Due to the observations that raised questions, a new theory of pain was developed in the early s to account for the clinically recognized importance of the mind and brain in pain perception.
It is called the gate control theory of pain , and it was initially developed by Ronald Melzack and Patrick Wall. Although the theory accounts for phenomena that are primarily mental in nature - that is, pain itself as well as some of the psychological factors influencing it - its scientific beauty is that it provides a physiological basis for the complex phenomenon of pain.
It does this by investigating the complex structure of the nervous system, which is comprised of the following two major divisions:. In the gate control theory, the experience of pain depends on a complex interplay of these two systems as they each process pain signals in their own way. Upon injury, pain messages originate in nerves associated with the damaged tissue and flow along the peripheral nerves to the spinal cord and on up to the brain.
So far, this is roughly equivalent to the specificity theory of pain described above. However, in the gate control theory, before they can reach the brain these pain messages encounter "nerve gates" in the spinal cord that open or close depending upon a number of factors possibly including instructions coming down from the brain.
When the gates are opening, pain messages "get through" more or less easily and pain can be intense. When the gates close, pain messages are prevented from reaching the brain and may not even be experienced. For us, the firing threshold will be defined as the value of net input that makes the neuron fire with probability equal to 0.
We have demonstrated elsewhere [ 16 ] that synaptic weights increment can be counterbalanced by the dynamic adjustment of the shift of the sigmoidal function so that the more the synaptic weights and accordingly the net input value grow, the more the shift grows. Thus, the steepest slope of the sigmoid tends to be placed over the average net input of the neuron see Figure 2.
Such dynamic adjustment makes synaptic weights stop increasing and stabilize in specific values. The following equation modeling intrinsic plasticity calculates the shift parameter of the activation function, , at time in terms of the shift parameter and output probability of the neuron at time : where is a small arbitrary factor that adjusts the shifting rate of the activation function.
In this paper, we show that the dynamic interactions between synaptic and intrinsic plasticity are the factors that allow the stabilization of parameters in the gate circuit.
Once parameters are stabilized under either standard or altered modes of operation, they give rise to either normal or altered pain sensations. In this research, we use a phenomenological type of neuron modelling. We take into account that, at least in mammals, rate coding is the way neurons communicate with one another.
Such rate can be expressed in the form of a probability ranging from zero to one. When , it means that the neuron fires every time it is possible. Probabilities are dimensionless measurements because they are obtained from the quotient of equal type of magnitudes see 4.
Synaptic weights are also calculated as conditional probabilities and are, therefore, dimensionless. For this reason, graphs representing weights do not have unit specifications in their axes.
In a similar way, variables that are obtained as a combination of dimensionless variables are also dimensionless like the net input, net, and the shift parameter,. When these variables appear in a graph, they are devoid of unit specifications. A comparison with binary bits might help in the understanding of these ideas. A binary bit is a dimensionless magnitude that is either 1 or 0. For computers to work, binary 1 is arbitrarily associated with a certain voltage level that depends on the available technology.
In a similar way, the net input value, that is dimensionless, is associated in real neurons with specific postsynaptic voltage levels. An iteration is typically a sequence of tasks in a programming loop. In order to relate iterations, time windows, and evolution of patients with stimulation protocols, patterned experimental tests with real patients should be necessary.
This section is devoted to analyzing the behavior of the gate circuit under standard Section 3. In Section 3. During this transitory period, a standard training epoch depicted in Table 1 b is input to the gate, being the mechanoreceptor input higher than the nociceptor input. As it will be demonstrated, synaptic and intrinsic plasticity interact for allowing parameters stabilization and convergence because one type of plasticity counterbalances the other.
Under such condition, the operation emerging from the circuit consists of pain only being elicited CT neurons fire when nociceptive signals are the only input to the circuit which is the conventional gate circuit operation. At the end of Section 3. The interplay of both synaptic and intrinsic plasticity is necessary to create the conditions for the circuit to reach the setpoint that makes the circuit respond in the conventional way.
As previously mentioned, in this section, we will study the evolution of synaptic weights, , and shift parameters, , when a standard type of stimulation is input to the gate circuit. In order to allow the conventional gate operation, the interplay between intrinsic and synaptic plasticity is necessary for parameter stabilization and convergence.
Equation 4 has been used for modeling weights modification and 5 was used to model the gradual shift of the activation function. Figure 3 shows five trajectories of weights Figure 3 a and shifts Figure 3 b corresponding to five different simulations five colored lines with the same color numbers , each of them starting with different shifts and weight values.
As we have four weights , , , and see location of each of these weights in Figure 1 b , a four-dimensional coordinate system would be necessary for representing their evolution along iterations.
We have managed to represent the trajectory of the four weights along iterations in a three-dimensional coordinate system see Figure 3 a by representing the value of as a colored point over each trajectory. The color of each point corresponds to a color scale in which dark blue means and red means.
The values of , , and are represented in a conventional three-dimensional system. All weight values are dimensionless and range from 0 to 1 because they are obtained from a conditional probability equation 4.
Notice that the five weight trajectories converge to the same coordinate: , , and. Shift trajectories Figure 3 b also converge to the final shifts values and that, according to 6 , correspond to the firing thresholds: and.
The table in Figure 3 c is obtained at the final points of each colored trajectory. The table shows the output value, , of the CT neuron when applying a standard epoch to the circuit using the final parameters of each of the th trajectories. In this case, the final parameters in the five trajectories are equal, so that the output when applying a standard epoch to each of the five circuit configurations is the same. In this case, the outputs correspond to the standard or conventional modus operandi of the gate circuit in which the CT neuron is only active when only the nociceptive input is active.
The output probability that is equal to 0. If a more realistic approximation of the activation function was used, in which , the output probability would certainly yield zero.
Graph of Figure 4 also represents the output of the gate circuit i. Figure 4 , instead, shows the response of the circuit along iterations for each combination of inputs in this case, in a standard epoch. For example, the dark blue ribbon shows the output of CT neuron when both inputs are null and from the very first iteration until iteration The cyan ribbon represents the response of the circuit along iterations when only a sensory signal is given and.
The yellow ribbon represents the output when only a nociceptive signal and is input. Finally, the flat dark red ribbon shows that no pain signal is elicited by the gate circuit along all iterations when both sensory and nociceptive input are simultaneously entering the circuit. In summary, Figure 4 shows that the interplay between intrinsic and synaptic plasticity, together with the presentation of a standard type of input patterns, leads the gate circuit to the gate conventional modus operandi : when both sensory and nociceptive inputs are simultaneously input to the gate, CT neurons are silent.
We performed other similar simulations, but only considering either intrinsic Figure 5 or synaptic plasticity Figure 6. As before, the five thin colored lines represent parameters evolution from five different initial conditions. It can be noticed that with only one type of plasticity there is no convergence of parameters and the gate circuit does not respond in a standard manner when a standard type of stimulation is applied.
Our premise here is that the gate circuit is a kind of neural network that is trained achieves different parameter configurations depending on the type of external stimulation. In this way, at iteration 50, the gate circuit is programmed as in Section 3. In this paper, we model different syndromes: phantom pain, demyelinating pain syndromes like multiple scleroses or Guillain-Barre syndrome, breakthrough pain, wind-up pain, and wind-down pain. Once the circuit settles down, we model the amputation by zeroing both sensory and nociceptive inputs along the following 50 iterations see Figure 7 a.
From iteration to iteration , very subtle sensory and nociceptive inputs enter the circuit, simulating abnormal action potentials fired by neuromas formed from injured nerve endings at the stump site. Figure 7 b shows the CT neuron output, , along the mentioned iterations for the different nociceptive and sensory combinations.
We can see that, from iteration ahead, two cases are possible depending on the stability point in which model parameters settle down. In graph b , we notice that a CT neurons output a pain signal is produced when either a nociceptive input is present yellow ribbon or no inputs are present at all dark blue ribbon. In graph c , a pain sensation CT neuron output appears when there are no input signals at all dark blue ribbon.
Although the peripheric pain component due to neuromas is not difficult to accept, pain when no input at all is present at the gate is more difficult to understand.
This case is consistent with clinical findings that demonstrate that phantom pain remains even when local anesthesia is applied to the stump.
The general consensus trying to explain this last case is that phantom pain is a top-down phenomenon caused by maladaptive cortical plasticity. However, a recent article [ 18 ] has reopened the discussion regarding the peripheral versus central origin of phantom pain.
Instead of always mitigating pain, the gate circuit is also able to create pain produce a CT output , even in the absence of sensorial and nociceptive stimuli.
For a neuron to fire in the absence of stimuli, the only possibility is that it has incremented its excitability by lowering its firing threshold according to intrinsic plasticity. The whole set of parameters for the case of Figure 7 c is shown in Figure 12 b.
This case is also an example on how we translate a verbal expression like the one presented at the end of Section 2.
In this case, the demyelinating syndrome makes mechanoreceptors convey weaker signals to the gate circuit. Table in Figure 8 a shows the training epoch used from iteration 50 to iteration Notice that sensory stimuli are weaker than in the standard training epoch, almost similar to stimuli from nociceptive nonmyelinated fibers. Here, we see that dysesthesia appears from iteration , at the onset of some concomitant event generating more intense nociceptive signals in this case, a new training epoch was applied with nociceptive stimuli rising from 0.
In these new conditions, pain sensations appear with sensorial stimuli cyan ribbon. Although gate parameters usually converge to an equilibrium point like the previously described, other equilibrium points are possible in demyelinating syndromes.
In this case, after the preliminary standard setup during the first 50 iterations, the training epoch in Figure 9 a is input to the gate circuit. After a few iterations, a wind-down phenomenon takes place in which nociceptive input signals see yellow ribbon produce decreasing pains sensations CT neurons output.
When pain sensations seem to be less intense, pain is triggered again when a moderate nociceptive stimulus is applied. Under the same circumstances, that is, intense nociceptive stimuli, other sequences are also possible. Nonstandard pain responses differ from the standard case in that in nonstandard cases there are usually secondary stability points in which pain responses are not so easy to predict.
In this case, we will analyze wind-down pain which also was a pain response that appeared in previous case. Wind-down pain is usually experienced when intense aversive nociceptive stimuli are constantly applied.
We performed the computational model of this case by letting the circuit settle under standard conditions during the first 50 iterations. From iterations 50 to , the unique stimulus applied was an intense input in the nociceptive entrance. In this way, we tested the circuit with all the combination of nociceptive and mechanoreceptor inputs. In cancer pain, this wind-down component can be masked by the concomitant usage of analgesics like morphine [ 23 ], so that pain relief is erroneously thought to be due to the pharmacological treatment rather than being derived from neural plasticity dynamics.
Wind-up pain is the pain response elicited when a constant sensory weak stimulus is input through mechanoreceptors. The consequence of this apparently innocuous procedure is that, in the long run, an intense pain appears in the subject.
We have simulated the conditions of wind-up pain from standard conditions Figure 11 by initially letting the circuit settle in a stable point along the 50 initial iterations. From iterations 50 to , the only stimulus is a weak 0. As it can be seen, there is an intense pain when no input is applied to the circuit. In this case, the pain that is actually felt by the subject undertaking repetitive weak sensory stimulation seems to be a type of dysesthesia that possibly comes up between repetitive sensorial stimuli.
One of the objectives of this work is to highlight the dependence of pain responses on gate circuit parameters synaptic weight and firing threshold values and on the relative contribution of afferent mechanoreceptors and nociceptors.
Usually afferent mechanoreceptors have myelinated axons generating more intense responses than nociceptors.
At the same time, sensory and nociceptive stimuli are usually delivered to the central nervous system according to a stimulation protocol epoch that is similar to the standard one presented in Section 2. Under these standard conditions, the synaptic weights and firing thresholds of the gate circuit evolve until settling in a stable point that allows the conventional operation of the circuit see synaptic weight values, , and firing threshold values, , in Figure 12 a. For calculating the output of neuron 1, , the computer program applies the sigmoidal activation function of 3 to its net input see 2.
As our sigmoid is very similar to a step function, it is also possible to obtain by simply comparing Net 1 with threshold. If the net input of neuron 1 is higher than its threshold, neuron 1 output is 1, which is almost the same result of applying the sigmoid to Net 1. This gating mechanism takes place in the dorsal horn of the body's spinal cord. Both small nerve fibers pain fibers and large nerve fibers normal fibers for touch, pressure, and other skin senses both carry information to two areas of the dorsal horn.
These two areas are either the transmission cells that carry information up to the spinal cord to the brain or the inhibitory interneurons which halt or impede the transmission of sensory information. While it is perhaps the most influential theory of pain perception, gate control is not without problems.
Many of the ideas suggested by Melzack and Wall have not been substantiated by research, including the very existence of an actual gating system in the spinal cord. Melzack and Wall suggest that this process explains why we tend to rub injuries after they happen.
When you bang your shin on a chair or table, for example, you might stop to rub the injured spot for a few moments. The increase in normal touch sensory information helps inhibit pain fiber activity, therefore reducing the perception of pain. Gate control theory is also often used to explain why massage and touch can be helpful pain management strategies during childbirth.
Because the touch increases large fiber activity, it has an inhibitory effect on pain signals. Melzack and Wall themselves noted that the "gate" metaphor for pain perception served as a helpful way of helping people understand the basic concept, regardless of whether they grasped the complex physiological processes behind the theory.
Doctors often utilize the gate metaphor to help patients understand how and why pain can fluctuate so much. While gate control theory does not explain every aspect of how people experience pain, Melzack and Wall's theory was the first to consider the psychological factors that influence the perception and experience of pain. Initially, there was resistance to the theory, but evidence increasingly pointed to the existence of a spinal gating mechanism.
The theory helped transform approaches pain management. Ever wonder what your personality type means? Sign up to find out more in our Healthy Mind newsletter. Katz J, Rosenbloom BN. The golden anniversary of Melzack and Wall's gate control theory of pain: Celebrating 50 years of pain research and management. Pain Res Manag. Melzack R, Wall PD. Pain mechanisms: a new theory. Your Privacy Rights.
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