength. They can, if sufficiently sensitive, act quickly to reduce discrepancies from reference points to very low levels. Too much sensitivity, however, may lead to instability and undesirable excursions of the regulated variable.
Other mechanisms involve minimization or optimization. For example, evidence indicates that rate and depth of breathing are adjusted to minimize the work expenditure for ventilation of a given magnitude. In other words, the controller decides whether to use a large breath with its attendant large elastic load or more frequent smaller breaths with their associated higher resistive load. This strategy requires afferent neural information about lung volume, rate of volume change, and transpulmonary pressures, which can be provided by lung and chest wall mechanoreceptors. During exercise, such a controller would act in concert with, among other things, the feedback control of carbon dioxide described earlier. As a final example, an optimization model using two pieces of information is illustrated in Figure 22.5. Breathing is adjusted to minimize the sum of the muscle effort and the sensory "cost" of tolerating a raised Paco2.
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This ebook provides an introductory explanation of the workings of the human body, with an effort to draw connections between the body systems and explain their interdependencies. A framework for the book is homeostasis and how the body maintains balance within each system. This is intended as a first introduction to physiology for a college-level course.