Effect of Depth-Dependent Nociceptor Density on the Heat-Induced Withdrawal Reflex ()
1. Introduction
The rapid development of applications such as wireless communications, security scanning, tissue diagnosis, and non-lethal weapons for crowd control or perimeter security has considerably increased human exposure to high-frequency millimeter waves (MMW) ranging from 30 to 300 gigahertz (GHz). For the purpose of biological risk assessment, it is vital to understand the effects of this irradiation on humans.
Many experiments have shown that exposure to MMW at sufficiently high intensities primarily produces fast heating near the surface of the skin [1] - [7]. The transmitted MMW power is absorbed in the skin to a depth of less than 0.5 mm at 100 GHz [8] and is attenuated exponentially as a function of skin depth. The skin generally consists of three different layers, namely epidermis, dermis, and hypodermis [9]. These layers have different thickness depending on the location of the skin. In particular, the epidermis is the outermost layer of skin containing both living and dead cells with thickness 0.075 - 0.15 mm. The dermis lies beneath the epidermis and is much thicker (1 - 4 mm). There are blood vessels and nerves in the dermis. The third layer is the hypodermis, which is composed of mainly subcutaneous fat. The hypodermis is about 1.1 - 5.6 mm in thickness. Studies performed at 60 GHz demonstrated that while the maximum value of the power density and specific absorption rate occurs at the epidermis, up to 60% of the incident power reaches the dermis, and only 10% gets to the hypodermis [10] [11]. Absorption of the MMW energy causes the local temperature of the skin to rise and can activate nociceptors [12] and consequently lead to a sensation of pain [13] [14].
Nociceptors are sensory nerve cells that respond to painful stimuli by sending out signals to the spinal cord via a chain of nerve fibers. When the collective signal becomes strong enough, the withdrawal reflex is triggered and the subject moves away from the exposure [15] [16]. Previously, we formulated a dose-response model for the heat-induced withdrawal reflex from MMV radiation [17]. The concise model predicts the occurrence of withdrawal reflex from a given spatial temperature profile of the skin. A prominent feature of the model is that it contains only two parameters. One key assumption in the concise model is that the nociceptor density in skin does not vary with the depth. In this paper we extend our earlier study by relaxing this assumption. Our goal is to determine the effect of depth-dependent nociceptor density on the heat-induced withdrawal reflex.
2. Mathematical Formulation in the Case of Depth Dependent Nociceptor Density
In this section, we study the mathematical formulation when the nociceptor density is a function of depth in the skin. We start by introducing proper mathematical notations:
· y: the depth coordinate (the skin surface is
).
·
: 2-D coordinates on the skin surface;
is the 3-D coordinates.
·
: 3-D spatial temperature profile of the skin.
·
: activation temperature of nociceptors; given
, the activation status of a nociceptor at
is governed by the indicator function
·
: nociceptor density at depth y (number per volume).
·
: total number of activated nociceptors in the skin,
·
: characteristic reference nociceptor density.
When the nociceptor density is uniform,
, the activated volume is proportional to the number of activated nociceptors. In this case, we adopted the activated volume as the single metric predictor variable (the input dose) for predicting withdrawal reflex [17].
Input dose in the case of uniform density:
(1)
The advantages of this input dose are 1) it makes the dose quantity independent of the nociceptor density
, and 2) it shifts the effect of
into the dose threshold
. In the dose response model, withdrawal reflex occurs when
where the effects of
and all other factors are reflected the single metric quantity
.
In the case of non-uniform nociceptor density, the activated volume is no longer proportional to the number of activated nociceptors, and thus is no longer a valid candidate for the input dose. We like to define the dose such that it contains (1) as a special case. For that purpose, we define the equivalent activated volume
based on reference density
, and use
as the input dose.
Input dose in the case of non-uniform density:
(2)
The dose response relation has the same form as in the case of uniform density.
In this study, we consider the hypothetical situation where the time of withdrawal reflex and the spatial temperature profile at reflex are measurable in experiments. With these two measurable entities, we explore the behaviors of several parameterized nociceptor density types. The objectives of the study are 1) to distinguish these candidate density types from each other based on the measurable entities, and 2) to infer the parameter values.
The calculation of
defined in (2) requires only the relative density
. The effect of
is contained in the dose threshold
. When
is given, the dose response model has only two unknown parameters:
and
. In a test, the measured temperature profile
at reflex provides a constraint on
. Mathematically, we construct constraint function
as follows. For any value of
, by definition, the corresponding value of
is the value of
calculated based on
and
.
(3)
when
is given, function
is completely determined by the measured
. Constraint functions
based on
measured at different test conditions are potentially distinct from each other. All these constraint functions have one common intersection, which gives the true values of
.
When the true relative density
is unknown, we work with a trial relative density
. We use
to replace
in (3) and construct trial constraint function
from the test data. Note that the true values of
satisfy only the true constraint function calculated using the true
. When
deviates from the true
, the true values of
are not on the trial constraint curve calculated using
. Consequently, for a pair of trial constraint functions calculated using
(based on measured
of two distinct test conditions), their intersection is not at the true values of
, and the intersection varies with the test conditions of the pair. The test-condition-dependence of the intersection serves as an indication that the trial density
is incorrect. The specific behavior of test-condition-dependence of the intersection provides a venue for us to tune
toward the true
.
We examine several types of parameterized density. We study the test-condition dependence of 1) the reflex time and 2) the intersection of a pair of trial constraint functions. The goal is to identify system behaviors that a) help us distinguish these density types from each other and b) guide us to tune the trial parameter toward its true value.
We carry out the analysis in the idealized situation where
· the electromagnetic heating is uniform over the beam cross-section (with area A) and it decays exponentially with depth y;
· the initial temperature is uniform everywhere;
· the heat conduction is included only in the depth direction.
This is the same as case B in our previous study [17]. At any given time, the temperature inside the beam cross-section is a function of depth y only and it decreases with y. The time evolution of temperature distribution
is governed by
where
·
is the mass density of skin,
·
is the specific heat capacity of the skin,
· K is the thermal conductivity of the skin,
·
is the absorption coefficient of the skin,
·
is the beam power density deposited on (absorbed into) the skin, and
·
is the initial temperature of the skin.
In this idealized situation, the region of activated nociceptors (
) is a cylinder with depth
governed by
. By definition,
,
and the reflex time
are constrained by the temperature distribution, which we assume is measurable.
(4)
We consider a general 1-parameter form for the relative density of nociceptors
where
can be any positive function. The activated depth
and the dose
both vary with the activation temperature
, and are related by Equation (2) as
(5)
(6)
where A is the beam spot area,
, and
is the inverse function of
. Since
is positive, function
is monotonically increasing and the inverse function
is well-defined over the range of
. By definition, functions
and
satisfy
and
. Recall that the dose threshold is defined as
. Equation (6) gives
(7)
Equation (4) in combination with (7) provides a constraint on
,
,
and
, which can be used for different purposes, depending on which parameters are known. When
and
are measured, (4) gives us a constraint on
,
and
. On the other hand, when
,
and
are given, (4) can be viewed as a governing equation for
. This is useful, for example, for examining the behavior of
vs. A.
In the analysis of subsequent sections, we need the expansions of
and
,
and their derivatives. We now derive these expansions. We first write out the Taylor expansion of
around
.
The expansion of
is derived from that of
using an iterative method. It depends on which term in the expansion of
is the leading non-zero term. Let
. The inverse function
maps u back to s. We discuss two cases.
· Case 1:
. Function
has the expansion
Based on that, we built an iteration formula:
The iteration gives us expansions of
and
(8)
(9)
· Case 2:
but
. Function
has the expansion
Based on that, we construct an iteration formula for
:
which yields the expansion of
in terms of u
Taking square roots of both sides, we obtain
(10)
(11)
with the mathematical results above, we study the behavior of
vs A.
3. Analysis of Reflex Time vs. Beam Spot Area
When
,
and
are given, (4) with (7) governs
vs A. In our previous study [17], we scaled and shifted the physical temperature distribution to the normalized non-dimensional temperature
.
(12)
where the non-dimensional depth
and the non-dimensional time
are defined as
The normalized temperature
has the expression
(13)
It is important to notice that the normalized temperature
is parameter-free. The non-dimensional version of (4) with (7) has the form
(14)
In the above,
is the non-dimensional beam spot area. In the limit of
, we have
and the equation for
becomes
where
. Let
. We have
. We examine the asymptotic behavior of this convergence. We seek an expansion of the form
(15)
Expanding
around
and substituting (15), we get
(16)
Exponent
and coefficient
are determined using the leading term expansion of
. The result depends on whether the nociceptor density at the skin surface is zero.
· Case 1:
.
The expansion of
given in (8). Substituting it into (16), we have
(17)
Here we have used
and
derived in our previous study.
· Case 2:
but
The expansion of
given in (10). Substituting it into (16), we arrive at
(18)
Returning to the physical quantities before the non-dimensionalization, the reflex time vs beam spot area is given by
(19)
4. Analysis of Constraint Functions on
When
and
are measured, (4) with (7) serves as a constraint on
,
and
with beam spot area A as a parameter describing the test condition. We denote the constraint function as
and use (4) to write it as
(20)
where
is the temperature profile at reflex with beam spot area
. We represent the effect of A via variable
since
is a smooth function of
as
. To facilitate the discussion, we introduce two sets of mathematical notations. These two sets of notations are used to distinguish a quantity’s true value from its role as a variable in a function.
·
: true value of coefficient
.
·
: a trial value of coefficient
.
·
: true values of model parameters
and
.
·
: constraint function (20) calculated using trial value
; in
,
denotes the independent variable, and
the dependent variable.
Note that the data is generated with the true value
. In the calculation constraint function (20), we use the measured data and a trial value
since
is unknown. When
, the constraint function shares one common intersection for all values of A:
(21)
when
, in general
is not on constraint curve
, and the intersection of a pair of constraint functions varies with the test conditions (values of A). We study the behavior of the intersection vs A. We make use of (21) and expand
around
. For conciseness, we introduce
and write (20) as:
We apply the chain rule to calculate partial derivatives.
(22)
(23)
Using these derivatives, we write out the expansion of
.
(24)
The slope of
vs.
is
, which is given in (22). In Appendix A, we show that the slope converges to zero as
. It follows that
(25)
(25) shows that in the limit of
, the constraint curve is a horizontal line at
, independent of
and
. For finite A,
and
in (24) varies with
and
. We consider the intersection of
and
. The
-coordinate of the intersection is
. Let
denote the
-coordinate of the intersection. Solving for
from (24) and (25) leads to
(26)
The dependence of
on A is contained in
given in (23). Using the expansion of
derived in (73) in Appendix A, we investigate the behavior of
at large A.
· Case 1:
.
In case 1, as
, the intersection
given by (26) converges to the true value
regardless of trial value
.
The residual in convergence is proportional to
and has the same sign as
. More specifically, we have
(27)
· Case 2:
but
(28)
In case 2, as
, the intersection
given by (26) converges to
which is proportional to trial value
. The residual in convergence is proportional to
and has the same sign as
. Asymptotically,
is
(29)
with the analytical preparations above, we examine four types of parametric form for nociceptor density vs depth, depicted in Figure 17.
5. Type 1 Nociceptor Density:
For type 1 nociceptor density, the relative density takes the parametric form
(30)
The graph of type 1
is plotted in Figure 17. It has the properties
5.1. Reflex Time
The reflex time vs beam spot area is given by (19) and (17), namely,
(31)
As A increases,
converges to its limit with the residual proportional to 1/A2:
Figure 1 plots the relation between
and A in two ways. Left panel:
vs A. Right panel:
vs. 1/A. In particular, the right panel confirms that the residual in the convergence of
decays faster than 1/A for large A, as predicted in the analysis above.
Figure 1. The relation between reflex time (
) and beam spot area (A) for type 1 nociceptor density (30) with
. Left panel:
vs A. Right panel:
vs 1/A.
It is interesting to notice that expansion (31) is independent of
. Consequently, the measurements of
vs. A do not contain any information for estimating
.
5.2. Constraint Function
We consider the intersection of the pair
and
. The
-coordinate of the intersection,
, is generally described by (26). For type 1 density, we have
,
and
, and the specific expression of
is given by (27).
(32)
At
, the intersection
is independent of A. When
, the trend of
vs. A tells us whether
or
.
· For
,
ascends toward
from below as A increases.
· For
,
descends toward
from above as A increases.
Figure 2 displays simulated
for several values of A, respectively for
and for
. Here constraint function
is based on test data (which is generated with true value
) and is calculated using formulation (20) with trial value
. The trend of
vs A is illustrated in Figure 3. The simulation results in Figure 2 and Figure 3 confirm the theoretically predicted trend above.
When it is known that the nociceptor density has the parametric form of type 1 given in (30), we can tune the trial value
down or up toward the true value
depending on whether the calculated
increases or decreases with A.
5.3. Constraint Function Calculated Using the Uniform Density
We now consider the situation where the type of parametric form of
Figure 2. Constraint curves for type 1 density (30).
is based on test data generated with true value
, and calculated using (20) with trial value
. Left panel:
. Right panel:
.
Figure 3.
vs. A, respectively for
and for
. Here
is the intersection of
and
from Figure 2.
is unknown. With no information on the type of density form, we use the uniform density as the trial density in calculating the constraint function. Let
denote the constraint function based on the test data (which is generated using type 1 density (30) with true value
) and calculated using framework (20) with the uniform trial density
. Notice that the uniform density is a member of type 1 family (30) with
. Thus, the two constraint functions
and
are related by
. Let
denote the
-coordinate of the intersection of the pair
and
. Setting
in (32), we obtain
(33)
Since the uniform density is parameter-free, the calculation of constraint function
and intersection
is based solely on the test data. It does not require any input parameter or knowledge of the function form of the true density. Once the test data is available,
and
can be calculated. When the true underlying density is type 1 given in (30), result (33) predicts that
converges to
as
with the difference proportional to 1/A. Figure 4 compares simulated
and
. The simulation results validate the theoretical prediction. In particular,
varies linearly with 1/A. We fit function
to data of
vs 1/A. The fitting coefficients give us
Before we end this section, we clarify that result (33) predicts the behavior of constraint function
calculated using the uniform trial density when the true underlying density affecting the test data is type 1 given in (30). When the true underlying density is of a different type, the behavior will be different. One objective of examining
and
is to identity the type of nociceptor density from the observed behavior of
vs A, based on the theoretically predicted behaviors for a list of density types. In the subsequent sections, we will study more density types.
6. Type 2 Nociceptor Density:
For type 2 nociceptor density, the relative density has the parametric form
(34)
Figure 4. Simulated results of
for type 1 density given in (30). Here
is based on the data from the true density (with
) but is calculated using the uniform trial density. Left panel:
vs A. Right panel:
vs. 1/A.
The graph of type 2
is shown in Figure 17. It is straightforward to see that
6.1. Reflex Time
The reflex time vs beam spot area is described by (19) and (18).
(35)
As A increases,
converges to its limit with the difference proportional to 1/A.
Figure 5 plots the relation between
and A in two ways. Left panel:
vs A. Right panel:
vs 1/A. In particular, the right panel confirms that
is linear with respect to 1/A for large A, as predicted in the analysis above. This is in contrast with the convergence of 1/A2 for type 1 nociceptor density (30). To distinguish between these two density forms, we introduce an auxiliary quantity Q
(36)
The theoretical prediction above tells us
(37)
In (35), coefficient
does contain
. However, in (35)
is tangled with
Figure 5. The relation between reflex time (
) and beam spot area (A) for type 2 nociceptor density (34) with
. Left panel:
vs A. Right panel:
vs 1/A.
other parameters. It is not possible to extract the value of
unless all other parameters are known.
6.2. Constraint Function
We consider the intersection of the pair
and
. The
-coordinate of the intersection,
, is generally described by (26). For type 2 density, we have
,
,
and
, and the specific expression of
is given by (29).
At
, the intersection
is independent of beam spot area A. When
, the trend of
vs A tells us whether
or
.
· For
,
ascends toward
as A increases.
· For
,
descends toward
as A increases.
The increase/decrease trend of
vs. A is qualitatively the same as that for type 1 density given in (30). There are two differences. For type 2 density (34), we have 1)
, which varies with trial value
, and 2) the residual in convergence is proportional to
, instead of 1/A.
Figure 6 shows simulated
for several values of A, respectively for
and for
. Here constraint function
is based on test data (which is generated with true value
) and is calculated using formulation (20) with trial value
. The trend of
vs A is shown in Figure 7. The simulation results in Figure 6 and Figure 7 confirm the theoretically predicted trend above.
Figure 6. Constraint curves for type 2 density (34).
is based on test data generated with true value
, and calculated using (20) with trial value
. Left panel:
. Right panel:
.
Figure 7.
vs. A, respectively for
and for
. Here
is the intersection of
and
from Figure 6.
When it is known that the nociceptor density has the parametric form of type 2 given in (34), we can tune the trial value
down or up toward the true value
depending on whether the calculated
increases or decreases with A.
6.3. Constraint Function Calculated Using the Uniform Density
When the type of parametric form of
is unknown, we like to design a method to identify the true underlying density type among a set of candidate density types based on test data measured in experiments. In the analysis of reflex time above, we used quantity Q defined in (36), with behaviors described in (37), to distinguish between type 1 and type 2 densities. Now we look into using constraint functions as a tool for that purpose. With no information on the density type, we use the uniform density as the trial density in calculating the constraint function.
Let
denote the constraint function based on the test data (which is generated using type 2 density (34) with true value
) and calculated using framework (20) with the uniform trial density
. We select the uniform density to make the calculation of constraint function parameter-free, based solely on the test data.
Note that the uniform density is not a member of type 2 family (34). Consequently, constraint function
is not a special case of
. To study the behavior of
, we try to connect it to
, the true constraint function. Both
and
are constructed from the same test data generated with the true underlying density (34). Each of them is calculated in framework (20) using a different type of trial density, and has different features:
·
is the constraint function calculated using the correct density type (34) with the true parameter value
. Operationally, the calculation of
is not realistic unless we know
. The theoretical advantage of
is that it shares a common intersection
for all values of A.
·
is the constraint function calculated using the uniform trial density. Operationally, we can always calculate
from test data. However, the point
in general is not on the curve
.
The two constraint functions above are unified in
via
, as described in (4).
(38)
The mapping between
and
, however, depends on the trial density. As a result, variable
is related to
differently in the two constraint functions. For clarity of the discussion, we use
to denote the variable in
, and use
for the variable in
. The difference lies in the trial density used in the formulation. For the true density,
is related to
in (5). For the uniform density,
. Expressing
in terms of
or
, we obtain
(39)
Combining (39) and (38), we write out
and
(40)
Since
is calculated using the true density, it satisfies
Taking the limit as
yields
In (40), letting
and using
, we obtain
Let
denote the intersection of the pair
and
. Both
and
are calculated by first mapping
to
and then mapping to
. Given the measured temperature profile at reflex, Equation (38) maps
to a unique
, independent of the trial density. It follows that in (39), line 1 with
and line 2 with
are both equal to
.
Therefore,
and
are related by
(41)
For type 2 density (34), the expansion of
is given in (10) with
and
. Substituting the expansion of
into (41) yields
(42)
Figure 8 compares simulated
and
. The simulation results confirm the theoretical prediction. In particular,
varies linearly with
. We fit function
to the data of
vs
. The fitting coefficients give us
is the intersection of two constraint functions calculated from test data using the uniform trial density. Given the test data, the process of calculating
is parameter-free. However,
depends on the test data, which is affected by the underlying true density. When the true density has the type 2 form given in (34), result (42) predicts that
increases linearly with
, unbounded as
. Result (42) for type 2 density is in sharp contrast
Figure 8. Simulated results of
for type 2 density given in (34). Here
is based on the data from the true density (with
) but is calculated using the uniform trial density. Left panel:
vs A. Right panel:
vs.
.
with the situation for type 1 density, described in (33) of Section 5, where
converges to the true value
as
. This difference in the behavior of
vs. A provides another mechanism of distinguishing between type 1 and type 2 densities, in addition to the method of examining quantity Q, described in (37).
7. Type 3 Nociceptor Density:
We write type 3 density in the same parametric form as that of types 1 and 2.
(43)
where
is the Heaviside step function.
(44)
The graph of type 3
is shown in Figure 17. Type 3 density (43) is different from types 1 and 2 in that the nociceptor density jumps from 0 to
at depth
. Because of the discontinuity, the Taylor expansions of
and
derived in Section 3 are no longer valid. We write out
and
directly
The mapping between
and
is described by (7) and has the specific expression
(45)
7.1. Reflex Time
With the expression of
in (45), Equation (14) for
becomes
In the limit of
, we have
and the equation for
converges to
Consider
as a function of t with q as a parameter. Let
be the inverse of
and
. As
, we have
By definition,
satisfies
. We expand function
around
and write the equation for
as
(46)
Substituting
into (46) to calculate
and
, and then mapping back to the physical quantities, we obtain
As A increases,
converges to its limit with the residual proportional to 1/A.
This behavior is similar to that of type 2 density. For both type 2 and type 3, the nociceptor density is zero at the skin surface. Figure 9 plots the relation between
and A in two ways. Left panel:
vs A. Right panel:
vs 1/A. In particular, the right panel confirms that
is linear with respect to 1/A for large A, as predicted in the analysis above.
Figure 9. The reflex time (
) as a function of beam spot area (A) for type 3 nociceptor density (43) with
. Left panel:
vs A. Right panel:
vs. 1/A.
7.2. Constraint Function
For type 3 density, it is more sensible to use
as the parameter since it has the clear physical meaning of the depth at which the nociceptor density jumps from 0 to
. In the unified parametric form in (43), the generic parameter is
. Mathematically, working with
allows us to use general results of the unified parametric form obtained in previous sections. In the analysis below, we will go back and forth between
and
.
We adopt the general convention of using
to denote the true value and
to represent the variable. The general form of constraint function with trial value
is given in (20). Using
and
, we write it as
(47)
In the limit of
,
converges to a horizontal line
At
, the constraint function shares the common intersection
for all A.
In particular, we have
. We expand
around
and expand
around
to write them respectively as
(48)
(49)
Let
denote the
-coordinate of the intersection of
and
.
is governed by equating the RHSs of (48) and (49).
Solving for
yields
(50)
We expand
with respect to
into the form
(51)
Substituting the expansion into (50), we obtain
(52)
In Appendix B, we show that the coefficients satisfy
(53)
At
, the intersection
is independent of beam spot area A. When
, the trend of
vs A implies whether
or
.
· For
,
ascends toward
as A increases.
· For
,
descends toward
as A increases.
In terms of
, the increase/decrease trend of
vs A for type 3 density (43) resembles that for type 2 density (34). Both types of densities share the common feature that the nociceptor density is zero at skin surface:
.
Figure 10 depicts simulated
for several values of A, respectively for
and for
. Here constraint function
is based on test data (which is generated with true value
) and is calculated using formulation (47) with trial value
. The trend of
vs A is shown in Figure 11. The simulation results in Figure 10 and Figure 11 confirm the theoretically predicted trend above.
When it is known that the nociceptor density has the parametric form of type 3 given in (43), we can tune the trial value
up or down toward the true value
Figure 10. Constraint curves for type 3 density (43).
is based on test data generated with true value
, and calculated using (47) with trial value
. Left panel:
. Right panel:
. Notice that for type 3 density,
is a horizontal line, whose height varies with
.
Figure 11.
vs A, respectively for
and for
. Here
is the intersection of
and
from Figure 10.
depending on whether the calculated
increases or decreases with A.
7.3. Constraint Function Calculated Using the Uniform Density
When the type of parametric form of
is unknown, we apply the uniform trial density in calculating the constraint function and use it as a tool for probing the density type. This approach has the advantage of being operationally practical. Once the test data is available, the calculation of constraint function does not require any input parameters.
Let
denote the constraint function based on the test data (which is generated using type 3 density (43) with true value
) and calculated using framework (47) with the uniform trial density
. For type 3 parametric family (43), the uniform density is a special case at
. However, expansions (48) and (49) are only for the case of
near
, away from the skin surface. At
, the expansions will be different because of the insulated boundary condition
. At
, we have
We expand
around
. It follows that
Let
denote the intersection of
and
. It satisfies
All derivatives are evaluated at
. Solving for
, we obtain
(54)
Result (54) predicts that when the true underlying density is type 3 given in (43),
increases linearly with
, unbounded as
. This behavior is similar to that for type 2 density. Both type 3 and type 2 share the common feature of
. Figure 12 compares simulated
and
. The simulation results confirm the theoretical prediction.
8. Type 4 Nociceptor Density:
We represent type 4 density in the same parametric form as that of types 1 - 3.
(55)
where
is the Heaviside step function defined in (44). The graph of type 4
is shown in Figure 17. Type 4 relative density (55) has value 1 in
, and drops down to value 0.5 for
. When beam spot area A is sufficiently large, only a small depth of the skin needs to reach the activation temperature in order to trigger the withdrawal reflex. Thus, for large A, the behaviors of type 4 density are the same as those of the uniform density. For the purpose of revealing the effect of density jump at
, we examine the reflex time and the temperature profile at reflex in an intermediate range of A corresponding to the situation where the activated depth is around the density jump (
).
Figure 12. Simulated results of
for type 3 density given in (43). Here
is based on the data from the true density (with
) but is calculated using the uniform trial density. Left panel:
vs. A. Right panel:
vs
.
8.1. Reflex Time
Because of the discontinuity in density profile (8), the Taylor expansions of
and
derived in Section 3 are no longer valid. We write out
and
directly.
(56)
The activated depth at reflex given in (7) has the expression
(57)
The non-dimensional reflex time
is related to A via
in Equation (14).
(58)
Conventionally, we view
as a function of A since in experiments the beam spot area is prescribed in test design and the corresponding reflex time is observed. To facilitate the mathematically analysis, we view A as a function of
. We study function
for type 4 density and connect it to its counterpart for the uniform density. Let
·
: function A vs.
for type 4 density
·
: function A vs.
for the uniform density
·
: function
vs. 1/A for type 4 density
·
: function
vs. 1/A for the uniform density.
For any given value of
, the corresponding
is completely determined by Equation (58), independent of the nociceptor density. We use
to connect
and
. For type 4 density,
is given in (57). Its inverse function is
(59)
For the uniform density,
,
, and
is given by
(60)
Combining (59) and (60), we can express
in terms of
.
(61)
(61) described the relation between
and
. Now we treat
as the dependent variable and view it as a function of 1/A. (61) leads to
(62)
Equation (62) reveals that
is obtained from
by a piecewise linear scaling on the independent variable 1/A. Function
is smooth. After the piecewise linear scaling,
has a discontinuity in derivative. Figure 13 plots the relation between
and A in two ways. Left panel:
vs. 1/A. Right panel: derivative of
vs. 1/A. In particular, the right panel verifies that
vs. 1/A has a discontinuity in derivative, as predicted in the analysis above.
8.2. Constraint Function
For type 4 density, it is more sensible to choose
as the parameter since it has the clear physical meaning of the depth at which the nociceptor density drops sharply from
to
. In the unified parametric form in (55), the generic parameter is
. We adopt the general convention of using
to denote the true value and
to represent the variable. The general form of constraint function with trial value
is given in (20). Using
and
given in (56), we write it as
Figure 13. The relation between reflex time (
) and beam spot area (A) for type 4 density (55) with
. Left panel:
vs 1/A. Right panel: derivative of
vs. 1/A.
(63)
when beam spot area A is sufficiently large to make
, (57) gives
and the measured data for type 4 density is the same as that for the uniform density. Constraint function (63) is based on the data for true value
and is calculated with trial value
. For any positive trial value
and a finite interval of
near
in consideration, when A is sufficiently large, we have
and
Here
is the constraint function based on the data for the uniform density and is calculated using the uniform trial density.
is independent of trial value
, and passes through
for all values of A.
In the limit of
, we have
. Let
denote the
-coordinate of the intersection of
and
. Our analysis above shows that
· For any
,
when A is sufficiently large.
· For any
,
.
· For any
,
when A is sufficiently large.
More specifically, to have
, we only need
for
near
. The condition
becomes
. We conclude that
(64)
Thus, for large A,
is independent of
. To probe the position of trial value
relative to true value
, we need to examine the behavior of
for
. In this range of A, constraint function (63) takes the form
At true value
,
shares the common intersection
for all A.
We expand
around
.
Setting the LHS to
and solving for
, we obtain
(65)
For
and
, the activated depth at reflex is
. Substituting the expression of
into (57) and solving for
, we have
(66)
For
and
, the activated depth at reflex is
. This
may or may not be beyond depth
. The case of
corresponds to
. Substituting the expression of
into (57) and solving for
yields
(67)
Similarly, the case of
corresponds to
. We get
(68)
Summarizing results for various cases, we write out a complete description of
.
The case of
,
The case of
,
(69)
At
, we have
for all A. When
,
has non-trivial dependence on A. The trend of
vs A tells us whether
or
.
· When
, from small A to large A,
starts below
, decreases further below
, and then reverts rapidly back to
and stays there.
· When
, from small A to large A,
starts above
, increases further above
, and then reverts rapidly back to
and remains there.
Figure 14 shows simulated
for several values of A, respectively for
and for
. Here constraint function
is based on test data (which is generated with true value
) and is calculated using formulation (63) with trial value
. The trend of
vs A is shown in Figure 15. The simulation results in Figure 14 and Figure 15 confirm the theoretically predicted trend above.
When it is known that the nociceptor density has the parametric form of type 4 given in (55), we can tune the trial value
up or down toward the true value
depending on the behavior of the calculated
vs. A.
8.3. Constraint Function Calculated Using the Uniform Density
When the type of parametric form of
is unknown, we use the uniform
Figure 14. Constraint curves for type 4 density (55).
is based on test data generated with true value
, and calculated using (63) with trial value
. Left panel:
. Right panel:
.
Figure 15.
vs A, respectively for
and for
. Here
is the intersection of
and
from Figure 14.
trial density in calculating the constraint function. The goal is to use the resulting constraint function as a tool to probe the underlying unknown density type.
Let
denote the constraint function based on the test data (which is generated using type 4 density (55) with true value
) and calculated using framework (63) with the uniform trial density
. For type 4 parametric family (55), the uniform density is a special case with large
.
It follows that the behavior of
is the same as that of
for large
, which we analyzed in the previous subsection.
Let
denote the intersection of the pair
and
. Intersection
has the same behavior as
for large
. Based on result (69) for
in the previous subsection, we conclude for
that
(70)
Result (70) predicts that when the true underlying density is type 4 given in (55),
starts at slightly below
for small A; decreases linearly toward
as A increases; arrives at
at
and stays there for
. The qualitative behavior of
converging to
for large A is similar for type 1 and type 4 densities. Figure 16 compares simulated
and
. The simulation results confirm the theoretical prediction.
Figure 16. Simulated results of
for type 4 density given in (55). Here
is based on the data from the true density (with
) but is calculated using the uniform trial density.
9. Summary
In this study, we investigate the nociceptor density of the form:
, and its effect on heat-induced withdrawal reflex. We examine 4 types of
illustrated in Figure 17. Each
shown has a characteristic length 1. When scaled in the depth direction by parameter
, each
yields a family of density profiles.
We consider the situation where the reflex time and the temperature profile at the reflex are measurable in tests. We build the mathematical formulation for extracting 3 key parameters from test data:
· the activation temperature
for heat-sensitive nociceptors,
· the critical threshold
on the equivalent activated volume, and
· the parameter
in the relative density.
Our general strategy is to identify distinct behaviors for different densitytypes and distinct behaviors for different regions of parameter values. We compare these theoretical patterns with the observed patterns from test data to pinpoint the underlying unknown density type. We inspect the behavior calculated using a trial parameter value in the parametric form to determine whether the trial value is below or above the true value. Then we use that information to tune the trial value up or down accordingly toward the true value. The process is repeated with the new trial value until convergence. To best illustrate each key module individually in its own setting, we divide the task of finding the density type and the parameter value into stages and consider several problems. Mathematically, we proceed from the simplest problem to the realistic one in which both the density type and the parameter value are unknown. The solution of a simpler problem provides the building blocks for solving a more complicated problem.
Figure 17. Four types of nociceptor density vs. depth. Each type has been transformed into the standard form of maximum density 1 and characteristic depth 1.
Problem 1:
When both the density type
and the true scaling parameter
are known for the nociceptor density, the test data allow us to construct true constraint functions on
. Multiple constraint functions, obtained from tests at different values of beam spot area A, share a common point at the true value of
. Parameters
and
are determined by finding the intersection of these distinct constraint functions.
Problem 2:
When the density type
is given but the true scaling parameter
is unknown, we construct trial constraint functions using trial values of
. Let
denote the trial constraint function that is based on the measured data (which is generated with true value
) and that is calculated using the given parametric form with trial value
. When the trial value
is different from the true value
, in general, the true value
is not on trial constraint functions, and trial constraint functions at different values of beam spot area A do not share a common intersection. The behaviors of trial constraint functions
are demonstrated for the 4 density types, respectively in Figure 2, Figure 6, Figure 10, and Figure 14. We look at the intersection of a pair of trial constraint functions:
and
. Let
denote the intersection of the pair, which is affected by the trial value
used in calculating the trial constraint functions and by the beam spot area A used in tests. Given a trial value
, we examine the trend of
vs A. For each of the 4 density types, as the beam spot area A increases,
vs A demonstrates distinct trend of increasing or decreasing, respectively when
and when
. The trend of
vs. A is illustrated for the 4 density types, respectively in Figure 3, Figure 7, Figure 11, and Figure 15. Depending on the given density type and the observed trend of
vs. A, we tune the trial value
accordingly to approach the true value
. At the true value
,
is independent of A. Once we arrive at the true value
, the subsequent procedure of finding parameters
and
is the same as described in Problem 1 above: the intersection of
and
gives us the true value of
.
Problem 3:
When both the density type
and the true scaling parameter
are unknown, we construct trial constraint functions using the uniform trial density
, which is parameter-free. The trial constraint function using the uniform density is denoted by
. When the true density is not the uniform density, the true value
is not on trial constraint function
, and for different values of A trial constraint functions
do not share a common point. Similar to what we did on trial constraint functions
in Problem 2 (where the density type is given), here we look at the intersection of a pair of trial constraint functions:
and
. Let
denote the intersection of the pair, which is affected only by the beam spot area A used in tests. Given a collection of measured data sets at several values of A, we examine the trend of
vs. A. The trend behavior of
vs. A is displayed for the 4 density types, respectively in Figure 4, Figure 8, Figure 12, and Figure 16. The behaviors of
vs. A for types 1 and 4 are distinct from each other and are distinct from those for types 2 and 3. In addition to examining the trend of
vs. A, we also check the convergence pattern of reflex time
as beam spot area A increases, which is shown for the 4 density types, respectively in Figure 1, Figure 5, Figure 9, and Figure 13. Again, the patterns of
vs. A for types 1 and 4 are distinct from each other and are distinct from those for types 2 and 3. Density types 2 and 3 have similar behaviors in both
vs. A and
vs. A. This is not completely surprising since both density types share the common feature of having zero nociceptor density at the skin surface. When we are presented with the task of identifying the underlying nociceptor density from among the 4 density types, it is still a challenge to distinguish between type 2 and type 3 based on the trend of
vs. A or the trend of
vs. A. We need to explore other formulations and analytical tools for differentiating types 2 and 3. One possibility is to use alternative standardized parameter-free forms other than the uniform density as the trial density in calculating the trial constraint function. Another possibility is to use the trend of system behaviors vs. varying applied beam power, in addition to varying beam spot area. Once the density type is selected, the remaining task of determining the true values of parameters
and
is the same as described in Problem 2 above.
The goal of our study is to develop a methodology that utilizes test data to 1) identify the density type for the underlying nociceptor density, 2) find by trial and error the true scaling parameter in the parametric form, and 3) determine the activation temperature of nociceptors and the critical threshold on the equivalent activated volume. The results of 1) and 2) basically specify the nociceptor density profile vs depth. We assume the test data available include measurements of the reflex time and of the spatial temperature profile at reflex. Combining the procedures outlined in Problems 1, 2 and 3 above, we obtain such a methodology exactly for this purpose.
Disclaimer
The authors thank Dr. John Biddle and Dr. Stacy Teng of Institute for Defense Analyses for introducing the ADT CHEETEH-E model and for many helpful discussions. The authors acknowledge the Joint Intermediate Force Capabilities Office of U.S. Department of Defense and the Naval Postgraduate School for supporting this work. The views expressed in this document are those of the authors and do not reflect the official policy or position of the Department of Defense or the U.S. Government.
Appendix A
Derivation of
in (25)
We show the convergence in three steps.
Step 1: We first look at the factor
in the expression of
in (22). As
we have
. We expand
as
. In both cases 1 and 2, we have
. It follows that
(71)
Step 2: Next we look at the factor
in the expression of
in (22). With
and the insulated boundary condition
, we have
(72)
Taking the limit as
in (22), and using results (71) and (72), we conclude
Step 3: We show that the term
in (24) is bounded as
. In the expression of
in (23), we look at
, the component that varies with
. Using expansions (8) and (9) in case 1 or expansions (10) and (11) in case 2, we get
(73)
In both cases, we have
.
Combining the results of three steps above, we conclude that for any trial value
, as
, the constraint function converges to
, as described in (25) in the main text.
Appendix B
Derivation of property (53)
Property (53) describes coefficients in the expansion of
with respect to
. Recall that
where
. Function
as given in (12) and (13), has the properties below
1)
is always negative.
2) At any given depth y, the absolute value of
increases with time t.
3) At any given time t, the absolute value of
start with 0 value at
, increases with y until the inflection point at
, and then decreases to zeros beyond the inflection point. The depth of inflection point
increases with t.
We consider the situation of
. That is, the nociceptor density jump occurs before the inflection point of temperature profile at reflex. Combining properties 1 and 3 of
with
, we have
(74)
As beam spot area A increases
decreases and is bounded from below by
. In turn,
is bounded by
, reflecting that the presence of a skin layer with no nociceptor delays the occurrence of withdrawal reflex. Thus, we have
Applying property 2 of
to (74) with
, we obtain
(75)
The first part of (53),
, follows from inequality (75) and definition of
in (51). The second part of (53),
, is verified in simulations.